The NASA Langley Multidisciplinary Uncertainty Quantification Challenge
Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.
2014-01-01
This paper presents the formulation of an uncertainty quantification challenge problem consisting of five subproblems. These problems focus on key aspects of uncertainty characterization, sensitivity analysis, uncertainty propagation, extreme-case analysis, and robust design.
Uncertainty Quantification in Numerical Aerodynamics
Litvinenko, Alexander
2017-05-16
We consider uncertainty quantification problem in aerodynamic simulations. We identify input uncertainties, classify them, suggest an appropriate statistical model and, finally, estimate propagation of these uncertainties into the solution (pressure, velocity and density fields as well as the lift and drag coefficients). The deterministic problem under consideration is a compressible transonic Reynolds-averaged Navier-Strokes flow around an airfoil with random/uncertain data. Input uncertainties include: uncertain angle of attack, the Mach number, random perturbations in the airfoil geometry, mesh, shock location, turbulence model and parameters of this turbulence model. This problem requires efficient numerical/statistical methods since it is computationally expensive, especially for the uncertainties caused by random geometry variations which involve a large number of variables. In numerical section we compares five methods, including quasi-Monte Carlo quadrature, polynomial chaos with coefficients determined by sparse quadrature and gradient-enhanced version of Kriging, radial basis functions and point collocation polynomial chaos, in their efficiency in estimating statistics of aerodynamic performance upon random perturbation to the airfoil geometry [D.Liu et al \\'17]. For modeling we used the TAU code, developed in DLR, Germany.
Uncertainty quantification for environmental models
Hill, Mary C.; Lu, Dan; Kavetski, Dmitri; Clark, Martyn P.; Ye, Ming
2012-01-01
Environmental models are used to evaluate the fate of fertilizers in agricultural settings (including soil denitrification), the degradation of hydrocarbons at spill sites, and water supply for people and ecosystems in small to large basins and cities—to mention but a few applications of these models. They also play a role in understanding and diagnosing potential environmental impacts of global climate change. The models are typically mildly to extremely nonlinear. The persistent demand for enhanced dynamics and resolution to improve model realism [17] means that lengthy individual model execution times will remain common, notwithstanding continued enhancements in computer power. In addition, high-dimensional parameter spaces are often defined, which increases the number of model runs required to quantify uncertainty [2]. Some environmental modeling projects have access to extensive funding and computational resources; many do not. The many recent studies of uncertainty quantification in environmental model predictions have focused on uncertainties related to data error and sparsity of data, expert judgment expressed mathematically through prior information, poorly known parameter values, and model structure (see, for example, [1,7,9,10,13,18]). Approaches for quantifying uncertainty include frequentist (potentially with prior information [7,9]), Bayesian [13,18,19], and likelihood-based. A few of the numerous methods, including some sensitivity and inverse methods with consequences for understanding and quantifying uncertainty, are as follows: Bayesian hierarchical modeling and Bayesian model averaging; single-objective optimization with error-based weighting [7] and multi-objective optimization [3]; methods based on local derivatives [2,7,10]; screening methods like OAT (one at a time) and the method of Morris [14]; FAST (Fourier amplitude sensitivity testing) [14]; the Sobol' method [14]; randomized maximum likelihood [10]; Markov chain Monte Carlo (MCMC) [10
Inverse Problems and Uncertainty Quantification
Litvinenko, Alexander
2014-01-06
In a Bayesian setting, inverse problems and uncertainty quantification (UQ) - the propagation of uncertainty through a computational (forward) modelare strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. This is especially the case as together with a functional or spectral approach for the forward UQ there is no need for time- consuming and slowly convergent Monte Carlo sampling. The developed sampling- free non-linear Bayesian update is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisa- tion to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and quadratic Bayesian update on the small but taxing example of the chaotic Lorenz 84 model, where we experiment with the influence of different observation or measurement operators on the update.
Inverse Problems and Uncertainty Quantification
Litvinenko, Alexander; Matthies, Hermann G.
2014-01-01
In a Bayesian setting, inverse problems and uncertainty quantification (UQ) - the propagation of uncertainty through a computational (forward) modelare strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. This is especially the case as together with a functional or spectral approach for the forward UQ there is no need for time- consuming and slowly convergent Monte Carlo sampling. The developed sampling- free non-linear Bayesian update is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisa- tion to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and quadratic Bayesian update on the small but taxing example of the chaotic Lorenz 84 model, where we experiment with the influence of different observation or measurement operators on the update.
Inverse problems and uncertainty quantification
Litvinenko, Alexander
2013-12-18
In a Bayesian setting, inverse problems and uncertainty quantification (UQ)— the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. This is especially the case as together with a functional or spectral approach for the forward UQ there is no need for time- consuming and slowly convergent Monte Carlo sampling. The developed sampling- free non-linear Bayesian update is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisa- tion to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and quadratic Bayesian update on the small but taxing example of the chaotic Lorenz 84 model, where we experiment with the influence of different observation or measurement operators on the update.
Uncertainty quantification theory, implementation, and applications
Smith, Ralph C
2014-01-01
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models that require quantified uncertainties for large-scale applications, novel algorithm development, and new computational architectures that facilitate implementation of these algorithms. Uncertainty Quantification: Theory, Implementation, and Applications provides readers with the basic concepts, theory, and algorithms necessary to quantify input and response uncertainties for simulation models arising in a broad range of disciplines. The book begins with a detailed discussion of applications where uncertainty quantification is critical for both scientific understanding and policy. It then covers concepts from probability and statistics, parameter selection techniques, frequentist and Bayesian model calibration, propagation of uncertainties, quantification of model discrepancy, surrogate model construction, and local and global sensitivity analysis. The author maintains a complementary web page where readers ca...
Uncertainty Quantification in Aerodynamics Simulations, Phase I
National Aeronautics and Space Administration — The objective of the proposed work (Phases I and II) is to develop uncertainty quantification methodologies and software suitable for use in CFD simulations of...
Low cost high performance uncertainty quantification
Bekas, C.; Curioni, A.; Fedulova, I.
2009-01-01
Uncertainty quantification in risk analysis has become a key application. In this context, computing the diagonal of inverse covariance matrices is of paramount importance. Standard techniques, that employ matrix factorizations, incur a cubic cost
Stereo-particle image velocimetry uncertainty quantification
International Nuclear Information System (INIS)
Bhattacharya, Sayantan; Vlachos, Pavlos P; Charonko, John J
2017-01-01
Particle image velocimetry (PIV) measurements are subject to multiple elemental error sources and thus estimating overall measurement uncertainty is challenging. Recent advances have led to a posteriori uncertainty estimation methods for planar two-component PIV. However, no complete methodology exists for uncertainty quantification in stereo PIV. In the current work, a comprehensive framework is presented to quantify the uncertainty stemming from stereo registration error and combine it with the underlying planar velocity uncertainties. The disparity in particle locations of the dewarped images is used to estimate the positional uncertainty of the world coordinate system, which is then propagated to the uncertainty in the calibration mapping function coefficients. Next, the calibration uncertainty is combined with the planar uncertainty fields of the individual cameras through an uncertainty propagation equation and uncertainty estimates are obtained for all three velocity components. The methodology was tested with synthetic stereo PIV data for different light sheet thicknesses, with and without registration error, and also validated with an experimental vortex ring case from 2014 PIV challenge. Thorough sensitivity analysis was performed to assess the relative impact of the various parameters to the overall uncertainty. The results suggest that in absence of any disparity, the stereo PIV uncertainty prediction method is more sensitive to the planar uncertainty estimates than to the angle uncertainty, although the latter is not negligible for non-zero disparity. Overall the presented uncertainty quantification framework showed excellent agreement between the error and uncertainty RMS values for both the synthetic and the experimental data and demonstrated reliable uncertainty prediction coverage. This stereo PIV uncertainty quantification framework provides the first comprehensive treatment on the subject and potentially lays foundations applicable to volumetric
Adjoint-Based Uncertainty Quantification with MCNP
Energy Technology Data Exchange (ETDEWEB)
Seifried, Jeffrey E. [Univ. of California, Berkeley, CA (United States)
2011-09-01
This work serves to quantify the instantaneous uncertainties in neutron transport simulations born from nuclear data and statistical counting uncertainties. Perturbation and adjoint theories are used to derive implicit sensitivity expressions. These expressions are transformed into forms that are convenient for construction with MCNP6, creating the ability to perform adjoint-based uncertainty quantification with MCNP6. These new tools are exercised on the depleted-uranium hybrid LIFE blanket, quantifying its sensitivities and uncertainties to important figures of merit. Overall, these uncertainty estimates are small (< 2%). Having quantified the sensitivities and uncertainties, physical understanding of the system is gained and some confidence in the simulation is acquired.
Uncertainty quantification in resonance absorption
International Nuclear Information System (INIS)
Williams, M.M.R.
2012-01-01
We assess the uncertainty in the resonance escape probability due to uncertainty in the neutron and radiation line widths for the first 21 resonances in 232 Th as given by . Simulation, quadrature and polynomial chaos methods are used and the resonance data are assumed to obey a beta distribution. We find the uncertainty in the total resonance escape probability to be the equivalent, in reactivity, of 75–130 pcm. Also shown are pdfs of the resonance escape probability for each resonance and the variation of the uncertainty with temperature. The viability of the polynomial chaos expansion method is clearly demonstrated.
Quantification of margins and uncertainties: Alternative representations of epistemic uncertainty
International Nuclear Information System (INIS)
Helton, Jon C.; Johnson, Jay D.
2011-01-01
In 2001, the National Nuclear Security Administration of the U.S. Department of Energy in conjunction with the national security laboratories (i.e., Los Alamos National Laboratory, Lawrence Livermore National Laboratory and Sandia National Laboratories) initiated development of a process designated Quantification of Margins and Uncertainties (QMU) for the use of risk assessment methodologies in the certification of the reliability and safety of the nation's nuclear weapons stockpile. A previous presentation, 'Quantification of Margins and Uncertainties: Conceptual and Computational Basis,' describes the basic ideas that underlie QMU and illustrates these ideas with two notional examples that employ probability for the representation of aleatory and epistemic uncertainty. The current presentation introduces and illustrates the use of interval analysis, possibility theory and evidence theory as alternatives to the use of probability theory for the representation of epistemic uncertainty in QMU-type analyses. The following topics are considered: the mathematical structure of alternative representations of uncertainty, alternative representations of epistemic uncertainty in QMU analyses involving only epistemic uncertainty, and alternative representations of epistemic uncertainty in QMU analyses involving a separation of aleatory and epistemic uncertainty. Analyses involving interval analysis, possibility theory and evidence theory are illustrated with the same two notional examples used in the presentation indicated above to illustrate the use of probability to represent aleatory and epistemic uncertainty in QMU analyses.
An uncertainty inventory demonstration - a primary step in uncertainty quantification
Energy Technology Data Exchange (ETDEWEB)
Langenbrunner, James R. [Los Alamos National Laboratory; Booker, Jane M [Los Alamos National Laboratory; Hemez, Francois M [Los Alamos National Laboratory; Salazar, Issac F [Los Alamos National Laboratory; Ross, Timothy J [UNM
2009-01-01
Tools, methods, and theories for assessing and quantifying uncertainties vary by application. Uncertainty quantification tasks have unique desiderata and circumstances. To realistically assess uncertainty requires the engineer/scientist to specify mathematical models, the physical phenomena of interest, and the theory or framework for assessments. For example, Probabilistic Risk Assessment (PRA) specifically identifies uncertainties using probability theory, and therefore, PRA's lack formal procedures for quantifying uncertainties that are not probabilistic. The Phenomena Identification and Ranking Technique (PIRT) proceeds by ranking phenomena using scoring criteria that results in linguistic descriptors, such as importance ranked with words, 'High/Medium/Low.' The use of words allows PIRT to be flexible, but the analysis may then be difficult to combine with other uncertainty theories. We propose that a necessary step for the development of a procedure or protocol for uncertainty quantification (UQ) is the application of an Uncertainty Inventory. An Uncertainty Inventory should be considered and performed in the earliest stages of UQ.
Uncertainty Quantification with Applications to Engineering Problems
DEFF Research Database (Denmark)
Bigoni, Daniele
in measurements, predictions and manufacturing, and we can say that any dynamical system used in engineering is subject to some of these uncertainties. The first part of this work presents an overview of the mathematical framework used in Uncertainty Quantification (UQ) analysis and introduces the spectral tensor...... and thus the UQ analysis of the associated systems will benefit greatly from the application of methods which require few function evaluations. We first consider the propagation of the uncertainty and the sensitivity analysis of the non-linear dynamics of railway vehicles with suspension components whose......-scale problems, where efficient methods are necessary with today’s computational resources. The outcome of this work was also the creation of several freely available Python modules for Uncertainty Quantification, which are listed and described in the appendix....
Uncertainty quantification and error analysis
Energy Technology Data Exchange (ETDEWEB)
Higdon, Dave M [Los Alamos National Laboratory; Anderson, Mark C [Los Alamos National Laboratory; Habib, Salman [Los Alamos National Laboratory; Klein, Richard [Los Alamos National Laboratory; Berliner, Mark [OHIO STATE UNIV.; Covey, Curt [LLNL; Ghattas, Omar [UNIV OF TEXAS; Graziani, Carlo [UNIV OF CHICAGO; Seager, Mark [LLNL; Sefcik, Joseph [LLNL; Stark, Philip [UC/BERKELEY; Stewart, James [SNL
2010-01-01
UQ studies all sources of error and uncertainty, including: systematic and stochastic measurement error; ignorance; limitations of theoretical models; limitations of numerical representations of those models; limitations on the accuracy and reliability of computations, approximations, and algorithms; and human error. A more precise definition for UQ is suggested below.
Model Uncertainty Quantification Methods In Data Assimilation
Pathiraja, S. D.; Marshall, L. A.; Sharma, A.; Moradkhani, H.
2017-12-01
Data Assimilation involves utilising observations to improve model predictions in a seamless and statistically optimal fashion. Its applications are wide-ranging; from improving weather forecasts to tracking targets such as in the Apollo 11 mission. The use of Data Assimilation methods in high dimensional complex geophysical systems is an active area of research, where there exists many opportunities to enhance existing methodologies. One of the central challenges is in model uncertainty quantification; the outcome of any Data Assimilation study is strongly dependent on the uncertainties assigned to both observations and models. I focus on developing improved model uncertainty quantification methods that are applicable to challenging real world scenarios. These include developing methods for cases where the system states are only partially observed, where there is little prior knowledge of the model errors, and where the model error statistics are likely to be highly non-Gaussian.
Uncertainty Quantification in Alchemical Free Energy Methods.
Bhati, Agastya P; Wan, Shunzhou; Hu, Yuan; Sherborne, Brad; Coveney, Peter V
2018-05-02
Alchemical free energy methods have gained much importance recently from several reports of improved ligand-protein binding affinity predictions based on their implementation using molecular dynamics simulations. A large number of variants of such methods implementing different accelerated sampling techniques and free energy estimators are available, each claimed to be better than the others in its own way. However, the key features of reproducibility and quantification of associated uncertainties in such methods have barely been discussed. Here, we apply a systematic protocol for uncertainty quantification to a number of popular alchemical free energy methods, covering both absolute and relative free energy predictions. We show that a reliable measure of error estimation is provided by ensemble simulation-an ensemble of independent MD simulations-which applies irrespective of the free energy method. The need to use ensemble methods is fundamental and holds regardless of the duration of time of the molecular dynamics simulations performed.
Uncertainty quantification and stochastic modeling with Matlab
Souza de Cursi, Eduardo
2015-01-01
Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with many real-world applications within statistics, mathematics, probability and engineering, but also within the natural sciences. Literature on the topic has up until now been largely based on polynomial chaos, which raises difficulties when considering different types of approximation and does no
Uncertainty quantification for hyperbolic and kinetic equations
Pareschi, Lorenzo
2017-01-01
This book explores recent advances in uncertainty quantification for hyperbolic, kinetic, and related problems. The contributions address a range of different aspects, including: polynomial chaos expansions, perturbation methods, multi-level Monte Carlo methods, importance sampling, and moment methods. The interest in these topics is rapidly growing, as their applications have now expanded to many areas in engineering, physics, biology and the social sciences. Accordingly, the book provides the scientific community with a topical overview of the latest research efforts.
Uncertainty Quantification in High Throughput Screening ...
Using uncertainty quantification, we aim to improve the quality of modeling data from high throughput screening assays for use in risk assessment. ToxCast is a large-scale screening program that analyzes thousands of chemicals using over 800 assays representing hundreds of biochemical and cellular processes, including endocrine disruption, cytotoxicity, and zebrafish development. Over 2.6 million concentration response curves are fit to models to extract parameters related to potency and efficacy. Models built on ToxCast results are being used to rank and prioritize the toxicological risk of tested chemicals and to predict the toxicity of tens of thousands of chemicals not yet tested in vivo. However, the data size also presents challenges. When fitting the data, the choice of models, model selection strategy, and hit call criteria must reflect the need for computational efficiency and robustness, requiring hard and somewhat arbitrary cutoffs. When coupled with unavoidable noise in the experimental concentration response data, these hard cutoffs cause uncertainty in model parameters and the hit call itself. The uncertainty will then propagate through all of the models built on the data. Left unquantified, this uncertainty makes it difficult to fully interpret the data for risk assessment. We used bootstrap resampling methods to quantify the uncertainty in fitting models to the concentration response data. Bootstrap resampling determines confidence intervals for
Uncertainty quantification in flood risk assessment
Blöschl, Günter; Hall, Julia; Kiss, Andrea; Parajka, Juraj; Perdigão, Rui A. P.; Rogger, Magdalena; Salinas, José Luis; Viglione, Alberto
2017-04-01
Uncertainty is inherent to flood risk assessments because of the complexity of the human-water system, which is characterised by nonlinearities and interdependencies, because of limited knowledge about system properties and because of cognitive biases in human perception and decision-making. On top of the uncertainty associated with the assessment of the existing risk to extreme events, additional uncertainty arises because of temporal changes in the system due to climate change, modifications of the environment, population growth and the associated increase in assets. Novel risk assessment concepts are needed that take into account all these sources of uncertainty. They should be based on the understanding of how flood extremes are generated and how they change over time. They should also account for the dynamics of risk perception of decision makers and population in the floodplains. In this talk we discuss these novel risk assessment concepts through examples from Flood Frequency Hydrology, Socio-Hydrology and Predictions Under Change. We believe that uncertainty quantification in flood risk assessment should lead to a robust approach of integrated flood risk management aiming at enhancing resilience rather than searching for optimal defense strategies.
Information theoretic quantification of diagnostic uncertainty.
Westover, M Brandon; Eiseman, Nathaniel A; Cash, Sydney S; Bianchi, Matt T
2012-01-01
Diagnostic test interpretation remains a challenge in clinical practice. Most physicians receive training in the use of Bayes' rule, which specifies how the sensitivity and specificity of a test for a given disease combine with the pre-test probability to quantify the change in disease probability incurred by a new test result. However, multiple studies demonstrate physicians' deficiencies in probabilistic reasoning, especially with unexpected test results. Information theory, a branch of probability theory dealing explicitly with the quantification of uncertainty, has been proposed as an alternative framework for diagnostic test interpretation, but is even less familiar to physicians. We have previously addressed one key challenge in the practical application of Bayes theorem: the handling of uncertainty in the critical first step of estimating the pre-test probability of disease. This essay aims to present the essential concepts of information theory to physicians in an accessible manner, and to extend previous work regarding uncertainty in pre-test probability estimation by placing this type of uncertainty within a principled information theoretic framework. We address several obstacles hindering physicians' application of information theoretic concepts to diagnostic test interpretation. These include issues of terminology (mathematical meanings of certain information theoretic terms differ from clinical or common parlance) as well as the underlying mathematical assumptions. Finally, we illustrate how, in information theoretic terms, one can understand the effect on diagnostic uncertainty of considering ranges instead of simple point estimates of pre-test probability.
Uncertainty quantification for PZT bimorph actuators
Bravo, Nikolas; Smith, Ralph C.; Crews, John
2018-03-01
In this paper, we discuss the development of a high fidelity model for a PZT bimorph actuator used for micro-air vehicles, which includes the Robobee. We developed a high-fidelity model for the actuator using the homogenized energy model (HEM) framework, which quantifies the nonlinear, hysteretic, and rate-dependent behavior inherent to PZT in dynamic operating regimes. We then discussed an inverse problem on the model. We included local and global sensitivity analysis of the parameters in the high-fidelity model. Finally, we will discuss the results of Bayesian inference and uncertainty quantification on the HEM.
Survey and Evaluate Uncertainty Quantification Methodologies
Energy Technology Data Exchange (ETDEWEB)
Lin, Guang; Engel, David W.; Eslinger, Paul W.
2012-02-01
The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and academic institutions that will develop and deploy state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. The CCSI Toolset will provide end users in industry with a comprehensive, integrated suite of scientifically validated models with uncertainty quantification, optimization, risk analysis and decision making capabilities. The CCSI Toolset will incorporate commercial and open-source software currently in use by industry and will also develop new software tools as necessary to fill technology gaps identified during execution of the project. The CCSI Toolset will (1) enable promising concepts to be more quickly identified through rapid computational screening of devices and processes; (2) reduce the time to design and troubleshoot new devices and processes; (3) quantify the technical risk in taking technology from laboratory-scale to commercial-scale; and (4) stabilize deployment costs more quickly by replacing some of the physical operational tests with virtual power plant simulations. The goal of CCSI is to deliver a toolset that can simulate the scale-up of a broad set of new carbon capture technologies from laboratory scale to full commercial scale. To provide a framework around which the toolset can be developed and demonstrated, we will focus on three Industrial Challenge Problems (ICPs) related to carbon capture technologies relevant to U.S. pulverized coal (PC) power plants. Post combustion capture by solid sorbents is the technology focus of the initial ICP (referred to as ICP A). The goal of the uncertainty quantification (UQ) task (Task 6) is to provide a set of capabilities to the user community for the quantification of uncertainties associated with the carbon
On uncertainty quantification in hydrogeology and hydrogeophysics
Linde, Niklas; Ginsbourger, David; Irving, James; Nobile, Fabio; Doucet, Arnaud
2017-12-01
Recent advances in sensor technologies, field methodologies, numerical modeling, and inversion approaches have contributed to unprecedented imaging of hydrogeological properties and detailed predictions at multiple temporal and spatial scales. Nevertheless, imaging results and predictions will always remain imprecise, which calls for appropriate uncertainty quantification (UQ). In this paper, we outline selected methodological developments together with pioneering UQ applications in hydrogeology and hydrogeophysics. The applied mathematics and statistics literature is not easy to penetrate and this review aims at helping hydrogeologists and hydrogeophysicists to identify suitable approaches for UQ that can be applied and further developed to their specific needs. To bypass the tremendous computational costs associated with forward UQ based on full-physics simulations, we discuss proxy-modeling strategies and multi-resolution (Multi-level Monte Carlo) methods. We consider Bayesian inversion for non-linear and non-Gaussian state-space problems and discuss how Sequential Monte Carlo may become a practical alternative. We also describe strategies to account for forward modeling errors in Bayesian inversion. Finally, we consider hydrogeophysical inversion, where petrophysical uncertainty is often ignored leading to overconfident parameter estimation. The high parameter and data dimensions encountered in hydrogeological and geophysical problems make UQ a complicated and important challenge that has only been partially addressed to date.
Uncertainty Quantification in Geomagnetic Field Modeling
Chulliat, A.; Nair, M. C.; Alken, P.; Meyer, B.; Saltus, R.; Woods, A.
2017-12-01
Geomagnetic field models are mathematical descriptions of the various sources of the Earth's magnetic field, and are generally obtained by solving an inverse problem. They are widely used in research to separate and characterize field sources, but also in many practical applications such as aircraft and ship navigation, smartphone orientation, satellite attitude control, and directional drilling. In recent years, more sophisticated models have been developed, thanks to the continuous availability of high quality satellite data and to progress in modeling techniques. Uncertainty quantification has become an integral part of model development, both to assess the progress made and to address specific users' needs. Here we report on recent advances made by our group in quantifying the uncertainty of geomagnetic field models. We first focus on NOAA's World Magnetic Model (WMM) and the International Geomagnetic Reference Field (IGRF), two reference models of the main (core) magnetic field produced every five years. We describe the methods used in quantifying the model commission error as well as the omission error attributed to various un-modeled sources such as magnetized rocks in the crust and electric current systems in the atmosphere and near-Earth environment. A simple error model was derived from this analysis, to facilitate usage in practical applications. We next report on improvements brought by combining a main field model with a high resolution crustal field model and a time-varying, real-time external field model, like in NOAA's High Definition Geomagnetic Model (HDGM). The obtained uncertainties are used by the directional drilling industry to mitigate health, safety and environment risks.
Verification Validation and Uncertainty Quantification for CGS
Energy Technology Data Exchange (ETDEWEB)
Rider, William J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kamm, James R. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Weirs, V. Gregory [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-01-01
The overall conduct of verification, validation and uncertainty quantification (VVUQ) is discussed through the construction of a workflow relevant to computational modeling including the turbulence problem in the coarse grained simulation (CGS) approach. The workflow contained herein is defined at a high level and constitutes an overview of the activity. Nonetheless, the workflow represents an essential activity in predictive simulation and modeling. VVUQ is complex and necessarily hierarchical in nature. The particular characteristics of VVUQ elements depend upon where the VVUQ activity takes place in the overall hierarchy of physics and models. In this chapter, we focus on the differences between and interplay among validation, calibration and UQ, as well as the difference between UQ and sensitivity analysis. The discussion in this chapter is at a relatively high level and attempts to explain the key issues associated with the overall conduct of VVUQ. The intention is that computational physicists can refer to this chapter for guidance regarding how VVUQ analyses fit into their efforts toward conducting predictive calculations.
Low cost high performance uncertainty quantification
Bekas, C.
2009-01-01
Uncertainty quantification in risk analysis has become a key application. In this context, computing the diagonal of inverse covariance matrices is of paramount importance. Standard techniques, that employ matrix factorizations, incur a cubic cost which quickly becomes intractable with the current explosion of data sizes. In this work we reduce this complexity to quadratic with the synergy of two algorithms that gracefully complement each other and lead to a radically different approach. First, we turned to stochastic estimation of the diagonal. This allowed us to cast the problem as a linear system with a relatively small number of multiple right hand sides. Second, for this linear system we developed a novel, mixed precision, iterative refinement scheme, which uses iterative solvers instead of matrix factorizations. We demonstrate that the new framework not only achieves the much needed quadratic cost but in addition offers excellent opportunities for scaling at massively parallel environments. We based our implementation on BLAS 3 kernels that ensure very high processor performance. We achieved a peak performance of 730 TFlops on 72 BG/P racks, with a sustained performance 73% of theoretical peak. We stress that the techniques presented in this work are quite general and applicable to several other important applications. Copyright © 2009 ACM.
Exploring Heterogeneous Multicore Architectures for Advanced Embedded Uncertainty Quantification.
Energy Technology Data Exchange (ETDEWEB)
Phipps, Eric T.; Edwards, Harold C.; Hu, Jonathan J.
2014-09-01
We explore rearrangements of classical uncertainty quantification methods with the aim of achieving higher aggregate performance for uncertainty quantification calculations on emerging multicore and manycore architectures. We show a rearrangement of the stochastic Galerkin method leads to improved performance and scalability on several computational architectures whereby un- certainty information is propagated at the lowest levels of the simulation code improving memory access patterns, exposing new dimensions of fine grained parallelism, and reducing communica- tion. We also develop a general framework for implementing such rearrangements for a diverse set of uncertainty quantification algorithms as well as computational simulation codes to which they are applied.
Quantification of Uncertainties in Integrated Spacecraft System Models, Phase I
National Aeronautics and Space Administration — The proposed effort is to investigate a novel uncertainty quantification (UQ) approach based on non-intrusive polynomial chaos (NIPC) for computationally efficient...
The Method of Manufactured Universes for validating uncertainty quantification methods
Stripling, H.F.; Adams, M.L.; McClarren, R.G.; Mallick, B.K.
2011-01-01
The Method of Manufactured Universes is presented as a validation framework for uncertainty quantification (UQ) methodologies and as a tool for exploring the effects of statistical and modeling assumptions embedded in these methods. The framework
Multi data reservior history matching and uncertainty quantification framework
Katterbauer, Klemens; Hoteit, Ibrahim; Sun, Shuyu
2015-01-01
A multi-data reservoir history matching and uncertainty quantification framework is provided. The framework can utilize multiple data sets such as production, seismic, electromagnetic, gravimetric and surface deformation data for improving
Uncertainty quantification in nanomechanical measurements using the atomic force microscope
Ryan Wagner; Robert Moon; Jon Pratt; Gordon Shaw; Arvind Raman
2011-01-01
Quantifying uncertainty in measured properties of nanomaterials is a prerequisite for the manufacture of reliable nanoengineered materials and products. Yet, rigorous uncertainty quantification (UQ) is rarely applied for material property measurements with the atomic force microscope (AFM), a widely used instrument that can measure properties at nanometer scale...
Decay heat uncertainty quantification of MYRRHA
Directory of Open Access Journals (Sweden)
Fiorito Luca
2017-01-01
Full Text Available MYRRHA is a lead-bismuth cooled MOX-fueled accelerator driven system (ADS currently in the design phase at SCK·CEN in Belgium. The correct evaluation of the decay heat and of its uncertainty level is very important for the safety demonstration of the reactor. In the first part of this work we assessed the decay heat released by the MYRRHA core using the ALEPH-2 burnup code. The second part of the study focused on the nuclear data uncertainty and covariance propagation to the MYRRHA decay heat. Radioactive decay data, independent fission yield and cross section uncertainties/covariances were propagated using two nuclear data sampling codes, namely NUDUNA and SANDY. According to the results, 238U cross sections and fission yield data are the largest contributors to the MYRRHA decay heat uncertainty. The calculated uncertainty values are deemed acceptable from the safety point of view as they are well within the available regulatory limits.
Quantification of uncertainties of modeling and simulation
International Nuclear Information System (INIS)
Ma Zhibo; Yin Jianwei
2012-01-01
The principles of Modeling and Simulation (M and S) is interpreted by a functional relation, from which the total uncertainties of M and S are identified and sorted to three parts considered to vary along with the conceptual models' parameters. According to the idea of verification and validation, the space of the parameters is parted to verified and applied domains, uncertainties in the verified domain are quantified by comparison between numerical and standard results, and those in the applied domain are quantified by a newly developed extrapolating method. Examples are presented to demonstrate and qualify the ideas aimed to build a framework to quantify the uncertainties of M and S. (authors)
Nuclear Data Uncertainty Quantification: Past, Present and Future
International Nuclear Information System (INIS)
Smith, D.L.
2015-01-01
An historical overview is provided of the mathematical foundations of uncertainty quantification and the roles played in the more recent past by nuclear data uncertainties in nuclear data evaluations and nuclear applications. Significant advances that have established the mathematical framework for contemporary nuclear data evaluation methods, as well as the use of uncertainty information in nuclear data evaluation and nuclear applications, are described. This is followed by a brief examination of the current status concerning nuclear data evaluation methodology, covariance data generation, and the application of evaluated nuclear data uncertainties in contemporary nuclear technology. A few possible areas for future investigation of this subject are also suggested
Nuclear Data Uncertainty Quantification: Past, Present and Future
Smith, D. L.
2015-01-01
An historical overview is provided of the mathematical foundations of uncertainty quantification and the roles played in the more recent past by nuclear data uncertainties in nuclear data evaluations and nuclear applications. Significant advances that have established the mathematical framework for contemporary nuclear data evaluation methods, as well as the use of uncertainty information in nuclear data evaluation and nuclear applications, are described. This is followed by a brief examination of the current status concerning nuclear data evaluation methodology, covariance data generation, and the application of evaluated nuclear data uncertainties in contemporary nuclear technology. A few possible areas for future investigation of this subject are also suggested.
Decay heat uncertainty quantification of MYRRHA
Fiorito Luca; Buss Oliver; Hoefer Axel; Stankovskiy Alexey; Eynde Gert Van den
2017-01-01
MYRRHA is a lead-bismuth cooled MOX-fueled accelerator driven system (ADS) currently in the design phase at SCK·CEN in Belgium. The correct evaluation of the decay heat and of its uncertainty level is very important for the safety demonstration of the reactor. In the first part of this work we assessed the decay heat released by the MYRRHA core using the ALEPH-2 burnup code. The second part of the study focused on the nuclear data uncertainty and covariance propagation to the MYRRHA decay hea...
Collaborative framework for PIV uncertainty quantification: comparative assessment of methods
International Nuclear Information System (INIS)
Sciacchitano, Andrea; Scarano, Fulvio; Neal, Douglas R; Smith, Barton L; Warner, Scott O; Vlachos, Pavlos P; Wieneke, Bernhard
2015-01-01
A posteriori uncertainty quantification of particle image velocimetry (PIV) data is essential to obtain accurate estimates of the uncertainty associated with a given experiment. This is particularly relevant when measurements are used to validate computational models or in design and decision processes. In spite of the importance of the subject, the first PIV uncertainty quantification (PIV-UQ) methods have been developed only in the last three years. The present work is a comparative assessment of four approaches recently proposed in the literature: the uncertainty surface method (Timmins et al 2012), the particle disparity approach (Sciacchitano et al 2013), the peak ratio criterion (Charonko and Vlachos 2013) and the correlation statistics method (Wieneke 2015). The analysis is based upon experiments conducted for this specific purpose, where several measurement techniques are employed simultaneously. The performances of the above approaches are surveyed across different measurement conditions and flow regimes. (paper)
Uncertainty quantification in wind farm flow models
DEFF Research Database (Denmark)
Murcia Leon, Juan Pablo
uncertainties through a model chain are presented and applied to several wind energy related problems such as: annual energy production estimation, wind turbine power curve estimation, wake model calibration and validation, and estimation of lifetime equivalent fatigue loads on a wind turbine. Statistical...
Quantification of Uncertainty in Thermal Building Simulation
DEFF Research Database (Denmark)
Brohus, Henrik; Haghighat, F.; Frier, Christian
In order to quantify uncertainty in thermal building simulation stochastic modelling is applied on a building model. An application of stochastic differential equations is presented in Part 1 comprising a general heat balance for an arbitrary number of loads and zones in a building to determine...
Uncertainty quantification and race car aerodynamics
Bradford, J; Montomoli, F; D'Ammaro, A
2014-01-01
28.04.15 KB. Ok to add accepted version to spiral, embargo expired Car aerodynamics are subjected to a number of random variables which introduce uncertainty into the downforce performance. These can include, but are not limited to, pitch variations and ride height variations. Studying the effect of the random variations in these parameters is important to predict accurately the car performance during the race. Despite their importance the assessment of these variations is difficult and it...
Uncertainty Quantification of Multi-Phase Closures
Energy Technology Data Exchange (ETDEWEB)
Nadiga, Balasubramanya T. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Baglietto, Emilio [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
2017-10-27
In the ensemble-averaged dispersed phase formulation used for CFD of multiphase ows in nuclear reactor thermohydraulics, closures of interphase transfer of mass, momentum, and energy constitute, by far, the biggest source of error and uncertainty. Reliable estimators of this source of error and uncertainty are currently non-existent. Here, we report on how modern Validation and Uncertainty Quanti cation (VUQ) techniques can be leveraged to not only quantify such errors and uncertainties, but also to uncover (unintended) interactions between closures of di erent phenomena. As such this approach serves as a valuable aide in the research and development of multiphase closures. The joint modeling of lift, drag, wall lubrication, and turbulent dispersion|forces that lead to tranfer of momentum between the liquid and gas phases|is examined in the frame- work of validation of the adiabatic but turbulent experiments of Liu and Banko , 1993. An extensive calibration study is undertaken with a popular combination of closure relations and the popular k-ϵ turbulence model in a Bayesian framework. When a wide range of super cial liquid and gas velocities and void fractions is considered, it is found that this set of closures can be validated against the experimental data only by allowing large variations in the coe cients associated with the closures. We argue that such an extent of variation is a measure of uncertainty induced by the chosen set of closures. We also nd that while mean uid velocity and void fraction pro les are properly t, uctuating uid velocity may or may not be properly t. This aspect needs to be investigated further. The popular set of closures considered contains ad-hoc components and are undesirable from a predictive modeling point of view. Consequently, we next consider improvements that are being developed by the MIT group under CASL and which remove the ad-hoc elements. We use non-intrusive methodologies for sensitivity analysis and calibration (using
A posteriori uncertainty quantification of PIV-based pressure data
Azijli, I.; Sciacchitano, A.; Ragni, D.; Palha Da Silva Clérigo, A.; Dwight, R.P.
2016-01-01
A methodology for a posteriori uncertainty quantification of pressure data retrieved from particle image velocimetry (PIV) is proposed. It relies upon the Bayesian framework, where the posterior distribution (probability distribution of the true velocity, given the PIV measurements) is obtained from
Uncertainty Quantification for Large-Scale Ice Sheet Modeling
Energy Technology Data Exchange (ETDEWEB)
Ghattas, Omar [Univ. of Texas, Austin, TX (United States)
2016-02-05
This report summarizes our work to develop advanced forward and inverse solvers and uncertainty quantification capabilities for a nonlinear 3D full Stokes continental-scale ice sheet flow model. The components include: (1) forward solver: a new state-of-the-art parallel adaptive scalable high-order-accurate mass-conservative Newton-based 3D nonlinear full Stokes ice sheet flow simulator; (2) inverse solver: a new adjoint-based inexact Newton method for solution of deterministic inverse problems governed by the above 3D nonlinear full Stokes ice flow model; and (3) uncertainty quantification: a novel Hessian-based Bayesian method for quantifying uncertainties in the inverse ice sheet flow solution and propagating them forward into predictions of quantities of interest such as ice mass flux to the ocean.
PIV uncertainty quantification by image matching
International Nuclear Information System (INIS)
Sciacchitano, Andrea; Scarano, Fulvio; Wieneke, Bernhard
2013-01-01
A novel method is presented to quantify the uncertainty of PIV data. The approach is a posteriori, i.e. the unknown actual error of the measured velocity field is estimated using the velocity field itself as input along with the original images. The principle of the method relies on the concept of super-resolution: the image pair is matched according to the cross-correlation analysis and the residual distance between matched particle image pairs (particle disparity vector) due to incomplete match between the two exposures is measured. The ensemble of disparity vectors within the interrogation window is analyzed statistically. The dispersion of the disparity vector returns the estimate of the random error, whereas the mean value of the disparity indicates the occurrence of a systematic error. The validity of the working principle is first demonstrated via Monte Carlo simulations. Two different interrogation algorithms are considered, namely the cross-correlation with discrete window offset and the multi-pass with window deformation. In the simulated recordings, the effects of particle image displacement, its gradient, out-of-plane motion, seeding density and particle image diameter are considered. In all cases good agreement is retrieved, indicating that the error estimator is able to follow the trend of the actual error with satisfactory precision. Experiments where time-resolved PIV data are available are used to prove the concept under realistic measurement conditions. In this case the ‘exact’ velocity field is unknown; however a high accuracy estimate is obtained with an advanced interrogation algorithm that exploits the redundant information of highly temporally oversampled data (pyramid correlation, Sciacchitano et al (2012 Exp. Fluids 53 1087–105)). The image-matching estimator returns the instantaneous distribution of the estimated velocity measurement error. The spatial distribution compares very well with that of the actual error with maxima in the
Uncertainty quantification in capacitive RF MEMS switches
Pax, Benjamin J.
Development of radio frequency micro electrical-mechanical systems (RF MEMS) has led to novel approaches to implement electrical circuitry. The introduction of capacitive MEMS switches, in particular, has shown promise in low-loss, low-power devices. However, the promise of MEMS switches has not yet been completely realized. RF-MEMS switches are known to fail after only a few months of operation, and nominally similar designs show wide variability in lifetime. Modeling switch operation using nominal or as-designed parameters cannot predict the statistical spread in the number of cycles to failure, and probabilistic methods are necessary. A Bayesian framework for calibration, validation and prediction offers an integrated approach to quantifying the uncertainty in predictions of MEMS switch performance. The objective of this thesis is to use the Bayesian framework to predict the creep-related deflection of the PRISM RF-MEMS switch over several thousand hours of operation. The PRISM switch used in this thesis is the focus of research at Purdue's PRISM center, and is a capacitive contacting RF-MEMS switch. It employs a fixed-fixed nickel membrane which is electrostatically actuated by applying voltage between the membrane and a pull-down electrode. Creep plays a central role in the reliability of this switch. The focus of this thesis is on the creep model, which is calibrated against experimental data measured for a frog-leg varactor fabricated and characterized at Purdue University. Creep plasticity is modeled using plate element theory with electrostatic forces being generated using either parallel plate approximations where appropriate, or solving for the full 3D potential field. For the latter, structure-electrostatics interaction is determined through immersed boundary method. A probabilistic framework using generalized polynomial chaos (gPC) is used to create surrogate models to mitigate the costly full physics simulations, and Bayesian calibration and forward
Statistical Uncertainty Quantification of Physical Models during Reflood of LBLOCA
Energy Technology Data Exchange (ETDEWEB)
Oh, Deog Yeon; Seul, Kwang Won; Woo, Sweng Woong [Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of)
2015-05-15
The use of the best-estimate (BE) computer codes in safety analysis for loss-of-coolant accident (LOCA) is the major trend in many countries to reduce the significant conservatism. A key feature of this BE evaluation requires the licensee to quantify the uncertainty of the calculations. So, it is very important how to determine the uncertainty distribution before conducting the uncertainty evaluation. Uncertainty includes those of physical model and correlation, plant operational parameters, and so forth. The quantification process is often performed mainly by subjective expert judgment or obtained from reference documents of computer code. In this respect, more mathematical methods are needed to reasonably determine the uncertainty ranges. The first uncertainty quantification are performed with the various increments for two influential uncertainty parameters to get the calculated responses and their derivatives. The different data set with two influential uncertainty parameters for FEBA tests, are chosen applying more strict criteria for selecting responses and their derivatives, which may be considered as the user’s effect in the CIRCÉ applications. Finally, three influential uncertainty parameters are considered to study the effect on the number of uncertainty parameters due to the limitation of CIRCÉ method. With the determined uncertainty ranges, uncertainty evaluations for FEBA tests are performed to check whether the experimental responses such as the cladding temperature or pressure drop are inside the limits of calculated uncertainty bounds. A confirmation step will be performed to evaluate the quality of the information in the case of the different reflooding PERICLES experiments. The uncertainty ranges of physical model in MARS-KS thermal-hydraulic code during the reflooding were quantified by CIRCÉ method using FEBA experiment tests, instead of expert judgment. Also, through the uncertainty evaluation for FEBA and PERICLES tests, it was confirmed
Stochastic Systems Uncertainty Quantification and Propagation
Grigoriu, Mircea
2012-01-01
Uncertainty is an inherent feature of both properties of physical systems and the inputs to these systems that needs to be quantified for cost effective and reliable designs. The states of these systems satisfy equations with random entries, referred to as stochastic equations, so that they are random functions of time and/or space. The solution of stochastic equations poses notable technical difficulties that are frequently circumvented by heuristic assumptions at the expense of accuracy and rigor. The main objective of Stochastic Systems is to promoting the development of accurate and efficient methods for solving stochastic equations and to foster interactions between engineers, scientists, and mathematicians. To achieve these objectives Stochastic Systems presents: · A clear and brief review of essential concepts on probability theory, random functions, stochastic calculus, Monte Carlo simulation, and functional analysis · Probabilistic models for random variables an...
Uncertainty quantification in nanomechanical measurements using the atomic force microscope
International Nuclear Information System (INIS)
Wagner, Ryan; Raman, Arvind; Moon, Robert; Pratt, Jon; Shaw, Gordon
2011-01-01
Quantifying uncertainty in measured properties of nanomaterials is a prerequisite for the manufacture of reliable nanoengineered materials and products. Yet, rigorous uncertainty quantification (UQ) is rarely applied for material property measurements with the atomic force microscope (AFM), a widely used instrument that can measure properties at nanometer scale resolution of both inorganic and biological surfaces and nanomaterials. We present a framework to ascribe uncertainty to local nanomechanical properties of any nanoparticle or surface measured with the AFM by taking into account the main uncertainty sources inherent in such measurements. We demonstrate the framework by quantifying uncertainty in AFM-based measurements of the transverse elastic modulus of cellulose nanocrystals (CNCs), an abundant, plant-derived nanomaterial whose mechanical properties are comparable to Kevlar fibers. For a single, isolated CNC the transverse elastic modulus was found to have a mean of 8.1 GPa and a 95% confidence interval of 2.7–20 GPa. A key result is that multiple replicates of force–distance curves do not sample the important sources of uncertainty, which are systematic in nature. The dominant source of uncertainty is the nondimensional photodiode sensitivity calibration rather than the cantilever stiffness or Z-piezo calibrations. The results underscore the great need for, and open a path towards, quantifying and minimizing uncertainty in AFM-based material property measurements of nanoparticles, nanostructured surfaces, thin films, polymers and biomaterials.
Multi data reservior history matching and uncertainty quantification framework
Katterbauer, Klemens
2015-11-26
A multi-data reservoir history matching and uncertainty quantification framework is provided. The framework can utilize multiple data sets such as production, seismic, electromagnetic, gravimetric and surface deformation data for improving the history matching process. The framework can consist of a geological model that is interfaced with a reservoir simulator. The reservoir simulator can interface with seismic, electromagnetic, gravimetric and surface deformation modules to predict the corresponding observations. The observations can then be incorporated into a recursive filter that subsequently updates the model state and parameters distributions, providing a general framework to quantify and eventually reduce with the data, uncertainty in the estimated reservoir state and parameters.
Final Report: Quantification of Uncertainty in Extreme Scale Computations (QUEST)
Energy Technology Data Exchange (ETDEWEB)
Marzouk, Youssef [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Conrad, Patrick [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Bigoni, Daniele [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Parno, Matthew [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
2017-06-09
QUEST (\\url{www.quest-scidac.org}) is a SciDAC Institute that is focused on uncertainty quantification (UQ) in large-scale scientific computations. Our goals are to (1) advance the state of the art in UQ mathematics, algorithms, and software; and (2) provide modeling, algorithmic, and general UQ expertise, together with software tools, to other SciDAC projects, thereby enabling and guiding a broad range of UQ activities in their respective contexts. QUEST is a collaboration among six institutions (Sandia National Laboratories, Los Alamos National Laboratory, the University of Southern California, Massachusetts Institute of Technology, the University of Texas at Austin, and Duke University) with a history of joint UQ research. Our vision encompasses all aspects of UQ in leadership-class computing. This includes the well-founded setup of UQ problems; characterization of the input space given available data/information; local and global sensitivity analysis; adaptive dimensionality and order reduction; forward and inverse propagation of uncertainty; handling of application code failures, missing data, and hardware/software fault tolerance; and model inadequacy, comparison, validation, selection, and averaging. The nature of the UQ problem requires the seamless combination of data, models, and information across this landscape in a manner that provides a self-consistent quantification of requisite uncertainties in predictions from computational models. Accordingly, our UQ methods and tools span an interdisciplinary space across applied math, information theory, and statistics. The MIT QUEST effort centers on statistical inference and methods for surrogate or reduced-order modeling. MIT personnel have been responsible for the development of adaptive sampling methods, methods for approximating computationally intensive models, and software for both forward uncertainty propagation and statistical inverse problems. A key software product of the MIT QUEST effort is the MIT
Subspace-based Inverse Uncertainty Quantification for Nuclear Data Assessment
Energy Technology Data Exchange (ETDEWEB)
Khuwaileh, B.A., E-mail: bakhuwai@ncsu.edu; Abdel-Khalik, H.S.
2015-01-15
Safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. An inverse problem can be defined and solved to assess the sources of uncertainty, and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this work a subspace-based algorithm for inverse sensitivity/uncertainty quantification (IS/UQ) has been developed to enable analysts account for all sources of nuclear data uncertainties in support of target accuracy assessment-type analysis. An approximate analytical solution of the optimization problem is used to guide the search for the dominant uncertainty subspace. By limiting the search to a subspace, the degrees of freedom available for the optimization search are significantly reduced. A quarter PWR fuel assembly is modeled and the accuracy of the multiplication factor and the fission reaction rate are used as reactor attributes whose uncertainties are to be reduced. Numerical experiments are used to demonstrate the computational efficiency of the proposed algorithm. Our ongoing work is focusing on extending the proposed algorithm to account for various forms of feedback, e.g., thermal-hydraulics and depletion effects.
Quantification of Safety-Critical Software Test Uncertainty
International Nuclear Information System (INIS)
Khalaquzzaman, M.; Cho, Jaehyun; Lee, Seung Jun; Jung, Wondea
2015-01-01
The method, conservatively assumes that the failure probability of a software for the untested inputs is 1, and the failure probability turns in 0 for successful testing of all test cases. However, in reality the chance of failure exists due to the test uncertainty. Some studies have been carried out to identify the test attributes that affect the test quality. Cao discussed the testing effort, testing coverage, and testing environment. Management of the test uncertainties was discussed in. In this study, the test uncertainty has been considered to estimate the software failure probability because the software testing process is considered to be inherently uncertain. A reliability estimation of software is very important for a probabilistic safety analysis of a digital safety critical system of NPPs. This study focused on the estimation of the probability of a software failure that considers the uncertainty in software testing. In our study, BBN has been employed as an example model for software test uncertainty quantification. Although it can be argued that the direct expert elicitation of test uncertainty is much simpler than BBN estimation, however the BBN approach provides more insights and a basis for uncertainty estimation
Ideas underlying the Quantification of Margins and Uncertainties
Energy Technology Data Exchange (ETDEWEB)
Pilch, Martin, E-mail: mpilch@sandia.gov [Department 1514, Sandia National Laboratories, Albuquerque, NM 87185-0828 (United States); Trucano, Timothy G. [Department 1411, Sandia National Laboratories, Albuquerque, NM 87185-0370 (United States); Helton, Jon C. [Department of Mathematics and Statistics, Arizona State University, Tempe, AZ 85287-1804 (United States)
2011-09-15
Key ideas underlying the application of Quantification of Margins and Uncertainties (QMU) to nuclear weapons stockpile lifecycle decisions are described. While QMU is a broad process and methodology for generating critical technical information to be used in U.S. nuclear weapon stockpile management, this paper emphasizes one component, which is information produced by computational modeling and simulation. In particular, the following topics are discussed: (i) the key principles of developing QMU information in the form of Best Estimate Plus Uncertainty, (ii) the need to separate aleatory and epistemic uncertainty in QMU, and (iii) the properties of risk-informed decision making (RIDM) that are best suited for effective application of QMU. The paper is written at a high level, but provides an extensive bibliography of useful papers for interested readers to deepen their understanding of the presented ideas.
Aerosol-type retrieval and uncertainty quantification from OMI data
Kauppi, Anu; Kolmonen, Pekka; Laine, Marko; Tamminen, Johanna
2017-11-01
We discuss uncertainty quantification for aerosol-type selection in satellite-based atmospheric aerosol retrieval. The retrieval procedure uses precalculated aerosol microphysical models stored in look-up tables (LUTs) and top-of-atmosphere (TOA) spectral reflectance measurements to solve the aerosol characteristics. The forward model approximations cause systematic differences between the modelled and observed reflectance. Acknowledging this model discrepancy as a source of uncertainty allows us to produce more realistic uncertainty estimates and assists the selection of the most appropriate LUTs for each individual retrieval.This paper focuses on the aerosol microphysical model selection and characterisation of uncertainty in the retrieved aerosol type and aerosol optical depth (AOD). The concept of model evidence is used as a tool for model comparison. The method is based on Bayesian inference approach, in which all uncertainties are described as a posterior probability distribution. When there is no single best-matching aerosol microphysical model, we use a statistical technique based on Bayesian model averaging to combine AOD posterior probability densities of the best-fitting models to obtain an averaged AOD estimate. We also determine the shared evidence of the best-matching models of a certain main aerosol type in order to quantify how plausible it is that it represents the underlying atmospheric aerosol conditions.The developed method is applied to Ozone Monitoring Instrument (OMI) measurements using a multiwavelength approach for retrieving the aerosol type and AOD estimate with uncertainty quantification for cloud-free over-land pixels. Several larger pixel set areas were studied in order to investigate the robustness of the developed method. We evaluated the retrieved AOD by comparison with ground-based measurements at example sites. We found that the uncertainty of AOD expressed by posterior probability distribution reflects the difficulty in model
Aerosol-type retrieval and uncertainty quantification from OMI data
Directory of Open Access Journals (Sweden)
A. Kauppi
2017-11-01
Full Text Available We discuss uncertainty quantification for aerosol-type selection in satellite-based atmospheric aerosol retrieval. The retrieval procedure uses precalculated aerosol microphysical models stored in look-up tables (LUTs and top-of-atmosphere (TOA spectral reflectance measurements to solve the aerosol characteristics. The forward model approximations cause systematic differences between the modelled and observed reflectance. Acknowledging this model discrepancy as a source of uncertainty allows us to produce more realistic uncertainty estimates and assists the selection of the most appropriate LUTs for each individual retrieval.This paper focuses on the aerosol microphysical model selection and characterisation of uncertainty in the retrieved aerosol type and aerosol optical depth (AOD. The concept of model evidence is used as a tool for model comparison. The method is based on Bayesian inference approach, in which all uncertainties are described as a posterior probability distribution. When there is no single best-matching aerosol microphysical model, we use a statistical technique based on Bayesian model averaging to combine AOD posterior probability densities of the best-fitting models to obtain an averaged AOD estimate. We also determine the shared evidence of the best-matching models of a certain main aerosol type in order to quantify how plausible it is that it represents the underlying atmospheric aerosol conditions.The developed method is applied to Ozone Monitoring Instrument (OMI measurements using a multiwavelength approach for retrieving the aerosol type and AOD estimate with uncertainty quantification for cloud-free over-land pixels. Several larger pixel set areas were studied in order to investigate the robustness of the developed method. We evaluated the retrieved AOD by comparison with ground-based measurements at example sites. We found that the uncertainty of AOD expressed by posterior probability distribution reflects the
An EPGPT-based approach for uncertainty quantification
International Nuclear Information System (INIS)
Wang, C.; Abdel-Khalik, H. S.
2012-01-01
Generalized Perturbation Theory (GPT) has been widely used by many scientific disciplines to perform sensitivity analysis and uncertainty quantification. This manuscript employs recent developments in GPT theory, collectively referred to as Exact-to-Precision Generalized Perturbation Theory (EPGPT), to enable uncertainty quantification for computationally challenging models, e.g. nonlinear models associated with many input parameters and many output responses and with general non-Gaussian parameters distributions. The core difference between EPGPT and existing GPT is in the way the problem is formulated. GPT formulates an adjoint problem that is dependent on the response of interest. It tries to capture via the adjoint solution the relationship between the response of interest and the constraints on the state variations. EPGPT recasts the problem in terms of a smaller set of what is referred to as the 'active' responses which are solely dependent on the physics model and the boundary and initial conditions rather than on the responses of interest. The objective of this work is to apply an EPGPT methodology to propagate cross-sections variations in typical reactor design calculations. The goal is to illustrate its use and the associated impact for situations where the typical Gaussian assumption for parameters uncertainties is not valid and when nonlinear behavior must be considered. To allow this demonstration, exaggerated variations will be employed to stimulate nonlinear behavior in simple prototypical neutronics models. (authors)
Complex Empiricism and the Quantification of Uncertainty in Paleoclimate Reconstructions
Brumble, K. C.
2014-12-01
Because the global climate cannot be observed directly, and because of vast and noisy data sets, climate science is a rich field to study how computational statistics informs what it means to do empirical science. Traditionally held virtues of empirical science and empirical methods like reproducibility, independence, and straightforward observation are complicated by representational choices involved in statistical modeling and data handling. Examining how climate reconstructions instantiate complicated empirical relationships between model, data, and predictions reveals that the path from data to prediction does not match traditional conceptions of empirical inference either. Rather, the empirical inferences involved are "complex" in that they require articulation of a good deal of statistical processing wherein assumptions are adopted and representational decisions made, often in the face of substantial uncertainties. Proxy reconstructions are both statistical and paleoclimate science activities aimed at using a variety of proxies to reconstruct past climate behavior. Paleoclimate proxy reconstructions also involve complex data handling and statistical refinement, leading to the current emphasis in the field on the quantification of uncertainty in reconstructions. In this presentation I explore how the processing needed for the correlation of diverse, large, and messy data sets necessitate the explicit quantification of the uncertainties stemming from wrangling proxies into manageable suites. I also address how semi-empirical pseudo-proxy methods allow for the exploration of signal detection in data sets, and as intermediary steps for statistical experimentation.
Uncertainty quantification applied to the radiological characterization of radioactive waste.
Zaffora, B; Magistris, M; Saporta, G; Chevalier, J-P
2017-09-01
This paper describes the process adopted at the European Organization for Nuclear Research (CERN) to quantify uncertainties affecting the characterization of very-low-level radioactive waste. Radioactive waste is a by-product of the operation of high-energy particle accelerators. Radioactive waste must be characterized to ensure its safe disposal in final repositories. Characterizing radioactive waste means establishing the list of radionuclides together with their activities. The estimated activity levels are compared to the limits given by the national authority of the waste disposal. The quantification of the uncertainty affecting the concentration of the radionuclides is therefore essential to estimate the acceptability of the waste in the final repository but also to control the sorting, volume reduction and packaging phases of the characterization process. The characterization method consists of estimating the activity of produced radionuclides either by experimental methods or statistical approaches. The uncertainties are estimated using classical statistical methods and uncertainty propagation. A mixed multivariate random vector is built to generate random input parameters for the activity calculations. The random vector is a robust tool to account for the unknown radiological history of legacy waste. This analytical technique is also particularly useful to generate random chemical compositions of materials when the trace element concentrations are not available or cannot be measured. The methodology was validated using a waste population of legacy copper activated at CERN. The methodology introduced here represents a first approach for the uncertainty quantification (UQ) of the characterization process of waste produced at particle accelerators. Copyright © 2017 Elsevier Ltd. All rights reserved.
Uncertainty Quantification for Monitoring of Civil Structures from Vibration Measurements
Döhler, Michael; Mevel, Laurent
2014-05-01
Health Monitoring of civil structures can be performed by detecting changes in the modal parameters of a structure, or more directly in the measured vibration signals. For a continuous monitoring the excitation of a structure is usually ambient, thus unknown and assumed to be noise. Hence, all estimates from the vibration measurements are realizations of random variables with inherent uncertainty due to (unknown) process and measurement noise and finite data length. In this talk, a strategy for quantifying the uncertainties of modal parameter estimates from a subspace-based system identification approach is presented and the importance of uncertainty quantification in monitoring approaches is shown. Furthermore, a damage detection method is presented, which is based on the direct comparison of the measured vibration signals without estimating modal parameters, while taking the statistical uncertainty in the signals correctly into account. The usefulness of both strategies is illustrated on data from a progressive damage action on a prestressed concrete bridge. References E. Carden and P. Fanning. Vibration based condition monitoring: a review. Structural Health Monitoring, 3(4):355-377, 2004. M. Döhler and L. Mevel. Efficient multi-order uncertainty computation for stochastic subspace identification. Mechanical Systems and Signal Processing, 38(2):346-366, 2013. M. Döhler, L. Mevel, and F. Hille. Subspace-based damage detection under changes in the ambient excitation statistics. Mechanical Systems and Signal Processing, 45(1):207-224, 2014.
Molecular nonlinear dynamics and protein thermal uncertainty quantification
Xia, Kelin; Wei, Guo-Wei
2014-01-01
This work introduces molecular nonlinear dynamics (MND) as a new approach for describing protein folding and aggregation. By using a mode system, we show that the MND of disordered proteins is chaotic while that of folded proteins exhibits intrinsically low dimensional manifolds (ILDMs). The stability of ILDMs is found to strongly correlate with protein energies. We propose a novel method for protein thermal uncertainty quantification based on persistently invariant ILDMs. Extensive comparison with experimental data and the state-of-the-art methods in the field validate the proposed new method for protein B-factor prediction. PMID:24697365
Preliminary Results on Uncertainty Quantification for Pattern Analytics
Energy Technology Data Exchange (ETDEWEB)
Stracuzzi, David John [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Brost, Randolph [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Chen, Maximillian Gene [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Malinas, Rebecca [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Peterson, Matthew Gregor [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Phillips, Cynthia A. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Robinson, David G. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Woodbridge, Diane [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
2015-09-01
This report summarizes preliminary research into uncertainty quantification for pattern ana- lytics within the context of the Pattern Analytics to Support High-Performance Exploitation and Reasoning (PANTHER) project. The primary focus of PANTHER was to make large quantities of remote sensing data searchable by analysts. The work described in this re- port adds nuance to both the initial data preparation steps and the search process. Search queries are transformed from does the specified pattern exist in the data? to how certain is the system that the returned results match the query? We show example results for both data processing and search, and discuss a number of possible improvements for each.
Uncertainty Quantification and Statistical Engineering for Hypersonic Entry Applications
Cozmuta, Ioana
2011-01-01
NASA has invested significant resources in developing and validating a mathematical construct for TPS margin management: a) Tailorable for low/high reliability missions; b) Tailorable for ablative/reusable TPS; c) Uncertainty Quantification and Statistical Engineering are valuable tools not exploited enough; and d) Need to define strategies combining both Theoretical Tools and Experimental Methods. The main reason for this lecture is to give a flavor of where UQ and SE could contribute and hope that the broader community will work with us to improve in these areas.
Uncertainty quantification in ion–solid interaction simulations
Energy Technology Data Exchange (ETDEWEB)
Preuss, R., E-mail: preuss@ipp.mpg.de; Toussaint, U. von
2017-02-15
Within the framework of Bayesian uncertainty quantification we propose a non-intrusive reduced-order spectral approach (polynomial chaos expansion) to the simulation of ion–solid interactions. The method not only reduces the number of function evaluations but provides simultaneously a quantitative measure for which combinations of inputs have the most important impact on the result. It is applied to SDTRIM-simulations (Möller et al., 1988) with several uncertain and Gaussian distributed input parameters (i.e. angle, projectile energy, surface binding energy, target composition) and the results are compared to full-grid based approaches and sampling based methods with respect to reliability, efficiency and scalability.
Uncertainty Quantification for Ice Sheet Science and Sea Level Projections
Boening, C.; Schlegel, N.; Limonadi, D.; Schodlok, M.; Seroussi, H. L.; Larour, E. Y.; Watkins, M. M.
2017-12-01
In order to better quantify uncertainties in global mean sea level rise projections and in particular upper bounds, we aim at systematically evaluating the contributions from ice sheets and potential for extreme sea level rise due to sudden ice mass loss. Here, we take advantage of established uncertainty quantification tools embedded within the Ice Sheet System Model (ISSM) as well as sensitivities to ice/ocean interactions using melt rates and melt potential derived from MITgcm/ECCO2. With the use of these tools, we conduct Monte-Carlo style sampling experiments on forward simulations of the Antarctic ice sheet, by varying internal parameters and boundary conditions of the system over both extreme and credible worst-case ranges. Uncertainty bounds for climate forcing are informed by CMIP5 ensemble precipitation and ice melt estimates for year 2100, and uncertainty bounds for ocean melt rates are derived from a suite of regional sensitivity experiments using MITgcm. Resulting statistics allow us to assess how regional uncertainty in various parameters affect model estimates of century-scale sea level rise projections. The results inform efforts to a) isolate the processes and inputs that are most responsible for determining ice sheet contribution to sea level; b) redefine uncertainty brackets for century-scale projections; and c) provide a prioritized list of measurements, along with quantitative information on spatial and temporal resolution, required for reducing uncertainty in future sea level rise projections. Results indicate that ice sheet mass loss is dependent on the spatial resolution of key boundary conditions - such as bedrock topography and melt rates at the ice-ocean interface. This work is performed at and supported by the California Institute of Technology's Jet Propulsion Laboratory. Supercomputing time is also supported through a contract with the National Aeronautics and Space Administration's Cryosphere program.
Statistical approach for uncertainty quantification of experimental modal model parameters
DEFF Research Database (Denmark)
Luczak, M.; Peeters, B.; Kahsin, M.
2014-01-01
Composite materials are widely used in manufacture of aerospace and wind energy structural components. These load carrying structures are subjected to dynamic time-varying loading conditions. Robust structural dynamics identification procedure impose tight constraints on the quality of modal models...... represent different complexity levels ranging from coupon, through sub-component up to fully assembled aerospace and wind energy structural components made of composite materials. The proposed method is demonstrated on two application cases of a small and large wind turbine blade........ This paper aims at a systematic approach for uncertainty quantification of the parameters of the modal models estimated from experimentally obtained data. Statistical analysis of modal parameters is implemented to derive an assessment of the entire modal model uncertainty measure. Investigated structures...
Uncertainty quantification in computational fluid dynamics and aircraft engines
Montomoli, Francesco; D'Ammaro, Antonio; Massini, Michela; Salvadori, Simone
2015-01-01
This book introduces novel design techniques developed to increase the safety of aircraft engines. The authors demonstrate how the application of uncertainty methods can overcome problems in the accurate prediction of engine lift, caused by manufacturing error. This in turn ameliorates the difficulty of achieving required safety margins imposed by limits in current design and manufacturing methods. This text shows that even state-of-the-art computational fluid dynamics (CFD) are not able to predict the same performance measured in experiments; CFD methods assume idealised geometries but ideal geometries do not exist, cannot be manufactured and their performance differs from real-world ones. By applying geometrical variations of a few microns, the agreement with experiments improves dramatically, but unfortunately the manufacturing errors in engines or in experiments are unknown. In order to overcome this limitation, uncertainty quantification considers the probability density functions of manufacturing errors...
An Uncertainty Quantification Framework for Remote Sensing Retrievals
Braverman, A. J.; Hobbs, J.
2017-12-01
Remote sensing data sets produced by NASA and other space agencies are the result of complex algorithms that infer geophysical state from observed radiances using retrieval algorithms. The processing must keep up with the downlinked data flow, and this necessitates computational compromises that affect the accuracies of retrieved estimates. The algorithms are also limited by imperfect knowledge of physics and of ancillary inputs that are required. All of this contributes to uncertainties that are generally not rigorously quantified by stepping outside the assumptions that underlie the retrieval methodology. In this talk we discuss a practical framework for uncertainty quantification that can be applied to a variety of remote sensing retrieval algorithms. Ours is a statistical approach that uses Monte Carlo simulation to approximate the sampling distribution of the retrieved estimates. We will discuss the strengths and weaknesses of this approach, and provide a case-study example from the Orbiting Carbon Observatory 2 mission.
Uncertainty Quantification Bayesian Framework for Porous Media Flows
Demyanov, V.; Christie, M.; Erbas, D.
2005-12-01
Uncertainty quantification is an increasingly important aspect of many areas of applied science, where the challenge is to make reliable predictions about the performance of complex physical systems in the absence of complete or reliable data. Predicting flows of fluids through undersurface reservoirs is an example of a complex system where accuracy in prediction is needed (e.g. in oil industry it is essential for financial reasons). Simulation of fluid flow in oil reservoirs is usually carried out using large commercially written finite difference simulators solving conservation equations describing the multi-phase flow through the porous reservoir rocks, which is a highly computationally expensive task. This work examines a Bayesian Framework for uncertainty quantification in porous media flows that uses a stochastic sampling algorithm to generate models that match observed time series data. The framework is flexible for a wide range of general physical/statistical parametric models, which are used to describe the underlying hydro-geological process in its temporal dynamics. The approach is based on exploration of the parameter space and update of the prior beliefs about what the most likely model definitions are. Optimization problem for a highly parametric physical model usually have multiple solutions, which impact the uncertainty of the made predictions. Stochastic search algorithm (e.g. genetic algorithm) allows to identify multiple "good enough" models in the parameter space. Furthermore, inference of the generated model ensemble via MCMC based algorithm evaluates the posterior probability of the generated models and quantifies uncertainty of the predictions. Machine learning algorithm - Artificial Neural Networks - are used to speed up the identification of regions in parameter space where good matches to observed data can be found. Adaptive nature of ANN allows to develop different ways of integrating them into the Bayesian framework: as direct time
Forensic Uncertainty Quantification of Explosive Dispersal of Particles
Hughes, Kyle; Park, Chanyoung; Haftka, Raphael; Kim, Nam-Ho
2017-06-01
In addition to the numerical challenges of simulating the explosive dispersal of particles, validation of the simulation is often plagued with poor knowledge of the experimental conditions. The level of experimental detail required for validation is beyond what is usually included in the literature. This presentation proposes the use of forensic uncertainty quantification (UQ) to investigate validation-quality experiments to discover possible sources of uncertainty that may have been missed in initial design of experiments or under-reported. The current experience of the authors has found that by making an analogy to crime scene investigation when looking at validation experiments, valuable insights may be gained. One examines all the data and documentation provided by the validation experimentalists, corroborates evidence, and quantifies large sources of uncertainty a posteriori with empirical measurements. In addition, it is proposed that forensic UQ may benefit from an independent investigator to help remove possible implicit biases and increases the likelihood of discovering unrecognized uncertainty. Forensic UQ concepts will be discussed and then applied to a set of validation experiments performed at Eglin Air Force Base. This work was supported in part by the U.S. Department of Energy, National Nuclear Security Administration, Advanced Simulation and Computing Program.
Numerical Continuation Methods for Intrusive Uncertainty Quantification Studies
Energy Technology Data Exchange (ETDEWEB)
Safta, Cosmin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Najm, Habib N. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Phipps, Eric Todd [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2014-09-01
Rigorous modeling of engineering systems relies on efficient propagation of uncertainty from input parameters to model outputs. In recent years, there has been substantial development of probabilistic polynomial chaos (PC) Uncertainty Quantification (UQ) methods, enabling studies in expensive computational models. One approach, termed ”intrusive”, involving reformulation of the governing equations, has been found to have superior computational performance compared to non-intrusive sampling-based methods in relevant large-scale problems, particularly in the context of emerging architectures. However, the utility of intrusive methods has been severely limited due to detrimental numerical instabilities associated with strong nonlinear physics. Previous methods for stabilizing these constructions tend to add unacceptably high computational costs, particularly in problems with many uncertain parameters. In order to address these challenges, we propose to adapt and improve numerical continuation methods for the robust time integration of intrusive PC system dynamics. We propose adaptive methods, starting with a small uncertainty for which the model has stable behavior and gradually moving to larger uncertainty where the instabilities are rampant, in a manner that provides a suitable solution.
Overview of hybrid subspace methods for uncertainty quantification, sensitivity analysis
International Nuclear Information System (INIS)
Abdel-Khalik, Hany S.; Bang, Youngsuk; Wang, Congjian
2013-01-01
Highlights: ► We overview the state-of-the-art in uncertainty quantification and sensitivity analysis. ► We overview new developments in above areas using hybrid methods. ► We give a tutorial introduction to above areas and the new developments. ► Hybrid methods address the explosion in dimensionality in nonlinear models. ► Representative numerical experiments are given. -- Abstract: The role of modeling and simulation has been heavily promoted in recent years to improve understanding of complex engineering systems. To realize the benefits of modeling and simulation, concerted efforts in the areas of uncertainty quantification and sensitivity analysis are required. The manuscript intends to serve as a pedagogical presentation of the material to young researchers and practitioners with little background on the subjects. We believe this is important as the role of these subjects is expected to be integral to the design, safety, and operation of existing as well as next generation reactors. In addition to covering the basics, an overview of the current state-of-the-art will be given with particular emphasis on the challenges pertaining to nuclear reactor modeling. The second objective will focus on presenting our own development of hybrid subspace methods intended to address the explosion in the computational overhead required when handling real-world complex engineering systems.
Collaborative framework for PIV uncertainty quantification: the experimental database
International Nuclear Information System (INIS)
Neal, Douglas R; Sciacchitano, Andrea; Scarano, Fulvio; Smith, Barton L
2015-01-01
The uncertainty quantification of particle image velocimetry (PIV) measurements has recently become a topic of great interest as shown by the recent appearance of several different methods within the past few years. These approaches have different working principles, merits and limitations, which have been speculated upon in subsequent studies. This paper reports a unique experiment that has been performed specifically to test the efficacy of PIV uncertainty methods. The case of a rectangular jet, as previously studied by Timmins et al (2012) and Wilson and Smith (2013b), is used. The novel aspect of the experiment is simultaneous velocity measurements using two different time-resolved PIV systems and a hot-wire anemometry (HWA) system. The first PIV system, called the PIV measurement system (‘PIV-MS’), is intended for nominal measurements of which the uncertainty is to be evaluated. It is based on a single camera and features a dynamic velocity range (DVR) representative of typical PIV experiments. The second PIV system, called the ‘PIV-HDR’ (high dynamic range) system, features a significantly higher DVR obtained with a higher digital imaging resolution. The hot-wire is placed in close proximity to the PIV measurement domain. The three measurement systems were carefully set to simultaneously measure the flow velocity at the same time and location. The comparison between the PIV-HDR system and the HWA provides an estimate of the measurement precision of the reference velocity for evaluation of the instantaneous error in the measurement system. The discrepancy between the PIV-MS and the reference data provides the measurement error, which is later used to assess the different uncertainty quantification methods proposed in the literature. A detailed comparison of the uncertainty estimation methods based on the present datasets is presented in a second paper from Sciacchitano et al (2015). Furthermore, this database offers the potential to be used for
Efficient Quantification of Uncertainties in Complex Computer Code Results, Phase I
National Aeronautics and Space Administration — This proposal addresses methods for efficient quantification of margins and uncertainties (QMU) for models that couple multiple, large-scale commercial or...
Bayesian uncertainty quantification in linear models for diffusion MRI.
Sjölund, Jens; Eklund, Anders; Özarslan, Evren; Herberthson, Magnus; Bånkestad, Maria; Knutsson, Hans
2018-03-29
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification. Copyright © 2018 Elsevier Inc. All rights reserved.
The Method of Manufactured Universes for validating uncertainty quantification methods
Stripling, H.F.
2011-09-01
The Method of Manufactured Universes is presented as a validation framework for uncertainty quantification (UQ) methodologies and as a tool for exploring the effects of statistical and modeling assumptions embedded in these methods. The framework calls for a manufactured reality from which experimental data are created (possibly with experimental error), an imperfect model (with uncertain inputs) from which simulation results are created (possibly with numerical error), the application of a system for quantifying uncertainties in model predictions, and an assessment of how accurately those uncertainties are quantified. The application presented in this paper manufactures a particle-transport universe, models it using diffusion theory with uncertain material parameters, and applies both Gaussian process and Bayesian MARS algorithms to make quantitative predictions about new experiments within the manufactured reality. The results of this preliminary study indicate that, even in a simple problem, the improper application of a specific UQ method or unrealized effects of a modeling assumption may produce inaccurate predictions. We conclude that the validation framework presented in this paper is a powerful and flexible tool for the investigation and understanding of UQ methodologies. © 2011 Elsevier Ltd. All rights reserved.
Towards an uncertainty quantification methodology with CASMO-5
International Nuclear Information System (INIS)
Wieselquist, W.; Vasiliev, A.; Ferroukhi, H.
2011-01-01
We present the development of an uncertainty quantification (UQ) methodology for the CASMO-5 lattice physics code, used extensively at the Paul Scherrer Institut for standalone neutronics calculations, as well as the generation of nuclear fuel segment libraries for the downstream core simulator, SIMULATE-3. We focus here on propagation of nuclear data uncertainties and describe the framework required for 'black box' UQ--in this case minor modifications of the code are necessary to allow perturbation of the CASMO-5 nuclear data library. We then implement a basic rst-order UQ method, direct perturbation, which directly produces sensitivity coefficients and when folded with the input nuclear data variance-covariance matrix (VCM) yields output uncertainties in the form of an output VCM. We discuss the implementation, including how to map the VCMs of a different group structure to the code library group structure (in our case the ENDF/B-VII-based 586-group library in CASMO-5), present some results for pin cell calculations, and conclude with future work. (author)
Quantification of Uncertainty in Extreme Scale Computations (QUEST)
Energy Technology Data Exchange (ETDEWEB)
Ghanem, Roger [Univ. of Southern California, Los Angeles, CA (United States)
2017-04-18
QUEST was a SciDAC Institute comprising Sandia National Laboratories, Los Alamos National Laboratory, the University of Southern California, the Massachusetts Institute of Technology, the University of Texas at Austin, and Duke University. The mission of QUEST is to: (1) develop a broad class of uncertainty quantification (UQ) methods/tools, and (2) provide UQ expertise and software to other SciDAC projects, thereby enabling/guiding their UQ activities. The USC effort centered on the development of reduced models and efficient algorithms for implementing various components of the UQ pipeline. USC personnel were responsible for the development of adaptive bases, adaptive quadrature, and reduced models to be used in estimation and inference.
High-Throughput Thermodynamic Modeling and Uncertainty Quantification for ICME
Otis, Richard A.; Liu, Zi-Kui
2017-05-01
One foundational component of the integrated computational materials engineering (ICME) and Materials Genome Initiative is the computational thermodynamics based on the calculation of phase diagrams (CALPHAD) method. The CALPHAD method pioneered by Kaufman has enabled the development of thermodynamic, atomic mobility, and molar volume databases of individual phases in the full space of temperature, composition, and sometimes pressure for technologically important multicomponent engineering materials, along with sophisticated computational tools for using the databases. In this article, our recent efforts will be presented in terms of developing new computational tools for high-throughput modeling and uncertainty quantification based on high-throughput, first-principles calculations and the CALPHAD method along with their potential propagations to downstream ICME modeling and simulations.
Multiscale Modeling and Uncertainty Quantification for Nuclear Fuel Performance
Energy Technology Data Exchange (ETDEWEB)
Estep, Donald [Colorado State Univ., Fort Collins, CO (United States); El-Azab, Anter [Florida State Univ., Tallahassee, FL (United States); Pernice, Michael [Idaho National Lab. (INL), Idaho Falls, ID (United States); Peterson, John W. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Polyakov, Peter [Univ. of Wyoming, Laramie, WY (United States); Tavener, Simon [Colorado State Univ., Fort Collins, CO (United States); Xiu, Dongbin [Purdue Univ., West Lafayette, IN (United States); Univ. of Utah, Salt Lake City, UT (United States)
2017-03-23
In this project, we will address the challenges associated with constructing high fidelity multiscale models of nuclear fuel performance. We (*) propose a novel approach for coupling mesoscale and macroscale models, (*) devise efficient numerical methods for simulating the coupled system, and (*) devise and analyze effective numerical approaches for error and uncertainty quantification for the coupled multiscale system. As an integral part of the project, we will carry out analysis of the effects of upscaling and downscaling, investigate efficient methods for stochastic sensitivity analysis of the individual macroscale and mesoscale models, and carry out a posteriori error analysis for computed results. We will pursue development and implementation of solutions in software used at Idaho National Laboratories on models of interest to the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program.
Plasticity models of material variability based on uncertainty quantification techniques
Energy Technology Data Exchange (ETDEWEB)
Jones, Reese E. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Rizzi, Francesco [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Boyce, Brad [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Templeton, Jeremy Alan [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Ostien, Jakob [Sandia National Lab. (SNL-CA), Livermore, CA (United States)
2017-11-01
The advent of fabrication techniques like additive manufacturing has focused attention on the considerable variability of material response due to defects and other micro-structural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. Lastly, we demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.
Mesh refinement for uncertainty quantification through model reduction
International Nuclear Information System (INIS)
Li, Jing; Stinis, Panos
2015-01-01
We present a novel way of deciding when and where to refine a mesh in probability space in order to facilitate uncertainty quantification in the presence of discontinuities in random space. A discontinuity in random space makes the application of generalized polynomial chaos expansion techniques prohibitively expensive. The reason is that for discontinuous problems, the expansion converges very slowly. An alternative to using higher terms in the expansion is to divide the random space in smaller elements where a lower degree polynomial is adequate to describe the randomness. In general, the partition of the random space is a dynamic process since some areas of the random space, particularly around the discontinuity, need more refinement than others as time evolves. In the current work we propose a way to decide when and where to refine the random space mesh based on the use of a reduced model. The idea is that a good reduced model can monitor accurately, within a random space element, the cascade of activity to higher degree terms in the chaos expansion. In turn, this facilitates the efficient allocation of computational sources to the areas of random space where they are more needed. For the Kraichnan–Orszag system, the prototypical system to study discontinuities in random space, we present theoretical results which show why the proposed method is sound and numerical results which corroborate the theory
3.8 Proposed approach to uncertainty quantification and sensitivity analysis in the next PA
Energy Technology Data Exchange (ETDEWEB)
Flach, Greg [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Wohlwend, Jen [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)
2017-10-02
This memorandum builds upon Section 3.8 of SRNL (2016) and Flach (2017) by defining key error analysis, uncertainty quantification, and sensitivity analysis concepts and terms, in preparation for the next E-Area Performance Assessment (WSRC 2008) revision.
Prudencio, Ernesto E.; Schulz, Karl W.
2012-01-01
QUESO is a collection of statistical algorithms and programming constructs supporting research into the uncertainty quantification (UQ) of models and their predictions. It has been designed with three objectives: it should (a) be sufficiently
A Study on Uncertainty Quantification of Reflood Model using CIRCE Methodology
International Nuclear Information System (INIS)
Jeon, Seongsu; Hong, Soonjoon; Oh, Deogyeon; Bang, Youngseok
2013-01-01
The CIRCE method is intended to quantify the uncertainties of the correlations of a code. It may replace the expert judgment generally used. In this study, an uncertainty quantification of reflood model was performed using CIRCE methodology. In this paper, the application process of CIRCE methodology and main results are briefly described. This research is expected to be useful to improve the present audit calculation methodology, KINS-REM. In this study, an uncertainty quantification of reflood model was performed using CIRCE methodology. The application of CIRCE provided the satisfactory results. This research is expected to be useful to improve the present audit calculation methodology, KINS-REM
Soize, Christian
2017-01-01
This book presents the fundamental notions and advanced mathematical tools in the stochastic modeling of uncertainties and their quantification for large-scale computational models in sciences and engineering. In particular, it focuses in parametric uncertainties, and non-parametric uncertainties with applications from the structural dynamics and vibroacoustics of complex mechanical systems, from micromechanics and multiscale mechanics of heterogeneous materials. Resulting from a course developed by the author, the book begins with a description of the fundamental mathematical tools of probability and statistics that are directly useful for uncertainty quantification. It proceeds with a well carried out description of some basic and advanced methods for constructing stochastic models of uncertainties, paying particular attention to the problem of calibrating and identifying a stochastic model of uncertainty when experimental data is available. < This book is intended to be a graduate-level textbook for stu...
Greenhouse Gas Source Attribution: Measurements Modeling and Uncertainty Quantification
Energy Technology Data Exchange (ETDEWEB)
Liu, Zhen [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Safta, Cosmin [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sargsyan, Khachik [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Najm, Habib N. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); van Bloemen Waanders, Bart Gustaaf [Sandia National Lab. (SNL-CA), Livermore, CA (United States); LaFranchi, Brian W. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Ivey, Mark D. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Schrader, Paul E. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Michelsen, Hope A. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Bambha, Ray P. [Sandia National Lab. (SNL-CA), Livermore, CA (United States)
2014-09-01
In this project we have developed atmospheric measurement capabilities and a suite of atmospheric modeling and analysis tools that are well suited for verifying emissions of green- house gases (GHGs) on an urban-through-regional scale. We have for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate atmospheric CO_{2} . This will allow for the examination of regional-scale transport and distribution of CO_{2} along with air pollutants traditionally studied using CMAQ at relatively high spatial and temporal resolution with the goal of leveraging emissions verification efforts for both air quality and climate. We have developed a bias-enhanced Bayesian inference approach that can remedy the well-known problem of transport model errors in atmospheric CO_{2} inversions. We have tested the approach using data and model outputs from the TransCom3 global CO_{2} inversion comparison project. We have also performed two prototyping studies on inversion approaches in the generalized convection-diffusion context. One of these studies employed Polynomial Chaos Expansion to accelerate the evaluation of a regional transport model and enable efficient Markov Chain Monte Carlo sampling of the posterior for Bayesian inference. The other approach uses de- terministic inversion of a convection-diffusion-reaction system in the presence of uncertainty. These approaches should, in principle, be applicable to realistic atmospheric problems with moderate adaptation. We outline a regional greenhouse gas source inference system that integrates (1) two ap- proaches of atmospheric dispersion simulation and (2) a class of Bayesian inference and un- certainty quantification algorithms. We use two different and complementary approaches to simulate atmospheric dispersion. Specifically, we use a Eulerian chemical transport model CMAQ and a Lagrangian Particle Dispersion Model - FLEXPART-WRF. These two models share the same WRF
Uncertainty quantification using evidence theory in multidisciplinary design optimization
International Nuclear Information System (INIS)
Agarwal, Harish; Renaud, John E.; Preston, Evan L.; Padmanabhan, Dhanesh
2004-01-01
Advances in computational performance have led to the development of large-scale simulation tools for design. Systems generated using such simulation tools can fail in service if the uncertainty of the simulation tool's performance predictions is not accounted for. In this research an investigation of how uncertainty can be quantified in multidisciplinary systems analysis subject to epistemic uncertainty associated with the disciplinary design tools and input parameters is undertaken. Evidence theory is used to quantify uncertainty in terms of the uncertain measures of belief and plausibility. To illustrate the methodology, multidisciplinary analysis problems are introduced as an extension to the epistemic uncertainty challenge problems identified by Sandia National Laboratories. After uncertainty has been characterized mathematically the designer seeks the optimum design under uncertainty. The measures of uncertainty provided by evidence theory are discontinuous functions. Such non-smooth functions cannot be used in traditional gradient-based optimizers because the sensitivities of the uncertain measures are not properly defined. In this research surrogate models are used to represent the uncertain measures as continuous functions. A sequential approximate optimization approach is used to drive the optimization process. The methodology is illustrated in application to multidisciplinary example problems
A rigorous treatment of uncertainty quantification for Silicon damage metrics
International Nuclear Information System (INIS)
Griffin, P.
2016-01-01
These report summaries the contributions made by Sandia National Laboratories in support of the International Atomic Energy Agency (IAEA) Nuclear Data Section (NDS) Technical Meeting (TM) on Nuclear Reaction Data and Uncertainties for Radiation Damage. This work focused on a rigorous treatment of the uncertainties affecting the characterization of the displacement damage seen in silicon semiconductors. (author)
Structural and parameteric uncertainty quantification in cloud microphysics parameterization schemes
van Lier-Walqui, M.; Morrison, H.; Kumjian, M. R.; Prat, O. P.; Martinkus, C.
2017-12-01
Atmospheric model parameterization schemes employ approximations to represent the effects of unresolved processes. These approximations are a source of error in forecasts, caused in part by considerable uncertainty about the optimal value of parameters within each scheme -- parameteric uncertainty. Furthermore, there is uncertainty regarding the best choice of the overarching structure of the parameterization scheme -- structrual uncertainty. Parameter estimation can constrain the first, but may struggle with the second because structural choices are typically discrete. We address this problem in the context of cloud microphysics parameterization schemes by creating a flexible framework wherein structural and parametric uncertainties can be simultaneously constrained. Our scheme makes no assuptions about drop size distribution shape or the functional form of parametrized process rate terms. Instead, these uncertainties are constrained by observations using a Markov Chain Monte Carlo sampler within a Bayesian inference framework. Our scheme, the Bayesian Observationally-constrained Statistical-physical Scheme (BOSS), has flexibility to predict various sets of prognostic drop size distribution moments as well as varying complexity of process rate formulations. We compare idealized probabilistic forecasts from versions of BOSS with varying levels of structural complexity. This work has applications in ensemble forecasts with model physics uncertainty, data assimilation, and cloud microphysics process studies.
Evidence-based quantification of uncertainties induced via simulation-based modeling
International Nuclear Information System (INIS)
Riley, Matthew E.
2015-01-01
The quantification of uncertainties in simulation-based modeling traditionally focuses upon quantifying uncertainties in the parameters input into the model, referred to as parametric uncertainties. Often neglected in such an approach are the uncertainties induced by the modeling process itself. This deficiency is often due to a lack of information regarding the problem or the models considered, which could theoretically be reduced through the introduction of additional data. Because of the nature of this epistemic uncertainty, traditional probabilistic frameworks utilized for the quantification of uncertainties are not necessarily applicable to quantify the uncertainties induced in the modeling process itself. This work develops and utilizes a methodology – incorporating aspects of Dempster–Shafer Theory and Bayesian model averaging – to quantify uncertainties of all forms for simulation-based modeling problems. The approach expands upon classical parametric uncertainty approaches, allowing for the quantification of modeling-induced uncertainties as well, ultimately providing bounds on classical probability without the loss of epistemic generality. The approach is demonstrated on two different simulation-based modeling problems: the computation of the natural frequency of a simple two degree of freedom non-linear spring mass system and the calculation of the flutter velocity coefficient for the AGARD 445.6 wing given a subset of commercially available modeling choices. - Highlights: • Modeling-induced uncertainties are often mishandled or ignored in the literature. • Modeling-induced uncertainties are epistemic in nature. • Probabilistic representations of modeling-induced uncertainties are restrictive. • Evidence theory and Bayesian model averaging are integrated. • Developed approach is applicable for simulation-based modeling problems
Quantification of margins and mixed uncertainties using evidence theory and stochastic expansions
International Nuclear Information System (INIS)
Shah, Harsheel; Hosder, Serhat; Winter, Tyler
2015-01-01
The objective of this paper is to implement Dempster–Shafer Theory of Evidence (DSTE) in the presence of mixed (aleatory and multiple sources of epistemic) uncertainty to the reliability and performance assessment of complex engineering systems through the use of quantification of margins and uncertainties (QMU) methodology. This study focuses on quantifying the simulation uncertainties, both in the design condition and the performance boundaries along with the determination of margins. To address the possibility of multiple sources and intervals for epistemic uncertainty characterization, DSTE is used for uncertainty quantification. An approach to incorporate aleatory uncertainty in Dempster–Shafer structures is presented by discretizing the aleatory variable distributions into sets of intervals. In view of excessive computational costs for large scale applications and repetitive simulations needed for DSTE analysis, a stochastic response surface based on point-collocation non-intrusive polynomial chaos (NIPC) has been implemented as the surrogate for the model response. The technique is demonstrated on a model problem with non-linear analytical functions representing the outputs and performance boundaries of two coupled systems. Finally, the QMU approach is demonstrated on a multi-disciplinary analysis of a high speed civil transport (HSCT). - Highlights: • Quantification of margins and uncertainties (QMU) methodology with evidence theory. • Treatment of both inherent and epistemic uncertainties within evidence theory. • Stochastic expansions for representation of performance metrics and boundaries. • Demonstration of QMU on an analytical problem. • QMU analysis applied to an aerospace system (high speed civil transport)
A Bayesian approach for quantification of model uncertainty
International Nuclear Information System (INIS)
Park, Inseok; Amarchinta, Hemanth K.; Grandhi, Ramana V.
2010-01-01
In most engineering problems, more than one model can be created to represent an engineering system's behavior. Uncertainty is inevitably involved in selecting the best model from among the models that are possible. Uncertainty in model selection cannot be ignored, especially when the differences between the predictions of competing models are significant. In this research, a methodology is proposed to quantify model uncertainty using measured differences between experimental data and model outcomes under a Bayesian statistical framework. The adjustment factor approach is used to propagate model uncertainty into prediction of a system response. A nonlinear vibration system is used to demonstrate the processes for implementing the adjustment factor approach. Finally, the methodology is applied on the engineering benefits of a laser peening process, and a confidence band for residual stresses is established to indicate the reliability of model prediction.
Uncertainty quantification of CO2 emission reduction for maritime shipping
International Nuclear Information System (INIS)
Yuan, Jun; Ng, Szu Hui; Sou, Weng Sut
2016-01-01
The International Maritime Organization (IMO) has recently proposed several operational and technical measures to improve shipping efficiency and reduce the greenhouse gases (GHG) emissions. The abatement potentials estimated for these measures have been further used by many organizations to project future GHG emission reductions and plot Marginal Abatement Cost Curves (MACC). However, the abatement potentials estimated for many of these measures can be highly uncertain as many of these measures are new, with limited sea trial information. Furthermore, the abatements obtained are highly dependent on ocean conditions, trading routes and sailing patterns. When the estimated abatement potentials are used for projections, these ‘input’ uncertainties are often not clearly displayed or accounted for, which can lead to overly optimistic or pessimistic outlooks. In this paper, we propose a methodology to systematically quantify and account for these input uncertainties on the overall abatement potential forecasts. We further propose improvements to MACCs to better reflect the uncertainties in marginal abatement costs and total emissions. This approach provides a fuller and more accurate picture of abatement forecasts and potential reductions achievable, and will be useful to policy makers and decision makers in the shipping industry to better assess the cost effective measures for CO 2 emission reduction. - Highlights: • We propose a systematic method to quantify uncertainty in emission reduction. • Marginal abatement cost curves are improved to better reflect the uncertainties. • Percentage reduction probability is given to determine emission reduction target. • The methodology is applied to a case study on maritime shipping.
Uncertainty Quantification of CFD Data Generated for a Model Scramjet Isolator Flowfield
Baurle, R. A.; Axdahl, E. L.
2017-01-01
Computational fluid dynamics is now considered to be an indispensable tool for the design and development of scramjet engine components. Unfortunately, the quantification of uncertainties is rarely addressed with anything other than sensitivity studies, so the degree of confidence associated with the numerical results remains exclusively with the subject matter expert that generated them. This practice must be replaced with a formal uncertainty quantification process for computational fluid dynamics to play an expanded role in the system design, development, and flight certification process. Given the limitations of current hypersonic ground test facilities, this expanded role is believed to be a requirement by some in the hypersonics community if scramjet engines are to be given serious consideration as a viable propulsion system. The present effort describes a simple, relatively low cost, nonintrusive approach to uncertainty quantification that includes the basic ingredients required to handle both aleatoric (random) and epistemic (lack of knowledge) sources of uncertainty. The nonintrusive nature of the approach allows the computational fluid dynamicist to perform the uncertainty quantification with the flow solver treated as a "black box". Moreover, a large fraction of the process can be automated, allowing the uncertainty assessment to be readily adapted into the engineering design and development workflow. In the present work, the approach is applied to a model scramjet isolator problem where the desire is to validate turbulence closure models in the presence of uncertainty. In this context, the relevant uncertainty sources are determined and accounted for to allow the analyst to delineate turbulence model-form errors from other sources of uncertainty associated with the simulation of the facility flow.
Uncertainty quantification and validation of combined hydrological and macroeconomic analyses.
Energy Technology Data Exchange (ETDEWEB)
Hernandez, Jacquelynne; Parks, Mancel Jordan; Jennings, Barbara Joan; Kaplan, Paul Garry; Brown, Theresa Jean; Conrad, Stephen Hamilton
2010-09-01
Changes in climate can lead to instabilities in physical and economic systems, particularly in regions with marginal resources. Global climate models indicate increasing global mean temperatures over the decades to come and uncertainty in the local to national impacts means perceived risks will drive planning decisions. Agent-based models provide one of the few ways to evaluate the potential changes in behavior in coupled social-physical systems and to quantify and compare risks. The current generation of climate impact analyses provides estimates of the economic cost of climate change for a limited set of climate scenarios that account for a small subset of the dynamics and uncertainties. To better understand the risk to national security, the next generation of risk assessment models must represent global stresses, population vulnerability to those stresses, and the uncertainty in population responses and outcomes that could have a significant impact on U.S. national security.
Conceptual and computational basis for the quantification of margins and uncertainty
International Nuclear Information System (INIS)
Helton, Jon Craig
2009-01-01
In 2001, the National Nuclear Security Administration of the U.S. Department of Energy in conjunction with the national security laboratories (i.e, Los Alamos National Laboratory, Lawrence Livermore National Laboratory and Sandia National Laboratories) initiated development of a process designated Quantification of Margins and Uncertainty (QMU) for the use of risk assessment methodologies in the certification of the reliability and safety of the nation's nuclear weapons stockpile. This presentation discusses and illustrates the conceptual and computational basis of QMU in analyses that use computational models to predict the behavior of complex systems. Topics considered include (1) the role of aleatory and epistemic uncertainty in QMU, (2) the representation of uncertainty with probability, (3) the probabilistic representation of uncertainty in QMU analyses involving only epistemic uncertainty, (4) the probabilistic representation of uncertainty in QMU analyses involving aleatory and epistemic uncertainty, (5) procedures for sampling-based uncertainty and sensitivity analysis, (6) the representation of uncertainty with alternatives to probability such as interval analysis, possibility theory and evidence theory, (7) the representation of uncertainty with alternatives to probability in QMU analyses involving only epistemic uncertainty, and (8) the representation of uncertainty with alternatives to probability in QMU analyses involving aleatory and epistemic uncertainty. Concepts and computational procedures are illustrated with both notional examples and examples from reactor safety and radioactive waste disposal.
Schwabe, O.; Shehab, E.; Erkoyuncu, J.
2015-08-01
The lack of defensible methods for quantifying cost estimate uncertainty over the whole product life cycle of aerospace innovations such as propulsion systems or airframes poses a significant challenge to the creation of accurate and defensible cost estimates. Based on the axiomatic definition of uncertainty as the actual prediction error of the cost estimate, this paper provides a comprehensive overview of metrics used for the uncertainty quantification of cost estimates based on a literature review, an evaluation of publicly funded projects such as part of the CORDIS or Horizon 2020 programs, and an analysis of established approaches used by organizations such NASA, the U.S. Department of Defence, the ESA, and various commercial companies. The metrics are categorized based on their foundational character (foundations), their use in practice (state-of-practice), their availability for practice (state-of-art) and those suggested for future exploration (state-of-future). Insights gained were that a variety of uncertainty quantification metrics exist whose suitability depends on the volatility of available relevant information, as defined by technical and cost readiness level, and the number of whole product life cycle phases the estimate is intended to be valid for. Information volatility and number of whole product life cycle phases can hereby be considered as defining multi-dimensional probability fields admitting various uncertainty quantification metric families with identifiable thresholds for transitioning between them. The key research gaps identified were the lacking guidance grounded in theory for the selection of uncertainty quantification metrics and lacking practical alternatives to metrics based on the Central Limit Theorem. An innovative uncertainty quantification framework consisting of; a set-theory based typology, a data library, a classification system, and a corresponding input-output model are put forward to address this research gap as the basis
Uncertainty quantification in lattice QCD calculations for nuclear physics
Energy Technology Data Exchange (ETDEWEB)
Beane, Silas R. [Univ. of Washington, Seattle, WA (United States); Detmold, William [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Orginos, Kostas [College of William and Mary, Williamsburg, VA (United States); Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Savage, Martin J. [Institute for Nuclear Theory, Seattle, WA (United States)
2015-02-05
The numerical technique of Lattice QCD holds the promise of connecting the nuclear forces, nuclei, the spectrum and structure of hadrons, and the properties of matter under extreme conditions with the underlying theory of the strong interactions, quantum chromodynamics. A distinguishing, and thus far unique, feature of this formulation is that all of the associated uncertainties, both statistical and systematic can, in principle, be systematically reduced to any desired precision with sufficient computational and human resources. As a result, we review the sources of uncertainty inherent in Lattice QCD calculations for nuclear physics, and discuss how each is quantified in current efforts.
Huan, Xun; Safta, Cosmin; Sargsyan, Khachik; Geraci, Gianluca; Eldred, Michael S.; Vane, Zachary P.; Lacaze, Guilhem; Oefelein, Joseph C.; Najm, Habib N.
2018-03-01
The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis is conducted to identify influential uncertain input parameters, which can help reduce the systems stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. These methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.
Energy Technology Data Exchange (ETDEWEB)
Huan, Xun [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Safta, Cosmin [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sargsyan, Khachik [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Geraci, Gianluca [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eldred, Michael S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vane, Zachary P. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Lacaze, Guilhem [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Oefelein, Joseph C. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Najm, Habib N. [Sandia National Lab. (SNL-CA), Livermore, CA (United States)
2018-02-09
The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis is conducted to identify influential uncertain input parameters, which can help reduce the system’s stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. Finally, these methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.
Uncertainty quantification in Rothermel's Model using an efficient sampling method
Edwin Jimenez; M. Yousuff Hussaini; Scott L. Goodrick
2007-01-01
The purpose of the present work is to quantify parametric uncertainty in Rothermelâs wildland fire spread model (implemented in software such as BehavePlus3 and FARSITE), which is undoubtedly among the most widely used fire spread models in the United States. This model consists of a nonlinear system of equations that relates environmental variables (input parameter...
Laser tracker TSPI uncertainty quantification via centrifuge trajectory
Romero, Edward; Paez, Thomas; Brown, Timothy; Miller, Timothy
2009-08-01
Sandia National Laboratories currently utilizes two laser tracking systems to provide time-space-position-information (TSPI) and high speed digital imaging of test units under flight. These laser trackers have been in operation for decades under the premise of theoretical accuracies based on system design and operator estimates. Advances in optical imaging and atmospheric tracking technology have enabled opportunities to provide more precise six degree of freedom measurements from these trackers. Applying these technologies to the laser trackers requires quantified understanding of their current errors and uncertainty. It was well understood that an assortment of variables contributed to laser tracker uncertainty but the magnitude of these contributions was not quantified and documented. A series of experiments was performed at Sandia National Laboratories large centrifuge complex to quantify TSPI uncertainties of Sandia National Laboratories laser tracker III. The centrifuge was used to provide repeatable and economical test unit trajectories of a test-unit to use for TSPI comparison and uncertainty analysis. On a centrifuge, testunits undergo a known trajectory continuously with a known angular velocity. Each revolution may represent an independent test, which may be repeated many times over for magnitudes of data practical for statistical analysis. Previously these tests were performed at Sandia's rocket sled track facility but were found to be costly with challenges in the measurement ground truth TSPI. The centrifuge along with on-board measurement equipment was used to provide known ground truth position of test units. This paper discusses the experimental design and techniques used to arrive at measures of laser tracker error and uncertainty.
Quantification of Uncertainty in Predicting Building Energy Consumption
DEFF Research Database (Denmark)
Brohus, Henrik; Frier, Christian; Heiselberg, Per
2012-01-01
Traditional building energy consumption calculation methods are characterised by rough approaches providing approximate figures with high and unknown levels of uncertainty. Lack of reliable energy resources and increasing concerns about climate change call for improved predictive tools. A new...... approach for the prediction of building energy consumption is presented. The approach quantifies the uncertainty of building energy consumption by means of stochastic differential equations. The approach is applied to a general heat balance for an arbitrary number of loads and zones in a building...... for the dynamic thermal behaviour of buildings. However, for air flow and energy consumption it is found to be much more significant due to less “damping”. Probabilistic methods establish a new approach to the prediction of building energy consumption, enabling designers to include stochastic parameters like...
Survey of Existing Uncertainty Quantification Capabilities for Army Relevant Problems
2017-11-27
first of these introductory sections is an overview of UQ and its various methods. The second of these discusses issues pertaining to the use of UQ...can be readily assessed, as well as the variance or other statistical measures of the distribu- tion of parameters. The uncertainty in the parameters is... statistics of the outputs of these methods, such as the moments of the probability distributions of model outputs. The module does not explicitly support
Discriminative Random Field Models for Subsurface Contamination Uncertainty Quantification
Arshadi, M.; Abriola, L. M.; Miller, E. L.; De Paolis Kaluza, C.
2017-12-01
Application of flow and transport simulators for prediction of the release, entrapment, and persistence of dense non-aqueous phase liquids (DNAPLs) and associated contaminant plumes is a computationally intensive process that requires specification of a large number of material properties and hydrologic/chemical parameters. Given its computational burden, this direct simulation approach is particularly ill-suited for quantifying both the expected performance and uncertainty associated with candidate remediation strategies under real field conditions. Prediction uncertainties primarily arise from limited information about contaminant mass distributions, as well as the spatial distribution of subsurface hydrologic properties. Application of direct simulation to quantify uncertainty would, thus, typically require simulating multiphase flow and transport for a large number of permeability and release scenarios to collect statistics associated with remedial effectiveness, a computationally prohibitive process. The primary objective of this work is to develop and demonstrate a methodology that employs measured field data to produce equi-probable stochastic representations of a subsurface source zone that capture the spatial distribution and uncertainty associated with key features that control remediation performance (i.e., permeability and contamination mass). Here we employ probabilistic models known as discriminative random fields (DRFs) to synthesize stochastic realizations of initial mass distributions consistent with known, and typically limited, site characterization data. Using a limited number of full scale simulations as training data, a statistical model is developed for predicting the distribution of contaminant mass (e.g., DNAPL saturation and aqueous concentration) across a heterogeneous domain. Monte-Carlo sampling methods are then employed, in conjunction with the trained statistical model, to generate realizations conditioned on measured borehole data
Uncertainty Quantification in Control Problems for Flocking Models
Directory of Open Access Journals (Sweden)
Giacomo Albi
2015-01-01
Full Text Available The optimal control of flocking models with random inputs is investigated from a numerical point of view. The effect of uncertainty in the interaction parameters is studied for a Cucker-Smale type model using a generalized polynomial chaos (gPC approach. Numerical evidence of threshold effects in the alignment dynamic due to the random parameters is given. The use of a selective model predictive control permits steering of the system towards the desired state even in unstable regimes.
Uncertainty Quantification of Water Quality in Tamsui River in Taiwan
Kao, D.; Tsai, C.
2017-12-01
In Taiwan, modeling of non-point source pollution is unavoidably associated with uncertainty. The main purpose of this research is to better understand water contamination in the metropolitan Taipei area, and also to provide a new analysis method for government or companies to establish related control and design measures. In this research, three methods are utilized to carry out the uncertainty analysis step by step with Mike 21, which is widely used for hydro-dynamics and water quality modeling, and the study area is focused on Tamsui river watershed. First, a sensitivity analysis is conducted which can be used to rank the order of influential parameters and variables such as Dissolved Oxygen, Nitrate, Ammonia and Phosphorous. Then we use the First-order error method (FOEA) to determine the number of parameters that could significantly affect the variability of simulation results. Finally, a state-of-the-art method for uncertainty analysis called the Perturbance moment method (PMM) is applied in this research, which is more efficient than the Monte-Carlo simulation (MCS). For MCS, the calculations may become cumbersome when involving multiple uncertain parameters and variables. For PMM, three representative points are used for each random variable, and the statistical moments (e.g., mean value, standard deviation) for the output can be presented by the representative points and perturbance moments based on the parallel axis theorem. With the assumption of the independent parameters and variables, calculation time is significantly reduced for PMM as opposed to MCS for a comparable modeling accuracy.
Bayesian Mars for uncertainty quantification in stochastic transport problems
International Nuclear Information System (INIS)
Stripling, Hayes F.; McClarren, Ryan G.
2011-01-01
We present a method for estimating solutions to partial differential equations with uncertain parameters using a modification of the Bayesian Multivariate Adaptive Regression Splines (BMARS) emulator. The BMARS algorithm uses Markov chain Monte Carlo (MCMC) to construct a basis function composed of polynomial spline functions, for which derivatives and integrals are straightforward to compute. We use these calculations and a modification of the curve-fitting BMARS algorithm to search for a basis function (response surface) which, in combination with its derivatives/integrals, satisfies a governing differential equation and specified boundary condition. We further show that this fit can be improved by enforcing a conservation or other physics-based constraint. Our results indicate that estimates to solutions of simple first order partial differential equations (without uncertainty) can be efficiently computed with very little regression error. We then extend the method to estimate uncertainties in the solution to a pure absorber transport problem in a medium with uncertain cross-section. We describe and compare two strategies for propagating the uncertain cross-section through the BMARS algorithm; the results from each method are in close comparison with analytic results. We discuss the scalability of the algorithm to parallel architectures and the applicability of the two strategies to larger problems with more degrees of uncertainty. (author)
Uncertainty Quantification of Turbulence Model Closure Coefficients for Transonic Wall-Bounded Flows
Schaefer, John; West, Thomas; Hosder, Serhat; Rumsey, Christopher; Carlson, Jan-Renee; Kleb, William
2015-01-01
The goal of this work was to quantify the uncertainty and sensitivity of commonly used turbulence models in Reynolds-Averaged Navier-Stokes codes due to uncertainty in the values of closure coefficients for transonic, wall-bounded flows and to rank the contribution of each coefficient to uncertainty in various output flow quantities of interest. Specifically, uncertainty quantification of turbulence model closure coefficients was performed for transonic flow over an axisymmetric bump at zero degrees angle of attack and the RAE 2822 transonic airfoil at a lift coefficient of 0.744. Three turbulence models were considered: the Spalart-Allmaras Model, Wilcox (2006) k-w Model, and the Menter Shear-Stress Trans- port Model. The FUN3D code developed by NASA Langley Research Center was used as the flow solver. The uncertainty quantification analysis employed stochastic expansions based on non-intrusive polynomial chaos as an efficient means of uncertainty propagation. Several integrated and point-quantities are considered as uncertain outputs for both CFD problems. All closure coefficients were treated as epistemic uncertain variables represented with intervals. Sobol indices were used to rank the relative contributions of each closure coefficient to the total uncertainty in the output quantities of interest. This study identified a number of closure coefficients for each turbulence model for which more information will reduce the amount of uncertainty in the output significantly for transonic, wall-bounded flows.
Interval-based reconstruction for uncertainty quantification in PET
Kucharczak, Florentin; Loquin, Kevin; Buvat, Irène; Strauss, Olivier; Mariano-Goulart, Denis
2018-02-01
A new directed interval-based tomographic reconstruction algorithm, called non-additive interval based expectation maximization (NIBEM) is presented. It uses non-additive modeling of the forward operator that provides intervals instead of single-valued projections. The detailed approach is an extension of the maximum likelihood—expectation maximization algorithm based on intervals. The main motivation for this extension is that the resulting intervals have appealing properties for estimating the statistical uncertainty associated with the reconstructed activity values. After reviewing previously published theoretical concepts related to interval-based projectors, this paper describes the NIBEM algorithm and gives examples that highlight the properties and advantages of this interval valued reconstruction.
Quantification of Uncertainty in the Flood Frequency Analysis
Kasiapillai Sudalaimuthu, K.; He, J.; Swami, D.
2017-12-01
Flood frequency analysis (FFA) is usually carried out for planning and designing of water resources and hydraulic structures. Owing to the existence of variability in sample representation, selection of distribution and estimation of distribution parameters, the estimation of flood quantile has been always uncertain. Hence, suitable approaches must be developed to quantify the uncertainty in the form of prediction interval as an alternate to deterministic approach. The developed framework in the present study to include uncertainty in the FFA discusses a multi-objective optimization approach to construct the prediction interval using ensemble of flood quantile. Through this approach, an optimal variability of distribution parameters is identified to carry out FFA. To demonstrate the proposed approach, annual maximum flow data from two gauge stations (Bow river at Calgary and Banff, Canada) are used. The major focus of the present study was to evaluate the changes in magnitude of flood quantiles due to the recent extreme flood event occurred during the year 2013. In addition, the efficacy of the proposed method was further verified using standard bootstrap based sampling approaches and found that the proposed method is reliable in modeling extreme floods as compared to the bootstrap methods.
International Nuclear Information System (INIS)
Perko, Z.; Gilli, L.; Lathouwers, D.; Kloosterman, J. L.
2013-01-01
Uncertainty quantification plays an increasingly important role in the nuclear community, especially with the rise of Best Estimate Plus Uncertainty methodologies. Sensitivity analysis, surrogate models, Monte Carlo sampling and several other techniques can be used to propagate input uncertainties. In recent years however polynomial chaos expansion has become a popular alternative providing high accuracy at affordable computational cost. This paper presents such polynomial chaos (PC) methods using adaptive sparse grids and adaptive basis set construction, together with an application to a Gas Cooled Fast Reactor transient. Comparison is made between a new sparse grid algorithm and the traditionally used technique proposed by Gerstner. An adaptive basis construction method is also introduced and is proved to be advantageous both from an accuracy and a computational point of view. As a demonstration the uncertainty quantification of a 50% loss of flow transient in the GFR2400 Gas Cooled Fast Reactor design was performed using the CATHARE code system. The results are compared to direct Monte Carlo sampling and show the superior convergence and high accuracy of the polynomial chaos expansion. Since PC techniques are easy to implement, they can offer an attractive alternative to traditional techniques for the uncertainty quantification of large scale problems. (authors)
Inverse problem and uncertainty quantification: application to compressible gas dynamics
International Nuclear Information System (INIS)
Birolleau, Alexandre
2014-01-01
This thesis deals with uncertainty propagation and the resolution of inverse problems together with their respective acceleration via Polynomial Chaos. The object of this work is to present a state of the art and a numerical analysis of this stochastic spectral method, in order to understand its pros and cons when tackling the probabilistic study of hydrodynamical instabilities in Richtmyer-Meshkov shock tube experiments. The first chapter is introductory and allows understanding the stakes of being able to accurately take into account uncertainties in compressible gas dynamics simulations. The second chapter is both an illustrative state of the art on generalized Polynomial Chaos and a full numerical analysis of the method keeping in mind the final application on hydrodynamical problems developing shocks and discontinuous solutions. In this chapter, we introduce a new method, naming iterative generalized Polynomial Chaos, which ensures a gain with respect to generalized Polynomial Chaos, especially with non smooth solutions. Chapter three is closely related to an accepted publication in Communication in Computational Physics. It deals with stochastic inverse problems and introduces bayesian inference. It also emphasizes the possibility of accelerating the bayesian inference thanks to iterative generalized Polynomial Chaos described in the previous chapter. Theoretical convergence is established and illustrated on several test-cases. The last chapter consists in the application of the above materials to a complex and ambitious compressible gas dynamics problem (Richtmyer-Meshkov shock tube configuration) together with a deepened study of the physico-numerical phenomenon at stake. Finally, in the appendix, we also present some interesting research paths we quickly tackled during this thesis. (author) [fr
Sampling-based nuclear data uncertainty quantification for continuous energy Monte-Carlo codes
International Nuclear Information System (INIS)
Zhu, T.
2015-01-01
Research on the uncertainty of nuclear data is motivated by practical necessity. Nuclear data uncertainties can propagate through nuclear system simulations into operation and safety related parameters. The tolerance for uncertainties in nuclear reactor design and operation can affect the economic efficiency of nuclear power, and essentially its sustainability. The goal of the present PhD research is to establish a methodology of nuclear data uncertainty quantification (NDUQ) for MCNPX, the continuous-energy Monte-Carlo (M-C) code. The high fidelity (continuous-energy treatment and flexible geometry modelling) of MCNPX makes it the choice of routine criticality safety calculations at PSI/LRS, but also raises challenges for NDUQ by conventional sensitivity/uncertainty (S/U) methods. For example, only recently in 2011, the capability of calculating continuous energy κ_e_f_f sensitivity to nuclear data was demonstrated in certain M-C codes by using the method of iterated fission probability. The methodology developed during this PhD research is fundamentally different from the conventional S/U approach: nuclear data are treated as random variables and sampled in accordance to presumed probability distributions. When sampled nuclear data are used in repeated model calculations, the output variance is attributed to the collective uncertainties of nuclear data. The NUSS (Nuclear data Uncertainty Stochastic Sampling) tool is based on this sampling approach and implemented to work with MCNPX’s ACE format of nuclear data, which also gives NUSS compatibility with MCNP and SERPENT M-C codes. In contrast, multigroup uncertainties are used for the sampling of ACE-formatted pointwise-energy nuclear data in a groupwise manner due to the more limited quantity and quality of nuclear data uncertainties. Conveniently, the usage of multigroup nuclear data uncertainties allows consistent comparison between NUSS and other methods (both S/U and sampling-based) that employ the same
Sampling-based nuclear data uncertainty quantification for continuous energy Monte-Carlo codes
Energy Technology Data Exchange (ETDEWEB)
Zhu, T.
2015-07-01
Research on the uncertainty of nuclear data is motivated by practical necessity. Nuclear data uncertainties can propagate through nuclear system simulations into operation and safety related parameters. The tolerance for uncertainties in nuclear reactor design and operation can affect the economic efficiency of nuclear power, and essentially its sustainability. The goal of the present PhD research is to establish a methodology of nuclear data uncertainty quantification (NDUQ) for MCNPX, the continuous-energy Monte-Carlo (M-C) code. The high fidelity (continuous-energy treatment and flexible geometry modelling) of MCNPX makes it the choice of routine criticality safety calculations at PSI/LRS, but also raises challenges for NDUQ by conventional sensitivity/uncertainty (S/U) methods. For example, only recently in 2011, the capability of calculating continuous energy κ{sub eff} sensitivity to nuclear data was demonstrated in certain M-C codes by using the method of iterated fission probability. The methodology developed during this PhD research is fundamentally different from the conventional S/U approach: nuclear data are treated as random variables and sampled in accordance to presumed probability distributions. When sampled nuclear data are used in repeated model calculations, the output variance is attributed to the collective uncertainties of nuclear data. The NUSS (Nuclear data Uncertainty Stochastic Sampling) tool is based on this sampling approach and implemented to work with MCNPX’s ACE format of nuclear data, which also gives NUSS compatibility with MCNP and SERPENT M-C codes. In contrast, multigroup uncertainties are used for the sampling of ACE-formatted pointwise-energy nuclear data in a groupwise manner due to the more limited quantity and quality of nuclear data uncertainties. Conveniently, the usage of multigroup nuclear data uncertainties allows consistent comparison between NUSS and other methods (both S/U and sampling-based) that employ the same
International Nuclear Information System (INIS)
Ortiz, M.G.; Ghan, L.S.; Vogl, J.
1991-01-01
The Nuclear Regulatory Commission (NRC) revised the Emergency Core Cooling System (ECCS) licensing rule to allow the use of Best Estimate (BE) computer codes, provided the uncertainty of the calculations are quantified and used in the licensing and regulation process. The NRC developed a generic methodology called Code Scaling, Applicability and Uncertainty (CSAU) to evaluate BE code uncertainties. The CSAU methodology was demonstrated with a specific application to a pressurized water reactor (PWR), experiencing a postulated large break loss-of-coolant accident (LBLOCA). The current work is part of an effort to adapt and demonstrate the CSAU methodology to a small break (SB) LOCA in a PWR of B and W design using RELAP5/MOD3 as the simulation tool. The subject of this paper is the Assessment and Ranging of Parameters (Element 2 of the CSAU methodology), which determines the contribution to uncertainty of specific models in the code
Pore-scale uncertainty quantification with multilevel Monte Carlo
Icardi, Matteo
2014-01-06
Computational fluid dynamics (CFD) simulations of pore-scale transport processes in porous media have recently gained large popularity. However the geometrical details of the pore structures can be known only in a very low number of samples and the detailed flow computations can be carried out only on a limited number of cases. The explicit introduction of randomness in the geometry and in other setup parameters can be crucial for the optimization of pore-scale investigations for random homogenization. Since there are no generic ways to parametrize the randomness in the porescale structures, Monte Carlo techniques are the most accessible to compute statistics. We propose a multilevel Monte Carlo (MLMC) technique to reduce the computational cost of estimating quantities of interest within a prescribed accuracy constraint. Random samples of pore geometries with a hierarchy of geometrical complexities and grid refinements, are synthetically generated and used to propagate the uncertainties in the flow simulations and compute statistics of macro-scale effective parameters.
Uncertainty quantification for mean field games in social interactions
Dia, Ben Mansour
2016-01-09
We present an overview of mean field games formulation. A comparative analysis of the optimality for a stochastic McKean-Vlasov process with time-dependent probability is presented. Then we examine mean-field games for social interactions and we show that optimizing the long-term well-being through effort and social feeling state distribution (mean-field) will help to stabilize couple (marriage). However , if the cost of effort is very high, the couple fluctuates in a bad feeling state or the marriage breaks down. We then examine the influence of society on a couple using mean field sentimental games. We show that, in mean-field equilibrium, the optimal effort is always higher than the one-shot optimal effort. Finally we introduce the Wiener chaos expansion for the construction of solution of stochastic differential equations of Mckean-Vlasov type. The method is based on the Cameron-Martin version of the Wiener Chaos expansion and allow to quantify the uncertainty in the optimality system.
Uncertainty quantification for mean field games in social interactions
Dia, Ben Mansour
2016-01-01
We present an overview of mean field games formulation. A comparative analysis of the optimality for a stochastic McKean-Vlasov process with time-dependent probability is presented. Then we examine mean-field games for social interactions and we show that optimizing the long-term well-being through effort and social feeling state distribution (mean-field) will help to stabilize couple (marriage). However , if the cost of effort is very high, the couple fluctuates in a bad feeling state or the marriage breaks down. We then examine the influence of society on a couple using mean field sentimental games. We show that, in mean-field equilibrium, the optimal effort is always higher than the one-shot optimal effort. Finally we introduce the Wiener chaos expansion for the construction of solution of stochastic differential equations of Mckean-Vlasov type. The method is based on the Cameron-Martin version of the Wiener Chaos expansion and allow to quantify the uncertainty in the optimality system.
Quantification of tomographic PIV uncertainty using controlled experimental measurements.
Liu, Ning; Wu, Yue; Ma, Lin
2018-01-20
The goal of this work was to experimentally quantify the uncertainty of three-dimensional (3D) and three-component (3C) velocity measurements using tomographic particle image velocimetry (tomo-PIV). Controlled measurements were designed using tracer particles embedded in a solid sample, and tomo-PIV measurements were performed on the sample while it was moved both translationally and rotationally to simulate various known displacement fields, so the 3D3C displacements measured by tomo-PIV can be directly compared to the known displacements created by the sample. The results illustrated that (1) the tomo-PIV technique was able to reconstruct the 3D3C velocity with an averaged error of 0.8-1.4 voxels in terms of magnitude and 1.7°-1.9° in terms of orientation for the velocity fields tested; (2) view registration (VR) plays a significant role in tomo-PIV, and by reducing VR error from 0.6° to 0.1°, the 3D3C measurement accuracy can be improved by at least 2.5 times in terms of both magnitude and orientation; and (3) the use of additional cameras in tomo-PIV can extend the 3D3C velocity measurement to a larger volume, while maintaining acceptable accuracy. These results obtained from controlled tests are expected to aid the error analysis and the design of tomo-PIV measurements.
Dittes, Beatrice; Špačková, Olga; Ebrahimian, Negin; Kaiser, Maria; Rieger, Wolfgang; Disse, Markus; Straub, Daniel
2017-04-01
Flood risk estimates are subject to significant uncertainties, e.g. due to limited records of historic flood events, uncertainty in flood modeling, uncertain impact of climate change or uncertainty in the exposure and loss estimates. In traditional design of flood protection systems, these uncertainties are typically just accounted for implicitly, based on engineering judgment. In the AdaptRisk project, we develop a fully quantitative framework for planning of flood protection systems under current and future uncertainties using quantitative pre-posterior Bayesian decision analysis. In this contribution, we focus on the quantification of the uncertainties and study their relative influence on the flood risk estimate and on the planning of flood protection systems. The following uncertainty components are included using a Bayesian approach: 1) inherent and statistical (i.e. limited record length) uncertainty; 2) climate uncertainty that can be learned from an ensemble of GCM-RCM models; 3) estimates of climate uncertainty components not covered in 2), such as bias correction, incomplete ensemble, local specifics not captured by the GCM-RCM models; 4) uncertainty in the inundation modelling; 5) uncertainty in damage estimation. We also investigate how these uncertainties are possibly reduced in the future when new evidence - such as new climate models, observed extreme events, and socio-economic data - becomes available. Finally, we look into how this new evidence influences the risk assessment and effectivity of flood protection systems. We demonstrate our methodology for a pre-alpine catchment in southern Germany: the Mangfall catchment in Bavaria that includes the city of Rosenheim, which suffered significant losses during the 2013 flood event.
Aeroelastic Uncertainty Quantification Studies Using the S4T Wind Tunnel Model
Nikbay, Melike; Heeg, Jennifer
2017-01-01
This paper originates from the joint efforts of an aeroelastic study team in the Applied Vehicle Technology Panel from NATO Science and Technology Organization, with the Task Group number AVT-191, titled "Application of Sensitivity Analysis and Uncertainty Quantification to Military Vehicle Design." We present aeroelastic uncertainty quantification studies using the SemiSpan Supersonic Transport wind tunnel model at the NASA Langley Research Center. The aeroelastic study team decided treat both structural and aerodynamic input parameters as uncertain and represent them as samples drawn from statistical distributions, propagating them through aeroelastic analysis frameworks. Uncertainty quantification processes require many function evaluations to asses the impact of variations in numerous parameters on the vehicle characteristics, rapidly increasing the computational time requirement relative to that required to assess a system deterministically. The increased computational time is particularly prohibitive if high-fidelity analyses are employed. As a remedy, the Istanbul Technical University team employed an Euler solver in an aeroelastic analysis framework, and implemented reduced order modeling with Polynomial Chaos Expansion and Proper Orthogonal Decomposition to perform the uncertainty propagation. The NASA team chose to reduce the prohibitive computational time by employing linear solution processes. The NASA team also focused on determining input sample distributions.
A statistical methodology for quantification of uncertainty in best estimate code physical models
International Nuclear Information System (INIS)
Vinai, Paolo; Macian-Juan, Rafael; Chawla, Rakesh
2007-01-01
A novel uncertainty assessment methodology, based on a statistical non-parametric approach, is presented in this paper. It achieves quantification of code physical model uncertainty by making use of model performance information obtained from studies of appropriate separate-effect tests. Uncertainties are quantified in the form of estimated probability density functions (pdf's), calculated with a newly developed non-parametric estimator. The new estimator objectively predicts the probability distribution of the model's 'error' (its uncertainty) from databases reflecting the model's accuracy on the basis of available experiments. The methodology is completed by applying a novel multi-dimensional clustering technique based on the comparison of model error samples with the Kruskall-Wallis test. This takes into account the fact that a model's uncertainty depends on system conditions, since a best estimate code can give predictions for which the accuracy is affected by the regions of the physical space in which the experiments occur. The final result is an objective, rigorous and accurate manner of assigning uncertainty to coded models, i.e. the input information needed by code uncertainty propagation methodologies used for assessing the accuracy of best estimate codes in nuclear systems analysis. The new methodology has been applied to the quantification of the uncertainty in the RETRAN-3D void model and then used in the analysis of an independent separate-effect experiment. This has clearly demonstrated the basic feasibility of the approach, as well as its advantages in yielding narrower uncertainty bands in quantifying the code's accuracy for void fraction predictions
Energy Technology Data Exchange (ETDEWEB)
Nicholson, Andrew D. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Croft, Stephen [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); McElroy, Robert Dennis [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2017-08-01
The various methods of nondestructive assay (NDA) of special nuclear material (SNM) have applications in nuclear nonproliferation, including detection and identification of illicit SNM at border crossings and quantifying SNM at nuclear facilities for safeguards. No assay method is complete without “error bars,” which provide one way of expressing confidence in the assay result. Consequently, NDA specialists typically provide error bars and also partition total uncertainty into “random” and “systematic” components so that, for example, an error bar can be developed for the total mass estimate in multiple items. Uncertainty Quantification (UQ) for NDA has always been important, but it is recognized that greater rigor is needed and achievable using modern statistical methods.
Practical reliability and uncertainty quantification in complex systems : final report.
Energy Technology Data Exchange (ETDEWEB)
Grace, Matthew D.; Ringland, James T.; Marzouk, Youssef M. (Massachusetts Institute of Technology, Cambridge, MA); Boggs, Paul T.; Zurn, Rena M.; Diegert, Kathleen V. (Sandia National Laboratories, Albuquerque, NM); Pebay, Philippe Pierre; Red-Horse, John Robert (Sandia National Laboratories, Albuquerque, NM)
2009-09-01
The purpose of this project was to investigate the use of Bayesian methods for the estimation of the reliability of complex systems. The goals were to find methods for dealing with continuous data, rather than simple pass/fail data; to avoid assumptions of specific probability distributions, especially Gaussian, or normal, distributions; to compute not only an estimate of the reliability of the system, but also a measure of the confidence in that estimate; to develop procedures to address time-dependent or aging aspects in such systems, and to use these models and results to derive optimal testing strategies. The system is assumed to be a system of systems, i.e., a system with discrete components that are themselves systems. Furthermore, the system is 'engineered' in the sense that each node is designed to do something and that we have a mathematical description of that process. In the time-dependent case, the assumption is that we have a general, nonlinear, time-dependent function describing the process. The major results of the project are described in this report. In summary, we developed a sophisticated mathematical framework based on modern probability theory and Bayesian analysis. This framework encompasses all aspects of epistemic uncertainty and easily incorporates steady-state and time-dependent systems. Based on Markov chain, Monte Carlo methods, we devised a computational strategy for general probability density estimation in the steady-state case. This enabled us to compute a distribution of the reliability from which many questions, including confidence, could be addressed. We then extended this to the time domain and implemented procedures to estimate the reliability over time, including the use of the method to predict the reliability at a future time. Finally, we used certain aspects of Bayesian decision analysis to create a novel method for determining an optimal testing strategy, e.g., we can estimate the 'best' location to
Development of Uncertainty Quantification Method for MIR-PIV Measurement using BOS Technique
International Nuclear Information System (INIS)
Seong, Jee Hyun; Song, Min Seop; Kim, Eung Soo
2014-01-01
Matching Index of Refraction (MIR) is frequently used for obtaining high quality PIV measurement data. ven small distortion by unmatched refraction index of test section can result in uncertainty problems. In this context, it is desirable to construct new concept for checking errors of MIR and following uncertainty of PIV measurement. This paper proposes a couple of experimental concept and relative results. This study developed an MIR uncertainty quantification method for PIV measurement using SBOS technique. From the reference data of the BOS, the reliable SBOS experiment procedure was constructed. Then with the combination of SBOS technique with MIR-PIV technique, velocity vector and refraction displacement vector field was measured simultaneously. MIR errors are calculated through mathematical equation, in which PIV and SBOS data are put. These errors are also verified by another BOS experiment. Finally, with the applying of calculated MIR-PIV uncertainty, correct velocity vector field can be obtained regardless of MIR errors
Swinburne, Thomas D.; Perez, Danny
2018-05-01
A massively parallel method to build large transition rate matrices from temperature-accelerated molecular dynamics trajectories is presented. Bayesian Markov model analysis is used to estimate the expected residence time in the known state space, providing crucial uncertainty quantification for higher-scale simulation schemes such as kinetic Monte Carlo or cluster dynamics. The estimators are additionally used to optimize where exploration is performed and the degree of temperature acceleration on the fly, giving an autonomous, optimal procedure to explore the state space of complex systems. The method is tested against exactly solvable models and used to explore the dynamics of C15 interstitial defects in iron. Our uncertainty quantification scheme allows for accurate modeling of the evolution of these defects over timescales of several seconds.
International Nuclear Information System (INIS)
Zhang, Hongbin; Zhao, Haihua; Zou, Ling; Burns, Douglas; Ladd, Jacob
2017-01-01
BISON is an advanced fuels performance code being developed at Idaho National Laboratory and is the code of choice for fuels performance by the U.S. Department of Energy (DOE)’s Consortium for Advanced Simulation of Light Water Reactors (CASL) Program. An approach to uncertainty quantification and sensitivity analysis with BISON was developed and a new toolkit was created. A PWR fuel rod model was developed and simulated by BISON, and uncertainty quantification and sensitivity analysis were performed with eighteen uncertain input parameters. The maximum fuel temperature and gap conductance were selected as the figures of merit (FOM). Pearson, Spearman, and partial correlation coefficients were considered for all of the figures of merit in sensitivity analysis. (author)
Energy Technology Data Exchange (ETDEWEB)
Zhang, Hongbin; Ladd, Jacob; Zhao, Haihua; Zou, Ling; Burns, Douglas
2015-11-01
BISON is an advanced fuels performance code being developed at Idaho National Laboratory and is the code of choice for fuels performance by the U.S. Department of Energy (DOE)’s Consortium for Advanced Simulation of Light Water Reactors (CASL) Program. An approach to uncertainty quantification and sensitivity analysis with BISON was developed and a new toolkit was created. A PWR fuel rod model was developed and simulated by BISON, and uncertainty quantification and sensitivity analysis were performed with eighteen uncertain input parameters. The maximum fuel temperature and gap conductance were selected as the figures of merit (FOM). Pearson, Spearman, and partial correlation coefficients were considered for all of the figures of merit in sensitivity analysis.
Energy Technology Data Exchange (ETDEWEB)
Tsao, Jeffrey Y. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Trucano, Timothy G. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kleban, Stephen D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Naugle, Asmeret Bier [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Verzi, Stephen Joseph [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Swiler, Laura Painton [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Johnson, Curtis M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Smith, Mark A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Flanagan, Tatiana Paz [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vugrin, Eric D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Gabert, Kasimir Georg [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Lave, Matthew Samuel [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Chen, Wei [Northwestern Univ., Evanston, IL (United States); DeLaurentis, Daniel [Purdue Univ., West Lafayette, IN (United States); Hubler, Alfred [Univ. of Illinois, Urbana, IL (United States); Oberkampf, Bill [WLO Consulting, Austin, TX (United States)
2016-08-01
This report contains the written footprint of a Sandia-hosted workshop held in Albuquerque, New Mexico, June 22-23, 2016 on “Complex Systems Models and Their Applications: Towards a New Science of Verification, Validation and Uncertainty Quantification,” as well as of pre-work that fed into the workshop. The workshop’s intent was to explore and begin articulating research opportunities at the intersection between two important Sandia communities: the complex systems (CS) modeling community, and the verification, validation and uncertainty quantification (VVUQ) community The overarching research opportunity (and challenge) that we ultimately hope to address is: how can we quantify the credibility of knowledge gained from complex systems models, knowledge that is often incomplete and interim, but will nonetheless be used, sometimes in real-time, by decision makers?
USACM Thematic Workshop On Uncertainty Quantification And Data-Driven Modeling.
Energy Technology Data Exchange (ETDEWEB)
Stewart, James R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-05-01
The USACM Thematic Workshop on Uncertainty Quantification and Data-Driven Modeling was held on March 23-24, 2017, in Austin, TX. The organizers of the technical program were James R. Stewart of Sandia National Laboratories and Krishna Garikipati of University of Michigan. The administrative organizer was Ruth Hengst, who serves as Program Coordinator for the USACM. The organization of this workshop was coordinated through the USACM Technical Thrust Area on Uncertainty Quantification and Probabilistic Analysis. The workshop website (http://uqpm2017.usacm.org) includes the presentation agenda as well as links to several of the presentation slides (permission to access the presentations was granted by each of those speakers, respectively). Herein, this final report contains the complete workshop program that includes the presentation agenda, the presentation abstracts, and the list of posters.
International Nuclear Information System (INIS)
Schneiders, Jan F G; Sciacchitano, Andrea
2017-01-01
The track benchmarking method (TBM) is proposed for uncertainty quantification of particle tracking velocimetry (PTV) data mapped onto a regular grid. The method provides statistical uncertainty for a velocity time-series and can in addition be used to obtain instantaneous uncertainty at increased computational cost. Interpolation techniques are typically used to map velocity data from scattered PTV (e.g. tomographic PTV and Shake-the-Box) measurements onto a Cartesian grid. Recent examples of these techniques are the FlowFit and VIC+ methods. The TBM approach estimates the random uncertainty in dense velocity fields by performing the velocity interpolation using a subset of typically 95% of the particle tracks and by considering the remaining tracks as an independent benchmarking reference. In addition, also a bias introduced by the interpolation technique is identified. The numerical assessment shows that the approach is accurate when particle trajectories are measured over an extended number of snapshots, typically on the order of 10. When only short particle tracks are available, the TBM estimate overestimates the measurement error. A correction to TBM is proposed and assessed to compensate for this overestimation. The experimental assessment considers the case of a jet flow, processed both by tomographic PIV and by VIC+. The uncertainty obtained by TBM provides a quantitative evaluation of the measurement accuracy and precision and highlights the regions of high error by means of bias and random uncertainty maps. In this way, it is possible to quantify the uncertainty reduction achieved by advanced interpolation algorithms with respect to standard correlation-based tomographic PIV. The use of TBM for uncertainty quantification and comparison of different processing techniques is demonstrated. (paper)
Chen, Cheng; Xu, Weijie; Guo, Tong; Chen, Kai
2017-10-01
Uncertainties in structure properties can result in different responses in hybrid simulations. Quantification of the effect of these uncertainties would enable researchers to estimate the variances of structural responses observed from experiments. This poses challenges for real-time hybrid simulation (RTHS) due to the existence of actuator delay. Polynomial chaos expansion (PCE) projects the model outputs on a basis of orthogonal stochastic polynomials to account for influences of model uncertainties. In this paper, PCE is utilized to evaluate effect of actuator delay on the maximum displacement from real-time hybrid simulation of a single degree of freedom (SDOF) structure when accounting for uncertainties in structural properties. The PCE is first applied for RTHS without delay to determine the order of PCE, the number of sample points as well as the method for coefficients calculation. The PCE is then applied to RTHS with actuator delay. The mean, variance and Sobol indices are compared and discussed to evaluate the effects of actuator delay on uncertainty quantification for RTHS. Results show that the mean and the variance of the maximum displacement increase linearly and exponentially with respect to actuator delay, respectively. Sensitivity analysis through Sobol indices also indicates the influence of the single random variable decreases while the coupling effect increases with the increase of actuator delay.
Quantification of Airfoil Geometry-Induced Aerodynamic Uncertainties---Comparison of Approaches
Liu, Dishi; Litvinenko, Alexander; Schillings, Claudia; Schulz, Volker
2015-01-01
Uncertainty quantification in aerodynamic simulations calls for efficient numerical methods to reduce computational cost, especially for uncertainties caused by random geometry variations which involve a large number of variables. This paper compares five methods, including quasi-Monte Carlo quadrature, polynomial chaos with coefficients determined by sparse quadrature and by point collocation, radial basis function and a gradient-enhanced version of kriging, and examines their efficiency in estimating statistics of aerodynamic performance upon random perturbation to the airfoil geometry which is parameterized by independent Gaussian variables. The results show that gradient-enhanced surrogate methods achieve better accuracy than direct integration methods with the same computational cost.
Uncertainty Quantification for Complex RF-structures Using the State-space Concatenation Approach
Heller, Johann; Schmidt, Christian; Van Rienen, Ursula
2015-01-01
as well as to employ robust optimizations, a so-called uncertainty quantification (UQ) is applied. For large and complex structures such computations are heavily demanding and cannot be carried out using standard brute-force approaches. In this paper, we propose a combination of established techniques to perform UQ for long and complex structures, where the uncertainty is located only in parts of the structure. As exemplary structure, we investigate the third-harmonic cavity, which is being used at the FLASH accelerator at DESY, assuming an uncertain...
Quantification of Airfoil Geometry-Induced Aerodynamic Uncertainties---Comparison of Approaches
Liu, Dishi
2015-04-14
Uncertainty quantification in aerodynamic simulations calls for efficient numerical methods to reduce computational cost, especially for uncertainties caused by random geometry variations which involve a large number of variables. This paper compares five methods, including quasi-Monte Carlo quadrature, polynomial chaos with coefficients determined by sparse quadrature and by point collocation, radial basis function and a gradient-enhanced version of kriging, and examines their efficiency in estimating statistics of aerodynamic performance upon random perturbation to the airfoil geometry which is parameterized by independent Gaussian variables. The results show that gradient-enhanced surrogate methods achieve better accuracy than direct integration methods with the same computational cost.
Energy Technology Data Exchange (ETDEWEB)
Picard, Richard Roy [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Bhat, Kabekode Ghanasham [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-07-18
We examine sensitivity analysis and uncertainty quantification for molecular dynamics simulation. Extreme (large or small) output values for the LAMMPS code often occur at the boundaries of input regions, and uncertainties in those boundary values are overlooked by common SA methods. Similarly, input values for which code outputs are consistent with calibration data can also occur near boundaries. Upon applying approaches in the literature for imprecise probabilities (IPs), much more realistic results are obtained than for the complacent application of standard SA and code calibration.
Directory of Open Access Journals (Sweden)
Theodoros T. Zygiridis
2017-01-01
Full Text Available We provide a review of selected computational methodologies that are based on the deterministic finite-difference time-domain algorithm and are suitable for the investigation of electromagnetic problems involving uncertainties. As it will become apparent, several alternatives capable of performing uncertainty quantification in a variety of cases exist, each one exhibiting different qualities and ranges of applicability, which we intend to point out here. Given the numerous available approaches, the purpose of this paper is to clarify the main strengths and weaknesses of the described methodologies and help the potential readers to safely select the most suitable approach for their problem under consideration.
Bayesian Statistics and Uncertainty Quantification for Safety Boundary Analysis in Complex Systems
He, Yuning; Davies, Misty Dawn
2014-01-01
The analysis of a safety-critical system often requires detailed knowledge of safe regions and their highdimensional non-linear boundaries. We present a statistical approach to iteratively detect and characterize the boundaries, which are provided as parameterized shape candidates. Using methods from uncertainty quantification and active learning, we incrementally construct a statistical model from only few simulation runs and obtain statistically sound estimates of the shape parameters for safety boundaries.
Banks, H T; Holm, Kathleen; Robbins, Danielle
2010-11-01
We computationally investigate two approaches for uncertainty quantification in inverse problems for nonlinear parameter dependent dynamical systems. We compare the bootstrapping and asymptotic theory approaches for problems involving data with several noise forms and levels. We consider both constant variance absolute error data and relative error which produces non-constant variance data in our parameter estimation formulations. We compare and contrast parameter estimates, standard errors, confidence intervals, and computational times for both bootstrapping and asymptotic theory methods.
An Open Source Computational Framework for Uncertainty Quantification of Plasma Chemistry Models
Zaheri Sarabi, Shadi
2017-01-01
The current thesis deals with the development of a computational framework for performing plasma chemistry simulations and their uncertainty quantification analysis by suitably combining and extending existing open source computational tools. A plasma chemistry solver is implemented in the OpenFOAM C++ solver suite. The OpenFOAM plasma chemistry application solves the species conservation equations and the electron energy equation by accounting suitably for various production and loss terms b...
Uncertainty quantification and sensitivity analysis with CASL Core Simulator VERA-CS
International Nuclear Information System (INIS)
Brown, C.S.; Zhang, Hongbin
2016-01-01
VERA-CS (Virtual Environment for Reactor Applications, Core Simulator) is a coupled neutron transport and thermal-hydraulics code under development by the Consortium for Advanced Simulation of Light Water Reactors (CASL). An approach to uncertainty quantification and sensitivity analysis with VERA-CS was developed and a new toolkit was created to perform uncertainty quantification and sensitivity analysis. A 2 × 2 fuel assembly model was developed and simulated by VERA-CS, and uncertainty quantification and sensitivity analysis were performed with fourteen uncertain input parameters. The minimum departure from nucleate boiling ratio (MDNBR), maximum fuel center-line temperature, and maximum outer clad surface temperature were chosen as the selected figures of merit. Pearson, Spearman, and partial correlation coefficients were considered for all of the figures of merit in sensitivity analysis and coolant inlet temperature was consistently the most influential parameter. Parameters used as inputs to the critical heat flux calculation with the W-3 correlation were shown to be the most influential on the MDNBR, maximum fuel center-line temperature, and maximum outer clad surface temperature.
Energy Technology Data Exchange (ETDEWEB)
Yankov, A.; Downar, T. [University of Michigan, 2355 Bonisteel Blvd, Ann Arbor, MI 48109 (United States)
2013-07-01
Recent efforts in the application of uncertainty quantification to nuclear systems have utilized methods based on generalized perturbation theory and stochastic sampling. While these methods have proven to be effective they both have major drawbacks that may impede further progress. A relatively new approach based on spectral elements for uncertainty quantification is applied in this paper to several problems in reactor simulation. Spectral methods based on collocation attempt to couple the approximation free nature of stochastic sampling methods with the determinism of generalized perturbation theory. The specific spectral method used in this paper employs both the Smolyak algorithm and adaptivity by using Newton-Cotes collocation points along with linear hat basis functions. Using this approach, a surrogate model for the outputs of a computer code is constructed hierarchically by adaptively refining the collocation grid until the interpolant is converged to a user-defined threshold. The method inherently fits into the framework of parallel computing and allows for the extraction of meaningful statistics and data that are not within reach of stochastic sampling and generalized perturbation theory. This paper aims to demonstrate the advantages of spectral methods-especially when compared to current methods used in reactor physics for uncertainty quantification-and to illustrate their full potential. (authors)
Mondal, A.; Efendiev, Y.; Mallick, B.; Datta-Gupta, A.
2010-01-01
. Within each channel, the permeability is assumed to have a lognormal distribution. Uncertainty quantification in history matching is carried out hierarchically by constructing geologic facies boundaries as well as permeability fields within each facies
Akram, Muhammad Farooq Bin
The management of technology portfolios is an important element of aerospace system design. New technologies are often applied to new product designs to ensure their competitiveness at the time they are introduced to market. The future performance of yet-to- be designed components is inherently uncertain, necessitating subject matter expert knowledge, statistical methods and financial forecasting. Estimates of the appropriate parameter settings often come from disciplinary experts, who may disagree with each other because of varying experience and background. Due to inherent uncertain nature of expert elicitation in technology valuation process, appropriate uncertainty quantification and propagation is very critical. The uncertainty in defining the impact of an input on performance parameters of a system makes it difficult to use traditional probability theory. Often the available information is not enough to assign the appropriate probability distributions to uncertain inputs. Another problem faced during technology elicitation pertains to technology interactions in a portfolio. When multiple technologies are applied simultaneously on a system, often their cumulative impact is non-linear. Current methods assume that technologies are either incompatible or linearly independent. It is observed that in case of lack of knowledge about the problem, epistemic uncertainty is the most suitable representation of the process. It reduces the number of assumptions during the elicitation process, when experts are forced to assign probability distributions to their opinions without sufficient knowledge. Epistemic uncertainty can be quantified by many techniques. In present research it is proposed that interval analysis and Dempster-Shafer theory of evidence are better suited for quantification of epistemic uncertainty in technology valuation process. Proposed technique seeks to offset some of the problems faced by using deterministic or traditional probabilistic approaches for
International Nuclear Information System (INIS)
Chiba, Go; Tsuji, Masashi; Narabayashi, Tadashi
2014-01-01
In order to properly quantify fission reactor neutronics parameter uncertainties, we have to use covariance data and sensitivity profiles consistently. In the present paper, we establish two consistent methodologies for uncertainty quantification: a self-shielded cross section-based consistent methodology and an infinitely-diluted cross section-based consistent methodology. With these methodologies and the covariance data of uranium-238 nuclear data given in JENDL-3.3, we quantify uncertainties of infinite neutron multiplication factors of light water reactor and fast reactor fuel cells. While an inconsistent methodology gives results which depend on the energy group structure of neutron flux and neutron-nuclide reaction cross section representation, both the consistent methodologies give fair results with no such dependences.
Ideas underlying quantification of margins and uncertainties(QMU): a white paper.
Energy Technology Data Exchange (ETDEWEB)
Helton, Jon Craig; Trucano, Timothy Guy; Pilch, Martin M.
2006-09-01
This report describes key ideas underlying the application of Quantification of Margins and Uncertainties (QMU) to nuclear weapons stockpile lifecycle decisions at Sandia National Laboratories. While QMU is a broad process and methodology for generating critical technical information to be used in stockpile management, this paper emphasizes one component, which is information produced by computational modeling and simulation. In particular, we discuss the key principles of developing QMU information in the form of Best Estimate Plus Uncertainty, the need to separate aleatory and epistemic uncertainty in QMU, and the risk-informed decision making that is best suited for decisive application of QMU. The paper is written at a high level, but provides a systematic bibliography of useful papers for the interested reader to deepen their understanding of these ideas.
Directory of Open Access Journals (Sweden)
GO CHIBA
2014-06-01
Full Text Available In order to properly quantify fission reactor neutronics parameter uncertainties, we have to use covariance data and sensitivity profiles consistently. In the present paper, we establish two consistent methodologies for uncertainty quantification: a self-shielded cross section-based consistent methodology and an infinitely-diluted cross section-based consistent methodology. With these methodologies and the covariance data of uranium-238 nuclear data given in JENDL-3.3, we quantify uncertainties of infinite neutron multiplication factors of light water reactor and fast reactor fuel cells. While an inconsistent methodology gives results which depend on the energy group structure of neutron flux and neutron-nuclide reaction cross section representation, both the consistent methodologies give fair results with no such dependences.
A method for uncertainty quantification in the life prediction of gas turbine components
Energy Technology Data Exchange (ETDEWEB)
Lodeby, K.; Isaksson, O.; Jaervstraat, N. [Volvo Aero Corporation, Trolhaettan (Sweden)
1998-12-31
A failure in an aircraft jet engine can have severe consequences which cannot be accepted and high requirements are therefore raised on engine reliability. Consequently, assessment of the reliability of life predictions used in design and maintenance are important. To assess the validity of the predicted life a method to quantify the contribution to the total uncertainty in the life prediction from different uncertainty sources is developed. The method is a structured approach for uncertainty quantification that uses a generic description of the life prediction process. It is based on an approximate error propagation theory combined with a unified treatment of random and systematic errors. The result is an approximate statistical distribution for the predicted life. The method is applied on life predictions for three different jet engine components. The total uncertainty became of reasonable order of magnitude and a good qualitative picture of the distribution of the uncertainty contribution from the different sources was obtained. The relative importance of the uncertainty sources differs between the three components. It is also highly dependent on the methods and assumptions used in the life prediction. Advantages and disadvantages of this method is discussed. (orig.) 11 refs.
Monte Carlo approaches for uncertainty quantification of criticality for system dimensions
International Nuclear Information System (INIS)
Kiedrowski, B.C.; Brown, F.B.
2013-01-01
One of the current challenges in nuclear engineering computations is the issue of performing uncertainty analysis for either calculations or experimental measurements. This paper specifically focuses on the issue of estimating the uncertainties arising from geometric tolerances. For this paper, two techniques for uncertainty quantification are studied. The first is the forward propagation technique, which can be thought of as a 'brute force' approach; uncertain system parameters are randomly sampled, the calculation is run, and uncertainties are found from the empirically obtained distribution of results. This approach need make no approximations in principle, but is very computationally expensive. The other approach investigated is the adjoint-based approach; system sensitivities are computed via a single Monte Carlo calculation and those are used with a covariance matrix to provide a linear estimate of the uncertainty. Demonstration calculations are performed with the MCNP6 code for both techniques. The 2 techniques are tested on 2 cases: the first case is a solid, bare cylinder of Pu-metal while the second case is a can of plutonium nitrate solution. The results show that the forward and adjoint approaches appear to agree in some cases where the responses are not non-linearly correlated. In other cases, the uncertainties in the effective multiplication k disagree for reasons not yet known
International Nuclear Information System (INIS)
Xue, Zhenyu; Charonko, John J; Vlachos, Pavlos P
2014-01-01
In particle image velocimetry (PIV) the measurement signal is contained in the recorded intensity of the particle image pattern superimposed on a variety of noise sources. The signal-to-noise-ratio (SNR) strength governs the resulting PIV cross correlation and ultimately the accuracy and uncertainty of the resulting PIV measurement. Hence we posit that correlation SNR metrics calculated from the correlation plane can be used to quantify the quality of the correlation and the resulting uncertainty of an individual measurement. In this paper we extend the original work by Charonko and Vlachos and present a framework for evaluating the correlation SNR using a set of different metrics, which in turn are used to develop models for uncertainty estimation. Several corrections have been applied in this work. The SNR metrics and corresponding models presented herein are expanded to be applicable to both standard and filtered correlations by applying a subtraction of the minimum correlation value to remove the effect of the background image noise. In addition, the notion of a ‘valid’ measurement is redefined with respect to the correlation peak width in order to be consistent with uncertainty quantification principles and distinct from an ‘outlier’ measurement. Finally the type and significance of the error distribution function is investigated. These advancements lead to more robust and reliable uncertainty estimation models compared with the original work by Charonko and Vlachos. The models are tested against both synthetic benchmark data as well as experimental measurements. In this work, U 68.5 uncertainties are estimated at the 68.5% confidence level while U 95 uncertainties are estimated at 95% confidence level. For all cases the resulting calculated coverage factors approximate the expected theoretical confidence intervals, thus demonstrating the applicability of these new models for estimation of uncertainty for individual PIV measurements. (paper)
Quantification of water resources uncertainties in the Luvuvhu sub-basin of the Limpopo river basin
Oosthuizen, N.; Hughes, D.; Kapangaziwiri, E.; Mwenge Kahinda, J.; Mvandaba, V.
2018-06-01
In the absence of historical observed data, models are generally used to describe the different hydrological processes and generate data and information that will inform management and policy decision making. Ideally, any hydrological model should be based on a sound conceptual understanding of the processes in the basin and be backed by quantitative information for the parameterization of the model. However, these data are often inadequate in many sub-basins, necessitating the incorporation of the uncertainty related to the estimation process. This paper reports on the impact of the uncertainty related to the parameterization of the Pitman monthly model and water use data on the estimates of the water resources of the Luvuvhu, a sub-basin of the Limpopo river basin. The study reviews existing information sources associated with the quantification of water balance components and gives an update of water resources of the sub-basin. The flows generated by the model at the outlet of the basin were between 44.03 Mm3 and 45.48 Mm3 per month when incorporating +20% uncertainty to the main physical runoff generating parameters. The total predictive uncertainty of the model increased when water use data such as small farm and large reservoirs and irrigation were included. The dam capacity data was considered at an average of 62% uncertainty mainly as a result of the large differences between the available information in the national water resources database and that digitised from satellite imagery. Water used by irrigated crops was estimated with an average of about 50% uncertainty. The mean simulated monthly flows were between 38.57 Mm3 and 54.83 Mm3 after the water use uncertainty was added. However, it is expected that the uncertainty could be reduced by using higher resolution remote sensing imagery.
Xue, Zhenyu; Charonko, John J.; Vlachos, Pavlos P.
2014-11-01
In particle image velocimetry (PIV) the measurement signal is contained in the recorded intensity of the particle image pattern superimposed on a variety of noise sources. The signal-to-noise-ratio (SNR) strength governs the resulting PIV cross correlation and ultimately the accuracy and uncertainty of the resulting PIV measurement. Hence we posit that correlation SNR metrics calculated from the correlation plane can be used to quantify the quality of the correlation and the resulting uncertainty of an individual measurement. In this paper we extend the original work by Charonko and Vlachos and present a framework for evaluating the correlation SNR using a set of different metrics, which in turn are used to develop models for uncertainty estimation. Several corrections have been applied in this work. The SNR metrics and corresponding models presented herein are expanded to be applicable to both standard and filtered correlations by applying a subtraction of the minimum correlation value to remove the effect of the background image noise. In addition, the notion of a ‘valid’ measurement is redefined with respect to the correlation peak width in order to be consistent with uncertainty quantification principles and distinct from an ‘outlier’ measurement. Finally the type and significance of the error distribution function is investigated. These advancements lead to more robust and reliable uncertainty estimation models compared with the original work by Charonko and Vlachos. The models are tested against both synthetic benchmark data as well as experimental measurements. In this work, {{U}68.5} uncertainties are estimated at the 68.5% confidence level while {{U}95} uncertainties are estimated at 95% confidence level. For all cases the resulting calculated coverage factors approximate the expected theoretical confidence intervals, thus demonstrating the applicability of these new models for estimation of uncertainty for individual PIV measurements.
Energy Technology Data Exchange (ETDEWEB)
Reeve, Samuel Temple; Strachan, Alejandro, E-mail: strachan@purdue.edu
2017-04-01
We use functional, Fréchet, derivatives to quantify how thermodynamic outputs of a molecular dynamics (MD) simulation depend on the potential used to compute atomic interactions. Our approach quantifies the sensitivity of the quantities of interest with respect to the input functions as opposed to its parameters as is done in typical uncertainty quantification methods. We show that the functional sensitivity of the average potential energy and pressure in isothermal, isochoric MD simulations using Lennard–Jones two-body interactions can be used to accurately predict those properties for other interatomic potentials (with different functional forms) without re-running the simulations. This is demonstrated under three different thermodynamic conditions, namely a crystal at room temperature, a liquid at ambient pressure, and a high pressure liquid. The method provides accurate predictions as long as the change in potential can be reasonably described to first order and does not significantly affect the region in phase space explored by the simulation. The functional uncertainty quantification approach can be used to estimate the uncertainties associated with constitutive models used in the simulation and to correct predictions if a more accurate representation becomes available.
Online updating and uncertainty quantification using nonstationary output-only measurement
Yuen, Ka-Veng; Kuok, Sin-Chi
2016-01-01
Extended Kalman filter (EKF) is widely adopted for state estimation and parametric identification of dynamical systems. In this algorithm, it is required to specify the covariance matrices of the process noise and measurement noise based on prior knowledge. However, improper assignment of these noise covariance matrices leads to unreliable estimation and misleading uncertainty estimation on the system state and model parameters. Furthermore, it may induce diverging estimation. To resolve these problems, we propose a Bayesian probabilistic algorithm for online estimation of the noise parameters which are used to characterize the noise covariance matrices. There are three major appealing features of the proposed approach. First, it resolves the divergence problem in the conventional usage of EKF due to improper choice of the noise covariance matrices. Second, the proposed approach ensures the reliability of the uncertainty quantification. Finally, since the noise parameters are allowed to be time-varying, nonstationary process noise and/or measurement noise are explicitly taken into account. Examples using stationary/nonstationary response of linear/nonlinear time-varying dynamical systems are presented to demonstrate the efficacy of the proposed approach. Furthermore, comparison with the conventional usage of EKF will be provided to reveal the necessity of the proposed approach for reliable model updating and uncertainty quantification.
Resseguier, V.; Memin, E.; Chapron, B.; Fox-Kemper, B.
2017-12-01
In order to better observe and predict geophysical flows, ensemble-based data assimilation methods are of high importance. In such methods, an ensemble of random realizations represents the variety of the simulated flow's likely behaviors. For this purpose, randomness needs to be introduced in a suitable way and physically-based stochastic subgrid parametrizations are promising paths. This talk will propose a new kind of such a parametrization referred to as modeling under location uncertainty. The fluid velocity is decomposed into a resolved large-scale component and an aliased small-scale one. The first component is possibly random but time-correlated whereas the second is white-in-time but spatially-correlated and possibly inhomogeneous and anisotropic. With such a velocity, the material derivative of any - possibly active - tracer is modified. Three new terms appear: a correction of the large-scale advection, a multiplicative noise and a possibly heterogeneous and anisotropic diffusion. This parameterization naturally ensures attractive properties such as energy conservation for each realization. Additionally, this stochastic material derivative and the associated Reynolds' transport theorem offer a systematic method to derive stochastic models. In particular, we will discuss the consequences of the Quasi-Geostrophic assumptions in our framework. Depending on the turbulence amount, different models with different physical behaviors are obtained. Under strong turbulence assumptions, a simplified diagnosis of frontolysis and frontogenesis at the surface of the ocean is possible in this framework. A Surface Quasi-Geostrophic (SQG) model with a weaker noise influence has also been simulated. A single realization better represents small scales than a deterministic SQG model at the same resolution. Moreover, an ensemble accurately predicts extreme events, bifurcations as well as the amplitudes and the positions of the simulation errors. Figure 1 highlights this last
Sargsyan, K.; Safta, C.; Debusschere, B.; Najm, H.
2010-12-01
Uncertainty quantification in complex climate models is challenged by the sparsity of available climate model predictions due to the high computational cost of model runs. Another feature that prevents classical uncertainty analysis from being readily applicable is bifurcative behavior in climate model response with respect to certain input parameters. A typical example is the Atlantic Meridional Overturning Circulation. The predicted maximum overturning stream function exhibits discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO2 forcing. We outline a methodology for uncertainty quantification given discontinuous model response and a limited number of model runs. Our approach is two-fold. First we detect the discontinuity with Bayesian inference, thus obtaining a probabilistic representation of the discontinuity curve shape and location for arbitrarily distributed input parameter values. Then, we construct spectral representations of uncertainty, using Polynomial Chaos (PC) expansions on either side of the discontinuity curve, leading to an averaged-PC representation of the forward model that allows efficient uncertainty quantification. The approach is enabled by a Rosenblatt transformation that maps each side of the discontinuity to regular domains where desirable orthogonality properties for the spectral bases hold. We obtain PC modes by either orthogonal projection or Bayesian inference, and argue for a hybrid approach that targets a balance between the accuracy provided by the orthogonal projection and the flexibility provided by the Bayesian inference - where the latter allows obtaining reasonable expansions without extra forward model runs. The model output, and its associated uncertainty at specific design points, are then computed by taking an ensemble average over PC expansions corresponding to possible realizations of the discontinuity curve. The methodology is tested on synthetic examples of
Quantification of uncertainties in source term estimates for a BWR with Mark I containment
International Nuclear Information System (INIS)
Khatib-Rahbar, M.; Cazzoli, E.; Davis, R.; Ishigami, T.; Lee, M.; Nourbakhsh, H.; Schmidt, E.; Unwin, S.
1988-01-01
A methodology for quantification and uncertainty analysis of source terms for severe accident in light water reactors (QUASAR) has been developed. The objectives of the QUASAR program are (1) to develop a framework for performing an uncertainty evaluation of the input parameters of the phenomenological models used in the Source Term Code Package (STCP), and (2) to quantify the uncertainties in certain phenomenological aspects of source terms (that are not modeled by STCP) using state-of-the-art methods. The QUASAR methodology consists of (1) screening sensitivity analysis, where the most sensitive input variables are selected for detailed uncertainty analysis, (2) uncertainty analysis, where probability density functions (PDFs) are established for the parameters identified by the screening stage and propagated through the codes to obtain PDFs for the outputs (i.e., release fractions to the environment), and (3) distribution sensitivity analysis, which is performed to determine the sensitivity of the output PDFs to the input PDFs. In this paper attention is limited to a single accident progression sequence, namely; a station blackout accident in a BWR with a Mark I containment buildings. Identified as an important accident in the draft NUREG-1150 a station blackout involves loss of both off-site power and DC power resulting in failure of the diesels to start and in the unavailability of the high pressure injection and core isolation coding systems
Quantification of Back-End Nuclear Fuel Cycle Metrics Uncertainties Due to Cross Sections
International Nuclear Information System (INIS)
Tracy E. Stover Jr.
2007-01-01
This work examines uncertainties in the back end fuel cycle metrics of isotopic composition, decay heat, radioactivity, and radiotoxicity. Most advanced fuel cycle scenarios, including the ones represented in this work, are limited by one or more of these metrics, so that quantification of them becomes of great importance in order to optimize or select one of these scenarios. Uncertainty quantification, in this work, is performed by propagating cross-section covariance data, and later number density covariance data, through a reactor physics and depletion code sequence. Propagation of uncertainty is performed primarily via the Efficient Subspace Method (ESM). ESM decomposes the covariance data into singular pairs and perturbs input data along independent directions of the uncertainty and only for the most significant values of that uncertainty. Results of these perturbations being collected, ESM directly calculates the covariance of the observed output posteriori. By exploiting the rank deficient nature of the uncertainty data, ESM works more efficiently than traditional stochastic sampling, but is shown to produce equivalent results. ESM is beneficial for very detailed models with large amounts of input data that make stochastic sampling impractical. In this study various fuel cycle scenarios are examined. Simplified, representative models of pressurized water reactor (PWR) and boiling water reactor (BWR) fuels composed of both uranium oxide and mixed oxides are examined. These simple models are intended to give a representation of the uncertainty that can be associated with open uranium oxide fuel cycles and closed mixed oxide fuel cycles. The simplified models also serve as a demonstration to show that ESM and stochastic sampling produce equivalent results, because these models require minimum computer resources and have amounts of input data small enough such that either method can be quickly implemented and a numerical experiment performed. The simplified
Prudencio, Ernesto E.
2012-01-01
QUESO is a collection of statistical algorithms and programming constructs supporting research into the uncertainty quantification (UQ) of models and their predictions. It has been designed with three objectives: it should (a) be sufficiently abstract in order to handle a large spectrum of models, (b) be algorithmically extensible, allowing an easy insertion of new and improved algorithms, and (c) take advantage of parallel computing, in order to handle realistic models. Such objectives demand a combination of an object-oriented design with robust software engineering practices. QUESO is written in C++, uses MPI, and leverages libraries already available to the scientific community. We describe some UQ concepts, present QUESO, and list planned enhancements.
Directory of Open Access Journals (Sweden)
Arun Kaintura
2018-02-01
Full Text Available Advances in manufacturing process technology are key ensembles for the production of integrated circuits in the sub-micrometer region. It is of paramount importance to assess the effects of tolerances in the manufacturing process on the performance of modern integrated circuits. The polynomial chaos expansion has emerged as a suitable alternative to standard Monte Carlo-based methods that are accurate, but computationally cumbersome. This paper provides an overview of the most recent developments and challenges in the application of polynomial chaos-based techniques for uncertainty quantification in integrated circuits, with particular focus on high-dimensional problems.
DEFF Research Database (Denmark)
Sunyer Pinya, Maria Antonia; Madsen, H.; Rosbjerg, Dan
Climate Models (RCMs) and General Circulation Models (GCMs). These multi-model ensembles provide the information needed to estimate probabilistic climate change projections. Several probabilistic methods have been suggested. One common assumption in most of these methods is that the climate models...... are independent. The effects of this assumption on the uncertainty quantification of extreme rainfall projections are addressed in this study. First, the interdependency of the 95% quantile of wet days in the ENSEMBLES RCMs is estimated. For this statistic and the region studied, the RCMs cannot be assumed...
Statistical emulation of a tsunami model for sensitivity analysis and uncertainty quantification
Directory of Open Access Journals (Sweden)
A. Sarri
2012-06-01
Full Text Available Due to the catastrophic consequences of tsunamis, early warnings need to be issued quickly in order to mitigate the hazard. Additionally, there is a need to represent the uncertainty in the predictions of tsunami characteristics corresponding to the uncertain trigger features (e.g. either position, shape and speed of a landslide, or sea floor deformation associated with an earthquake. Unfortunately, computer models are expensive to run. This leads to significant delays in predictions and makes the uncertainty quantification impractical. Statistical emulators run almost instantaneously and may represent well the outputs of the computer model. In this paper, we use the outer product emulator to build a fast statistical surrogate of a landslide-generated tsunami computer model. This Bayesian framework enables us to build the emulator by combining prior knowledge of the computer model properties with a few carefully chosen model evaluations. The good performance of the emulator is validated using the leave-one-out method.
Uncertainty of a detected spatial cluster in 1D: quantification and visualization
Lee, Junho; Gangnon, Ronald E.; Zhu, Jun; Liang, Jingjing
2017-01-01
Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.
Uncertainty Quantification Techniques for Sensor Calibration Monitoring in Nuclear Power Plants
Energy Technology Data Exchange (ETDEWEB)
Ramuhalli, Pradeep; Lin, Guang; Crawford, Susan L.; Konomi, Bledar A.; Coble, Jamie B.; Shumaker, Brent; Hashemian, Hash
2014-04-30
This report describes research towards the development of advanced algorithms for online calibration monitoring. The objective of this research is to develop the next generation of online monitoring technologies for sensor calibration interval extension and signal validation in operating and new reactors. These advances are expected to improve the safety and reliability of current and planned nuclear power systems as a result of higher accuracies and increased reliability of sensors used to monitor key parameters. The focus of this report is on documenting the outcomes of the first phase of R&D under this project, which addressed approaches to uncertainty quantification (UQ) in online monitoring that are data-driven, and can therefore adjust estimates of uncertainty as measurement conditions change. Such data-driven approaches to UQ are necessary to address changing plant conditions, for example, as nuclear power plants experience transients, or as next-generation small modular reactors (SMR) operate in load-following conditions.
Uncertainty Quantification Techniques for Sensor Calibration Monitoring in Nuclear Power Plants
Energy Technology Data Exchange (ETDEWEB)
Ramuhalli, Pradeep; Lin, Guang; Crawford, Susan L.; Konomi, Bledar A.; Braatz, Brett G.; Coble, Jamie B.; Shumaker, Brent; Hashemian, Hash
2013-09-01
This report describes the status of ongoing research towards the development of advanced algorithms for online calibration monitoring. The objective of this research is to develop the next generation of online monitoring technologies for sensor calibration interval extension and signal validation in operating and new reactors. These advances are expected to improve the safety and reliability of current and planned nuclear power systems as a result of higher accuracies and increased reliability of sensors used to monitor key parameters. The focus of this report is on documenting the outcomes of the first phase of R&D under this project, which addressed approaches to uncertainty quantification (UQ) in online monitoring that are data-driven, and can therefore adjust estimates of uncertainty as measurement conditions change. Such data-driven approaches to UQ are necessary to address changing plant conditions, for example, as nuclear power plants experience transients, or as next-generation small modular reactors (SMR) operate in load-following conditions.
Uncertainty of a detected spatial cluster in 1D: quantification and visualization
Lee, Junho
2017-10-19
Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.
Advanced Modeling and Uncertainty Quantification for Flight Dynamics; Interim Results and Challenges
Hyde, David C.; Shweyk, Kamal M.; Brown, Frank; Shah, Gautam
2014-01-01
As part of the NASA Vehicle Systems Safety Technologies (VSST), Assuring Safe and Effective Aircraft Control Under Hazardous Conditions (Technical Challenge #3), an effort is underway within Boeing Research and Technology (BR&T) to address Advanced Modeling and Uncertainty Quantification for Flight Dynamics (VSST1-7). The scope of the effort is to develop and evaluate advanced multidisciplinary flight dynamics modeling techniques, including integrated uncertainties, to facilitate higher fidelity response characterization of current and future aircraft configurations approaching and during loss-of-control conditions. This approach is to incorporate multiple flight dynamics modeling methods for aerodynamics, structures, and propulsion, including experimental, computational, and analytical. Also to be included are techniques for data integration and uncertainty characterization and quantification. This research shall introduce new and updated multidisciplinary modeling and simulation technologies designed to improve the ability to characterize airplane response in off-nominal flight conditions. The research shall also introduce new techniques for uncertainty modeling that will provide a unified database model comprised of multiple sources, as well as an uncertainty bounds database for each data source such that a full vehicle uncertainty analysis is possible even when approaching or beyond Loss of Control boundaries. Methodologies developed as part of this research shall be instrumental in predicting and mitigating loss of control precursors and events directly linked to causal and contributing factors, such as stall, failures, damage, or icing. The tasks will include utilizing the BR&T Water Tunnel to collect static and dynamic data to be compared to the GTM extended WT database, characterizing flight dynamics in off-nominal conditions, developing tools for structural load estimation under dynamic conditions, devising methods for integrating various modeling elements
Uncertainty analysis methods for quantification of source terms using a large computer code
International Nuclear Information System (INIS)
Han, Seok Jung
1997-02-01
Quantification of uncertainties in the source term estimations by a large computer code, such as MELCOR and MAAP, is an essential process of the current probabilistic safety assessments (PSAs). The main objectives of the present study are (1) to investigate the applicability of a combined procedure of the response surface method (RSM) based on input determined from a statistical design and the Latin hypercube sampling (LHS) technique for the uncertainty analysis of CsI release fractions under a hypothetical severe accident sequence of a station blackout at Young-Gwang nuclear power plant using MAAP3.0B code as a benchmark problem; and (2) to propose a new measure of uncertainty importance based on the distributional sensitivity analysis. On the basis of the results obtained in the present work, the RSM is recommended to be used as a principal tool for an overall uncertainty analysis in source term quantifications, while using the LHS in the calculations of standardized regression coefficients (SRC) and standardized rank regression coefficients (SRRC) to determine the subset of the most important input parameters in the final screening step and to check the cumulative distribution functions (cdfs) obtained by RSM. Verification of the response surface model for its sufficient accuracy is a prerequisite for the reliability of the final results obtained by the combined procedure proposed in the present work. In the present study a new measure has been developed to utilize the metric distance obtained from cumulative distribution functions (cdfs). The measure has been evaluated for three different cases of distributions in order to assess the characteristics of the measure: The first case and the second are when the distribution is known as analytical distributions and the other case is when the distribution is unknown. The first case is given by symmetry analytical distributions. The second case consists of two asymmetry distributions of which the skewness is non zero
Bermejo-Moreno, Ivan; Campo, Laura; Larsson, Johan; Emory, Mike; Bodart, Julien; Palacios, Francisco; Iaccarino, Gianluca; Eaton, John
2013-11-01
We study the interaction between an oblique shock wave and the turbulent boundary layers inside a nearly-square duct by combining wall-modeled LES, 2D and 3D RANS simulations, targeting the experiment of Campo, Helmer & Eaton, 2012 (nominal conditions: M = 2 . 05 , Reθ = 6 , 500). A primary objective is to quantify the effect of aleatory and epistemic uncertainties on the STBLI. Aleatory uncertainties considered include the inflow conditions (Mach number of the incoming air stream and thickness of the boundary layers) and perturbations of the duct geometry upstream of the interaction. The epistemic uncertainty under consideration focuses on the RANS turbulence model form by injecting perturbations in the Reynolds stress anisotropy in regions of the flow where the model assumptions (in particular, the Boussinesq eddy-viscosity hypothesis) may be invalid. These perturbations are then propagated through the flow solver into the solution. The uncertainty quantification (UQ) analysis is done through 2D and 3D RANS simulations, assessing the importance of the three-dimensional effects imposed by the nearly-square duct geometry. Wall-modeled LES are used to verify elements of the UQ methodology and to explore the flow features and physics of the STBLI for multiple shock strengths. Financial support from the United States Department of Energy under the PSAAP program is gratefully acknowledged.
Uncertainty Quantification of Fork Detector Measurements from Spent Fuel Loading Campaigns
International Nuclear Information System (INIS)
Vaccaro, S.; De Baere, P.; Schwalbach, P.; Gauld, I.; Hu, J.
2015-01-01
With increasing activities at the end of the fuel cycle, the requirements for the verification of spent nuclear fuel for safeguards purposes are continuously growing. In the European Union we are experiencing a dramatic increase in the number of cask loadings for interim dry storage. This is caused by the progressive shut-down of reactors, related to facility ageing but also due to politically motivated phase-out of nuclear power. On the other hand there are advanced plans for the construction of encapsulation plants and geological repositories. The cask loading or the encapsulation process will provide the last occasion to verify the spent fuel assemblies. In this context, Euratom and the US DOE have carried out a critical review of the widely used Fork measurements method of irradiated assemblies. The Nuclear Safeguards directorates of the European Commission's Directorate General for Energy and Oak Ridge National Laboratory have collaborated to improve the Fork data evaluation process and simplify its use for inspection applications. Within the Commission's standard data evaluation package CRISP, we included a SCALE/ORIGEN-based irradiation and depletion simulation of the measured assembly and modelled the fork transfer function to calculate expected count rates based on operator's declarations. The complete acquisition and evaluation process has been automated to compare expected (calculated) with measured count rates. This approach allows a physics-based improvement of the data review and evaluation process. At the same time the new method provides the means for better measurement uncertainty quantification. The present paper will address the implications of the combined approach involving measured and simulated data to the quantification of measurement uncertainty and the consequences of these uncertainties in the possible use of the Fork detector as a partial defect detection method. (author)
Seo, Jongmin; Schiavazzi, Daniele; Marsden, Alison
2017-11-01
Cardiovascular simulations are increasingly used in clinical decision making, surgical planning, and disease diagnostics. Patient-specific modeling and simulation typically proceeds through a pipeline from anatomic model construction using medical image data to blood flow simulation and analysis. To provide confidence intervals on simulation predictions, we use an uncertainty quantification (UQ) framework to analyze the effects of numerous uncertainties that stem from clinical data acquisition, modeling, material properties, and boundary condition selection. However, UQ poses a computational challenge requiring multiple evaluations of the Navier-Stokes equations in complex 3-D models. To achieve efficiency in UQ problems with many function evaluations, we implement and compare a range of iterative linear solver and preconditioning techniques in our flow solver. We then discuss applications to patient-specific cardiovascular simulation and how the problem/boundary condition formulation in the solver affects the selection of the most efficient linear solver. Finally, we discuss performance improvements in the context of uncertainty propagation. Support from National Institute of Health (R01 EB018302) is greatly appreciated.
Subber, Waad; Salvadori, Alberto; Lee, Sangmin; Matous, Karel
2017-06-01
The reverse Taylor impact is a common experiment to investigate the dynamical response of materials at high strain rates. To better understand the physical phenomena and to provide a platform for code validation and Uncertainty Quantification (UQ), a co-designed simulation and experimental paradigm is investigated. For validation under uncertainty, quantities of interest (QOIs) within subregions of the computational domain are introduced. For such simulations where regions of interest can be identified, the computational cost for UQ can be reduced by confining the random variability within these regions of interest. This observation inspired us to develop an asynchronous space and time computational algorithm with localized UQ. In the region of interest, the high resolution space and time discretization schemes are used for a stochastic model. Apart from the region of interest, low spatial and temporal resolutions are allowed for a stochastic model with low dimensional representation of uncertainty. The model is exercised on the linear elastodynamics and shows a potential in reducing the UQ computational cost. Although, we consider wave prorogation in solid, the proposed framework is general and can be used for fluid flow problems as well. Department of Energy, National Nuclear Security Administration (PSAAP-II).
McDonnell, J D; Schunck, N; Higdon, D; Sarich, J; Wild, S M; Nazarewicz, W
2015-03-27
Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models, to estimate model errors and thereby improve predictive capability, to extrapolate beyond the regions reached by experiment, and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squares optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. The example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.
Energy Technology Data Exchange (ETDEWEB)
McDonnell, J. D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Schunck, N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Higdon, D. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Sarich, J. [Argonne National Lab. (ANL), Argonne, IL (United States); Wild, S. M. [Argonne National Lab. (ANL), Argonne, IL (United States); Nazarewicz, W. [Michigan State Univ., East Lansing, MI (United States); Oak Ridge National Lab., Oak Ridge, TN (United States); Univ. of Warsaw, Warsaw (Poland)
2015-03-24
Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models, to estimate model errors and thereby improve predictive capability, to extrapolate beyond the regions reached by experiment, and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squares optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. As a result, the example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.
Uncertainty quantification in Eulerian-Lagrangian models for particle-laden flows
Fountoulakis, Vasileios; Jacobs, Gustaaf; Udaykumar, Hs
2017-11-01
A common approach to ameliorate the computational burden in simulations of particle-laden flows is to use a point-particle based Eulerian-Lagrangian model, which traces individual particles in their Lagrangian frame and models particles as mathematical points. The particle motion is determined by Stokes drag law, which is empirically corrected for Reynolds number, Mach number and other parameters. The empirical corrections are subject to uncertainty. Treating them as random variables renders the coupled system of PDEs and ODEs stochastic. An approach to quantify the propagation of this parametric uncertainty to the particle solution variables is proposed. The approach is based on averaging of the governing equations and allows for estimation of the first moments of the quantities of interest. We demonstrate the feasibility of our proposed methodology of uncertainty quantification of particle-laden flows on one-dimensional linear and nonlinear Eulerian-Lagrangian systems. This research is supported by AFOSR under Grant FA9550-16-1-0008.
International Nuclear Information System (INIS)
Vierow, Karen; Aldemir, Tunc
2009-01-01
The project entitled, 'Uncertainty Quantification in the Reliability and Risk Assessment of Generation IV Reactors', was conducted as a DOE NERI project collaboration between Texas A and M University and The Ohio State University between March 2006 and June 2009. The overall goal of the proposed project was to develop practical approaches and tools by which dynamic reliability and risk assessment techniques can be used to augment the uncertainty quantification process in probabilistic risk assessment (PRA) methods and PRA applications for Generation IV reactors. This report is the Final Scientific/Technical Report summarizing the project.
Energy Technology Data Exchange (ETDEWEB)
Vierow, Karen; Aldemir, Tunc
2009-09-10
The project entitled, “Uncertainty Quantification in the Reliability and Risk Assessment of Generation IV Reactors”, was conducted as a DOE NERI project collaboration between Texas A&M University and The Ohio State University between March 2006 and June 2009. The overall goal of the proposed project was to develop practical approaches and tools by which dynamic reliability and risk assessment techniques can be used to augment the uncertainty quantification process in probabilistic risk assessment (PRA) methods and PRA applications for Generation IV reactors. This report is the Final Scientific/Technical Report summarizing the project.
International Nuclear Information System (INIS)
Skorek, Tomasz; Crecy, Agnes de
2013-01-01
PREMIUM (Post BEMUSE Reflood Models Input Uncertainty Methods) is an activity launched with the aim to push forward the methods of quantification of physical models uncertainties in thermal-hydraulic codes. It is endorsed by OECD/NEA/CSNI/WGAMA. The benchmark PREMIUM is addressed to all who applies uncertainty evaluation methods based on input uncertainties quantification and propagation. The benchmark is based on a selected case of uncertainty analysis application to the simulation of quench front propagation in an experimental test facility. Application to an experiment enables evaluation and confirmation of the quantified probability distribution functions on the basis of experimental data. The scope of the benchmark comprises a review of the existing methods, selection of potentially important uncertain input parameters, preliminary quantification of the ranges and distributions of the identified parameters, evaluation of the probability density function using experimental results of tests performed on FEBA test facility and confirmation/validation of the performed quantification on the basis of blind calculation of Reflood 2-D PERICLES experiment. (authors)
Energy Technology Data Exchange (ETDEWEB)
Aldegunde, Manuel, E-mail: M.A.Aldegunde-Rodriguez@warwick.ac.uk; Kermode, James R., E-mail: J.R.Kermode@warwick.ac.uk; Zabaras, Nicholas
2016-04-15
This paper presents the development of a new exchange–correlation functional from the point of view of machine learning. Using atomization energies of solids and small molecules, we train a linear model for the exchange enhancement factor using a Bayesian approach which allows for the quantification of uncertainties in the predictions. A relevance vector machine is used to automatically select the most relevant terms of the model. We then test this model on atomization energies and also on bulk properties. The average model provides a mean absolute error of only 0.116 eV for the test points of the G2/97 set but a larger 0.314 eV for the test solids. In terms of bulk properties, the prediction for transition metals and monovalent semiconductors has a very low test error. However, as expected, predictions for types of materials not represented in the training set such as ionic solids show much larger errors.
A machine learning approach for efficient uncertainty quantification using multiscale methods
Chan, Shing; Elsheikh, Ahmed H.
2018-02-01
Several multiscale methods account for sub-grid scale features using coarse scale basis functions. For example, in the Multiscale Finite Volume method the coarse scale basis functions are obtained by solving a set of local problems over dual-grid cells. We introduce a data-driven approach for the estimation of these coarse scale basis functions. Specifically, we employ a neural network predictor fitted using a set of solution samples from which it learns to generate subsequent basis functions at a lower computational cost than solving the local problems. The computational advantage of this approach is realized for uncertainty quantification tasks where a large number of realizations has to be evaluated. We attribute the ability to learn these basis functions to the modularity of the local problems and the redundancy of the permeability patches between samples. The proposed method is evaluated on elliptic problems yielding very promising results.
Zimoń, Małgorzata; Sawko, Robert; Emerson, David; Thompson, Christopher
2017-11-01
Uncertainty quantification (UQ) is increasingly becoming an indispensable tool for assessing the reliability of computational modelling. Efficient handling of stochastic inputs, such as boundary conditions, physical properties or geometry, increases the utility of model results significantly. We discuss the application of non-intrusive generalised polynomial chaos techniques in the context of fluid engineering simulations. Deterministic and Monte Carlo integration rules are applied to a set of problems, including ordinary differential equations and the computation of aerodynamic parameters subject to random perturbations. In particular, we analyse acoustic wave propagation in a heterogeneous medium to study the effects of mesh resolution, transients, number and variability of stochastic inputs. We consider variants of multi-level Monte Carlo and perform a novel comparison of the methods with respect to numerical and parametric errors, as well as computational cost. The results provide a comprehensive view of the necessary steps in UQ analysis and demonstrate some key features of stochastic fluid flow systems.
Module-based Hybrid Uncertainty Quantification for Multi-physics Applications: Theory and Software
Energy Technology Data Exchange (ETDEWEB)
Tong, Charles [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Chen, Xiao [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Iaccarino, Gianluca [Stanford Univ., CA (United States); Mittal, Akshay [Stanford Univ., CA (United States)
2013-10-08
In this project we proposed to develop an innovative uncertainty quantification methodology that captures the best of the two competing approaches in UQ, namely, intrusive and non-intrusive approaches. The idea is to develop the mathematics and the associated computational framework and algorithms to facilitate the use of intrusive or non-intrusive UQ methods in different modules of a multi-physics multi-module simulation model in a way that physics code developers for different modules are shielded (as much as possible) from the chores of accounting for the uncertain ties introduced by the other modules. As the result of our research and development, we have produced a number of publications, conference presentations, and a software product.
Robust approaches to quantification of margin and uncertainty for sparse data
Energy Technology Data Exchange (ETDEWEB)
Hund, Lauren [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Schroeder, Benjamin B. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Rumsey, Kelin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Murchison, Nicole [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-09-01
Characterizing the tails of probability distributions plays a key role in quantification of margins and uncertainties (QMU), where the goal is characterization of low probability, high consequence events based on continuous measures of performance. When data are collected using physical experimentation, probability distributions are typically fit using statistical methods based on the collected data, and these parametric distributional assumptions are often used to extrapolate about the extreme tail behavior of the underlying probability distribution. In this project, we character- ize the risk associated with such tail extrapolation. Specifically, we conducted a scaling study to demonstrate the large magnitude of the risk; then, we developed new methods for communicat- ing risk associated with tail extrapolation from unvalidated statistical models; lastly, we proposed a Bayesian data-integration framework to mitigate tail extrapolation risk through integrating ad- ditional information. We conclude that decision-making using QMU is a complex process that cannot be achieved using statistical analyses alone.
Mai, P. M.; Schorlemmer, D.; Page, M.
2012-04-01
Earthquake source inversions image the spatio-temporal rupture evolution on one or more fault planes using seismic and/or geodetic data. Such studies are critically important for earthquake seismology in general, and for advancing seismic hazard analysis in particular, as they reveal earthquake source complexity and help (i) to investigate earthquake mechanics; (ii) to develop spontaneous dynamic rupture models; (iii) to build models for generating rupture realizations for ground-motion simulations. In applications (i - iii), the underlying finite-fault source models are regarded as "data" (input information), but their uncertainties are essentially unknown. After all, source models are obtained from solving an inherently ill-posed inverse problem to which many a priori assumptions and uncertain observations are applied. The Source Inversion Validation (SIV) project is a collaborative effort to better understand the variability between rupture models for a single earthquake (as manifested in the finite-source rupture model database) and to develop robust uncertainty quantification for earthquake source inversions. The SIV project highlights the need to develop a long-standing and rigorous testing platform to examine the current state-of-the-art in earthquake source inversion, and to develop and test novel source inversion approaches. We will review the current status of the SIV project, and report the findings and conclusions of the recent workshops. We will briefly discuss several source-inversion methods, how they treat uncertainties in data, and assess the posterior model uncertainty. Case studies include initial forward-modeling tests on Green's function calculations, and inversion results for synthetic data from spontaneous dynamic crack-like strike-slip earthquake on steeply dipping fault, embedded in a layered crustal velocity-density structure.
Gomez, Luis J; Yücel, Abdulkadir C; Hernandez-Garcia, Luis; Taylor, Stephan F; Michielssen, Eric
2015-01-01
A computational framework for uncertainty quantification in transcranial magnetic stimulation (TMS) is presented. The framework leverages high-dimensional model representations (HDMRs), which approximate observables (i.e., quantities of interest such as electric (E) fields induced inside targeted cortical regions) via series of iteratively constructed component functions involving only the most significant random variables (i.e., parameters that characterize the uncertainty in a TMS setup such as the position and orientation of TMS coils, as well as the size, shape, and conductivity of the head tissue). The component functions of HDMR expansions are approximated via a multielement probabilistic collocation (ME-PC) method. While approximating each component function, a quasi-static finite-difference simulator is used to compute observables at integration/collocation points dictated by the ME-PC method. The proposed framework requires far fewer simulations than traditional Monte Carlo methods for providing highly accurate statistical information (e.g., the mean and standard deviation) about the observables. The efficiency and accuracy of the proposed framework are demonstrated via its application to the statistical characterization of E-fields generated by TMS inside cortical regions of an MRI-derived realistic head model. Numerical results show that while uncertainties in tissue conductivities have negligible effects on TMS operation, variations in coil position/orientation and brain size significantly affect the induced E-fields. Our numerical results have several implications for the use of TMS during depression therapy: 1) uncertainty in the coil position and orientation may reduce the response rates of patients; 2) practitioners should favor targets on the crest of a gyrus to obtain maximal stimulation; and 3) an increasing scalp-to-cortex distance reduces the magnitude of E-fields on the surface and inside the cortex.
Miller, K. L.; Berg, S. J.; Davison, J. H.; Sudicky, E. A.; Forsyth, P. A.
2018-01-01
Although high performance computers and advanced numerical methods have made the application of fully-integrated surface and subsurface flow and transport models such as HydroGeoSphere common place, run times for large complex basin models can still be on the order of days to weeks, thus, limiting the usefulness of traditional workhorse algorithms for uncertainty quantification (UQ) such as Latin Hypercube simulation (LHS) or Monte Carlo simulation (MCS), which generally require thousands of simulations to achieve an acceptable level of accuracy. In this paper we investigate non-intrusive polynomial chaos for uncertainty quantification, which in contrast to random sampling methods (e.g., LHS and MCS), represents a model response of interest as a weighted sum of polynomials over the random inputs. Once a chaos expansion has been constructed, approximating the mean, covariance, probability density function, cumulative distribution function, and other common statistics as well as local and global sensitivity measures is straightforward and computationally inexpensive, thus making PCE an attractive UQ method for hydrologic models with long run times. Our polynomial chaos implementation was validated through comparison with analytical solutions as well as solutions obtained via LHS for simple numerical problems. It was then used to quantify parametric uncertainty in a series of numerical problems with increasing complexity, including a two-dimensional fully-saturated, steady flow and transient transport problem with six uncertain parameters and one quantity of interest; a one-dimensional variably-saturated column test involving transient flow and transport, four uncertain parameters, and two quantities of interest at 101 spatial locations and five different times each (1010 total); and a three-dimensional fully-integrated surface and subsurface flow and transport problem for a small test catchment involving seven uncertain parameters and three quantities of interest at
Debry, E.; Malherbe, L.; Schillinger, C.; Bessagnet, B.; Rouil, L.
2009-04-01
Evaluation of human exposure to atmospheric pollution usually requires the knowledge of pollutants concentrations in ambient air. In the framework of PAISA project, which studies the influence of socio-economical status on relationships between air pollution and short term health effects, the concentrations of gas and particle pollutants are computed over Strasbourg with the ADMS-Urban model. As for any modeling result, simulated concentrations come with uncertainties which have to be characterized and quantified. There are several sources of uncertainties related to input data and parameters, i.e. fields used to execute the model like meteorological fields, boundary conditions and emissions, related to the model formulation because of incomplete or inaccurate treatment of dynamical and chemical processes, and inherent to the stochastic behavior of atmosphere and human activities [1]. Our aim is here to assess the uncertainties of the simulated concentrations with respect to input data and model parameters. In this scope the first step consisted in bringing out the input data and model parameters that contribute most effectively to space and time variability of predicted concentrations. Concentrations of several pollutants were simulated for two months in winter 2004 and two months in summer 2004 over five areas of Strasbourg. The sensitivity analysis shows the dominating influence of boundary conditions and emissions. Among model parameters, the roughness and Monin-Obukhov lengths appear to have non neglectable local effects. Dry deposition is also an important dynamic process. The second step of the characterization and quantification of uncertainties consists in attributing a probability distribution to each input data and model parameter and in propagating the joint distribution of all data and parameters into the model so as to associate a probability distribution to the modeled concentrations. Several analytical and numerical methods exist to perform an
International Nuclear Information System (INIS)
Burr, T.; Croft, S.; Krieger, T.; Martin, K.; Norman, C.; Walsh, S.
2016-01-01
One example of top-down uncertainty quantification (UQ) involves comparing two or more measurements on each of multiple items. One example of bottom-up UQ expresses a measurement result as a function of one or more input variables that have associated errors, such as a measured count rate, which individually (or collectively) can be evaluated for impact on the uncertainty in the resulting measured value. In practice, it is often found that top-down UQ exhibits larger error variances than bottom-up UQ, because some error sources are present in the fielded assay methods used in top-down UQ that are not present (or not recognized) in the assay studies used in bottom-up UQ. One would like better consistency between the two approaches in order to claim understanding of the measurement process. The purpose of this paper is to refine bottom-up uncertainty estimation by using calibration information so that if there are no unknown error sources, the refined bottom-up uncertainty estimate will agree with the top-down uncertainty estimate to within a specified tolerance. Then, in practice, if the top-down uncertainty estimate is larger than the refined bottom-up uncertainty estimate by more than the specified tolerance, there must be omitted sources of error beyond those predicted from calibration uncertainty. The paper develops a refined bottom-up uncertainty approach for four cases of simple linear calibration: (1) inverse regression with negligible error in predictors, (2) inverse regression with non-negligible error in predictors, (3) classical regression followed by inversion with negligible error in predictors, and (4) classical regression followed by inversion with non-negligible errors in predictors. Our illustrations are of general interest, but are drawn from our experience with nuclear material assay by non-destructive assay. The main example we use is gamma spectroscopy that applies the enrichment meter principle. Previous papers that ignore error in predictors
Uncertainties and quantification of common cause failure rates and probabilities for system analyses
International Nuclear Information System (INIS)
Vaurio, Jussi K.
2005-01-01
Simultaneous failures of multiple components due to common causes at random times are modelled by constant multiple-failure rates. A procedure is described for quantification of common cause failure (CCF) basic event probabilities for system models using plant-specific and multiple-plant failure-event data. Methodology is presented for estimating CCF-rates from event data contaminated with assessment uncertainties. Generalised impact vectors determine the moments for the rates of individual systems or plants. These moments determine the effective numbers of events and observation times to be input to a Bayesian formalism to obtain plant-specific posterior CCF-rates. The rates are used to determine plant-specific common cause event probabilities for the basic events of explicit fault tree models depending on test intervals, test schedules and repair policies. Three methods are presented to determine these probabilities such that the correct time-average system unavailability can be obtained with single fault tree quantification. Recommended numerical values are given and examples illustrate different aspects of the methodology
Kim, Daesang
2015-07-01
Uncertainty quantification (UQ) methods are implemented to obtain a quantitative characterization of the evolution of electrons and ions during the ignition of methane-oxygen mixtures under lean and stoichiometric conditions. The GRI-Mech 3.0 mechanism is combined with an extensive set of ion chemistry pathways and the forward propagation of uncertainty from model parameters to observables is performed using response surfaces. The UQ analysis considers 22 uncertain rate parameters, which include both chemi-ionization, proton transfer, and electron attachment reactions as well as neutral reactions pertaining to the chemistry of the CH radical. The uncertainty ranges for each rate parameter are discussed. Our results indicate that the uncertainty in the time evolution of the electron number density is due mostly to the chemi-ionization reaction CH+O⇌HCO+ +E- and to the main CH consumption reaction CH+O2 ⇌O+HCO. Similar conclusions hold for the hydronium ion H3O+, since electrons and H3O+ account for more than 99% of the total negative and positive charge density, respectively. Surprisingly, the statistics of the number density of charged species show very little sensitivity to the uncertainty in the rate of the recombination reaction H3O+ +E- →products, until very late in the decay process, when the electron number density has fallen below 20% of its peak value. Finally, uncertainties in the secondary reactions within networks leading to the formation of minor ions (e.g., C2H3O+, HCO+, OH-, and O-) do not play any role in controlling the mean and variance of electrons and H3O+, but do affect the statistics of the minor ions significantly. The observed trends point to the role of key neutral reactions in controlling the mean and variance of the charged species number density in an indirect fashion. Furthermore, total sensitivity indices provide quantitative metrics to focus future efforts aiming at improving the rates of key reactions responsible for the
Kim, Daesang; Rizzi, Francesco; Cheng, Kwok Wah; Han, Jie; Bisetti, Fabrizio; Knio, Omar Mohamad
2015-01-01
Uncertainty quantification (UQ) methods are implemented to obtain a quantitative characterization of the evolution of electrons and ions during the ignition of methane-oxygen mixtures under lean and stoichiometric conditions. The GRI-Mech 3.0 mechanism is combined with an extensive set of ion chemistry pathways and the forward propagation of uncertainty from model parameters to observables is performed using response surfaces. The UQ analysis considers 22 uncertain rate parameters, which include both chemi-ionization, proton transfer, and electron attachment reactions as well as neutral reactions pertaining to the chemistry of the CH radical. The uncertainty ranges for each rate parameter are discussed. Our results indicate that the uncertainty in the time evolution of the electron number density is due mostly to the chemi-ionization reaction CH+O⇌HCO+ +E- and to the main CH consumption reaction CH+O2 ⇌O+HCO. Similar conclusions hold for the hydronium ion H3O+, since electrons and H3O+ account for more than 99% of the total negative and positive charge density, respectively. Surprisingly, the statistics of the number density of charged species show very little sensitivity to the uncertainty in the rate of the recombination reaction H3O+ +E- →products, until very late in the decay process, when the electron number density has fallen below 20% of its peak value. Finally, uncertainties in the secondary reactions within networks leading to the formation of minor ions (e.g., C2H3O+, HCO+, OH-, and O-) do not play any role in controlling the mean and variance of electrons and H3O+, but do affect the statistics of the minor ions significantly. The observed trends point to the role of key neutral reactions in controlling the mean and variance of the charged species number density in an indirect fashion. Furthermore, total sensitivity indices provide quantitative metrics to focus future efforts aiming at improving the rates of key reactions responsible for the
Iskandarani, Mohamed; Wang, Shitao; Srinivasan, Ashwanth; Carlisle Thacker, W.; Winokur, Justin; Knio, Omar
2016-01-01
We give an overview of four different ensemble-based techniques for uncertainty quantification and illustrate their application in the context of oil plume simulations. These techniques share the common paradigm of constructing a model proxy that efficiently captures the functional dependence of the model output on uncertain model inputs. This proxy is then used to explore the space of uncertain inputs using a large number of samples, so that reliable estimates of the model's output statistics can be calculated. Three of these techniques use polynomial chaos (PC) expansions to construct the model proxy, but they differ in their approach to determining the expansions' coefficients; the fourth technique uses Gaussian Process Regression (GPR). An integral plume model for simulating the Deepwater Horizon oil-gas blowout provides examples for illustrating the different techniques. A Monte Carlo ensemble of 50,000 model simulations is used for gauging the performance of the different proxies. The examples illustrate how regression-based techniques can outperform projection-based techniques when the model output is noisy. They also demonstrate that robust uncertainty analysis can be performed at a fraction of the cost of the Monte Carlo calculation.
Multi-fidelity uncertainty quantification in large-scale predictive simulations of turbulent flow
Geraci, Gianluca; Jofre-Cruanyes, Lluis; Iaccarino, Gianluca
2017-11-01
The performance characterization of complex engineering systems often relies on accurate, but computationally intensive numerical simulations. It is also well recognized that in order to obtain a reliable numerical prediction the propagation of uncertainties needs to be included. Therefore, Uncertainty Quantification (UQ) plays a fundamental role in building confidence in predictive science. Despite the great improvement in recent years, even the more advanced UQ algorithms are still limited to fairly simplified applications and only moderate parameter dimensionality. Moreover, in the case of extremely large dimensionality, sampling methods, i.e. Monte Carlo (MC) based approaches, appear to be the only viable alternative. In this talk we describe and compare a family of approaches which aim to accelerate the convergence of standard MC simulations. These methods are based on hierarchies of generalized numerical resolutions (multi-level) or model fidelities (multi-fidelity), and attempt to leverage the correlation between Low- and High-Fidelity (HF) models to obtain a more accurate statistical estimator without introducing additional HF realizations. The performance of these methods are assessed on an irradiated particle laden turbulent flow (PSAAP II solar energy receiver). This investigation was funded by the United States Department of Energy's (DoE) National Nuclear Security Administration (NNSA) under the Predicitive Science Academic Alliance Program (PSAAP) II at Stanford University.
Iskandarani, Mohamed
2016-04-22
We give an overview of four different ensemble-based techniques for uncertainty quantification and illustrate their application in the context of oil plume simulations. These techniques share the common paradigm of constructing a model proxy that efficiently captures the functional dependence of the model output on uncertain model inputs. This proxy is then used to explore the space of uncertain inputs using a large number of samples, so that reliable estimates of the model\\'s output statistics can be calculated. Three of these techniques use polynomial chaos (PC) expansions to construct the model proxy, but they differ in their approach to determining the expansions\\' coefficients; the fourth technique uses Gaussian Process Regression (GPR). An integral plume model for simulating the Deepwater Horizon oil-gas blowout provides examples for illustrating the different techniques. A Monte Carlo ensemble of 50,000 model simulations is used for gauging the performance of the different proxies. The examples illustrate how regression-based techniques can outperform projection-based techniques when the model output is noisy. They also demonstrate that robust uncertainty analysis can be performed at a fraction of the cost of the Monte Carlo calculation.
multiUQ: An intrusive uncertainty quantification tool for gas-liquid multiphase flows
Turnquist, Brian; Owkes, Mark
2017-11-01
Uncertainty quantification (UQ) can improve our understanding of the sensitivity of gas-liquid multiphase flows to variability about inflow conditions and fluid properties, creating a valuable tool for engineers. While non-intrusive UQ methods (e.g., Monte Carlo) are simple and robust, the cost associated with these techniques can render them unrealistic. In contrast, intrusive UQ techniques modify the governing equations by replacing deterministic variables with stochastic variables, adding complexity, but making UQ cost effective. Our numerical framework, called multiUQ, introduces an intrusive UQ approach for gas-liquid flows, leveraging a polynomial chaos expansion of the stochastic variables: density, momentum, pressure, viscosity, and surface tension. The gas-liquid interface is captured using a conservative level set approach, including a modified reinitialization equation which is robust and quadrature free. A least-squares method is leveraged to compute the stochastic interface normal and curvature needed in the continuum surface force method for surface tension. The solver is tested by applying uncertainty to one or two variables and verifying results against the Monte Carlo approach. NSF Grant #1511325.
Małolepszy, Zbigniew; Szynkaruk, Ewa
2015-04-01
The multiscale static modeling of regional structure of the Lublin Basin is carried on in the Polish Geological Institute, in accordance with principles of integrated 3D geological modelling. The model is based on all available geospatial data from Polish digital databases and analogue archives. Mapped regional structure covers the area of 260x80 km located between Warsaw and Polish-Ukrainian border, along NW-SE-trending margin of the East European Craton. Within the basin, the Paleozoic beds with coalbearing Carboniferous and older formations containing hydrocarbons and unconventional prospects are covered unconformably by Permo-Mesozoic and younger rocks. Vertical extent of the regional model is set from topographic surface to 6000 m ssl and at the bottom includes some Proterozoic crystalline formations of the craton. The project focuses on internal consistency of the models built at different scales - from basin (small) scale to field-scale (large-scale). The models, nested in the common structural framework, are being constructed with regional geological knowledge, ensuring smooth transition in the 3D model resolution and amount of geological detail. Major challenge of the multiscale approach to subsurface modelling is the assessment and consistent quantification of various types of geological uncertainties tied to those various scale sub-models. Decreasing amount of information with depth and, particularly, very limited data collected below exploration targets, as well as accuracy and quality of data, all have the most critical impact on the modelled structure. In deeper levels of the Lublin Basin model, seismic interpretation of 2D surveys is sparsely tied to well data. Therefore time-to-depth conversion carries one of the major uncertainties in the modeling of structures, especially below 3000 m ssl. Furthermore, as all models at different scales are based on the same dataset, we must deal with different levels of generalization of geological structures. The
Energy Technology Data Exchange (ETDEWEB)
Alwan, Aravind; Aluru, N.R.
2013-12-15
This paper presents a data-driven framework for performing uncertainty quantification (UQ) by choosing a stochastic model that accurately describes the sources of uncertainty in a system. This model is propagated through an appropriate response surface function that approximates the behavior of this system using stochastic collocation. Given a sample of data describing the uncertainty in the inputs, our goal is to estimate a probability density function (PDF) using the kernel moment matching (KMM) method so that this PDF can be used to accurately reproduce statistics like mean and variance of the response surface function. Instead of constraining the PDF to be optimal for a particular response function, we show that we can use the properties of stochastic collocation to make the estimated PDF optimal for a wide variety of response functions. We contrast this method with other traditional procedures that rely on the Maximum Likelihood approach, like kernel density estimation (KDE) and its adaptive modification (AKDE). We argue that this modified KMM method tries to preserve what is known from the given data and is the better approach when the available data is limited in quantity. We test the performance of these methods for both univariate and multivariate density estimation by sampling random datasets from known PDFs and then measuring the accuracy of the estimated PDFs, using the known PDF as a reference. Comparing the output mean and variance estimated with the empirical moments using the raw data sample as well as the actual moments using the known PDF, we show that the KMM method performs better than KDE and AKDE in predicting these moments with greater accuracy. This improvement in accuracy is also demonstrated for the case of UQ in electrostatic and electrothermomechanical microactuators. We show how our framework results in the accurate computation of statistics in micromechanical systems.
International Nuclear Information System (INIS)
Hartmann, C.; Sanchez, V.; Tietsch, W.; Stieglitz, R.
2012-01-01
The KIT is involved in the development and qualification of best estimate methodologies for BWR transient analysis in cooperation with industrial partners. The goal is to establish the most advanced thermal hydraulic system codes coupled with 3D reactor dynamic codes to be able to perform a more realistic evaluation of the BWR behavior under accidental conditions. For this purpose a computational chain based on the lattice code (SCALE6/GenPMAXS), the coupled neutronic/thermal hydraulic code (TRACE/PARCS) as well as a Monte Carlo based uncertainty and sensitivity package (SUSA) has been established and applied to different kind of transients of a Boiling Water Reactor (BWR). This paper will describe the multidimensional models of the plant elaborated for TRACE and PARCS to perform the investigations mentioned before. For the uncertainty quantification of the coupled code TRACE/PARCS and specifically to take into account the influence of the kinetics parameters in such studies, the PARCS code has been extended to facilitate the change of model parameters in such a way that the SUSA package can be used in connection with TRACE/PARCS for the U and S studies. This approach will be presented in detail. The results obtained for a rod drop transient with TRACE/PARCS using the SUSA-methodology showed clearly the importance of some kinetic parameters on the transient progression demonstrating that the coupling of a best-estimate coupled codes with uncertainty and sensitivity tools is very promising and of great importance for the safety assessment of nuclear reactors. (authors)
Zielke, O.; McDougall, D.; Mai, P. M.; Babuska, I.
2014-12-01
One fundamental aspect of seismic hazard mitigation is gaining a better understanding of the rupture process. Because direct observation of the relevant parameters and properties is not possible, other means such as kinematic source inversions are used instead. By constraining the spatial and temporal evolution of fault slip during an earthquake, those inversion approaches may enable valuable insights in the physics of the rupture process. However, due to the underdetermined nature of this inversion problem (i.e., inverting a kinematic source model for an extended fault based on seismic data), the provided solutions are generally non-unique. Here we present a statistical (Bayesian) inversion approach based on an open-source library for uncertainty quantification (UQ) called QUESO that was developed at ICES (UT Austin). The approach has advantages with respect to deterministic inversion approaches as it provides not only a single (non-unique) solution but also provides uncertainty bounds with it. Those uncertainty bounds help to qualitatively and quantitatively judge how well constrained an inversion solution is and how much rupture complexity the data reliably resolve. The presented inversion scheme uses only tele-seismically recorded body waves but future developments may lead us towards joint inversion schemes. After giving an insight in the inversion scheme ifself (based on delayed rejection adaptive metropolis, DRAM) we explore the method's resolution potential. For that, we synthetically generate tele-seismic data, add for example different levels of noise and/or change fault plane parameterization and then apply our inversion scheme in the attempt to extract the (known) kinematic rupture model. We conclude with exemplary inverting real tele-seismic data of a recent large earthquake and compare those results with deterministically derived kinematic source models provided by other research groups.
International Nuclear Information System (INIS)
Alwan, Aravind; Aluru, N.R.
2013-01-01
This paper presents a data-driven framework for performing uncertainty quantification (UQ) by choosing a stochastic model that accurately describes the sources of uncertainty in a system. This model is propagated through an appropriate response surface function that approximates the behavior of this system using stochastic collocation. Given a sample of data describing the uncertainty in the inputs, our goal is to estimate a probability density function (PDF) using the kernel moment matching (KMM) method so that this PDF can be used to accurately reproduce statistics like mean and variance of the response surface function. Instead of constraining the PDF to be optimal for a particular response function, we show that we can use the properties of stochastic collocation to make the estimated PDF optimal for a wide variety of response functions. We contrast this method with other traditional procedures that rely on the Maximum Likelihood approach, like kernel density estimation (KDE) and its adaptive modification (AKDE). We argue that this modified KMM method tries to preserve what is known from the given data and is the better approach when the available data is limited in quantity. We test the performance of these methods for both univariate and multivariate density estimation by sampling random datasets from known PDFs and then measuring the accuracy of the estimated PDFs, using the known PDF as a reference. Comparing the output mean and variance estimated with the empirical moments using the raw data sample as well as the actual moments using the known PDF, we show that the KMM method performs better than KDE and AKDE in predicting these moments with greater accuracy. This improvement in accuracy is also demonstrated for the case of UQ in electrostatic and electrothermomechanical microactuators. We show how our framework results in the accurate computation of statistics in micromechanical systems
Uncertainty quantification tools for multiphase gas-solid flow simulations using MFIX
Energy Technology Data Exchange (ETDEWEB)
Fox, Rodney O. [Iowa State Univ., Ames, IA (United States); Passalacqua, Alberto [Iowa State Univ., Ames, IA (United States)
2016-02-01
Computational fluid dynamics (CFD) has been widely studied and used in the scientific community and in the industry. Various models were proposed to solve problems in different areas. However, all models deviate from reality. Uncertainty quantification (UQ) process evaluates the overall uncertainties associated with the prediction of quantities of interest. In particular it studies the propagation of input uncertainties to the outputs of the models so that confidence intervals can be provided for the simulation results. In the present work, a non-intrusive quadrature-based uncertainty quantification (QBUQ) approach is proposed. The probability distribution function (PDF) of the system response can be then reconstructed using extended quadrature method of moments (EQMOM) and extended conditional quadrature method of moments (ECQMOM). The report first explains the theory of QBUQ approach, including methods to generate samples for problems with single or multiple uncertain input parameters, low order statistics, and required number of samples. Then methods for univariate PDF reconstruction (EQMOM) and multivariate PDF reconstruction (ECQMOM) are explained. The implementation of QBUQ approach into the open-source CFD code MFIX is discussed next. At last, QBUQ approach is demonstrated in several applications. The method is first applied to two examples: a developing flow in a channel with uncertain viscosity, and an oblique shock problem with uncertain upstream Mach number. The error in the prediction of the moment response is studied as a function of the number of samples, and the accuracy of the moments required to reconstruct the PDF of the system response is discussed. The QBUQ approach is then demonstrated by considering a bubbling fluidized bed as example application. The mean particle size is assumed to be the uncertain input parameter. The system is simulated with a standard two-fluid model with kinetic theory closures for the particulate phase implemented into
Quantification of Dynamic Model Validation Metrics Using Uncertainty Propagation from Requirements
Brown, Andrew M.; Peck, Jeffrey A.; Stewart, Eric C.
2018-01-01
The Space Launch System, NASA's new large launch vehicle for long range space exploration, is presently in the final design and construction phases, with the first launch scheduled for 2019. A dynamic model of the system has been created and is critical for calculation of interface loads and natural frequencies and mode shapes for guidance, navigation, and control (GNC). Because of the program and schedule constraints, a single modal test of the SLS will be performed while bolted down to the Mobile Launch Pad just before the first launch. A Monte Carlo and optimization scheme will be performed to create thousands of possible models based on given dispersions in model properties and to determine which model best fits the natural frequencies and mode shapes from modal test. However, the question still remains as to whether this model is acceptable for the loads and GNC requirements. An uncertainty propagation and quantification (UP and UQ) technique to develop a quantitative set of validation metrics that is based on the flight requirements has therefore been developed and is discussed in this paper. There has been considerable research on UQ and UP and validation in the literature, but very little on propagating the uncertainties from requirements, so most validation metrics are "rules-of-thumb;" this research seeks to come up with more reason-based metrics. One of the main assumptions used to achieve this task is that the uncertainty in the modeling of the fixed boundary condition is accurate, so therefore that same uncertainty can be used in propagating the fixed-test configuration to the free-free actual configuration. The second main technique applied here is the usage of the limit-state formulation to quantify the final probabilistic parameters and to compare them with the requirements. These techniques are explored with a simple lumped spring-mass system and a simplified SLS model. When completed, it is anticipated that this requirements-based validation
Heusinkveld, Maarten H G; Quicken, Sjeng; Holtackers, Robert J; Huberts, Wouter; Reesink, Koen D; Delhaas, Tammo; Spronck, Bart
2018-02-01
Quantification of the uncertainty in constitutive model predictions describing arterial wall mechanics is vital towards non-invasive assessment of vascular drug therapies. Therefore, we perform uncertainty quantification to determine uncertainty in mechanical characteristics describing the vessel wall response upon loading. Furthermore, a global variance-based sensitivity analysis is performed to pinpoint measurements that are most rewarding to be measured more precisely. We used previously published carotid diameter-pressure and intima-media thickness (IMT) data (measured in triplicate), and Holzapfel-Gasser-Ogden models. A virtual data set containing 5000 diastolic and systolic diameter-pressure points, and IMT values was generated by adding measurement error to the average of the measured data. The model was fitted to single-exponential curves calculated from the data, obtaining distributions of constitutive parameters and constituent load bearing parameters. Additionally, we (1) simulated vascular drug treatment to assess the relevance of model uncertainty and (2) evaluated how increasing the number of measurement repetitions influences model uncertainty. We found substantial uncertainty in constitutive parameters. Simulating vascular drug treatment predicted a 6% point reduction in collagen load bearing ([Formula: see text]), approximately 50% of its uncertainty. Sensitivity analysis indicated that the uncertainty in [Formula: see text] was primarily caused by noise in distension and IMT measurements. Spread in [Formula: see text] could be decreased by 50% when increasing the number of measurement repetitions from 3 to 10. Model uncertainty, notably that in [Formula: see text], could conceal effects of vascular drug therapy. However, this uncertainty could be reduced by increasing the number of measurement repetitions of distension and wall thickness measurements used for model parameterisation.
Barkstrom, B. R.; Loeb, N. G.; Wielicki, B. A.
2017-12-01
Verification, Validation, and Uncertainty Quantification (VVUQ) are key actions that support conclusions based on Earth science data. Communities of data producers and users must undertake VVUQ when they create and use their data. The strategies [S] and tools [T] suggested below come from successful use on two large NASA projects. The first was the Earth Radiation Budget Experiment (ERBE). The second is the investigation of Clouds and the Earth's Radiant Energy System (CERES). [S] 1. Partition the production system into subsystems that deal with data transformations confined to limited space and time scales. Simplify the subsystems to minimize the number of data transformations in each subsystem. [S] 2. Derive algorithms from the fundamental physics and chemistry governing the parameters in each subsystem including those for instrument calibration. [S] 3. Use preliminary uncertainty estimates to detect unexpected discrepancies. Removing these requires diagnostic work as well as development and testing of fixes. [S] 4. Make sure there are adequate resources to support multiple end-to-end reprocessing of all data products. [T] 1. Create file identifiers that accommodate temporal and spatial sequences of data files and subsystem version changes. [T] 2. Create libraries of parameters used in common by different subsystems to reduce errors due to inconsistent values. [T] 3. Maintain a list of action items to record progress on resolving discrepancies. [T] 4. Plan on VVUQ activities that use independent data sources and peer review before distributing and archiving data. The goal of VVUQ is to provide a transparent link between the data and the physics and chemistry governing the measured quantities. The VVUQ effort also involves specialized domain experience and nomenclature. It often requires as much effort as the original system development. ERBE and CERES demonstrated that these strategies and tools can reduce the cost of VVUQ for Earth science data products.
Zielke, Olaf; McDougall, Damon; Mai, Martin; Babuska, Ivo
2014-05-01
Seismic, often augmented with geodetic data, are frequently used to invert for the spatio-temporal evolution of slip along a rupture plane. The resulting images of the slip evolution for a single event, inferred by different research teams, often vary distinctly, depending on the adopted inversion approach and rupture model parameterization. This observation raises the question, which of the provided kinematic source inversion solutions is most reliable and most robust, and — more generally — how accurate are fault parameterization and solution predictions? These issues are not included in "standard" source inversion approaches. Here, we present a statistical inversion approach to constrain kinematic rupture parameters from teleseismic body waves. The approach is based a) on a forward-modeling scheme that computes synthetic (body-)waves for a given kinematic rupture model, and b) on the QUESO (Quantification of Uncertainty for Estimation, Simulation, and Optimization) library that uses MCMC algorithms and Bayes theorem for sample selection. We present Bayesian inversions for rupture parameters in synthetic earthquakes (i.e. for which the exact rupture history is known) in an attempt to identify the cross-over at which further model discretization (spatial and temporal resolution of the parameter space) is no longer attributed to a decreasing misfit. Identification of this cross-over is of importance as it reveals the resolution power of the studied data set (i.e. teleseismic body waves), enabling one to constrain kinematic earthquake rupture histories of real earthquakes at a resolution that is supported by data. In addition, the Bayesian approach allows for mapping complete posterior probability density functions of the desired kinematic source parameters, thus enabling us to rigorously assess the uncertainties in earthquake source inversions.
Extreme-Scale Bayesian Inference for Uncertainty Quantification of Complex Simulations
Energy Technology Data Exchange (ETDEWEB)
Biros, George [Univ. of Texas, Austin, TX (United States)
2018-01-12
Uncertainty quantification (UQ)—that is, quantifying uncertainties in complex mathematical models and their large-scale computational implementations—is widely viewed as one of the outstanding challenges facing the field of CS&E over the coming decade. The EUREKA project set to address the most difficult class of UQ problems: those for which both the underlying PDE model as well as the uncertain parameters are of extreme scale. In the project we worked on these extreme-scale challenges in the following four areas: 1. Scalable parallel algorithms for sampling and characterizing the posterior distribution that exploit the structure of the underlying PDEs and parameter-to-observable map. These include structure-exploiting versions of the randomized maximum likelihood method, which aims to overcome the intractability of employing conventional MCMC methods for solving extreme-scale Bayesian inversion problems by appealing to and adapting ideas from large-scale PDE-constrained optimization, which have been very successful at exploring high-dimensional spaces. 2. Scalable parallel algorithms for construction of prior and likelihood functions based on learning methods and non-parametric density estimation. Constructing problem-specific priors remains a critical challenge in Bayesian inference, and more so in high dimensions. Another challenge is construction of likelihood functions that capture unmodeled couplings between observations and parameters. We will create parallel algorithms for non-parametric density estimation using high dimensional N-body methods and combine them with supervised learning techniques for the construction of priors and likelihood functions. 3. Bayesian inadequacy models, which augment physics models with stochastic models that represent their imperfections. The success of the Bayesian inference framework depends on the ability to represent the uncertainty due to imperfections of the mathematical model of the phenomena of interest. This is a
Badawy, B.; Fletcher, C. G.
2017-12-01
The parameterization of snow processes in land surface models is an important source of uncertainty in climate simulations. Quantifying the importance of snow-related parameters, and their uncertainties, may therefore lead to better understanding and quantification of uncertainty within integrated earth system models. However, quantifying the uncertainty arising from parameterized snow processes is challenging due to the high-dimensional parameter space, poor observational constraints, and parameter interaction. In this study, we investigate the sensitivity of the land simulation to uncertainty in snow microphysical parameters in the Canadian LAnd Surface Scheme (CLASS) using an uncertainty quantification (UQ) approach. A set of training cases (n=400) from CLASS is used to sample each parameter across its full range of empirical uncertainty, as determined from available observations and expert elicitation. A statistical learning model using support vector regression (SVR) is then constructed from the training data (CLASS output variables) to efficiently emulate the dynamical CLASS simulations over a much larger (n=220) set of cases. This approach is used to constrain the plausible range for each parameter using a skill score, and to identify the parameters with largest influence on the land simulation in CLASS at global and regional scales, using a random forest (RF) permutation importance algorithm. Preliminary sensitivity tests indicate that snow albedo refreshment threshold and the limiting snow depth, below which bare patches begin to appear, have the highest impact on snow output variables. The results also show a considerable reduction of the plausible ranges of the parameters values and hence reducing their uncertainty ranges, which can lead to a significant reduction of the model uncertainty. The implementation and results of this study will be presented and discussed in details.
Hadjidoukas, P. E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.
2015-03-01
We present Π4U, an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.
International Nuclear Information System (INIS)
Hadjidoukas, P.E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.
2015-01-01
We present Π4U, 1 an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow
Adaptive multiscale MCMC algorithm for uncertainty quantification in seismic parameter estimation
Tan, Xiaosi
2014-08-05
Formulating an inverse problem in a Bayesian framework has several major advantages (Sen and Stoffa, 1996). It allows finding multiple solutions subject to flexible a priori information and performing uncertainty quantification in the inverse problem. In this paper, we consider Bayesian inversion for the parameter estimation in seismic wave propagation. The Bayes\\' theorem allows writing the posterior distribution via the likelihood function and the prior distribution where the latter represents our prior knowledge about physical properties. One of the popular algorithms for sampling this posterior distribution is Markov chain Monte Carlo (MCMC), which involves making proposals and calculating their acceptance probabilities. However, for large-scale problems, MCMC is prohibitevely expensive as it requires many forward runs. In this paper, we propose a multilevel MCMC algorithm that employs multilevel forward simulations. Multilevel forward simulations are derived using Generalized Multiscale Finite Element Methods that we have proposed earlier (Efendiev et al., 2013a; Chung et al., 2013). Our overall Bayesian inversion approach provides a substantial speed-up both in the process of the sampling via preconditioning using approximate posteriors and the computation of the forward problems for different proposals by using the adaptive nature of multiscale methods. These aspects of the method are discussed n the paper. This paper is motivated by earlier work of M. Sen and his collaborators (Hong and Sen, 2007; Hong, 2008) who proposed the development of efficient MCMC techniques for seismic applications. In the paper, we present some preliminary numerical results.
Uncertainty Quantification in Scale-Dependent Models of Flow in Porous Media: SCALE-DEPENDENT UQ
Energy Technology Data Exchange (ETDEWEB)
Tartakovsky, A. M. [Computational Mathematics Group, Pacific Northwest National Laboratory, Richland WA USA; Panzeri, M. [Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Milano Italy; Tartakovsky, G. D. [Hydrology Group, Pacific Northwest National Laboratory, Richland WA USA; Guadagnini, A. [Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Milano Italy
2017-11-01
Equations governing flow and transport in heterogeneous porous media are scale-dependent. We demonstrate that it is possible to identify a support scale $\\eta^*$, such that the typically employed approximate formulations of Moment Equations (ME) yield accurate (statistical) moments of a target environmental state variable. Under these circumstances, the ME approach can be used as an alternative to the Monte Carlo (MC) method for Uncertainty Quantification in diverse fields of Earth and environmental sciences. MEs are directly satisfied by the leading moments of the quantities of interest and are defined on the same support scale as the governing stochastic partial differential equations (PDEs). Computable approximations of the otherwise exact MEs can be obtained through perturbation expansion of moments of the state variables in orders of the standard deviation of the random model parameters. As such, their convergence is guaranteed only for the standard deviation smaller than one. We demonstrate our approach in the context of steady-state groundwater flow in a porous medium with a spatially random hydraulic conductivity.
Final Technical Report: Quantification of Uncertainty in Extreme Scale Computations (QUEST)
Energy Technology Data Exchange (ETDEWEB)
Knio, Omar M. [Duke Univ., Durham, NC (United States). Dept. of Mechanical Engineering and Materials Science
2017-06-06
QUEST is a SciDAC Institute comprising Sandia National Laboratories, Los Alamos National Laboratory, University of Southern California, Massachusetts Institute of Technology, University of Texas at Austin, and Duke University. The mission of QUEST is to: (1) develop a broad class of uncertainty quantification (UQ) methods/tools, and (2) provide UQ expertise and software to other SciDAC projects, thereby enabling/guiding their UQ activities. The Duke effort focused on the development of algorithms and utility software for non-intrusive sparse UQ representations, and on participation in the organization of annual workshops and tutorials to disseminate UQ tools to the community, and to gather input in order to adapt approaches to the needs of SciDAC customers. In particular, fundamental developments were made in (a) multiscale stochastic preconditioners, (b) gradient-based approaches to inverse problems, (c) adaptive pseudo-spectral approximations, (d) stochastic limit cycles, and (e) sensitivity analysis tools for noisy systems. In addition, large-scale demonstrations were performed, namely in the context of ocean general circulation models.
Energy Technology Data Exchange (ETDEWEB)
Hadjidoukas, P.E.; Angelikopoulos, P. [Computational Science and Engineering Laboratory, ETH Zürich, CH-8092 (Switzerland); Papadimitriou, C. [Department of Mechanical Engineering, University of Thessaly, GR-38334 Volos (Greece); Koumoutsakos, P., E-mail: petros@ethz.ch [Computational Science and Engineering Laboratory, ETH Zürich, CH-8092 (Switzerland)
2015-03-01
We present Π4U,{sup 1} an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.
Final Technical Report: Mathematical Foundations for Uncertainty Quantification in Materials Design
Energy Technology Data Exchange (ETDEWEB)
Plechac, Petr [Univ. of Delaware, Newark, DE (United States); Vlachos, Dionisios G. [Univ. of Delaware, Newark, DE (United States)
2018-01-23
We developed path-wise information theory-based and goal-oriented sensitivity analysis and parameter identification methods for complex high-dimensional dynamics and in particular of non-equilibrium extended molecular systems. The combination of these novel methodologies provided the first methods in the literature which are capable to handle UQ questions for stochastic complex systems with some or all of the following features: (a) multi-scale stochastic models such as (bio)chemical reaction networks, with a very large number of parameters, (b) spatially distributed systems such as Kinetic Monte Carlo or Langevin Dynamics, (c) non-equilibrium processes typically associated with coupled physico-chemical mechanisms, driven boundary conditions, hybrid micro-macro systems, etc. A particular computational challenge arises in simulations of multi-scale reaction networks and molecular systems. Mathematical techniques were applied to in silico prediction of novel materials with emphasis on the effect of microstructure on model uncertainty quantification (UQ). We outline acceleration methods to make calculations of real chemistry feasible followed by two complementary tasks on structure optimization and microstructure-induced UQ.
Elsheikh, A. H.
2013-12-01
Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans, and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known as nested sampling (NS), which can simultaneously sample the posterior distribution for uncertainty quantification, and estimate the Bayesian evidence for model selection. Model selection statistics, such as the Bayesian evidence, are needed to choose or assign different weights to different models of different levels of complexities. In this work, we report the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems. The estimated Bayesian evidence by the NS algorithm is used to weight different parameterizations of the subsurface flow models (prior model selection). The results of the numerical evaluation implicitly enforced Occam\\'s razor where simpler models with fewer number of parameters are favored over complex models. The proper level of model complexity was automatically determined based on the information content of the calibration data and the data mismatch of the calibrated model.
Larour, E.; Schlegel, N.
2016-11-01
With the Amazon EC2 Cloud becoming available as a viable platform for parallel computing, Earth System Models are increasingly interested in leveraging its capabilities towards improving climate projections. In particular, faced with long wait periods on high-end clusters, the elasticity of the Cloud presents a unique opportunity of potentially "infinite" availability of small-sized clusters running on high-performance instances. Among specific applications of this new paradigm, we show here how uncertainty quantification in climate projections of polar ice sheets (Antarctica and Greenland) can be significantly accelerated using the Cloud. Indeed, small-sized clusters are very efficient at delivering sensitivity and sampling analysis, core tools of uncertainty quantification. We demonstrate how this approach was used to carry out an extensive analysis of ice-flow projections on one of the largest basins in Greenland, the North-East Greenland Glacier, using the Ice Sheet System Model, the public-domain NASA-funded ice-flow modeling software. We show how errors in the projections were accurately quantified using Monte-Carlo sampling analysis on the EC2 Cloud, and how a judicious mix of high-end parallel computing and Cloud use can best leverage existing infrastructures, and significantly accelerate delivery of potentially ground-breaking climate projections, and in particular, enable uncertainty quantification that were previously impossible to achieve.
Directory of Open Access Journals (Sweden)
A. Roobaert
2018-03-01
Full Text Available The calculation of the air–water CO2 exchange (FCO2 in the ocean not only depends on the gradient in CO2 partial pressure at the air–water interface but also on the parameterization of the gas exchange transfer velocity (k and the choice of wind product. Here, we present regional and global-scale quantifications of the uncertainty in FCO2 induced by several widely used k formulations and four wind speed data products (CCMP, ERA, NCEP1 and NCEP2. The analysis is performed at a 1° × 1° resolution using the sea surface pCO2 climatology generated by Landschützer et al. (2015a for the 1991–2011 period, while the regional assessment relies on the segmentation proposed by the Regional Carbon Cycle Assessment and Processes (RECCAP project. First, we use k formulations derived from the global 14C inventory relying on a quadratic relationship between k and wind speed (k = c ⋅ U102; Sweeney et al., 2007; Takahashi et al., 2009; Wanninkhof, 2014, where c is a calibration coefficient and U10 is the wind speed measured 10 m above the surface. Our results show that the range of global FCO2, calculated with these k relationships, diverge by 12 % when using CCMP, ERA or NCEP1. Due to differences in the regional wind patterns, regional discrepancies in FCO2 are more pronounced than global. These global and regional differences significantly increase when using NCEP2 or other k formulations which include earlier relationships (i.e., Wanninkhof, 1992; Wanninkhof et al., 2009 as well as numerous local and regional parameterizations derived experimentally. To minimize uncertainties associated with the choice of wind product, it is possible to recalculate the coefficient c globally (hereafter called c∗ for a given wind product and its spatio-temporal resolution, in order to match the last evaluation of the global k value. We thus performed these recalculations for each wind product at the resolution and time period of our study
Roobaert, Alizée; Laruelle, Goulven G.; Landschützer, Peter; Regnier, Pierre
2018-03-01
The calculation of the air-water CO2 exchange (FCO2) in the ocean not only depends on the gradient in CO2 partial pressure at the air-water interface but also on the parameterization of the gas exchange transfer velocity (k) and the choice of wind product. Here, we present regional and global-scale quantifications of the uncertainty in FCO2 induced by several widely used k formulations and four wind speed data products (CCMP, ERA, NCEP1 and NCEP2). The analysis is performed at a 1° × 1° resolution using the sea surface pCO2 climatology generated by Landschützer et al. (2015a) for the 1991-2011 period, while the regional assessment relies on the segmentation proposed by the Regional Carbon Cycle Assessment and Processes (RECCAP) project. First, we use k formulations derived from the global 14C inventory relying on a quadratic relationship between k and wind speed (k = c ṡ U102; Sweeney et al., 2007; Takahashi et al., 2009; Wanninkhof, 2014), where c is a calibration coefficient and U10 is the wind speed measured 10 m above the surface. Our results show that the range of global FCO2, calculated with these k relationships, diverge by 12 % when using CCMP, ERA or NCEP1. Due to differences in the regional wind patterns, regional discrepancies in FCO2 are more pronounced than global. These global and regional differences significantly increase when using NCEP2 or other k formulations which include earlier relationships (i.e., Wanninkhof, 1992; Wanninkhof et al., 2009) as well as numerous local and regional parameterizations derived experimentally. To minimize uncertainties associated with the choice of wind product, it is possible to recalculate the coefficient c globally (hereafter called c∗) for a given wind product and its spatio-temporal resolution, in order to match the last evaluation of the global k value. We thus performed these recalculations for each wind product at the resolution and time period of our study but the resulting global FCO2 estimates
International Nuclear Information System (INIS)
Wang, Jian-Xun; Sun, Rui; Xiao, Heng
2016-01-01
Highlights: • Compared physics-based and random matrix methods to quantify RANS model uncertainty. • Demonstrated applications of both methods in channel ow over periodic hills. • Examined the amount of information introduced in the physics-based approach. • Discussed implications to modeling turbulence in both near-wall and separated regions. - Abstract: Numerical models based on Reynolds-Averaged Navier-Stokes (RANS) equations are widely used in engineering turbulence modeling. However, the RANS predictions have large model-form uncertainties for many complex flows, e.g., those with non-parallel shear layers or strong mean flow curvature. Quantification of these large uncertainties originating from the modeled Reynolds stresses has attracted attention in the turbulence modeling community. Recently, a physics-based Bayesian framework for quantifying model-form uncertainties has been proposed with successful applications to several flows. Nonetheless, how to specify proper priors without introducing unwarranted, artificial information remains challenging to the current form of the physics-based approach. Another recently proposed method based on random matrix theory provides the prior distributions with maximum entropy, which is an alternative for model-form uncertainty quantification in RANS simulations. This method has better mathematical rigorousness and provides the most non-committal prior distributions without introducing artificial constraints. On the other hand, the physics-based approach has the advantages of being more flexible to incorporate available physical insights. In this work, we compare and discuss the advantages and disadvantages of the two approaches on model-form uncertainty quantification. In addition, we utilize the random matrix theoretic approach to assess and possibly improve the specification of priors used in the physics-based approach. The comparison is conducted through a test case using a canonical flow, the flow past
Sapsis, Themistoklis P; Majda, Andrew J
2013-08-20
A framework for low-order predictive statistical modeling and uncertainty quantification in turbulent dynamical systems is developed here. These reduced-order, modified quasilinear Gaussian (ROMQG) algorithms apply to turbulent dynamical systems in which there is significant linear instability or linear nonnormal dynamics in the unperturbed system and energy-conserving nonlinear interactions that transfer energy from the unstable modes to the stable modes where dissipation occurs, resulting in a statistical steady state; such turbulent dynamical systems are ubiquitous in geophysical and engineering turbulence. The ROMQG method involves constructing a low-order, nonlinear, dynamical system for the mean and covariance statistics in the reduced subspace that has the unperturbed statistics as a stable fixed point and optimally incorporates the indirect effect of non-Gaussian third-order statistics for the unperturbed system in a systematic calibration stage. This calibration procedure is achieved through information involving only the mean and covariance statistics for the unperturbed equilibrium. The performance of the ROMQG algorithm is assessed on two stringent test cases: the 40-mode Lorenz 96 model mimicking midlatitude atmospheric turbulence and two-layer baroclinic models for high-latitude ocean turbulence with over 125,000 degrees of freedom. In the Lorenz 96 model, the ROMQG algorithm with just a single mode captures the transient response to random or deterministic forcing. For the baroclinic ocean turbulence models, the inexpensive ROMQG algorithm with 252 modes, less than 0.2% of the total, captures the nonlinear response of the energy, the heat flux, and even the one-dimensional energy and heat flux spectra.
International Nuclear Information System (INIS)
Laborda, Francisco; Medrano, Jesus; Castillo, Juan R.
2004-01-01
The quality of the quantitative results obtained from transient signals in high-performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICPMS) and flow injection-inductively coupled plasma mass spectrometry (FI-ICPMS) was investigated under multielement conditions. Quantification methods were based on multiple-point calibration by simple and weighted linear regression, and double-point calibration (measurement of the baseline and one standard). An uncertainty model, which includes the main sources of uncertainty from FI-ICPMS and HPLC-ICPMS (signal measurement, sample flow rate and injection volume), was developed to estimate peak area uncertainties and statistical weights used in weighted linear regression. The behaviour of the ICPMS instrument was characterized in order to be considered in the model, concluding that the instrument works as a concentration detector when it is used to monitorize transient signals from flow injection or chromatographic separations. Proper quantification by the three calibration methods was achieved when compared to reference materials, although the double-point calibration allowed to obtain results of the same quality as the multiple-point calibration, shortening the calibration time. Relative expanded uncertainties ranged from 10-20% for concentrations around the LOQ to 5% for concentrations higher than 100 times the LOQ
Energy Technology Data Exchange (ETDEWEB)
Eldred, Michael Scott; Vigil, Dena M.; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Lefantzi, Sophia (Sandia National Laboratories, Livermore, CA); Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Eddy, John P.
2011-12-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the DAKOTA software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of DAKOTA-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of DAKOTA's iterative analysis capabilities.
Modgil, Girish A.
Stage (HWSS) turbine blisk provides a baseline to demonstrate the process. The generalized polynomial chaos (gPC) toolbox which was developed includes sampling methods and constructs polynomial approximations. The toolbox provides not only the means for uncertainty quantification of the final blade design, but also facilitates construction of the surrogate models used for the blade optimization. This paper shows that gPC , with a small number of samples, achieves very fast rates of convergence and high accuracy in describing probability distributions without loss of detail in the tails . First, an optimization problem maximizes stage efficiency using turbine aerodynamic design rules as constraints; the function evaluations for this optimization are surrogate models from detailed 3D steady Computational Fluid Dynamics (CFD) analyses. The resulting optimal shape provides a starting point for the 3D high-fidelity aeromechanics (unsteady CFD and 3D Finite Element Analysis (FEA)) UQ study assuming three uncertain input parameters. This investigation seeks to find the steady and vibratory stresses associated with the first torsion mode for the HWSS turbine blisk near maximum operating speed of the engine. Using gPC to provide uncertainty estimates of the steady and vibratory stresses enables the creation of a Probabilistic Goodman Diagram, which - to the authors' best knowledge - is the first of its kind using high fidelity aeromechanics for turbomachinery blades. The Probabilistic Goodman Diagram enables turbine blade designers to make more informed design decisions and it allows the aeromechanics expert to assess quantitatively the risk associated with HCF for any mode crossing based on high fidelity simulations.
International Nuclear Information System (INIS)
Lee, Sang Yong; Chung, Bob Dong; Lee, Young Jin; Park, Chan Eok; Lee, Guy Hyung; Choi, Chul Jin
1994-06-01
This research aims to develop reliable, advanced system thermal-hydraulic computer code and to quantify the uncertainties of code to introduce the best estimate methodology of ECCS for LBLOCA. Although the one of best estimate code, RELAP5/MOD3.1 was introduced from USNRC, several deficiencies in its reflood model and some improvements have been made. The improvements consist of modification of reflood wall heat transfer package and adjusting the drop size in dispersed flow regime. The tome smoothing of wall vaporization and level tracking model are also added to eliminate the pressure spike and level oscillation. For the verification of improved model and quantification of associated uncertainty, the FLECHT-SEASET data were used and upper limit of uncertainty at 95% confidence level is evaluated. (Author) 30 refs., 49 figs., 2 tabs
Energy Technology Data Exchange (ETDEWEB)
Lee, Sang Yong; Chung, Bob Dong; Lee, Young Jin; Park, Chan Eok; Lee, Guy Hyung; Choi, Chul Jin [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)
1994-06-01
This research aims to develop reliable, advanced system thermal-hydraulic computer code and to quantify the uncertainties of code to introduce the best estimate methodology of ECCS for LBLOCA. Although the one of best estimate code, RELAP5/MOD3.1 was introduced from USNRC, several deficiencies in its reflood model and some improvements have been made. The improvements consist of modification of reflood wall heat transfer package and adjusting the drop size in dispersed flow regime. The tome smoothing of wall vaporization and level tracking model are also added to eliminate the pressure spike and level oscillation. For the verification of improved model and quantification of associated uncertainty, the FLECHT-SEASET data were used and upper limit of uncertainty at 95% confidence level is evaluated. (Author) 30 refs., 49 figs., 2 tabs.
Brehm, Maik; Deraemaeker, Arnaud
2015-04-01
For the development of innovative materials, construction types or maintenance strategies, experimental investigations are inevitable to validate theoretical approaches in praxis. Numerical simulations, embedded in a general virtual testing approach, are alternatives to expensive experimental investigations. The statistical properties of the dynamic response in the frequency domain obtained from continuously measured data are often the basis for many developments, such as the optimization of damage indicators for structural health monitoring systems or the investigation of data-based frequency response function estimates. Two straightforward numerical simulation approaches exist to derive the statistics of a response due to random excitation and measurement errors. One approach is the sample-based technique, wherein for each excitation sample a time integration solution is needed. This can be computationally very demanding if a high accuracy of the statistical properties is of interest. The other approach consists in using the relationship between the excitation and the response directly in the frequency domain, wherein a weakly stationary process is assumed. This approach is inherently related to an infinite time response, which can hardly be derived from measured data. In this paper, a novel approach is proposed that overcomes the limitation of both aforementioned methods, by providing a fast analytical probabilistic framework for uncertainty quantification to determine accurately the statistics of short time dynamic responses. It is assumed that the structural system is known and can be described by deterministic parameters. The influences of signal processing techniques, such as linear combinations, windowing, and segmentation used in Welch's method, are considered as well. The performance of the new algorithm is investigated in comparison to both previous approaches on a three degrees of freedom system. The benchmark shows that the novel approach outperforms
Qi, Di
Turbulent dynamical systems are ubiquitous in science and engineering. Uncertainty quantification (UQ) in turbulent dynamical systems is a grand challenge where the goal is to obtain statistical estimates for key physical quantities. In the development of a proper UQ scheme for systems characterized by both a high-dimensional phase space and a large number of instabilities, significant model errors compared with the true natural signal are always unavoidable due to both the imperfect understanding of the underlying physical processes and the limited computational resources available. One central issue in contemporary research is the development of a systematic methodology for reduced order models that can recover the crucial features both with model fidelity in statistical equilibrium and with model sensitivity in response to perturbations. In the first part, we discuss a general mathematical framework to construct statistically accurate reduced-order models that have skill in capturing the statistical variability in the principal directions of a general class of complex systems with quadratic nonlinearity. A systematic hierarchy of simple statistical closure schemes, which are built through new global statistical energy conservation principles combined with statistical equilibrium fidelity, are designed and tested for UQ of these problems. Second, the capacity of imperfect low-order stochastic approximations to model extreme events in a passive scalar field advected by turbulent flows is investigated. The effects in complicated flow systems are considered including strong nonlinear and non-Gaussian interactions, and much simpler and cheaper imperfect models with model error are constructed to capture the crucial statistical features in the stationary tracer field. Several mathematical ideas are introduced to improve the prediction skill of the imperfect reduced-order models. Most importantly, empirical information theory and statistical linear response theory are
Jacquin, A. P.
2012-04-01
This study is intended to quantify the impact of uncertainty about precipitation spatial distribution on predictive uncertainty of a snowmelt runoff model. This problem is especially relevant in mountain catchments with a sparse precipitation observation network and relative short precipitation records. The model analysed is a conceptual watershed model operating at a monthly time step. The model divides the catchment into five elevation zones, where the fifth zone corresponds to the catchment's glaciers. Precipitation amounts at each elevation zone i are estimated as the product between observed precipitation at a station and a precipitation factor FPi. If other precipitation data are not available, these precipitation factors must be adjusted during the calibration process and are thus seen as parameters of the model. In the case of the fifth zone, glaciers are seen as an inexhaustible source of water that melts when the snow cover is depleted.The catchment case study is Aconcagua River at Chacabuquito, located in the Andean region of Central Chile. The model's predictive uncertainty is measured in terms of the output variance of the mean squared error of the Box-Cox transformed discharge, the relative volumetric error, and the weighted average of snow water equivalent in the elevation zones at the end of the simulation period. Sobol's variance decomposition (SVD) method is used for assessing the impact of precipitation spatial distribution, represented by the precipitation factors FPi, on the models' predictive uncertainty. In the SVD method, the first order effect of a parameter (or group of parameters) indicates the fraction of predictive uncertainty that could be reduced if the true value of this parameter (or group) was known. Similarly, the total effect of a parameter (or group) measures the fraction of predictive uncertainty that would remain if the true value of this parameter (or group) was unknown, but all the remaining model parameters could be fixed
Energy Technology Data Exchange (ETDEWEB)
Labarile, A.; Barrachina, T.; Miró, R.; Verdú, G., E-mail: alabarile@iqn.upv.es, E-mail: tbarrachina@iqn.upv.es, E-mail: rmiro@iqn.upv.es, E-mail: gverdu@iqn.upv.es [Institute for Industrial, Radiophysical and Environmental Safety - ISIRYM, Valencia (Spain); Pereira, C., E-mail: claubia@nuclear.ufmg.br [Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Departamento de Engenharia Nuclear
2017-07-01
The use of Best-Estimate computer codes is one of the greatest concerns in the nuclear industry especially for licensing analysis. Of paramount importance is the estimation of the uncertainties of the whole system to establish the safety margins based on highly reliable results. The estimation of these uncertainties should be performed by applying a methodology to propagate the uncertainties from the input parameters and the models implemented in the code to the output parameters. This study employs two different approaches for the Sensitivity Analysis (SA) and Uncertainty Quantification (UQ), the adjoint-based perturbation theory of TSUNAMI-3D, and the stochastic sampling technique of SAMPLER/KENO. The cases studied are two models of Light Water Reactors in the framework of the OECD/NEA UAM-LWR benchmark, a Boiling Water Reactor (BWR) and a Pressurized Water Reactor (PWR). Both of them at Hot Full Power (HFP) and Hot Zero Power (HZP) conditions, with and without control rod. This work presents the results of k{sub eff} from different simulation, and discuss the comparison of the two methods employed. In particular, a list of the major contributors to the uncertainty of k{sub eff} in terms of microscopic cross sections; their sensitivity coefficients; a comparison between the results of the two modules and with reference values; statistical information from the stochastic approach, and the probability and statistical confidence reached in the simulations. The reader will find all these information discussed in this paper. (author)
Van Steenbergen, N.; Willems, P.
2012-04-01
Reliable flood forecasts are the most important non-structural measures to reduce the impact of floods. However flood forecasting systems are subject to uncertainty originating from the input data, model structure and model parameters of the different hydraulic and hydrological submodels. To quantify this uncertainty a non-parametric data-based approach has been developed. This approach analyses the historical forecast residuals (differences between the predictions and the observations at river gauging stations) without using a predefined statistical error distribution. Because the residuals are correlated with the value of the forecasted water level and the lead time, the residuals are split up into discrete classes of simulated water levels and lead times. For each class, percentile values are calculated of the model residuals and stored in a 'three dimensional error' matrix. By 3D interpolation in this error matrix, the uncertainty in new forecasted water levels can be quantified. In addition to the quantification of the uncertainty, the communication of this uncertainty is equally important. The communication has to be done in a consistent way, reducing the chance of misinterpretation. Also, the communication needs to be adapted to the audience; the majority of the larger public is not interested in in-depth information on the uncertainty on the predicted water levels, but only is interested in information on the likelihood of exceedance of certain alarm levels. Water managers need more information, e.g. time dependent uncertainty information, because they rely on this information to undertake the appropriate flood mitigation action. There are various ways in presenting uncertainty information (numerical, linguistic, graphical, time (in)dependent, etc.) each with their advantages and disadvantages for a specific audience. A useful method to communicate uncertainty of flood forecasts is by probabilistic flood mapping. These maps give a representation of the
Energy Technology Data Exchange (ETDEWEB)
Voegl, E
1970-07-01
This study is to acquaint the oil geologist, reservoir engineer, and manager with modern methods of appraising geological/technical projects and decision problems under uncertainty. Uncertainty attaches to any appraisal of investment projects whose income lies in the future. The greater that uncertainty, the less important become the appraisal methods proper while the computation procedures concerning uncertainty gain in significance. There are briefly discussed the tools of risk determination, i.e., mathematical statistics and probability theory, and some of the most common methods of quantifying the uncertainty are explained. The best known methods of decision finding under multivalent or uncertain expectations, such as conditional and sensibility analyses, minimax and minimax-risk rule, and preference theory are set forth. The risk is defined, and the most common methods of genuine risk determination in exploration and exploitation are discussed. Practical examples illustrate the solution of decision problems under uncertainty, and examples of genuine risk determination are furnished. (29 refs.)
Energy Technology Data Exchange (ETDEWEB)
Domino, Stefan Paul; Figueroa, Victor G.; Romero, Vicente Jose; Glaze, David Jason; Sherman, Martin P.; Luketa-Hanlin, Anay Josephine
2009-12-01
The objective of this work is to perform an uncertainty quantification (UQ) and model validation analysis of simulations of tests in the cross-wind test facility (XTF) at Sandia National Laboratories. In these tests, a calorimeter was subjected to a fire and the thermal response was measured via thermocouples. The UQ and validation analysis pertains to the experimental and predicted thermal response of the calorimeter. The calculations were performed using Sierra/Fuego/Syrinx/Calore, an Advanced Simulation and Computing (ASC) code capable of predicting object thermal response to a fire environment. Based on the validation results at eight diversely representative TC locations on the calorimeter the predicted calorimeter temperatures effectively bound the experimental temperatures. This post-validates Sandia's first integrated use of fire modeling with thermal response modeling and associated uncertainty estimates in an abnormal-thermal QMU analysis.
Sraj, Ihab; Mandli, Kyle T.; Knio, Omar; Dawson, Clint N.; Hoteit, Ibrahim
2014-01-01
Tsunami computational models are employed to explore multiple flooding scenarios and to predict water elevations. However, accurate estimation of water elevations requires accurate estimation of many model parameters including the Manning's n friction parameterization. Our objective is to develop an efficient approach for the uncertainty quantification and inference of the Manning's n coefficient which we characterize here by three different parameters set to be constant in the on-shore, near-shore and deep-water regions as defined using iso-baths. We use Polynomial Chaos (PC) to build an inexpensive surrogate for the G. eoC. law model and employ Bayesian inference to estimate and quantify uncertainties related to relevant parameters using the DART buoy data collected during the Tōhoku tsunami. The surrogate model significantly reduces the computational burden of the Markov Chain Monte-Carlo (MCMC) sampling of the Bayesian inference. The PC surrogate is also used to perform a sensitivity analysis.
Sraj, Ihab
2014-11-01
Tsunami computational models are employed to explore multiple flooding scenarios and to predict water elevations. However, accurate estimation of water elevations requires accurate estimation of many model parameters including the Manning\\'s n friction parameterization. Our objective is to develop an efficient approach for the uncertainty quantification and inference of the Manning\\'s n coefficient which we characterize here by three different parameters set to be constant in the on-shore, near-shore and deep-water regions as defined using iso-baths. We use Polynomial Chaos (PC) to build an inexpensive surrogate for the G. eoC. law model and employ Bayesian inference to estimate and quantify uncertainties related to relevant parameters using the DART buoy data collected during the Tōhoku tsunami. The surrogate model significantly reduces the computational burden of the Markov Chain Monte-Carlo (MCMC) sampling of the Bayesian inference. The PC surrogate is also used to perform a sensitivity analysis.
Raben, Jaime S; Hariharan, Prasanna; Robinson, Ronald; Malinauskas, Richard; Vlachos, Pavlos P
2016-03-01
the previous work through increased PIV image resolution, use of robust image processing algorithms for near-wall velocity measurements and wall shear stress calculations, and uncertainty analyses for both velocity and wall shear stress measurements. The velocity and shear stress analysis, with spatially distributed uncertainty estimates, highlights the challenges of flow quantification in medical devices and provides potential methods to overcome such challenges.
Gilli, L.
2013-01-01
This thesis presents the development and the implementation of an uncertainty propagation algorithm based on the concept of spectral expansion. The first part of the thesis is dedicated to the study of uncertainty propagation methodologies and to the analysis of spectral techniques. The concepts
Schlegel, Nicole-Jeanne; Boening, Carmen; Larour, Eric; Limonadi, Daniel; Schodlok, Michael; Seroussi, Helene; Watkins, Michael
2017-04-01
Research and development activities at the Jet Propulsion Laboratory (JPL) currently support the creation of a framework to formally evaluate the observational needs within earth system science. One of the pilot projects of this effort aims to quantify uncertainties in global mean sea level rise projections, due to contributions from the continental ice sheets. Here, we take advantage of established uncertainty quantification tools embedded within the JPL-University of California at Irvine Ice Sheet System Model (ISSM). We conduct sensitivity and Monte-Carlo style sampling experiments on forward simulations of the Greenland and Antarctic ice sheets. By varying internal parameters and boundary conditions of the system over both extreme and credible worst-case ranges, we assess the impact of the different parameter ranges on century-scale sea level rise projections. The results inform efforts to a) isolate the processes and inputs that are most responsible for determining ice sheet contribution to sea level; b) redefine uncertainty brackets for century-scale projections; and c) provide a prioritized list of measurements, along with quantitative information on spatial and temporal resolution, required for reducing uncertainty in future sea level rise projections. Results indicate that ice sheet mass loss is dependent on the spatial resolution of key boundary conditions - such as bedrock topography and melt rates at the ice-ocean interface. This work is performed at and supported by the California Institute of Technology's Jet Propulsion Laboratory. Supercomputing time is also supported through a contract with the National Aeronautics and Space Administration's Cryosphere program.
DEFF Research Database (Denmark)
Minsley, Burke; Christensen, Nikolaj Kruse; Christensen, Steen
of airborne electromagnetic (AEM) data to estimate large-scale model structural geometry, i.e. the spatial distribution of different lithological units based on assumed or estimated resistivity-lithology relationships, and the uncertainty in those structures given imperfect measurements. Geophysically derived...... estimates of model structural uncertainty are then combined with hydrologic observations to assess the impact of model structural error on hydrologic calibration and prediction errors. Using a synthetic numerical model, we describe a sequential hydrogeophysical approach that: (1) uses Bayesian Markov chain...... Monte Carlo (McMC) methods to produce a robust estimate of uncertainty in electrical resistivity parameter values, (2) combines geophysical parameter uncertainty estimates with borehole observations of lithology to produce probabilistic estimates of model structural uncertainty over the entire AEM...
Optimal design and uncertainty quantification in blood flow simulations for congenital heart disease
Marsden, Alison
2009-11-01
Recent work has demonstrated substantial progress in capabilities for patient-specific cardiovascular flow simulations. Recent advances include increasingly complex geometries, physiological flow conditions, and fluid structure interaction. However inputs to these simulations, including medical image data, catheter-derived pressures and material properties, can have significant uncertainties associated with them. For simulations to predict clinically useful and reliable output information, it is necessary to quantify the effects of input uncertainties on outputs of interest. In addition, blood flow simulation tools can now be efficiently coupled to shape optimization algorithms for surgery design applications, and these tools should incorporate uncertainty information. We present a unified framework to systematically and efficient account for uncertainties in simulations using adaptive stochastic collocation. In addition, we present a framework for derivative-free optimization of cardiovascular geometries, and layer these tools to perform optimization under uncertainty. These methods are demonstrated using simulations and surgery optimization to improve hemodynamics in pediatric cardiology applications.
Mondal, A.
2010-03-01
In this paper, we study the uncertainty quantification in inverse problems for flows in heterogeneous porous media. Reversible jump Markov chain Monte Carlo algorithms (MCMC) are used for hierarchical modeling of channelized permeability fields. Within each channel, the permeability is assumed to have a lognormal distribution. Uncertainty quantification in history matching is carried out hierarchically by constructing geologic facies boundaries as well as permeability fields within each facies using dynamic data such as production data. The search with Metropolis-Hastings algorithm results in very low acceptance rate, and consequently, the computations are CPU demanding. To speed-up the computations, we use a two-stage MCMC that utilizes upscaled models to screen the proposals. In our numerical results, we assume that the channels intersect the wells and the intersection locations are known. Our results show that the proposed algorithms are capable of capturing the channel boundaries and describe the permeability variations within the channels using dynamic production history at the wells. © 2009 Elsevier Ltd. All rights reserved.
A Two-Step Approach to Uncertainty Quantification of Core Simulators
Directory of Open Access Journals (Sweden)
Artem Yankov
2012-01-01
Full Text Available For the multiple sources of error introduced into the standard computational regime for simulating reactor cores, rigorous uncertainty analysis methods are available primarily to quantify the effects of cross section uncertainties. Two methods for propagating cross section uncertainties through core simulators are the XSUSA statistical approach and the “two-step” method. The XSUSA approach, which is based on the SUSA code package, is fundamentally a stochastic sampling method. Alternatively, the two-step method utilizes generalized perturbation theory in the first step and stochastic sampling in the second step. The consistency of these two methods in quantifying uncertainties in the multiplication factor and in the core power distribution was examined in the framework of phase I-3 of the OECD Uncertainty Analysis in Modeling benchmark. With the Three Mile Island Unit 1 core as a base model for analysis, the XSUSA and two-step methods were applied with certain limitations, and the results were compared to those produced by other stochastic sampling-based codes. Based on the uncertainty analysis results, conclusions were drawn as to the method that is currently more viable for computing uncertainties in burnup and transient calculations.
Hughes, J. D.; Metz, P. A.
2014-12-01
Most watershed studies include observation-based water budget analyses to develop first-order estimates of significant flow terms. Surface-water/groundwater (SWGW) exchange is typically assumed to be equal to the residual of the sum of inflows and outflows in a watershed. These estimates of SWGW exchange, however, are highly uncertain as a result of the propagation of uncertainty inherent in the calculation or processing of the other terms of the water budget, such as stage-area-volume relations, and uncertainties associated with land-cover based evapotranspiration (ET) rate estimates. Furthermore, the uncertainty of estimated SWGW exchanges can be magnified in large wetland systems that transition from dry to wet during wet periods. Although it is well understood that observation-based estimates of SWGW exchange are uncertain it is uncommon for the uncertainty of these estimates to be directly quantified. High-level programming languages like Python can greatly reduce the effort required to (1) quantify the uncertainty of estimated SWGW exchange in large wetland systems and (2) evaluate how different approaches for partitioning land-cover data in a watershed may affect the water-budget uncertainty. We have used Python with the Numpy, Scipy.stats, and pyDOE packages to implement an unconstrained Monte Carlo approach with Latin Hypercube sampling to quantify the uncertainty of monthly estimates of SWGW exchange in the Floral City watershed of the Tsala Apopka wetland system in west-central Florida, USA. Possible sources of uncertainty in the water budget analysis include rainfall, ET, canal discharge, and land/bathymetric surface elevations. Each of these input variables was assigned a probability distribution based on observation error or spanning the range of probable values. The Monte Carlo integration process exposes the uncertainties in land-cover based ET rate estimates as the dominant contributor to the uncertainty in SWGW exchange estimates. We will discuss
DEFF Research Database (Denmark)
Ambühl, Simon; Kofoed, Jens Peter; Sørensen, John Dalsgaard
2015-01-01
Wave models used for site assessments are subjected to model uncertainties, which need to be quantified when using wave model results for probabilistic reliability assessments. This paper focuses on determination of wave model uncertainties. Four different wave models are considered, and validation...... data are collected from published scientific research. The bias and the root-mean-square error, as well as the scatter index, are considered for the significant wave height as well as the mean zero-crossing wave period. Based on an illustrative generic example, this paper presents how the quantified...... uncertainties can be implemented in probabilistic reliability assessments....
DEFF Research Database (Denmark)
Minsley, B. J.; Christensen, Nikolaj Kruse; Christensen, Steen
Model structure, or the spatial arrangement of subsurface lithological units, is fundamental to the hydrological behavior of Earth systems. Knowledge of geological model structure is critically important in order to make informed hydrological predictions and management decisions. Model structure...... is never perfectly known, however, and incorrect assumptions can be a significant source of error when making model predictions. We describe a systematic approach for quantifying model structural uncertainty that is based on the integration of sparse borehole observations and large-scale airborne...... electromagnetic (AEM) data. Our estimates of model structural uncertainty follow a Bayesian framework that accounts for both the uncertainties in geophysical parameter estimates given AEM data, and the uncertainties in the relationship between lithology and geophysical parameters. Using geostatistical sequential...
Gurney, K. R.; Chandrasekaran, V.; Mendoza, D. L.; Geethakumar, S.
2010-12-01
The Vulcan Project has estimated United States fossil fuel CO2 emissions at the hourly time scale and at spatial scales below the county level for the year 2002. Vulcan is built from a wide variety of observational data streams including regulated air pollutant emissions reporting, traffic monitoring, energy statistics, and US census data. In addition to these data sets, Vulcan relies on a series of modeling assumptions and constructs to interpolate in space, time and transform non-CO2 reporting into an estimate of CO2 combustion emissions. The recent version 2.0 of the Vulcan inventory has produced advances in a number of categories with particular emphasis on improved temporal structure. Onroad transportation emissions now avail of roughly 5000 automated traffic count monitors allowing for much improved diurnal and weekly time structure in our onroad transportation emissions. Though the inventory shows excellent agreement with independent national-level CO2 emissions estimates, uncertainty quantification has been a challenging task given the large number of data sources and numerous modeling assumptions. However, we have now accomplished a complete uncertainty estimate across all the Vulcan economic sectors and will present uncertainty estimates as a function of space, time, sector and fuel. We find that, like the underlying distribution of CO2 emissions themselves, the uncertainty is also strongly lognormal with high uncertainty associated with a relatively small number of locations. These locations typically are locations reliant upon coal combustion as the dominant CO2 source. We will also compare and contrast Vulcan fossil fuel CO2 emissions estimates against estimates built from DOE fuel-based surveys at the state level. We conclude that much of the difference between the Vulcan inventory and DOE statistics are not due to biased estimation but mechanistic differences in supply versus demand and combustion in space/time.
Ben Abdessalem, A.; Jenson, F.; Calmon, P.
2016-02-01
This contribution provides an example of the possible advantages of adopting a Bayesian inversion approach to uncertainty quantification in nondestructive inspection methods. In such problem, the uncertainty associated to the random parameters is not always known and needs to be characterised from scattering signal measurements. The uncertainties may then correctly propagated in order to determine a reliable probability of detection curve. To this end, we establish a general Bayesian framework based on a non-parametric maximum likelihood function formulation and some priors from expert knowledge. However, the presented inverse problem is time-consuming and computationally intensive. To cope with this difficulty, we replace the real model by a surrogate one in order to speed-up the model evaluation and to make the problem to be computationally feasible for implementation. The least squares support vector regression is adopted as metamodelling technique due to its robustness to deal with non-linear problems. We illustrate the usefulness of this methodology through the control of tube with enclosed defect using ultrasonic inspection method.
Energy Technology Data Exchange (ETDEWEB)
Wu, Xu, E-mail: xuwu2@illinois.edu; Kozlowski, Tomasz
2017-03-15
Modeling and simulations are naturally augmented by extensive Uncertainty Quantification (UQ) and sensitivity analysis requirements in the nuclear reactor system design, in which uncertainties must be quantified in order to prove that the investigated design stays within acceptance criteria. Historically, expert judgment has been used to specify the nominal values, probability density functions and upper and lower bounds of the simulation code random input parameters for the forward UQ process. The purpose of this paper is to replace such ad-hoc expert judgment of the statistical properties of input model parameters with inverse UQ process. Inverse UQ seeks statistical descriptions of the model random input parameters that are consistent with the experimental data. Bayesian analysis is used to establish the inverse UQ problems based on experimental data, with systematic and rigorously derived surrogate models based on Polynomial Chaos Expansion (PCE). The methods developed here are demonstrated with the Point Reactor Kinetics Equation (PRKE) coupled with lumped parameter thermal-hydraulics feedback model. Three input parameters, external reactivity, Doppler reactivity coefficient and coolant temperature coefficient are modeled as uncertain input parameters. Their uncertainties are inversely quantified based on synthetic experimental data. Compared with the direct numerical simulation, surrogate model by PC expansion shows high efficiency and accuracy. In addition, inverse UQ with Bayesian analysis can calibrate the random input parameters such that the simulation results are in a better agreement with the experimental data.
Frey, H Christopher; Bammi, Sachin
2002-04-01
Variability refers to real differences in emissions among multiple emission sources at any given time or over time for any individual emission source. Variability in emissions can be attributed to variation in fuel or feedstock composition, ambient temperature, design, maintenance, or operation. Uncertainty refers to lack of knowledge regarding the true value of emissions. Sources of uncertainty include small sample sizes, bias or imprecision in measurements, nonrepresentativeness, or lack of data. Quantitative methods for characterizing both variability and uncertainty are demonstrated and applied to case studies of emission factors for lawn and garden (L&G) equipment engines. Variability was quantified using empirical and parametric distributions. Bootstrap simulation was used to characterize confidence intervals for the fitted distributions. The 95% confidence intervals for the mean grams per brake horsepower/hour (g/hp-hr) emission factors for two-stroke engine total hydrocarbon (THC) and NOx emissions were from -30 to +41% and from -45 to +75%, respectively. The confidence intervals for four-stroke engines were from -33 to +46% for THCs and from -27 to +35% for NOx. These quantitative measures of uncertainty convey information regarding the quality of the emission factors and serve as a basis for calculation of uncertainty in emission inventories (EIs).
A High-Performance Embedded Hybrid Methodology for Uncertainty Quantification With Applications
Energy Technology Data Exchange (ETDEWEB)
Iaccarino, Gianluca
2014-04-01
Multiphysics processes modeled by a system of unsteady di erential equations are natu- rally suited for partitioned (modular) solution strategies. We consider such a model where probabilistic uncertainties are present in each module of the system and represented as a set of random input parameters. A straightforward approach in quantifying uncertainties in the predicted solution would be to sample all the input parameters into a single set, and treat the full system as a black-box. Although this method is easily parallelizable and requires minimal modi cations to deterministic solver, it is blind to the modular structure of the underlying multiphysical model. On the other hand, using spectral representations polynomial chaos expansions (PCE) can provide richer structural information regarding the dynamics of these uncertainties as they propagate from the inputs to the predicted output, but can be prohibitively expensive to implement in the high-dimensional global space of un- certain parameters. Therefore, we investigated hybrid methodologies wherein each module has the exibility of using sampling or PCE based methods of capturing local uncertainties while maintaining accuracy in the global uncertainty analysis. For the latter case, we use a conditional PCE model which mitigates the curse of dimension associated with intru- sive Galerkin or semi-intrusive Pseudospectral methods. After formalizing the theoretical framework, we demonstrate our proposed method using a numerical viscous ow simulation and benchmark the performance against a solely Monte-Carlo method and solely spectral method.
Uncertainty quantification for proton–proton fusion in chiral effective field theory
Energy Technology Data Exchange (ETDEWEB)
Acharya, B. [Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996 (United States); Carlsson, B.D. [Department of Physics, Chalmers University of Technology, SE-412 96 Göteborg (Sweden); Ekström, A. [Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996 (United States); Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Department of Physics, Chalmers University of Technology, SE-412 96 Göteborg (Sweden); Forssén, C. [Department of Physics, Chalmers University of Technology, SE-412 96 Göteborg (Sweden); Platter, L., E-mail: lplatter@utk.edu [Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996 (United States); Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)
2016-09-10
We compute the S-factor of the proton–proton (pp) fusion reaction using chiral effective field theory (χEFT) up to next-to-next-to-leading order (NNLO) and perform a rigorous uncertainty analysis of the results. We quantify the uncertainties due to (i) the computational method used to compute the pp cross section in momentum space, (ii) the statistical uncertainties in the low-energy coupling constants of χEFT, (iii) the systematic uncertainty due to the χEFT cutoff, and (iv) systematic variations in the database used to calibrate the nucleon–nucleon interaction. We also examine the robustness of the polynomial extrapolation procedure, which is commonly used to extract the threshold S-factor and its energy-derivatives. By performing a statistical analysis of the polynomial fit of the energy-dependent S-factor at several different energy intervals, we eliminate a systematic uncertainty that can arise from the choice of the fit interval in our calculations. In addition, we explore the statistical correlations between the S-factor and few-nucleon observables such as the binding energies and point-proton radii of {sup 2,3}H and {sup 3}He as well as the D-state probability and quadrupole moment of {sup 2}H, and the β-decay of {sup 3}H. We find that, with the state-of-the-art optimization of the nuclear Hamiltonian, the statistical uncertainty in the threshold S-factor cannot be reduced beyond 0.7%.
Uncertainty Quantification of Composite Laminate Damage with the Generalized Information Theory
Energy Technology Data Exchange (ETDEWEB)
J. Lucero; F. Hemez; T. Ross; K.Kline; J.Hundhausen; T. Tippetts
2006-05-01
This work presents a survey of five theories to assess the uncertainty of projectile impact induced damage on multi-layered carbon-epoxy composite plates. Because the types of uncertainty dealt with in this application are multiple (variability, ambiguity, and conflict) and because the data sets collected are sparse, characterizing the amount of delamination damage with probability theory alone is possible but incomplete. This motivates the exploration of methods contained within a broad Generalized Information Theory (GIT) that rely on less restrictive assumptions than probability theory. Probability, fuzzy sets, possibility, and imprecise probability (probability boxes (p-boxes) and Dempster-Shafer) are used to assess the uncertainty in composite plate damage. Furthermore, this work highlights the usefulness of each theory. The purpose of the study is not to compare directly the different GIT methods but to show that they can be deployed on a practical application and to compare the assumptions upon which these theories are based. The data sets consist of experimental measurements and finite element predictions of the amount of delamination and fiber splitting damage as multilayered composite plates are impacted by a projectile at various velocities. The physical experiments consist of using a gas gun to impact suspended plates with a projectile accelerated to prescribed velocities, then, taking ultrasound images of the resulting delamination. The nonlinear, multiple length-scale numerical simulations couple local crack propagation implemented through cohesive zone modeling to global stress-displacement finite element analysis. The assessment of damage uncertainty is performed in three steps by, first, considering the test data only; then, considering the simulation data only; finally, performing an assessment of total uncertainty where test and simulation data sets are combined. This study leads to practical recommendations for reducing the uncertainty and
Quantification of uncertainty in gamma spectrometric analysis of food and environmental samples
International Nuclear Information System (INIS)
Yii Mei Wo; Zaharudin Ahmad; Norfaizal Mohamed
2005-01-01
Gamma Spectrometry is widely used to determine the activity of gamma-ray emitter radionuclide inside a sample. Reporting the activity of the measurement for a sample should not be a single value only but it shall be associated with a reasonable uncertainty value since disintegration of radionuclide is a random/spontaneous process. This paper will focus on how the uncertainty was estimated, quantified and calculated, when measuring the activity of Cs-134 and Cs-137 in food and Ra-226, Ra-228 and K-40 in the environmental samples. (Author)
Intergenerational equity and long-term stewardship plans
International Nuclear Information System (INIS)
Hocking, E. K.
2002-01-01
For an untold number of contaminated sites throughout the world, stewardship will be inevitable. For many such sites, stewardship will be a reasonable approach because of the uncertainties associated with present and future site conditions and site contaminants, the limited performance of available technologies, the nonavailability of technologies, and the risk and cost associated with complete cleanup. Regardless of whether stewardship is a realistic approach to site situations or simply a convenient default, it could be required at most contaminated sites for multiple generations. Because the stewardship plan is required to protect the release of hazardous contaminants to the environment, some use restrictions will be put in place to provide that protection. These use restrictions will limit access to resources for as long as the protection is required. The intergenerational quality of long-term stewardship plans and their inherent limitations on resource use require that they be designed to achieve equity among the affected generations. Intergenerational equity, defined here as the fairness of access to resources across generations, could be achieved through a well-developed stewardship plan that provides future generations with the information they need to make wise decisions about resource use. Developing and implementing such a plan would take into account the failure mechanisms of the plan's components, feature short stewardship time blocks that would allow for periodic reassessments of the site and of the stewardship program's performance, and provide present and future generations with necessary site information
Robust Trajectory Optimization of a Ski Jumper for Uncertainty Influence and Safety Quantification
Directory of Open Access Journals (Sweden)
Patrick Piprek
2018-02-01
Full Text Available This paper deals with the development of a robust optimal control framework for a previously developed multi-body ski jumper simulation model by the authors. This framework is used to model uncertainties acting on the jumper during his jump, e.g., wind or mass, to enhance the performance, but also to increase the fairness and safety of the competition. For the uncertainty modeling the method of generalized polynomial chaos together with the discrete expansion by stochastic collocation is applied: This methodology offers a very flexible framework to model multiple uncertainties using a small number of required optimizations to calculate an uncertain trajectory. The results are then compared to the results of the Latin-Hypercube sampling method to show the correctness of the applied methods. Finally, the results are examined with respect to two major metrics: First, the influence of the uncertainties on the jumper, his positioning with respect to the air, and his maximal achievable flight distance are examined. Then, the results are used in a further step to quantify the safety of the jumper.
Uncertainty Quantification for a Sailing Yacht Hull, Using Multi-Fidelity Kriging
de Baar, J.H.S.; Roberts, S; Dwight, R.P.; Mallol, B.
2015-01-01
Uncertainty Quantication (UQ) for CFD-based ship design can require a large number of simulations, resulting in signicant overall computational cost. Presently, we use an existing method, multi-delity Kriging, to reduce the number of simulations required for the UQ analysis of the performance of a
DEFF Research Database (Denmark)
Vezzaro, Luca; Mikkelsen, Peter Steen
2012-01-01
of uncertainty in a conceptual lumped dynamic stormwater runoff quality model that is used in a study catchment to estimate (i) copper loads, (ii) compliance with dissolved Cu concentration limits on stormwater discharge and (iii) the fraction of Cu loads potentially intercepted by a planned treatment facility...
Crevillén-García, D.; Power, H.
2017-08-01
In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen-Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.
Crevillén-García, D; Power, H
2017-08-01
In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen-Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.
Debry, Edouard; Mallet, Vivien; Garaud, Damien; Malherbe, Laure; Bessagnet, Bertrand; Rouïl, Laurence
2010-05-01
Prev'Air is the French operational system for air pollution forecasting. It is developed and maintained by INERIS with financial support from the French Ministry for Environment. On a daily basis it delivers forecasts up to three days ahead for ozone, nitrogene dioxide and particles over France and Europe. Maps of concentration peaks and daily averages are freely available to the general public. More accurate data can be provided to customers and modelers. Prev'Air forecasts are based on the Chemical Transport Model CHIMERE. French authorities rely more and more on this platform to alert the general public in case of high pollution events and to assess the efficiency of regulation measures when such events occur. For example the road speed limit may be reduced in given areas when the ozone level exceeds one regulatory threshold. These operational applications require INERIS to assess the quality of its forecasts and to sensitize end users about the confidence level. Indeed concentrations always remain an approximation of the true concentrations because of the high uncertainty on input data, such as meteorological fields and emissions, because of incomplete or inaccurate representation of physical processes, and because of efficiencies in numerical integration [1]. We would like to present in this communication the uncertainty analysis of the CHIMERE model led in the framework of an INERIS research project aiming, on the one hand, to assess the uncertainty of several deterministic models and, on the other hand, to propose relevant indicators describing air quality forecast and their uncertainty. There exist several methods to assess the uncertainty of one model. Under given assumptions the model may be differentiated into an adjoint model which directly provides the concentrations sensitivity to given parameters. But so far Monte Carlo methods seem to be the most widely and oftenly used [2,3] as they are relatively easy to implement. In this framework one
Uncertainty Quantification of the Real-Time Reserves for Offshore Wind Power Plants
DEFF Research Database (Denmark)
Göçmen, Tuhfe; Giebel, Gregor; Réthoré, Pierre-Elouan
In order to retain the system stability, the wind power plants are required to provide ancillary services. One of those services is reserve power. Here in this study, we focus on the real-time reserves which can be traded in the balancing markets and are currently used for compensation under...... mandatory downregulation stated by the transmission system operators (TSOs). The PossPOW project (Possible Power of down-regulated Offshore Wind power plants) developed a real-time power curve of available power for offshore wind farms for use during down-regulation. The follow-up Concert project......(control and uncertainties in real-time power curves of offshore wind power plants) aims to quantify and finally reduce the uncertainty in reserve power, bringing the PossPOW algorithm and the state of the art forecasting methods together. The experiments designed to test the available power estimated by the Poss...
Energy Technology Data Exchange (ETDEWEB)
Nielsen, Joseph, E-mail: joseph.nielsen@inl.gov [Idaho National Laboratory, 1955 N. Fremont Avenue, P.O. Box 1625, Idaho Falls, ID 83402 (United States); University of Idaho, Department of Mechanical Engineering and Nuclear Engineering Program, 1776 Science Center Drive, Idaho Falls, ID 83402-1575 (United States); Tokuhiro, Akira [University of Idaho, Department of Mechanical Engineering and Nuclear Engineering Program, 1776 Science Center Drive, Idaho Falls, ID 83402-1575 (United States); Hiromoto, Robert [University of Idaho, Department of Computer Science, 1776 Science Center Drive, Idaho Falls, ID 83402-1575 (United States); Tu, Lei [University of Idaho, Department of Mechanical Engineering and Nuclear Engineering Program, 1776 Science Center Drive, Idaho Falls, ID 83402-1575 (United States)
2015-12-15
Highlights: • Dynamic Event Tree solutions have been optimized using the Branch-and-Bound algorithm. • A 60% efficiency in optimization has been achieved. • Modeling uncertainty within a risk-informed framework is evaluated. - Abstract: Evaluation of the impacts of uncertainty and sensitivity in modeling presents a significant set of challenges in particular to high fidelity modeling. Computational costs and validation of models creates a need for cost effective decision making with regards to experiment design. Experiments designed to validate computation models can be used to reduce uncertainty in the physical model. In some cases, large uncertainty in a particular aspect of the model may or may not have a large impact on the final results. For example, modeling of a relief valve may result in large uncertainty, however, the actual effects on final peak clad temperature in a reactor transient may be small and the large uncertainty with respect to valve modeling may be considered acceptable. Additionally, the ability to determine the adequacy of a model and the validation supporting it should be considered within a risk informed framework. Low fidelity modeling with large uncertainty may be considered adequate if the uncertainty is considered acceptable with respect to risk. In other words, models that are used to evaluate the probability of failure should be evaluated more rigorously with the intent of increasing safety margin. Probabilistic risk assessment (PRA) techniques have traditionally been used to identify accident conditions and transients. Traditional classical event tree methods utilize analysts’ knowledge and experience to identify the important timing of events in coordination with thermal-hydraulic modeling. These methods lack the capability to evaluate complex dynamic systems. In these systems, time and energy scales associated with transient events may vary as a function of transition times and energies to arrive at a different physical
International Nuclear Information System (INIS)
Nielsen, Joseph; Tokuhiro, Akira; Hiromoto, Robert; Tu, Lei
2015-01-01
Highlights: • Dynamic Event Tree solutions have been optimized using the Branch-and-Bound algorithm. • A 60% efficiency in optimization has been achieved. • Modeling uncertainty within a risk-informed framework is evaluated. - Abstract: Evaluation of the impacts of uncertainty and sensitivity in modeling presents a significant set of challenges in particular to high fidelity modeling. Computational costs and validation of models creates a need for cost effective decision making with regards to experiment design. Experiments designed to validate computation models can be used to reduce uncertainty in the physical model. In some cases, large uncertainty in a particular aspect of the model may or may not have a large impact on the final results. For example, modeling of a relief valve may result in large uncertainty, however, the actual effects on final peak clad temperature in a reactor transient may be small and the large uncertainty with respect to valve modeling may be considered acceptable. Additionally, the ability to determine the adequacy of a model and the validation supporting it should be considered within a risk informed framework. Low fidelity modeling with large uncertainty may be considered adequate if the uncertainty is considered acceptable with respect to risk. In other words, models that are used to evaluate the probability of failure should be evaluated more rigorously with the intent of increasing safety margin. Probabilistic risk assessment (PRA) techniques have traditionally been used to identify accident conditions and transients. Traditional classical event tree methods utilize analysts’ knowledge and experience to identify the important timing of events in coordination with thermal-hydraulic modeling. These methods lack the capability to evaluate complex dynamic systems. In these systems, time and energy scales associated with transient events may vary as a function of transition times and energies to arrive at a different physical
DEFF Research Database (Denmark)
Bigoni, Daniele; Engsig-Karup, Allan Peter; Eskilsson, Claes
2016-01-01
A major challenge in next-generation industrial applications is to improve numerical analysis by quantifying uncertainties in predictions. In this work we present a formulation of a fully nonlinear and dispersive potential flow water wave model with random inputs for the probabilistic description...... at different points in the parameter space, allowing for the reuse of existing simulation software. The choice of the applied methods is driven by the number of uncertain input parameters and by the fact that finding the solution of the considered model is computationally intensive. We revisit experimental...... benchmarks often used for validation of deterministic water wave models. Based on numerical experiments and assumed uncertainties in boundary data, our analysis reveals that some of the known discrepancies from deterministic simulation in comparison with experimental measurements could be partially explained...
Directory of Open Access Journals (Sweden)
Wang Mingjie
2017-01-01
Full Text Available A framework of non-intrusive polynomial chaos expansion method (PC was proposed to investigate the statistic characteristics of the response of structural-acoustic system containing random uncertainty. The PC method does not need to reformulate model equations, and the statistics of the response can be evaluated directly. The results show that compared to the direct Monte Carlo method (MCM based on the original numerical model, the PC method is effective and more efficient.
2015-12-02
of completely new nonlinear Malliavin calculus . This type of calculus is important for the analysis and simulation of stationary and/or “causal...been limited by the fact that it requires the solution of an optimization problem with noisy gradients . When using deterministic optimization schemes...under uncertainty. We tested new developments on nonlinear Malliavin calculus , combining reduced basis methods with ANOVA, model validation, on
Uncertainty Quantification and Comparison of Weld Residual Stress Measurements and Predictions.
Energy Technology Data Exchange (ETDEWEB)
Lewis, John R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Brooks, Dusty Marie [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2016-10-01
In pressurized water reactors, the prevention, detection, and repair of cracks within dissimilar metal welds is essential to ensure proper plant functionality and safety. Weld residual stresses, which are difficult to model and cannot be directly measured, contribute to the formation and growth of cracks due to primary water stress corrosion cracking. Additionally, the uncertainty in weld residual stress measurements and modeling predictions is not well understood, further complicating the prediction of crack evolution. The purpose of this document is to develop methodology to quantify the uncertainty associated with weld residual stress that can be applied to modeling predictions and experimental measurements. Ultimately, the results can be used to assess the current state of uncertainty and to build confidence in both modeling and experimental procedures. The methodology consists of statistically modeling the variation in the weld residual stress profiles using functional data analysis techniques. Uncertainty is quantified using statistical bounds (e.g. confidence and tolerance bounds) constructed with a semi-parametric bootstrap procedure. Such bounds describe the range in which quantities of interest, such as means, are expected to lie as evidenced by the data. The methodology is extended to provide direct comparisons between experimental measurements and modeling predictions by constructing statistical confidence bounds for the average difference between the two quantities. The statistical bounds on the average difference can be used to assess the level of agreement between measurements and predictions. The methodology is applied to experimental measurements of residual stress obtained using two strain relief measurement methods and predictions from seven finite element models developed by different organizations during a round robin study.
Gaignaire , Roman; Scorretti , Riccardo; Sabariego , Ruth ,; Geuzaine , Christophe
2011-01-01
The finite element method can be used to compute the electromagnetic fields induced in the human body by environmental extremely low frequency (ELF) fields. However, the electric properties of tissues are not precisely known and may vary depending on the individual, his/her age and other physiological parameters. In this paper, we account for the uncertainties on the conductivities of the brain tissues and spread them out to the induced fields by means of a nonintrusive approach based on Herm...
Monte Carlo Uncertainty Quantification Using Quasi-1D SRM Ballistic Model
Directory of Open Access Journals (Sweden)
Davide Viganò
2016-01-01
Full Text Available Compactness, reliability, readiness, and construction simplicity of solid rocket motors make them very appealing for commercial launcher missions and embarked systems. Solid propulsion grants high thrust-to-weight ratio, high volumetric specific impulse, and a Technology Readiness Level of 9. However, solid rocket systems are missing any throttling capability at run-time, since pressure-time evolution is defined at the design phase. This lack of mission flexibility makes their missions sensitive to deviations of performance from nominal behavior. For this reason, the reliability of predictions and reproducibility of performances represent a primary goal in this field. This paper presents an analysis of SRM performance uncertainties throughout the implementation of a quasi-1D numerical model of motor internal ballistics based on Shapiro’s equations. The code is coupled with a Monte Carlo algorithm to evaluate statistics and propagation of some peculiar uncertainties from design data to rocker performance parameters. The model has been set for the reproduction of a small-scale rocket motor, discussing a set of parametric investigations on uncertainty propagation across the ballistic model.
Rios, Richard; Acosta, Oscar; Lafond, Caroline; Espinosa, Jairo; de Crevoisier, Renaud
2017-11-01
In radiotherapy for prostate cancer the dose at the treatment planning for the bladder may be a bad surrogate of the actual delivered dose as the bladder presents the largest inter-fraction shape variations during treatment. This paper presents PCA models as a virtual tool to estimate dosimetric uncertainties for the bladder produced by motion and deformation between fractions. Our goal is to propose a methodology to determine the minimum number of modes required to quantify dose uncertainties of the bladder for motion/deformation models based on PCA. We trained individual PCA models using the bladder contours available from three patients with a planning computed tomography (CT) and on-treatment cone-beam CTs (CBCTs). Based on the above models and via deformable image registration (DIR), we estimated two accumulated doses: firstly, an accumulated dose obtained by integrating the planning dose over the Gaussian probability distribution of the PCA model; and secondly, an accumulated dose obtained by simulating treatment courses via a Monte Carlo approach. We also computed a reference accumulated dose for each patient using his available images via DIR. Finally, we compared the planning dose with the three accumulated doses, and we calculated local dose variability and dose-volume histogram uncertainties.
Kalman filter approach for uncertainty quantification in time-resolved laser-induced incandescence.
Hadwin, Paul J; Sipkens, Timothy A; Thomson, Kevin A; Liu, Fengshan; Daun, Kyle J
2018-03-01
Time-resolved laser-induced incandescence (TiRe-LII) data can be used to infer spatially and temporally resolved volume fractions and primary particle size distributions of soot-laden aerosols, but these estimates are corrupted by measurement noise as well as uncertainties in the spectroscopic and heat transfer submodels used to interpret the data. Estimates of the temperature, concentration, and size distribution of soot primary particles within a sample aerosol are typically made by nonlinear regression of modeled spectral incandescence decay, or effective temperature decay, to experimental data. In this work, we employ nonstationary Bayesian estimation techniques to infer aerosol properties from simulated and experimental LII signals, specifically the extended Kalman filter and Schmidt-Kalman filter. These techniques exploit the time-varying nature of both the measurements and the models, and they reveal how uncertainty in the estimates computed from TiRe-LII data evolves over time. Both techniques perform better when compared with standard deterministic estimates; however, we demonstrate that the Schmidt-Kalman filter produces more realistic uncertainty estimates.
Qi, W.; Zhang, C.; Fu, G.; Sweetapple, C.; Zhou, H.
2016-02-01
The applicability of six fine-resolution precipitation products, including precipitation radar, infrared, microwave and gauge-based products, using different precipitation computation recipes, is evaluated using statistical and hydrological methods in northeastern China. In addition, a framework quantifying uncertainty contributions of precipitation products, hydrological models, and their interactions to uncertainties in ensemble discharges is proposed. The investigated precipitation products are Tropical Rainfall Measuring Mission (TRMM) products (TRMM3B42 and TRMM3B42RT), Global Land Data Assimilation System (GLDAS)/Noah, Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and a Global Satellite Mapping of Precipitation (GSMAP-MVK+) product. Two hydrological models of different complexities, i.e. a water and energy budget-based distributed hydrological model and a physically based semi-distributed hydrological model, are employed to investigate the influence of hydrological models on simulated discharges. Results show APHRODITE has high accuracy at a monthly scale compared with other products, and GSMAP-MVK+ shows huge advantage and is better than TRMM3B42 in relative bias (RB), Nash-Sutcliffe coefficient of efficiency (NSE), root mean square error (RMSE), correlation coefficient (CC), false alarm ratio, and critical success index. These findings could be very useful for validation, refinement, and future development of satellite-based products (e.g. NASA Global Precipitation Measurement). Although large uncertainty exists in heavy precipitation, hydrological models contribute most of the uncertainty in extreme discharges. Interactions between precipitation products and hydrological models can have the similar magnitude of contribution to discharge uncertainty as the hydrological models. A
Indian Academy of Sciences (India)
To reflect this uncertainty in the climate scenarios, the use of AOGCMs that explicitly simulate the carbon cycle and chemistry of all the substances are needed. The Hadley Centre has developed a version of the climate model that allows the effect of climate change on the carbon cycle and its feedback into climate, to be ...
International Nuclear Information System (INIS)
Silva, T.A. da
1988-01-01
The comparison between the uncertainty method recommended by International Atomic Energy Agency (IAEA) and the and the International Weight and Measure Commitee (CIPM) are showed, for the calibration of clinical dosimeters in the secondary standard Dosimetry Laboratory (SSDL). (C.G.C.) [pt
Uncertainty Quantification and Sensitivity Analysis in the CICE v5.1 Sea Ice Model
Urrego-Blanco, J. R.; Urban, N. M.
2015-12-01
Changes in the high latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with mid latitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. In this work we characterize parametric uncertainty in Los Alamos Sea Ice model (CICE) and quantify the sensitivity of sea ice area, extent and volume with respect to uncertainty in about 40 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one-at-a-time, this study uses a global variance-based approach in which Sobol sequences are used to efficiently sample the full 40-dimensional parameter space. This approach requires a very large number of model evaluations, which are expensive to run. A more computationally efficient approach is implemented by training and cross-validating a surrogate (emulator) of the sea ice model with model output from 400 model runs. The emulator is used to make predictions of sea ice extent, area, and volume at several model configurations, which are then used to compute the Sobol sensitivity indices of the 40 parameters. A ranking based on the sensitivity indices indicates that model output is most sensitive to snow parameters such as conductivity and grain size, and the drainage of melt ponds. The main effects and interactions among the most influential parameters are also estimated by a non-parametric regression technique based on generalized additive models. It is recommended research to be prioritized towards more accurately determining these most influential parameters values by observational studies or by improving existing parameterizations in the sea ice model.
A norm-based approach to the quantification of model uncertainty
International Nuclear Information System (INIS)
Zio, E.; Apostolakis, G.E.
1996-01-01
Various mathematical formulations have been proposed for the treatment of model uncertainty. These formulations can be categorized as model-focused or prediction focused, according to whether the attention is directed towards the plausibility of the model hypotheses or to the accuracy of its predictions. In this paper we embrace the model-focused approach and propose a new tool for the quantitative analysis of the alternate models hypotheses, and for the evaluation of the probabilities representing the degree of belief on the validity of these hypotheses
Uncertainty Quantification of the FUN3D-Predicted NASA CRM Flutter Boundary
Stanford, Bret K.; Massey, Steven J.
2017-01-01
A nonintrusive point collocation method is used to propagate parametric uncertainties of the flexible Common Research Model, a generic transport configuration, through the unsteady aeroelastic CFD solver FUN3D. A range of random input variables are considered, including atmospheric flow variables, structural variables, and inertial (lumped mass) variables. UQ results are explored for a range of output metrics (with a focus on dynamic flutter stability), for both subsonic and transonic Mach numbers, for two different CFD mesh refinements. A particular focus is placed on computing failure probabilities: the probability that the wing will flutter within the flight envelope.
Feng, Jinchao; Lansford, Joshua; Mironenko, Alexander; Pourkargar, Davood Babaei; Vlachos, Dionisios G.; Katsoulakis, Markos A.
2018-03-01
We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data). The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.
Directory of Open Access Journals (Sweden)
Jinchao Feng
2018-03-01
Full Text Available We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data. The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.
DEFF Research Database (Denmark)
Sunyer Pinya, Maria Antonia; Madsen, H.; Rosbjerg, Dan
are independent. This study investigates the validity of this assumption and its effects on the estimated probabilistic projections of the changes in the 95% quantile of wet days. The methodology is divided in two main parts. First, the interdependency of the ENSEMBLES RCMs is estimated using the methodology...... developed by Pennell and Reichler (2011). The results show that the projections from the ENSEMBLES RCMs cannot be assumed independent. This result is then used to estimate the uncertainty in climate model projections. A Bayesian approach has been developed using the procedure suggested by Tebaldi et al...
DEFF Research Database (Denmark)
Sunyer, M. A.; Rosbjerg, Dan; Arnbjerg-Nielsen, Karsten
2017-01-01
are independent. This study investigates the validity of this assumption and its effects on the estimated probabilistic projections of the changes in the 95% quantile of wet days. The methodology is divided in two main parts. First, the interdependency of the ENSEMBLES RCMs is estimated using the methodology...... developed by Pennell and Reichler (2011). The results show that the projections from the ENSEMBLES RCMs cannot be assumed independent. This result is then used to estimate the uncertainty in climate model projections. A Bayesian approach has been developed using the procedure suggested by Tebaldi et al...
DEFF Research Database (Denmark)
Luczak, Marcin; Peeters, Bart; Kahsin, Maciej
2014-01-01
for uncertainty evaluation in experimentally estimated models. Investigated structures are plates, fuselage panels and helicopter main rotor blades as they represent different complexity levels ranging from coupon, through sub-component up to fully assembled structures made of composite materials. To evaluate......Aerospace and wind energy structures are extensively using components made of composite materials. Since these structures are subjected to dynamic environments with time-varying loading conditions, it is important to model their dynamic behavior and validate these models by means of vibration...
Zarlenga, Antonio; de Barros, Felipe; Fiori, Aldo
2016-04-01
We present a probabilistic framework for assessing human health risk due to groundwater contamination. Our goal is to quantify how physical hydrogeological and biochemical parameters control the magnitude and uncertainty of human health risk. Our methodology captures the whole risk chain from the aquifer contamination to the tap water assumption by human population. The contaminant concentration, the key parameter for the risk estimation, is governed by the interplay between the large-scale advection, caused by heterogeneity and the degradation processes strictly related to the local scale dispersion processes. The core of the hazard identification and of the methodology is the reactive transport model: erratic displacement of contaminant in groundwater, due to the spatial variability of hydraulic conductivity (K), is characterized by a first-order Lagrangian stochastic model; different dynamics are considered as possible ways of biodegradation in aerobic and anaerobic conditions. With the goal of quantifying uncertainty, the Beta distribution is assumed for the concentration probability density function (pdf) model, while different levels of approximation are explored for the estimation of the one-point concentration moments. The information pertaining the flow and transport is connected with a proper dose response assessment which generally involves the estimation of physiological parameters of the exposed population. Human health response depends on the exposed individual metabolism (e.g. variability) and is subject to uncertainty. Therefore, the health parameters are intrinsically a stochastic. As a consequence, we provide an integrated in a global probabilistic human health risk framework which allows the propagation of the uncertainty from multiple sources. The final result, the health risk pdf, is expressed as function of a few relevant, physically-based parameters such as the size of the injection area, the Péclet number, the K structure metrics and
Pathmanathan, Pras; Shotwell, Matthew S; Gavaghan, David J; Cordeiro, Jonathan M; Gray, Richard A
2015-01-01
Perhaps the most mature area of multi-scale systems biology is the modelling of the heart. Current models are grounded in over fifty years of research in the development of biophysically detailed models of the electrophysiology (EP) of cardiac cells, but one aspect which is inadequately addressed is the incorporation of uncertainty and physiological variability. Uncertainty quantification (UQ) is the identification and characterisation of the uncertainty in model parameters derived from experimental data, and the computation of the resultant uncertainty in model outputs. It is a necessary tool for establishing the credibility of computational models, and will likely be expected of EP models for future safety-critical clinical applications. The focus of this paper is formal UQ of one major sub-component of cardiac EP models, the steady-state inactivation of the fast sodium current, INa. To better capture average behaviour and quantify variability across cells, we have applied for the first time an 'individual-based' statistical methodology to assess voltage clamp data. Advantages of this approach over a more traditional 'population-averaged' approach are highlighted. The method was used to characterise variability amongst cells isolated from canine epi and endocardium, and this variability was then 'propagated forward' through a canine model to determine the resultant uncertainty in model predictions at different scales, such as of upstroke velocity and spiral wave dynamics. Statistically significant differences between epi and endocardial cells (greater half-inactivation and less steep slope of steady state inactivation curve for endo) was observed, and the forward propagation revealed a lack of robustness of the model to underlying variability, but also surprising robustness to variability at the tissue scale. Overall, the methodology can be used to: (i) better analyse voltage clamp data; (ii) characterise underlying population variability; (iii) investigate
Directory of Open Access Journals (Sweden)
Kelly C. Chang
2017-11-01
Full Text Available The Comprehensive in vitro Proarrhythmia Assay (CiPA is a global initiative intended to improve drug proarrhythmia risk assessment using a new paradigm of mechanistic assays. Under the CiPA paradigm, the relative risk of drug-induced Torsade de Pointes (TdP is assessed using an in silico model of the human ventricular action potential (AP that integrates in vitro pharmacology data from multiple ion channels. Thus, modeling predictions of cardiac risk liability will depend critically on the variability in pharmacology data, and uncertainty quantification (UQ must comprise an essential component of the in silico assay. This study explores UQ methods that may be incorporated into the CiPA framework. Recently, we proposed a promising in silico TdP risk metric (qNet, which is derived from AP simulations and allows separation of a set of CiPA training compounds into Low, Intermediate, and High TdP risk categories. The purpose of this study was to use UQ to evaluate the robustness of TdP risk separation by qNet. Uncertainty in the model parameters used to describe drug binding and ionic current block was estimated using the non-parametric bootstrap method and a Bayesian inference approach. Uncertainty was then propagated through AP simulations to quantify uncertainty in qNet for each drug. UQ revealed lower uncertainty and more accurate TdP risk stratification by qNet when simulations were run at concentrations below 5× the maximum therapeutic exposure (Cmax. However, when drug effects were extrapolated above 10× Cmax, UQ showed that qNet could no longer clearly separate drugs by TdP risk. This was because for most of the pharmacology data, the amount of current block measured was <60%, preventing reliable estimation of IC50-values. The results of this study demonstrate that the accuracy of TdP risk prediction depends both on the intrinsic variability in ion channel pharmacology data as well as on experimental design considerations that preclude an
International Nuclear Information System (INIS)
2009-03-01
The Canadian Association of Petroleum Producers prepares an annual stewardship report as part of the industry's commitment to stewardship through the open and transparent reporting of progress on environmental, health and safety, and social issues. These reports also serve to provide annual benchmarking targets for the industry to surpass. This report presented the eighth annual stewardship report for 2008 and discussed indicators relating to several areas. The first involved air quality as it relates to climate change and greenhouse gases and technological solutions such as toe-to-heel air injection; geothermal energy; and carbon capture and sequestration. The issues of releasing greenhouse gases through flaring and venting were also examined along with other issues such as returning the land to a sustainable landscape; using water to produce oil and gas; ensuring the workplace is safe; and maintaining positive relationships. It was concluded that while greenhouse gas intensity has dropped, overall emissions have increase. Surface water use has also slightly increased. figs
Cheaib, Alissar; Badeau, Vincent; Boe, Julien; Chuine, Isabelle; Delire, Christine; Dufrêne, Eric; François, Christophe; Gritti, Emmanuel S; Legay, Myriam; Pagé, Christian; Thuiller, Wilfried; Viovy, Nicolas; Leadley, Paul
2012-06-01
Model-based projections of shifts in tree species range due to climate change are becoming an important decision support tool for forest management. However, poorly evaluated sources of uncertainty require more scrutiny before relying heavily on models for decision-making. We evaluated uncertainty arising from differences in model formulations of tree response to climate change based on a rigorous intercomparison of projections of tree distributions in France. We compared eight models ranging from niche-based to process-based models. On average, models project large range contractions of temperate tree species in lowlands due to climate change. There was substantial disagreement between models for temperate broadleaf deciduous tree species, but differences in the capacity of models to account for rising CO(2) impacts explained much of the disagreement. There was good quantitative agreement among models concerning the range contractions for Scots pine. For the dominant Mediterranean tree species, Holm oak, all models foresee substantial range expansion. © 2012 Blackwell Publishing Ltd/CNRS.
Phenomena-based Uncertainty Quantification in Predictive Coupled- Physics Reactor Simulations
Energy Technology Data Exchange (ETDEWEB)
Adams, Marvin [Texas A & M Univ., College Station, TX (United States)
2017-06-12
This project has sought to develop methodologies, tailored to phenomena that govern nuclearreactor behavior, to produce predictions (including uncertainties) for quantities of interest (QOIs) in the simulation of steady-state and transient reactor behavior. Examples of such predictions include, for each QOI, an expected value as well as a distribution around this value and an assessment of how much of the distribution stems from each major source of uncertainty. The project has sought to test its methodologies by comparing against measured experimental outcomes. The main experimental platform has been a 1-MW TRIGA reactor. This is a flexible platform for a wide range of experiments, including steady state with and without temperature feedback, slow transients with and without feedback, and rapid transients with strong feedback. The original plan was for the primary experimental data to come from in-core neutron detectors. We made considerable progress toward this goal but did not get as far along as we had planned. We have designed, developed, installed, and tested vertical guide tubes, each able to accept a detector or stack of detectors that can be moved axially inside the tube, and we have tested several new detector designs. One of these shows considerable promise.
Phenomena-based Uncertainty Quantification in Predictive Coupled- Physics Reactor Simulations
International Nuclear Information System (INIS)
Adams, Marvin
2017-01-01
This project has sought to develop methodologies, tailored to phenomena that govern nuclearreactor behavior, to produce predictions (including uncertainties) for quantities of interest (QOIs) in the simulation of steady-state and transient reactor behavior. Examples of such predictions include, for each QOI, an expected value as well as a distribution around this value and an assessment of how much of the distribution stems from each major source of uncertainty. The project has sought to test its methodologies by comparing against measured experimental outcomes. The main experimental platform has been a 1-MW TRIGA reactor. This is a flexible platform for a wide range of experiments, including steady state with and without temperature feedback, slow transients with and without feedback, and rapid transients with strong feedback. The original plan was for the primary experimental data to come from in-core neutron detectors. We made considerable progress toward this goal but did not get as far along as we had planned. We have designed, developed, installed, and tested vertical guide tubes, each able to accept a detector or stack of detectors that can be moved axially inside the tube, and we have tested several new detector designs. One of these shows considerable promise.
International Nuclear Information System (INIS)
Gilli, L.; Lathouwers, D.; Kloosterman, J.L.; Hagen, T.H.J.J. van der; Koning, A.J.; Rochman, D.
2013-01-01
Highlights: ► Non-intrusive spectral techniques are applied to perform UQ of criticality problems. ► A new adaptive algorithm based on the definition of sparse grid is derived. ► The method is applied to two reference criticality problems. - Abstract: In this paper we present the implementation and the application of non-intrusive spectral techniques for uncertainty analysis of criticality problems. Spectral techniques can be used to reconstruct stochastic quantities of interest by means of a Fourier-like expansion. Their application to uncertainty propagation problems can be performed in a non-intrusive fashion by evaluating a set of projection integrals that are used to reconstruct the spectral expansion. This can be done either by using standard Monte Carlo integration approaches or by adopting numerical quadrature rules. We present the derivation of a new adaptive quadrature algorithm, based on the definition of a sparse grid, which can be used to reduce the computational cost associated with non-intrusive spectral techniques. This new adaptive algorithm and the Monte Carlo integration alternative are then applied to two reference problems. First, a stochastic multigroup diffusion problem is introduced by considering the microscopic cross-sections of the system to be random quantities. Then a criticality benchmark is defined for which a set of resonance parameters in the resolved region are assumed to be stochastic
Uncertainty Quantification of Calculated Temperatures for the U.S. Capsules in the AGR-2 Experiment
Energy Technology Data Exchange (ETDEWEB)
Lybeck, Nancy [Idaho National Lab. (INL), Idaho Falls, ID (United States); Einerson, Jeffrey J. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Pham, Binh T. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Hawkes, Grant L. [Idaho National Lab. (INL), Idaho Falls, ID (United States)
2015-03-01
A series of Advanced Gas Reactor (AGR) irradiation experiments are being conducted within the Advanced Reactor Technology (ART) Fuel Development and Qualification Program. The main objectives of the fuel experimental campaign are to provide the necessary data on fuel performance to support fuel process development, qualify a fuel design and fabrication process for normal operation and accident conditions, and support development and validation of fuel performance and fission product transport models and codes (PLN-3636). The AGR-2 test was inserted in the B-12 position in the Advanced Test Reactor (ATR) core at Idaho National Laboratory (INL) in June 2010 and successfully completed irradiation in October 2013, resulting in irradiation of the TRISO fuel for 559.2 effective full power days (EFPDs) during approximately 3.3 calendar years. The AGR-2 data, including the irradiation data and calculated results, were qualified and stored in the Nuclear Data Management and Analysis System (NDMAS) (Pham and Einerson 2014). To support the U.S. TRISO fuel performance assessment and to provide data for validation of fuel performance and fission product transport models and codes, the daily as-run thermal analysis has been performed separately on each of four AGR-2 U.S. capsules for the entire irradiation as discussed in (Hawkes 2014). The ABAQUS code’s finite element-based thermal model predicts the daily average volume-average fuel temperature and peak fuel temperature in each capsule. This thermal model involves complex physical mechanisms (e.g., graphite holder and fuel compact shrinkage) and properties (e.g., conductivity and density). Therefore, the thermal model predictions are affected by uncertainty in input parameters and by incomplete knowledge of the underlying physics leading to modeling assumptions. Therefore, alongside with the deterministic predictions from a set of input thermal conditions, information about prediction uncertainty is instrumental for the ART
Experimental data bases useful for quantification of model uncertainties in best estimate codes
International Nuclear Information System (INIS)
Wilson, G.E.; Katsma, K.R.; Jacobson, J.L.; Boodry, K.S.
1988-01-01
A data base is necessary for assessment of thermal hydraulic codes within the context of the new NRC ECCS Rule. Separate effect tests examine particular phenomena that may be used to develop and/or verify models and constitutive relationships in the code. Integral tests are used to demonstrate the capability of codes to model global characteristics and sequence of events for real or hypothetical transients. The nuclear industry has developed a large experimental data base of fundamental nuclear, thermal-hydraulic phenomena for code validation. Given a particular scenario, and recognizing the scenario's important phenomena, selected information from this data base may be used to demonstrate applicability of a particular code to simulate the scenario and to determine code model uncertainties. LBLOCA experimental data bases useful to this objective are identified in this paper. 2 tabs
Quantification of parameter uncertainty for robust control of shape memory alloy bending actuators
International Nuclear Information System (INIS)
Crews, John H; McMahan, Jerry A; Smith, Ralph C; Hannen, Jennifer C
2013-01-01
In this paper, we employ Bayesian parameter estimation techniques to derive gains for robust control of smart materials. Specifically, we demonstrate the feasibility of utilizing parameter uncertainty estimation provided by Markov chain Monte Carlo (MCMC) methods to determine controller gains for a shape memory alloy bending actuator. We treat the parameters in the equations governing the actuator’s temperature dynamics as uncertain and use the MCMC method to construct the probability densities for these parameters. The densities are then used to derive parameter bounds for robust control algorithms. For illustrative purposes, we construct a sliding mode controller based on the homogenized energy model and experimentally compare its performance to a proportional-integral controller. While sliding mode control is used here, the techniques described in this paper provide a useful starting point for many robust control algorithms. (paper)
DEFF Research Database (Denmark)
Tegeltija, Miroslava; Oehmen, Josef; Kozin, Igor
2016-01-01
made by PD managers and engineers have a significant impact on the strategic value of the asset delivered, and these decisions depend on the quality of information on which they are based [Ewejeet al. 2012]. Uncertainty plays an important role in decision making. Decision making quality improves...... that addresses the different ways in which factual statements may be interpreted by different individuals [Klinke and Renn 2002].[Flyvbjerg 2007] observed that the main challenges of large projects, including PD projects, are inadequate, unreliable or misleading information; and conflicts between decision making......, policy and planning. It has been proven by empirical studies (e.g. [Levi 1990], [Sahlin 2012]) that the amount and quality of information behind probabilities and utilities is an important factor when making decisions, in other words, people tend to make different decisions if they are aware...
Uncertainty Quantification and Bifurcation Analysis of an Airfoil with Multiple Nonlinearities
Directory of Open Access Journals (Sweden)
Haitao Liao
2013-01-01
Full Text Available In order to calculate the limit cycle oscillations and bifurcations of nonlinear aeroelastic system, the problem of finding periodic solutions with maximum vibration amplitude is transformed into a nonlinear optimization problem. An algebraic system of equations obtained by the harmonic balance method and the stability condition derived from the Floquet theory are used to construct the general nonlinear equality and inequality constraints. The resulting constrained maximization problem is then solved by using the MultiStart algorithm. Finally, the proposed approach is validated, and the effects of structural parameter uncertainty on the limit cycle oscillations and bifurcations of an airfoil with multiple nonlinearities are studied. Numerical examples show that the coexistence of multiple nonlinearities may lead to low amplitude limit cycle oscillation.
Kassem, Hussein A.; Chehab, Ghassan R.; Najjar, Shadi S.
2017-08-01
Advanced material characterization of asphalt concrete is essential for realistic and accurate performance prediction of flexible pavements. However, such characterization requires rigorous testing regimes that involve mechanical testing of a large number of laboratory samples at various conditions and set-ups. Advanced measurement instrumentation in addition to meticulous and accurate data analysis and analytical representation are also of high importance. Such steps as well as the heterogeneous nature of asphalt concrete (AC) constitute major factors of inherent variability. Thus, it is imperative to model and quantify the variability of the needed asphalt material's properties, mainly the linear viscoelastic response functions such as: relaxation modulus, E(t), and creep compliance, D(t). The objective of this paper is to characterize the inherent uncertainty of both E(t) and D(t) over the time domain of their master curves. This is achieved through a probabilistic framework using Monte Carlo simulations and First Order approximations, utilizing E^{*} data for six AC mixes with at least eight replicates per mix. The study shows that the inherent variability, presented by the coefficient of variation (COV), in E(t) and D(t) is low at small reduced times, and increases with the increase in reduced time. At small reduced times, the COV in E(t) and D(t) are similar in magnitude; however, differences become significant at large reduced times. Additionally, the probability distributions and COVs of E(t) and D(t) are mix dependent. Finally, a case study is considered in which the inherent uncertainty in D(t) is forward propagated to assess the effect of variability on the predicted number of cycles to fatigue failure of an asphalt mix.
Energy Technology Data Exchange (ETDEWEB)
Aryal, M; Cao, Y [The University of Michigan, Ann Arbor, MI (United States)
2015-06-15
Purpose: Quantification of dynamic contrast enhanced (DCE) MRI requires native longitudinal relaxation time (T1) measurement. This study aimed to assess uncertainty in T1 measurements using two different methods. Methods and Materials: Brain MRI scans were performed on a 3T scanner in 9 patients who had low grade/benign tumors and partial brain radiotherapy without chemotherapy at pre-RT, week-3 during RT (wk-3), end-RT, and 1, 6 and 18 months after RT. T1-weighted images were acquired using gradient echo sequences with 1) 2 different flip angles (50 and 150), and 2) 5 variable TRs (100–2000ms). After creating quantitative T1 maps, average T1 was calculated in regions of interest (ROI), which were distant from tumors and received a total of accumulated radiation doses < 5 Gy at wk-3. ROIs included left and right normal Putamen and Thalamus (gray matter: GM), and frontal and parietal white matter (WM). Since there were no significant or even a trend of T1 changes from pre-RT to wk-3 in these ROIs, a relative repeatability coefficient (RC) of T1 as a measure of uncertainty was estimated in each ROI using the data pre-RT and at wk-3. The individual T1 changes at later time points were evaluated compared to the estimated RCs. Results: The 2-flip angle method produced small RCs in GM (9.7–11.7%) but large RCs in WM (12.2–13.6%) compared to the saturation-recovery (SR) method (11.0–17.7% for GM and 7.5–11.2% for WM). More than 81% of individual T1 changes were within T1 uncertainty ranges defined by RCs. Conclusion: Our study suggests that the impact of T1 uncertainty on physiological parameters derived from DCE MRI is not negligible. A short scan with 2 flip angles is able to achieve repeatability of T1 estimates similar to a long scan with 5 different TRs, and is desirable to be integrated in the DCE protocol. Present study was supported by National Institute of Health (NIH) under grant numbers; UO1 CA183848 and RO1 NS064973.
Bayesian Uncertainty Quantification for Subsurface Inversion Using a Multiscale Hierarchical Model
Mondal, Anirban
2014-07-03
We consider a Bayesian approach to nonlinear inverse problems in which the unknown quantity is a random field (spatial or temporal). The Bayesian approach contains a natural mechanism for regularization in the form of prior information, can incorporate information from heterogeneous sources and provide a quantitative assessment of uncertainty in the inverse solution. The Bayesian setting casts the inverse solution as a posterior probability distribution over the model parameters. The Karhunen-Loeve expansion is used for dimension reduction of the random field. Furthermore, we use a hierarchical Bayes model to inject multiscale data in the modeling framework. In this Bayesian framework, we show that this inverse problem is well-posed by proving that the posterior measure is Lipschitz continuous with respect to the data in total variation norm. Computational challenges in this construction arise from the need for repeated evaluations of the forward model (e.g., in the context of MCMC) and are compounded by high dimensionality of the posterior. We develop two-stage reversible jump MCMC that has the ability to screen the bad proposals in the first inexpensive stage. Numerical results are presented by analyzing simulated as well as real data from hydrocarbon reservoir. This article has supplementary material available online. © 2014 American Statistical Association and the American Society for Quality.
Low energy peripheral scaling in nucleon-nucleon scattering and uncertainty quantification
Ruiz Simo, I.; Amaro, J. E.; Ruiz Arriola, E.; Navarro Pérez, R.
2018-03-01
We analyze the peripheral structure of the nucleon-nucleon interaction for LAB energies below 350 MeV. To this end we transform the scattering matrix into the impact parameter representation by analyzing the scaled phase shifts (L + 1/2) δ JLS (p) and the scaled mixing parameters (L + 1/2)ɛ JLS (p) in terms of the impact parameter b = (L + 1/2)/p. According to the eikonal approximation, at large angular momentum L these functions should become an universal function of b, independent on L. This allows to discuss in a rather transparent way the role of statistical and systematic uncertainties in the different long range components of the two-body potential. Implications for peripheral waves obtained in chiral perturbation theory interactions to fifth order (N5LO) or from the large body of NN data considered in the SAID partial wave analysis are also drawn from comparing them with other phenomenological high-quality interactions, constructed to fit scattering data as well. We find that both N5LO and SAID peripheral waves disagree more than 5σ with the Granada-2013 statistical analysis, more than 2σ with the 6 statistically equivalent potentials fitting the Granada-2013 database and about 1σ with the historical set of 13 high-quality potentials developed since the 1993 Nijmegen analysis.
Katterbauer, Klemens
2015-04-01
Reservoir simulations and history matching are critical for fine-tuning reservoir production strategies, improving understanding of the subsurface formation, and forecasting remaining reserves. Production data have long been incorporated for adjusting reservoir parameters. However, the sparse spatial sampling of this data set has posed a significant challenge for efficiently reducing uncertainty of reservoir parameters. Seismic, electromagnetic, gravity and InSAR techniques have found widespread applications in enhancing exploration for oil and gas and monitoring reservoirs. These data have however been interpreted and analyzed mostly separately, rarely exploiting the synergy effects that could result from combining them. We present a multi-data ensemble Kalman filter-based history matching framework for the simultaneous incorporation of various reservoir data such as seismic, electromagnetics, gravimetry and InSAR for best possible characterization of the reservoir formation. We apply an ensemble-based sensitivity method to evaluate the impact of each observation on the estimated reservoir parameters. Numerical experiments for different test cases demonstrate considerable matching enhancements when integrating all data sets in the history matching process. Results from the sensitivity analysis further suggest that electromagnetic data exhibit the strongest impact on the matching enhancements due to their strong differentiation between water fronts and hydrocarbons in the test cases.
DEFF Research Database (Denmark)
Marhadi, Kun Saptohartyadi; Skrimpas, Georgios Alexandros
2015-01-01
Setting optimal alarm thresholds in vibration based condition monitoring system is inherently difficult. There are no established thresholds for many vibration based measurements. Most of the time, the thresholds are set based on statistics of the collected data available. Often times the underly......Setting optimal alarm thresholds in vibration based condition monitoring system is inherently difficult. There are no established thresholds for many vibration based measurements. Most of the time, the thresholds are set based on statistics of the collected data available. Often times...... the underlying probability distribution that describes the data is not known. Choosing an incorrect distribution to describe the data and then setting up thresholds based on the chosen distribution could result in sub-optimal thresholds. Moreover, in wind turbine applications the collected data available may...... not represent the whole operating conditions of a turbine, which results in uncertainty in the parameters of the fitted probability distribution and the thresholds calculated. In this study, Johnson, Normal, and Weibull distributions are investigated; which distribution can best fit vibration data collected...
Uncertainty quantification-based robust aerodynamic optimization of laminar flow nacelle
Xiong, Neng; Tao, Yang; Liu, Zhiyong; Lin, Jun
2018-05-01
The aerodynamic performance of laminar flow nacelle is highly sensitive to uncertain working conditions, especially the surface roughness. An efficient robust aerodynamic optimization method on the basis of non-deterministic computational fluid dynamic (CFD) simulation and Efficient Global Optimization (EGO)algorithm was employed. A non-intrusive polynomial chaos method is used in conjunction with an existing well-verified CFD module to quantify the uncertainty propagation in the flow field. This paper investigates the roughness modeling behavior with the γ-Ret shear stress transport model including modeling flow transition and surface roughness effects. The roughness effects are modeled to simulate sand grain roughness. A Class-Shape Transformation-based parametrical description of the nacelle contour as part of an automatic design evaluation process is presented. A Design-of-Experiments (DoE) was performed and surrogate model by Kriging method was built. The new design nacelle process demonstrates that significant improvements of both mean and variance of the efficiency are achieved and the proposed method can be applied to laminar flow nacelle design successfully.
Rigorous Statistical Bounds in Uncertainty Quantification for One-Layer Turbulent Geophysical Flows
Qi, Di; Majda, Andrew J.
2018-04-01
Statistical bounds controlling the total fluctuations in mean and variance about a basic steady-state solution are developed for the truncated barotropic flow over topography. Statistical ensemble prediction is an important topic in weather and climate research. Here, the evolution of an ensemble of trajectories is considered using statistical instability analysis and is compared and contrasted with the classical deterministic instability for the growth of perturbations in one pointwise trajectory. The maximum growth of the total statistics in fluctuations is derived relying on the statistical conservation principle of the pseudo-energy. The saturation bound of the statistical mean fluctuation and variance in the unstable regimes with non-positive-definite pseudo-energy is achieved by linking with a class of stable reference states and minimizing the stable statistical energy. Two cases with dependence on initial statistical uncertainty and on external forcing and dissipation are compared and unified under a consistent statistical stability framework. The flow structures and statistical stability bounds are illustrated and verified by numerical simulations among a wide range of dynamical regimes, where subtle transient statistical instability exists in general with positive short-time exponential growth in the covariance even when the pseudo-energy is positive-definite. Among the various scenarios in this paper, there exist strong forward and backward energy exchanges between different scales which are estimated by the rigorous statistical bounds.
Dettmer, J.; Benavente, R. F.; Cummins, P. R.
2017-12-01
This work considers probabilistic, non-linear centroid moment tensor inversion of data from earthquakes at teleseismic distances. The moment tensor is treated as deviatoric and centroid location is parametrized with fully unknown latitude, longitude, depth and time delay. The inverse problem is treated as fully non-linear in a Bayesian framework and the posterior density is estimated with interacting Markov chain Monte Carlo methods which are implemented in parallel and allow for chain interaction. The source mechanism and location, including uncertainties, are fully described by the posterior probability density and complex trade-offs between various metrics are studied. These include the percent of double couple component as well as fault orientation and the probabilistic results are compared to results from earthquake catalogs. Additional focus is on the analysis of complex events which are commonly not well described by a single point source. These events are studied by jointly inverting for multiple centroid moment tensor solutions. The optimal number of sources is estimated by the Bayesian information criterion to ensure parsimonious solutions. [Supported by NSERC.
Energy Technology Data Exchange (ETDEWEB)
Rider, William J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Witkowski, Walter R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Mousseau, Vincent Andrew [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2016-04-13
The importance of credible, trustworthy numerical simulations is obvious especially when using the results for making high-consequence decisions. Determining the credibility of such numerical predictions is much more difficult and requires a systematic approach to assessing predictive capability, associated uncertainties and overall confidence in the computational simulation process for the intended use of the model. This process begins with an evaluation of the computational modeling of the identified, important physics of the simulation for its intended use. This is commonly done through a Phenomena Identification Ranking Table (PIRT). Then an assessment of the evidence basis supporting the ability to computationally simulate these physics can be performed using various frameworks such as the Predictive Capability Maturity Model (PCMM). There were several critical activities that follow in the areas of code and solution verification, validation and uncertainty quantification, which will be described in detail in the following sections. Here, we introduce the subject matter for general applications but specifics are given for the failure prediction project. In addition, the first task that must be completed in the verification & validation procedure is to perform a credibility assessment to fully understand the requirements and limitations of the current computational simulation capability for the specific application intended use. The PIRT and PCMM are tools used at Sandia National Laboratories (SNL) to provide a consistent manner to perform such an assessment. Ideally, all stakeholders should be represented and contribute to perform an accurate credibility assessment. PIRTs and PCMMs are both described in brief detail below and the resulting assessments for an example project are given.
Calvetti, Daniela; Cheng, Yougan; Somersalo, Erkki
2016-12-01
Identifying feasible steady state solutions of a brain energy metabolism model is an inverse problem that allows infinitely many solutions. The characterization of the non-uniqueness, or the uncertainty quantification of the flux balance analysis, is tantamount to identifying the degrees of freedom of the solution. The degrees of freedom of multi-compartment mathematical models for energy metabolism of a neuron-astrocyte complex may offer a key to understand the different ways in which the energetic needs of the brain are met. In this paper we study the uncertainty in the solution, using techniques of linear algebra to identify the degrees of freedom in a lumped model, and Markov chain Monte Carlo methods in its extension to a spatially distributed case. The interpretation of the degrees of freedom in metabolic terms, more specifically, glucose and oxygen partitioning, is then leveraged to derive constraints on the free parameters to guarantee that the model is energetically feasible. We demonstrate how the model can be used to estimate the stoichiometric energy needs of the cells as well as the household energy based on the measured oxidative cerebral metabolic rate of glucose and glutamate cycling. Moreover, our analysis shows that in the lumped model the net direction of lactate dehydrogenase (LDH) in the cells can be deduced from the glucose partitioning between the compartments. The extension of the lumped model to a spatially distributed multi-compartment setting that includes diffusion fluxes from capillary to tissue increases the number of degrees of freedom, requiring the use of statistical sampling techniques. The analysis of the distributed model reveals that some of the conclusions valid for the spatially lumped model, e.g., concerning the LDH activity and glucose partitioning, may no longer hold.
Regional uncertainty of GOSAT XCO2 retrievals in China: quantification and attribution
Directory of Open Access Journals (Sweden)
N. Bie
2018-03-01
Full Text Available The regional uncertainty of the column-averaged dry air mole fraction of CO2 (XCO2 retrieved using different algorithms from the Greenhouse gases Observing SATellite (GOSAT and its attribution are still not well understood. This paper investigates the regional performance of XCO2 within a latitude band of 37–42° N segmented into 8 cells in a grid of 5° from west to east (80–120° E in China, where typical land surface types and geographic conditions exist. The former includes desert, grassland and built-up areas mixed with cropland; and the latter includes anthropogenic emissions that change from small to large from west to east, including those from the megacity of Beijing. For these specific cells, we evaluate the regional uncertainty of GOSAT XCO2 retrievals by quantifying and attributing the consistency of XCO2 retrievals from four algorithms (ACOS, NIES, OCFP and SRFP by intercomparison. These retrievals are then specifically compared with simulated XCO2 from the high-resolution nested model in East Asia of the Goddard Earth Observing System 3-D chemical transport model (GEOS-Chem. We also introduce the anthropogenic CO2 emissions data generated from the investigation of surface emitting point sources that was conducted by the Ministry of Environmental Protection of China to GEOS-Chem simulations of XCO2 over the Chinese mainland. The results indicate that (1 regionally, the four algorithms demonstrate smaller absolute biases of 0.7–1.1 ppm in eastern cells, which are covered by built-up areas mixed with cropland with intensive anthropogenic emissions, than those in the western desert cells (1.0–1.6 ppm with a high-brightness surface from the pairwise comparison results of XCO2 retrievals. (2 Compared with XCO2 simulated by GEOS-Chem (GEOS-XCO2, the XCO2 values from ACOS and SRFP have better agreement, while values from OCFP are the least consistent with GEOS-XCO2. (3 Viewing attributions of XCO2 in the spatio
Validation and quantification of uncertainty in coupled climate models using network analysis
Energy Technology Data Exchange (ETDEWEB)
Bracco, Annalisa [Georgia Inst. of Technology, Atlanta, GA (United States)
2015-08-10
We developed a fast, robust and scalable methodology to examine, quantify, and visualize climate patterns and their relationships. It is based on a set of notions, algorithms and metrics used in the study of graphs, referred to as complex network analysis. This approach can be applied to explain known climate phenomena in terms of an underlying network structure and to uncover regional and global linkages in the climate system, while comparing general circulation models outputs with observations. The proposed method is based on a two-layer network representation, and is substantially new within the available network methodologies developed for climate studies. At the first layer, gridded climate data are used to identify ‘‘areas’’, i.e., geographical regions that are highly homogeneous in terms of the given climate variable. At the second layer, the identified areas are interconnected with links of varying strength, forming a global climate network. The robustness of the method (i.e. the ability to separate between topological distinct fields, while identifying correctly similarities) has been extensively tested. It has been proved that it provides a reliable, fast framework for comparing and ranking the ability of climate models of reproducing observed climate patterns and their connectivity. We further developed the methodology to account for lags in the connectivity between climate patterns and refined our area identification algorithm to account for autocorrelation in the data. The new methodology based on complex network analysis has been applied to state-of-the-art climate model simulations that participated to the last IPCC (International Panel for Climate Change) assessment to verify their performances, quantify uncertainties, and uncover changes in global linkages between past and future projections. Network properties of modeled sea surface temperature and rainfall over 1956–2005 have been constrained towards observations or reanalysis data sets
Regional uncertainty of GOSAT XCO2 retrievals in China: quantification and attribution
Bie, Nian; Lei, Liping; Zeng, ZhaoCheng; Cai, Bofeng; Yang, Shaoyuan; He, Zhonghua; Wu, Changjiang; Nassar, Ray
2018-03-01
The regional uncertainty of the column-averaged dry air mole fraction of CO2 (XCO2) retrieved using different algorithms from the Greenhouse gases Observing SATellite (GOSAT) and its attribution are still not well understood. This paper investigates the regional performance of XCO2 within a latitude band of 37-42° N segmented into 8 cells in a grid of 5° from west to east (80-120° E) in China, where typical land surface types and geographic conditions exist. The former includes desert, grassland and built-up areas mixed with cropland; and the latter includes anthropogenic emissions that change from small to large from west to east, including those from the megacity of Beijing. For these specific cells, we evaluate the regional uncertainty of GOSAT XCO2 retrievals by quantifying and attributing the consistency of XCO2 retrievals from four algorithms (ACOS, NIES, OCFP and SRFP) by intercomparison. These retrievals are then specifically compared with simulated XCO2 from the high-resolution nested model in East Asia of the Goddard Earth Observing System 3-D chemical transport model (GEOS-Chem). We also introduce the anthropogenic CO2 emissions data generated from the investigation of surface emitting point sources that was conducted by the Ministry of Environmental Protection of China to GEOS-Chem simulations of XCO2 over the Chinese mainland. The results indicate that (1) regionally, the four algorithms demonstrate smaller absolute biases of 0.7-1.1 ppm in eastern cells, which are covered by built-up areas mixed with cropland with intensive anthropogenic emissions, than those in the western desert cells (1.0-1.6 ppm) with a high-brightness surface from the pairwise comparison results of XCO2 retrievals. (2) Compared with XCO2 simulated by GEOS-Chem (GEOS-XCO2), the XCO2 values from ACOS and SRFP have better agreement, while values from OCFP are the least consistent with GEOS-XCO2. (3) Viewing attributions of XCO2 in the spatio-temporal pattern, ACOS and SRFP
Qi, D.; Majda, A.
2017-12-01
A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with
Uncertainty quantification methodologies development for stress corrosion cracking of canister welds
Energy Technology Data Exchange (ETDEWEB)
Dingreville, Remi Philippe Michel [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bryan, Charles R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2016-09-30
This letter report presents a probabilistic performance assessment model to evaluate the probability of canister failure (through-wall penetration) by SCC. The model first assesses whether environmental conditions for SCC – the presence of an aqueous film – are present at canister weld locations (where tensile stresses are likely to occur) on the canister surface. Geometry-specific storage system thermal models and weather data sets representative of U.S. spent nuclear fuel (SNF) storage sites are implemented to evaluate location-specific canister surface temperature and relative humidity (RH). As the canister cools and aqueous conditions become possible, the occurrence of corrosion is evaluated. Corrosion is modeled as a two-step process: first, pitting is initiated, and the extent and depth of pitting is a function of the chloride surface load and the environmental conditions (temperature and RH). Second, as corrosion penetration increases, the pit eventually transitions to a SCC crack, with crack initiation becoming more likely with increasing pit depth. Once pits convert to cracks, a crack growth model is implemented. The SCC growth model includes rate dependencies on both temperature and crack tip stress intensity factor, and crack growth only occurs in time steps when aqueous conditions are predicted. The model suggests that SCC is likely to occur over potential SNF interim storage intervals; however, this result is based on many modeling assumptions. Sensitivity analyses provide information on the model assumptions and parameter values that have the greatest impact on predicted storage canister performance, and provide guidance for further research to reduce uncertainties.
Generalized Uncertainty Quantification for Linear Inverse Problems in X-ray Imaging
Energy Technology Data Exchange (ETDEWEB)
Fowler, Michael James [Clarkson Univ., Potsdam, NY (United States)
2014-04-25
In industrial and engineering applications, X-ray radiography has attained wide use as a data collection protocol for the assessment of material properties in cases where direct observation is not possible. The direct measurement of nuclear materials, particularly when they are under explosive or implosive loading, is not feasible, and radiography can serve as a useful tool for obtaining indirect measurements. In such experiments, high energy X-rays are pulsed through a scene containing material of interest, and a detector records a radiograph by measuring the radiation that is not attenuated in the scene. One approach to the analysis of these radiographs is to model the imaging system as an operator that acts upon the object being imaged to produce a radiograph. In this model, the goal is to solve an inverse problem to reconstruct the values of interest in the object, which are typically material properties such as density or areal density. The primary objective in this work is to provide quantitative solutions with uncertainty estimates for three separate applications in X-ray radiography: deconvolution, Abel inversion, and radiation spot shape reconstruction. For each problem, we introduce a new hierarchical Bayesian model for determining a posterior distribution on the unknowns and develop efficient Markov chain Monte Carlo (MCMC) methods for sampling from the posterior. A Poisson likelihood, based on a noise model for photon counts at the detector, is combined with a prior tailored to each application: an edge-localizing prior for deconvolution; a smoothing prior with non-negativity constraints for spot reconstruction; and a full covariance sampling prior based on a Wishart hyperprior for Abel inversion. After developing our methods in a general setting, we demonstrate each model on both synthetically generated datasets, including those from a well known radiation transport code, and real high energy radiographs taken at two U. S. Department of Energy
Energy Technology Data Exchange (ETDEWEB)
Schunert, Sebastian; Wang, Congjian; Wang, Yaqi; Kong, Fande; Ortensi, Javier; Baker, Benjamin; Gleicher, Frederick; DeHart, Mark; Martineau, Richard
2017-04-01
Rattlesnake and MAMMOTH are the designated TREAT analysis tools currently being developed at the Idaho National Laboratory. Concurrent with development of the multi-physics, multi-scale capabilities, sensitivity analysis and uncertainty quantification (SA/UQ) capabilities are required for predicitive modeling of the TREAT reactor. For steady-state SA/UQ, that is essential for setting initial conditions for the transients, generalized perturbation theory (GPT) will be used. This work describes the implementation of a PETSc based solver for the generalized adjoint equations that constitute a inhomogeneous, rank deficient problem. The standard approach is to use an outer iteration strategy with repeated removal of the fundamental mode contamination. The described GPT algorithm directly solves the GPT equations without the need of an outer iteration procedure by using Krylov subspaces that are orthogonal to the operator’s nullspace. Three test problems are solved and provide sufficient verification for the Rattlesnake’s GPT capability. We conclude with a preliminary example evaluating the impact of the Boron distribution in the TREAT reactor using perturbation theory.
International Nuclear Information System (INIS)
Sugiyama, Daisuke; Chida, Taiji; Fujita, Tomonari; Tsukamoto, Masaki
2011-01-01
A quantification methodology of epistemic uncertainty by expert judgement based on the risk-informed approach is developed to assess inevitable uncertainty for the long-term safety assessment of radioactive waste disposal. The proposed method in this study employs techniques of logic tree, by which options of models and/or scenarios are identified, and Evidential Support Logic (ESL), by which possibility of each option is quantified. In this report, the effect of a feedback process of discussion between experts and input of state-of-the-art knowledge in the proposed method is discussed to estimate alteration of the distribution of expert judgements which is one of the factors causing uncertainty. In a preliminary quantification experiment of uncertainty of degradation of the engineering barrier materials in a tentative sub-surface disposal using the proposed methodology, experts themselves modified questions appropriately to facilitate sound judgements and to correlate those with scientific evidences clearly. The result suggests that the method effectively improves confidence of expert judgement. Also, the degree of consensus of expert judgement was sort of improved in some cases, since scientific knowledge and information of expert judgement in other fields became common understanding. It is suggested that the proposed method could facilitate consensus on uncertainty between interested persons. (author)
Arnst, M.; Abello Álvarez, B.; Ponthot, J.-P.; Boman, R.
2017-11-01
This paper is concerned with the characterization and the propagation of errors associated with data limitations in polynomial-chaos-based stochastic methods for uncertainty quantification. Such an issue can arise in uncertainty quantification when only a limited amount of data is available. When the available information does not suffice to accurately determine the probability distributions that must be assigned to the uncertain variables, the Bayesian method for assigning these probability distributions becomes attractive because it allows the stochastic model to account explicitly for insufficiency of the available information. In previous work, such applications of the Bayesian method had already been implemented by using the Metropolis-Hastings and Gibbs Markov Chain Monte Carlo (MCMC) methods. In this paper, we present an alternative implementation, which uses an alternative MCMC method built around an Itô stochastic differential equation (SDE) that is ergodic for the Bayesian posterior. We draw together from the mathematics literature a number of formal properties of this Itô SDE that lend support to its use in the implementation of the Bayesian method, and we describe its discretization, including the choice of the free parameters, by using the implicit Euler method. We demonstrate the proposed methodology on a problem of uncertainty quantification in a complex nonlinear engineering application relevant to metal forming.
Energy Technology Data Exchange (ETDEWEB)
Shahnam, Mehrdad [National Energy Technology Lab. (NETL), Morgantown, WV (United States). Research and Innovation Center, Energy Conversion Engineering Directorate; Gel, Aytekin [ALPEMI Consulting, LLC, Phoeniz, AZ (United States); Subramaniyan, Arun K. [GE Global Research Center, Niskayuna, NY (United States); Musser, Jordan [National Energy Technology Lab. (NETL), Morgantown, WV (United States). Research and Innovation Center, Energy Conversion Engineering Directorate; Dietiker, Jean-Francois [West Virginia Univ. Research Corporation, Morgantown, WV (United States)
2017-10-02
Adequate assessment of the uncertainties in modeling and simulation is becoming an integral part of the simulation based engineering design. The goal of this study is to demonstrate the application of non-intrusive Bayesian uncertainty quantification (UQ) methodology in multiphase (gas-solid) flows with experimental and simulation data, as part of our research efforts to determine the most suited approach for UQ of a bench scale fluidized bed gasifier. UQ analysis was first performed on the available experimental data. Global sensitivity analysis performed as part of the UQ analysis shows that among the three operating factors, steam to oxygen ratio has the most influence on syngas composition in the bench-scale gasifier experiments. An analysis for forward propagation of uncertainties was performed and results show that an increase in steam to oxygen ratio leads to an increase in H2 mole fraction and a decrease in CO mole fraction. These findings are in agreement with the ANOVA analysis performed in the reference experimental study. Another contribution in addition to the UQ analysis is the optimization-based approach to guide to identify next best set of additional experimental samples, should the possibility arise for additional experiments. Hence, the surrogate models constructed as part of the UQ analysis is employed to improve the information gain and make incremental recommendation, should the possibility to add more experiments arise. In the second step, series of simulations were carried out with the open-source computational fluid dynamics software MFiX to reproduce the experimental conditions, where three operating factors, i.e., coal flow rate, coal particle diameter, and steam-to-oxygen ratio, were systematically varied to understand their effect on the syngas composition. Bayesian UQ analysis was performed on the numerical results. As part of Bayesian UQ analysis, a global sensitivity analysis was performed based on the simulation results, which shows
Fahrenholtz, S; Fuentes, D; Stafford, R; Hazle, J
2012-06-01
Magnetic resonance-guided laser induced thermal therapy (MRgLITT) is a minimally invasive thermal treatment for metastatic brain lesions, offering an alternative to conventional surgery. The purpose of this investigation is to incorporate uncertainty quantification (UQ) into the biothermal parameters used in the Pennes bioheat transfer equation (BHT), in order to account for imprecise values available in the literature. The BHT is a partial differential equation commonly used in thermal therapy models. MRgLITT was performed on an in vivo canine brain in a previous investigation. The canine MRgLITT was modeled using the BHT. The BHT has four parameters'" microperfusion, conductivity, optical absorption, and optical scattering'"which lack precise measurements in living brain and tumor. The uncertainties in the parameters were expressed as probability distribution functions derived from literature values. A univariate generalized polynomial chaos (gPC) expansion was applied to the stochastic BHT. The gPC approach to UQ provides a novel methodology to calculate spatio-temporal voxel-wise means and variances of the predicted temperature distributions. The performance of the gPC predictions were evaluated retrospectively by comparison with MR thermal imaging (MRTI) acquired during the MRgLITT procedure in the canine model. The comparison was evaluated with root mean square difference (RMSD), isotherm contours, spatial profiles, and z-tests. The peak RMSD was ∼1.5 standard deviations for microperfusion, conductivity, and optical absorption, while optical scattering was ∼2.2 standard deviations. Isotherm contours and spatial profiles of the simulation's predicted mean plus or minus two standard deviations demonstrate the MRTI temperature was enclosed by the model's isotherm confidence interval predictions. An a = 0.01 z-test demonstrates agreement. The application of gPC for UQ is a potentially powerful means for providing predictive simulations despite poorly known
Vesselinov, V. V.
2017-12-01
Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical species. Numerous geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. As a result, these types of model analyses are typically extremely challenging. Here, we demonstrate a new contaminant source identification approach that performs decomposition of the observation mixtures based on Nonnegative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. We also demonstrate how NMFk can be extended to perform uncertainty quantification and experimental design related to real-world site characterization. The NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios). The NMFk algorithm has been extensively tested on synthetic datasets; NMFk analyses have been actively performed on real-world data collected at the Los Alamos National
Uncertainty Quantification - an Overview
Litvinenko, Alexander
2018-03-01
1. Introduction to UQ 2. Low-rank tensors for representation of big/high-dimensional data 3. Inverse Problem via Bayesian Update 4. R-INLA and advance numerics for spatio-temporal statistics 5. High Performance Computing, parallel algorithms
Ecosystem stewardship: sustainability strategies for a rapidly changing planet
F. Stuart Chapin; Stephen R. Carpenter; Gary P. Kofinas; Carl Folke; Nick Abel; William C. Clark; Per Olsson; D. Mark Stafford Smith; Brian Walker; Oran R. Young; Fikret Berkes; Reinette Biggs; J. Morgan Grove; Rosamond L. Naylor; Evelyn Pinkerton; Will Steffen; Frederick J. Swanson
2010-01-01
Ecosystem stewardship is an action-oriented framework intended to foster the social-ecological sustainability of a rapidly changing planet. Recent developments identify three strategies that make optimal use of current understanding in an environment of inevitable uncertainty and abrupt change: reducing the magnitude of, and exposure and sensitivity to, known stresses...
International Nuclear Information System (INIS)
Brown, P.G.
1997-01-01
A trustee is someone who cares for a resource on behalf of another. In the case of climate, one generation cares for the climate and the myriad things climate effects on behalf of subsequent generations. This article offers reasons for accepting trusteeship as a framework for thinking about climate change; discusses what trustee duties are: considers their implications for the construction of an economics of stewardship; shows how tradeoffs would be assessed within this framework, and points towards a reconceptualization of international relations based on these ideas. 1 ref
Newell, P.; Yoon, H.; Martinez, M. J.; Bishop, J. E.; Arnold, B. W.; Bryant, S.
2013-12-01
It is essential to couple multiphase flow and geomechanical response in order to predict a consequence of geological storage of CO2. In this study, we estimate key hydrogeologic features to govern the geomechanical response (i.e., surface uplift) at a large-scale CO2 injection project at In Salah, Algeria using the Sierra Toolkit - a multi-physics simulation code developed at Sandia National Laboratories. Importantly, a jointed rock model is used to study the effect of postulated fractures in the injection zone on the surface uplift. The In Salah Gas Project includes an industrial-scale demonstration of CO2 storage in an active gas field where CO2 from natural gas production is being re-injected into a brine-filled portion of the structure downdip of the gas accumulation. The observed data include millimeter scale surface deformations (e.g., uplift) reported in the literature and injection well locations and rate histories provided by the operators. Our preliminary results show that the intrinsic permeability and Biot coefficient of the injection zone are important. Moreover pre-existing fractures within the injection zone affect the uplift significantly. Estimation of additional (i.e., anisotropy ratio) and coupled parameters will help us to develop models, which account for the complex relationship between mechanical integrity and CO2 injection-induced pressure changes. Uncertainty quantification of model predictions will be also performed using various algorithms including null-space Monte Carlo and polynomial-chaos expansion methods. This work will highlight that our coupled reservoir and geomechanical simulations associated with parameter estimation can provide a practical solution for designing operating conditions and understanding subsurface processes associated with the CO2 injection. This work is supported as part of the Center for Frontiers of Subsurface Energy Security, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office
Energy Technology Data Exchange (ETDEWEB)
Eldred, Michael Scott; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Gay, David M.; Eddy, John P.; Haskell, Karen H.
2010-05-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a reference manual for the commands specification for the DAKOTA software, providing input overviews, option descriptions, and example specifications.
Energy Technology Data Exchange (ETDEWEB)
Griffin, Joshua D. (Sandai National Labs, Livermore, CA); Eldred, Michael Scott; Martinez-Canales, Monica L. (Sandai National Labs, Livermore, CA); Watson, Jean-Paul; Kolda, Tamara Gibson (Sandai National Labs, Livermore, CA); Adams, Brian M.; Swiler, Laura Painton; Williams, Pamela J. (Sandai National Labs, Livermore, CA); Hough, Patricia Diane (Sandai National Labs, Livermore, CA); Gay, David M.; Dunlavy, Daniel M.; Eddy, John P.; Hart, William Eugene; Guinta, Anthony A.; Brown, Shannon L.
2006-10-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a reference manual for the commands specification for the DAKOTA software, providing input overviews, option descriptions, and example specifications.
Antimicrobial Stewardship in Daily Practice: Managing an Important Resource
Directory of Open Access Journals (Sweden)
Nicole Le Saux
2014-01-01
Full Text Available Antimicrobial stewardship is a recent concept that embodies the practical, judicious use of antimicrobials to decrease adverse outcomes from antimicrobials while optimizing the treatment of bacterial infections to reduce the emergence of resistant pathogens. The objectives of the present statement are to illustrate the principles of antimicrobial stewardship and to offer practical examples of how to make antimicrobial stewardship part of everyday hospital and outpatient practice. Vital components of antimicrobial stewardship include appropriate testing to diagnose whether infections are viral or bacterial, and using clinical follow-up rather than antibiotics in cases in which the child is not very ill and uncertainty exists. Other specific, important actions include questioning whether positive urine cultures are contaminated when there is no evidence of pyuria or inflammatory changes, and obtaining a chest radiograph to support a diagnosis of bacterial pneumonia. Optimizing the choice and dosage of antimicrobials also reduces the probability of clinical failures and subsequent courses of antimicrobials. A list of common clinical scenarios to promote stewardship is included.
Antimicrobial stewardship: Limits for implementation
Sinha, Bhanu
2014-01-01
Antibiotic stewardship programme (ASP) is a multifaceted approach to improve patients' clinical outcomes, prevent the emergence of antimicrobial resistance, and reduce hospital costs by prudent and focused antimicrobial use. Development of local treatment guidelines according to local ecology, rapid
Foster, L. K.; Clark, B. R.; Duncan, L. L.; Tebo, D. T.; White, J.
2017-12-01
Several historical groundwater models exist within the Coastal Lowlands Aquifer System (CLAS), which spans the Gulf Coastal Plain in Texas, Louisiana, Mississippi, Alabama, and Florida. The largest of these models, called the Gulf Coast Regional Aquifer System Analysis (RASA) model, has been brought into a new framework using the Newton formulation for MODFLOW-2005 (MODFLOW-NWT) and serves as the starting point of a new investigation underway by the U.S. Geological Survey to improve understanding of the CLAS and provide predictions of future groundwater availability within an uncertainty quantification (UQ) framework. The use of an UQ framework will not only provide estimates of water-level observation worth, hydraulic parameter uncertainty, boundary-condition uncertainty, and uncertainty of future potential predictions, but it will also guide the model development process. Traditionally, model development proceeds from dataset construction to the process of deterministic history matching, followed by deterministic predictions using the model. This investigation will combine the use of UQ with existing historical models of the study area to assess in a quantitative framework the effect model package and property improvements have on the ability to represent past-system states, as well as the effect on the model's ability to make certain predictions of water levels, water budgets, and base-flow estimates. Estimates of hydraulic property information and boundary conditions from the existing models and literature, forming the prior, will be used to make initial estimates of model forecasts and their corresponding uncertainty, along with an uncalibrated groundwater model run within an unconstrained Monte Carlo analysis. First-Order Second-Moment (FOSM) analysis will also be used to investigate parameter and predictive uncertainty, and guide next steps in model development prior to rigorous history matching by using PEST++ parameter estimation code.
Efendiev, Y.; Datta-Gupta, A.; Ma, X.; Mallick, B.
2009-01-01
the fine-scale simulations. Numerical results for three-phase flow and transport demonstrate the advantages, efficiency, and utility of the method for uncertainty assessment in the history matching. Copyright 2009 by the American Geophysical Union.
Ahmad, Zeeshan; Viswanathan, Venkatasubramanian
2016-08-01
Computationally-guided material discovery is being increasingly employed using a descriptor-based screening through the calculation of a few properties of interest. A precise understanding of the uncertainty associated with first-principles density functional theory calculated property values is important for the success of descriptor-based screening. The Bayesian error estimation approach has been built in to several recently developed exchange-correlation functionals, which allows an estimate of the uncertainty associated with properties related to the ground state energy, for example, adsorption energies. Here, we propose a robust and computationally efficient method for quantifying uncertainty in mechanical properties, which depend on the derivatives of the energy. The procedure involves calculating energies around the equilibrium cell volume with different strains and fitting the obtained energies to the corresponding energy-strain relationship. At each strain, we use instead of a single energy, an ensemble of energies, giving us an ensemble of fits and thereby, an ensemble of mechanical properties associated with each fit, whose spread can be used to quantify its uncertainty. The generation of ensemble of energies is only a post-processing step involving a perturbation of parameters of the exchange-correlation functional and solving for the energy non-self-consistently. The proposed method is computationally efficient and provides a more robust uncertainty estimate compared to the approach of self-consistent calculations employing several different exchange-correlation functionals. We demonstrate the method by calculating the uncertainty bounds for several materials belonging to different classes and having different structures using the developed method. We show that the calculated uncertainty bounds the property values obtained using three different GGA functionals: PBE, PBEsol, and RPBE. Finally, we apply the approach to calculate the uncertainty
International Nuclear Information System (INIS)
Ohnishi, S.; Thornton, B.; Kamada, S.; Hirao, Y.; Ura, T.; Odano, N.
2016-01-01
Factors to convert the count rate of a NaI(Tl) scintillation detector to the concentration of radioactive cesium in marine sediments are estimated for a towed gamma-ray detector system. The response of the detector against a unit concentration of radioactive cesium is calculated by Monte Carlo radiation transport simulation considering the vertical profile of radioactive material measured in core samples. The conversion factors are acquired by integrating the contribution of each layer and are normalized by the concentration in the surface sediment layer. At the same time, the uncertainty of the conversion factors are formulated and estimated. The combined standard uncertainty of the radioactive cesium concentration by the towed gamma-ray detector is around 25 percent. The values of uncertainty, often referred to as relative root mean squat errors in other works, between sediment core sampling measurements and towed detector measurements were 16 percent in the investigation made near the Abukuma River mouth and 5.2 percent in Sendai Bay, respectively. Most of the uncertainty is due to interpolation of the conversion factors between core samples and uncertainty of the detector's burial depth. The results of the towed measurements agree well with laboratory analysed sediment samples. Also, the concentrations of radioactive cesium at the intersection of each survey line are consistent. The consistency with sampling results and between different lines' transects demonstrate the availability and reproducibility of towed gamma-ray detector system.
Energy Technology Data Exchange (ETDEWEB)
Ohnishi, S., E-mail: ohnishi@nmri.go.jp [National Maritime Research Institute, 6-38-1, Shinkawa, Mitaka, Tokyo 181-0004 (Japan); Thornton, B. [Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8505 (Japan); Kamada, S.; Hirao, Y.; Ura, T.; Odano, N. [National Maritime Research Institute, 6-38-1, Shinkawa, Mitaka, Tokyo 181-0004 (Japan)
2016-05-21
Factors to convert the count rate of a NaI(Tl) scintillation detector to the concentration of radioactive cesium in marine sediments are estimated for a towed gamma-ray detector system. The response of the detector against a unit concentration of radioactive cesium is calculated by Monte Carlo radiation transport simulation considering the vertical profile of radioactive material measured in core samples. The conversion factors are acquired by integrating the contribution of each layer and are normalized by the concentration in the surface sediment layer. At the same time, the uncertainty of the conversion factors are formulated and estimated. The combined standard uncertainty of the radioactive cesium concentration by the towed gamma-ray detector is around 25 percent. The values of uncertainty, often referred to as relative root mean squat errors in other works, between sediment core sampling measurements and towed detector measurements were 16 percent in the investigation made near the Abukuma River mouth and 5.2 percent in Sendai Bay, respectively. Most of the uncertainty is due to interpolation of the conversion factors between core samples and uncertainty of the detector's burial depth. The results of the towed measurements agree well with laboratory analysed sediment samples. Also, the concentrations of radioactive cesium at the intersection of each survey line are consistent. The consistency with sampling results and between different lines' transects demonstrate the availability and reproducibility of towed gamma-ray detector system.
Antimicrobial stewardship in small animal veterinary practice
DEFF Research Database (Denmark)
Guardabassi, Luca; Prescott, John F
2015-01-01
Despite the increasing recognition of the critical role for antimicrobial stewardship in preventing the spread of multidrug-resistant bacteria, examples of effective antimicrobial stewardship programs are rare in small animal veterinary practice. This article highlights the basic requirements...
DeLannoy, Gabrielle J. M.; Reichle, Rolf H.; Vrugt, Jasper A.
2013-01-01
Uncertainties in L-band (1.4 GHz) radiative transfer modeling (RTM) affect the simulation of brightness temperatures (Tb) over land and the inversion of satellite-observed Tb into soil moisture retrievals. In particular, accurate estimates of the microwave soil roughness, vegetation opacity and scattering albedo for large-scale applications are difficult to obtain from field studies and often lack an uncertainty estimate. Here, a Markov Chain Monte Carlo (MCMC) simulation method is used to determine satellite-scale estimates of RTM parameters and their posterior uncertainty by minimizing the misfit between long-term averages and standard deviations of simulated and observed Tb at a range of incidence angles, at horizontal and vertical polarization, and for morning and evening overpasses. Tb simulations are generated with the Goddard Earth Observing System (GEOS-5) and confronted with Tb observations from the Soil Moisture Ocean Salinity (SMOS) mission. The MCMC algorithm suggests that the relative uncertainty of the RTM parameter estimates is typically less than 25 of the maximum a posteriori density (MAP) parameter value. Furthermore, the actual root-mean-square-differences in long-term Tb averages and standard deviations are found consistent with the respective estimated total simulation and observation error standard deviations of m3.1K and s2.4K. It is also shown that the MAP parameter values estimated through MCMC simulation are in close agreement with those obtained with Particle Swarm Optimization (PSO).
De Lannoy, G.J.M.; Reichle, R.H.; Vrugt, J.A.
2014-01-01
Uncertainties in L-band (1.4 GHz) microwave radiative transfer modeling (RTM) affect the simulation of brightness temperatures (Tb) over land and the inversion of satellite-observed Tb into soil moisture retrievals. In particular, accurate estimates of the microwave soil roughness, vegetation
Energy Technology Data Exchange (ETDEWEB)
Mezghani, M.
1999-02-11
The aim of this thesis is to propose a new inversion method to allow both an improved reservoir characterization and a management of uncertainties. In this approach, the identification of the permeability distribution is conducted using the sentinel method in order to match the pressure data. This approach, based on optimal control theory, can be seen as an alternative of least-squares method. Here, we prove the existence of exact sentinels under regularity hypothesis. From a numerical point of view, we consider regularized sentinels. We suggest a novel approach to update the penalization coefficient in order to improve numerical robustness. Moreover, the flexibility of the sentinel method enables to develop a way to treat noisy pressure data. To deal with geostatistical modelling of permeability distribution, we propose to link the pilot point method with sentinels to reach the identification of permeability. We particularly focus on the optimal location of pilot points. Finally, we present an original method, based on adjoint state computations, to quantify the dynamic data contribution to the characterisation of a calibrated geostatistical model. (author) 67 refs.
International Nuclear Information System (INIS)
Chilenski, M.A.; Greenwald, M.; Howard, N.T.; White, A.E.; Rice, J.E.; Walk, J.R.; Marzouk, Y.
2015-01-01
The need to fit smooth temperature and density profiles to discrete observations is ubiquitous in plasma physics, but the prevailing techniques for this have many shortcomings that cast doubt on the statistical validity of the results. This issue is amplified in the context of validation of gyrokinetic transport models (Holland et al 2009 Phys. Plasmas 16 052301), where the strong sensitivity of the code outputs to input gradients means that inadequacies in the profile fitting technique can easily lead to an incorrect assessment of the degree of agreement with experimental measurements. In order to rectify the shortcomings of standard approaches to profile fitting, we have applied Gaussian process regression (GPR), a powerful non-parametric regression technique, to analyse an Alcator C-Mod L-mode discharge used for past gyrokinetic validation work (Howard et al 2012 Nucl. Fusion 52 063002). We show that the GPR techniques can reproduce the previous results while delivering more statistically rigorous fits and uncertainty estimates for both the value and the gradient of plasma profiles with an improved level of automation. We also discuss how the use of GPR can allow for dramatic increases in the rate of convergence of uncertainty propagation for any code that takes experimental profiles as inputs. The new GPR techniques for profile fitting and uncertainty propagation are quite useful and general, and we describe the steps to implementation in detail in this paper. These techniques have the potential to substantially improve the quality of uncertainty estimates on profile fits and the rate of convergence of uncertainty propagation, making them of great interest for wider use in fusion experiments and modelling efforts. (paper)
Gonzales-Gustavson, Eloy; Cárdenas-Youngs, Yexenia; Calvo, Miquel; da Silva, Marcelle Figueira Marques; Hundesa, Ayalkibet; Amorós, Inmaculada; Moreno, Yolanda; Moreno-Mesonero, Laura; Rosell, Rosa; Ganges, Llilianne; Araujo, Rosa; Girones, Rosina
2017-03-01
In this study, the use of skimmed milk flocculation (SMF) to simultaneously concentrate viruses, bacteria and protozoa was evaluated. We selected strains of faecal indicator bacteria and pathogens, such as Escherichia coli and Helicobacter pylori. The viruses selected were adenovirus (HAdV 35), rotavirus (RoV SA-11), the bacteriophage MS2 and bovine viral diarrhoea virus (BVDV). The protozoa tested were Acanthamoeba, Giardia and Cryptosporidium. The mean recoveries with q(RT)PCR were 66% (HAdV 35), 24% (MS2), 28% (RoV SA-11), 15% (BVDV), 60% (E. coli), 30% (H. pylori) and 21% (Acanthamoeba castellanii). When testing the infectivity, the mean recoveries were 59% (HAdV 35), 12% (MS2), 26% (RoV SA-11) and 0.7% (BVDV). The protozoa Giardia lamblia and Cryptosporidium parvum were studied by immunofluorescence with recoveries of 18% and 13%, respectively. Although q(RT)PCR consistently showed higher quantification values (as expected), q(RT)PCR and the infectivity assays showed similar recoveries for HAdV 35 and RoV SA-11. Additionally, we investigated modelling the variability and uncertainty of the recovery with this method to extrapolate the quantification obtained by q(RT)PCR and estimate the real concentration. The 95% prediction intervals of the real concentration of the microorganisms inoculated were calculated using a general non-parametric bootstrap procedure adapted in our context to estimate the technical error of the measurements. SMF shows recoveries with a low variability that permits the use of a mathematical approximation to predict the concentration of the pathogen and indicator with acceptable low intervals. The values of uncertainty may be used for a quantitative microbial risk analysis or diagnostic purposes. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.
The organisational structure of urban environmental stewardship
Dana R. Fisher; Lindsay Campbell; Erika S. Svendsen
2012-01-01
How is the organisational structure of urban environmental stewardship groups related to the diverse ways that civic stewardship is taking place in urban settings? The findings of the limited number of studies that have explored the organisational structure of civic environmentalism are combined with the research on civic stewardship to answer this question. By...
Tauxe, J.; Black, P.; Carilli, J.; Catlett, K.; Crowe, B.; Hooten, M.; Rawlinson, S.; Schuh, A.; Stockton, T.; Yucel, V.
2002-12-01
The disposal of low-level radioactive waste (LLW) in the United States (U.S.) is a highly regulated undertaking. The U.S. Department of Energy (DOE), itself a large generator of such wastes, requires a substantial amount of analysis and assessment before permitting disposal of LLW at its facilities. One of the requirements that must be met in assessing the performance of a disposal site and technology is that a Performance Assessment (PA) demonstrate "reasonable expectation" that certain performance objectives, such as dose to a hypothetical future receptor, not be exceeded. The phrase "reasonable expectation" implies recognition of uncertainty in the assessment process. In order for this uncertainty to be quantified and communicated to decision makers, the PA computer model must accept probabilistic (uncertain) input (parameter values) and produce results which reflect that uncertainty as it is propagated through the model calculations. The GoldSim modeling software was selected for the task due to its unique facility with both probabilistic analysis and radioactive contaminant transport. Probabilistic model parameters range from water content and other physical properties of alluvium to the activity of radionuclides disposed to the amount of time a future resident might be expected to spend tending a garden. Although these parameters govern processes which are defined in isolation as rather simple differential equations, the complex interaction of couple processes makes for a highly nonlinear system with often unanticipated results. The decision maker has the difficult job of evaluating the uncertainty of modeling results in the context of granting permission for LLW disposal. This job also involves the evaluation of alternatives, such as the selection of disposal technologies. Various scenarios can be evaluated in the model, so that the effects of, for example, using a thicker soil cap over the waste cell can be assessed. This ability to evaluate mitigation
International Nuclear Information System (INIS)
Ingale, S. V.; Datta, D.
2010-01-01
Consequence of the accidental release of radioactivity from a nuclear power plant is assessed in terms of exposure or dose to the members of the public. Assessment of risk is routed through this dose computation. Dose computation basically depends on the basic dose assessment model and exposure pathways. One of the exposure pathways is the ingestion of contaminated food. The aim of the present paper is to compute the uncertainty associated with the risk to the members of the public due to the ingestion of contaminated food. The governing parameters of the ingestion dose assessment model being imprecise, we have approached evidence theory to compute the bound of the risk. The uncertainty is addressed by the belief and plausibility fuzzy measures.
Energy Technology Data Exchange (ETDEWEB)
Marzouk, Youssef [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
2016-08-31
Predictive simulation of complex physical systems increasingly rests on the interplay of experimental observations with computational models. Key inputs, parameters, or structural aspects of models may be incomplete or unknown, and must be developed from indirect and limited observations. At the same time, quantified uncertainties are needed to qualify computational predictions in the support of design and decision-making. In this context, Bayesian statistics provides a foundation for inference from noisy and limited data, but at prohibitive computional expense. This project intends to make rigorous predictive modeling *feasible* in complex physical systems, via accelerated and scalable tools for uncertainty quantification, Bayesian inference, and experimental design. Specific objectives are as follows: 1. Develop adaptive posterior approximations and dimensionality reduction approaches for Bayesian inference in high-dimensional nonlinear systems. 2. Extend accelerated Bayesian methodologies to large-scale {\\em sequential} data assimilation, fully treating nonlinear models and non-Gaussian state and parameter distributions. 3. Devise efficient surrogate-based methods for Bayesian model selection and the learning of model structure. 4. Develop scalable simulation/optimization approaches to nonlinear Bayesian experimental design, for both parameter inference and model selection. 5. Demonstrate these inferential tools on chemical kinetic models in reacting flow, constructing and refining thermochemical and electrochemical models from limited data. Demonstrate Bayesian filtering on canonical stochastic PDEs and in the dynamic estimation of inhomogeneous subsurface properties and flow fields.
Energy Technology Data Exchange (ETDEWEB)
Griffin, Joshua D. (Sandia National lababoratory, Livermore, CA); Eldred, Michael Scott; Martinez-Canales, Monica L. (Sandia National lababoratory, Livermore, CA); Watson, Jean-Paul; Kolda, Tamara Gibson (Sandia National lababoratory, Livermore, CA); Giunta, Anthony Andrew; Adams, Brian M.; Swiler, Laura Painton; Williams, Pamela J. (Sandia National lababoratory, Livermore, CA); Hough, Patricia Diane (Sandia National lababoratory, Livermore, CA); Gay, David M.; Dunlavy, Daniel M.; Eddy, John P.; Hart, William Eugene; Brown, Shannon L.
2006-10-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a developers manual for the DAKOTA software and describes the DAKOTA class hierarchies and their interrelationships. It derives directly from annotation of the actual source code and provides detailed class documentation, including all member functions and attributes.
Energy Technology Data Exchange (ETDEWEB)
Eldred, Michael Scott; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Gay, David M.; Eddy, John P.; Haskell, Karen H.
2010-05-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a developers manual for the DAKOTA software and describes the DAKOTA class hierarchies and their interrelationships. It derives directly from annotation of the actual source code and provides detailed class documentation, including all member functions and attributes.
Energy Technology Data Exchange (ETDEWEB)
Griffin, Joshua D. (Sandai National Labs, Livermore, CA); Eldred, Michael Scott; Martinez-Canales, Monica L. (Sandai National Labs, Livermore, CA); Watson, Jean-Paul; Kolda, Tamara Gibson (Sandai National Labs, Livermore, CA); Giunta, Anthony Andrew; Adams, Brian M.; Swiler, Laura Painton; Williams, Pamela J. (Sandai National Labs, Livermore, CA); Hough, Patricia Diane (Sandai National Labs, Livermore, CA); Gay, David M.; Dunlavy, Daniel M.; Eddy, John P.; Hart, William Eugene; Brown, Shannon L.
2006-10-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the DAKOTA software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
Energy Technology Data Exchange (ETDEWEB)
Eldred, Michael Scott; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Gay, David M.; Eddy, John P.; Haskell, Karen H.
2010-05-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the DAKOTA software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
Efendiev, Y.
2009-11-01
The Markov chain Monte Carlo (MCMC) is a rigorous sampling method to quantify uncertainty in subsurface characterization. However, the MCMC usually requires many flow and transport simulations in evaluating the posterior distribution and can be computationally expensive for fine-scale geological models. We propose a methodology that combines coarse- and fine-scale information to improve the efficiency of MCMC methods. The proposed method employs off-line computations for modeling the relation between coarse- and fine-scale error responses. This relation is modeled using nonlinear functions with prescribed error precisions which are used in efficient sampling within the MCMC framework. We propose a two-stage MCMC where inexpensive coarse-scale simulations are performed to determine whether or not to run the fine-scale (resolved) simulations. The latter is determined on the basis of a statistical model developed off line. The proposed method is an extension of the approaches considered earlier where linear relations are used for modeling the response between coarse-scale and fine-scale models. The approach considered here does not rely on the proximity of approximate and resolved models and can employ much coarser and more inexpensive models to guide the fine-scale simulations. Numerical results for three-phase flow and transport demonstrate the advantages, efficiency, and utility of the method for uncertainty assessment in the history matching. Copyright 2009 by the American Geophysical Union.
International Nuclear Information System (INIS)
Abdallh, A; Crevecoeur, G; Dupré, L
2010-01-01
The measured voltage signals picked up by the needle probe method can be interpreted by a numerical method so as to identify the magnetic material properties of the magnetic circuit of an electromagnetic device. However, when solving this electromagnetic inverse problem, the uncertainties in the numerical method give rise to recovery errors since the calculated needle signals in the forward problem are sensitive to these uncertainties. This paper proposes a stochastic Cramér–Rao bound method for determining the optimal sensor placement in the experimental setup. The numerical method is computationally time efficient where the geometrical parameters need to be provided. We apply the method for the non-destructive magnetic material characterization of an EI inductor where we ascertain the optimal experiment design. This design corresponds to the highest possible resolution that can be obtained when solving the inverse problem. Moreover, the presented results are validated by comparison with the exact material characteristics. The results show that the proposed methodology is independent of the values of the material parameter so that it can be applied before solving the inverse problem, i.e. as a priori estimation stage
Energy Technology Data Exchange (ETDEWEB)
Tobias, Benjamin John [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Palaniyappan, Sasikumar [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Gautier, Donald Cort [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Mendez, Jacob [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Burris-Mog, Trevor John [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Huang, Chengkun K. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Favalli, Andrea [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Hunter, James F. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Espy, Michelle E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Schmidt, Derek William [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Nelson, Ronald Owen [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Sefkow, Adam [Univ. of Rochester, NY (United States); Shimada, Tsutomu [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Johnson, Randall Philip [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Fernandez, Juan Carlos [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-10-24
Images of the R2DTO resolution target were obtained during laser-driven-radiography experiments performed at the TRIDENT laser facility, and analysis of these images using the Bayesian Inference Engine (BIE) determines a most probable full-width half maximum (FWHM) spot size of 78 μm. However, significant uncertainty prevails due to variation in the measured detector blur. Propagating this uncertainty in detector blur through the forward model results in an interval of probabilistic ambiguity spanning approximately 35-195 μm when the laser energy impinges on a thick (1 mm) tantalum target. In other phases of the experiment, laser energy is deposited on a thin (~100 nm) aluminum target placed 250 μm ahead of the tantalum converter. When the energetic electron beam is generated in this manner, upstream from the bremsstrahlung converter, the inferred spot size shifts to a range of much larger values, approximately 270-600 μm FWHM. This report discusses methods applied to obtain these intervals as well as concepts necessary for interpreting the result within a context of probabilistic quantitative inference.
Energy Technology Data Exchange (ETDEWEB)
Adams, Brian M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ebeida, Mohamed Salah [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eldred, Michael S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jakeman, John Davis [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Swiler, Laura Painton [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Stephens, John Adam [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vigil, Dena M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wildey, Timothy Michael [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bohnhoff, William J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eddy, John P. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hu, Kenneth T. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Dalbey, Keith R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bauman, Lara E [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hough, Patricia Diane [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2014-05-01
The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
International Nuclear Information System (INIS)
Irishkin, M.; Imbeaux, F.; Aniel, T.; Artaud, J.F.
2015-01-01
Highlights: • We developed a method for automated comparison of experimental data with models. • A unique platform implements Bayesian analysis and integrated modelling tools. • The method is tokamak-generic and is applied to Tore Supra and JET pulses. • Validation of a heat transport model is carried out. • We quantified the uncertainties due to Te profiles in current diffusion simulations. - Abstract: In the context of present and future long pulse tokamak experiments yielding a growing size of measured data per pulse, automating data consistency analysis and comparisons of measurements with models is a critical matter. To address these issues, the present work describes an expert system that carries out in an integrated and fully automated way (i) a reconstruction of plasma profiles from the measurements, using Bayesian analysis (ii) a prediction of the reconstructed quantities, according to some models and (iii) a comparison of the first two steps. The first application shown is devoted to the development of an automated comparison method between the experimental plasma profiles reconstructed using Bayesian methods and time dependent solutions of the transport equations. The method was applied to model validation of a simple heat transport model with three radial shape options. It has been tested on a database of 21 Tore Supra and 14 JET shots. The second application aims at quantifying uncertainties due to the electron temperature profile in current diffusion simulations. A systematic reconstruction of the Ne, Te, Ti profiles was first carried out for all time slices of the pulse. The Bayesian 95% highest probability intervals on the Te profile reconstruction were then used for (i) data consistency check of the flux consumption and (ii) defining a confidence interval for the current profile simulation. The method has been applied to one Tore Supra pulse and one JET pulse.
Maina, Fadji Zaouna; Guadagnini, Alberto
2018-01-01
We study the contribution of typically uncertain subsurface flow parameters to gravity changes that can be recorded during pumping tests in unconfined aquifers. We do so in the framework of a Global Sensitivity Analysis and quantify the effects of uncertainty of such parameters on the first four statistical moments of the probability distribution of gravimetric variations induced by the operation of the well. System parameters are grouped into two main categories, respectively, governing groundwater flow in the unsaturated and saturated portions of the domain. We ground our work on the three-dimensional analytical model proposed by Mishra and Neuman (2011), which fully takes into account the richness of the physical process taking place across the unsaturated and saturated zones and storage effects in a finite radius pumping well. The relative influence of model parameter uncertainties on drawdown, moisture content, and gravity changes are quantified through (a) the Sobol' indices, derived from a classical decomposition of variance and (b) recently developed indices quantifying the relative contribution of each uncertain model parameter to the (ensemble) mean, skewness, and kurtosis of the model output. Our results document (i) the importance of the effects of the parameters governing the unsaturated flow dynamics on the mean and variance of local drawdown and gravity changes; (ii) the marked sensitivity (as expressed in terms of the statistical moments analyzed) of gravity changes to the employed water retention curve model parameter, specific yield, and storage, and (iii) the influential role of hydraulic conductivity of the unsaturated and saturated zones to the skewness and kurtosis of gravimetric variation distributions. The observed temporal dynamics of the strength of the relative contribution of system parameters to gravimetric variations suggest that gravity data have a clear potential to provide useful information for estimating the key hydraulic
Energy Technology Data Exchange (ETDEWEB)
Smith, P.J.; Eddings, E.G.; Ring, T.; Thornock, J.; Draper, T.; Isaac, B.; Rezeai, D.; Toth, P.; Wu, Y.; Kelly, K.
2014-08-01
The objective of this task is to produce predictive capability with quantified uncertainty bounds for the heat flux in commercial-scale, tangentially fired, oxy-coal boilers. Validation data came from the Alstom Boiler Simulation Facility (BSF) for tangentially fired, oxy-coal operation. This task brings together experimental data collected under Alstom’s DOE project for measuring oxy-firing performance parameters in the BSF with this University of Utah project for large eddy simulation (LES) and validation/uncertainty quantification (V/UQ). The Utah work includes V/UQ with measurements in the single-burner facility where advanced strategies for O2 injection can be more easily controlled and data more easily obtained. Highlights of the work include: • Simulations of Alstom’s 15 megawatt (MW) BSF, exploring the uncertainty in thermal boundary conditions. A V/UQ analysis showed consistency between experimental results and simulation results, identifying uncertainty bounds on the quantities of interest for this system (Subtask 9.1) • A simulation study of the University of Utah’s oxy-fuel combustor (OFC) focused on heat flux (Subtask 9.2). A V/UQ analysis was used to show consistency between experimental and simulation results. • Measurement of heat flux and temperature with new optical diagnostic techniques and comparison with conventional measurements (Subtask 9.3). Various optical diagnostics systems were created to provide experimental data to the simulation team. The final configuration utilized a mid-wave infrared (MWIR) camera to measure heat flux and temperature, which was synchronized with a high-speed, visible camera to utilize two-color pyrometry to measure temperature and soot concentration. • Collection of heat flux and temperature measurements in the University of Utah’s OFC for use is subtasks 9.2 and 9.3 (Subtask 9.4). Several replicates were carried to better assess the experimental error. Experiments were specifically designed for the
Energy Technology Data Exchange (ETDEWEB)
Ge Hong [Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina (United States); Department of Radiation Oncology, Henan Cancer Hospital, the Affiliated Cancer Hospital of Zhengzhou University, Henan (China); Cai Jing; Kelsey, Chris R. [Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina (United States); Yin Fangfang, E-mail: fangfang.yin@duke.edu [Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina (United States)
2013-02-01
Purpose: To quantify uncertainties in delineating an internal target volume (ITV) and to understand how these uncertainties may be individually minimized for stereotactic body radiation therapy (SBRT) of early stage non-small cell lung cancer (NSCLC). Methods and Materials: Twenty patients with NSCLC who were undergoing SBRT were imaged with free-breathing 3-dimensional computed tomography (3DCT) and 10-phase 4-dimensional CT (4DCT) for delineating gross tumor volume (GTV){sub 3D} and ITV{sub 10Phase} (ITV3). The maximum intensity projection (MIP) CT was also calculated from 10-phase 4DCT for contouring ITV{sub MIP} (ITV1). Then, ITV{sub COMB} (ITV2), ITV{sub 10Phase+GTV3D} (ITV4), and ITV{sub 10Phase+ITVCOMB} (ITV5) were generated by combining ITV{sub MIP} and GTV{sub 3D}, ITV{sub 10phase} and GTV{sub 3D}, and ITV{sub 10phase} and ITV{sub COMB}, respectively. All 6 volumes (GTV{sub 3D} and ITV1 to ITV5) were delineated in the same lung window by the same radiation oncologist. The percentage of volume difference (PVD) between any 2 different volumes was determined and was correlated to effective tumor diameter (ETD), tumor motion ranges, R{sub 3D}, and the amplitude variability of the recorded breathing signal (v) to assess their volume variations. Results: The mean (range) tumor motion (R{sub SI}, R{sub AP}, R{sub ML}, and R{sub 3D}) and breathing variability (v) were 7.6 mm (2-18 mm), 4.0 mm (2-8 mm), 3.3 mm (0-7.5 mm), 9.9 mm (4.1-18.7 mm), and 0.17 (0.07-0.37), respectively. The trend of volume variation was GTV{sub 3D}
Energy Technology Data Exchange (ETDEWEB)
Schulze-Riegert, R.; Krosche, M.; Stekolschikov, K. [Scandpower Petroleum Technology GmbH, Hamburg (Germany); Fahimuddin, A. [Technische Univ. Braunschweig (Germany)
2007-09-13
History Matching in Reservoir Simulation, well location and production optimization etc. is generally a multi-objective optimization problem. The problem statement of history matching for a realistic field case includes many field and well measurements in time and type, e.g. pressure measurements, fluid rates, events such as water and gas break-throughs, etc. Uncertainty parameters modified as part of the history matching process have varying impact on the improvement of the match criteria. Competing match criteria often reduce the likelihood of finding an acceptable history match. It is an engineering challenge in manual history matching processes to identify competing objectives and to implement the changes required in the simulation model. In production optimization or scenario optimization the focus on one key optimization criterion such as NPV limits the identification of alternatives and potential opportunities, since multiple objectives are summarized in a predefined global objective formulation. Previous works primarily focus on a specific optimization method. Few works actually concentrate on the objective formulation and multi-objective optimization schemes have not yet been applied to reservoir simulations. This paper presents a multi-objective optimization approach applicable to reservoir simulation. It addresses the problem of multi-objective criteria in a history matching study and presents analysis techniques identifying competing match criteria. A Pareto-Optimizer is discussed and the implementation of that multi-objective optimization scheme is applied to a case study. Results are compared to a single-objective optimization method. (orig.)
Antimicrobial stewardship in wound care
DEFF Research Database (Denmark)
Lipsky, Benjamin A; Dryden, Matthew; Gottrup, Finn
2016-01-01
BACKGROUND: With the growing global problem of antibiotic resistance it is crucial that clinicians use antibiotics wisely, which largely means following the principles of antimicrobial stewardship (AMS). Treatment of various types of wounds is one of the more common reasons for prescribing...... of experts in infectious diseases/clinical microbiology (from the British Society for Antimicrobial Chemotherapy) and wound management (from the European Wound Management Association) who, after thoroughly reviewing the available literature and holding teleconferences, jointly produced this guidance document...
Applications of Nuclear Science for Stewardship Science
International Nuclear Information System (INIS)
Cizewski, Jolie A
2013-01-01
Stewardship science is research important to national security interests that include stockpile stewardship science, homeland security, nuclear forensics, and non-proliferation. To help address challenges in stewardship science and workforce development, the Stewardship Science Academic Alliances (SSAA) was inaugurated ten years ago by the National Nuclear Security Administration of the U. S. Department of Energy. The goal was to enhance connections between NNSA laboratories and the activities of university scientists and their students in research areas important to NNSA, including low-energy nuclear science. This paper presents an overview of recent research in low-energy nuclear science supported by the Stewardship Science Academic Alliances and the applications of this research to stewardship science.
NOAA's Scientific Data Stewardship Program
Bates, J. J.
2004-12-01
The NOAA mission is to understand and predict changes in the Earth's environment and conserve and manage coastal and marine resources to meet the Nation's economic, social and environmental needs. NOAA has responsibility for long-term archiving of the United States environmental data and has recently integrated several data management functions into a concept called Scientific Data Stewardship. Scientific Data Stewardship a new paradigm in data management consisting of an integrated suite of functions to preserve and exploit the full scientific value of NOAA's, and the world's, environmental data These functions include careful monitoring of observing system performance for long-term applications, the generation of authoritative long-term climate records from multiple observing platforms, and the proper archival of and timely access to data and metadata. NOAA has developed a conceptual framework to implement the functions of scientific data stewardship. This framework has five objectives: 1) develop real-time monitoring of all satellite observing systems for climate applications, 2) process large volumes of satellite data extending up to decades in length to account for systematic errors and to eliminate artifacts in the raw data (referred to as fundamental climate data records, FCDRs), 3) generate retrieved geophysical parameters from the FCDRs (referred to as thematic climate data records TCDRs) including combining observations from all sources, 4) conduct monitoring and research by analyzing data sets to uncover climate trends and to provide evaluation and feedback for steps 2) and 3), and 5) provide archives of metadata, FCDRs, and TCDRs, and facilitate distribution of these data to the user community. The term `climate data record' and related terms, such as climate data set, have been used for some time, but the climate community has yet to settle on a concensus definition. A recent United States National Academy of Sciences report recommends using the
The Six Principles of Facilities Stewardship
Kaiser, Harvey H.; Klein, Eva
2010-01-01
Facilities stewardship means high-level and pervasive commitment to optimize capital investments, in order to achieve a high-functioning and attractive campus. It includes a major commitment to capital asset preservation and quality. Stewardship is about the long view of an institution's past and future. It ultimately forms the backdrop for…
Uncertainty Quantification in Numerical Aerodynamics
Litvinenko, Alexander; Matthies, Hermann G.; Liu, Dishi; Schillings, Claudia; Schulz, Volker
2017-01-01
In numerical section we compares five methods, including quasi-Monte Carlo quadrature, polynomial chaos with coefficients determined by sparse quadrature and gradient-enhanced version of Kriging, radial basis functions and point collocation polynomial chaos, in their efficiency in estimating statistics of aerodynamic performance upon random perturbation to the airfoil geometry [D.Liu et al '17]. For modeling we used the TAU code, developed in DLR, Germany.
Inverse problems and uncertainty quantification
Litvinenko, Alexander; Matthies, Hermann G.
2013-01-01
computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and quadratic Bayesian update on the small but taxing example
PIV Uncertainty Quantification and Beyond
Wieneke, B.F.A.
2017-01-01
The fundamental properties of computed flow fields using particle imaging velocimetry (PIV) have been investigated, viewing PIV processing as a black box without going in detail into algorithmic details. PIV processing can be analyzed using a linear filter model, i.e. assuming that the computed
William Salas; Steve Hagen
2013-01-01
This presentation will provide an overview of an approach for quantifying uncertainty in spatial estimates of carbon emission from land use change. We generate uncertainty bounds around our final emissions estimate using a randomized, Monte Carlo (MC)-style sampling technique. This approach allows us to combine uncertainty from different sources without making...
Energy Technology Data Exchange (ETDEWEB)
Tanaka, Masaaki, E-mail: tanaka.masaaki@jaea.go.jp
2014-11-15
Highlights: • Outline of numerical simulation code MUGTHES for fluid-structure thermal interaction was described. • The grid convergence index (GCI) method was applied according to the ASME V and V-20 guide. • Uncertainty of MUGTHES can be successfully quantified for thermal-hydraulic problems and unsteady heat conduction problems in the structure. • Validation for fluid-structure thermal interaction problem in a T-junction piping system was well conducted. - Abstract: Thermal fatigue caused by thermal mixing phenomena is one of the most important issues in design and safety assessment of fast breeder reactors. A numerical simulation code MUGTHES consisting of two calculation modules for unsteady thermal-hydraulics analysis and unsteady heat conduction analysis in structure has been developed to predict thermal mixing phenomena and to estimate thermal response of structure under the thermal interaction between fluid and structure fields. Although verification and validation (V and V) of MUGTHES has been required, actual procedure for uncertainty quantification is not fixed yet. In order to specify an actual procedure of V and V, uncertainty quantifications with the grid convergence index (GCI) estimation according to the existing guidelines were conducted in fundamental laminar flow problems for the thermal-hydraulics analysis module, and also uncertainty for the structure heat conduction analysis module and conjugate heat transfer model was quantified in comparison with the theoretical solutions of unsteady heat conduction problems. After the verification, MUGTHES was validated for a practical fluid-structure thermal interaction problem in T-junction piping system compared with measured results of velocity and temperatures of fluid and structure. Through the numerical simulations in the verification and validation, uncertainty of the code was successfully estimated and applicability of the code to the thermal fatigue issue was confirmed.
STEWARDSHIP: A Conceptual Imperative For Managerial ...
African Journals Online (AJOL)
resources for the management of the health sector. (Stewardship of ... of health sector development and performance. Examples of ... attempt at health sector decentralization and improving ... organizations could create inherent limitations on.
Measuring the impact of antimicrobial stewardship programs
Dik, Jan-Willem H.; Hendrix, Ron; Poelman, Randy; Niesters, Hubert G.; Postma, Maarten J.; Sinha, Bhanu; Friedrich, Alexander W.
Antimicrobial Stewardship Programs (ASPs) are being implemented worldwide to optimize antimicrobial therapy, and thereby improve patient safety and quality of care. Additionally, this should counteract resistance development. It is, however, vital that correct and timely diagnostics are performed in
Microbiological surveillance and antimicrobial stewardship minimise ...
African Journals Online (AJOL)
Microbiological surveillance and antimicrobial stewardship minimise the need for ultrabroad-spectrum combination therapy for treatment of nosocomial infections in a trauma intensive care unit: An audit of an evidence-based empiric antimicrobial policy.
Stockpile Stewardship at Los Alamos(U)
Energy Technology Data Exchange (ETDEWEB)
Webster, Robert B. [Los Alamos National Laboratory
2012-06-29
Stockpile stewardship is the retention of nuclear weapons in the stockpile beyond their original design life. These older weapons have potential changes inconsistent with the original design intent and military specifications. The Stockpile Stewardship Program requires us to develop high-fidelity, physics-based capabilities to predict, assess, certify and design nuclear weapons without conducting a nuclear test. Each year, the Lab Directors are required to provide an assessment of the safety, security, and reliability our stockpile to the President of the United States. This includes assessing whether a need to return to testing exists. This is a talk to provide an overview of Stockpile Stewardship's scientific requirements and how stewardship has changed in the absence of nuclear testing. The talk is adapted from an HQ talk to the War college, and historical unclassified talks on weapon's physics.
FY 2015 - Stockpile Stewardship and Management Plan
Energy Technology Data Exchange (ETDEWEB)
None
2014-04-01
This Department of Energy’s (DOE) National Nuclear Security Administration (NNSA) Fiscal Year Stockpile Stewardship and Management Plan (SSMP) is a key planning document for the nuclear security enterprise.
FY 2016 - Stockpile Stewardship and Management Plan
Energy Technology Data Exchange (ETDEWEB)
None
2015-03-01
This Department of Energy’s (DOE) National Nuclear Security Administration (NNSA) Fiscal Year Stockpile Stewardship and Management Plan (SSMP) is a key planning document for the nuclear security enterprise.
Embedding Data Stewardship in Geoscience Australia
Bastrakova, I.; Fyfe, S.
2013-12-01
Ten years of technological innovation now enable vast amounts of data to be collected, managed, processed and shared. At the same time, organisations have witnessed government legislative and policy requirements for open access to public sector data, and a demand for flexibility in access to data by both machine-to-machine and human consumption. Geoscience Australia (GA) has adopted Data Stewardship as an organisation-wide initiative to improve the way we manage and share our data. The benefits to GA including: - Consolidated understanding of GA's data assets and their value to the Agency; - Recognition of the significant role of data custodianship and data management; - Well-defined governance, policies, standards, practices and accountabilities that promote the accessibility, quality and interoperability of GA's data; - Integration of disparate data sets into cohesive information products available online in real time and equally accessible to researchers, government, industry and the public. Although the theory behind data stewardship is well-defined and accepted and the benefits are generally well-understood, practical implementation requires an organisation to prepare for a long-term commitment of resources, both financial and human. Fundamentally this involves: 1. Raising awareness in the organisation of the need for data stewardship and the challenges this entails; 2. Establishing a data stewardship framework including a data governance office to set policy and drive organisational change; and 3. Embedding the functions and a culture of data stewardship into business as usual operations. GA holds a vast amount of data ranging from petabytes of Big Data to significant quantities of relatively small ';long tail' geoscientific observations and measurements. Over the past four years, GA has undertaken strategic activities that prepare us for Data Stewardship: - Organisation-wide audits of GA's data holdings and identification of custodians for each dataset
Antibiotic stewardship in community-acquired pneumonia.
Viasus, Diego; Vecino-Moreno, Milly; De La Hoz, Juan M; Carratalà, Jordi
2017-04-01
Community-acquired pneumonia (CAP) continues to be associated with significant mortality and morbidity. As with other infectious diseases, in recent years there has been a marked increase in resistance to the antibiotics commonly used against the pathogens that cause CAP. Antimicrobial stewardship denotes coordinated interventions to improve and measure the appropriate use of antibiotics by encouraging the selection of optimal drug regimens. Areas covered: Several elements can be applied to antibiotic stewardship strategies for CAP in order to maintain or improve patient outcomes. In this regard, antibiotic de-escalation, duration of antibiotic treatment, adherence to CAP guidelines recommendations about empirical treatment, and switching from intravenous to oral antibiotic therapy may each be relevant in this context. Antimicrobial stewardship strategies, such as prospective audit with intervention and feedback, clinical pathways, and dedicated multidisciplinary teams, that have included some of these elements have demonstrated improvements in antimicrobial use for CAP without negatively affecting clinical outcomes. Expert commentary: Although there are a limited number of randomized clinical studies addressing antimicrobial stewardship strategies in CAP, there is evidence that antibiotic stewardship initiatives can be securely applied, providing benefits to both healthcare systems and patients.
DEFF Research Database (Denmark)
Vezzaro, Luca; Eriksson, Eva; Ledin, Anna
2012-01-01
Strategies for reduction of micropollutant (MP) discharges from stormwater drainage systems require accurate estimation of the potential MP removal in stormwater treatment systems. However, the high uncertainty commonly affecting stormwater runoff quality modelling also influences stormwater trea...
2015 Stewardship Science Academic Programs Annual
Energy Technology Data Exchange (ETDEWEB)
Stone, Terri [NNSA Office of Research, Development, Test, and Evaluation, Washington, DC (United States); Mischo, Millicent [NNSA Office of Research, Development, Test, and Evaluation, Washington, DC (United States)
2015-02-01
The Stockpile Stewardship Academic Programs (SSAP) are essential to maintaining a pipeline of professionals to support the technical capabilities that reside at the National Nuclear Security Administration (NNSA) national laboratories, sites, and plants. Since 1992, the United States has observed the moratorium on nuclear testing while significantly decreasing the nuclear arsenal. To accomplish this without nuclear testing, NNSA and its laboratories developed a science-based Stockpile Stewardship Program to maintain and enhance the experimental and computational tools required to ensure the continued safety, security, and reliability of the stockpile. NNSA launched its academic program portfolio more than a decade ago to engage students skilled in specific technical areas of relevance to stockpile stewardship. The success of this program is reflected by the large number of SSAP students choosing to begin their careers at NNSA national laboratories.
Ihssane, B; Bouchafra, H; El Karbane, M; Azougagh, M; Saffaj, T
2016-05-01
We propose in this work an efficient way to evaluate the measurement of uncertainty at the end of the development step of an analytical method, since this assessment provides an indication of the performance of the optimization process. The estimation of the uncertainty is done through a robustness test by applying a Placquett-Burman design, investigating six parameters influencing the simultaneous chromatographic assay of five water-soluble vitamins. The estimated effects of the variation of each parameter are translated into standard uncertainty value at each concentration level. The values obtained of the relative uncertainty do not exceed the acceptance limit of 5%, showing that the procedure development was well done. In addition, a statistical comparison conducted to compare standard uncertainty after the development stage and those of the validation step indicates that the estimated uncertainty are equivalent. The results obtained show clearly the performance and capacity of the chromatographic method to simultaneously assay the five vitamins and suitability for use in routine application. Copyright © 2015 Académie Nationale de Pharmacie. Published by Elsevier Masson SAS. All rights reserved.
Zhang, Kejiang; Achari, Gopal; Li, Hua
2009-11-03
Traditionally, uncertainty in parameters are represented as probabilistic distributions and incorporated into groundwater flow and contaminant transport models. With the advent of newer uncertainty theories, it is now understood that stochastic methods cannot properly represent non random uncertainties. In the groundwater flow and contaminant transport equations, uncertainty in some parameters may be random, whereas those of others may be non random. The objective of this paper is to develop a fuzzy-stochastic partial differential equation (FSPDE) model to simulate conditions where both random and non random uncertainties are involved in groundwater flow and solute transport. Three potential solution techniques namely, (a) transforming a probability distribution to a possibility distribution (Method I) then a FSPDE becomes a fuzzy partial differential equation (FPDE), (b) transforming a possibility distribution to a probability distribution (Method II) and then a FSPDE becomes a stochastic partial differential equation (SPDE), and (c) the combination of Monte Carlo methods and FPDE solution techniques (Method III) are proposed and compared. The effects of these three methods on the predictive results are investigated by using two case studies. The results show that the predictions obtained from Method II is a specific case of that got from Method I. When an exact probabilistic result is needed, Method II is suggested. As the loss or gain of information during a probability-possibility (or vice versa) transformation cannot be quantified, their influences on the predictive results is not known. Thus, Method III should probably be preferred for risk assessments.
Directory of Open Access Journals (Sweden)
K. Butterbach-Bahl
2012-10-01
Full Text Available Assessing the uncertainties of simulation results of ecological models is becoming increasingly important, specifically if these models are used to estimate greenhouse gas emissions on site to regional/national levels. Four general sources of uncertainty effect the outcome of process-based models: (i uncertainty of information used to initialise and drive the model, (ii uncertainty of model parameters describing specific ecosystem processes, (iii uncertainty of the model structure, and (iv accurateness of measurements (e.g., soil-atmosphere greenhouse gas exchange which are used for model testing and development. The aim of our study was to assess the simulation uncertainty of the process-based biogeochemical model LandscapeDNDC. For this we set up a Bayesian framework using a Markov Chain Monte Carlo (MCMC method, to estimate the joint model parameter distribution. Data for model testing, parameter estimation and uncertainty assessment were taken from observations of soil fluxes of nitrous oxide (N2O, nitric oxide (NO and carbon dioxide (CO2 as observed over a 10 yr period at the spruce site of the Höglwald Forest, Germany. By running four independent Markov Chains in parallel with identical properties (except for the parameter start values, an objective criteria for chain convergence developed by Gelman et al. (2003 could be used. Our approach shows that by means of the joint parameter distribution, we were able not only to limit the parameter space and specify the probability of parameter values, but also to assess the complex dependencies among model parameters used for simulating soil C and N trace gas emissions. This helped to improve the understanding of the behaviour of the complex LandscapeDNDC model while simulating soil C and N turnover processes and associated C and N soil-atmosphere exchange. In a final step the parameter distribution of the most sensitive parameters determining soil-atmosphere C and N exchange were used to obtain
Towards Materials Sustainability through Materials Stewardship
Directory of Open Access Journals (Sweden)
Christopher D. Taylor
2016-10-01
Full Text Available Materials sustainability requires a concerted change in philosophy across the entire materials lifecycle, orienting around the theme of materials stewardship. In this paper, we address the opportunities for improved materials conservation through dematerialization, durability, design for second life, and diversion of waste streams through industrial symbiosis.
Stewardship, Learning, and Memory in Disaster Resilience
Tidball, Keith G.; Krasny, Marianne E.; Svendsen, Erika; Campbell, Lindsay; Helphand, Kenneth
2010-01-01
In this contribution, we propose and explore the following hypothesis: civic ecology practices, including urban community forestry, community gardening, and other self-organized forms of stewardship of green spaces in cities, are manifestations of how memories of the role of greening in healing can be instrumentalized through social learning to…
Stewardship, learning, and memory in disaster resilience
Keith G. Tidball; Marianne E. Krasny; Erika Svendsen; Lindsay Campbell; Kenneth. Helphand
2010-01-01
In this contribution, we propose and explore the following hypothesis: civic ecology practices, including urban community forestry, community gardening, and other self-organized forms of stewardship of green spaces in cities, are manifestations of how memories of the role of greening in healing can be instrumentalized through social learning to foster social-ecological...
Predicting volunteer commitment in environmental stewardship programmes
Robert L. Ryan; Rachel Kaplan; Robert E. Grese
2001-01-01
The natural environment benefits greatly from the work of volunteers in environmental stewardship programmes. However, little is known about volunteers' motivations for continued participation in these programmes. This study looked at the relationship between volunteer commitment and motivation, as well as the effect that volunteering has on participants'...
Energy Technology Data Exchange (ETDEWEB)
Adams, Brian M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ebeida, Mohamed Salah [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eldred, Michael S [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jakeman, John Davis [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Swiler, Laura Painton [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Stephens, John Adam [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vigil, Dena M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wildey, Timothy Michael [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bohnhoff, William J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eddy, John P. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hu, Kenneth T. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Dalbey, Keith R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bauman, Lara E [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hough, Patricia Diane [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2014-05-01
The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quanti cation, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.
Energy Technology Data Exchange (ETDEWEB)
Behler, Matthias; Bock, Matthias; Stuke, Maik; Wagner, Markus
2014-06-15
This work describes statistical analyses based on Monte Carlo sampling methods for criticality safety analyses. The methods analyse a large number of calculations of a given problem with statistically varied model parameters to determine uncertainties and sensitivities of the computed results. The GRS development SUnCISTT (Sensitivities and Uncertainties in Criticality Inventory and Source Term Tool) is a modular, easily extensible abstract interface program, designed to perform such Monte Carlo sampling based uncertainty and sensitivity analyses in the field of criticality safety. It couples different criticality and depletion codes commonly used in nuclear criticality safety assessments to the well-established GRS tool SUSA for sensitivity and uncertainty analyses. For uncertainty analyses of criticality calculations, SunCISTT couples various SCALE sequences developed at Oak Ridge National Laboratory and the general Monte Carlo N-particle transport code MCNP from Los Alamos National Laboratory to SUSA. The impact of manufacturing tolerances of a fuel assembly configuration on the neutron multiplication factor for the various sequences is shown. Uncertainties in nuclear inventories, dose rates, or decay heat can be investigated via the coupling of the GRS depletion system OREST to SUSA. Some results for a simplified irradiated Pressurized Water Reactor (PWR) UO{sub 2} fuel assembly are shown. SUnCISTT also combines the two aforementioned modules for burnup credit criticality analysis of spent nuclear fuel to ensures an uncertainty and sensitivity analysis using the variations of manufacturing tolerances in the burn-up code and criticality code simultaneously. Calculations and results for a storage cask loaded with typical irradiated PWR UO{sub 2} fuel are shown, including Monte Carlo sampled axial burn-up profiles. The application of SUnCISTT in the field of code validation, specifically, how it is applied to compare a simulation model to available benchmark
International Nuclear Information System (INIS)
Wu, Xu; Kozlowski, Tomasz
2015-01-01
Highlights: • Coupling of Monte Carlo code Serpent and thermal–hydraulics code RELAP5. • A convergence criterion is developed based on the statistical uncertainty of power. • Correlation between MC statistical uncertainty and coupled error is quantified. • Both UO 2 and MOX single assembly models are used in the coupled simulation. • Validation of coupling results with a multi-group transport code DeCART. - Abstract: Coupled multi-physics approach plays an important role in improving computational accuracy. Compared with deterministic neutronics codes, Monte Carlo codes have the advantage of a higher resolution level. In the present paper, a three-dimensional continuous-energy Monte Carlo reactor physics burnup calculation code, Serpent, is coupled with a thermal–hydraulics safety analysis code, RELAP5. The coupled Serpent/RELAP5 code capability is demonstrated by the improved axial power distribution of UO 2 and MOX single assembly models, based on the OECD-NEA/NRC PWR MOX/UO 2 Core Transient Benchmark. Comparisons of calculation results using the coupled code with those from the deterministic methods, specifically heterogeneous multi-group transport code DeCART, show that the coupling produces more precise results. A new convergence criterion for the coupled simulation is developed based on the statistical uncertainty in power distribution in the Monte Carlo code, rather than ad-hoc criteria used in previous research. The new convergence criterion is shown to be more rigorous, equally convenient to use but requiring a few more coupling steps to converge. Finally, the influence of Monte Carlo statistical uncertainty on the coupled error of power and thermal–hydraulics parameters is quantified. The results are presented such that they can be used to find the statistical uncertainty to use in Monte Carlo in order to achieve a desired precision in coupled simulation
Nelson, N.; Azmy, Y.; Gardner, R. P.; Mattingly, J.; Smith, R.; Worrall, L. G.; Dewji, S.
2017-11-01
Detector response functions (DRFs) are often used for inverse analysis. We compute the DRF of a sodium iodide (NaI) nuclear material holdup field detector using the code named g03 developed by the Center for Engineering Applications of Radioisotopes (CEAR) at NC State University. Three measurement campaigns were performed in order to validate the DRF's constructed by g03: on-axis detection of calibration sources, off-axis measurements of a highly enriched uranium (HEU) disk, and on-axis measurements of the HEU disk with steel plates inserted between the source and the detector to provide attenuation. Furthermore, this work quantifies the uncertainty of the Monte Carlo simulations used in and with g03, as well as the uncertainties associated with each semi-empirical model employed in the full DRF representation. Overall, for the calibration source measurements, the response computed by the DRF for the prediction of the full-energy peak region of responses was good, i.e. within two standard deviations of the experimental response. In contrast, the DRF tended to overestimate the Compton continuum by about 45-65% due to inadequate tuning of the electron range multiplier fit variable that empirically represents physics associated with electron transport that is not modeled explicitly in g03. For the HEU disk measurements, computed DRF responses tended to significantly underestimate (more than 20%) the secondary full-energy peaks (any peak of lower energy than the highest-energy peak computed) due to scattering in the detector collimator and aluminum can, which is not included in the g03 model. We ran a sufficiently large number of histories to ensure for all of the Monte Carlo simulations that the statistical uncertainties were lower than their experimental counterpart's Poisson uncertainties. The uncertainties associated with least-squares fits to the experimental data tended to have parameter relative standard deviations lower than the peak channel relative standard
International Nuclear Information System (INIS)
Kovscek, A.R.; Wang, Y.
2005-01-01
Carbon dioxide (CO 2 ) is already injected into a limited class of reservoirs for oil recovery purposes; however, the engineering design question for simultaneous oil recovery and storage of anthropogenic CO 2 is significantly different from that of oil recovery alone. Currently, the volumes of CO 2 injected solely for oil recovery are minimized due to the purchase cost of CO 2 . If and when CO 2 emissions to the atmosphere are managed, it will be necessary to maximize simultaneously both economic oil recovery and the volumes of CO 2 emplaced in oil reservoirs. This process is coined 'cooptimization'. This paper proposes a work flow for cooptimization of oil recovery and geologic CO 2 storage. An important component of the work flow is the assessment of uncertainty in predictions of performance. Typical methods for quantifying uncertainty employ exhaustive flow simulation of multiple stochastic realizations of the geologic architecture of a reservoir. Such approaches are computationally intensive and thereby time consuming. An analytic streamline based proxy for full reservoir simulation is proposed and tested. Streamline trajectories represent the three-dimensional velocity field during multiphase flow in porous media and so are useful for quantifying the similarity and differences among various reservoir models. The proxy allows rational selection of a representative subset of equi-probable reservoir models that encompass uncertainty with respect to true reservoir geology. The streamline approach is demonstrated to be thorough and rapid
Environmental Stewardship: A Conceptual Review and Analytical Framework
Bennett, Nathan J.; Whitty, Tara S.; Finkbeiner, Elena; Pittman, Jeremy; Bassett, Hannah; Gelcich, Stefan; Allison, Edward H.
2018-04-01
There has been increasing attention to and investment in local environmental stewardship in conservation and environmental management policies and programs globally. Yet environmental stewardship has not received adequate conceptual attention. Establishing a clear definition and comprehensive analytical framework could strengthen our ability to understand the factors that lead to the success or failure of environmental stewardship in different contexts and how to most effectively support and enable local efforts. Here we propose such a definition and framework. First, we define local environmental stewardship as the actions taken by individuals, groups or networks of actors, with various motivations and levels of capacity, to protect, care for or responsibly use the environment in pursuit of environmental and/or social outcomes in diverse social-ecological contexts. Next, drawing from a review of the environmental stewardship, management and governance literatures, we unpack the elements of this definition to develop an analytical framework that can facilitate research on local environmental stewardship. Finally, we discuss potential interventions and leverage points for promoting or supporting local stewardship and future applications of the framework to guide descriptive, evaluative, prescriptive or systematic analysis of environmental stewardship. Further application of this framework in diverse environmental and social contexts is recommended to refine the elements and develop insights that will guide and improve the outcomes of environmental stewardship initiatives and investments. Ultimately, our aim is to raise the profile of environmental stewardship as a valuable and holistic concept for guiding productive and sustained relationships with the environment.
Environmental Stewardship: A Conceptual Review and Analytical Framework.
Bennett, Nathan J; Whitty, Tara S; Finkbeiner, Elena; Pittman, Jeremy; Bassett, Hannah; Gelcich, Stefan; Allison, Edward H
2018-04-01
There has been increasing attention to and investment in local environmental stewardship in conservation and environmental management policies and programs globally. Yet environmental stewardship has not received adequate conceptual attention. Establishing a clear definition and comprehensive analytical framework could strengthen our ability to understand the factors that lead to the success or failure of environmental stewardship in different contexts and how to most effectively support and enable local efforts. Here we propose such a definition and framework. First, we define local environmental stewardship as the actions taken by individuals, groups or networks of actors, with various motivations and levels of capacity, to protect, care for or responsibly use the environment in pursuit of environmental and/or social outcomes in diverse social-ecological contexts. Next, drawing from a review of the environmental stewardship, management and governance literatures, we unpack the elements of this definition to develop an analytical framework that can facilitate research on local environmental stewardship. Finally, we discuss potential interventions and leverage points for promoting or supporting local stewardship and future applications of the framework to guide descriptive, evaluative, prescriptive or systematic analysis of environmental stewardship. Further application of this framework in diverse environmental and social contexts is recommended to refine the elements and develop insights that will guide and improve the outcomes of environmental stewardship initiatives and investments. Ultimately, our aim is to raise the profile of environmental stewardship as a valuable and holistic concept for guiding productive and sustained relationships with the environment.
Antifungal stewardship considerations for adults and pediatrics.
Hamdy, Rana F; Zaoutis, Theoklis E; Seo, Susan K
2017-08-18
Antifungal stewardship refers to coordinated interventions to monitor and direct the appropriate use of antifungal agents in order to achieve the best clinical outcomes and minimize selective pressure and adverse events. Antifungal utilization has steadily risen over time in concert with the increase in number of immunocompromised adults and children at risk for invasive fungal infections (IFI). Challenges in diagnosing IFI often lead to delays in treatment and poorer outcomes. There are also emerging data linking prior antifungal exposure and suboptimal dosing to the emergence of antifungal resistance, particularly for Candida. Antimicrobial stewardship programs can take a multi-pronged bundle approach to ensure suitable prescribing of antifungals via post-prescription review and feedback and/or prior authorization. Institutional guidelines can also be developed to guide diagnostic testing in at-risk populations; appropriate choice, dose, and duration of antifungal agent; therapeutic drug monitoring; and opportunities for de-escalation and intravenous-to-oral conversion.
Multi-generational stewardship of plutonium
International Nuclear Information System (INIS)
Pillay, K.K.S.
1997-01-01
The post-cold war era has greatly enhanced the interest in the long-term stewardship of plutonium. The management of excess plutonium from proposed nuclear weapons dismantlement has been the subject of numerous intellectual discussions during the past several years. In this context, issues relevant to long-term management of all plutonium as a valuable energy resource are also being examined. While there are differing views about the future role of plutonium in the economy, there is a recognition of the environmental and health related problems and proliferation potentials of weapons-grade plutonium. The long-term management of plutonium as an energy resource will require a new strategy to maintain stewardship for many generations to come
Kountouris, Panagiotis; Gerbig, Christoph; Rödenbeck, Christian; Karstens, Ute; Koch, Thomas F.; Heimann, Martin
2018-03-01
Optimized biogenic carbon fluxes for Europe were estimated from high-resolution regional-scale inversions, utilizing atmospheric CO2 measurements at 16 stations for the year 2007. Additional sensitivity tests with different data-driven error structures were performed. As the atmospheric network is rather sparse and consequently contains large spatial gaps, we use a priori biospheric fluxes to further constrain the inversions. The biospheric fluxes were simulated by the Vegetation Photosynthesis and Respiration Model (VPRM) at a resolution of 0.1° and optimized against eddy covariance data. Overall we estimate an a priori uncertainty of 0.54 GtC yr-1 related to the poor spatial representation between the biospheric model and the ecosystem sites. The sink estimated from the atmospheric inversions for the area of Europe (as represented in the model domain) ranges between 0.23 and 0.38 GtC yr-1 (0.39 and 0.71 GtC yr-1 up-scaled to geographical Europe). This is within the range of posterior flux uncertainty estimates of previous studies using ground-based observations.
Antimicrobial stewardship: Strategies for a global response
Directory of Open Access Journals (Sweden)
Jenny Grunwald
2014-01-01
Full Text Available The increasing antimicrobial resistance worldwide, combined with dwindling antimicrobial armamentarium, has resulted in a critical threat to the public health and safety of patients. To combat this hazard, antimicrobial stewardship programs (ASPs have emerged. Antimicrobial stewardship programs prevent or slow the emergence of antimicrobial resistance by coordinated interventions designed to optimize antimicrobial use to achieve the best clinical outcomes and limiting selective pressures that drive the emergence of resistance. This also reduces excessive costs attributable to suboptimal antimicrobial use. Even though an ideal effective ASP should incorporate more than one element simultaneously, it also requires a multidisciplinary team, which should include an infectious diseases physician, a clinical pharmacist with infectious diseases training, infection control professionals, hospital epidemiologist, a clinical microbiologist and an information specialist. However, for antimicrobial stewardship (AMS programs to be successful, they must address the specific needs of individual institutions, must be built on available resources, the limitations and advantages of each institution, and the available staffing and technological infrastructure.
Raffo, A; D'Aloise, A; Magrì, A D; Leclercq, C
2012-01-01
One source of uncertainty in the estimation of dietary exposure to flavouring substances is the uncertainty in the occurrence and concentration levels of these substances naturally present or added to foodstuffs. The aim of this study was to assess the variability of concentration levels of allyl hexanoate, considered as a case study, in two main food categories to which it is often added: pineapple juice-based beverages and yogurts containing pineapple. Thirty-four beverages and 29 yogurts, with pineapple fruit or juice and added flavourings declared as ingredients on the package, were purchased from the local market (in Rome) and analysed. Analytical methods based on the stir bar sorptive extraction (SBSE) technique for the isolation of the target analyte, and on GC-MS analysis for final determination, were developed for the two food categories. In beverages, allyl hexanoate concentrations ranged from less than 0.01 to 16.71 mg l(-1), whereas in yogurts they ranged from 0.02 to 89.41 mg kg(-1). Average concentrations in beverages and yogurts with pineapple as the main fruit ingredient (1.91 mg l(-1) for beverages, 9.61 mg kg(-1) for yogurts) were in fair agreement with average use level data reported from industry surveys for the relevant food categories (4.5 and 6.0 mg kg(-1), respectively). Within the group of yogurts a single product was found to contain a level of allyl hexanoate more than 10-fold higher than the average reported use level. The screening techniques developed by the European Food Safety Authority (EFSA) using use level data provided by industry gave estimates of exposure that were of the same order of magnitude as the estimates obtained for regular consumers who would be loyal to the pineapple yogurt and beverage products containing the highest observed concentration of the substance of interest. In this specific case the uncertainty in the results obtained with the use of standard screening techniques for exposure assessment based on industry
Mama Software Features: Uncertainty Testing
Energy Technology Data Exchange (ETDEWEB)
Ruggiero, Christy E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Porter, Reid B. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2014-05-30
This document reviews how the uncertainty in the calculations is being determined with test image data. The results of this testing give an ‘initial uncertainty’ number than can be used to estimate the ‘back end’ uncertainty in digital image quantification in images. Statisticians are refining these numbers as part of a UQ effort.
2011-03-01
... Stakeholders To Inform the National Framework for Electronics Stewardship AGENCY: Environmental Protection... inform the national framework for electronics stewardship that is being developed by the Interagency Task Force on Electronics Stewardship. On November 15, 2010, President Obama signed a presidential...
Report #2005-P-00007, February 17, 2005. We asked stakeholders to define stewardship, list motivators and obstacles to participating in stewardship programs, and outline key roles for EPA to play to foster participating in environmental stewardship.
Pukala, Jason; Meeks, Sanford L; Staton, Robert J; Bova, Frank J; Mañon, Rafael R; Langen, Katja M
2013-11-01
Deformable image registration (DIR) is being used increasingly in various clinical applications. However, the underlying uncertainties of DIR are not well-understood and a comprehensive methodology has not been developed for assessing a range of interfraction anatomic changes during head and neck cancer radiotherapy. This study describes the development of a library of clinically relevant virtual phantoms for the purpose of aiding clinicians in the QA of DIR software. These phantoms will also be available to the community for the independent study and comparison of other DIR algorithms and processes. Each phantom was derived from a pair of kVCT volumetric image sets. The first images were acquired of head and neck cancer patients prior to the start-of-treatment and the second were acquired near the end-of-treatment. A research algorithm was used to autosegment and deform the start-of-treatment (SOT) images according to a biomechanical model. This algorithm allowed the user to adjust the head position, mandible position, and weight loss in the neck region of the SOT images to resemble the end-of-treatment (EOT) images. A human-guided thin-plate splines algorithm was then used to iteratively apply further deformations to the images with the objective of matching the EOT anatomy as closely as possible. The deformations from each algorithm were combined into a single deformation vector field (DVF) and a simulated end-of-treatment (SEOT) image dataset was generated from that DVF. Artificial noise was added to the SEOT images and these images, along with the original SOT images, created a virtual phantom where the underlying "ground-truth" DVF is known. Images from ten patients were deformed in this fashion to create ten clinically relevant virtual phantoms. The virtual phantoms were evaluated to identify unrealistic DVFs using the normalized cross correlation (NCC) and the determinant of the Jacobian matrix. A commercial deformation algorithm was applied to the virtual
Ezzedine, S. M.; Pitarka, A.; Vorobiev, O.; Glenn, L.; Antoun, T.
2017-12-01
We have performed three-dimensional high resolution simulations of underground chemical explosions conducted recently in jointed rock outcrop as part of the Source Physics Experiments (SPE) being conducted at the Nevada National Security Site (NNSS). The main goal of the current study is to investigate the effects of the structural and geomechanical properties on the spall phenomena due to underground chemical explosions and its subsequent effect on the seismo-acoustic signature at far distances. Two parametric studies have been undertaken to assess the impact of different 1) conceptual geological models including a single layer and two layers model, with and without joints and with and without varying geomechanical properties, and 2) depth of bursts of the chemical explosions and explosion yields. Through these investigations we have explored not only the near-field response of the chemical explosions but also the far-field responses of the seismic and the acoustic signatures. The near-field simulations were conducted using the Eulerian and Lagrangian codes, GEODYN and GEODYN -L, respectively, while the far-field seismic simulations were conducted using the elastic wave propagation code, WPP, and the acoustic response using the Kirchhoff-Helmholtz-Rayleigh time-dependent approximation code, KHR. Though a series of simulations we have recorded the velocity field histories a) at the ground surface on an acoustic-source-patch for the acoustic simulations, and 2) on a seismic-source-box for the seismic simulations. We first analyzed the SPE3 experimental data and simulated results, then simulated SPE4-prime, SPE5, and SPE6 to anticipate their seismo-acoustic responses given conditions of uncertainties. SPE experiments were conducted in a granitic formation; we have extended the parametric study to include other geological settings such dolomite and alluvial formations. These parametric studies enabled us 1) investigating the geotechnical and geophysical key parameters
Windham-Myers, L.; Holmquist, J. R.; Bergamaschi, B. A.; Byrd, K. B.; Callaway, J.; Crooks, S.; Drexler, J. Z.; Feagin, R. A.; Ferner, M. C.; Gonneea, M. E.; Kroeger, K. D.; Megonigal, P.; Morris, J. T.; Schile, L. M.; Simard, M.; Sutton-Grier, A.; Takekawa, J.; Troxler, T.; Weller, D.; Woo, I.
2015-12-01
Despite their high rates of long-term carbon (C) sequestration when compared to upland ecosystems, coastal C accounting is only recently receiving the attention of policy makers and carbon markets. Assessing accuracy and uncertainty in net C flux estimates requires both direct and derived measurements based on both short and long term dynamics in key drivers, particularly soil accretion rates and soil organic content. We are testing the ability of remote sensing products and national scale datasets to estimate biomass and soil stocks and fluxes over a wide range of spatial and temporal scales. For example, the 2013 Wetlands Supplement to the 2006 IPCC GHG national inventory reporting guidelines requests information on development of Tier I-III reporting, which express increasing levels of detail. We report progress toward development of a Carbon Monitoring System for "blue carbon" that may be useful for IPCC reporting guidelines at Tier II levels. Our project uses a current dataset of publically available and contributed field-based measurements to validate models of changing soil C stocks, across a broad range of U.S. tidal wetland types and landuse conversions. Additionally, development of biomass algorithms for both radar and spectral datasets will be tested and used to determine the "price of precision" of different satellite products. We discuss progress in calculating Tier II estimates focusing on variation introduced by the different input datasets. These include the USFWS National Wetlands Inventory, NOAA Coastal Change Analysis Program, and combinations to calculate tidal wetland area. We also assess the use of different attributes and depths from the USDA-SSURGO database to map soil C density. Finally, we examine the relative benefit of radar, spectral and hybrid approaches to biomass mapping in tidal marshes and mangroves. While the US currently plans to report GHG emissions at a Tier I level, we argue that a Tier II analysis is possible due to national
Physical Uncertainty Bounds (PUB)
Energy Technology Data Exchange (ETDEWEB)
Vaughan, Diane Elizabeth [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Preston, Dean L. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2015-03-19
This paper introduces and motivates the need for a new methodology for determining upper bounds on the uncertainties in simulations of engineered systems due to limited fidelity in the composite continuum-level physics models needed to simulate the systems. We show that traditional uncertainty quantification methods provide, at best, a lower bound on this uncertainty. We propose to obtain bounds on the simulation uncertainties by first determining bounds on the physical quantities or processes relevant to system performance. By bounding these physics processes, as opposed to carrying out statistical analyses of the parameter sets of specific physics models or simply switching out the available physics models, one can obtain upper bounds on the uncertainties in simulated quantities of interest.
Stewardship Reporting in the DOD Agency-Wide Financial Statements for FY 1998
National Research Council Canada - National Science Library
1999-01-01
...; heritage assets, stewardship land, and stewardship investments were presented on the financial statements accurately and in accordance with generally accepted accounting standards for Federal agencies...
US antibiotic stewardship and penicillin allergy.
Wada, Kara J; Calhoun, Karen H
2017-06-01
The purpose of this review is to improve otolaryngologists' antibiotic stewardship by detailing current approaches to penicillin allergy. Although up to 15% of hospitalized patients in the United States have a penicillin allergy recorded on their charts, fewer than 10% of these have a true penicillin allergy. Using a combination of a detailed allergy history, skin testing and graded-dose administration, many patients whose charts say 'penicillin-allergic' can safely be treated with penicillin and cross-reacting antibiotics. This permits use of narrower-spectrum antibiotics and saves money.
Antimicrobial Stewardship and Urinary Tract Infections
Directory of Open Access Journals (Sweden)
Lilian M. Abbo
2014-05-01
Full Text Available Urinary tract infections are the most common bacterial infections encountered in ambulatory and long-term care settings in the United States. Urine samples are the largest single category of specimens received by most microbiology laboratories and many such cultures are collected from patients who have no or questionable urinary symptoms. Unfortunately, antimicrobials are often prescribed inappropriately in such patients. Antimicrobial use, whether appropriate or inappropriate, is associated with the selection for antimicrobial-resistant organisms colonizing or infecting the urinary tract. Infections caused by antimicrobial-resistant organisms are associated with higher rates of treatment failures, prolonged hospitalizations, increased costs and mortality. Antimicrobial stewardship consists of avoidance of antimicrobials when appropriate and, when antimicrobials are indicated, use of strategies to optimize the selection, dosing, route of administration, duration and timing of antimicrobial therapy to maximize clinical cure while limiting the unintended consequences of antimicrobial use, including toxicity and selection of resistant microorganisms. This article reviews successful antimicrobial stewardship strategies in the diagnosis and treatment of urinary tract infections.
Accident sequence quantification with KIRAP
International Nuclear Information System (INIS)
Kim, Tae Un; Han, Sang Hoon; Kim, Kil You; Yang, Jun Eon; Jeong, Won Dae; Chang, Seung Cheol; Sung, Tae Yong; Kang, Dae Il; Park, Jin Hee; Lee, Yoon Hwan; Hwang, Mi Jeong.
1997-01-01
The tasks of probabilistic safety assessment(PSA) consists of the identification of initiating events, the construction of event tree for each initiating event, construction of fault trees for event tree logics, the analysis of reliability data and finally the accident sequence quantification. In the PSA, the accident sequence quantification is to calculate the core damage frequency, importance analysis and uncertainty analysis. Accident sequence quantification requires to understand the whole model of the PSA because it has to combine all event tree and fault tree models, and requires the excellent computer code because it takes long computation time. Advanced Research Group of Korea Atomic Energy Research Institute(KAERI) has developed PSA workstation KIRAP(Korea Integrated Reliability Analysis Code Package) for the PSA work. This report describes the procedures to perform accident sequence quantification, the method to use KIRAP's cut set generator, and method to perform the accident sequence quantification with KIRAP. (author). 6 refs
Accident sequence quantification with KIRAP
Energy Technology Data Exchange (ETDEWEB)
Kim, Tae Un; Han, Sang Hoon; Kim, Kil You; Yang, Jun Eon; Jeong, Won Dae; Chang, Seung Cheol; Sung, Tae Yong; Kang, Dae Il; Park, Jin Hee; Lee, Yoon Hwan; Hwang, Mi Jeong
1997-01-01
The tasks of probabilistic safety assessment(PSA) consists of the identification of initiating events, the construction of event tree for each initiating event, construction of fault trees for event tree logics, the analysis of reliability data and finally the accident sequence quantification. In the PSA, the accident sequence quantification is to calculate the core damage frequency, importance analysis and uncertainty analysis. Accident sequence quantification requires to understand the whole model of the PSA because it has to combine all event tree and fault tree models, and requires the excellent computer code because it takes long computation time. Advanced Research Group of Korea Atomic Energy Research Institute(KAERI) has developed PSA workstation KIRAP(Korea Integrated Reliability Analysis Code Package) for the PSA work. This report describes the procedures to perform accident sequence quantification, the method to use KIRAP`s cut set generator, and method to perform the accident sequence quantification with KIRAP. (author). 6 refs.
Best Practices for Curriculum, Teaching, and Evaluation Components of Aquatic Stewardship Education.
Siemer, William F.
This paper reviews the literature to outline principles and best practices for aquatic stewardship education. Stewardship education develops an internalized stewardship ethic and the skills needed for decision making and environmentally responsible actions. Successful stewardship education programs are designed to influence beliefs, values,…
Final Programmatic Environmental Impact Statement for stockpile stewardship and management
International Nuclear Information System (INIS)
1996-09-01
The Department of Energy (DOE) has been directed by the President and Congress to maintain the safety and reliability of the reduced nuclear weapons stockpile in the absence of underground nuclear testing. In order to fulfill that responsibility, DOE has developed a Stockpile Stewardship and Management Program to provide a single highly integrated technical program for maintaining the continued safety and reliability of the nuclear stockpile. The Stockpile Stewardship and Management Programmatic Environmental Impact Statement (PEIS) describes and analyzes alternative ways to implement the proposed actions for the Stockpile Stewardship and Management Program. This document contains Volume II which consists of Appendices A through H
Uncertainty Propagation in OMFIT
Smith, Sterling; Meneghini, Orso; Sung, Choongki
2017-10-01
A rigorous comparison of power balance fluxes and turbulent model fluxes requires the propagation of uncertainties in the kinetic profiles and their derivatives. Making extensive use of the python uncertainties package, the OMFIT framework has been used to propagate covariant uncertainties to provide an uncertainty in the power balance calculation from the ONETWO code, as well as through the turbulent fluxes calculated by the TGLF code. The covariant uncertainties arise from fitting 1D (constant on flux surface) density and temperature profiles and associated random errors with parameterized functions such as a modified tanh. The power balance and model fluxes can then be compared with quantification of the uncertainties. No effort is made at propagating systematic errors. A case study will be shown for the effects of resonant magnetic perturbations on the kinetic profiles and fluxes at the top of the pedestal. A separate attempt at modeling the random errors with Monte Carlo sampling will be compared to the method of propagating the fitting function parameter covariant uncertainties. Work supported by US DOE under DE-FC02-04ER54698, DE-FG2-95ER-54309, DE-SC 0012656.
The Roles of the Accountant and Auditor in Stewardship and ...
African Journals Online (AJOL)
While much attention has been given to accountability in public sector which has not even yielded the desired result, the same can not be said of organised private sector. ... Keywords: Stewardship, corporate governance, accountability,
Long-Term Stewardship Science and Technology Requirements
International Nuclear Information System (INIS)
McDonald, J.K.; Nickelson, R.A.
2002-01-01
To ensure technology developed for long-term stewardship will meet existing requirements, a review of requirements was performed. In addition to identifying existing science and technology related requirements, gaps and conflicts of requirements were identified
Airport Capital Improvement Planning: Stewardship for Airport Development
1997-09-01
"Airport Capital Improvement Planning: Stewardship for Airport Development", was : originally written in October, 1995. It documented an effort to implement the : concept of capital improvement planning with the airport development industry. : Airpor...
An integrated stewardship model : Antimicrobial, infection prevention and diagnostic (AID)
Dik, Jan-Willem H.; Poelman, Randy; Friedrich, Alexander W.; Panday, Prashant Nannan; Lo-Ten-Foe, Jerome R.; van Assen, Sander; van Gemert-Pijnen, Julia E. W. C.; Niesters, Hubert G. M.; Hendrix, Ron; Sinha, Bhanu
2016-01-01
Considering the threat of antimicrobial resistance and the difficulties it entails in treating infections, it is necessary to cross borders and approach infection management in an integrated, multidisciplinary manner. We propose the antimicrobial, infection prevention and diagnostic stewardship
An integrated stewardship model: antimicrobial, infection prevention and diagnostic (AID)
Dik, Jan-Willem H.; Poelman, Randy; Friedrich, Alexander W.; Panday, Prashant N.; Lo-Ten-Foe, Jerome R.; van Assen, Sander; van Gemert-Pijnen, Julia E.W.C.; Niesters, Hubert G.M.; Hendrix, Ron; Sinha, Bhanu
2015-01-01
Considering the threat of antimicrobial resistance and the difficulties it entails in treating infections, it is necessary to cross borders and approach infection management in an integrated, multidisciplinary manner. We propose the antimicrobial, infection prevention and diagnostic stewardship
FY 2014 - Stockpile and Stewardship and Management Plan
Energy Technology Data Exchange (ETDEWEB)
None
2013-06-01
This Department of Energy’s (DOE) National Nuclear Security Administration (NNSA) Fiscal Year Stockpile Stewardship and Management Plan (SSMP) is a key planning document for the nuclear security enterprise.
Long-Term Stewardship Baseline Report and Transition Guidance
Energy Technology Data Exchange (ETDEWEB)
Kristofferson, Keith
2001-11-01
Long-term stewardship consists of those actions necessary to maintain and demonstrate continued protection of human health and the environment after facility cleanup is complete. As the Department of Energy’s (DOE) lead laboratory for environmental management programs, the Idaho National Engineering and Environmental Laboratory (INEEL) administers DOE’s long-term stewardship science and technology efforts. The INEEL provides DOE with technical, and scientific expertise needed to oversee its long-term environmental management obligations complexwide. Long-term stewardship is administered and overseen by the Environmental Management Office of Science and Technology. The INEEL Long-Term Stewardship Program is currently developing the management structures and plans to complete INEEL-specific, long-term stewardship obligations. This guidance document (1) assists in ensuring that the program leads transition planning for the INEEL with respect to facility and site areas and (2) describes the classes and types of criteria and data required to initiate transition for areas and sites where the facility mission has ended and cleanup is complete. Additionally, this document summarizes current information on INEEL facilities, structures, and release sites likely to enter long-term stewardship at the completion of DOE’s cleanup mission. This document is not intended to function as a discrete checklist or local procedure to determine readiness to transition. It is an overarching document meant as guidance in implementing specific transition procedures. Several documents formed the foundation upon which this guidance was developed. Principal among these documents was the Long-Term Stewardship Draft Technical Baseline; A Report to Congress on Long-Term Stewardship, Volumes I and II; Infrastructure Long-Range Plan; Comprehensive Facility Land Use Plan; INEEL End-State Plan; and INEEL Institutional Plan.
Nuclear materials stewardship: Our enduring mission
International Nuclear Information System (INIS)
Isaacs, T.H.
1998-01-01
The US Department of Energy (DOE) and its predecessors have handled a remarkably wide variety of nuclear materials over the past 50 yr. Two fundamental changes have occurred that shape the current landscape regarding nuclear materials. If one recognizes the implications and opportunities, one sees that the stewardship of nuclear materials will be a fundamental and important job of the DOE for the foreseeable future. The first change--the breakup of the Soviet Union and the resulting end to the nuclear arms race--altered US objectives. Previously, the focus was on materials production, weapon design, nuclear testing, and stockpile enhancements. Now the attention is on dismantlement of weapons, excess special nuclear material inventories, accompanying increased concern over the protection afforded to such materials; new arms control measures; and importantly, maintenance of the safety and reliability of the remaining arsenal without testing. The second change was the raised consciousness and sense of responsibility for dealing with the environmental legacies of past nuclear arms programs. Recognition of the need to clean up radioactive contamination, manage the wastes, conduct current operations responsibly, and restore the environment have led to the establishment of what is now the largest program in the DOE. Two additional features add to the challenge and drive the need for recognition of nuclear materials stewardship as a fundamental, enduring, and compelling mission of the DOE. The first is the extraordinary time frames. No matter what the future of nuclear weapons and no matter what the future of nuclear power, the DOE will be responsible for most of the country's nuclear materials and wastes for generations. Even if the Yucca Mountain program is successful and on schedule, it will last more than 100 yr. Second, the use, management, and disposition of nuclear materials and wastes affect a variety of nationally important and diverse objectives, from national
Needs of the CSAU uncertainty method
International Nuclear Information System (INIS)
Prosek, A.; Mavko, B.
2000-01-01
The use of best estimate codes for safety analysis requires quantification of the uncertainties. These uncertainties are inherently linked to the chosen safety analysis methodology. Worldwide, various methods were proposed for this quantification. The purpose of this paper was to identify the needs of the Code Scaling, Applicability, and Uncertainty (CSAU) methodology and then to answer the needs. The specific procedural steps were combined from other methods for uncertainty evaluation and new tools and procedures were proposed. The uncertainty analysis approach and tools were then utilized for confirmatory study. The uncertainty was quantified for the RELAP5/MOD3.2 thermalhydraulic computer code. The results of the adapted CSAU approach to the small-break loss-of-coolant accident (SB LOCA) show that the adapted CSAU can be used for any thermal-hydraulic safety analysis with uncertainty evaluation. However, it was indicated that there are still some limitations in the CSAU approach that need to be resolved. (author)
Sustainable Stewardship of the Landrace Diversity
Directory of Open Access Journals (Sweden)
Ioannis Tokatlidis
2016-12-01
Full Text Available Landraces are heterogeneous populations and their variability goes through continuous alterations because of physical, genetic, and epigenetic procedures exacerbated by the ongoing climatic changes. Appropriate stewardship of landrace diversity is pivotal to promote its longevity in a manner that is sustainable from the farming perspective. A seed multiplication procedure is presented based on the assumption that in order to improve effectiveness in resource use and increase seed productivity, landraces should comprise genotypes which minimize intra-species competition. These aforementioned genotypes should be of the “weak competitor” ideotype, which are selected so as to alleviate the interplant competition and reach as high as possible crop stand uniformity. Stand uniformity is essential to ensure the same growing conditions for each plant. Reduced intra-crop inequality and equal use of inputs by individual plants will optimize crop performance. Precisely, the “weak competitor” is most often of high yield potential due to a negative association between yielding and competitive ability. Therefore, the suggested procedure involves initial reproduction at nil-competition (widely spaced plants to preclude any plant-to-plant interference for inputs where “off-type” and low yielding plants are omitted, followed by subsequent multiplication at dense stands. This may represent an effective cultural practice to improve also the landrace health status concerning seed-borne diseases in the absence of certification systems.
NASA'S Earth Science Data Stewardship Activities
Lowe, Dawn R.; Murphy, Kevin J.; Ramapriyan, Hampapuram
2015-01-01
NASA has been collecting Earth observation data for over 50 years using instruments on board satellites, aircraft and ground-based systems. With the inception of the Earth Observing System (EOS) Program in 1990, NASA established the Earth Science Data and Information System (ESDIS) Project and initiated development of the Earth Observing System Data and Information System (EOSDIS). A set of Distributed Active Archive Centers (DAACs) was established at locations based on science discipline expertise. Today, EOSDIS consists of 12 DAACs and 12 Science Investigator-led Processing Systems (SIPS), processing data from the EOS missions, as well as the Suomi National Polar Orbiting Partnership mission, and other satellite and airborne missions. The DAACs archive and distribute the vast majority of data from NASA’s Earth science missions, with data holdings exceeding 12 petabytes The data held by EOSDIS are available to all users consistent with NASA’s free and open data policy, which has been in effect since 1990. The EOSDIS archives consist of raw instrument data counts (level 0 data), as well as higher level standard products (e.g., geophysical parameters, products mapped to standard spatio-temporal grids, results of Earth system models using multi-instrument observations, and long time series of Earth System Data Records resulting from multiple satellite observations of a given type of phenomenon). EOSDIS data stewardship responsibilities include ensuring that the data and information content are reliable, of high quality, easily accessible, and usable for as long as they are considered to be of value.
WASH-1400: quantifying the uncertainties
International Nuclear Information System (INIS)
Erdmann, R.C.; Leverenz, F.L. Jr.; Lellouche, G.S.
1981-01-01
The purpose of this paper is to focus on the limitations of the WASH-1400 analysis in estimating the risk from light water reactors (LWRs). This assessment attempts to modify the quantification of the uncertainty in and estimate of risk as presented by the RSS (reactor safety study). 8 refs
Messacar, Kevin; Parker, Sarah K; Todd, James K; Dominguez, Samuel R
2017-03-01
New rapid molecular diagnostic technologies for infectious diseases enable expedited accurate microbiological diagnoses. However, diagnostic stewardship and antimicrobial stewardship are necessary to ensure that these technologies conserve, rather than consume, additional health care resources and optimally affect patient care. Diagnostic stewardship is needed to implement appropriate tests for the clinical setting and to direct testing toward appropriate patients. Antimicrobial stewardship is needed to ensure prompt appropriate clinical action to translate faster diagnostic test results in the laboratory into improved outcomes at the bedside. This minireview outlines the roles of diagnostic stewardship and antimicrobial stewardship in the implementation of rapid molecular infectious disease diagnostics. Copyright © 2017 American Society for Microbiology.
Directory of Open Access Journals (Sweden)
A. Paige. Fischer
2015-12-01
Full Text Available Although people and organizations in the Great Lakes region, USA take seriously their role as stewards of natural resources, many lack capacity to fulfill that role in a meaningful way. Stepping into that gap, The Stewardship Network (TSN envisions "a world of empowered, connected communities caring for land and water, now and forever," and fulfills that vision through its mission to "connect, equip, and mobilize people and organizations to care for land and water in their communities." TSN uses a scalable model of linked local and regional capacity building, science communication, civic engagement, and on-the-ground stewardship activities to achieve these goals. The model engages local and regional groups in an ongoing process of learning around conservation and restoration that improves social and ecological knowledge. I share the story of TSN to demonstrate how transdisciplinary science can take hold locally and expand regionally to bring people from diverse disciplines and functional roles together to solve common problems. I demonstrate how researchers and practitioners can collaborate to create enduring mechanisms of social and ecological change.
Evaluation of pharmacy generalists performing antimicrobial stewardship services.
Carreno, Joseph J; Kenney, Rachel M; Bloome, Mary; McDonnell, Jane; Rodriguez, Jennifer; Weinmann, Allison; Kilgore, Paul E; Davis, Susan L
2015-08-01
Improvements in medication use achieved by pharmacy generalists using a care bundle approach to antimicrobial stewardship are reported. A six-month prospective, repeated-treatment, quasi-experimental study involving three month-long intervention periods and three month-long control periods was conducted in the setting of an existing antimicrobial stewardship program at a large hospital. The intervention involved prospective audit and feedback conducted by pharmacy generalists who were trained in an antimicrobial stewardship care bundle approach. During control months, a pharmacy generalist who was not trained in antimicrobial stewardship rounded with the multidisciplinary team and provided standard-of-care pharmacy services. The primary endpoint was compliance with a care bundle of four antimicrobial stewardship metrics: documentation of indication for therapy in the medical record, selection of empirical therapy according to institutional guidelines, documented performance of indicated culture testing, and deescalation of therapy when indicated. Two-hundred eighty-six patients were enrolled in the study: 124 in the intervention group and 162 in the control group. The cumulative rate of full compliance with all care bundle components during the six-month study was significantly greater during intervention months than during control months (68.5% versus 45.7%, p management. Copyright © 2015 by the American Society of Health-System Pharmacists, Inc. All rights reserved.
Long-Term Stewardship Program Science and Technology Requirements
Energy Technology Data Exchange (ETDEWEB)
Joan McDonald
2002-09-01
Many of the United States’ hazardous and radioactively contaminated waste sites will not be sufficiently remediated to allow unrestricted land use because funding and technology limitations preclude cleanup to pristine conditions. This means that after cleanup is completed, the Department of Energy will have long-term stewardship responsibilities to monitor and safeguard more than 100 sites that still contain residual contamination. Long-term stewardship encompasses all physical and institutional controls, institutions, information, and other mechanisms required to protect human health and the environment from the hazards remaining. The Department of Energy Long-Term Stewardship National Program is in the early stages of development, so considerable planning is still required to identify all the specific roles and responsibilities, policies, and activities needed over the next few years to support the program’s mission. The Idaho National Engineering and Environmental Laboratory was tasked with leading the development of Science and Technology within the Long-Term Stewardship National Program. As part of that role, a task was undertaken to identify the existing science and technology related requirements, identify gaps and conflicts that exist, and make recommendations to the Department of Energy for future requirements related to science and technology requirements for long-term stewardship. This work is summarized in this document.
Integrating Information Networks for Collective Planetary Stewardship
Tiwari, A.
2016-12-01
Responsible behaviour resulting from climate literacy in global environmental movement is limited to policy and planning institutions in the Global South, while remaining absent for ends-user. Thus, planetary stewardship exists only at earth system boundaries where pressures sink to the local scale while ethics remains afloat. Existing citizen participation is restricted within policy spheres, appearing synonymous to enforcements in social psychology. Much, accounted reason is that existing information mechanisms operate mostly through linear exchanges between institutions and users, therefore reinforcing only hierarchical relationships. This study discloses such relationships that contribute to broad networking gaps through information demand assessment of stakeholders in a dozen development projects based in South Asia. Two parameters widely used for this purpose are: a. Feedback: Ends-user feedback to improve consumption literacy of climate sensitive resources (through consumption displays, billing, advisory services ecolabelling, sensors) and, b. Institutional Policy: Rewarding punishing to enforce desired behaviour (subsidies, taxation). Research answered: 1. Who gets the information (Equity in Information Distribution)? As existing information publishing mechanisms are designed by and for analysts, 2. How information translates to climate action Transparency of Execution)? Findings suggested that climate goals manifested in economic policy, than environmental policy, have potential clear short-term benefits and costs, and coincide with people's economic goals Also grassroots roles for responsible behaviour are empowered with presence of end user information. Barier free climate communication process and decision making is ensured among multiplicity of stakeholders with often conflicting perspectives. Research finds significance where collaboration among information networks can better translate regional policies into local action for climate adaptation and
Lindley, Dennis V
2013-01-01
Praise for the First Edition ""...a reference for everyone who is interested in knowing and handling uncertainty.""-Journal of Applied Statistics The critically acclaimed First Edition of Understanding Uncertainty provided a study of uncertainty addressed to scholars in all fields, showing that uncertainty could be measured by probability, and that probability obeyed three basic rules that enabled uncertainty to be handled sensibly in everyday life. These ideas were extended to embrace the scientific method and to show how decisions, containing an uncertain element, could be rationally made.
Final programmatic environmental impact statement for stockpile stewardship and management
International Nuclear Information System (INIS)
1996-09-01
In response to the end of the Cold War and changes in the world's political regimes, the United States is not producing new-design nuclear weapons. Instead, the emphasis of the U.S. nuclear weapons program is on reducing the size of the Nation's nuclear stockpile by dismantling existing nuclear weapons. The Department of Energy (DOE) has been directed by the President and Congress to maintain the safety and reliability of the reduced nuclear weapons stockpile in the absence of underground nuclear testing. In order to fulfill that responsibility, DOE has developed a Stockpile Stewardship and Management Program to provide a single highly integrated technical program for maintaining the continued safety and reliability of the nuclear stockpile. The Stockpile Stewardship and Management PEIS describes and analyzes alternative ways to implement the proposed actions for the Stockpile Stewardship and Management Program
Without Testing: Stockpile Stewardship in the Second Nuclear Age
Energy Technology Data Exchange (ETDEWEB)
Martz, Joseph C. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2014-01-07
Stockpile stewardship is a topic dear to my heart. I’ve been fascinated by it, and I’ve lived it—mostly on the technical side but also on the policy side from 2009 to 2010 at Stanford University as a visiting scholar and the inaugural William J. Perry Fellow. At Stanford I worked with Perry, former secretary of defense, and Sig Hecker, former Los Alamos Lab director (1986–1997), looking at nuclear deterrence, nuclear policy, and stockpile stewardship and at where all this was headed.
Improving Wellbeing and Environmental Stewardship Through Volunteering in Nature.
Molsher, Robyn; Townsend, Mardie
2016-03-01
Environmental volunteering (EV) can provide a unique way to optimise the wellbeing of participants while fostering environmental stewardship. However, the potential of EV to create human health benefits remains an under-researched area. This study provides evidence for improved wellbeing and mood state for 32 participants from diverse backgrounds undertaking EV activities. Most participants also reported improved environmental stewardship with a greatly improved understanding of the environment and the need to conserve it. Other benefits included: 31% of those seeking work obtained it; and 50% joined a volunteer group at program completion. EV provides a unique mechanism to enhance the wellbeing of the participants, while conserving the environment.
A system simulation to enhance stockpile stewardship (ASSESS)
International Nuclear Information System (INIS)
Yoshimura, A.S.; Plantenga, T.D.; Napolitano, L.M.; Johnson, M.M.
1997-01-01
This paper describes the ASSESS project, whose goal is to construct a policy driven enterprise simulation of the DOE nuclear weapons complex (DOE/NWC). ASSESS encompasses the full range of stockpile stewardship activities by incorporating simulation component models that are developed and managed by local experts. ASSESS runs on a heterogeneous distributed computing environment and implements multi-layered user access capabilities. ASSESS allows the user to create hypothetical policies governing stockpile stewardship, simulate the resulting operation of the DOE/NWC, and analyze the relative impact of each policy
Organizing urban ecosystem services through environmental stewardship governance in New York City
James J. Connolly; Erika S. Svendsen; Dana R. Fisher; Lindsay K. Campbell
2013-01-01
How do stewardship groups contribute to the management of urban ecosystem services? In this paper, we integrate the research on environmental stewardship with the social-ecological systems literature to explain how stewardship groups serve as bridge organizations between public agencies and civic organizations, working across scales and sectors to build the flexible...
Koch, Michael
Measurement uncertainty is one of the key issues in quality assurance. It became increasingly important for analytical chemistry laboratories with the accreditation to ISO/IEC 17025. The uncertainty of a measurement is the most important criterion for the decision whether a measurement result is fit for purpose. It also delivers help for the decision whether a specification limit is exceeded or not. Estimation of measurement uncertainty often is not trivial. Several strategies have been developed for this purpose that will shortly be described in this chapter. In addition the different possibilities to take into account the uncertainty in compliance assessment are explained.
Eco-Visualization: Promoting Environmental Stewardship in the Museum
Holmes, Tiffany
2007-01-01
Eco-visualizations are artworks that reinterpret environmental data with custom software to promote stewardship. Eco-visualization technology offers a new way to dynamically picture environmental data and make it meaningful to a museum population. The questions are: How might museums create new projects and programs around place-based information?…
Antimicrobial stewardship in long term care facilities: what is effective?
Nicolle, Lindsay E
2014-02-12
Intense antimicrobial use in long term care facilities promotes the emergence and persistence of antimicrobial resistant organisms and leads to adverse effects such as C. difficile colitis. Guidelines recommend development of antimicrobial stewardship programs for these facilities to promote optimal antimicrobial use. However, the effectiveness of these programs or the contribution of any specific program component is not known. For this review, publications describing evaluation of antimicrobial stewardship programs for long term care facilities were identified through a systematic literature search. Interventions included education, guidelines development, feedback to practitioners, and infectious disease consultation. The studies reviewed varied in types of facilities, interventions used, implementation, and evaluation. Comprehensive programs addressing all infections were reported to have improved antimicrobial use for at least some outcomes. Targeted programs for treatment of pneumonia were minimally effective, and only for indicators of uncertain relevance for stewardship. Programs focusing on specific aspects of treatment of urinary infection - limiting treatment of asymptomatic bacteriuria or prophylaxis of urinary infection - were reported to be effective. There were no reports of cost-effectiveness, and the sustainability of most of the programs is unclear. There is a need for further evaluation to characterize effective antimicrobial stewardship for long term care facilities.
Stewardship - De nieuwe facilitaire werkelijkheid, Facto Magazine, nr. 12
Kok, H.B.
2011-01-01
Voor het succesvol besturen van organisaties bestaan in de managementliteratuur diverse modellen en theorieën. Het lijkt er op dat de stewardship-theorie, die uitgaat van wederzijds vertrouwen, beter past bij de ontwikkeling die FM op dit moment doormaakt dan de agencytheorie
A state-stewardship view on executive compensation
Liang, Hao; Renneboog, Luc; Li Sun, Sunny; Choi, J.; Powers, M.; Zhang, X.
2016-01-01
We take a state-stewardship view on the corporate governance model and executive compensation policies in economies with strong political involvement. In such a highly politically-oriented institutional environment, the business elites are not just professional managers but are also de facto
76 FR 17180 - Meeting of the Regional Resource Stewardship Council
2011-03-28
... area components of the draft NRP and the benefits and challenges stemming from such programs. 5. Public... and will be called on during the public comment period. Handout materials should be limited to one printed page. Written comments are also invited and may be mailed to the Regional Resource Stewardship...
United States Department of Energy Nuclear Materials Stewardship
International Nuclear Information System (INIS)
Newton, J. W.
2002-01-01
The Department of Energy launched the Nuclear Materials Stewardship Initiative in January 2000 to accelerate the work of achieving integration and cutting long-term costs associated with the management of the Department's nuclear materials, with the principal focus on excess materials. Management of nuclear materials is a fundamental and enduring responsibility that is essential to meeting the Department's national security, nonproliferation, energy, science, and environmental missions into the distant future. The effective management of nuclear materials is important for a set of reasons: (1) some materials are vital to our national defense; (2) the materials pose physical and security risks; (3) managing them is costly; and (4) costs are likely to extend well into the future. The Department currently manages nuclear materials under eight programs, with offices in 36 different locations. Through the Nuclear Materials Stewardship Initiative, progress was during calendar year 20 00 in achieving better coordination and integration of nuclear materials management responsibilities and in evaluating opportunities to further coordinate and integrate cross-program responsibilities for the treatment, storage, and disposition of excess nuclear materials. During CY 2001 the Departmental approach to nuclear materials stewardship changed consistent with the business processes followed by the new administration. This paper reports on the progress of the Nuclear Materials Stewardship Initiative in evaluating and implementing these opportunities, and the remaining challenges in integrating the long-term management of nuclear materials
ICMR programme on Antibiotic Stewardship, Prevention of Infection & Control (ASPIC).
Chandy, Sujith J; Michael, Joy Sarojini; Veeraraghavan, Balaji; Abraham, O C; Bachhav, Sagar S; Kshirsagar, Nilima A
2014-02-01
Antimicrobial resistance and hospital infections have increased alarmingly in India. Antibiotic stewardship and hospital infection control are two broad strategies which have been employed globally to contain the problems of resistance and infections. For this to succeed, it is important to bring on board the various stakeholders in hospitals, especially the clinical pharmacologists. The discipline of clinical pharmacology needs to be involved in themes such as antimicrobial resistance and hospital infection which truly impact patient care. Clinical pharmacologists need to collaborate with faculty in other disciplines such as microbiology to achieve good outcomes for optimal patient care in the hospital setting. The ASPIC programme was initiated by the Indian Council of Medical Research (ICMR) in response to the above need and was designed to bring together faculty from clinical pharmacology, microbiology and other disciplines to collaborate on initiating and improving antibiotic stewardship and concurrently curbing hospital infections through feasible infection control practices. This programme involves the participation of 20 centres per year throughout the country which come together for a training workshop. Topics pertaining to the above areas are discussed in addition to planning a project which helps to improve antibiotic stewardship and infection control practices in the various centres. It is hoped that this programme would empower hospitals and institutions throughout the country to improve antibiotic stewardship and infection control and ultimately contain antimicrobial resistance.
Antimicrobial Stewardship Initiatives Throughout Europe: Proven Value for Money
Oberje, E.J.M.; Tanke, M.A.C.; Jeurissen, P.P.T.
2017-01-01
Antimicrobial stewardship is recognized as a key component to stop the current European spread of antimicrobial resistance. It has also become evident that antimicrobial resistance is a problem that cannot be tackled by single institutions or physicians. Prevention of antimicrobial resistance needs
Antimicrobial stewardship: attempting to preserve a strategic resource
Directory of Open Access Journals (Sweden)
Trevor Van Schooneveld, Md
2011-07-01
Full Text Available Antimicrobials hold a unique place in our drug armamentarium. Unfortunately the increase in resistance among both gram-positive and gram-negative pathogens coupled with a lack of new antimicrobial agents is threatening our ability to treat infections. Antimicrobial use is the driving force behind this rise in resistance and much of this use is suboptimal. Antimicrobial stewardship programs (ASP have been advocated as a strategy to improve antimicrobial use. The goals of ASP are to improve patient outcomes while minimizing toxicity and selection for resistant strains by assisting in the selection of the correct agent, right dose, and best duration. Two major strategies for ASP exist: restriction/pre-authorization that controls use at the time of ordering and audit and feedback that reviews ordered antimicrobials and makes suggestions for improvement. Both strategies have some limitations, but have been effective at achieving stewardship goals. Other supplemental strategies such as education, clinical prediction rules, biomarkers, clinical decision support software, and institutional guidelines have been effective at improving antimicrobial use. The most effective antimicrobial stewardship programs have employed multiple strategies to impact antimicrobial use. Using these strategies stewardship programs have been able to decrease antimicrobial use, the spread of resistant pathogens, the incidence of C. difficile infection, pharmacy costs, and improved patient outcomes.
36 CFR 230.6 - Landowner forest stewardship plan.
2010-07-01
... manage soil, water, aesthetic qualities, recreation, timber, and fish and wildlife resources in a manner... sells or otherwise conveys land covered by a landowner forest stewardship plan, such plan shall remain... plan. 230.6 Section 230.6 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE...
Sparse grid-based polynomial chaos expansion for aerodynamics of an airfoil with uncertainties
Directory of Open Access Journals (Sweden)
Xiaojing WU
2018-05-01
Full Text Available The uncertainties can generate fluctuations with aerodynamic characteristics. Uncertainty Quantification (UQ is applied to compute its impact on the aerodynamic characteristics. In addition, the contribution of each uncertainty to aerodynamic characteristics should be computed by uncertainty sensitivity analysis. Non-Intrusive Polynomial Chaos (NIPC has been successfully applied to uncertainty quantification and uncertainty sensitivity analysis. However, the non-intrusive polynomial chaos method becomes inefficient as the number of random variables adopted to describe uncertainties increases. This deficiency becomes significant in stochastic aerodynamic analysis considering the geometric uncertainty because the description of geometric uncertainty generally needs many parameters. To solve the deficiency, a Sparse Grid-based Polynomial Chaos (SGPC expansion is used to do uncertainty quantification and sensitivity analysis for stochastic aerodynamic analysis considering geometric and operational uncertainties. It is proved that the method is more efficient than non-intrusive polynomial chaos and Monte Carlo Simulation (MSC method for the stochastic aerodynamic analysis. By uncertainty quantification, it can be learnt that the flow characteristics of shock wave and boundary layer separation are sensitive to the geometric uncertainty in transonic region. The uncertainty sensitivity analysis reveals the individual and coupled effects among the uncertainty parameters. Keywords: Non-intrusive polynomial chaos, Sparse grid, Stochastic aerodynamic analysis, Uncertainty sensitivity analysis, Uncertainty quantification
Considerations for Future Climate Data Stewardship
Halem, M.; Nguyen, P. T.; Chapman, D. R.
2009-12-01
In this talk, we will describe the lessons learned based on processing and generating a decade of gridded AIRS and MODIS IR sounding data. We describe the challenges faced in accessing and sharing very large data sets, maintaining data provenance under evolving technologies, obtaining access to legacy calibration data and the permanent preservation of Earth science data records for on demand services. These lessons suggest a new approach to data stewardship will be required for the next decade of hyper spectral instruments combined with cloud resolving models. It will not be sufficient for stewards of future data centers to just provide the public with access to archived data but our experience indicates that data needs to reside close to computers with ultra large disc farms and tens of thousands of processors to deliver complex services on demand over very high speed networks much like the offerings of search engines today. Over the first decade of the 21st century, petabyte data records were acquired from the AIRS instrument on Aqua and the MODIS instrument on Aqua and Terra. NOAA data centers also maintain petabytes of operational IR sounders collected over the past four decades. The UMBC Multicore Computational Center (MC2) developed a Service Oriented Atmospheric Radiance gridding system (SOAR) to allow users to select IR sounding instruments from multiple archives and choose space-time- spectral periods of Level 1B data to download, grid, visualize and analyze on demand. Providing this service requires high data rate bandwidth access to the on line disks at Goddard. After 10 years, cost effective disk storage technology finally caught up with the MODIS data volume making it possible for Level 1B MODIS data to be available on line. However, 10Ge fiber optic networks to access large volumes of data are still not available from CSFC to serve the broader community. Data transfer rates are well below 10MB/s limiting their usefulness for climate studies. During
Sharing Responsibility for Data Stewardship Between Scientists and Curators
Hedstrom, M. L.
2012-12-01
Data stewardship is becoming increasingly important to support accurate conclusions from new forms of data, integration of and computation across heterogeneous data types, interactions between models and data, replication of results, data governance and long-term archiving. In addition to increasing recognition of the importance of data management, data science, and data curation by US and international scientific agencies, the National Academies of Science Board on Research Data and Information is sponsoring a study on Data Curation Education and Workforce Issues. Effective data stewardship requires a distributed effort among scientists who produce data, IT staff and/or vendors who provide data storage and computational facilities and services, and curators who enhance data quality, manage data governance, provide access to third parties, and assume responsibility for long-term archiving of data. The expertise necessary for scientific data management includes a mix of knowledge of the scientific domain; an understanding of domain data requirements, standards, ontologies and analytical methods; facility with leading edge information technology; and knowledge of data governance, standards, and best practices for long-term preservation and access that rarely are found in a single individual. Rather than developing data science and data curation as new and distinct occupations, this paper examines the set of tasks required for data stewardship. The paper proposes an alternative model that embeds data stewardship in scientific workflows and coordinates hand-offs between instruments, repositories, analytical processing, publishers, distributors, and archives. This model forms the basis for defining knowledge and skill requirements for specific actors in the processes required for data stewardship and the corresponding educational and training needs.
Liu, Baoding
2015-01-01
When no samples are available to estimate a probability distribution, we have to invite some domain experts to evaluate the belief degree that each event will happen. Perhaps some people think that the belief degree should be modeled by subjective probability or fuzzy set theory. However, it is usually inappropriate because both of them may lead to counterintuitive results in this case. In order to rationally deal with belief degrees, uncertainty theory was founded in 2007 and subsequently studied by many researchers. Nowadays, uncertainty theory has become a branch of axiomatic mathematics for modeling belief degrees. This is an introductory textbook on uncertainty theory, uncertain programming, uncertain statistics, uncertain risk analysis, uncertain reliability analysis, uncertain set, uncertain logic, uncertain inference, uncertain process, uncertain calculus, and uncertain differential equation. This textbook also shows applications of uncertainty theory to scheduling, logistics, networks, data mining, c...
Uncertainty enabled Sensor Observation Services
Cornford, Dan; Williams, Matthew; Bastin, Lucy
2010-05-01
Almost all observations of reality are contaminated with errors, which introduce uncertainties into the actual observation result. Such uncertainty is often held to be a data quality issue, and quantification of this uncertainty is essential for the principled exploitation of the observations. Many existing systems treat data quality in a relatively ad-hoc manner, however if the observation uncertainty is a reliable estimate of the error on the observation with respect to reality then knowledge of this uncertainty enables optimal exploitation of the observations in further processes, or decision making. We would argue that the most natural formalism for expressing uncertainty is Bayesian probability theory. In this work we show how the Open Geospatial Consortium Sensor Observation Service can be implemented to enable the support of explicit uncertainty about observations. We show how the UncertML candidate standard is used to provide a rich and flexible representation of uncertainty in this context. We illustrate this on a data set of user contributed weather data where the INTAMAP interpolation Web Processing Service is used to help estimate the uncertainty on the observations of unknown quality, using observations with known uncertainty properties. We then go on to discuss the implications of uncertainty for a range of existing Open Geospatial Consortium standards including SWE common and Observations and Measurements. We discuss the difficult decisions in the design of the UncertML schema and its relation and usage within existing standards and show various options. We conclude with some indications of the likely future directions for UncertML in the context of Open Geospatial Consortium services.
Uncertainty in geological and hydrogeological data
Directory of Open Access Journals (Sweden)
B. Nilsson
2007-09-01
Full Text Available Uncertainty in conceptual model structure and in environmental data is of essential interest when dealing with uncertainty in water resources management. To make quantification of uncertainty possible is it necessary to identify and characterise the uncertainty in geological and hydrogeological data. This paper discusses a range of available techniques to describe the uncertainty related to geological model structure and scale of support. Literature examples on uncertainty in hydrogeological variables such as saturated hydraulic conductivity, specific yield, specific storage, effective porosity and dispersivity are given. Field data usually have a spatial and temporal scale of support that is different from the one on which numerical models for water resources management operate. Uncertainty in hydrogeological data variables is characterised and assessed within the methodological framework of the HarmoniRiB classification.
Uncertainty analysis comes to integrated assessment models for climate change and conversely
Cooke, R.M.
2012-01-01
This article traces the development of uncertainty analysis through three generations punctuated by large methodology investments in the nuclear sector. Driven by a very high perceived legitimation burden, these investments aimed at strengthening the scientific basis of uncertainty quantification.
Model uncertainty in safety assessment
International Nuclear Information System (INIS)
Pulkkinen, U.; Huovinen, T.
1996-01-01
The uncertainty analyses are an essential part of any risk assessment. Usually the uncertainties of reliability model parameter values are described by probability distributions and the uncertainty is propagated through the whole risk model. In addition to the parameter uncertainties, the assumptions behind the risk models may be based on insufficient experimental observations and the models themselves may not be exact descriptions of the phenomena under analysis. The description and quantification of this type of uncertainty, model uncertainty, is the topic of this report. The model uncertainty is characterized and some approaches to model and quantify it are discussed. The emphasis is on so called mixture models, which have been applied in PSAs. Some of the possible disadvantages of the mixture model are addressed. In addition to quantitative analyses, also qualitative analysis is discussed shortly. To illustrate the models, two simple case studies on failure intensity and human error modeling are described. In both examples, the analysis is based on simple mixture models, which are observed to apply in PSA analyses. (orig.) (36 refs., 6 figs., 2 tabs.)
Model uncertainty in safety assessment
Energy Technology Data Exchange (ETDEWEB)
Pulkkinen, U; Huovinen, T [VTT Automation, Espoo (Finland). Industrial Automation
1996-01-01
The uncertainty analyses are an essential part of any risk assessment. Usually the uncertainties of reliability model parameter values are described by probability distributions and the uncertainty is propagated through the whole risk model. In addition to the parameter uncertainties, the assumptions behind the risk models may be based on insufficient experimental observations and the models themselves may not be exact descriptions of the phenomena under analysis. The description and quantification of this type of uncertainty, model uncertainty, is the topic of this report. The model uncertainty is characterized and some approaches to model and quantify it are discussed. The emphasis is on so called mixture models, which have been applied in PSAs. Some of the possible disadvantages of the mixture model are addressed. In addition to quantitative analyses, also qualitative analysis is discussed shortly. To illustrate the models, two simple case studies on failure intensity and human error modeling are described. In both examples, the analysis is based on simple mixture models, which are observed to apply in PSA analyses. (orig.) (36 refs., 6 figs., 2 tabs.).
Stewardship to tackle global phosphorus inefficiency: The case of Europe.
Withers, Paul J A; van Dijk, Kimo C; Neset, Tina-Simone S; Nesme, Thomas; Oenema, Oene; Rubæk, Gitte H; Schoumans, Oscar F; Smit, Bert; Pellerin, Sylvain
2015-03-01
The inefficient use of phosphorus (P) in the food chain is a threat to the global aquatic environment and the health and well-being of citizens, and it is depleting an essential finite natural resource critical for future food security and ecosystem function. We outline a strategic framework of 5R stewardship (Re-align P inputs, Reduce P losses, Recycle P in bioresources, Recover P in wastes, and Redefine P in food systems) to help identify and deliver a range of integrated, cost-effective, and feasible technological innovations to improve P use efficiency in society and reduce Europe's dependence on P imports. Their combined adoption facilitated by interactive policies, co-operation between upstream and downstream stakeholders (researchers, investors, producers, distributors, and consumers), and more harmonized approaches to P accounting would maximize the resource and environmental benefits and help deliver a more competitive, circular, and sustainable European economy. The case of Europe provides a blueprint for global P stewardship.
Volunteer Environmental Stewardship and Affective Labour in Philadelphia
Directory of Open Access Journals (Sweden)
Alec Foster
2018-01-01
Full Text Available Recent research has critically evaluated the rapid growth of volunteer urban environmental stewardship. Framings of this phenomenon have largely focused upon environmentality and/or neoliberal environments, unfortunately often presenting a totalising picture of the state and/or market utilising power from above to create environmental subjects with limited agency available to local citizens. Based upon qualitative research with volunteer urban environmental stewards in Philadelphia, affective labour is proposed as an alternative explanation for participation. Stewards volunteered their time and labour due to the intense emotional attachments they formed with their neighbourhoods, neighbours, and nonhuman others in relationships of affective labour. Volunteer urban environmental stewardship as affective labour provides room for agency on the part of individuals and groups involved in volunteer urban environmental reproduction and opens up new ways of relating to and being with human and nonhuman others.
Spatially explicit data: stewardship and ethical challenges in science.
Hartter, Joel; Ryan, Sadie J; Mackenzie, Catrina A; Parker, John N; Strasser, Carly A
2013-09-01
Scholarly communication is at an unprecedented turning point created in part by the increasing saliency of data stewardship and data sharing. Formal data management plans represent a new emphasis in research, enabling access to data at higher volumes and more quickly, and the potential for replication and augmentation of existing research. Data sharing has recently transformed the practice, scope, content, and applicability of research in several disciplines, in particular in relation to spatially specific data. This lends exciting potentiality, but the most effective ways in which to implement such changes, particularly for disciplines involving human subjects and other sensitive information, demand consideration. Data management plans, stewardship, and sharing, impart distinctive technical, sociological, and ethical challenges that remain to be adequately identified and remedied. Here, we consider these and propose potential solutions for their amelioration.
Science-based stockpile stewardship at Los Alamos National Laboratory
International Nuclear Information System (INIS)
Immele, J.
1995-01-01
I would like to start by working from Vic Reis's total quality management diagram in which he began with the strategy and then worked through the customer requirements-what the Department of Defense (DoD) is hoping for from the science-based stockpile stewardship program. Maybe our customer's requirements will help guide some of the issues that we should be working on. ONe quick answer to open-quotes why have we adopted a science-based strategyclose quotes is that nuclear weapons are a 50-year responsibility, not just a 5-year responsibility, and stewardship without testing is a grand challenge. While we can do engineering maintenance and turn over and remake a few things on the short time scale, without nuclear testing, without new weapons development, and without much of the manufacturing base that we had in the past, we need to learn better just how these weapons are actually working
Leveraging best practices to promote health, safety, sustainability, and stewardship.
Weiss, Marjorie D
2013-08-01
Strategically leveraging health and safety initiatives with sustainability and stewardship helps organizations improve profitability and positively impact team member and customer attachment to the organization. Collective efficacy enhances the triple bottom line: healthy people, healthy planet, and healthy profits. The HS(3)™ Best Practice Exchanges group demonstrated that collective efficacy can leverage the social cohesion, communication channels, and activities within workplaces to promote a healthy, sustainable work culture. This in turn (1) protects the health and safety of workers, (2) preserves the natural environment, and (3) increases attachment to the organization. Community-based participatory research using the Attach21 survey assessed the progress of these companies in their efforts to integrate health, safety, sustainability, and stewardship. Monthly Best Practice Exchanges promoted collective efficacy by providing support, encouragement, and motivation to share and adopt new ideas. Copyright 2013, SLACK Incorporated.
International Nuclear Information System (INIS)
Youssef, M.Z.; Konno, C.; Maekawa, F.; Ikeda, Y.; Kosako, K.; Nakagawa, M.; Mori, T.; Maekawa, H.
1995-01-01
Several fusion integral experiments were performed within a collaboration between the USA and Japan on fusion breeder neutronics aimed at verifying the prediction accuracy of key neutronics parameters in a fusion reactor blanket based on current neutron transport codes and basic nuclear databases. The focus has been on the tritium production rate (TRP) as an important design parameter to resolve the issue of tritium self-sufficiency in a fusion reactor. In this paper, the calculational and experimental uncertainties (errors) in local TPR in each experiment performed i were interpolated and propagated to estimate the prediction uncertainty u i in the line-integrated TPR and its standard deviation σ i . The measured data are based on Li-glass and NE213 detectors. From the quantities u i and σ i , normalized density functions (NDFs) were constructed, considering all the experiments and their associated analyses performed independently by the UCLA and JAERI. Several statistical parameters were derived, including the mean prediction uncertainties u and the possible spread ±σ u around them. Design margins and safety factors were derived from these NDFs. Distinction was made between the results obtained by UCLA and JAERI and between calculational results based on the discrete ordinates and Monte Carlo methods. The prediction uncertainties, their standard deviations and the design margins and safety factors were derived for the line-integrated TPR from Li-6 T 6 , and Li-7 T 7 . These parameters were used to estimate the corresponding uncertainties and safety factor for the line-integrated TPR from natural lithium T n . (orig.)
Water appropriation and ecosystem stewardship in the Baja desert
de las Heras Alejandro; Rodriguez Mario A.; Islas-Espinoza Marina
2014-01-01
The UNESCO San Francisco Rock Paintings polygon within El Vizcaino Biosphere Reserve in the Baja California Peninsula derives its moisture from the North American monsoon. There, ranchers have depended on the desert since the 18th century. More recently, the desert has depended on the environmental stewardship of the ranchers who have allayed mining exploitation and archaeological looting. Using a Rapid Assessment Procedure (RAP), climate data, and geographical informa...
Antimicrobial stewardship in long term care facilities: what is effective?
Nicolle, Lindsay E
2014-01-01
Intense antimicrobial use in long term care facilities promotes the emergence and persistence of antimicrobial resistant organisms and leads to adverse effects such as C. difficile colitis. Guidelines recommend development of antimicrobial stewardship programs for these facilities to promote optimal antimicrobial use. However, the effectiveness of these programs or the contribution of any specific program component is not known. For this review, publications describing evaluation of antimicro...
Antibiotic stewardship er etableret på Herlev Hospital
DEFF Research Database (Denmark)
Arpi, Magnus; Gjørup, Ida; Boel, Jonas Bredtoft
2014-01-01
A high incidence of Clostridium difficile and multiresistant organisms and increasing consumption of cephalosporins and quinolones have required an antibiotic stewardship programme, and antibiotic audits with feedback, revised guidelines and stringent prescription rules have been successful....... The hospital intervention was managed by an antibiotic team combined with contact persons in all departments, a pocket edition of the guideline was available, and monthly commented reports about antibiotic consumption in each department were presented on the intranet. Declining use of restricted antibiotics...
Chang, Li-Na; Luo, Shun-Long; Sun, Yuan
2017-11-01
The principle of superposition is universal and lies at the heart of quantum theory. Although ever since the inception of quantum mechanics a century ago, superposition has occupied a central and pivotal place, rigorous and systematic studies of the quantification issue have attracted significant interests only in recent years, and many related problems remain to be investigated. In this work we introduce a figure of merit which quantifies superposition from an intuitive and direct perspective, investigate its fundamental properties, connect it to some coherence measures, illustrate it through several examples, and apply it to analyze wave-particle duality. Supported by Science Challenge Project under Grant No. TZ2016002, Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, Key Laboratory of Random Complex Structures and Data Science, Chinese Academy of Sciences, Grant under No. 2008DP173182
Nosocomial Candidiasis: Antifungal Stewardship and the Importance of Rapid Diagnosis.
Pfaller, Michael A; Castanheira, Mariana
2016-01-01
Candidemia and other forms of candidiasis are associated with considerable excess mortality and costs. Despite the addition of several new antifungal agents with improved spectrum and potency, the frequency of Candida infection and associated mortality have not decreased in the past two decades. The lack of rapid and sensitive diagnostic tests has led to considerable overuse of antifungal agents resulting in increased costs, selection pressure for resistance, unnecessary drug toxicity, and adverse drug interactions. Both the lack of timely diagnostic tests and emergence of antifungal resistance pose considerable problems for antifungal stewardship. Whereas antifungal stewardship with a focus on nosocomial candidiasis should be able to improve the administration of antifungal therapy in terms of drug selection, proper dose and duration, source control and de-escalation therapy, an important parameter, timeliness of antifungal therapy, remains a victim of slow and insensitive diagnostic tests. Fortunately, new proteomic and molecular diagnostic tools are improving the time to species identification and detection. In this review we will describe the potential impact that rapid diagnostic testing and antifungal stewardship can have on the management of nosocomial candidiasis. © The Author 2015. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
2003 Stewardship progress report : committed to continuous improvement
Energy Technology Data Exchange (ETDEWEB)
NONE
2003-12-01
The stewardship initiative is a mandatory requirement for members of the Canadian Association of Petroleum Producers (CAPP). It involves performance management and benchmarking, voluntary audits and verification, as well as training and improved communication inside and outside the industry. This fourth annual progress report describes the environment, health, safety and socio-economic stewardship initiative. This report presents an aggregate of industry performance. Stewardship of Excellence awards were presented in 2003, celebrating outstanding performance by members who demonstrated their commitment to responsible development and continuous improvement within a business framework. The awards were presented in three categories, namely environment, health and safety, and socio-economic. Northrock Resources was presented with the award in the environment category for its voluntary waste gas reduction. The health and safety recognition went to Burlington Resources Canada Ltd. for superior office ergonomics, while the award in the socio-economic category was presented to Suncor Energy Inc. for Aboriginal business development. A brief overview of the achievements of each of these three companies was presented. tabs., figs.
Duerdoth, Ian
2009-01-01
The subject of uncertainties (sometimes called errors) is traditionally taught (to first-year science undergraduates) towards the end of a course on statistics that defines probability as the limit of many trials, and discusses probability distribution functions and the Gaussian distribution. We show how to introduce students to the concepts of…
DEFF Research Database (Denmark)
Heydorn, Kaj; Anglov, Thomas
2002-01-01
Methods recommended by the International Standardization Organisation and Eurachem are not satisfactory for the correct estimation of calibration uncertainty. A novel approach is introduced and tested on actual calibration data for the determination of Pb by ICP-AES. The improved calibration...
A cost-effectiveness analysis of two different antimicrobial stewardship programs
Okumura, Lucas Miyake; Riveros, Bruno Salgado; Gomes-da-Silva, Monica Maria; Veroneze, Izelandia
2016-01-01
Abstract There is a lack of formal economic analysis to assess the efficiency of antimicrobial stewardship programs. Herein, we conducted a cost-effectiveness study to assess two different strategies of Antimicrobial Stewardship Programs. A 30-day Markov model was developed to analyze how cost-effective was a Bundled Antimicrobial Stewardship implemented in a university hospital in Brazil. Clinical data derived from a historical cohort that compared two different strategies of antimicrobial s...
A cost-effectiveness analysis of two different antimicrobial stewardship programs
Lucas Miyake Okumura; Bruno Salgado Riveros; Monica Maria Gomes-da-Silva; Izelandia Veroneze
2016-01-01
There is a lack of formal economic analysis to assess the efficiency of antimicrobial stewardship programs. Herein, we conducted a cost-effectiveness study to assess two different strategies of Antimicrobial Stewardship Programs. A 30-day Markov model was developed to analyze how cost-effective was a Bundled Antimicrobial Stewardship implemented in a university hospital in Brazil. Clinical data derived from a historical cohort that compared two different strategies of antimicrobial stewardshi...
McPherson, Christopher; Lee, Brian R; Terrill, Cindy; Hersh, Adam L; Gerber, Jeffrey S; Kronman, Matthew P; Newland, Jason G
2018-01-25
In response to the growing epidemic of antibiotic-resistant bacterial infections, antimicrobial stewardship programs (ASP) have been rapidly implemented in the United States (US). This study examines the prevalence of the Centers for Disease Control and Prevention's (CDC) seven core elements of a successful ASP within a large subset of US Children's Hospitals. In 2016, a survey was conducted of 52 pediatric hospitals assessing the presence of the seven core elements: leadership commitment, accountability, drug expertise, action, tracking, reporting, and education. Forty-nine hospitals (94%) had established ASPs and 41 hospitals (79%) included all seven core elements. Physician accountability (87%) and a dedicated ASP pharmacist or drug expert (88%) were present in the vast majority of hospitals. However, substantial variability existed in the financial support allotted to these positions. This variability did not predict program actions, tracking, reporting, and education. When compared with previous surveys, these results document a dramatic increase in the prevalence and resources of pediatric stewardship programs, although continued expansion is warranted. Further research is required to understand the feasibility of various core stewardship activities and the impact on patient outcomes in the setting of finite resources.
Directory of Open Access Journals (Sweden)
Christopher McPherson
2018-01-01
Full Text Available In response to the growing epidemic of antibiotic-resistant bacterial infections, antimicrobial stewardship programs (ASP have been rapidly implemented in the United States (US. This study examines the prevalence of the Centers for Disease Control and Prevention’s (CDC seven core elements of a successful ASP within a large subset of US Children’s Hospitals. In 2016, a survey was conducted of 52 pediatric hospitals assessing the presence of the seven core elements: leadership commitment, accountability, drug expertise, action, tracking, reporting, and education. Forty-nine hospitals (94% had established ASPs and 41 hospitals (79% included all seven core elements. Physician accountability (87% and a dedicated ASP pharmacist or drug expert (88% were present in the vast majority of hospitals. However, substantial variability existed in the financial support allotted to these positions. This variability did not predict program actions, tracking, reporting, and education. When compared with previous surveys, these results document a dramatic increase in the prevalence and resources of pediatric stewardship programs, although continued expansion is warranted. Further research is required to understand the feasibility of various core stewardship activities and the impact on patient outcomes in the setting of finite resources.
DEFF Research Database (Denmark)
Nguyen, Daniel Xuyen
This paper presents a model of trade that explains why firms wait to export and why many exporters fail. Firms face uncertain demands that are only realized after the firm enters the destination. The model retools the timing of uncertainty resolution found in productivity heterogeneity models....... This retooling addresses several shortcomings. First, the imperfect correlation of demands reconciles the sales variation observed in and across destinations. Second, since demands for the firm's output are correlated across destinations, a firm can use previously realized demands to forecast unknown demands...... in untested destinations. The option to forecast demands causes firms to delay exporting in order to gather more information about foreign demand. Third, since uncertainty is resolved after entry, many firms enter a destination and then exit after learning that they cannot profit. This prediction reconciles...
A broader view of stewardship to achieve conservation and sustainability goals in South Africa
Directory of Open Access Journals (Sweden)
Jaco Barendse
2016-05-01
Full Text Available Stewardship is a popular term for the principles and actions aimed at improving sustainability and resilience of social-ecological systems at various scales and in different contexts. Participation in stewardship is voluntary, and is based on values of altruism and long-term benefits. At a global scale, "earth stewardship" is viewed as a successor to earlier natural resource management systems. However, in South Africa, stewardship is narrowly applied to biodiversity conservation agreements on private land. Using a broader definition of stewardship, we identify all potentially related schemes that may contribute to sustainability and conservation outcomes. Stewardship schemes and actors are represented as a social network and placed in a simple typology based on objectives, mechanisms of action and operational scales. The predominant type was biodiversity stewardship programmes. The main actors were environmental non-governmental organisations participating in prominent bioregional landscape partnerships, together acting as important "bridging organisations" within local stewardship networks. This bridging enables a high degree of collaboration between non-governmental and governmental bodies, especially provincial conservation agencies via mutual projects and conservation objectives. An unintended consequence may be that management accountability is relinquished or neglected by government because of inadequate implementation capacity. Other stewardship types, such as market-based and landscape initiatives, complemented primarily biodiversity ones, as part of national spatial conservation priorities. Not all schemes related to biodiversity, especially those involving common pool resources, markets and supply chains. Despite an apparent narrow biodiversity focus, there is evidence of diversification of scope to include more civic and community-level stewardship activities, in line with the earth stewardship metaphor.
Uncertainty, joint uncertainty, and the quantum uncertainty principle
International Nuclear Information System (INIS)
Narasimhachar, Varun; Poostindouz, Alireza; Gour, Gilad
2016-01-01
Historically, the element of uncertainty in quantum mechanics has been expressed through mathematical identities called uncertainty relations, a great many of which continue to be discovered. These relations use diverse measures to quantify uncertainty (and joint uncertainty). In this paper we use operational information-theoretic principles to identify the common essence of all such measures, thereby defining measure-independent notions of uncertainty and joint uncertainty. We find that most existing entropic uncertainty relations use measures of joint uncertainty that yield themselves to a small class of operational interpretations. Our notion relaxes this restriction, revealing previously unexplored joint uncertainty measures. To illustrate the utility of our formalism, we derive an uncertainty relation based on one such new measure. We also use our formalism to gain insight into the conditions under which measure-independent uncertainty relations can be found. (paper)
Idaho National Laboratory Comprehensive Land Use and Environmental Stewardship Report
Energy Technology Data Exchange (ETDEWEB)
No name listed on publication
2011-08-01
Land and facility use planning and decisions at the Idaho National Laboratory (INL) Site are guided by a comprehensive site planning process in accordance with Department of Energy Policy 430.1, 'Land and Facility Use Policy,' that integrates mission, economic, ecologic, social, and cultural factors. The INL Ten-Year Site Plan, prepared in accordance with Department of Energy Order 430.1B, 'Real Property Asset Management,' outlines the vision and strategy to transform INL to deliver world-leading capabilities that will enable the Department of Energy to accomplish its mission. Land use planning is the overarching function within real property asset management that integrates the other functions of acquisition, recapitalization, maintenance, disposition, real property utilization, and long-term stewardship into a coordinated effort to ensure current and future mission needs are met. All land and facility use projects planned at the INL Site are considered through a formal planning process that supports the Ten-Year Site Plan. This Comprehensive Land Use and Environmental Stewardship Report describes that process. The land use planning process identifies the current condition of existing land and facility assets and the scope of constraints across INL and in the surrounding region. Current land use conditions are included in the Comprehensive Land Use and Environmental Stewardship Report and facility assets and scope of constraints are discussed in the Ten-Year Site Plan. This report also presents the past, present, and future uses of land at the INL Site that are considered during the planning process, as well as outlining the future of the INL Site for the 10, 30, and 100-year timeframes.
Optimizing Antibiotic Use in Nursing Homes Through Antibiotic Stewardship.
Sloane, Philip D; Huslage, Kirk; Kistler, Christine E; Zimmerman, Sheryl
2016-01-01
Antibiotic stewardship is becoming a requirement for nursing homes. Programs should be interdisciplinary and multifaceted; should have support from nursing home administrators; and should aim to promote antibiotics only when needed, not just in case. Recommended components include use of evidence-based guidelines; ongoing monitoring of antibiotic prescriptions, cultures, and study results; monitoring of health outcomes; use of nursing home-specific antibiograms; regular reporting and feedback to medical providers and nurses; and education of residents and families. ©2016 by the North Carolina Institute of Medicine and The Duke Endowment. All rights reserved.
Zou, Xiao-Duan; Li, Jian-Yang; Clark, Beth Ellen; Golish, Dathon
2018-01-01
The OSIRIS-REx spacecraft, launched in September, 2016, will study the asteroid Bennu and return a sample from its surface to Earth in 2023. Bennu is a near-Earth carbonaceous asteroid which will provide insight into the formation and evolution of the solar system. OSIRIS-REx will first approach Bennu in August 2018 and will study the asteroid for approximately two years before sampling. OSIRIS-REx will develop its photometric model (including Lommel-Seelinger, ROLO, McEwen, Minnaert and Akimov) of Bennu with OCAM and OVIRS during the Detailed Survey mission phase. The model developed during this phase will be used to photometrically correct the OCAM and OVIRS data.Here we present the analysis of the error for the photometric corrections. Based on our testing data sets, we find:1. The model uncertainties is only correct when we use the covariance matrix to calculate, because the parameters are highly correlated.2. No evidence of domination of any parameter in each model.3. And both model error and the data error contribute to the final correction error comparably.4. We tested the uncertainty module on fake and real data sets, and find that model performance depends on the data coverage and data quality. These tests gave us a better understanding of how different model behave in different case.5. L-S model is more reliable than others. Maybe because the simulated data are based on L-S model. However, the test on real data (SPDIF) does show slight advantage of L-S, too. ROLO is not reliable to use when calculating bond albedo. The uncertainty of McEwen model is big in most cases. Akimov performs unphysical on SOPIE 1 data.6. Better use L-S as our default choice, this conclusion is based mainly on our test on SOPIE data and IPDIF.
Erika s. Svendsen; Lindsay K. Campbell
2008-01-01
Urban environmental stewardship activities are on the rise in cities throughout the Northeast. Groups participating in stewardship activities range in age, size, and geography and represent an increasingly complex and dynamic arrangement of civil society, government and business sectors. To better understand the structure, function and network of these community-based...
Schuts, Emelie C.; Hulscher, Marlies E. J. L.; Mouton, Johan W.; Verduin, Cees M.; Stuart, James W. T. Cohen; Overdiek, Hans W. P. M.; van der Linden, Paul D.; Natsch, Stephanie; Hertogh, Cees M. P. M.; Wolfs, Tom F. W.; Schouten, Jeroen A.; Kullberg, Bart Jan; Prins, Jan M.
2016-01-01
Antimicrobial stewardship is advocated to improve the quality of antimicrobial use. We did a systematic review and meta-analysis to assess whether antimicrobial stewardship objectives had any effects in hospitals and long-term care facilities on four predefined patients' outcomes: clinical outcomes,
Schuts, Emelie C.; Hulscher, Marlies E J L; Mouton, Johan W.; Verduin, Cees M.; Stuart, James W T Cohen; Overdiek, Hans W P M; van der Linden, Paul D.; Natsch, Stephanie; Hertogh, Cees M P M; Wolfs, Tom F W; Schouten, Jeroen A.; Kullberg, Bart Jan; Prins, Jan M.
2016-01-01
BACKGROUND: Antimicrobial stewardship is advocated to improve the quality of antimicrobial use. We did a systematic review and meta-analysis to assess whether antimicrobial stewardship objectives had any effects in hospitals and long-term care facilities on four predefined patients' outcomes:
Schuts, E.C.; Hulscher, M.E.J.L.; Mouton, J.W.; Verduin, C.M.; Stuart, J.W.; Overdiek, H.W.; Linden, P.D. van der; Natsch, S.S.; Hertogh, C.M.; Wolfs, T.F.; Schouten, J.A.; Kullberg, B.J.; Prins, J.M.
2016-01-01
BACKGROUND: Antimicrobial stewardship is advocated to improve the quality of antimicrobial use. We did a systematic review and meta-analysis to assess whether antimicrobial stewardship objectives had any effects in hospitals and long-term care facilities on four predefined patients' outcomes:
Antecedents and consequences of environmental stewardship in boundary-spanning B2B teams
Ruyter, de J.C.; Jong, de A.; Wetzels, M.G.M.
2009-01-01
The authors examine antecedents and consequences of environmental stewardship in frontline business-to-business teams. On the basis of data from members of 34 teams organized into regional networks, they demonstrate the differential impact of team environmental stewardship on customer satisfaction
1997-01-01
Opening comments to this session share observations on the current management climate within the USDA Forest Service. Partnerships and collaborative stewardship as agency philosophy are discussed. Silviculturists roles, as scientists and managers are compared, and the need for internal and external cooperation stressed as we strive to meet forest stewardship goals....
What is urban environmental stewardship? Constructing a practitioner-derived framework
M. Romolini; W. Brinkley; K.L. Wolf
2012-01-01
Agencies and organizations deploy various strategies in response to environmental challenges, including the formulation of policy, programs, and regulations. Citizen-based environmental stewardship is increasingly seen as an innovative and important approach to improving and conserving landscape health. A new research focus on the stewardship of urban natural resources...
International Nuclear Information System (INIS)
Thomas, R.E.
1982-03-01
An evaluation is made of the suitability of analytical and statistical sampling methods for making uncertainty analyses. The adjoint method is found to be well-suited for obtaining sensitivity coefficients for computer programs involving large numbers of equations and input parameters. For this purpose the Latin Hypercube Sampling method is found to be inferior to conventional experimental designs. The Latin hypercube method can be used to estimate output probability density functions, but requires supplementary rank transformations followed by stepwise regression to obtain uncertainty information on individual input parameters. A simple Cork and Bottle problem is used to illustrate the efficiency of the adjoint method relative to certain statistical sampling methods. For linear models of the form Ax=b it is shown that a complete adjoint sensitivity analysis can be made without formulating and solving the adjoint problem. This can be done either by using a special type of statistical sampling or by reformulating the primal problem and using suitable linear programming software
Gasoline toxicology: overview of regulatory and product stewardship programs.
Swick, Derek; Jaques, Andrew; Walker, J C; Estreicher, Herb
2014-11-01
Significant efforts have been made to characterize the toxicological properties of gasoline. There have been both mandatory and voluntary toxicology testing programs to generate hazard characterization data for gasoline, the refinery process streams used to blend gasoline, and individual chemical constituents found in gasoline. The Clean Air Act (CAA) (Clean Air Act, 2012: § 7401, et seq.) is the primary tool for the U.S. Environmental Protection Agency (EPA) to regulate gasoline and this supplement presents the results of the Section 211(b) Alternative Tier 2 studies required for CAA Fuel and Fuel Additive registration. Gasoline blending streams have also been evaluated by EPA under the voluntary High Production Volume (HPV) Challenge Program through which the petroleum industry provide data on over 80 refinery streams used in gasoline. Product stewardship efforts by companies and associations such as the American Petroleum Institute (API), Conservation of Clean Air and Water Europe (CONCAWE), and the Petroleum Product Stewardship Council (PPSC) have contributed a significant amount of hazard characterization data on gasoline and related substances. The hazard of gasoline and anticipated exposure to gasoline vapor has been well characterized for risk assessment purposes. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.
Antimicrobial stewardship initiatives throughout Europe: proven value for money
Directory of Open Access Journals (Sweden)
Edwin J.M. Oberjé
2017-03-01
Full Text Available Antimicrobial stewardship is recognized as a key component to stop the current European spread of antimicrobial resistance. It has also become evident that antimicrobial resistance is a problem that cannot be tackled by single institutions or physicians. Prevention of antimicrobial resistance needs rigorous actions at ward level, institution level, national level and at supra-national levels. Countries can learn from each other and possibly transplant best practices across borders to prevent antimicrobial resistance. The aim of this study is to highlight some of the success stories of proven cost-effective interventions, and to describe the actions that have been taken, the outcomes that have been found, and the difficulties that have been met. In some cases we came across substantial scope for real-life cost savings. Although the best approach to effectively hinder the spread of antimicrobial resistance remains unclear and may vary significantly among settings, several EU-wide examples demonstrate that cost-effective antimicrobial stewardship is possible. Such examples can encourage others to implement (the most cost-effective elements in their system.
Antimicrobial Stewardship: A Call to Action for Surgeons
Duane, Therese M.; Catena, Fausto; Tessier, Jeffrey M.; Coccolini, Federico; Kao, Lillian S.; De Simone, Belinda; Labricciosa, Francesco M.; May, Addison K.; Ansaloni, Luca; Mazuski, John E.
2016-01-01
Abstract Despite current antimicrobial stewardship programs (ASPs) being advocated by infectious disease specialists and discussed by national and international policy makers, ASPs coverage remains limited to only certain hospitals as well as specific service lines within hospitals. The ASPs incorporate a variety of strategies to optimize antimicrobial agent use in the hospital, yet the exact set of interventions essential to ASP success remains unknown. Promotion of ASPs across clinical practice is crucial to their success to ensure standardization of antimicrobial agent use within an institution. To effectively accomplish this standardization, providers who actively engage in antimicrobial agent prescribing should participate in the establishment and support of these programs. Hence, surgeons need to play a major role in these collaborations. Surgeons must be aware that judicious antibiotic utilization is an integral part of any stewardship program and necessary to maximize clinical cure and minimize emergence of antimicrobial resistance. The battle against antibiotic resistance should be fought by all healthcare professionals. If surgeons around the world participate in this global fight and demonstrate awareness of the major problem of antimicrobial resistance, they will be pivotal leaders. If surgeons fail to actively engage and use antibiotics judiciously, they will find themselves deprived of the autonomy to treat their patients. PMID:27828764
Improving Stewardship of Marine Resources: Linking Strategy to Opportunity
Directory of Open Access Journals (Sweden)
Franciska von Heland
2014-07-01
Full Text Available The need for improved stewardship of coastal and marine resources is evident worldwide. However, complex ecosystem dynamics, institutional inertia, and budgetary constraints impede such action. This study explores how networks of change-oriented individuals or “institutional entrepreneurs” can introduce new types of human-environment interaction. The focus is on investigating the interplay between the strategies of institutional entrepreneurs and broader system dynamics that shape the context in which they are working, and possible impacts of institutional entrepreneurship on marine governance. We explore these issues in the context of Wakatobi National Park in eastern Indonesia. We suggest that creating links between different social spheres, such as between marine resource management and spirituality or between marine resource management and education, may accelerate the development of a new ecosystem stewardship. We further suggest that the use of media has significant power to show alternative futures, but that media may also serve to objectify certain resource users and increase the complexity of marine resource management. In general, institutional entrepreneurs play an important role in capturing and managing opportunity to open up space for experimentation and novel ideas, for example by linking their ideas to broader political priorities. Yet, such strategies bear the risk of institutional capture. Finally, institutional entrepreneurs sometimes have vested interests in certain solutions that may forsake experimentation toward a sustainable future.
Methodology for characterizing modeling and discretization uncertainties in computational simulation
Energy Technology Data Exchange (ETDEWEB)
ALVIN,KENNETH F.; OBERKAMPF,WILLIAM L.; RUTHERFORD,BRIAN M.; DIEGERT,KATHLEEN V.
2000-03-01
This research effort focuses on methodology for quantifying the effects of model uncertainty and discretization error on computational modeling and simulation. The work is directed towards developing methodologies which treat model form assumptions within an overall framework for uncertainty quantification, for the purpose of developing estimates of total prediction uncertainty. The present effort consists of work in three areas: framework development for sources of uncertainty and error in the modeling and simulation process which impact model structure; model uncertainty assessment and propagation through Bayesian inference methods; and discretization error estimation within the context of non-deterministic analysis.
Tadini, A.; Bevilacqua, A.; Neri, A.; Cioni, R.; Aspinall, W. P.; Bisson, M.; Isaia, R.; Mazzarini, F.; Valentine, G. A.; Vitale, S.; Baxter, P. J.; Bertagnini, A.; Cerminara, M.; de Michieli Vitturi, M.; Di Roberto, A.; Engwell, S.; Esposti Ongaro, T.; Flandoli, F.; Pistolesi, M.
2017-06-01
In this study, we combine reconstructions of volcanological data sets and inputs from a structured expert judgment to produce a first long-term probability map for vent opening location for the next Plinian or sub-Plinian eruption of Somma-Vesuvio. In the past, the volcano has exhibited significant spatial variability in vent location; this can exert a significant control on where hazards materialize (particularly of pyroclastic density currents). The new vent opening probability mapping has been performed through (i) development of spatial probability density maps with Gaussian kernel functions for different data sets and (ii) weighted linear combination of these spatial density maps. The epistemic uncertainties affecting these data sets were quantified explicitly with expert judgments and implemented following a doubly stochastic approach. Various elicitation pooling metrics and subgroupings of experts and target questions were tested to evaluate the robustness of outcomes. Our findings indicate that (a) Somma-Vesuvio vent opening probabilities are distributed inside the whole caldera, with a peak corresponding to the area of the present crater, but with more than 50% probability that the next vent could open elsewhere within the caldera; (b) there is a mean probability of about 30% that the next vent will open west of the present edifice; (c) there is a mean probability of about 9.5% that the next medium-large eruption will enlarge the present Somma-Vesuvio caldera, and (d) there is a nonnegligible probability (mean value of 6-10%) that the next Plinian or sub-Plinian eruption will have its initial vent opening outside the present Somma-Vesuvio caldera.
Results from the Application of Uncertainty Methods in the CSNI Uncertainty Methods Study (UMS)
International Nuclear Information System (INIS)
Glaeser, H.
2008-01-01
Within licensing procedures there is the incentive to replace the conservative requirements for code application by a - best estimate - concept supplemented by an uncertainty analysis to account for predictive uncertainties of code results. Methods have been developed to quantify these uncertainties. The Uncertainty Methods Study (UMS) Group, following a mandate from CSNI, has compared five methods for calculating the uncertainty in the predictions of advanced -best estimate- thermal-hydraulic codes. Most of the methods identify and combine input uncertainties. The major differences between the predictions of the methods came from the choice of uncertain parameters and the quantification of the input uncertainties, i.e. the wideness of the uncertainty ranges. Therefore, suitable experimental and analytical information has to be selected to specify these uncertainty ranges or distributions. After the closure of the Uncertainty Method Study (UMS) and after the report was issued comparison calculations of experiment LSTF-SB-CL-18 were performed by University of Pisa using different versions of the RELAP 5 code. It turned out that the version used by two of the participants calculated a 170 K higher peak clad temperature compared with other versions using the same input deck. This may contribute to the differences of the upper limit of the uncertainty ranges.
Medical table: A major tool for antimicrobial stewardship policy.
Roger, P-M; Demonchy, E; Risso, K; Courjon, J; Leroux, S; Leroux, E; Cua, É
2017-09-01
Infectious diseases are unpredictable, with heterogeneous clinical presentations, diverse pathogens, and various susceptibility rates to anti-infective agents. These features lead to a wide variety of clinical practices, which in turn strongly limits their evaluation. We have been using a medical table since 2005 to monitor the medical activity in our department. The observation of heterogeneous therapeutic practices led to drafting up our own antibiotic guidelines and to implementing a continuous evaluation of their observance and impact on morbidity and morta