Electro-optical parameters of bond polarizability model for aluminosilicates.

Science.gov (United States)

Smirnov, Konstantin S; Bougeard, Daniel; Tandon, Poonam

2006-04-06

Electro-optical parameters (EOPs) of bond polarizability model (BPM) for aluminosilicate structures were derived from quantum-chemical DFT calculations of molecular models. The tensor of molecular polarizability and the derivatives of the tensor with respect to the bond length are well reproduced with the BPM, and the EOPs obtained are in a fair agreement with available experimental data. The parameters derived were found to be transferable to larger molecules. This finding suggests that the procedure used can be applied to systems with partially ionic chemical bonds. The transferability of the parameters to periodic systems was tested in molecular dynamics simulation of the polarized Raman spectra of alpha-quartz. It appeared that the molecular Si-O bond EOPs failed to reproduce the intensity of peaks in the spectra. This limitation is due to large values of the longitudinal components of the bond polarizability and its derivative found in the molecular calculations as compared to those obtained from periodic DFT calculations of crystalline silica polymorphs by Umari et al. (Phys. Rev. B 2001, 63, 094305). It is supposed that the electric field of the solid is responsible for the difference of the parameters. Nevertheless, the EOPs obtained can be used as an initial set of parameters for calculations of polarizability related characteristics of relevant systems in the framework of BPM.

Historic Low Wall Detection via Topographic Parameter Images Derived from Fine-Resolution DEM

Directory of Open Access Journals (Sweden)

Hone-Jay Chu

2017-11-01

Full Text Available Coral walls protect vegetation gardens from strong winds that sweep across Xiji Island, Taiwan Strait for half the year. Topographic parameters based on light detection and ranging (LiDAR-based high-resolution digital elevation model (DEM provide obvious correspondence with the expected form of landscape features. The information on slope, curvature, and openness can help identify the location of landscape features. This study applied the automatic landscape line detection to extract historic vegetable garden wall lines from a LiDAR-derived DEM. The three rapid processes used in this study included the derivation of topographic parameters, line extraction, and aggregation. The rules were extracted from a decision tree to check the line detection from multiple topographic parameters. Results show that wall line detection with multiple topographic parameter images is an alternative means of obtaining essential historic wall feature information. Multiple topographic parameters are highly related to low wall feature identification. Furthermore, the accuracy of wall feature detection is 74% compared with manual interpretation. Thus, this study provides rapid wall detection systems with multiple topographic parameters for further historic landscape management.

Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning

Science.gov (United States)

Chaney, Nathaniel W.; Herman, Jonathan D.; Ek, Michael B.; Wood, Eric F.

2016-11-01

With their origins in numerical weather prediction and climate modeling, land surface models aim to accurately partition the surface energy balance. An overlooked challenge in these schemes is the role of model parameter uncertainty, particularly at unmonitored sites. This study provides global parameter estimates for the Noah land surface model using 85 eddy covariance sites in the global FLUXNET network. The at-site parameters are first calibrated using a Latin Hypercube-based ensemble of the most sensitive parameters, determined by the Sobol method, to be the minimum stomatal resistance (rs,min), the Zilitinkevich empirical constant (Czil), and the bare soil evaporation exponent (fxexp). Calibration leads to an increase in the mean Kling-Gupta Efficiency performance metric from 0.54 to 0.71. These calibrated parameter sets are then related to local environmental characteristics using the Extra-Trees machine learning algorithm. The fitted Extra-Trees model is used to map the optimal parameter sets over the globe at a 5 km spatial resolution. The leave-one-out cross validation of the mapped parameters using the Noah land surface model suggests that there is the potential to skillfully relate calibrated model parameter sets to local environmental characteristics. The results demonstrate the potential to use FLUXNET to tune the parameterizations of surface fluxes in land surface models and to provide improved parameter estimates over the globe.

PARAMETER ESTIMATION IN BREAD BAKING MODEL

Directory of Open Access Journals (Sweden)

Hadiyanto Hadiyanto

2012-05-01

Full Text Available Bread product quality is highly dependent to the baking process. A model for the development of product quality, which was obtained by using quantitative and qualitative relationships, was calibrated by experiments at a fixed baking temperature of 200°C alone and in combination with 100 W microwave powers. The model parameters were estimated in a stepwise procedure i.e. first, heat and mass transfer related parameters, then the parameters related to product transformations and finally product quality parameters. There was a fair agreement between the calibrated model results and the experimental data. The results showed that the applied simple qualitative relationships for quality performed above expectation. Furthermore, it was confirmed that the microwave input is most meaningful for the internal product properties and not for the surface properties as crispness and color. The model with adjusted parameters was applied in a quality driven food process design procedure to derive a dynamic operation pattern, which was subsequently tested experimentally to calibrate the model. Despite the limited calibration with fixed operation settings, the model predicted well on the behavior under dynamic convective operation and on combined convective and microwave operation. It was expected that the suitability between model and baking system could be improved further by performing calibration experiments at higher temperature and various microwave power levels.  Abstrak  PERKIRAAN PARAMETER DALAM MODEL UNTUK PROSES BAKING ROTI. Kualitas produk roti sangat tergantung pada proses baking yang digunakan. Suatu model yang telah dikembangkan dengan metode kualitatif dan kuantitaif telah dikalibrasi dengan percobaan pada temperatur 200oC dan dengan kombinasi dengan mikrowave pada 100 Watt. Parameter-parameter model diestimasi dengan prosedur bertahap yaitu pertama, parameter pada model perpindahan masa dan panas, parameter pada model transformasi, dan

Parameter Estimation for Thurstone Choice Models

Energy Technology Data Exchange (ETDEWEB)

Vojnovic, Milan [London School of Economics (United Kingdom); Yun, Seyoung [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-04-24

We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one or more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.

Unsteady Vibration Aerodynamic Modeling and Evaluation of Dynamic Derivatives Using Computational Fluid Dynamics

Directory of Open Access Journals (Sweden)

Xu Liu

2015-01-01

Full Text Available Unsteady aerodynamic system modeling is widely used to solve the dynamic stability problems encountering aircraft design. In this paper, single degree-of-freedom (SDF vibration model and forced simple harmonic motion (SHM model for dynamic derivative prediction are developed on the basis of modified Etkin model. In the light of the characteristics of SDF time domain solution, the free vibration identification methods for dynamic stability parameters are extended and applied to the time domain numerical simulation of blunted cone calibration model examples. The dynamic stability parameters by numerical identification are no more than 0.15% deviated from those by experimental simulation, confirming the correctness of SDF vibration model. The acceleration derivatives, rotary derivatives, and combination derivatives of Army-Navy Spinner Rocket are numerically identified by using unsteady N-S equation and solving different SHV patterns. Comparison with the experimental result of Army Ballistic Research Laboratories confirmed the correctness of the SHV model and dynamic derivative identification. The calculation result of forced SHM is better than that by the slender body theory of engineering approximation. SDF vibration model and SHM model for dynamic stability parameters provide a solution to the dynamic stability problem encountering aircraft design.

Seasonal and spatial variation in broadleaf forest model parameters

Science.gov (United States)

Groenendijk, M.; van der Molen, M. K.; Dolman, A. J.

2009-04-01

Process based, coupled ecosystem carbon, energy and water cycle models are used with the ultimate goal to project the effect of future climate change on the terrestrial carbon cycle. A typical dilemma in such exercises is how much detail the model must be given to describe the observations reasonably realistic while also be general. We use a simple vegetation model (5PM) with five model parameters to study the variability of the parameters. These parameters are derived from the observed carbon and water fluxes from the FLUXNET database. For 15 broadleaf forests the model parameters were derived for different time resolutions. It appears that in general for all forests, the correlation coefficient between observed and simulated carbon and water fluxes improves with a higher parameter time resolution. The quality of the simulations is thus always better when a higher time resolution is used. These results show that annual parameters are not capable of properly describing weather effects on ecosystem fluxes, and that two day time resolution yields the best results. A first indication of the climate constraints can be found by the seasonal variation of the covariance between Jm, which describes the maximum electron transport for photosynthesis, and climate variables. A general seasonality we found is that during winter the covariance with all climate variables is zero. Jm increases rapidly after initial spring warming, resulting in a large covariance with air temperature and global radiation. During summer Jm is less variable, but co-varies negatively with air temperature and vapour pressure deficit and positively with soil water content. A temperature response appears during spring and autumn for broadleaf forests. This shows that an annual model parameter cannot be representative for the entire year. And relations with mean annual temperature are not possible. During summer the photosynthesis parameters are constrained by water availability, soil water content and

Incorporating model parameter uncertainty into inverse treatment planning

International Nuclear Information System (INIS)

Lian Jun; Xing Lei

2004-01-01

Radiobiological treatment planning depends not only on the accuracy of the models describing the dose-response relation of different tumors and normal tissues but also on the accuracy of tissue specific radiobiological parameters in these models. Whereas the general formalism remains the same, different sets of model parameters lead to different solutions and thus critically determine the final plan. Here we describe an inverse planning formalism with inclusion of model parameter uncertainties. This is made possible by using a statistical analysis-based frameset developed by our group. In this formalism, the uncertainties of model parameters, such as the parameter a that describes tissue-specific effect in the equivalent uniform dose (EUD) model, are expressed by probability density function and are included in the dose optimization process. We found that the final solution strongly depends on distribution functions of the model parameters. Considering that currently available models for computing biological effects of radiation are simplistic, and the clinical data used to derive the models are sparse and of questionable quality, the proposed technique provides us with an effective tool to minimize the effect caused by the uncertainties in a statistical sense. With the incorporation of the uncertainties, the technique has potential for us to maximally utilize the available radiobiology knowledge for better IMRT treatment

Significance of uncertainties derived from settling tank model structure and parameters on predicting WWTP performance - A global sensitivity analysis study

DEFF Research Database (Denmark)

Ramin, Elham; Sin, Gürkan; Mikkelsen, Peter Steen

2011-01-01

Uncertainty derived from one of the process models – such as one-dimensional secondary settling tank (SST) models – can impact the output of the other process models, e.g., biokinetic (ASM1), as well as the integrated wastewater treatment plant (WWTP) models. The model structure and parameter...... and from the last aerobic bioreactor upstream to the SST (Garrett/hydraulic method). For model structure uncertainty, two one-dimensional secondary settling tank (1-D SST) models are assessed, including a first-order model (the widely used Takács-model), in which the feasibility of using measured...... uncertainty of settler models can therefore propagate, and add to the uncertainties in prediction of any plant performance criteria. Here we present an assessment of the relative significance of secondary settling model performance in WWTP simulations. We perform a global sensitivity analysis (GSA) based...

The mobilisation model and parameter sensitivity

International Nuclear Information System (INIS)

Blok, B.M.

1993-12-01

In the PRObabillistic Safety Assessment (PROSA) of radioactive waste in a salt repository one of the nuclide release scenario's is the subrosion scenario. A new subrosion model SUBRECN has been developed. In this model the combined effect of a depth-dependent subrosion, glass dissolution, and salt rise has been taken into account. The subrosion model SUBRECN and the implementation of this model in the German computer program EMOS4 is presented. A new computer program PANTER is derived from EMOS4. PANTER models releases of radionuclides via subrosion from a disposal site in a salt pillar into the biosphere. For uncertainty and sensitivity analyses the new subrosion model Latin Hypercube Sampling has been used for determine the different values for the uncertain parameters. The influence of the uncertainty in the parameters on the dose calculations has been investigated by the following sensitivity techniques: Spearman Rank Correlation Coefficients, Partial Rank Correlation Coefficients, Standardised Rank Regression Coefficients, and the Smirnov Test. (orig./HP)

Predicting Collateral Status With Magnetic Resonance Perfusion Parameters: Probabilistic Approach With a Tmax-Derived Prediction Model.

Science.gov (United States)

Lee, Mi Ji; Son, Jeong Pyo; Kim, Suk Jae; Ryoo, Sookyung; Woo, Sook-Young; Cha, Jihoon; Kim, Gyeong-Moon; Chung, Chin-Sang; Lee, Kwang Ho; Bang, Oh Young

2015-10-01

Good collateral flow is an important predictor for favorable responses to recanalization therapy and successful outcomes after acute ischemic stroke. Magnetic resonance perfusion-weighted imaging (MRP) is widely used in patients with stroke. However, it is unclear whether the perfusion parameters and thresholds would predict collateral status. The present study evaluated the relationship between hypoperfusion severity and collateral status to develop a predictive model for good collaterals using MRP parameters. Patients who were eligible for recanalization therapy that underwent both serial diffusion-weighted imaging and serial MRP were enrolled into the study. A collateral flow map derived from MRP source data was generated through automatic postprocessing. Hypoperfusion severity, presented as proportions of every 2-s Tmax strata to the entire hypoperfusion volume (Tmax≥2 s), was compared between patients with good and poor collaterals. Prediction models for good collaterals were developed with each Tmax strata proportion and cerebral blood volumes. Among 66 patients, 53 showed good collaterals based on MRP-based collateral grading. Although no difference was noted in delays within 16 s, more severe Tmax delays (Tmax16-18 s, Tmax18-22 s, Tmax22-24 s, and Tmax>24 s) were associated with poor collaterals. The probability equation model using Tmax strata proportion demonstrated high predictive power in a receiver operating characteristic analysis (area under the curve=0.9303; 95% confidence interval, 0.8682-0.9924). The probability score was negatively correlated with the volume of infarct growth (P=0.030). Collateral status is associated with more severe Tmax delays than previously defined. The present Tmax severity-weighted model can determine good collaterals and subsequent infarct growth. © 2015 American Heart Association, Inc.

Systematic parameter inference in stochastic mesoscopic modeling

Energy Technology Data Exchange (ETDEWEB)

Lei, Huan; Yang, Xiu [Pacific Northwest National Laboratory, Richland, WA 99352 (United States); Li, Zhen [Division of Applied Mathematics, Brown University, Providence, RI 02912 (United States); Karniadakis, George Em, E-mail: george_karniadakis@brown.edu [Division of Applied Mathematics, Brown University, Providence, RI 02912 (United States)

2017-02-01

We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are “sparse”. The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space. Fully access to the response surfaces within the confidence range enables us to infer the optimal force parameters given the desirable values of target properties at the macroscopic scale. Moreover, it enables us to investigate the intrinsic relationship between the model parameters, identify possible degeneracies in the parameter space, and optimize the model by eliminating model redundancies. The proposed method provides an efficient alternative approach for constructing mesoscopic models by inferring model parameters to recover target properties of the physics systems (e.g., from experimental measurements), where those force field parameters and formulation cannot be derived from the microscopic level in a straight forward way.

On the equivalence between the thirring model and a derivative coupling model

International Nuclear Information System (INIS)

Gomes, M.; Silva, A.J. da.

1986-07-01

The equivalence between the Thirring model and the fermionic sector of the theory of a Dirac field interacting via derivate coupling with two boson fields is analysed. For a certain choice of the parameters the two models have the same fermionic Green functions. (Author) [pt

Geometry parameters for musculoskeletal modelling of the shoulder system

NARCIS (Netherlands)

Van der Helm, F C; Veeger, DirkJan (H. E. J.); Pronk, G M; Van der Woude, L H; Rozendal, R H

A dynamical finite-element model of the shoulder mechanism consisting of thorax, clavicula, scapula and humerus is outlined. The parameters needed for the model are obtained in a cadaver experiment consisting of both shoulders of seven cadavers. In this paper, in particular, the derivation of

A Note on the Item Information Function of the Four-Parameter Logistic Model

Science.gov (United States)

Magis, David

2013-01-01

This article focuses on four-parameter logistic (4PL) model as an extension of the usual three-parameter logistic (3PL) model with an upper asymptote possibly different from 1. For a given item with fixed item parameters, Lord derived the value of the latent ability level that maximizes the item information function under the 3PL model. The…

Biological parameters for lung cancer in mathematical models of carcinogenesis

International Nuclear Information System (INIS)

Jacob, P.; Jacob, V.

2003-01-01

Applications of the two-step model of carcinogenesis with clonal expansion (TSCE) to lung cancer data are reviewed, including those on atomic bomb survivors from Hiroshima and Nagasaki, British doctors, Colorado Plateau miners, and Chinese tin miners. Different sets of identifiable model parameters are used in the literature. The parameter set which could be determined with the lowest uncertainty consists of the net proliferation rate gamma of intermediate cells, the hazard h 55 at an intermediate age, and the hazard H? at an asymptotically large age. Also, the values of these three parameters obtained in the various studies are more consistent than other identifiable combinations of the biological parameters. Based on representative results for these three parameters, implications for the biological parameters in the TSCE model are derived. (author)

Derivation of potential model for LiAlO2 by simple and effective optimization of model parameters

International Nuclear Information System (INIS)

Tsuchihira, H.; Oda, T.; Tanaka, S.

2009-01-01

Interatomic potentials of LiAlO 2 were constructed by a simple and effective method. In this method, the model function consists of multiple inverse polynomial functions with an exponential truncation function, and parameters in the potential model can be optimized as a solution of simultaneous linear equations. Potential energies obtained by ab initio calculation are used as fitting targets for model parameter optimization. Lattice constants, elastic properties, defect-formation energy, thermal expansions and the melting point were calculated under the constructed potential models. The results showed good agreement with experimental values and ab initio calculation results, which underscores the validity of the presented method.

Lumped-parameter models

Energy Technology Data Exchange (ETDEWEB)

Ibsen, Lars Bo; Liingaard, M.

2006-12-15

A lumped-parameter model represents the frequency dependent soil-structure interaction of a massless foundation placed on or embedded into an unbounded soil domain. In this technical report the steps of establishing a lumped-parameter model are presented. Following sections are included in this report: Static and dynamic formulation, Simple lumped-parameter models and Advanced lumped-parameter models. (au)

Large deflection of viscoelastic beams using fractional derivative model

International Nuclear Information System (INIS)

Bahranini, Seyed Masoud Sotoodeh; Eghtesad, Mohammad; Ghavanloo, Esmaeal; Farid, Mehrdad

2013-01-01

This paper deals with large deflection of viscoelastic beams using a fractional derivative model. For this purpose, a nonlinear finite element formulation of viscoelastic beams in conjunction with the fractional derivative constitutive equations has been developed. The four-parameter fractional derivative model has been used to describe the constitutive equations. The deflected configuration for a uniform beam with different boundary conditions and loads is presented. The effect of the order of fractional derivative on the large deflection of the cantilever viscoelastic beam, is investigated after 10, 100, and 1000 hours. The main contribution of this paper is finite element implementation for nonlinear analysis of viscoelastic fractional model using the storage of both strain and stress histories. The validity of the present analysis is confirmed by comparing the results with those found in the literature.

A note on modeling of tumor regression for estimation of radiobiological parameters

International Nuclear Information System (INIS)

Zhong, Hualiang; Chetty, Indrin

2014-01-01

Purpose: Accurate calculation of radiobiological parameters is crucial to predicting radiation treatment response. Modeling differences may have a significant impact on derived parameters. In this study, the authors have integrated two existing models with kinetic differential equations to formulate a new tumor regression model for estimation of radiobiological parameters for individual patients. Methods: A system of differential equations that characterizes the birth-and-death process of tumor cells in radiation treatment was analytically solved. The solution of this system was used to construct an iterative model (Z-model). The model consists of three parameters: tumor doubling time T d , half-life of dead cells T r , and cell survival fraction SF D under dose D. The Jacobian determinant of this model was proposed as a constraint to optimize the three parameters for six head and neck cancer patients. The derived parameters were compared with those generated from the two existing models: Chvetsov's model (C-model) and Lim's model (L-model). The C-model and L-model were optimized with the parameter T d fixed. Results: With the Jacobian-constrained Z-model, the mean of the optimized cell survival fractions is 0.43 ± 0.08, and the half-life of dead cells averaged over the six patients is 17.5 ± 3.2 days. The parameters T r and SF D optimized with the Z-model differ by 1.2% and 20.3% from those optimized with the T d -fixed C-model, and by 32.1% and 112.3% from those optimized with the T d -fixed L-model, respectively. Conclusions: The Z-model was analytically constructed from the differential equations of cell populations that describe changes in the number of different tumor cells during the course of radiation treatment. The Jacobian constraints were proposed to optimize the three radiobiological parameters. The generated model and its optimization method may help develop high-quality treatment regimens for individual patients

SP_Ace: a new code to derive stellar parameters and elemental abundances

Science.gov (United States)

Boeche, C.; Grebel, E. K.

2016-03-01

Context. Ongoing and future massive spectroscopic surveys will collect large numbers (106-107) of stellar spectra that need to be analyzed. Highly automated software is needed to derive stellar parameters and chemical abundances from these spectra. Aims: We developed a new method of estimating the stellar parameters Teff, log g, [M/H], and elemental abundances. This method was implemented in a new code, SP_Ace (Stellar Parameters And Chemical abundances Estimator). This is a highly automated code suitable for analyzing the spectra of large spectroscopic surveys with low or medium spectral resolution (R = 2000-20 000). Methods: After the astrophysical calibration of the oscillator strengths of 4643 absorption lines covering the wavelength ranges 5212-6860 Å and 8400-8924 Å, we constructed a library that contains the equivalent widths (EW) of these lines for a grid of stellar parameters. The EWs of each line are fit by a polynomial function that describes the EW of the line as a function of the stellar parameters. The coefficients of these polynomial functions are stored in a library called the "GCOG library". SP_Ace, a code written in FORTRAN95, uses the GCOG library to compute the EWs of the lines, constructs models of spectra as a function of the stellar parameters and abundances, and searches for the model that minimizes the χ2 deviation when compared to the observed spectrum. The code has been tested on synthetic and real spectra for a wide range of signal-to-noise and spectral resolutions. Results: SP_Ace derives stellar parameters such as Teff, log g, [M/H], and chemical abundances of up to ten elements for low to medium resolution spectra of FGK-type stars with precision comparable to the one usually obtained with spectra of higher resolution. Systematic errors in stellar parameters and chemical abundances are presented and identified with tests on synthetic and real spectra. Stochastic errors are automatically estimated by the code for all the parameters

Assimilation of Earth rotation parameters into a global ocean model (FESOM)

Science.gov (United States)

Androsov, A.; Schröter, J.; Brunnabend, S.; Saynisch, J.

2012-04-01

Earth Rotation Parameters (ERP) are used to improve estimates of the ocean circulation and mass budget. GRACE data can be used for verification or for further improvements. The Finite Element Sea-ice Ocean Model (FESOM) is used to simulate weekly ocean circulation and mass variations. The FESOM model is a hydrostatic ocean circulation model with a fully non-linear free surface. It solves the hydrostatic primitive equations with volume (Boussinesq approximation) and mass (Greatbatch correction) conservation. Fresh water exchange with the atmosphere and land is modelled as mass flux. This flux is the weakest part of the mass budget as it is the difference of large and uncertain quantities: evaporation, precipitation and river runoff. All uncertainties included in these parameters are directly reflected in the model results. ERP help in closing the budget in a realistic manner. Our strategy is designed for testing parametric estimation on a weekly basis. First, Oceanographic Earth rotation parameters (OERP) are calculated by subtracting atmospheric and hydrologic estimates from observed ERP. They are compared to OERP derived from a global ocean circulation model. The difference can be inverted to diagnose a correction of the oceanic mass budget. Additionally mass variations measured by GRACE are used for verification. In a second step, the global mass correction parameter, derived by the inversion, is used to improve the fresh water budget of FESOM.

Soil hydraulic parameters and surface soil moisture of a tilled bare soil plot inversely derived from l-band brightness temperatures

KAUST Repository

Dimitrov, Marin

2014-01-01

We coupled a radiative transfer model and a soil hydrologic model (HYDRUS 1D) with an optimization routine to derive soil hydraulic parameters, surface roughness, and soil moisture of a tilled bare soil plot using measured brightness temperatures at 1.4 GHz (L-band), rainfall, and potential soil evaporation. The robustness of the approach was evaluated using five 28-d data sets representing different meteorological conditions. We considered two soil hydraulic property models: the unimodal Mualem-van Genuchten and the bimodal model of Durner. Microwave radiative transfer was modeled by three different approaches: the Fresnel equation with depth-averaged dielectric permittivity of either 2-or 5-cm-thick surface layers and a coherent radiative transfer model (CRTM) that accounts for vertical gradients in dielectric permittivity. Brightness temperatures simulated by the CRTM and the 2-cm-layer Fresnel model fitted well to the measured ones. L-band brightness temperatures are therefore related to the dielectric permittivity and soil moisture in a 2-cm-thick surface layer. The surface roughness parameter that was derived from brightness temperatures using inverse modeling was similar to direct estimates from laser profiler measurements. The laboratory-derived water retention curve was bimodal and could be retrieved consistently for the different periods from brightness temperatures using inverse modeling. A unimodal soil hydraulic property function underestimated the hydraulic conductivity near saturation. Surface soil moisture contents simulated using retrieved soil hydraulic parameters were compared with in situ measurements. Depth-specific calibration relations were essential to derive soil moisture from near-surface installed sensors. © Soil Science Society of America 5585 Guilford Rd., Madison, WI 53711 USA.

Analysis of pressure-flow data in terms of computer-derived urethral resistance parameters.

Science.gov (United States)

van Mastrigt, R; Kranse, M

1995-01-01

The simultaneous measurement of detrusor pressure and flow rate during voiding is at present the only way to measure or grade infravesical obstruction objectively. Numerous methods have been introduced to analyze the resulting data. These methods differ in aim (measurement of urethral resistance and/or diagnosis of obstruction), method (manual versus computerized data processing), theory or model used, and resolution (continuously variable parameters or a limited number of classes, the so-called monogram). In this paper, some aspects of these fundamental differences are discussed and illustrated. Subsequently, the properties and clinical performance of two computer-based methods for deriving continuous urethral resistance parameters are treated.

Lumped Parameter Modeling for Rapid Vibration Response Prototyping and Test Correlation for Electronic Units

Science.gov (United States)

Van Dyke, Michael B.

2013-01-01

Present preliminary work using lumped parameter models to approximate dynamic response of electronic units to random vibration; Derive a general N-DOF model for application to electronic units; Illustrate parametric influence of model parameters; Implication of coupled dynamics for unit/board design; Demonstrate use of model to infer printed wiring board (PWB) dynamics from external chassis test measurement.

Using sensitivity derivatives for design and parameter estimation in an atmospheric plasma discharge simulation

International Nuclear Information System (INIS)

Lange, Kyle J.; Anderson, W. Kyle

2010-01-01

The problem of applying sensitivity analysis to a one-dimensional atmospheric radio frequency plasma discharge simulation is considered. A fluid simulation is used to model an atmospheric pressure radio frequency helium discharge with a small nitrogen impurity. Sensitivity derivatives are computed for the peak electron density with respect to physical inputs to the simulation. These derivatives are verified using several different methods to compute sensitivity derivatives. It is then demonstrated how sensitivity derivatives can be used within a design cycle to change these physical inputs so as to increase the peak electron density. It is also shown how sensitivity analysis can be used in conjunction with experimental data to obtain better estimates for rate and transport parameters. Finally, it is described how sensitivity analysis could be used to compute an upper bound on the uncertainty for results from a simulation.

Physically based model for extracting dual permeability parameters using non-Newtonian fluids

Science.gov (United States)

Abou Najm, M. R.; Basset, C.; Stewart, R. D.; Hauswirth, S.

2017-12-01

Dual permeability models are effective for the assessment of flow and transport in structured soils with two dominant structures. The major challenge to those models remains in the ability to determine appropriate and unique parameters through affordable, simple, and non-destructive methods. This study investigates the use of water and a non-Newtonian fluid in saturated flow experiments to derive physically-based parameters required for improved flow predictions using dual permeability models. We assess the ability of these two fluids to accurately estimate the representative pore sizes in dual-domain soils, by determining the effective pore sizes of macropores and micropores. We developed two sub-models that solve for the effective macropore size assuming either cylindrical (e.g., biological pores) or planar (e.g., shrinkage cracks and fissures) pore geometries, with the micropores assumed to be represented by a single effective radius. Furthermore, the model solves for the percent contribution to flow (wi) corresponding to the representative macro and micro pores. A user-friendly solver was developed to numerically solve the system of equations, given that relevant non-Newtonian viscosity models lack forms conducive to analytical integration. The proposed dual-permeability model is a unique attempt to derive physically based parameters capable of measuring dual hydraulic conductivities, and therefore may be useful in reducing parameter uncertainty and improving hydrologic model predictions.

Models for estimating photosynthesis parameters from in situ production profiles

Science.gov (United States)

Kovač, Žarko; Platt, Trevor; Sathyendranath, Shubha; Antunović, Suzana

2017-12-01

The rate of carbon assimilation in phytoplankton primary production models is mathematically prescribed with photosynthesis irradiance functions, which convert a light flux (energy) into a material flux (carbon). Information on this rate is contained in photosynthesis parameters: the initial slope and the assimilation number. The exactness of parameter values is crucial for precise calculation of primary production. Here we use a model of the daily production profile based on a suite of photosynthesis irradiance functions and extract photosynthesis parameters from in situ measured daily production profiles at the Hawaii Ocean Time-series station Aloha. For each function we recover parameter values, establish parameter distributions and quantify model skill. We observe that the choice of the photosynthesis irradiance function to estimate the photosynthesis parameters affects the magnitudes of parameter values as recovered from in situ profiles. We also tackle the problem of parameter exchange amongst the models and the effect it has on model performance. All models displayed little or no bias prior to parameter exchange, but significant bias following parameter exchange. The best model performance resulted from using optimal parameter values. Model formulation was extended further by accounting for spectral effects and deriving a spectral analytical solution for the daily production profile. The daily production profile was also formulated with time dependent growing biomass governed by a growth equation. The work on parameter recovery was further extended by exploring how to extract photosynthesis parameters from information on watercolumn production. It was demonstrated how to estimate parameter values based on a linearization of the full analytical solution for normalized watercolumn production and from the solution itself, without linearization. The paper complements previous works on photosynthesis irradiance models by analysing the skill and consistency of

Statistical properties of compartmental model parameters extracted from dynamic positron emission tomography experiments

International Nuclear Information System (INIS)

Mazoyer, B.M.; Huesman, R.H.; Budinger, T.F.; Knittel, B.L.

1986-01-01

Over the past years a major focus of research in physiologic studies employing tracers has been the computer implementation of mathematical methods of kinetic modeling for extracting the desired physiological parameters from tomographically derived data. A study is reported of factors that affect the statistical properties of compartmental model parameters extracted from dynamic positron emission tomography (PET) experiments

Parameter estimation in nonlinear models for pesticide degradation

International Nuclear Information System (INIS)

Richter, O.; Pestemer, W.; Bunte, D.; Diekkrueger, B.

1991-01-01

A wide class of environmental transfer models is formulated as ordinary or partial differential equations. With the availability of fast computers, the numerical solution of large systems became feasible. The main difficulty in performing a realistic and convincing simulation of the fate of a substance in the biosphere is not the implementation of numerical techniques but rather the incomplete data basis for parameter estimation. Parameter estimation is a synonym for statistical and numerical procedures to derive reasonable numerical values for model parameters from data. The classical method is the familiar linear regression technique which dates back to the 18th century. Because it is easy to handle, linear regression has long been established as a convenient tool for analysing relationships. However, the wide use of linear regression has led to an overemphasis of linear relationships. In nature, most relationships are nonlinear and linearization often gives a poor approximation of reality. Furthermore, pure regression models are not capable to map the dynamics of a process. Therefore, realistic models involve the evolution in time (and space). This leads in a natural way to the formulation of differential equations. To establish the link between data and dynamical models, numerical advanced parameter identification methods have been developed in recent years. This paper demonstrates the application of these techniques to estimation problems in the field of pesticide dynamics. (7 refs., 5 figs., 2 tabs.)

Correlations between skin hydration parameters and corneocyte-derived parameters to characterize skin conditions.

Science.gov (United States)

Masaki, Hitoshi; Yamashita, Yuki; Kyotani, Daiki; Honda, Tatsuya; Takano, Kenichi; Tamura, Toshiyasu; Mizutani, Taeko; Okano, Yuri

2018-03-30

Skin hydration is generally assessed using the parameters of skin surface water content (SWC) and trans-epidermal water loss (TEWL). To date, few studies have characterized skin conditions using correlations between skin hydration parameters and corneocyte parameters. The parameters SWC and TEWL allow the classification of skin conditions into four distinct Groups. The purpose of this study was to assess the characteristics of skin conditions classified by SWC and TEWL for correlations with parameters from corneocytes. A human volunteer test was conducted that measured SWC and TEWL. As corneocyte-derived parameters, the size and thick abrasion ratios, the ratio of sulfhydryl groups and disulfide bonds (SH/SS) and CP levels were analyzed. Volunteers were classified by their median SWC and TEWL values into 4 Groups: Group I (high SWC/low TEWL), Group II (high SWC/high TEWL), Group III (low SWC/low TEWL), and Group IV (low SWC/high TEWL). Group IV showed a significantly smaller size of corneocytes. Groups III and IV had significantly higher thick abrasion ratios and CP levels. Group I had a significantly lower SH/SS value. The SWC/TEWL value showed a decline in order from Group I to Group IV. Groups classified by their SWC and TEWL values showed characteristic skin conditions. We propose that the SWC and TEWL ratio is a comprehensive parameter to assess skin conditions. © 2018 Wiley Periodicals, Inc.

Derivation of global vegetation biophysical parameters from EUMETSAT Polar System

Science.gov (United States)

García-Haro, Francisco Javier; Campos-Taberner, Manuel; Muñoz-Marí, Jordi; Laparra, Valero; Camacho, Fernando; Sánchez-Zapero, Jorge; Camps-Valls, Gustau

2018-05-01

This paper presents the algorithm developed in LSA-SAF (Satellite Application Facility for Land Surface Analysis) for the derivation of global vegetation parameters from the AVHRR (Advanced Very High Resolution Radiometer) sensor on board MetOp (Meteorological-Operational) satellites forming the EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Polar System (EPS). The suite of LSA-SAF EPS vegetation products includes the leaf area index (LAI), the fractional vegetation cover (FVC), and the fraction of absorbed photosynthetically active radiation (FAPAR). LAI, FAPAR, and FVC characterize the structure and the functioning of vegetation and are key parameters for a wide range of land-biosphere applications. The algorithm is based on a hybrid approach that blends the generalization capabilities offered by physical radiative transfer models with the accuracy and computational efficiency of machine learning methods. One major feature is the implementation of multi-output retrieval methods able to jointly and more consistently estimate all the biophysical parameters at the same time. We propose a multi-output Gaussian process regression (GPRmulti), which outperforms other considered methods over PROSAIL (coupling of PROSPECT and SAIL (Scattering by Arbitrary Inclined Leaves) radiative transfer models) EPS simulations. The global EPS products include uncertainty estimates taking into account the uncertainty captured by the retrieval method and input errors propagation. A sensitivity analysis is performed to assess several sources of uncertainties in retrievals and maximize the positive impact of modeling the noise in training simulations. The paper discusses initial validation studies and provides details about the characteristics and overall quality of the products, which can be of interest to assist the successful use of the data by a broad user's community. The consistent generation and distribution of the EPS vegetation products will

TWO-PARAMETER ISOTHERMS OF METHYL ORANGE SORPTION BY PINECONE DERIVED ACTIVATED CARBON

Directory of Open Access Journals (Sweden)

M. R. Samarghandi ، M. Hadi ، S. Moayedi ، F. Barjasteh Askari

2009-10-01

Full Text Available The adsorption of a mono azo dye methyl-orange (MeO onto granular pinecone derived activated carbon (GPAC, from aqueous solutions, was studied in a batch system. Seven two-parameter isotherm models Langmuir, Freundlich, Dubinin-Radushkevic, Temkin, Halsey, Jovanovic and Hurkins-Jura were used to fit the experimental data. The results revealed that the adsorption isotherm models fitted the data in the order of Jovanovic (X2=1.374 > Langmuir > Dubinin-Radushkevic > Temkin > Freundlich > Halsey > Hurkins-Jura isotherms. Adsorption isotherms modeling showed that the interaction of dye with activated carbon surface is localized monolayer adsorption. A comparison of kinetic models was evaluated for the pseudo-second order, Elovich and Lagergren kinetic models. Lagergren first order model was found to agree well with the experimental data (X2=9.231. In order to determine the best-fit isotherm and kinetic models, two error analysis methods of Residual Mean Square Error and Chi-square statistic (X2 were used to evaluate the data.

Parameter estimation and analysis of an automotive heavy-duty SCR catalyst model

DEFF Research Database (Denmark)

Åberg, Andreas; Widd, Anders; Abildskov, Jens

2017-01-01

A single channel model for a heavy-duty SCR catalyst was derived based on first principles. The model considered heat and mass transfer between the channel gas phase and the wash coat phase. The parameters of the kinetic model were estimated using bench-scale monolith isothermal data. Validation ...

Poromechanics Parameters of Fluid-Saturated Chemically Active Fibrous Media Derived from a Micromechanical Approach.

Science.gov (United States)

Misra, Anil; Parthasarathy, Ranganathan; Singh, Viraj; Spencer, Paulette

2013-01-01

The authors have derived macroscale poromechanics parameters for chemically active saturated fibrous media by combining microstructure-based homogenization with Hill's volume averaging. The stress-strain relationship of the dry fibrous media is first obtained by considering the fiber behavior. The constitutive relationships applicable to saturated media are then derived in the poromechanics framework using Hill's Lemmas. The advantage of this approach is that the resultant continuum model assumes a form suited to study porous materials, while retaining the effect of discrete fiber deformation. As a result, the model is able to predict the influence of microscale phenomena such as fiber buckling on the overall behavior, and in particular, on the poromechanics constants. The significance of the approach is demonstrated using the effect of drainage and fiber nonlinearity on monotonic compressive stress-strain behavior. The model predictions conform to the experimental observations for articular cartilage. The method can potentially be extended to other porous materials such as bone, clays, foams, and concrete.

A Conceptually Simple Modeling Approach for Jason-1 Sea State Bias Correction Based on 3 Parameters Exclusively Derived from Altimetric Information

Directory of Open Access Journals (Sweden)

Nelson Pires

2016-07-01

Full Text Available A conceptually simple formulation is proposed for a new empirical sea state bias (SSB model using information retrieved entirely from altimetric data. Nonparametric regression techniques are used, based on penalized smoothing splines adjusted to each predictor and then combined by a Generalized Additive Model. In addition to the significant wave height (SWH and wind speed (U10, a mediator parameter designed by the mean wave period derived from radar altimetry, has proven to improve the model performance in explaining some of the SSB variability, especially in swell ocean regions with medium-high SWH and low U10. A collinear analysis of scaled sea level anomalies (SLA variance differences shows conformity between the proposed model and the established SSB models. The new formulation aims to be a fast, reliable and flexible SSB model, in line with the well-settled SSB corrections, depending exclusively on altimetric information. The suggested method is computationally efficient and capable of generating a stable model with a small training dataset, a useful feature for forthcoming missions.

Derivation of parameters necessary for the evaluation of performance of sites for deep geological repositories with particular reference to bedded salt, Livermore, California. Volume I. Main text

International Nuclear Information System (INIS)

Ashby, J.P.; Rawlings, G.E.; Soto, C.A.; Wood, D.F.; Chorley, D.W.

1979-12-01

A survey of parameters to be considered in the evaluation of sites for deep geologic nuclear waste repositories is presented. As yet, no comprehensive site selection procedure or performance evaluation approach has been adopted. A basis is provided for the development of parameters by discussing both site selection and performance evaluation. Three major groups of parameters are considered in this report: geologic, mining/rock mechanics, and hydrogeologic. For each type, the role of the parameter in the evaluation of repository sites is discussed. The derivation of the parameter by measurement, correlation, inference, or other method is discussed. Geologic parameters define the framework of the repository site and can be used in development of conceptual models and the prediction of long-term performance. Methods for deriving geological parameters include mapping, surveying, drilling, geophysical investigation, and historical and regional analysis. Rock mechanics/mining parameters are essential for the prediction of short-term performance and the development of initial conditions for modeling of long-term performance. Rock mechanics/mapping parameters can be derived by field or laboratory investigation, correlation, and theoretically or empirically based inference. Hydrogeologic parameters are the most important for assessment of long-term radionuclide confinement, since transport throughout the regional hydrogeologic system is the most likely mode of radionuclide escape from geologic repositories. Hydrogeologic parameters can be derived by hydrogeologic mapping and interpretation, hydrogeologic system modeling, field measurements, and lab tests. Procedures used in determination and statistical evaluation of geologic and rock mechanics parameters are discussed

Derivation of parameters necessary for the evaluation of performance of sites for deep geological repositories with particular reference to bedded salt, Livermore, California. Volume I. Main text

Energy Technology Data Exchange (ETDEWEB)

Ashby, J.P.; Rawlings, G.E.; Soto, C.A.; Wood, D.F.; Chorley, D.W.

1979-12-01

A survey of parameters to be considered in the evaluation of sites for deep geologic nuclear waste repositories is presented. As yet, no comprehensive site selection procedure or performance evaluation approach has been adopted. A basis is provided for the development of parameters by discussing both site selection and performance evaluation. Three major groups of parameters are considered in this report: geologic, mining/rock mechanics, and hydrogeologic. For each type, the role of the parameter in the evaluation of repository sites is discussed. The derivation of the parameter by measurement, correlation, inference, or other method is discussed. Geologic parameters define the framework of the repository site and can be used in development of conceptual models and the prediction of long-term performance. Methods for deriving geological parameters include mapping, surveying, drilling, geophysical investigation, and historical and regional analysis. Rock mechanics/mining parameters are essential for the prediction of short-term performance and the development of initial conditions for modeling of long-term performance. Rock mechanics/mapping parameters can be derived by field or laboratory investigation, correlation, and theoretically or empirically based inference. Hydrogeologic parameters are the most important for assessment of long-term radionuclide confinement, since transport throughout the regional hydrogeologic system is the most likely mode of radionuclide escape from geologic repositories. Hydrogeologic parameters can be derived by hydrogeologic mapping and interpretation, hydrogeologic system modeling, field measurements, and lab tests. Procedures used in determination and statistical evaluation of geologic and rock mechanics parameters are discussed.

Identifying the connective strength between model parameters and performance criteria

Directory of Open Access Journals (Sweden)

B. Guse

2017-11-01

Full Text Available In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria. To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash–Sutcliffe efficiency (NSE, Kling–Gupta efficiency (KGE and its three components (alpha, beta and r as well as RSR (the ratio of the root mean square error to the standard deviation for different segments of the flow duration curve (FDC are calculated. With a joint analysis of two regression tree (RT approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter. In this study, a high bijective connective strength between model parameters and performance criteria

Using climate derivatives for assessment of meteorological parameter relationships in RCM and observations

Science.gov (United States)

Timuhins, Andrejs; Bethers, Uldis; Bethers, Peteris; Klints, Ilze; Sennikovs, Juris; Frishfelds, Vilnis

2017-04-01

In a changing climate it is essential to estimate its impacts on different economic fields. In our study we tried to create a framework for climate change assessment and climate change impact estimation for the territory of Latvia and to create results which are also understandable for non-scientists (stakeholder, media and public). This approach allowed us to more carefully assess the presentation and interpretation of results and their validation, for public viewing. For the presentation of our work a website was created (www.modlab.lv/klimats) containing two types of documents in a unified framework, meteorological parameter analysis of different easily interpretable derivative values. Both of these include analysis of the current situation as well as illustrate the projection for future time periods. Derivate values are calculated using two data sources: the bias corrected regional climate data and meteorological observation data. Derivative documents contain description of derived value, some interesting facts and conclusions. Additionally, all results may be viewed in temporal and spatial graphs and maps, for different time periods as well as different seasons. Bias correction (Sennikovs and Bethers, 2009) for the control period 1961-1990 is applied to RCM data series. Meteorological observation data of the Latvian Environment, Geology, and Meteorology Agency and ENSEMBLES project daily data of 13 RCM runs for the period 1960-2100 are used. All the documents are prepared in python notebooks, which allow for flexible changes. At the moment following derivative values have been published: forest fire risk index, wind energy, phenology (Degree days), road condition (friction, ice conditions), daily minimal meteorological visibility, headache occurrence rate, firs snow date and meteorological parameter analysis: temperature, precipitation, wind speed, relative humidity, and cloudiness. While creating these products RCM ability to represent the actual climate was

Improving weather predictability by including land-surface model parameter uncertainty

Science.gov (United States)

Orth, Rene; Dutra, Emanuel; Pappenberger, Florian

2016-04-01

The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogenous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model. Focusing on ECMWF's land-surface model HTESSEL we present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. We select 6 poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally we investigate the possibility to construct ensembles from the multiple land surface parameters. In the uncoupled runs we find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. We demonstrate the robustness of our findings by comparing multiple best performing parameter sets and multiple randomly chosen parameter sets. We find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings. Finally, we construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system. Orth, R., E. Dutra, and F. Pappenberger, 2016: Improving weather predictability by

Modeling Anti-HIV Activity of HEPT Derivatives Revisited. Multiregression Models Are Not Inferior Ones

International Nuclear Information System (INIS)

Basic, Ivan; Nadramija, Damir; Flajslik, Mario; Amic, Dragan; Lucic, Bono

2007-01-01

Several quantitative structure-activity studies for this data set containing 107 HEPT derivatives have been performed since 1997, using the same set of molecules by (more or less) different classes of molecular descriptors. Multivariate Regression (MR) and Artificial Neural Network (ANN) models were developed and in each study the authors concluded that ANN models are superior to MR ones. We re-calculated multivariate regression models for this set of molecules using the same set of descriptors, and compared our results with the previous ones. Two main reasons for overestimation of the quality of the ANN models in previous studies comparing with MR models are: (1) wrong calculation of leave-one-out (LOO) cross-validated (CV) correlation coefficient for MR models in Luco et al., J. Chem. Inf. Comput. Sci. 37 392-401 (1997), and (2) incorrect estimation/interpretation of leave-one-out (LOO) cross-validated and predictive performance and power of ANN models. More precise and fairer comparison of fit and LOO CV statistical parameters shows that MR models are more stable. In addition, MR models are much simpler than ANN ones. For real testing the predictive performance of both classes of models we need more HEPT derivatives, because all ANN models that presented results for external set of molecules used experimental values in optimization of modeling procedure and model parameters

CHARACTERIZING LANDSCAPE SPATIAL HETEROGENEITY USING SEMIVARIOGRAM PARAMETERS DERIVED FROM NDVI IMAGES

Directory of Open Access Journals (Sweden)

Eduarda Martiniano de Oliveira Silveira

2017-12-01

Full Text Available Assuming a relationship between landscape heterogeneity and measures of spatial dependence by using remotely sensed data, the aim of this work was to evaluate the potential of semivariogram parameters, derived from satellite images with different spatial resolutions, to characterize landscape spatial heterogeneity of forested and human modified areas. The NDVI (Normalized Difference Vegetation Index was generated in an area of Brazilian amazon tropical forest (1,000 km².We selected samples (1 x 1 km from forested and human modified areas distributed throughout the study area, to generate the semivariogram and extract the sill (σ²-overall spatial variability of the surface property and range (φ-the length scale of the spatial structures of objects parameters. The analysis revealed that image spatial resolution influenced the sill and range parameters. The average sill and range values increase from forested to human modified areas and the greatest between-class variation was found for LANDSAT 8 imagery, indicating that this image spatial resolution is the most appropriate for deriving sill and range parameters with the intention of describing landscape spatial heterogeneity. By combining remote sensing and geostatistical techniques, we have shown that the sill and range parameters of semivariograms derived from NDVI images are a simple indicator of landscape heterogeneity and can be used to provide landscape heterogeneity maps to enable researchers to design appropriate sampling regimes. In the future, more applications combining remote sensing and geostatistical features should be further investigated and developed, such as change detection and image classification using object-based image analysis (OBIA approaches.

Pivotal statistics for testing subsets of structural parameters in the IV Regression Model

NARCIS (Netherlands)

Kleibergen, F.R.

2000-01-01

We construct a novel statistic to test hypothezes on subsets of the structural parameters in anInstrumental Variables (IV) regression model. We derive the chi squared limiting distribution of thestatistic and show that it has a degrees of freedom parameter that is equal to the number ofstructural

A fractal derivative constitutive model for three stages in granite creep

Directory of Open Access Journals (Sweden)

R. Wang

Full Text Available In this paper, by replacing the Newtonian dashpot with the fractal dashpot and considering damage effect, a new constitutive model is proposed in terms of time fractal derivative to describe the full creep regions of granite. The analytic solutions of the fractal derivative creep constitutive equation are derived via scaling transform. The conventional triaxial compression creep tests are performed on MTS 815 rock mechanics test system to verify the efficiency of the new model. The granite specimen is taken from Beishan site, the most potential area for the China’s high-level radioactive waste repository. It is shown that the proposed fractal model can characterize the creep behavior of granite especially in accelerating stage which the classical models cannot predict. The parametric sensitivity analysis is also conducted to investigate the effects of model parameters on the creep strain of granite. Keywords: Beishan granite, Fractal derivative, Damage evolution, Scaling transformation

Parameters of Models of Structural Transformations in Alloy Steel Under Welding Thermal Cycle

Science.gov (United States)

Kurkin, A. S.; Makarov, E. L.; Kurkin, A. B.; Rubtsov, D. E.; Rubtsov, M. E.

2017-05-01

A mathematical model of structural transformations in an alloy steel under the thermal cycle of multipass welding is suggested for computer implementation. The minimum necessary set of parameters for describing the transformations under heating and cooling is determined. Ferritic-pearlitic, bainitic and martensitic transformations under cooling of a steel are considered. A method for deriving the necessary temperature and time parameters of the model from the chemical composition of the steel is described. Published data are used to derive regression models of the temperature ranges and parameters of transformation kinetics in alloy steels. It is shown that the disadvantages of the active visual methods of analysis of the final phase composition of steels are responsible for inaccuracy and mismatch of published data. The hardness of a specimen, which correlates with some other mechanical properties of the material, is chosen as the most objective and reproducible criterion of the final phase composition. The models developed are checked by a comparative analysis of computational results and experimental data on the hardness of 140 alloy steels after cooling at various rates.

PARAMETER ESTIMATION AND MODEL SELECTION FOR INDOOR ENVIRONMENTS BASED ON SPARSE OBSERVATIONS

Directory of Open Access Journals (Sweden)

Y. Dehbi

2017-09-01

Full Text Available This paper presents a novel method for the parameter estimation and model selection for the reconstruction of indoor environments based on sparse observations. While most approaches for the reconstruction of indoor models rely on dense observations, we predict scenes of the interior with high accuracy in the absence of indoor measurements. We use a model-based top-down approach and incorporate strong but profound prior knowledge. The latter includes probability density functions for model parameters and sparse observations such as room areas and the building footprint. The floorplan model is characterized by linear and bi-linear relations with discrete and continuous parameters. We focus on the stochastic estimation of model parameters based on a topological model derived by combinatorial reasoning in a first step. A Gauss-Markov model is applied for estimation and simulation of the model parameters. Symmetries are represented and exploited during the estimation process. Background knowledge as well as observations are incorporated in a maximum likelihood estimation and model selection is performed with AIC/BIC. The likelihood is also used for the detection and correction of potential errors in the topological model. Estimation results are presented and discussed.

Parameter Estimation and Model Selection for Indoor Environments Based on Sparse Observations

Science.gov (United States)

Dehbi, Y.; Loch-Dehbi, S.; Plümer, L.

2017-09-01

This paper presents a novel method for the parameter estimation and model selection for the reconstruction of indoor environments based on sparse observations. While most approaches for the reconstruction of indoor models rely on dense observations, we predict scenes of the interior with high accuracy in the absence of indoor measurements. We use a model-based top-down approach and incorporate strong but profound prior knowledge. The latter includes probability density functions for model parameters and sparse observations such as room areas and the building footprint. The floorplan model is characterized by linear and bi-linear relations with discrete and continuous parameters. We focus on the stochastic estimation of model parameters based on a topological model derived by combinatorial reasoning in a first step. A Gauss-Markov model is applied for estimation and simulation of the model parameters. Symmetries are represented and exploited during the estimation process. Background knowledge as well as observations are incorporated in a maximum likelihood estimation and model selection is performed with AIC/BIC. The likelihood is also used for the detection and correction of potential errors in the topological model. Estimation results are presented and discussed.

The derivative based variance sensitivity analysis for the distribution parameters and its computation

International Nuclear Information System (INIS)

Wang, Pan; Lu, Zhenzhou; Ren, Bo; Cheng, Lei

2013-01-01

The output variance is an important measure for the performance of a structural system, and it is always influenced by the distribution parameters of inputs. In order to identify the influential distribution parameters and make it clear that how those distribution parameters influence the output variance, this work presents the derivative based variance sensitivity decomposition according to Sobol′s variance decomposition, and proposes the derivative based main and total sensitivity indices. By transforming the derivatives of various orders variance contributions into the form of expectation via kernel function, the proposed main and total sensitivity indices can be seen as the “by-product” of Sobol′s variance based sensitivity analysis without any additional output evaluation. Since Sobol′s variance based sensitivity indices have been computed efficiently by the sparse grid integration method, this work also employs the sparse grid integration method to compute the derivative based main and total sensitivity indices. Several examples are used to demonstrate the rationality of the proposed sensitivity indices and the accuracy of the applied method

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 and variability in laboratory derived sorption parameters of sediments from a uranium in situ recovery site

Science.gov (United States)

Dangelmayr, Martin A.; Reimus, Paul W.; Johnson, Raymond H.; Clay, James T.; Stone, James J.

2018-06-01

This research assesses the ability of a GC SCM to simulate uranium transport under variable geochemical conditions typically encountered at uranium in-situ recovery (ISR) sites. Sediment was taken from a monitoring well at the SRH site at depths 192 and 193 m below ground and characterized by XRD, XRF, TOC, and BET. Duplicate column studies on the different sediment depths, were flushed with synthesized restoration waters at two different alkalinities (160 mg/l CaCO3 and 360 mg/l CaCO3) to study the effect of alkalinity on uranium mobility. Uranium breakthrough occurred 25% - 30% earlier in columns with 360 mg/l CaCO3 over columns fed with 160 mg/l CaCO3 influent water. A parameter estimation program (PEST) was coupled to PHREEQC to derive site densities from experimental data. Significant parameter fittings were produced for all models, demonstrating that the GC SCM approach can model the impact of carbonate on uranium in flow systems. Derived site densities for the two sediment depths were between 141 and 178 μmol-sites/kg-soil, demonstrating similar sorption capacities despite heterogeneity in sediment mineralogy. Model sensitivity to alkalinity and pH was shown to be moderate compared to fitted site densities, when calcite saturation was allowed to equilibrate. Calcite kinetics emerged as a potential source of error when fitting parameters in flow conditions. Fitted results were compared to data from previous batch and column studies completed on sediments from the Smith-Ranch Highland (SRH) site, to assess variability in derived parameters. Parameters from batch experiments were lower by a factor of 1.1 to 3.4 compared to column studies completed on the same sediments. The difference was attributed to errors in solid-solution ratios and the impact of calcite dissolution in batch experiments. Column studies conducted at two different laboratories showed almost an order of magnitude difference in fitted site densities suggesting that experimental methodology

Identifying the effects of parameter uncertainty on the reliability of riverbank stability modelling

Science.gov (United States)

Samadi, A.; Amiri-Tokaldany, E.; Darby, S. E.

2009-05-01

Bank retreat is a key process in fluvial dynamics affecting a wide range of physical, ecological and socioeconomic issues in the fluvial environment. To predict the undesirable effects of bank retreat and to inform effective measures to prevent it, a wide range of bank stability models have been presented in the literature. These models typically express bank stability by defining a factor of safety as the ratio of driving and resisting forces acting on the incipient failure block. These forces are affected by a range of controlling factors that include such aspects as the bank profile (bank height and angle), the geotechnical properties of the bank materials, as well as the hydrological status of the riverbanks. In this paper we evaluate the extent to which uncertainties in the parameterization of these controlling factors feed through to influence the reliability of the resulting bank stability estimate. This is achieved by employing a simple model of riverbank stability with respect to planar failure (which is the most common type of bank stability model) in a series of sensitivity tests and Monte Carlo analyses to identify, for each model parameter, the range of values that induce significant changes in the simulated factor of safety. These identified parameter value ranges are compared to empirically derived parameter uncertainties to determine whether they are likely to confound the reliability of the resulting bank stability calculations. Our results show that parameter uncertainties are typically high enough that the likelihood of generating unreliable predictions is typically very high (> ˜ 80% for predictions requiring a precision of < ± 15%). Because parameter uncertainties are derived primarily from the natural variability of the parameters, rather than measurement errors, much more careful attention should be paid to field sampling strategies, such that the parameter uncertainties and consequent prediction unreliabilities can be quantified more

A new unbiased stochastic derivative estimator for discontinuous sample performances with structural parameters

NARCIS (Netherlands)

Peng, Yijie; Fu, Michael C.; Hu, Jian Qiang; Heidergott, Bernd

In this paper, we propose a new unbiased stochastic derivative estimator in a framework that can handle discontinuous sample performances with structural parameters. This work extends the three most popular unbiased stochastic derivative estimators: (1) infinitesimal perturbation analysis (IPA), (2)

Extension of the direct statistical approach to a volume parameter model (non-integer splitting)

International Nuclear Information System (INIS)

Burn, K.W.

1990-01-01

The Direct Statistical Approach is a rigorous mathematical derivation of the second moment for surface splitting and Russian Roulette games attached to the Monte Carlo modelling of fixed source particle transport. It has been extended to a volume parameter model (involving non-integer ''expected value'' splitting), and then to a cell model. The cell model gives second moment and time functions that have a closed form. This suggests the possibility of two different methods of solution of the optimum splitting/Russian Roulette parameters. (author)

An iterative stochastic ensemble method for parameter estimation of subsurface flow models

International Nuclear Information System (INIS)

Elsheikh, Ahmed H.; Wheeler, Mary F.; Hoteit, Ibrahim

2013-01-01

Parameter estimation for subsurface flow models is an essential step for maximizing the value of numerical simulations for future prediction and the development of effective control strategies. We propose the iterative stochastic ensemble method (ISEM) as a general method for parameter estimation based on stochastic estimation of gradients using an ensemble of directional derivatives. ISEM eliminates the need for adjoint coding and deals with the numerical simulator as a blackbox. The proposed method employs directional derivatives within a Gauss–Newton iteration. The update equation in ISEM resembles the update step in ensemble Kalman filter, however the inverse of the output covariance matrix in ISEM is regularized using standard truncated singular value decomposition or Tikhonov regularization. We also investigate the performance of a set of shrinkage based covariance estimators within ISEM. The proposed method is successfully applied on several nonlinear parameter estimation problems for subsurface flow models. The efficiency of the proposed algorithm is demonstrated by the small size of utilized ensembles and in terms of error convergence rates

An iterative stochastic ensemble method for parameter estimation of subsurface flow models

KAUST Repository

Elsheikh, Ahmed H.

2013-06-01

Parameter estimation for subsurface flow models is an essential step for maximizing the value of numerical simulations for future prediction and the development of effective control strategies. We propose the iterative stochastic ensemble method (ISEM) as a general method for parameter estimation based on stochastic estimation of gradients using an ensemble of directional derivatives. ISEM eliminates the need for adjoint coding and deals with the numerical simulator as a blackbox. The proposed method employs directional derivatives within a Gauss-Newton iteration. The update equation in ISEM resembles the update step in ensemble Kalman filter, however the inverse of the output covariance matrix in ISEM is regularized using standard truncated singular value decomposition or Tikhonov regularization. We also investigate the performance of a set of shrinkage based covariance estimators within ISEM. The proposed method is successfully applied on several nonlinear parameter estimation problems for subsurface flow models. The efficiency of the proposed algorithm is demonstrated by the small size of utilized ensembles and in terms of error convergence rates. © 2013 Elsevier Inc.

A simple method for identifying parameter correlations in partially observed linear dynamic models.

Science.gov (United States)

Li, Pu; Vu, Quoc Dong

2015-12-14

Parameter estimation represents one of the most significant challenges in systems biology. This is because biological models commonly contain a large number of parameters among which there may be functional interrelationships, thus leading to the problem of non-identifiability. Although identifiability analysis has been extensively studied by analytical as well as numerical approaches, systematic methods for remedying practically non-identifiable models have rarely been investigated. We propose a simple method for identifying pairwise correlations and higher order interrelationships of parameters in partially observed linear dynamic models. This is made by derivation of the output sensitivity matrix and analysis of the linear dependencies of its columns. Consequently, analytical relations between the identifiability of the model parameters and the initial conditions as well as the input functions can be achieved. In the case of structural non-identifiability, identifiable combinations can be obtained by solving the resulting homogenous linear equations. In the case of practical non-identifiability, experiment conditions (i.e. initial condition and constant control signals) can be provided which are necessary for remedying the non-identifiability and unique parameter estimation. It is noted that the approach does not consider noisy data. In this way, the practical non-identifiability issue, which is popular for linear biological models, can be remedied. Several linear compartment models including an insulin receptor dynamics model are taken to illustrate the application of the proposed approach. Both structural and practical identifiability of partially observed linear dynamic models can be clarified by the proposed method. The result of this method provides important information for experimental design to remedy the practical non-identifiability if applicable. The derivation of the method is straightforward and thus the algorithm can be easily implemented into a

A simple but accurate procedure for solving the five-parameter model

International Nuclear Information System (INIS)

Mares, Oana; Paulescu, Marius; Badescu, Viorel

2015-01-01

Highlights: • A new procedure for extracting the parameters of the one-diode model is proposed. • Only the basic information listed in the datasheet of PV modules are required. • Results demonstrate a simple, robust and accurate procedure. - Abstract: The current–voltage characteristic of a photovoltaic module is typically evaluated by using a model based on the solar cell equivalent circuit. The complexity of the procedure applied for extracting the model parameters depends on data available in manufacture’s datasheet. Since the datasheet is not detailed enough, simplified models have to be used in many cases. This paper proposes a new procedure for extracting the parameters of the one-diode model in standard test conditions, using only the basic data listed by all manufactures in datasheet (short circuit current, open circuit voltage and maximum power point). The procedure is validated by using manufacturers’ data for six commercially crystalline silicon photovoltaic modules. Comparing the computed and measured current–voltage characteristics the determination coefficient is in the range 0.976–0.998. Thus, the proposed procedure represents a feasible tool for solving the five-parameter model applied to crystalline silicon photovoltaic modules. The procedure is described in detail, to guide potential users to derive similar models for other types of photovoltaic modules.

The constitutive distributed parameter model of multicomponent chemical processes in gas, fluid and solid phase

International Nuclear Information System (INIS)

Niemiec, W.

1985-01-01

In the literature of distributed parameter modelling of real processes is not considered the class of multicomponent chemical processes in gas, fluid and solid phase. The aim of paper is constitutive distributed parameter physicochemical model, constructed on kinetics and phenomenal analysis of multicomponent chemical processes in gas, fluid and solid phase. The mass, energy and momentum aspects of these multicomponent chemical reactions and adequate phenomena are utilized in balance operations, by conditions of: constitutive invariance for continuous media with space and time memories, reciprocity principle for isotropic and anisotropic nonhomogeneous media with space and time memories, application of definitions of following derivative and equation of continuity, to the construction of systems of partial differential constitutive state equations, in the following derivative forms for gas, fluid and solid phase. Couched in this way all physicochemical conditions of multicomponent chemical processes in gas, fluid and solid phase are new form of constitutive distributed parameter model for automatics and its systems of equations are new form of systems of partial differential constitutive state equations in sense of phenomenal distributed parameter control

Parameters Tuning of Model Free Adaptive Control Based on Minimum Entropy

Institute of Scientific and Technical Information of China (English)

Chao Ji; Jing Wang; Liulin Cao; Qibing Jin

2014-01-01

Dynamic linearization based model free adaptive control(MFAC) algorithm has been widely used in practical systems, in which some parameters should be tuned before it is successfully applied to process industries. Considering the random noise existing in real processes, a parameter tuning method based on minimum entropy optimization is proposed,and the feature of entropy is used to accurately describe the system uncertainty. For cases of Gaussian stochastic noise and non-Gaussian stochastic noise, an entropy recursive optimization algorithm is derived based on approximate model or identified model. The extensive simulation results show the effectiveness of the minimum entropy optimization for the partial form dynamic linearization based MFAC. The parameters tuned by the minimum entropy optimization index shows stronger stability and more robustness than these tuned by other traditional index,such as integral of the squared error(ISE) or integral of timeweighted absolute error(ITAE), when the system stochastic noise exists.

General hybrid projective complete dislocated synchronization with non-derivative and derivative coupling based on parameter identification in several chaotic and hyperchaotic systems

International Nuclear Information System (INIS)

Sun Jun-Wei; Shen Yi; Zhang Guo-Dong; Wang Yan-Feng; Cui Guang-Zhao

2013-01-01

According to the Lyapunov stability theorem, a new general hybrid projective complete dislocated synchronization scheme with non-derivative and derivative coupling based on parameter identification is proposed under the framework of drive-response systems. Every state variable of the response system equals the summation of the hybrid drive systems in the previous hybrid synchronization. However, every state variable of the drive system equals the summation of the hybrid response systems while evolving with time in our method. Complete synchronization, hybrid dislocated synchronization, projective synchronization, non-derivative and derivative coupling, and parameter identification are included as its special item. The Lorenz chaotic system, Rössler chaotic system, memristor chaotic oscillator system, and hyperchaotic Lü system are discussed to show the effectiveness of the proposed methods. (general)

Model parameters estimation and sensitivity by genetic algorithms

International Nuclear Information System (INIS)

Marseguerra, Marzio; Zio, Enrico; Podofillini, Luca

2003-01-01

In this paper we illustrate the possibility of extracting qualitative information on the importance of the parameters of a model in the course of a Genetic Algorithms (GAs) optimization procedure for the estimation of such parameters. The Genetic Algorithms' search of the optimal solution is performed according to procedures that resemble those of natural selection and genetics: an initial population of alternative solutions evolves within the search space through the four fundamental operations of parent selection, crossover, replacement, and mutation. During the search, the algorithm examines a large amount of solution points which possibly carries relevant information on the underlying model characteristics. A possible utilization of this information amounts to create and update an archive with the set of best solutions found at each generation and then to analyze the evolution of the statistics of the archive along the successive generations. From this analysis one can retrieve information regarding the speed of convergence and stabilization of the different control (decision) variables of the optimization problem. In this work we analyze the evolution strategy followed by a GA in its search for the optimal solution with the aim of extracting information on the importance of the control (decision) variables of the optimization with respect to the sensitivity of the objective function. The study refers to a GA search for optimal estimates of the effective parameters in a lumped nuclear reactor model of literature. The supporting observation is that, as most optimization procedures do, the GA search evolves towards convergence in such a way to stabilize first the most important parameters of the model and later those which influence little the model outputs. In this sense, besides estimating efficiently the parameters values, the optimization approach also allows us to provide a qualitative ranking of their importance in contributing to the model output. The

Estimating model parameters in nonautonomous chaotic systems using synchronization

International Nuclear Information System (INIS)

Yang, Xiaoli; Xu, Wei; Sun, Zhongkui

2007-01-01

In this Letter, a technique is addressed for estimating unknown model parameters of multivariate, in particular, nonautonomous chaotic systems from time series of state variables. This technique uses an adaptive strategy for tracking unknown parameters in addition to a linear feedback coupling for synchronizing systems, and then some general conditions, by means of the periodic version of the LaSalle invariance principle for differential equations, are analytically derived to ensure precise evaluation of unknown parameters and identical synchronization between the concerned experimental system and its corresponding receiver one. Exemplifies are presented by employing a parametrically excited 4D new oscillator and an additionally excited Ueda oscillator. The results of computer simulations reveal that the technique not only can quickly track the desired parameter values but also can rapidly respond to changes in operating parameters. In addition, the technique can be favorably robust against the effect of noise when the experimental system is corrupted by bounded disturbance and the normalized absolute error of parameter estimation grows almost linearly with the cutoff value of noise strength in simulation

Prediction of interest rate using CKLS model with stochastic parameters

International Nuclear Information System (INIS)

Ying, Khor Chia; Hin, Pooi Ah

2014-01-01

The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ (j) of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ (j) , we assume that φ (j) depends on φ (j−m) , φ (j−m+1) ,…, φ (j−1) and the interest rate r j+n at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r j+n+1 of the interest rate at the next time point when the value r j+n of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r j+n+d at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters

Prediction of interest rate using CKLS model with stochastic parameters

Energy Technology Data Exchange (ETDEWEB)

Ying, Khor Chia [Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Selangor (Malaysia); Hin, Pooi Ah [Sunway University Business School, No. 5, Jalan Universiti, Bandar Sunway, 47500 Subang Jaya, Selangor (Malaysia)

2014-06-19

The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ{sup (j)} of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ{sup (j)}, we assume that φ{sup (j)} depends on φ{sup (j−m)}, φ{sup (j−m+1)},…, φ{sup (j−1)} and the interest rate r{sub j+n} at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r{sub j+n+1} of the interest rate at the next time point when the value r{sub j+n} of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r{sub j+n+d} at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters.

A Study on Stability of Limit Cycle Walking Model with Feet: Parameter Study

Directory of Open Access Journals (Sweden)

Yonggwon Jeon

2013-01-01

Full Text Available In this paper, two kinds of feet, namely, curved and flat feet, are added to limit cycle walking model to investigate its stability properties. Although both models are already proposed and are investigated, most previous works are focused on efficiency and gait behaviors. Only the stability properties of the simplest walking model conceived Garcia et al. are well defined. Therefore, there is still a need for a precise research on the effect of feet, especially in the view of local stability, bifurcation route to chaos, global stability, falling boundary and energy balance line. Therefore, this article revisits the stability analysis of limit cycle walking model with various foot shape. To analyze the effects of feet, we re-derive the equation of motion of modified models by adding one more parameter of foot shape than the simplest walking model. Also, the falling boundary and energy balance line of modified models are derived to get proper initial conditions for stable walking and to explain global stability. Simulation results show us that the curved feet can enlarge both stable walking range and area of basin of attraction while the case of flat feet depends on foot shape parameter.

Neural network modelling of antifungal activity of a series of oxazole derivatives based on in silico pharmacokinetic parameters

Directory of Open Access Journals (Sweden)

Kovačević Strahinja Z.

2013-01-01

Full Text Available In the present paper, the antifungal activity of a series of benzoxazole and oxazolo[ 4,5-b]pyridine derivatives was evaluated against Candida albicans by using quantitative structure-activity relationships chemometric methodology with artificial neural network (ANN regression approach. In vitro antifungal activity of the tested compounds was presented by minimum inhibitory concentration expressed as log(1/cMIC. In silico pharmacokinetic parameters related to absorption, distribution, metabolism and excretion (ADME were calculated for all studied compounds by using PreADMET software. A feedforward back-propagation ANN with gradient descent learning algorithm was applied for modelling of the relationship between ADME descriptors (blood-brain barrier penetration, plasma protein binding, Madin-Darby cell permeability and Caco-2 cell permeability and experimental log(1/cMIC values. A 4-6-1 ANN was developed with the optimum momentum and learning rates of 0.3 and 0.05, respectively. An excellent correlation between experimental antifungal activity and values predicted by the ANN was obtained with a correlation coefficient of 0.9536. [Projekat Ministarstva nauke Republike Srbije, br. 172012 i br. 172014

Mathematical modeling of tetrahydroimidazole benzodiazepine-1-one derivatives as an anti HIV agent

Science.gov (United States)

Ojha, Lokendra Kumar

2017-07-01

The goal of the present work is the study of drug receptor interaction via QSAR (Quantitative Structure-Activity Relationship) analysis for 89 set of TIBO (Tetrahydroimidazole Benzodiazepine-1-one) derivatives. MLR (Multiple Linear Regression) method is utilized to generate predictive models of quantitative structure-activity relationships between a set of molecular descriptors and biological activity (IC50). The best QSAR model was selected having a correlation coefficient (r) of 0.9299 and Standard Error of Estimation (SEE) of 0.5022, Fisher Ratio (F) of 159.822 and Quality factor (Q) of 1.852. This model is statistically significant and strongly favours the substitution of sulphur atom, IS i.e. indicator parameter for -Z position of the TIBO derivatives. Two other parameter logP (octanol-water partition coefficient) and SAG (Surface Area Grid) also played a vital role in the generation of best QSAR model. All three descriptor shows very good stability towards data variation in leave-one-out (LOO).

The method for determination of parameters of the phenomenological continual model of soil organic matter transformation

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S. I. Bartsev

2015-06-01

Full Text Available A possible method for experimental determination of parameters of the previously proposed continual mathematical model of soil organic matter transformation is theoretically considered in this paper. The previously proposed by the authors continual model of soil organic matter transformation, based on using the rate of matter transformation as a continual scale of its recalcitrance, describes the transformation process phenomenologically without going into detail of microbiological mechanisms of transformation. Thereby simplicity of the model is achieved. The model is represented in form of one differential equation in first­order partial derivatives, which has an analytical solution in elementary functions. The model equation contains a small number of empirical parameters which generally characterize environmental conditions where the matter transformation process occurs and initial properties of the plant litter. Given the values of these parameters, it is possible to calculate dynamics of soil organic matter stocks and its distribution over transformation rate. In the present study, possible approaches for determination of the model parameters are considered and a simple method of their experimental measurement is proposed. An experiment of an incubation of chemically homogeneous samples in soil and multiple sequential measurement of the sample mass loss with time is proposed. An equation of time dynamics of mass loss of incubated homogeneous sample is derived from the basic assumption of the presented soil organic matter transformation model. Thus, fitting by the least squares method the parameters of sample mass loss curve calculated according the proposed mass loss dynamics equation allows to determine the parameters of the general equation of soil organic transformation model.

Uncertainty and variability in laboratory derived sorption parameters of sediments from a uranium in situ recovery site.

Science.gov (United States)

Dangelmayr, Martin A; Reimus, Paul W; Johnson, Raymond H; Clay, James T; Stone, James J

2018-06-01

This research assesses the ability of a GC SCM to simulate uranium transport under variable geochemical conditions typically encountered at uranium in-situ recovery (ISR) sites. Sediment was taken from a monitoring well at the SRH site at depths 192 and 193 m below ground and characterized by XRD, XRF, TOC, and BET. Duplicate column studies on the different sediment depths, were flushed with synthesized restoration waters at two different alkalinities (160 mg/l CaCO 3 and 360 mg/l CaCO 3 ) to study the effect of alkalinity on uranium mobility. Uranium breakthrough occurred 25% - 30% earlier in columns with 360 mg/l CaCO 3 over columns fed with 160 mg/l CaCO 3 influent water. A parameter estimation program (PEST) was coupled to PHREEQC to derive site densities from experimental data. Significant parameter fittings were produced for all models, demonstrating that the GC SCM approach can model the impact of carbonate on uranium in flow systems. Derived site densities for the two sediment depths were between 141 and 178 μmol-sites/kg-soil, demonstrating similar sorption capacities despite heterogeneity in sediment mineralogy. Model sensitivity to alkalinity and pH was shown to be moderate compared to fitted site densities, when calcite saturation was allowed to equilibrate. Calcite kinetics emerged as a potential source of error when fitting parameters in flow conditions. Fitted results were compared to data from previous batch and column studies completed on sediments from the Smith-Ranch Highland (SRH) site, to assess variability in derived parameters. Parameters from batch experiments were lower by a factor of 1.1 to 3.4 compared to column studies completed on the same sediments. The difference was attributed to errors in solid-solution ratios and the impact of calcite dissolution in batch experiments. Column studies conducted at two different laboratories showed almost an order of magnitude difference in fitted site densities suggesting that experimental

Multivariate modelling of prostate cancer combining magnetic resonance derived T2, diffusion, dynamic contrast-enhanced and spectroscopic parameters

Energy Technology Data Exchange (ETDEWEB)

Riches, S.F.; Payne, G.S.; Morgan, V.A.; DeSouza, N.M. [Royal Marsden NHS Foundation Trust and Institute of Cancer Research, CRUK and EPSRC Cancer Imaging Centre, Sutton, Surrey (United Kingdom); Dearnaley, D. [Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Department of Urology and Department of Academic Radiotherapy, Sutton, Surrey (United Kingdom); Morgan, S. [The Ottawa Hospital Cancer Centre and the University of Ottawa, Division of Radiation Oncology, Ottawa, Ontario (Canada); Partridge, M. [The Institute of Cancer Research, Section of Radiotherapy and Imaging, Sutton, Surrey (United Kingdom); University of Oxford, The Gray Institute for Radiation Oncology and Biology, Oxford (United Kingdom); Livni, N. [Royal Marsden NHS Foundation Trust Chelsea, Department of Histopathology, London (United Kingdom); Ogden, C. [Royal Marsden NHS Foundation Trust Chelsea, Department of Urology, London (United Kingdom)

2015-05-01

The objectives are determine the optimal combination of MR parameters for discriminating tumour within the prostate using linear discriminant analysis (LDA) and to compare model accuracy with that of an experienced radiologist. Multiparameter MRIs in 24 patients before prostatectomy were acquired. Tumour outlines from whole-mount histology, T{sub 2}-defined peripheral zone (PZ), and central gland (CG) were superimposed onto slice-matched parametric maps. T{sub 2,} Apparent Diffusion Coefficient, initial area under the gadolinium curve, vascular parameters (K{sup trans},K{sub ep},V{sub e}), and (choline+polyamines+creatine)/citrate were compared between tumour and non-tumour tissues. Receiver operating characteristic (ROC) curves determined sensitivity and specificity at spectroscopic voxel resolution and per lesion, and LDA determined the optimal multiparametric model for identifying tumours. Accuracy was compared with an expert observer. Tumours were significantly different from PZ and CG for all parameters (all p < 0.001). Area under the ROC curve for discriminating tumour from non-tumour was significantly greater (p < 0.001) for the multiparametric model than for individual parameters; at 90 % specificity, sensitivity was 41 % (MRSI voxel resolution) and 59 % per lesion. At this specificity, an expert observer achieved 28 % and 49 % sensitivity, respectively. The model was more accurate when parameters from all techniques were included and performed better than an expert observer evaluating these data. (orig.)

Coast-down model based on rated parameters of reactor coolant pump

International Nuclear Information System (INIS)

Jiang Maohua; Zou Zhichao; Wang Pengfei; Ruan Xiaodong

2014-01-01

For a sudden loss of power in reactor coolant pump (RCP), a calculation model of rotor speed and flow characteristics based on rated parameters was studied. The derived model was verified by comparing with the power-off experimental data of 100D RCP. The results indicate that it can be used in preliminary design calculation and verification analysis. Then a design criterion of RCP was described based on the calculation model. The moment of inertia in AP1000 RCP was verified by this criterion. (authors)

Derivation of a well-posed and multidimensional drift-flux model for boiling flows

International Nuclear Information System (INIS)

Gregoire, O.; Martin, M.

2005-01-01

In this note, we derive a multidimensional drift-flux model for boiling flows. Within this framework, the distribution parameter is no longer a scalar but a tensor that might account for the medium anisotropy and the flow regime. A new model for the drift-velocity vector is also derived. It intrinsically takes into account the effect of the friction pressure loss on the buoyancy force. On the other hand, we show that most drift-flux models might exhibit a singularity for large void fraction. In order to avoid this singularity, a remedy based on a simplified three field approach is proposed. (authors)

Sensitivity analysis and parameter estimation for distributed hydrological modeling: potential of variational methods

Directory of Open Access Journals (Sweden)

W. Castaings

2009-04-01

Full Text Available Variational methods are widely used for the analysis and control of computationally intensive spatially distributed systems. In particular, the adjoint state method enables a very efficient calculation of the derivatives of an objective function (response function to be analysed or cost function to be optimised with respect to model inputs.

In this contribution, it is shown that the potential of variational methods for distributed catchment scale hydrology should be considered. A distributed flash flood model, coupling kinematic wave overland flow and Green Ampt infiltration, is applied to a small catchment of the Thoré basin and used as a relatively simple (synthetic observations but didactic application case.

It is shown that forward and adjoint sensitivity analysis provide a local but extensive insight on the relation between the assigned model parameters and the simulated hydrological response. Spatially distributed parameter sensitivities can be obtained for a very modest calculation effort (~6 times the computing time of a single model run and the singular value decomposition (SVD of the Jacobian matrix provides an interesting perspective for the analysis of the rainfall-runoff relation.

For the estimation of model parameters, adjoint-based derivatives were found exceedingly efficient in driving a bound-constrained quasi-Newton algorithm. The reference parameter set is retrieved independently from the optimization initial condition when the very common dimension reduction strategy (i.e. scalar multipliers is adopted.

Furthermore, the sensitivity analysis results suggest that most of the variability in this high-dimensional parameter space can be captured with a few orthogonal directions. A parametrization based on the SVD leading singular vectors was found very promising but should be combined with another regularization strategy in order to prevent overfitting.

Applications of the solvation parameter model in reversed-phase liquid chromatography.

Science.gov (United States)

Poole, Colin F; Lenca, Nicole

2017-02-24

The solvation parameter model is widely used to provide insight into the retention mechanism in reversed-phase liquid chromatography, for column characterization, and in the development of surrogate chromatographic models for biopartitioning processes. The properties of the separation system are described by five system constants representing all possible intermolecular interactions for neutral molecules. The general model can be extended to include ions and enantiomers by adding new descriptors to encode the specific properties of these compounds. System maps provide a comprehensive overview of the separation system as a function of mobile phase composition and/or temperature for method development. The solvation parameter model has been applied to gradient elution separations but here theory and practice suggest a cautious approach since the interpretation of system and compound properties derived from its use are approximate. A growing application of the solvation parameter model in reversed-phase liquid chromatography is the screening of surrogate chromatographic systems for estimating biopartitioning properties. Throughout the discussion of the above topics success as well as known and likely deficiencies of the solvation parameter model are described with an emphasis on the role of the heterogeneous properties of the interphase region on the interpretation and understanding of the general retention mechanism in reversed-phase liquid chromatography for porous chemically bonded sorbents. Copyright © 2016 Elsevier B.V. All rights reserved.

Urban thermal environment and its biophysical parameters derived from satellite remote sensing imagery

Science.gov (United States)

Zoran, Maria A.; Savastru, Roxana S.; Savastru, Dan M.; Tautan, Marina N.; Baschir, Laurentiu V.

2013-10-01

In frame of global warming, the field of urbanization and urban thermal environment are important issues among scientists all over the world. This paper investigated the influences of urbanization on urban thermal environment as well as the relationships of thermal characteristics to other biophysical variables in Bucharest metropolitan area of Romania based on satellite remote sensing imagery Landsat TM/ETM+, time series MODIS Terra/Aqua data and IKONOS acquired during 1990 - 2012 period. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban growth. The land surface temperature (Ts), a key parameter for urban thermal characteristics analysis, was also retrieved from thermal infrared band of Landsat TM/ETM+, from MODIS Terra/Aqua datasets. Based on these parameters, the urban growth, urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters have been analyzed. Results indicated that the metropolitan area ratio of impervious surface in Bucharest increased significantly during two decades investigated period, the intensity of urban heat island and heat wave events being most significant. The correlation analyses revealed that, at the pixel-scale, Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively. This analysis provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling.

Derivatives of buckling loads and vibration frequencies with respect to stiffness and initial strain parameters

Science.gov (United States)

Haftka, Raphael T.; Cohen, Gerald A.; Mroz, Zenon

1990-01-01

A uniform variational approach to sensitivity analysis of vibration frequencies and bifurcation loads of nonlinear structures is developed. Two methods of calculating the sensitivities of bifurcation buckling loads and vibration frequencies of nonlinear structures, with respect to stiffness and initial strain parameters, are presented. A direct method requires calculation of derivatives of the prebuckling state with respect to these parameters. An adjoint method bypasses the need for these derivatives by using instead the strain field associated with the second-order postbuckling state. An operator notation is used and the derivation is based on the principle of virtual work. The derivative computations are easily implemented in structural analysis programs. This is demonstrated by examples using a general purpose, finite element program and a shell-of-revolution program.

A new analytical method for estimating lumped parameter constants of linear viscoelastic models from strain rate tests

Science.gov (United States)

Mattei, G.; Ahluwalia, A.

2018-04-01

We introduce a new function, the apparent elastic modulus strain-rate spectrum, E_{app} ( \\dot{ɛ} ), for the derivation of lumped parameter constants for Generalized Maxwell (GM) linear viscoelastic models from stress-strain data obtained at various compressive strain rates ( \\dot{ɛ}). The E_{app} ( \\dot{ɛ} ) function was derived using the tangent modulus function obtained from the GM model stress-strain response to a constant \\dot{ɛ} input. Material viscoelastic parameters can be rapidly derived by fitting experimental E_{app} data obtained at different strain rates to the E_{app} ( \\dot{ɛ} ) function. This single-curve fitting returns similar viscoelastic constants as the original epsilon dot method based on a multi-curve global fitting procedure with shared parameters. Its low computational cost permits quick and robust identification of viscoelastic constants even when a large number of strain rates or replicates per strain rate are considered. This method is particularly suited for the analysis of bulk compression and nano-indentation data of soft (bio)materials.

COMPREHENSIVE CHECK MEASUREMENT OF KEY PARAMETERS ON MODEL BELT CONVEYOR

Directory of Open Access Journals (Sweden)

Vlastimil MONI

2013-07-01

Full Text Available Complex measurements of characteristic parameters realised on a long distance model belt conveyor are described. The main objective was to complete and combine the regular measurements of electric power on drives of belt conveyors operated in Czech opencast mines with measurements of other physical quantities and to gain by this way an image of their mutual relations and relations of quantities derived from them. The paper includes a short description and results of the measurements on an experimental model conveyor with a closed material transport way.

Parameter dependence and outcome dependence in dynamical models for state vector reduction

International Nuclear Information System (INIS)

Ghirardi, G.C.; Grassi, R.; Butterfield, J.; Fleming, G.N.

1993-01-01

The authors apply the distinction between parameter independence and outcome independence to the linear and nonlinear models of a recent nonrelativistic theory of continuous state vector reduction. It is shown that in the nonlinear model there is a set of realizations of the stochastic process that drives the state vector reduction for which parameter independence is violated for parallel spin components in the EPR-Bohm setup. Such a set has an appreciable probability of occurrence (∼ 1/2). On the other hand, the linear model exhibits only extremely small parameter dependence effects. Some specific features of the models are investigated and it is recalled that, as has been pointed out recently, to be able to speak of definite outcomes (or equivalently of possessed objective elements of reality) at finite times, the criteria for their attribution to physical systems must be slightly changed. The concluding section is devoted to a detailed discussion of the difficulties met when attempting to take, as a starting point for the formulation of a relativistic theory, a nonrelativistic scheme which exhibits parameter dependence. Here the authors derive a theorem which identifies the precise sense in which the occurrence of parameter dependence forbids a genuinely relativistic generalization. Finally, the authors show how the appreciable parameter dependence of the nonlinear model gives rise to problems with relativity, while the extremely weak parameter dependence of the linear model does not give rise to any difficulty, provided the appropriate criteria for the attribution of definite outcomes are taken into account. 19 refs

HMM filtering and parameter estimation of an electricity spot price model

International Nuclear Information System (INIS)

Erlwein, Christina; Benth, Fred Espen; Mamon, Rogemar

2010-01-01

In this paper we develop a model for electricity spot price dynamics. The spot price is assumed to follow an exponential Ornstein-Uhlenbeck (OU) process with an added compound Poisson process. In this way, the model allows for mean-reversion and possible jumps. All parameters are modulated by a hidden Markov chain in discrete time. They are able to switch between different economic regimes representing the interaction of various factors. Through the application of reference probability technique, adaptive filters are derived, which in turn, provide optimal estimates for the state of the Markov chain and related quantities of the observation process. The EM algorithm is applied to find optimal estimates of the model parameters in terms of the recursive filters. We implement this self-calibrating model on a deseasonalised series of daily spot electricity prices from the Nordic exchange Nord Pool. On the basis of one-step ahead forecasts, we found that the model is able to capture the empirical characteristics of Nord Pool spot prices. (author)

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis

Directory of Open Access Journals (Sweden)

Tashkova Katerina

2011-10-01

Full Text Available Abstract Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. Results We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA, particle-swarm optimization (PSO, and differential evolution (DE, as well as a local-search derivative-based algorithm 717 (A717 to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Conclusions Overall, the global meta-heuristic methods (DASA, PSO, and DE clearly and significantly outperform the local derivative-based method (A717. Among the three meta-heuristics, differential evolution (DE performs best in terms of the objective function, i.e., reconstructing the output, and in terms of

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis.

Science.gov (United States)

Tashkova, Katerina; Korošec, Peter; Silc, Jurij; Todorovski, Ljupčo; Džeroski, Sašo

2011-10-11

We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and

Optimization of Soil Hydraulic Model Parameters Using Synthetic Aperture Radar Data: An Integrated Multidisciplinary Approach

DEFF Research Database (Denmark)

Pauwels, Valentijn; Balenzano, Anna; Satalino, Giuseppe

2009-01-01

It is widely recognized that Synthetic Aperture Radar (SAR) data are a very valuable source of information for the modeling of the interactions between the land surface and the atmosphere. During the last couple of decades, most of the research on the use of SAR data in hydrologic applications has...... that no direct relationships between the remote-sensing observations, more specifically radar backscatter values, and the parameter values can be derived. However, land surface models can provide these relationships. The objective of this paper is to retrieve a number of soil physical model parameters through...

Pharmacokinetic parameters derived from dynamic contrast enhanced MRI of cervical cancers predict chemoradiotherapy outcome

International Nuclear Information System (INIS)

Andersen, Erlend K.F.; Hole, Knut Håkon; Lund, Kjersti V.; Sundfør, Kolbein; Kristensen, Gunnar B.; Lyng, Heidi; Malinen, Eirik

2013-01-01

Purpose: To assess the prognostic value of pharmacokinetic parameters derived from pre-chemoradiotherapy dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of cervical cancer patients. Materials and methods: Seventy-eight patients with locally advanced cervical cancer underwent DCE-MRI with Gd-DTPA before chemoradiotherapy. The pharmacokinetic Brix and Tofts models were fitted to contrast enhancement curves in all tumor voxels, providing histograms of several pharmacokinetic parameters (Brix: A Brix , k ep , k el , Tofts: K trans , ν e ). A percentile screening approach including log-rank survival tests was undertaken to identify the clinically most relevant part of the intratumoral parameter distribution. Clinical endpoints were progression-free survival (PFS) and locoregional control (LRC). Multivariate analysis including FIGO stage and tumor volume was used to assess the prognostic significance of the imaging parameters. Results: A Brix , k el , and K trans were significantly (P e was significantly positively correlated with PFS only. k ep showed no association with any endpoint. A Brix was positively correlated with K trans and ν e , and showed the strongest association with endpoint in the log-rank testing. k el and K trans were independent prognostic factors in multivariate analysis with LRC as endpoint. Conclusions: Parameters estimated by pharmacokinetic analysis of DCE-MR images obtained prior to chemoradiotherapy may be used for identifying patients at risk of treatment failure

Irradiation-induced void evolution in iron: A phase-field approach with atomistic derived parameters

International Nuclear Information System (INIS)

Wang Yuan-Yuan; Ding Jian-Hua; Huang Shao-Song; Zhao Ji-Jun; Liu Wen-Bo; Ke Xiao-Qin; Wang Yun-Zhi; Zhang Chi

2017-01-01

A series of material parameters are derived from atomistic simulations and implemented into a phase field (PF) model to simulate void evolution in body-centered cubic (bcc) iron subjected to different irradiation doses at different temperatures. The simulation results show good agreement with experimental observations — the porosity as a function of temperature varies in a bell-shaped manner and the void density monotonically decreases with increasing temperatures; both porosity and void density increase with increasing irradiation dose at the same temperature. Analysis reveals that the evolution of void number and size is determined by the interplay among the production, diffusion and recombination of vacancy and interstitial. (paper)

Modeling ramp-hold indentation measurements based on Kelvin-Voigt fractional derivative model

Science.gov (United States)

Zhang, Hongmei; zhe Zhang, Qing; Ruan, Litao; Duan, Junbo; Wan, Mingxi; Insana, Michael F.

2018-03-01

Interpretation of experimental data from micro- and nano-scale indentation testing is highly dependent on the constitutive model selected to relate measurements to mechanical properties. The Kelvin-Voigt fractional derivative model (KVFD) offers a compact set of viscoelastic features appropriate for characterizing soft biological materials. This paper provides a set of KVFD solutions for converting indentation testing data acquired for different geometries and scales into viscoelastic properties of soft materials. These solutions, which are mostly in closed-form, apply to ramp-hold relaxation, load-unload and ramp-load creep-testing protocols. We report on applications of these model solutions to macro- and nano-indentation testing of hydrogels, gastric cancer cells and ex vivo breast tissue samples using an atomic force microscope (AFM). We also applied KVFD models to clinical ultrasonic breast data using a compression plate as required for elasticity imaging. Together the results show that KVFD models fit a broad range of experimental data with a correlation coefficient typically R 2  >  0.99. For hydrogel samples, estimation of KVFD model parameters from test data using spherical indentation versus plate compression as well as ramp relaxation versus load-unload compression all agree within one standard deviation. Results from measurements made using macro- and nano-scale indentation agree in trend. For gastric cell and ex vivo breast tissue measurements, KVFD moduli are, respectively, 1/3-1/2 and 1/6 of the elasticity modulus found from the Sneddon model. In vivo breast tissue measurements yield model parameters consistent with literature results. The consistency of results found for a broad range of experimental parameters suggest the KVFD model is a reliable tool for exploring intrinsic features of the cell/tissue microenvironments.

Robust estimation of hydrological model parameters

Directory of Open Access Journals (Sweden)

A. Bárdossy

2008-11-01

Full Text Available The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

Photovoltaic module parameters acquisition model

Energy Technology Data Exchange (ETDEWEB)

Cibira, Gabriel, E-mail: cibira@lm.uniza.sk; Koščová, Marcela, E-mail: mkoscova@lm.uniza.sk

2014-09-01

Highlights: • Photovoltaic five-parameter model is proposed using Matlab{sup ®} and Simulink. • The model acquisits input sparse data matrix from stigmatic measurement. • Computer simulations lead to continuous I–V and P–V characteristics. • Extrapolated I–V and P–V characteristics are in hand. • The model allows us to predict photovoltaics exploitation in different conditions. - Abstract: This paper presents basic procedures for photovoltaic (PV) module parameters acquisition using MATLAB and Simulink modelling. In first step, MATLAB and Simulink theoretical model are set to calculate I–V and P–V characteristics for PV module based on equivalent electrical circuit. Then, limited I–V data string is obtained from examined PV module using standard measurement equipment at standard irradiation and temperature conditions and stated into MATLAB data matrix as a reference model. Next, the theoretical model is optimized to keep-up with the reference model and to learn its basic parameters relations, over sparse data matrix. Finally, PV module parameters are deliverable for acquisition at different realistic irradiation, temperature conditions as well as series resistance. Besides of output power characteristics and efficiency calculation for PV module or system, proposed model validates computing statistical deviation compared to reference model.

Photovoltaic module parameters acquisition model

International Nuclear Information System (INIS)

Cibira, Gabriel; Koščová, Marcela

2014-01-01

Highlights: • Photovoltaic five-parameter model is proposed using Matlab ® and Simulink. • The model acquisits input sparse data matrix from stigmatic measurement. • Computer simulations lead to continuous I–V and P–V characteristics. • Extrapolated I–V and P–V characteristics are in hand. • The model allows us to predict photovoltaics exploitation in different conditions. - Abstract: This paper presents basic procedures for photovoltaic (PV) module parameters acquisition using MATLAB and Simulink modelling. In first step, MATLAB and Simulink theoretical model are set to calculate I–V and P–V characteristics for PV module based on equivalent electrical circuit. Then, limited I–V data string is obtained from examined PV module using standard measurement equipment at standard irradiation and temperature conditions and stated into MATLAB data matrix as a reference model. Next, the theoretical model is optimized to keep-up with the reference model and to learn its basic parameters relations, over sparse data matrix. Finally, PV module parameters are deliverable for acquisition at different realistic irradiation, temperature conditions as well as series resistance. Besides of output power characteristics and efficiency calculation for PV module or system, proposed model validates computing statistical deviation compared to reference model

Orbital parameters of extrasolar planets derived from polarimetry

Science.gov (United States)

Fluri, D. M.; Berdyugina, S. V.

2010-03-01

Context. Polarimetry of extrasolar planets becomes a new tool for their investigation, which requires the development of diagnostic techniques and parameter case studies. Aims: Our goal is to develop a theoretical model which can be applied to interpret polarimetric observations of extrasolar planets. Here we present a theoretical parameter study that shows the influence of the various involved parameters on the polarization curves. Furthermore, we investigate the robustness of the fitting procedure. We focus on the diagnostics of orbital parameters and the estimation of the scattering radius of the planet. Methods: We employ the physics of Rayleigh scattering to obtain polarization curves of an unresolved extrasolar planet. Calculations are made for two cases: (i) assuming an angular distribution for the intensity of the scattered light as from a Lambert sphere and for polarization as from a Rayleigh-type scatterer; and (ii) assuming that both the intensity and polarization of the scattered light are distributed according to the Rayleigh law. We show that the difference between these two cases is negligible for the shapes of the polarization curves. In addition, we take the size of the host star into account, which is relevant for hot Jupiters orbiting giant stars. Results: We discuss the influence of the inclination of the planetary orbit, the position angle of the ascending node, and the eccentricity on the linearly polarized light curves both in Stokes Q/I and U/I. We also analyze errors that arise from the assumption of a point-like star in numerical modeling of polarization as compared to consistent calculations accounting for the finite size of the host star. We find that errors due to the point-like star approximation are reduced with the size of the orbit, but still amount to about 5% for known hot Jupiters. Recovering orbital parameters from simulated data is shown to be very robust even for very noisy data because the polarization curves react

Dynamic model of cage induction motor with number of rotor bars as parameter

Directory of Open Access Journals (Sweden)

Gojko Joksimović

2017-05-01

Full Text Available A dynamic mathematical model, using number of rotor bars as parameter, is reached for cage induction motors through the use of coupled-circuits and the concept of winding functions. The exact MMFs waveforms are accounted for by the model which is derived in natural frames of reference. By knowing the initial motor parameters for a priori adopted number of stator slots and rotor bars model allows change of rotor bars number what results in new model parameters. During this process, the rated machine power, number of stator slots and stator winding scheme remain the same. Although presented model has a potentially broad application area it is primarily suitable for the analysis of the different stator/rotor slot combination on motor behaviour during the transients or in steady-state regime. The model is significant in its potential to provide analysis of dozen of different number of rotor bars in a few tens of minutes. Numerical example on cage rotor induction motor exemplifies this application, including three variants of number of rotor bars.

Study on Developing Degradation Model for Nuclear Power Plants With Ageing Elements Affected on Operation Parameter

International Nuclear Information System (INIS)

Choi, Yong Won; Lim, Sung Won; Lee, Un Chul; Kim, Man Woong; Kim, Kab; Ryu, Yong Ho

2009-01-01

As a part of development the evaluation system of safety margin effects for degradation of CANDU reactors, it is required that the degradation model represents the distribution of each ageing factor's value during operating year. Unfortunately, it is not easy to make an explicit relation between the RELAP-CANDU parameters and ageing mechanism because of insufficient data and lack of applicable models. So, operating parameter related with ageing is used for range determination of ageing factor. Then, relation between operating parameter and ageing elements is analyzed and ageing constant values for degradation model are determined. Also the other ageing factor is derived for more accurate ageing analysis

Impulsive synchronization and parameter mismatch of the three-variable autocatalator model

International Nuclear Information System (INIS)

Li, Yang; Liao, Xiaofeng; Li, Chuandong; Huang, Tingwen; Yang, Degang

2007-01-01

The synchronization problems of the three-variable autocatalator model via impulsive control approach are investigated; several theorems on the stability of impulsive control systems are also investigated. These theorems are then used to find the conditions under which the three-variable autocatalator model can be asymptotically controlled to the equilibrium point. This Letter derives some sufficient conditions for the stabilization and synchronization of a three-variable autocatalator model via impulsive control with varying impulsive intervals. Furthermore, we address the chaos quasi-synchronization in the presence of single-parameter mismatch. To illustrate the effectiveness of the new scheme, several numerical examples are given

Satellite-derived land surface parameters for mesoscale modelling of the Mexico City basin

Directory of Open Access Journals (Sweden)

B. de Foy

2006-01-01

Full Text Available Mesoscale meteorological modelling is an important tool to help understand air pollution and heat island effects in urban areas. Accurate wind simulations are difficult to obtain in areas of weak synoptic forcing. Local factors have a dominant role in the circulation and include land surface parameters and their interaction with the atmosphere. This paper examines an episode during the MCMA-2003 field campaign held in the Mexico City Metropolitan Area (MCMA in April of 2003. Because the episode has weak synoptic forcing, there is the potential for the surface heat budget to influence the local meteorology. High resolution satellite observations are used to specify the land use, vegetation fraction, albedo and surface temperature in the MM5 model. Making use of these readily available data leads to improved meteorological simulations in the MCMA, both for the wind circulation patterns and the urban heat island. Replacing values previously obtained from land-use tables with actual measurements removes the number of unknowns in the model and increases the accuracy of the energy budget. In addition to improving the understanding of local meteorology, this sets the stage for the use of advanced urban modules.

A relation between the Barbero-Immirzi parameter and the standard model

Energy Technology Data Exchange (ETDEWEB)

Broda, Boguslaw, E-mail: bobroda@uni.lodz.p [Department of Theoretical Physics, University of Lodz, Pomorska 149/153, PL-90-236 Lodz (Poland); Szanecki, Michal, E-mail: michalszanecki@wp.p [Department of Theoretical Physics, University of Lodz, Pomorska 149/153, PL-90-236 Lodz (Poland)

2010-06-07

It has been shown that Sakharov's induced, from the fields entering the standard model, Barbero-Immirzi parameter {gamma} assumes, in the framework of Euclidean formalism, the UV cutoff-independent value, 1/9. The calculus uses the Schwinger's proper-time formalism, the Seeley-DeWitt heat-kernel expansion, and it is akin to the derivation of the ABJ chiral anomaly in space-time with torsion.

Modeling phosphorus in the Lake Allatoona watershed using SWAT: I. Developing phosphorus parameter values.

Science.gov (United States)

Radcliffe, D E; Lin, Z; Risse, L M; Romeis, J J; Jackson, C R

2009-01-01

Lake Allatoona is a large reservoir north of Atlanta, GA, that drains an area of about 2870 km2 scheduled for a phosphorus (P) total maximum daily load (TMDL). The Soil and Water Assessment Tool (SWAT) model has been widely used for watershed-scale modeling of P, but there is little guidance on how to estimate P-related parameters, especially those related to in-stream P processes. In this paper, methods are demonstrated to individually estimate SWAT soil-related P parameters and to collectively estimate P parameters related to stream processes. Stream related parameters were obtained using the nutrient uptake length concept. In a manner similar to experiments conducted by stream ecologists, a small point source is simulated in a headwater sub-basin of the SWAT models, then the in-stream parameter values are adjusted collectively to get an uptake length of P similar to the values measured in the streams in the region. After adjusting the in-stream parameters, the P uptake length estimated in the simulations ranged from 53 to 149 km compared to uptake lengths measured by ecologists in the region of 11 to 85 km. Once the a priori P-related parameter set was developed, the SWAT models of main tributaries to Lake Allatoona were calibrated for daily transport. Models using SWAT P parameters derived from the methods in this paper outperformed models using default parameter values when predicting total P (TP) concentrations in streams during storm events and TP annual loads to Lake Allatoona.

Modeling the drain current and its equation parameters for lightly doped symmetrical double-gate MOSFETs

International Nuclear Information System (INIS)

Bhartia, Mini; Chatterjee, Arun Kumar

2015-01-01

A 2D model for the potential distribution in silicon film is derived for a symmetrical double gate MOSFET in weak inversion. This 2D potential distribution model is used to analytically derive an expression for the subthreshold slope and threshold voltage. A drain current model for lightly doped symmetrical DG MOSFETs is then presented by considering weak and strong inversion regions including short channel effects, series source to drain resistance and channel length modulation parameters. These derived models are compared with the simulation results of the SILVACO (Atlas) tool for different channel lengths and silicon film thicknesses. Lastly, the effect of the fixed oxide charge on the drain current model has been studied through simulation. It is observed that the obtained analytical models of symmetrical double gate MOSFETs are in good agreement with the simulated results for a channel length to silicon film thickness ratio greater than or equal to 2. (paper)

Modeling the drain current and its equation parameters for lightly doped symmetrical double-gate MOSFETs

Science.gov (United States)

Bhartia, Mini; Chatterjee, Arun Kumar

2015-04-01

A 2D model for the potential distribution in silicon film is derived for a symmetrical double gate MOSFET in weak inversion. This 2D potential distribution model is used to analytically derive an expression for the subthreshold slope and threshold voltage. A drain current model for lightly doped symmetrical DG MOSFETs is then presented by considering weak and strong inversion regions including short channel effects, series source to drain resistance and channel length modulation parameters. These derived models are compared with the simulation results of the SILVACO (Atlas) tool for different channel lengths and silicon film thicknesses. Lastly, the effect of the fixed oxide charge on the drain current model has been studied through simulation. It is observed that the obtained analytical models of symmetrical double gate MOSFETs are in good agreement with the simulated results for a channel length to silicon film thickness ratio greater than or equal to 2.

Revised Parameters for the AMOEBA Polarizable Atomic Multipole Water Model

Science.gov (United States)

Pande, Vijay S.; Head-Gordon, Teresa; Ponder, Jay W.

2016-01-01

A set of improved parameters for the AMOEBA polarizable atomic multipole water model is developed. The protocol uses an automated procedure, ForceBalance, to adjust model parameters to enforce agreement with ab initio-derived results for water clusters and experimentally obtained data for a variety of liquid phase properties across a broad temperature range. The values reported here for the new AMOEBA14 water model represent a substantial improvement over the previous AMOEBA03 model. The new AMOEBA14 water model accurately predicts the temperature of maximum density and qualitatively matches the experimental density curve across temperatures ranging from 249 K to 373 K. Excellent agreement is observed for the AMOEBA14 model in comparison to a variety of experimental properties as a function of temperature, including the 2nd virial coefficient, enthalpy of vaporization, isothermal compressibility, thermal expansion coefficient and dielectric constant. The viscosity, self-diffusion constant and surface tension are also well reproduced. In comparison to high-level ab initio results for clusters of 2 to 20 water molecules, the AMOEBA14 model yields results similar to the AMOEBA03 and the direct polarization iAMOEBA models. With advances in computing power, calibration data, and optimization techniques, we recommend the use of the AMOEBA14 water model for future studies employing a polarizable water model. PMID:25683601

Modeling Volcanic Eruption Parameters by Near-Source Internal Gravity Waves.

Science.gov (United States)

Ripepe, M; Barfucci, G; De Angelis, S; Delle Donne, D; Lacanna, G; Marchetti, E

2016-11-10

Volcanic explosions release large amounts of hot gas and ash into the atmosphere to form plumes rising several kilometers above eruptive vents, which can pose serious risk on human health and aviation also at several thousands of kilometers from the volcanic source. However the most sophisticate atmospheric models and eruptive plume dynamics require input parameters such as duration of the ejection phase and total mass erupted to constrain the quantity of ash dispersed in the atmosphere and to efficiently evaluate the related hazard. The sudden ejection of this large quantity of ash can perturb the equilibrium of the whole atmosphere triggering oscillations well below the frequencies of acoustic waves, down to much longer periods typical of gravity waves. We show that atmospheric gravity oscillations induced by volcanic eruptions and recorded by pressure sensors can be modeled as a compact source representing the rate of erupted volcanic mass. We demonstrate the feasibility of using gravity waves to derive eruption source parameters such as duration of the injection and total erupted mass with direct application in constraining plume and ash dispersal models.

Inflation and cosmological parameter estimation

Energy Technology Data Exchange (ETDEWEB)

Hamann, J.

2007-05-15

In this work, we focus on two aspects of cosmological data analysis: inference of parameter values and the search for new effects in the inflationary sector. Constraints on cosmological parameters are commonly derived under the assumption of a minimal model. We point out that this procedure systematically underestimates errors and possibly biases estimates, due to overly restrictive assumptions. In a more conservative approach, we analyse cosmological data using a more general eleven-parameter model. We find that regions of the parameter space that were previously thought ruled out are still compatible with the data; the bounds on individual parameters are relaxed by up to a factor of two, compared to the results for the minimal six-parameter model. Moreover, we analyse a class of inflation models, in which the slow roll conditions are briefly violated, due to a step in the potential. We show that the presence of a step generically leads to an oscillating spectrum and perform a fit to CMB and galaxy clustering data. We do not find conclusive evidence for a step in the potential and derive strong bounds on quantities that parameterise the step. (orig.)

The influence of land surface parameters on energy flux densities derived from remote sensing data

Energy Technology Data Exchange (ETDEWEB)

Tittebrand, A.; Schwiebus, A. [Inst. for Hydrology und Meteorology, TU Dresden (Germany); Berger, F.H. [Observatory Lindenberg, German Weather Service, Lindenberg (Germany)

2005-04-01

Knowledge of the vegetation properties surface reflectance, normalised difference vegetation index (NDVI) and leaf area index (LAI) are essential for the determination of the heat and water fluxes between terrestrial ecosystems and the atmosphere. Remote sensing data can be used to derive spatial estimates of the required surface properties. The determination of land surface parameters and their influence on radiant and energy flux densities is investigated with data of different remote sensing systems. Sensitivity studies show the importance of correctly derived land surface properties to estimate the key quantity of the hydrological cycle, the evapotranspiration (L.E), most exactly. In addition to variable parameters like LAI or NDVI there are also parameters which are can not be inferred from satellite data but needed for the Penman-Monteith approach. Fixed values are assumed for these variables because they have little influence on L.E. Data of Landsat-7 ETM+ and NOAA-16 AVHRR are used to show results in different spatial resolution. The satellite derived results are compared with ground truth data provided by the Observatory Lindenberg of the German Weather Service. (orig.)

Methodology for deriving hydrogeological input parameters for safety-analysis models - application to fractured crystalline rocks of Northern Switzerland

International Nuclear Information System (INIS)

Vomvoris, S.; Andrews, R.W.; Lanyon, G.W.; Voborny, O.; Wilson, W.

1996-04-01

Switzerland is one of many nations with nuclear power that is seeking to identify rock types and locations that would be suitable for the underground disposal of nuclear waste. A common challenge among these programs is to provide engineering designers and safety analysts with a reasonably representative hydrogeological input dataset that synthesizes the relevant information from direct field observations as well as inferences and model results derived from those observations. Needed are estimates of the volumetric flux through a volume of rock and the distribution of that flux into discrete pathways between the repository zones and the biosphere. These fluxes are not directly measurable but must be derived based on understandings of the range of plausible hydrogeologic conditions expected at the location investigated. The methodology described in this report utilizes conceptual and numerical models at various scales to derive the input dataset. The methodology incorporates an innovative approach, called the geometric approach, in which field observations and their associated uncertainty, together with a conceptual representation of those features that most significantly affect the groundwater flow regime, were rigorously applied to generate alternative possible realizations of hydrogeologic features in the geosphere. In this approach, the ranges in the output values directly reflect uncertainties in the input values. As a demonstration, the methodology is applied to the derivation of the hydrogeological dataset for the crystalline basement of Northern Switzerland. (author) figs., tabs., refs

Parameter Estimation of Partial Differential Equation Models.

Science.gov (United States)

Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J; Maity, Arnab

2013-01-01

Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data.

Deriving a model for influenza epidemics from historical data.

Energy Technology Data Exchange (ETDEWEB)

Ray, Jaideep; Lefantzi, Sophia

2011-09-01

In this report we describe how we create a model for influenza epidemics from historical data collected from both civilian and military societies. We derive the model when the population of the society is unknown but the size of the epidemic is known. Our interest lies in estimating a time-dependent infection rate to within a multiplicative constant. The model form fitted is chosen for its similarity to published models for HIV and plague, enabling application of Bayesian techniques to discriminate among infectious agents during an emerging epidemic. We have developed models for the progression of influenza in human populations. The model is framed as a integral, and predicts the number of people who exhibit symptoms and seek care over a given time-period. The start and end of the time period form the limits of integration. The disease progression model, in turn, contains parameterized models for the incubation period and a time-dependent infection rate. The incubation period model is obtained from literature, and the parameters of the infection rate are fitted from historical data including both military and civilian populations. The calibrated infection rate models display a marked difference in which the 1918 Spanish Influenza pandemic differed from the influenza seasons in the US between 2001-2008 and the progression of H1N1 in Catalunya, Spain. The data for the 1918 pandemic was obtained from military populations, while the rest are country-wide or province-wide data from the twenty-first century. We see that the initial growth of infection in all cases were about the same; however, military populations were able to control the epidemic much faster i.e., the decay of the infection-rate curve is much higher. It is not clear whether this was because of the much higher level of organization present in a military society or the seriousness with which the 1918 pandemic was addressed. Each outbreak to which the influenza model was fitted yields a separate set of

Permanently split capacitor motor-study of the design parameters

Science.gov (United States)

Sarac, Vasilija; Stefanov, Goce

2017-09-01

Paper analyzes the influence of various design parameters on torque of permanently split capacitor motor. Motor analytical model is derived and it is used for calculating the performance characteristics of basic motor model. The acquired analytical model is applied in optimization software that uses genetic algorithms (GA) as an optimization method. Optimized motor model with increased torque is derived by varying three motor parameters in GA program: winding turns ratio, average air gap flux density and motor stack length. Increase of torque has been achieved for nominal operation but also at motor starting. Accuracy of the derived models is verified by Simulink. The acquired values of several motor parameters from transient characteristics of Simulink models are compared with the corresponding values obtained from analytical models of both motors, basic and optimized. Numerical analysis, based on finite element method (FEM), is also performed for both motor models. As a result of the FEM analysis, magnetic flux density in motor cross-section is calculated and adequate conclusions are derived in relation to core saturation and air gap flux density in both motor models.

Modeling of a three-phase reactor for bitumen-derived gas oil hydrotreating

International Nuclear Information System (INIS)

Chacon, R.; Canale, A.; Bouza, A.; Sanchez, Y.

2012-01-01

A three-phase reactor model for describing the hydrotreating reactions of bitumen-derived gas oil was developed. The model incorporates the mass-transfer resistance at the gas-liquid and liquid-solid interfaces and a kinetic rate expression based on a Langmuir-Hinshelwood-type model. We derived three correlations for determining the solubility of hydrogen (H 2 ), hydrogen sulfide (H 2 S) and ammonia (NH 3 ) in hydrocarbon mixtures and the calculation of the catalyst effectiveness factor was included. Experimental data taken from the literature were used to determine the kinetic parameters (stoichiometric coefficients, reaction orders, reaction rate and adsorption constants for hydrodesulfuration (HDS) and hydrodenitrogenation (HDN)) and to validate the model under various operating conditions. Finally, we studied the effect of operating conditions such as pressure, temperature, LHSV, H 2 /feed ratio and the inhibiting effect of H 2 S on HDS and NH 3 on HDN. (author)

Quality assessment for radiological model parameters

International Nuclear Information System (INIS)

Funtowicz, S.O.

1989-01-01

A prototype framework for representing uncertainties in radiological model parameters is introduced. This follows earlier development in this journal of a corresponding framework for representing uncertainties in radiological data. Refinements and extensions to the earlier framework are needed in order to take account of the additional contextual factors consequent on using data entries to quantify model parameters. The parameter coding can in turn feed in to methods for evaluating uncertainties in calculated model outputs. (author)

Alien derivatives of the WKB solutions of the Gauss hypergeometric differential equation with a large parameter

Directory of Open Access Journals (Sweden)

Mika Tanda

2015-01-01

Full Text Available We compute alien derivatives of the WKB solutions of the Gauss hypergeometric differential equation with a large parameter and discuss the singularity structures of the Borel transforms of the WKB solution expressed in terms of its alien derivatives.

What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast.

Directory of Open Access Journals (Sweden)

Artémis Llamosi

2016-02-01

Full Text Available Significant cell-to-cell heterogeneity is ubiquitously observed in isogenic cell populations. Consequently, parameters of models of intracellular processes, usually fitted to population-averaged data, should rather be fitted to individual cells to obtain a population of models of similar but non-identical individuals. Here, we propose a quantitative modeling framework that attributes specific parameter values to single cells for a standard model of gene expression. We combine high quality single-cell measurements of the response of yeast cells to repeated hyperosmotic shocks and state-of-the-art statistical inference approaches for mixed-effects models to infer multidimensional parameter distributions describing the population, and then derive specific parameters for individual cells. The analysis of single-cell parameters shows that single-cell identity (e.g. gene expression dynamics, cell size, growth rate, mother-daughter relationships is, at least partially, captured by the parameter values of gene expression models (e.g. rates of transcription, translation and degradation. Our approach shows how to use the rich information contained into longitudinal single-cell data to infer parameters that can faithfully represent single-cell identity.

Interpretation of hydraulic conductivity data and parameter evaluation for groundwater flow models

International Nuclear Information System (INIS)

Niemi, A.

1991-01-01

The report reviews recent developments in evaluating effective permeabilities for groundwater flow models, starting from methods of well test interpretation for and proceeding to the principles of parameter estimation. Basic concepts of parameter evaluation as well as expressions derived for effective permeabilities in traditional porous medium are described. Due to the assumptions made, these do often not apply for fractured media. Specific features of fractured medium are discussed, including approaches used determining the size of a possible REV and questions related to the application of stochastic theories. Due to the difficulties encountered when applying traditional deterministic models for fractured media, stochastic and fracture network approaches have been developed. The application of these techniques is still under development, the main questions to be resolved being related to the scarcity of data

A tracer diffusion model derived from microstructure

International Nuclear Information System (INIS)

Lehikoinen, Jarmo; Muurinen, Arto; Olin, Markus

2012-01-01

of reference, is shown to be given by the ratio of the effective diffusivity to the apparent diffusivity for an assumed non-interacting solute, such as tritiated water. Finally, the utility of the model and derivation of the model parameters are demonstrated with tracer diffusion data from the open literature for compacted bentonite. (authors)

Parameter Estimation of Partial Differential Equation Models

KAUST Repository

Xun, Xiaolei

2013-09-01

Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.

Optimization of some electrochemical etching parameters for cellulose derivatives

International Nuclear Information System (INIS)

Chowdhury, Annis; Gammage, R.B.

1978-01-01

Electrochemical etching of fast neutron induced recoil particle tracks in cellulose derivatives and other polymers provides an inexpensive and sensitive means of fast neutron personnel dosimetry. A study of the shape, clarity, and size of the tracks in Transilwrap polycarbonate indicated that the optimum normality of the potassium hydroxide etching solution is 9 N. Optimizations have also been attempted for cellulose nitrate, triacetate, and acetobutyrate with respect to such electrochemical etching parameters as frequency, voltage gradient, and concentration of the etching solution. The measurement of differential leakage currents between the undamaged and the neutron damaged foils aided in the selection of optimum frequencies. (author)

Estimation efficiency of usage satellite derived and modelled biophysical products for yield forecasting

Science.gov (United States)

Kolotii, Andrii; Kussul, Nataliia; Skakun, Sergii; Shelestov, Andrii; Ostapenko, Vadim; Oliinyk, Tamara

2015-04-01

Efficient and timely crop monitoring and yield forecasting are important tasks for ensuring of stability and sustainable economic development [1]. As winter crops pay prominent role in agriculture of Ukraine - the main focus of this study is concentrated on winter wheat. In our previous research [2, 3] it was shown that usage of biophysical parameters of crops such as FAPAR (derived from Geoland-2 portal as for SPOT Vegetation data) is far more efficient for crop yield forecasting to NDVI derived from MODIS data - for available data. In our current work efficiency of usage such biophysical parameters as LAI, FAPAR, FCOVER (derived from SPOT Vegetation and PROBA-V data at resolution of 1 km and simulated within WOFOST model) and NDVI product (derived from MODIS) for winter wheat monitoring and yield forecasting is estimated. As the part of crop monitoring workflow (vegetation anomaly detection, vegetation indexes and products analysis) and yield forecasting SPIRITS tool developed by JRC is used. Statistics extraction is done for landcover maps created in SRI within FP-7 SIGMA project. Efficiency of usage satellite based and modelled with WOFOST model biophysical products is estimated. [1] N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "Sensor Web approach to Flood Monitoring and Risk Assessment", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 815-818. [2] F. Kogan, N. Kussul, T. Adamenko, S. Skakun, O. Kravchenko, O. Kryvobok, A. Shelestov, A. Kolotii, O. Kussul, and A. Lavrenyuk, "Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models," International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 192-203, 2013. [3] Kussul O., Kussul N., Skakun S., Kravchenko O., Shelestov A., Kolotii A, "Assessment of relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 3235 - 3238.

distributed parameter model of spiral-wound sepralator for treatment of uranyl nitrate effluents

International Nuclear Information System (INIS)

El-Bialy, S.H; Elsherbiny, A.E.

2004-01-01

in this paper, mathematical formulation of spiral-wound sepralator was derived and applied for the treatment of effluent stream which is produced during nuclear fuel processing stage. the concentration of the stream has a value up to 200 ppm . cross-flow characteristic of both feed and permeate streams was taken into account and their mutual effects on the values of system variables were investigated. of course, such a flow pattern leads to a heterogeneous system which leads-in turn-to six partial differential equations, beside a set of algebraic equations. those were solved numerically and the results were used to estimate the average values of both permeate flux and percent solute rejection. then, these were compared with both experimental data in addition to the results of lumped parameter model. the study showed that distributed parameter model gives better results than lumped parameter one compared with experimental data

Identification of Affine Linear Parameter Varying Models for Adaptive Interventions in Fibromyalgia Treatment.

Science.gov (United States)

Dos Santos, P Lopes; Deshpande, Sunil; Rivera, Daniel E; Azevedo-Perdicoúlis, T-P; Ramos, J A; Younger, Jarred

2013-12-31

There is good evidence that naltrexone, an opioid antagonist, has a strong neuroprotective role and may be a potential drug for the treatment of fibromyalgia. In previous work, some of the authors used experimental clinical data to identify input-output linear time invariant models that were used to extract useful information about the effect of this drug on fibromyalgia symptoms. Additional factors such as anxiety, stress, mood, and headache, were considered as additive disturbances. However, it seems reasonable to think that these factors do not affect the drug actuation, but only the way in which a participant perceives how the drug actuates on herself. Under this hypothesis the linear time invariant models can be replaced by State-Space Affine Linear Parameter Varying models where the disturbances are seen as a scheduling signal signal only acting at the parameters of the output equation. In this paper a new algorithm for identifying such a model is proposed. This algorithm minimizes a quadratic criterion of the output error. Since the output error is a linear function of some parameters, the Affine Linear Parameter Varying system identification is formulated as a separable nonlinear least squares problem. Likewise other identification algorithms using gradient optimization methods several parameter derivatives are dynamical systems that must be simulated. In order to increase time efficiency a canonical parametrization that minimizes the number of systems to be simulated is chosen. The effectiveness of the algorithm is assessed in a case study where an Affine Parameter Varying Model is identified from the experimental data used in the previous study and compared with the time-invariant model.

Model atmospheres and parameters of central stars of planetary nebulae

International Nuclear Information System (INIS)

Patriarchi, P.; Cerruti-sola, M.; Perinotto, M.

1989-01-01

Non-LTE hydrogen and helium model atmospheres have been obtained for temperatures and gravities relevant to the central stars of planetary nebulae. Low-resolution and high-resolution observations obtained by the IUE satellite have been used along with optical data to determine Zanstra temperatures of the central stars of NGC 1535, NGC 6210, NGC 7009, IC 418, and IC 4593. Comparison of the observed stellar continuum of these stars with theoretical results allowed further information on the stellar temperature to be derived. The final temperatures are used to calculate accurate stellar parameters. 62 refs

Calibration by Hydrological Response Unit of a National Hydrologic Model to Improve Spatial Representation and Distribution of Parameters

Science.gov (United States)

Norton, P. A., II

2015-12-01

The U. S. Geological Survey is developing a National Hydrologic Model (NHM) to support consistent hydrologic modeling across the conterminous United States (CONUS). The Precipitation-Runoff Modeling System (PRMS) simulates daily hydrologic and energy processes in watersheds, and is used for the NHM application. For PRMS each watershed is divided into hydrologic response units (HRUs); by default each HRU is assumed to have a uniform hydrologic response. The Geospatial Fabric (GF) is a database containing initial parameter values for input to PRMS and was created for the NHM. The parameter values in the GF were derived from datasets that characterize the physical features of the entire CONUS. The NHM application is composed of more than 100,000 HRUs from the GF. Selected parameter values commonly are adjusted by basin in PRMS using an automated calibration process based on calibration targets, such as streamflow. Providing each HRU with distinct values that captures variability within the CONUS may improve simulation performance of the NHM. During calibration of the NHM by HRU, selected parameter values are adjusted for PRMS based on calibration targets, such as streamflow, snow water equivalent (SWE) and actual evapotranspiration (AET). Simulated SWE, AET, and runoff were compared to value ranges derived from multiple sources (e.g. the Snow Data Assimilation System, the Moderate Resolution Imaging Spectroradiometer (i.e. MODIS) Global Evapotranspiration Project, the Simplified Surface Energy Balance model, and the Monthly Water Balance Model). This provides each HRU with a distinct set of parameter values that captures the variability within the CONUS, leading to improved model performance. We present simulation results from the NHM after preliminary calibration, including the results of basin-level calibration for the NHM using: 1) default initial GF parameter values, and 2) parameter values calibrated by HRU.

Covariance matrices for nuclear cross sections derived from nuclear model calculations

International Nuclear Information System (INIS)

Smith, D. L.

2005-01-01

The growing need for covariance information to accompany the evaluated cross section data libraries utilized in contemporary nuclear applications is spurring the development of new methods to provide this information. Many of the current general purpose libraries of evaluated nuclear data used in applications are derived either almost entirely from nuclear model calculations or from nuclear model calculations benchmarked by available experimental data. Consequently, a consistent method for generating covariance information under these circumstances is required. This report discusses a new approach to producing covariance matrices for cross sections calculated using nuclear models. The present method involves establishing uncertainty information for the underlying parameters of nuclear models used in the calculations and then propagating these uncertainties through to the derived cross sections and related nuclear quantities by means of a Monte Carlo technique rather than the more conventional matrix error propagation approach used in some alternative methods. The formalism to be used in such analyses is discussed in this report along with various issues and caveats that need to be considered in order to proceed with a practical implementation of the methodology

Parameter Estimation of a Delay Time Model of Wearing Parts Based on Objective Data

Directory of Open Access Journals (Sweden)

Y. Tang

2015-01-01

Full Text Available The wearing parts of a system have a very high failure frequency, making it necessary to carry out continual functional inspections and maintenance to protect the system from unscheduled downtime. This allows for the collection of a large amount of maintenance data. Taking the unique characteristics of the wearing parts into consideration, we establish their respective delay time models in ideal inspection cases and nonideal inspection cases. The model parameters are estimated entirely using the collected maintenance data. Then, a likelihood function of all renewal events is derived based on their occurring probability functions, and the model parameters are calculated with the maximum likelihood function method, which is solved by the CRM. Finally, using two wearing parts from the oil and gas drilling industry as examples—the filter element and the blowout preventer rubber core—the parameters of the distribution function of the initial failure time and the delay time for each example are estimated, and their distribution functions are obtained. Such parameter estimation based on objective data will contribute to the optimization of the reasonable function inspection interval and will also provide some theoretical models to support the integrity management of equipment or systems.

Modeling of a three-phase reactor for bitumen-derived gas oil hydrotreating

Energy Technology Data Exchange (ETDEWEB)

Chacon, R.; Canale, A.; Bouza, A. [Departamento de Termodinamica y Fenomenos de Transporte. Universidad Simon Bolivar, Caracas (Venezuela, Bolivarian Republic of); Sanchez, Y. [Departamento de Procesos y Sistemas. Universidad Simon Bolivar (Venezuela, Bolivarian Republic of)

2012-01-15

A three-phase reactor model for describing the hydrotreating reactions of bitumen-derived gas oil was developed. The model incorporates the mass-transfer resistance at the gas-liquid and liquid-solid interfaces and a kinetic rate expression based on a Langmuir-Hinshelwood-type model. We derived three correlations for determining the solubility of hydrogen (H{sub 2}), hydrogen sulfide (H{sub 2}S) and ammonia (NH{sub 3}) in hydrocarbon mixtures and the calculation of the catalyst effectiveness factor was included. Experimental data taken from the literature were used to determine the kinetic parameters (stoichiometric coefficients, reaction orders, reaction rate and adsorption constants for hydrodesulfuration (HDS) and hydrodenitrogenation (HDN)) and to validate the model under various operating conditions. Finally, we studied the effect of operating conditions such as pressure, temperature, LHSV, H{sub 2}/feed ratio and the inhibiting effect of H{sub 2}S on HDS and NH{sub 3} on HDN. (author)

Invariant models in the inversion of gravity and magnetic fields and their derivatives

Science.gov (United States)

Ialongo, Simone; Fedi, Maurizio; Florio, Giovanni

2014-11-01

In potential field inversion problems we usually solve underdetermined systems and realistic solutions may be obtained by introducing a depth-weighting function in the objective function. The choice of the exponent of such power-law is crucial. It was suggested to determine it from the field-decay due to a single source-block; alternatively it has been defined as the structural index of the investigated source distribution. In both cases, when k-order derivatives of the potential field are considered, the depth-weighting exponent has to be increased by k with respect that of the potential field itself, in order to obtain consistent source model distributions. We show instead that invariant and realistic source-distribution models are obtained using the same depth-weighting exponent for the magnetic field and for its k-order derivatives. A similar behavior also occurs in the gravity case. In practice we found that the depth weighting-exponent is invariant for a given source-model and equal to that of the corresponding magnetic field, in the magnetic case, and of the 1st derivative of the gravity field, in the gravity case. In the case of the regularized inverse problem, with depth-weighting and general constraints, the mathematical demonstration of such invariance is difficult, because of its non-linearity, and of its variable form, due to the different constraints used. However, tests performed on a variety of synthetic cases seem to confirm the invariance of the depth-weighting exponent. A final consideration regards the role of the regularization parameter; we show that the regularization can severely affect the depth to the source because the estimated depth tends to increase proportionally with the size of the regularization parameter. Hence, some care is needed in handling the combined effect of the regularization parameter and depth weighting.

'Generalizability' of a radial-aortic transfer function for the derivation of central aortic waveform parameters.

Science.gov (United States)

Hope, Sarah A; Meredith, Ian T; Tay, David; Cameron, James D

2007-09-01

Arterial transfer functions (TFs) describe the relationship between the pressure waveform at different arterial sites. Generalized TFs are used to reconstruct central aortic waveforms from non-invasively obtained peripheral waveforms and have been promoted as potentially clinically useful. A limitation is the paucity of information on their 'generalizability' with no information existing on the number of subjects required to construct a satisfactory TF, nor is adequate prospective validation available. We therefore investigated the uniformity of radial-aortic TFs and prospectively estimated the capacity of a generalized TF to reconstruct individual central blood pressure parameters. Ninety-three subjects (64 male) were studied by simultaneous radial applanation and high-fidelity (Millar Mikro-tip catheter) direct measurement of central aortic BP during elective coronary procedures. Subjects were prospectively randomized to either a derivation or validation group. Increasing numbers of individual TFs from the derivation group were averaged to form a generalized TF. There was minimal change with greater than 20 TFs averaged. In the validation group, the error in most reconstructed parameters related to the absolute value of the directly measured parameter [systolic blood pressure (SBP) and pulse pressure, Pcentral aortic SBP and pulse pressure (negatively) and time to peak systole (positively) (all PInclusion of more than 20 individual TFs in the construction of a generalized TF does not improve 'generalizability'. There appear to be systematic errors in derived central pressure waveforms and derived aortic augmentation index is inaccurate compared to the directly measured value.

A statistical model for deriving probability distributions of contamination for accidental releases

International Nuclear Information System (INIS)

ApSimon, H.M.; Davison, A.C.

1986-01-01

Results generated from a detailed long-range transport model, MESOS, simulating dispersal of a large number of hypothetical releases of radionuclides in a variety of meteorological situations over Western Europe have been used to derive a simpler statistical model, MESOSTAT. This model may be used to generate probability distributions of different levels of contamination at a receptor point 100-1000 km or so from the source (for example, across a frontier in another country) without considering individual release and dispersal scenarios. The model is embodied in a series of equations involving parameters which are determined from such factors as distance between source and receptor, nuclide decay and deposition characteristics, release duration, and geostrophic windrose at the source. Suitable geostrophic windrose data have been derived for source locations covering Western Europe. Special attention has been paid to the relatively improbable extreme values of contamination at the top end of the distribution. The MESOSTAT model and its development are described, with illustrations of its use and comparison with the original more detailed modelling techniques. (author)

Automated parameter estimation for biological models using Bayesian statistical model checking.

Science.gov (United States)

Hussain, Faraz; Langmead, Christopher J; Mi, Qi; Dutta-Moscato, Joyeeta; Vodovotz, Yoram; Jha, Sumit K

2015-01-01

Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem. Using an agent-based model of the dynamics of acute inflammation, we demonstrate a novel parameter estimation algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide that guarantee a set of observed clinical outcomes with high probability. We synthesized values of twenty-eight unknown parameters such that the parameterized model instantiated with these parameter values satisfies four specifications describing the dynamic behavior of the model. We have developed a new algorithmic technique for discovering parameters in complex stochastic models of biological systems given behavioral specifications written in a formal mathematical logic. Our algorithm uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to automatically synthesize parameters of probabilistic biological models.

On the relationship between NMR-derived amide order parameters and protein backbone entropy changes.

Science.gov (United States)

Sharp, Kim A; O'Brien, Evan; Kasinath, Vignesh; Wand, A Joshua

2015-05-01

Molecular dynamics simulations are used to analyze the relationship between NMR-derived squared generalized order parameters of amide NH groups and backbone entropy. Amide order parameters (O(2) NH ) are largely determined by the secondary structure and average values appear unrelated to the overall flexibility of the protein. However, analysis of the more flexible subset (O(2) NH  entropy than that reported by the side chain methyl axis order parameters, O(2) axis . A calibration curve for backbone entropy vs. O(2) NH is developed, which accounts for both correlations between amide group motions of different residues, and correlations between backbone and side chain motions. This calibration curve can be used with experimental values of O(2) NH changes obtained by NMR relaxation measurements to extract backbone entropy changes, for example, upon ligand binding. In conjunction with our previous calibration for side chain entropy derived from measured O(2) axis values this provides a prescription for determination of the total protein conformational entropy changes from NMR relaxation measurements. © 2015 Wiley Periodicals, Inc.

Estimation of inflation parameters for Perturbed Power Law model using recent CMB measurements

International Nuclear Information System (INIS)

Mukherjee, Suvodip; Das, Santanu; Souradeep, Tarun; Joy, Minu

2015-01-01

Cosmic Microwave Background (CMB) is an important probe for understanding the inflationary era of the Universe. We consider the Perturbed Power Law (PPL) model of inflation which is a soft deviation from Power Law (PL) inflationary model. This model captures the effect of higher order derivative of Hubble parameter during inflation, which in turn leads to a non-zero effective mass m eff for the inflaton field. The higher order derivatives of Hubble parameter at leading order sources constant difference in the spectral index for scalar and tensor perturbation going beyond PL model of inflation. PPL model have two observable independent parameters, namely spectral index for tensor perturbation ν t and change in spectral index for scalar perturbation ν st to explain the observed features in the scalar and tensor power spectrum of perturbation. From the recent measurements of CMB power spectra by WMAP, Planck and BICEP-2 for temperature and polarization, we estimate the feasibility of PPL model with standard ΛCDM model. Although BICEP-2 claimed a detection of r=0.2, estimates of dust contamination provided by Planck have left open the possibility that only upper bound on r will be expected in a joint analysis. As a result we consider different upper bounds on the value of r and show that PPL model can explain a lower value of tensor to scalar ratio (r<0.1 or r<0.01) for a scalar spectral index of n s =0.96 by having a non-zero value of effective mass of the inflaton field m 2 eff /H 2 . The analysis with WP + Planck likelihood shows a non-zero detection of m 2 eff /H 2 with 5.7 σ and 8.1 σ respectively for r<0.1 and r<0.01. Whereas, with BICEP-2 likelihood m 2 eff /H 2  = −0.0237 ± 0.0135 which is consistent with zero

Customized Steady-State Constraints for Parameter Estimation in Non-Linear Ordinary Differential Equation Models.

Science.gov (United States)

Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel

2016-01-01

Ordinary differential equation models have become a wide-spread approach to analyze dynamical systems and understand underlying mechanisms. Model parameters are often unknown and have to be estimated from experimental data, e.g., by maximum-likelihood estimation. In particular, models of biological systems contain a large number of parameters. To reduce the dimensionality of the parameter space, steady-state information is incorporated in the parameter estimation process. For non-linear models, analytical steady-state calculation typically leads to higher-order polynomial equations for which no closed-form solutions can be obtained. This can be circumvented by solving the steady-state equations for kinetic parameters, which results in a linear equation system with comparatively simple solutions. At the same time multiplicity of steady-state solutions is avoided, which otherwise is problematic for optimization. When solved for kinetic parameters, however, steady-state constraints tend to become negative for particular model specifications, thus, generating new types of optimization problems. Here, we present an algorithm based on graph theory that derives non-negative, analytical steady-state expressions by stepwise removal of cyclic dependencies between dynamical variables. The algorithm avoids multiple steady-state solutions by construction. We show that our method is applicable to most common classes of biochemical reaction networks containing inhibition terms, mass-action and Hill-type kinetic equations. Comparing the performance of parameter estimation for different analytical and numerical methods of incorporating steady-state information, we show that our approach is especially well-tailored to guarantee a high success rate of optimization.

Vegetation root zone storage and rooting depth, derived from local calibration of a global hydrological model

Science.gov (United States)

van der Ent, R.; Van Beek, R.; Sutanudjaja, E.; Wang-Erlandsson, L.; Hessels, T.; Bastiaanssen, W.; Bierkens, M. F.

2017-12-01

The storage and dynamics of water in the root zone control many important hydrological processes such as saturation excess overland flow, interflow, recharge, capillary rise, soil evaporation and transpiration. These processes are parameterized in hydrological models or land-surface schemes and the effect on runoff prediction can be large. Root zone parameters in global hydrological models are very uncertain as they cannot be measured directly at the scale on which these models operate. In this paper we calibrate the global hydrological model PCR-GLOBWB using a state-of-the-art ensemble of evaporation fields derived by solving the energy balance for satellite observations. We focus our calibration on the root zone parameters of PCR-GLOBWB and derive spatial patterns of maximum root zone storage. We find these patterns to correspond well with previous research. The parameterization of our model allows for the conversion of maximum root zone storage to root zone depth and we find that these correspond quite well to the point observations where available. We conclude that climate and soil type should be taken into account when regionalizing measured root depth for a certain vegetation type. We equally find that using evaporation rather than discharge better allows for local adjustment of root zone parameters within a basin and thus provides orthogonal data to diagnose and optimize hydrological models and land surface schemes.

Examining the Functional Specification of Two-Parameter Model under Location and Scale Parameter Condition

OpenAIRE

Nakashima, Takahiro

2006-01-01

The functional specification of mean-standard deviation approach is examined under location and scale parameter condition. Firstly, the full set of restrictions imposed on the mean-standard deviation function under the location and scale parameter condition are made clear. Secondly, the examination based on the restrictions mentioned in the previous sentence derives the new properties of the mean-standard deviation function on the applicability of additive separability and the curvature of ex...

Frequency-Domain Maximum-Likelihood Estimation of High-Voltage Pulse Transformer Model Parameters

CERN Document Server

Aguglia, D; Martins, C.D.A.

2014-01-01

This paper presents an offline frequency-domain nonlinear and stochastic identification method for equivalent model parameter estimation of high-voltage pulse transformers. Such kinds of transformers are widely used in the pulsed-power domain, and the difficulty in deriving pulsed-power converter optimal control strategies is directly linked to the accuracy of the equivalent circuit parameters. These components require models which take into account electric fields energies represented by stray capacitance in the equivalent circuit. These capacitive elements must be accurately identified, since they greatly influence the general converter performances. A nonlinear frequency-based identification method, based on maximum-likelihood estimation, is presented, and a sensitivity analysis of the best experimental test to be considered is carried out. The procedure takes into account magnetic saturation and skin effects occurring in the windings during the frequency tests. The presented method is validated by experim...

The influence of model parameters on catchment-response

International Nuclear Information System (INIS)

Shah, S.M.S.; Gabriel, H.F.; Khan, A.A.

2002-01-01

This paper deals with the study of influence of influence of conceptual rainfall-runoff model parameters on catchment response (runoff). A conceptual modified watershed yield model is employed to study the effects of model-parameters on catchment-response, i.e. runoff. The model is calibrated, using manual parameter-fitting approach, also known as trial and error parameter-fitting. In all, there are twenty one (21) parameters that control the functioning of the model. A lumped parametric approach is used. The detailed analysis was performed on Ling River near Kahuta, having catchment area of 56 sq. miles. The model includes physical parameters like GWSM, PETS, PGWRO, etc. fitting coefficients like CINF, CGWS, etc. and initial estimates of the surface-water and groundwater storages i.e. srosp and gwsp. Sensitivity analysis offers a good way, without repetititious computations, the proper weight and consideration that must be taken when each of the influencing factor is evaluated. Sensitivity-analysis was performed to evaluate the influence of model-parameters on runoff. The sensitivity and relative contributions of model parameters influencing catchment-response are studied. (author)

Analysis of deuterium relaxation-derived methyl axis order parameters and correlation with local structure

International Nuclear Information System (INIS)

Mittermaier, Anthony; Kay, Lewis E.; Forman-Kay, Julie D.

1999-01-01

Methyl axis (S2axis) and backbone NH (S2NH) order parameters derived from eight proteins have been analyzed. Similar distribution profiles for Ala S2axis and S2NH order parameters were observed. A good correlation between the two S2axis values of Val and Leu methyl groups is noted, although differences between order parameters can arise. The relation of S2axis or S2NH to solvent accessibility and packing density has also been investigated. Correlations are weak, likely reflecting the importance of collective, non-local motions in proteins. The lack of correlation between these simple structural parameters and dynamics emphasizes the importance of motional studies to fully characterize proteins

An Optimization Principle for Deriving Nonequilibrium Statistical Models of Hamiltonian Dynamics

Science.gov (United States)

Turkington, Bruce

2013-08-01

A general method for deriving closed reduced models of Hamiltonian dynamical systems is developed using techniques from optimization and statistical estimation. Given a vector of resolved variables, selected to describe the macroscopic state of the system, a family of quasi-equilibrium probability densities on phase space corresponding to the resolved variables is employed as a statistical model, and the evolution of the mean resolved vector is estimated by optimizing over paths of these densities. Specifically, a cost function is constructed to quantify the lack-of-fit to the microscopic dynamics of any feasible path of densities from the statistical model; it is an ensemble-averaged, weighted, squared-norm of the residual that results from submitting the path of densities to the Liouville equation. The path that minimizes the time integral of the cost function determines the best-fit evolution of the mean resolved vector. The closed reduced equations satisfied by the optimal path are derived by Hamilton-Jacobi theory. When expressed in terms of the macroscopic variables, these equations have the generic structure of governing equations for nonequilibrium thermodynamics. In particular, the value function for the optimization principle coincides with the dissipation potential that defines the relation between thermodynamic forces and fluxes. The adjustable closure parameters in the best-fit reduced equations depend explicitly on the arbitrary weights that enter into the lack-of-fit cost function. Two particular model reductions are outlined to illustrate the general method. In each example the set of weights in the optimization principle contracts into a single effective closure parameter.

On parameter estimation in deformable models

DEFF Research Database (Denmark)

Fisker, Rune; Carstensen, Jens Michael

1998-01-01

Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian form...

Modeling error and stability of endothelial cytoskeletal membrane parameters based on modeling transendothelial impedance as resistor and capacitor in series.

Science.gov (United States)

Bodmer, James E; English, Anthony; Brady, Megan; Blackwell, Ken; Haxhinasto, Kari; Fotedar, Sunaina; Borgman, Kurt; Bai, Er-Wei; Moy, Alan B

2005-09-01

Transendothelial impedance across an endothelial monolayer grown on a microelectrode has previously been modeled as a repeating pattern of disks in which the electrical circuit consists of a resistor and capacitor in series. Although this numerical model breaks down barrier function into measurements of cell-cell adhesion, cell-matrix adhesion, and membrane capacitance, such solution parameters can be inaccurate without understanding model stability and error. In this study, we have evaluated modeling stability and error by using a chi(2) evaluation and Levenberg-Marquardt nonlinear least-squares (LM-NLS) method of the real and/or imaginary data in which the experimental measurement is compared with the calculated measurement derived by the model. Modeling stability and error were dependent on current frequency and the type of experimental data modeled. Solution parameters of cell-matrix adhesion were most susceptible to modeling instability. Furthermore, the LM-NLS method displayed frequency-dependent instability of the solution parameters, regardless of whether the real or imaginary data were analyzed. However, the LM-NLS method identified stable and reproducible solution parameters between all types of experimental data when a defined frequency spectrum of the entire data set was selected on the basis of a criterion of minimizing error. The frequency bandwidth that produced stable solution parameters varied greatly among different data types. Thus a numerical model based on characterizing transendothelial impedance as a resistor and capacitor in series and as a repeating pattern of disks is not sufficient to characterize the entire frequency spectrum of experimental transendothelial impedance.

Parameter estimations in predictive microbiology: Statistically sound modelling of the microbial growth rate.

Science.gov (United States)

Akkermans, Simen; Logist, Filip; Van Impe, Jan F

2018-04-01

When building models to describe the effect of environmental conditions on the microbial growth rate, parameter estimations can be performed either with a one-step method, i.e., directly on the cell density measurements, or in a two-step method, i.e., via the estimated growth rates. The two-step method is often preferred due to its simplicity. The current research demonstrates that the two-step method is, however, only valid if the correct data transformation is applied and a strict experimental protocol is followed for all experiments. Based on a simulation study and a mathematical derivation, it was demonstrated that the logarithm of the growth rate should be used as a variance stabilizing transformation. Moreover, the one-step method leads to a more accurate estimation of the model parameters and a better approximation of the confidence intervals on the estimated parameters. Therefore, the one-step method is preferred and the two-step method should be avoided. Copyright © 2017. Published by Elsevier Ltd.

Parameter and State Estimator for State Space Models

Directory of Open Access Journals (Sweden)

Ruifeng Ding

2014-01-01

Full Text Available This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.

Deformation analysis of polymers composites: rheological model involving time-based fractional derivative

DEFF Research Database (Denmark)

Zhou, H. W.; Yi, H. Y.; Mishnaevsky, Leon

2017-01-01

A modeling approach to time-dependent property of Glass Fiber Reinforced Polymers (GFRP) composites is of special interest for quantitative description of long-term behavior. An electronic creep machine is employed to investigate the time-dependent deformation of four specimens of dog-bond-shaped......A modeling approach to time-dependent property of Glass Fiber Reinforced Polymers (GFRP) composites is of special interest for quantitative description of long-term behavior. An electronic creep machine is employed to investigate the time-dependent deformation of four specimens of dog......-bond-shaped GFRP composites at various stress level. A negative exponent function based on structural changes is introduced to describe the damage evolution of material properties in the process of creep test. Accordingly, a new creep constitutive equation, referred to fractional derivative Maxwell model...... by the fractional derivative Maxwell model proposed in the paper are in a good agreement with the experimental data. It is shown that the new creep constitutive model proposed in the paper needs few parameters to represent various time-dependent behaviors....

Complexity, parameter sensitivity and parameter transferability in the modelling of floodplain inundation

Science.gov (United States)

Bates, P. D.; Neal, J. C.; Fewtrell, T. J.

2012-12-01

In this we paper we consider two related questions. First, we address the issue of how much physical complexity is necessary in a model in order to simulate floodplain inundation to within validation data error. This is achieved through development of a single code/multiple physics hydraulic model (LISFLOOD-FP) where different degrees of complexity can be switched on or off. Different configurations of this code are applied to four benchmark test cases, and compared to the results of a number of industry standard models. Second we address the issue of how parameter sensitivity and transferability change with increasing complexity using numerical experiments with models of different physical and geometric intricacy. Hydraulic models are a good example system with which to address such generic modelling questions as: (1) they have a strong physical basis; (2) there is only one set of equations to solve; (3) they require only topography and boundary conditions as input data; and (4) they typically require only a single free parameter, namely boundary friction. In terms of complexity required we show that for the problem of sub-critical floodplain inundation a number of codes of different dimensionality and resolution can be found to fit uncertain model validation data equally well, and that in this situation Occam's razor emerges as a useful logic to guide model selection. We find also find that model skill usually improves more rapidly with increases in model spatial resolution than increases in physical complexity, and that standard approaches to testing hydraulic models against laboratory data or analytical solutions may fail to identify this important fact. Lastly, we find that in benchmark testing studies significant differences can exist between codes with identical numerical solution techniques as a result of auxiliary choices regarding the specifics of model implementation that are frequently unreported by code developers. As a consequence, making sound

Parameter estimation of variable-parameter nonlinear Muskingum model using excel solver

Science.gov (United States)

Kang, Ling; Zhou, Liwei

2018-02-01

Abstract . The Muskingum model is an effective flood routing technology in hydrology and water resources Engineering. With the development of optimization technology, more and more variable-parameter Muskingum models were presented to improve effectiveness of the Muskingum model in recent decades. A variable-parameter nonlinear Muskingum model (NVPNLMM) was proposed in this paper. According to the results of two real and frequently-used case studies by various models, the NVPNLMM could obtain better values of evaluation criteria, which are used to describe the superiority of the estimated outflows and compare the accuracies of flood routing using various models, and the optimal estimated outflows by the NVPNLMM were closer to the observed outflows than the ones by other models.

Modelling and simulation of a dynamical system with the Atangana-Baleanu fractional derivative

Science.gov (United States)

Owolabi, Kolade M.

2018-01-01

In this paper, we model an ecological system consisting of a predator and two preys with the newly derived two-step fractional Adams-Bashforth method via the Atangana-Baleanu derivative in the Caputo sense. We analyze the dynamical system for correct choice of parameter values that are biologically meaningful. The local analysis of the main model is based on the application of qualitative theory for ordinary differential equations. By using the fixed point theorem idea, we establish the existence and uniqueness of the solutions. Convergence results of the new scheme are verified in both space and time. Dynamical wave phenomena of solutions are verified via some numerical results obtained for different values of the fractional index, which have some interesting ecological implications.

Sensitivity of the model error parameter specification in weak-constraint four-dimensional variational data assimilation

Science.gov (United States)

Shaw, Jeremy A.; Daescu, Dacian N.

2017-08-01

This article presents the mathematical framework to evaluate the sensitivity of a forecast error aspect to the input parameters of a weak-constraint four-dimensional variational data assimilation system (w4D-Var DAS), extending the established theory from strong-constraint 4D-Var. Emphasis is placed on the derivation of the equations for evaluating the forecast sensitivity to parameters in the DAS representation of the model error statistics, including bias, standard deviation, and correlation structure. A novel adjoint-based procedure for adaptive tuning of the specified model error covariance matrix is introduced. Results from numerical convergence tests establish the validity of the model error sensitivity equations. Preliminary experiments providing a proof-of-concept are performed using the Lorenz multi-scale model to illustrate the theoretical concepts and potential benefits for practical applications.

Physical properties of asteroids derived from a novel approach to modeling of optical lightcurves and WISE thermalinfrared data

Science.gov (United States)

Durech, Josef; Hanus, Josef; Delbo, Marco; Ali-Lagoa, Victor; Carry, Benoit

2014-11-01

Convex shape models and spin vectors of asteroids are now routinely derived from their disk-integrated lightcurves by the lightcurve inversion method of Kaasalainen et al. (2001, Icarus 153, 37). These shape models can be then used in combination with thermal infrared data and a thermophysical model to derive other physical parameters - size, albedo, macroscopic roughness and thermal inertia of the surface. In this classical two-step approach, the shape and spin parameters are kept fixed during the thermophysical modeling when the emitted thermal flux is computed from the surface temperature, which is computed by solving a 1-D heat diffusion equation in sub-surface layers. A novel method of simultaneous inversion of optical and infrared data was presented by Durech et al. (2012, LPI Contribution No. 1667, id.6118). The new algorithm uses the same convex shape representation as the lightcurve inversion but optimizes all relevant physical parameters simultaneously (including the shape, size, rotation vector, thermal inertia, albedo, surface roughness, etc.), which leads to a better fit to the thermal data and a reliable estimation of model uncertainties. We applied this method to selected asteroids using their optical lightcurves from archives and thermal infrared data observed by the Wide-field Infrared Survey Explorer (WISE) satellite. We will (i) show several examples of how well our model fits both optical and infrared data, (ii) discuss the uncertainty of derived parameters (namely the thermal inertia), (iii) compare results obtained with the two-step approach with those obtained by our method, (iv) discuss the advantages of this simultaneous approach with respect to the classical two-step approach, and (v) advertise the possibility to use this approach to tens of thousands asteroids for which enough WISE and optical data exist.

Derivation of inner magnetospheric electric field (UNH-IMEF model using Cluster data set

Directory of Open Access Journals (Sweden)

H. Matsui

2008-09-01

Full Text Available We derive an inner magnetospheric electric field (UNH-IMEF model at L=2–10 using primarily Cluster electric field data for more than 5 years between February 2001 and October 2006. This electric field data set is divided into several ranges of the interplanetary electric field (IEF values measured by ACE. As ring current simulations which require electric field as an input parameter are often performed at L=2–6.6, we have included statistical results from ground radars and low altitude satellites inside the perigee of Cluster in our data set (L~4. Electric potential patterns are derived from the average electric fields by solving an inverse problem. The electric potential pattern for small IEF values is probably affected by the ionospheric dynamo. The magnitudes of the electric field increase around the evening local time as IEF increases, presumably due to the sub-auroral polarization stream (SAPS. Another region with enhanced electric fields during large IEF periods is located around 9 MLT at L>8, which is possibly related to solar wind-magnetosphere coupling. Our potential patterns are consistent with those derived from self-consistent simulations. As the potential patterns can be interpolated/extrapolated to any discrete IEF value within measured ranges, we thus derive an empirical electric potential model. The performance of the model is evaluated by comparing the electric field derived from the model with original one measured by Cluster and mapped to the equator. The model is open to the public through our website.

Derivation of inner magnetospheric electric field (UNH-IMEF model using Cluster data set

Directory of Open Access Journals (Sweden)

H. Matsui

2008-09-01

Full Text Available We derive an inner magnetospheric electric field (UNH-IMEF model at L=2–10 using primarily Cluster electric field data for more than 5 years between February 2001 and October 2006. This electric field data set is divided into several ranges of the interplanetary electric field (IEF values measured by ACE. As ring current simulations which require electric field as an input parameter are often performed at L=2–6.6, we have included statistical results from ground radars and low altitude satellites inside the perigee of Cluster in our data set (L~4. Electric potential patterns are derived from the average electric fields by solving an inverse problem. The electric potential pattern for small IEF values is probably affected by the ionospheric dynamo. The magnitudes of the electric field increase around the evening local time as IEF increases, presumably due to the sub-auroral polarization stream (SAPS. Another region with enhanced electric fields during large IEF periods is located around 9 MLT at L>8, which is possibly related to solar wind-magnetosphere coupling. Our potential patterns are consistent with those derived from self-consistent simulations. As the potential patterns can be interpolated/extrapolated to any discrete IEF value within measured ranges, we thus derive an empirical electric potential model. The performance of the model is evaluated by comparing the electric field derived from the model with original one measured by Cluster and mapped to the equator. The model is open to the public through our website.

Universally sloppy parameter sensitivities in systems biology models.

Directory of Open Access Journals (Sweden)

Ryan N Gutenkunst

2007-10-01

Full Text Available Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.

Universally sloppy parameter sensitivities in systems biology models.

Science.gov (United States)

Gutenkunst, Ryan N; Waterfall, Joshua J; Casey, Fergal P; Brown, Kevin S; Myers, Christopher R; Sethna, James P

2007-10-01

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models.

Directory of Open Access Journals (Sweden)

Jonathan R Karr

2015-05-01

Full Text Available Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.

Derivation of site-specific relationships between hydraulic parameters and p-wave velocities based on hydraulic and seismic tomography

Energy Technology Data Exchange (ETDEWEB)

Brauchler, R.; Doetsch, J.; Dietrich, P.; Sauter, M.

2012-01-10

In this study, hydraulic and seismic tomographic measurements were used to derive a site-specific relationship between the geophysical parameter p-wave velocity and the hydraulic parameters, diffusivity and specific storage. Our field study includes diffusivity tomograms derived from hydraulic travel time tomography, specific storage tomograms, derived from hydraulic attenuation tomography, and p-wave velocity tomograms, derived from seismic tomography. The tomographic inversion was performed in all three cases with the SIRT (Simultaneous Iterative Reconstruction Technique) algorithm, using a ray tracing technique with curved trajectories. The experimental set-up was designed such that the p-wave velocity tomogram overlaps the hydraulic tomograms by half. The experiments were performed at a wellcharacterized sand and gravel aquifer, located in the Leine River valley near Göttingen, Germany. Access to the shallow subsurface was provided by direct-push technology. The high spatial resolution of hydraulic and seismic tomography was exploited to derive representative site-specific relationships between the hydraulic and geophysical parameters, based on the area where geophysical and hydraulic tests were performed. The transformation of the p-wave velocities into hydraulic properties was undertaken using a k-means cluster analysis. Results demonstrate that the combination of hydraulic and geophysical tomographic data is a promising approach to improve hydrogeophysical site characterization.

Efficient Ensemble State-Parameters Estimation Techniques in Ocean Ecosystem Models: Application to the North Atlantic

Science.gov (United States)

El Gharamti, M.; Bethke, I.; Tjiputra, J.; Bertino, L.

2016-02-01

Given the recent strong international focus on developing new data assimilation systems for biological models, we present in this comparative study the application of newly developed state-parameters estimation tools to an ocean ecosystem model. It is quite known that the available physical models are still too simple compared to the complexity of the ocean biology. Furthermore, various biological parameters remain poorly unknown and hence wrong specifications of such parameters can lead to large model errors. Standard joint state-parameters augmentation technique using the ensemble Kalman filter (Stochastic EnKF) has been extensively tested in many geophysical applications. Some of these assimilation studies reported that jointly updating the state and the parameters might introduce significant inconsistency especially for strongly nonlinear models. This is usually the case for ecosystem models particularly during the period of the spring bloom. A better handling of the estimation problem is often carried out by separating the update of the state and the parameters using the so-called Dual EnKF. The dual filter is computationally more expensive than the Joint EnKF but is expected to perform more accurately. Using a similar separation strategy, we propose a new EnKF estimation algorithm in which we apply a one-step-ahead smoothing to the state. The new state-parameters estimation scheme is derived in a consistent Bayesian filtering framework and results in separate update steps for the state and the parameters. Unlike the classical filtering path, the new scheme starts with an update step and later a model propagation step is performed. We test the performance of the new smoothing-based schemes against the standard EnKF in a one-dimensional configuration of the Norwegian Earth System Model (NorESM) in the North Atlantic. We use nutrients profile (up to 2000 m deep) data and surface partial CO2 measurements from Mike weather station (66o N, 2o E) to estimate

Evaluation of Stem Cell-Derived Red Blood Cells as a Transfusion Product Using a Novel Animal Model.

Science.gov (United States)

Shah, Sandeep N; Gelderman, Monique P; Lewis, Emily M A; Farrel, John; Wood, Francine; Strader, Michael Brad; Alayash, Abdu I; Vostal, Jaroslav G

2016-01-01

Reliance on volunteer blood donors can lead to transfusion product shortages, and current liquid storage of red blood cells (RBCs) is associated with biochemical changes over time, known as 'the storage lesion'. Thus, there is a need for alternative sources of transfusable RBCs to supplement conventional blood donations. Extracorporeal production of stem cell-derived RBCs (stemRBCs) is a potential and yet untapped source of fresh, transfusable RBCs. A number of groups have attempted RBC differentiation from CD34+ cells. However, it is still unclear whether these stemRBCs could eventually be effective substitutes for traditional RBCs due to potential differences in oxygen carrying capacity, viability, deformability, and other critical parameters. We have generated ex vivo stemRBCs from primary human cord blood CD34+ cells and compared them to donor-derived RBCs based on a number of in vitro parameters. In vivo, we assessed stemRBC circulation kinetics in an animal model of transfusion and oxygen delivery in a mouse model of exercise performance. Our novel, chronically anemic, SCID mouse model can evaluate the potential of stemRBCs to deliver oxygen to tissues (muscle) under resting and exercise-induced hypoxic conditions. Based on our data, stem cell-derived RBCs have a similar biochemical profile compared to donor-derived RBCs. While certain key differences remain between donor-derived RBCs and stemRBCs, the ability of stemRBCs to deliver oxygen in a living organism provides support for further development as a transfusion product.

Evaluation of Stem Cell-Derived Red Blood Cells as a Transfusion Product Using a Novel Animal Model.

Directory of Open Access Journals (Sweden)

Sandeep N Shah

Full Text Available Reliance on volunteer blood donors can lead to transfusion product shortages, and current liquid storage of red blood cells (RBCs is associated with biochemical changes over time, known as 'the storage lesion'. Thus, there is a need for alternative sources of transfusable RBCs to supplement conventional blood donations. Extracorporeal production of stem cell-derived RBCs (stemRBCs is a potential and yet untapped source of fresh, transfusable RBCs. A number of groups have attempted RBC differentiation from CD34+ cells. However, it is still unclear whether these stemRBCs could eventually be effective substitutes for traditional RBCs due to potential differences in oxygen carrying capacity, viability, deformability, and other critical parameters. We have generated ex vivo stemRBCs from primary human cord blood CD34+ cells and compared them to donor-derived RBCs based on a number of in vitro parameters. In vivo, we assessed stemRBC circulation kinetics in an animal model of transfusion and oxygen delivery in a mouse model of exercise performance. Our novel, chronically anemic, SCID mouse model can evaluate the potential of stemRBCs to deliver oxygen to tissues (muscle under resting and exercise-induced hypoxic conditions. Based on our data, stem cell-derived RBCs have a similar biochemical profile compared to donor-derived RBCs. While certain key differences remain between donor-derived RBCs and stemRBCs, the ability of stemRBCs to deliver oxygen in a living organism provides support for further development as a transfusion product.

Derivation of the mean annual water-energy balance model based on an Ohms-type law

Science.gov (United States)

Li, X.; Shan, X.; Yang, H.

2017-12-01

The Budyko Hypothesis is used to describe the water partition and energy partition. Many empirical and analytical solutions have been proposed to evaluate the general solution which can be described as E/P = F(E0/P, c), where c is a parameter. And previous studies have given a derivation of Mezentsev-Choudhruy-Yang (MCY) model, based on dimensional analysis and mathematic reasoning, however, little hydrological process. Thus further hydrological meaning is limited to the boundary conditions which are difficult to explore. Note that hydrologic cycle is always forced by the energy conversions and atmospheric transportation, and the parallel in the electric circuits and the atmospheric motions, therefore we try to give a new derivation of MCY model from a conceptual model, considering hydrologic fluxes and atmospheric motions. Here an analogy of Ohms Law and the atmospheric cycle is used to aim at describing the partition of water in a long-term timescale. Then MCY model is derived in a new form, which is based on more physical explanation than mathematic reasoning proposed in previous studies. The implications of this derivation are also explored.

Higher derivative extensions of 3d Chern-Simons models: conservation laws and stability

International Nuclear Information System (INIS)

Kaparulin, D.S.; Karataeva, I.Yu.; Lyakhovich, S.L.

2015-01-01

We consider the class of higher derivative 3d vector field models with the field equation operator being a polynomial of the Chern-Simons operator. For the nth-order theory of this type, we provide a general recipe for constructing n-parameter family of conserved second rank tensors. The family includes the canonical energy-momentum tensor, which is unbounded, while there are bounded conserved tensors that provide classical stability of the system for certain combinations of the parameters in the Lagrangian. We also demonstrate the examples of consistent interactions which are compatible with the requirement of stability. (orig.)

Determination of remodeling parameters for a strain-adaptive finite element model of the distal ulna.

Science.gov (United States)

Neuert, Mark A C; Dunning, Cynthia E

2013-09-01

Strain energy-based adaptive material models are used to predict bone resorption resulting from stress shielding induced by prosthetic joint implants. Generally, such models are governed by two key parameters: a homeostatic strain-energy state (K) and a threshold deviation from this state required to initiate bone reformation (s). A refinement procedure has been performed to estimate these parameters in the femur and glenoid; this study investigates the specific influences of these parameters on resulting density distributions in the distal ulna. A finite element model of a human ulna was created using micro-computed tomography (µCT) data, initialized to a homogeneous density distribution, and subjected to approximate in vivo loading. Values for K and s were tested, and the resulting steady-state density distribution compared with values derived from µCT images. The sensitivity of these parameters to initial conditions was examined by altering the initial homogeneous density value. The refined model parameters selected were then applied to six additional human ulnae to determine their performance across individuals. Model accuracy using the refined parameters was found to be comparable with that found in previous studies of the glenoid and femur, and gross bone structures, such as the cortical shell and medullary canal, were reproduced. The model was found to be insensitive to initial conditions; however, a fair degree of variation was observed between the six specimens. This work represents an important contribution to the study of changes in load transfer in the distal ulna following the implementation of commercial orthopedic implants.

Test models for improving filtering with model errors through stochastic parameter estimation

International Nuclear Information System (INIS)

Gershgorin, B.; Harlim, J.; Majda, A.J.

2010-01-01

The filtering skill for turbulent signals from nature is often limited by model errors created by utilizing an imperfect model for filtering. Updating the parameters in the imperfect model through stochastic parameter estimation is one way to increase filtering skill and model performance. Here a suite of stringent test models for filtering with stochastic parameter estimation is developed based on the Stochastic Parameterization Extended Kalman Filter (SPEKF). These new SPEKF-algorithms systematically correct both multiplicative and additive biases and involve exact formulas for propagating the mean and covariance including the parameters in the test model. A comprehensive study is presented of robust parameter regimes for increasing filtering skill through stochastic parameter estimation for turbulent signals as the observation time and observation noise are varied and even when the forcing is incorrectly specified. The results here provide useful guidelines for filtering turbulent signals in more complex systems with significant model errors.

Dynamic Longitudinal and Directional Stability Derivatives for a 45 deg. Sweptback-Wing Airplane Model at Transonic Speeds

Science.gov (United States)

Bielat, Ralph P.; Wiley, Harleth G.

1959-01-01

An investigation was made at transonic speeds to determine some of the dynamic stability derivatives of a 45 deg. sweptback-wing airplane model. The model was sting mounted and was rigidly forced to perform a single-degree-of-freedom angular oscillation in pitch or yaw of +/- 2 deg. The investigation was made for angles of attack alpha, from -4 deg. to 14 deg. throughout most of the transonic speed range for values of reduced-frequency parameter from 0.015 to 0.040 based on wing mean aerodynamic chord and from 0.04 to 0.14 based on wing span. The results show that reduced frequency had only a small effect on the damping-in-pitch derivative and the oscillatory longitudinal stability derivative for all Mach numbers M and angles of attack with the exception of the values of damping coefficient near M = 1.03 and alpha = 8 deg. to 14 deg. In this region, the damping coefficient changed rapidly with reduced frequency and negative values of damping coefficient were measured at low values of reduced frequency. This abrupt variation of pitch damping with reduced frequency was a characteristic of the complete model or wing-body-vertical-tail combination. The damping-in-pitch derivative varied considerably with alpha and M for the horizontal-tail-on and horizontal-tail-off configurations, and the damping was relatively high at angles of attack corresponding to the onset of pitch-up for both configurations. The damping-in-yaw derivative was generally independent of reduced frequency and M at alpha = -4 deg. to 4 deg. At alpha = 8 deg. to 14 deg., the damping derivative increased with an increase in reduced frequency and alpha for the configurations having the wing, whereas the damping derivative was either independent of or decreased with increase in reduced frequency for the configuration without the wing. The oscillatory directional stability derivative for all configurations generally decreased with an increase in the reduced-frequency parameter, and, in some instances

Complex step-based low-rank extended Kalman filtering for state-parameter estimation in subsurface transport models

KAUST Repository

El Gharamti, Mohamad; Hoteit, Ibrahim

2014-01-01

The accuracy of groundwater flow and transport model predictions highly depends on our knowledge of subsurface physical parameters. Assimilation of contaminant concentration data from shallow dug wells could help improving model behavior, eventually resulting in better forecasts. In this paper, we propose a joint state-parameter estimation scheme which efficiently integrates a low-rank extended Kalman filtering technique, namely the Singular Evolutive Extended Kalman (SEEK) filter, with the prominent complex-step method (CSM). The SEEK filter avoids the prohibitive computational burden of the Extended Kalman filter by updating the forecast along the directions of error growth only, called filter correction directions. CSM is used within the SEEK filter to efficiently compute model derivatives with respect to the state and parameters along the filter correction directions. CSM is derived using complex Taylor expansion and is second order accurate. It is proven to guarantee accurate gradient computations with zero numerical round-off errors, but requires complexifying the numerical code. We perform twin-experiments to test the performance of the CSM-based SEEK for estimating the state and parameters of a subsurface contaminant transport model. We compare the efficiency and the accuracy of the proposed scheme with two standard finite difference-based SEEK filters as well as with the ensemble Kalman filter (EnKF). Assimilation results suggest that the use of the CSM in the context of the SEEK filter may provide up to 80% more accurate solutions when compared to standard finite difference schemes and is competitive with the EnKF, even providing more accurate results in certain situations. We analyze the results based on two different observation strategies. We also discuss the complexification of the numerical code and show that this could be efficiently implemented in the context of subsurface flow models. © 2013 Elsevier B.V.

Complex step-based low-rank extended Kalman filtering for state-parameter estimation in subsurface transport models

KAUST Repository

El Gharamti, Mohamad

2014-02-01

The accuracy of groundwater flow and transport model predictions highly depends on our knowledge of subsurface physical parameters. Assimilation of contaminant concentration data from shallow dug wells could help improving model behavior, eventually resulting in better forecasts. In this paper, we propose a joint state-parameter estimation scheme which efficiently integrates a low-rank extended Kalman filtering technique, namely the Singular Evolutive Extended Kalman (SEEK) filter, with the prominent complex-step method (CSM). The SEEK filter avoids the prohibitive computational burden of the Extended Kalman filter by updating the forecast along the directions of error growth only, called filter correction directions. CSM is used within the SEEK filter to efficiently compute model derivatives with respect to the state and parameters along the filter correction directions. CSM is derived using complex Taylor expansion and is second order accurate. It is proven to guarantee accurate gradient computations with zero numerical round-off errors, but requires complexifying the numerical code. We perform twin-experiments to test the performance of the CSM-based SEEK for estimating the state and parameters of a subsurface contaminant transport model. We compare the efficiency and the accuracy of the proposed scheme with two standard finite difference-based SEEK filters as well as with the ensemble Kalman filter (EnKF). Assimilation results suggest that the use of the CSM in the context of the SEEK filter may provide up to 80% more accurate solutions when compared to standard finite difference schemes and is competitive with the EnKF, even providing more accurate results in certain situations. We analyze the results based on two different observation strategies. We also discuss the complexification of the numerical code and show that this could be efficiently implemented in the context of subsurface flow models. © 2013 Elsevier B.V.

Geoelectrical parameter-based multivariate regression borehole yield model for predicting aquifer yield in managing groundwater resource sustainability

Directory of Open Access Journals (Sweden)

Kehinde Anthony Mogaji

2016-07-01

Full Text Available This study developed a GIS-based multivariate regression (MVR yield rate prediction model of groundwater resource sustainability in the hard-rock geology terrain of southwestern Nigeria. This model can economically manage the aquifer yield rate potential predictions that are often overlooked in groundwater resources development. The proposed model relates the borehole yield rate inventory of the area to geoelectrically derived parameters. Three sets of borehole yield rate conditioning geoelectrically derived parameters—aquifer unit resistivity (ρ, aquifer unit thickness (D and coefficient of anisotropy (λ—were determined from the acquired and interpreted geophysical data. The extracted borehole yield rate values and the geoelectrically derived parameter values were regressed to develop the MVR relationship model by applying linear regression and GIS techniques. The sensitivity analysis results of the MVR model evaluated at P ⩽ 0.05 for the predictors ρ, D and λ provided values of 2.68 × 10−05, 2 × 10−02 and 2.09 × 10−06, respectively. The accuracy and predictive power tests conducted on the MVR model using the Theil inequality coefficient measurement approach, coupled with the sensitivity analysis results, confirmed the model yield rate estimation and prediction capability. The MVR borehole yield prediction model estimates were processed in a GIS environment to model an aquifer yield potential prediction map of the area. The information on the prediction map can serve as a scientific basis for predicting aquifer yield potential rates relevant in groundwater resources sustainability management. The developed MVR borehole yield rate prediction mode provides a good alternative to other methods used for this purpose.

The use of quantum chemically derived descriptors for QSAR modelling of reductive dehalogenation of aromatic compounds

NARCIS (Netherlands)

Rorije E; Richter J; Peijnenburg WJGM; ECO; IHE Delft

1994-01-01

In this study, quantum-chemically derived parameters are developed for a limited number of halogenated aromatic compounds to model the anaerobic reductive dehalogenation reaction rate constants of these compounds. It is shown that due to the heterogeneity of the set of compounds used, no single

Nuclear Matter Bulk Parameter Scales and Correlations

International Nuclear Information System (INIS)

Santos, B. M.; Delfino, A.; Dutra, M.; Lourenço, O.

2015-01-01

We study the arising of correlations among some isovector bulk parameters in nonrelativistic and relativistic hadronic mean-field models. For the former, we investigate correlations in the nonrelativistic (NR) limit of relativistic point-coupling models. We provide analytical correlations, for the NR limit model, between the symmetry energy and its derivatives, namely, the symmetry energy slope, curvature, skewness and fourth order derivative, discussing the conditions in which they are linear ones. We also show that some correlations presented in the NR limit model are reproduced for relativistic models presenting cubic and quartic self-interactions in its scalar field. As a direct application of such linear correlations, we remark its association with possible crossing points in the density dependence of the linearly correlated bulk parameter. (author)

Inflationary models with non-minimally derivative coupling

International Nuclear Information System (INIS)

Yang, Nan; Fei, Qin; Gong, Yungui; Gao, Qing

2016-01-01

We derive the general formulae for the scalar and tensor spectral tilts to the second order for the inflationary models with non-minimally derivative coupling without taking the high friction limit. The non-minimally kinetic coupling to Einstein tensor brings the energy scale in the inflationary models down to be sub-Planckian. In the high friction limit, the Lyth bound is modified with an extra suppression factor, so that the field excursion of the inflaton is sub-Planckian. The inflationary models with non-minimally derivative coupling are more consistent with observations in the high friction limit. In particular, with the help of the non-minimally derivative coupling, the quartic power law potential is consistent with the observational constraint at 95% CL. (paper)

Analysis of the spatial variation in the parameters of the SWAT model with application in Flanders, Northern Belgium

Directory of Open Access Journals (Sweden)

G. Heuvelmans

2004-01-01

Full Text Available Operational applications of a hydrological model often require the prediction of stream flow in (future time periods without stream flow observations or in ungauged catchments. Data for a case-specific optimisation of model parameters are not available for such applications, so parameters have to be derived from other catchments or time periods. It has been demonstrated that for applications of the SWAT in Northern Belgium, temporal transfers of the parameters have less influence than spatial transfers on the performance of the model. This study examines the spatial variation in parameter optima in more detail. The aim was to delineate zones wherein model parameters can be transferred without a significant loss of model performance. SWAT was calibrated for 25 catchments that are part of eight larger sub-basins of the Scheldt river basin. Two approaches are discussed for grouping these units in zones with a uniform set of parameters: a single parameter approach considering each parameter separately and a parameter set approach evaluating the parameterisation as a whole. For every catchment, the SWAT model was run with the local parameter optima, with the average parameter values for the entire study region (Flanders, with the zones delineated with the single parameter approach and with the zones obtained by the parameter set approach. Comparison of the model performances of these four parameterisation strategies indicates that both the single parameter and the parameter set zones lead to stream flow predictions that are more accurate than if the entire study region were treated as one single zone. On the other hand, the use of zonal average parameter values results in a considerably worse model fit compared to local parameter optima. Clustering of parameter sets gives a more accurate result than the single parameter approach and is, therefore, the preferred technique for use in the parameterisation of ungauged sub-catchments as part of the

Exploiting intrinsic fluctuations to identify model parameters.

Science.gov (United States)

Zimmer, Christoph; Sahle, Sven; Pahle, Jürgen

2015-04-01

Parameterisation of kinetic models plays a central role in computational systems biology. Besides the lack of experimental data of high enough quality, some of the biggest challenges here are identification issues. Model parameters can be structurally non-identifiable because of functional relationships. Noise in measured data is usually considered to be a nuisance for parameter estimation. However, it turns out that intrinsic fluctuations in particle numbers can make parameters identifiable that were previously non-identifiable. The authors present a method to identify model parameters that are structurally non-identifiable in a deterministic framework. The method takes time course recordings of biochemical systems in steady state or transient state as input. Often a functional relationship between parameters presents itself by a one-dimensional manifold in parameter space containing parameter sets of optimal goodness. Although the system's behaviour cannot be distinguished on this manifold in a deterministic framework it might be distinguishable in a stochastic modelling framework. Their method exploits this by using an objective function that includes a measure for fluctuations in particle numbers. They show on three example models, immigration-death, gene expression and Epo-EpoReceptor interaction, that this resolves the non-identifiability even in the case of measurement noise with known amplitude. The method is applied to partially observed recordings of biochemical systems with measurement noise. It is simple to implement and it is usually very fast to compute. This optimisation can be realised in a classical or Bayesian fashion.

A parameter identification problem arising from a two-dimensional airfoil section model

International Nuclear Information System (INIS)

Cerezo, G.M.

1994-01-01

The development of state space models for aeroelastic systems, including unsteady aerodynamics, is particularly important for the design of highly maneuverable aircraft. In this work we present a state space formulation for a special class of singular neutral functional differential equations (SNFDE) with initial data in C(-1, 0). This work is motivated by the two-dimensional airfoil model presented by Burns, Cliff and Herdman in. In the same authors discuss the validity of the assumptions under which the model was formulated. They pay special attention to the derivation of the evolution equation for the circulation on the airfoil. This equation was coupled to the rigid-body dynamics of the airfoil in order to obtain a complete set of functional differential equations that describes the composite system. The resulting mathematical model for the aeroelastic system has a weakly singular component. In this work we consider a finite delay approximation to the model presented in. We work with a scalar model in which we consider the weak singularity appearing in the original problem. The main goal of this work is to develop numerical techniques for the identification of the parameters appearing in the kernel of the associated scalar integral equation. Clearly this is the first step in the study of parameter identification for the original model and the corresponding validation of this model for the aeroelastic system

Validation of mixing heights derived from the operational NWP models at the German weather service

Energy Technology Data Exchange (ETDEWEB)

Fay, B.; Schrodin, R.; Jacobsen, I. [Deutscher Wetterdienst, Offenbach (Germany); Engelbart, D. [Deutscher Wetterdienst, Meteorol. Observ. Lindenberg (Germany)

1997-10-01

NWP models incorporate an ever-increasing number of observations via four-dimensional data assimilation and are capable of providing comprehensive information about the atmosphere both in space and time. They describe not only near surface parameters but also the vertical structure of the atmosphere. They operate daily, are well verified and successfully used as meteorological pre-processors in large-scale dispersion modelling. Applications like ozone forecasts, emission or power plant control calculations require highly resolved, reliable, and routine values of the temporal evolution of the mixing height (MH) which is a critical parameter in determining the mixing and transformation of substances and the resulting pollution levels near the ground. The purpose of development at the German Weather Service is a straightforward mixing height scheme that uses only parameters derived from NWP model variables and thus automatically provides spatial and temporal fields of mixing heights on an operational basis. An universal parameter to describe stability is the Richardson number Ri. Compared to the usual diagnostic or rate equations, the Ri number concept of determining mixing heights has the advantage of using not only surface layer parameters but also regarding the vertical structure of the boundary layer resolved in the NWP models. (au)

Minimization and parameter estimation for seminorm regularization models with I-divergence constraints

International Nuclear Information System (INIS)

Teuber, T; Steidl, G; Chan, R H

2013-01-01

In this paper, we analyze the minimization of seminorms ‖L · ‖ on R n under the constraint of a bounded I-divergence D(b, H · ) for rather general linear operators H and L. The I-divergence is also known as Kullback–Leibler divergence and appears in many models in imaging science, in particular when dealing with Poisson data but also in the case of multiplicative Gamma noise. Often H represents, e.g., a linear blur operator and L is some discrete derivative or frame analysis operator. A central part of this paper consists in proving relations between the parameters of I-divergence constrained and penalized problems. To solve the I-divergence constrained problem, we consider various first-order primal–dual algorithms which reduce the problem to the solution of certain proximal minimization problems in each iteration step. One of these proximation problems is an I-divergence constrained least-squares problem which can be solved based on Morozov’s discrepancy principle by a Newton method. We prove that these algorithms produce not only a sequence of vectors which converges to a minimizer of the constrained problem but also a sequence of parameters which converges to a regularization parameter so that the corresponding penalized problem has the same solution. Furthermore, we derive a rule for automatically setting the constraint parameter for data corrupted by multiplicative Gamma noise. The performance of the various algorithms is finally demonstrated for different image restoration tasks both for images corrupted by Poisson noise and multiplicative Gamma noise. (paper)

Inverse modeling for seawater intrusion in coastal aquifers: Insights about parameter sensitivities, variances, correlations and estimation procedures derived from the Henry problem

Science.gov (United States)

Sanz, E.; Voss, C.I.

2006-01-01

Inverse modeling studies employing data collected from the classic Henry seawater intrusion problem give insight into several important aspects of inverse modeling of seawater intrusion problems and effective measurement strategies for estimation of parameters for seawater intrusion. Despite the simplicity of the Henry problem, it embodies the behavior of a typical seawater intrusion situation in a single aquifer. Data collected from the numerical problem solution are employed without added noise in order to focus on the aspects of inverse modeling strategies dictated by the physics of variable-density flow and solute transport during seawater intrusion. Covariances of model parameters that can be estimated are strongly dependent on the physics. The insights gained from this type of analysis may be directly applied to field problems in the presence of data errors, using standard inverse modeling approaches to deal with uncertainty in data. Covariance analysis of the Henry problem indicates that in order to generally reduce variance of parameter estimates, the ideal places to measure pressure are as far away from the coast as possible, at any depth, and the ideal places to measure concentration are near the bottom of the aquifer between the center of the transition zone and its inland fringe. These observations are located in and near high-sensitivity regions of system parameters, which may be identified in a sensitivity analysis with respect to several parameters. However, both the form of error distribution in the observations and the observation weights impact the spatial sensitivity distributions, and different choices for error distributions or weights can result in significantly different regions of high sensitivity. Thus, in order to design effective sampling networks, the error form and weights must be carefully considered. For the Henry problem, permeability and freshwater inflow can be estimated with low estimation variance from only pressure or only

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking.

Science.gov (United States)

Hussain, Faraz; Jha, Sumit K; Jha, Susmit; Langmead, Christopher J

2014-01-01

Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.

Establishing statistical models of manufacturing parameters

International Nuclear Information System (INIS)

Senevat, J.; Pape, J.L.; Deshayes, J.F.

1991-01-01

This paper reports on the effect of pilgering and cold-work parameters on contractile strain ratio and mechanical properties that were investigated using a large population of Zircaloy tubes. Statistical models were established between: contractile strain ratio and tooling parameters, mechanical properties (tensile test, creep test) and cold-work parameters, and mechanical properties and stress-relieving temperature

Some tests for parameter constancy in cointegrated VAR-models

DEFF Research Database (Denmark)

Hansen, Henrik; Johansen, Søren

1999-01-01

Some methods for the evaluation of parameter constancy in vector autoregressive (VAR) models are discussed. Two different ways of re-estimating the VAR model are proposed; one in which all parameters are estimated recursively based upon the likelihood function for the first observations, and anot...... be applied to test the constancy of the long-run parameters in the cointegrated VAR-model. All results are illustrated using a model for the term structure of interest rates on US Treasury securities. ......Some methods for the evaluation of parameter constancy in vector autoregressive (VAR) models are discussed. Two different ways of re-estimating the VAR model are proposed; one in which all parameters are estimated recursively based upon the likelihood function for the first observations......, and another in which the cointegrating relations are estimated recursively from a likelihood function, where the short-run parameters have been concentrated out. We suggest graphical procedures based on recursively estimated eigenvalues to evaluate the constancy of the long-run parameters in the model...

Edge Modeling by Two Blur Parameters in Varying Contrasts.

Science.gov (United States)

Seo, Suyoung

2018-06-01

This paper presents a method of modeling edge profiles with two blur parameters, and estimating and predicting those edge parameters with varying brightness combinations and camera-to-object distances (COD). First, the validity of the edge model is proven mathematically. Then, it is proven experimentally with edges from a set of images captured for specifically designed target sheets and with edges from natural images. Estimation of the two blur parameters for each observed edge profile is performed with a brute-force method to find parameters that produce global minimum errors. Then, using the estimated blur parameters, actual blur parameters of edges with arbitrary brightness combinations are predicted using a surface interpolation method (i.e., kriging). The predicted surfaces show that the two blur parameters of the proposed edge model depend on both dark-side edge brightness and light-side edge brightness following a certain global trend. This is similar across varying CODs. The proposed edge model is compared with a one-blur parameter edge model using experiments of the root mean squared error for fitting the edge models to each observed edge profile. The comparison results suggest that the proposed edge model has superiority over the one-blur parameter edge model in most cases where edges have varying brightness combinations.

Identifyability measures to select the parameters to be estimated in a solid-state fermentation distributed parameter model.

Science.gov (United States)

da Silveira, Christian L; Mazutti, Marcio A; Salau, Nina P G

2016-07-08

Process modeling can lead to of advantages such as helping in process control, reducing process costs and product quality improvement. This work proposes a solid-state fermentation distributed parameter model composed by seven differential equations with seventeen parameters to represent the process. Also, parameters estimation with a parameters identifyability analysis (PIA) is performed to build an accurate model with optimum parameters. Statistical tests were made to verify the model accuracy with the estimated parameters considering different assumptions. The results have shown that the model assuming substrate inhibition better represents the process. It was also shown that eight from the seventeen original model parameters were nonidentifiable and better results were obtained with the removal of these parameters from the estimation procedure. Therefore, PIA can be useful to estimation procedure, since it may reduce the number of parameters that can be evaluated. Further, PIA improved the model results, showing to be an important procedure to be taken. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:905-917, 2016. © 2016 American Institute of Chemical Engineers.

Derivation of Green's function of a spin Calogero-Sutherland model by Uglov's method

International Nuclear Information System (INIS)

Nakai, Ryota; Kato, Yusuke

2009-01-01

The hole propagator of a spin 1/2 Calogero-Sutherland model is derived using Uglov's method, which maps the exact eigenfunctions of the model, called the Yangian Gelfand-Zetlin basis, to a limit of Macdonald polynomials (gl 2 -Jack polynomials). To apply this mapping method to the calculation of 1-particle Green's function, we confirm that the sum of the field annihilation operator ψ u + ψ ↓ on a Yangian Gelfand-Zetlin basis is transformed to the field annihilation operator ψ on gl 2 -Jack polynomials by the mapping. The resultant expression for the hole propagator for a finite-size system is written in terms of renormalized momenta and spin of quasi-holes, and the expression in the thermodynamic limit coincides with the earlier result derived by another method. We also discuss the singularity of the spectral function for a specific coupling parameter where the hole propagator of the spin Calogero-Sutherland model becomes equivalent to the dynamical colour correlation function of an SU(3) Haldane-Shastry model

Models and methods for derivation of in vivo neuroreceptor parameters with PET and SPECT reversible radiotracers

International Nuclear Information System (INIS)

Slifstein, Mark; Laruelle, Marc

2001-01-01

The science of quantitative analysis of PET and SPECT neuroreceptor imaging studies has grown considerably over the past decade. A number of methods have been proposed in which receptor parameter estimation results from fitting data to a model of the underlying kinetics of ligand uptake in the brain. These approaches have come to be collectively known as model-based methods and several have received widespread use. Here, we briefly review the most frequently used methods and examine their strengths and weaknesses. Kinetic modeling is the most direct implementation of the compartment models, but with some tracers accurate input function measurement and good compartment configuration identification can be difficult to obtain. Other methods were designed to overcome some particular vulnerability to error of classical kinetic modeling, but introduced new vulnerabilities in the process. Reference region methods obviate the need for arterial plasma measurement, but are not as robust to violations of the underlying modeling assumptions as methods using the arterial input function. Graphical methods give estimates of V T without the requirement of compartment model specification, but provide a biased estimator in the presence of statistical noise. True equilibrium methods are quite robust, but their use is limited to experiments with tracers that are suitable for constant infusion. In conclusion, there is no universally 'best' method that is applicable to all neuroreceptor imaging studies, and carefully evaluation of model-based methods is required for each radiotracer

Environmental Transport Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

M. A. Wasiolek

2003-06-27

This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (TWP) (BSC 2003 [163602]). Some documents in Figure 1-1 may be under development and not available when this report is issued. This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA), but access to the listed documents is not required to understand the contents of this report. This report is one of the reports that develops input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2003 [160699]) describes the conceptual model, the mathematical model, and the input parameters. The purpose of this analysis is to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or volcanic ash). The analysis was performed in accordance with the TWP (BSC 2003 [163602]). This analysis develops values of parameters associated with many features, events, and processes (FEPs) applicable to the reference biosphere (DTN: M00303SEPFEPS2.000 [162452]), which are addressed in the biosphere model (BSC 2003 [160699]). The treatment of these FEPs is described in BSC (2003 [160699

Environmental Transport Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

Wasiolek, M. A.

2003-01-01

This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (TWP) (BSC 2003 [163602]). Some documents in Figure 1-1 may be under development and not available when this report is issued. This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA), but access to the listed documents is not required to understand the contents of this report. This report is one of the reports that develops input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2003 [160699]) describes the conceptual model, the mathematical model, and the input parameters. The purpose of this analysis is to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or volcanic ash). The analysis was performed in accordance with the TWP (BSC 2003 [163602]). This analysis develops values of parameters associated with many features, events, and processes (FEPs) applicable to the reference biosphere (DTN: M00303SEPFEPS2.000 [162452]), which are addressed in the biosphere model (BSC 2003 [160699]). The treatment of these FEPs is described in BSC (2003 [160699], Section 6.2). Parameter values

Parameter estimation in stochastic rainfall-runoff models

DEFF Research Database (Denmark)

Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur

2006-01-01

A parameter estimation method for stochastic rainfall-runoff models is presented. The model considered in the paper is a conceptual stochastic model, formulated in continuous-discrete state space form. The model is small and a fully automatic optimization is, therefore, possible for estimating all...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...

Modelling of slaughterhouse solid waste anaerobic digestion: determination of parameters and continuous reactor simulation.

Science.gov (United States)

López, Iván; Borzacconi, Liliana

2010-10-01

A model based on the work of Angelidaki et al. (1993) was applied to simulate the anaerobic biodegradation of ruminal contents. In this study, two fractions of solids with different biodegradation rates were considered. A first-order kinetic was used for the easily biodegradable fraction and a kinetic expression that is function of the extracellular enzyme concentration was used for the slowly biodegradable fraction. Batch experiments were performed to obtain an accumulated methane curve that was then used to obtain the model parameters. For this determination, a methodology derived from the "multiple-shooting" method was successfully used. Monte Carlo simulations allowed a confidence range to be obtained for each parameter. Simulations of a continuous reactor were performed using the optimal set of model parameters. The final steady-states were determined as functions of the operational conditions (solids load and residence time). The simulations showed that methane flow peaked at a flow rate of 0.5-0.8 Nm(3)/d/m(reactor)(3) at a residence time of 10-20 days. Simulations allow the adequate selection of operating conditions of a continuous reactor. (c) 2010 Elsevier Ltd. All rights reserved.

Modelling ocean-colour-derived chlorophyll a

Directory of Open Access Journals (Sweden)

S. Dutkiewicz

2018-01-01

Full Text Available This article provides a proof of concept for using a biogeochemical/ecosystem/optical model with a radiative transfer component as a laboratory to explore aspects of ocean colour. We focus here on the satellite ocean colour chlorophyll a (Chl a product provided by the often-used blue/green reflectance ratio algorithm. The model produces output that can be compared directly to the real-world ocean colour remotely sensed reflectance. This model output can then be used to produce an ocean colour satellite-like Chl a product using an algorithm linking the blue versus green reflectance similar to that used for the real world. Given that the model includes complete knowledge of the (model water constituents, optics and reflectance, we can explore uncertainties and their causes in this proxy for Chl a (called derived Chl a in this paper. We compare the derived Chl a to the actual model Chl a field. In the model we find that the mean absolute bias due to the algorithm is 22 % between derived and actual Chl a. The real-world algorithm is found using concurrent in situ measurement of Chl a and radiometry. We ask whether increased in situ measurements to train the algorithm would improve the algorithm, and find a mixed result. There is a global overall improvement, but at the expense of some regions, especially in lower latitudes where the biases increase. Not surprisingly, we find that region-specific algorithms provide a significant improvement, at least in the annual mean. However, in the model, we find that no matter how the algorithm coefficients are found there can be a temporal mismatch between the derived Chl a and the actual Chl a. These mismatches stem from temporal decoupling between Chl a and other optically important water constituents (such as coloured dissolved organic matter and detrital matter. The degree of decoupling differs regionally and over time. For example, in many highly seasonal regions, the timing of initiation

An Iterative Ensemble Kalman Filter with One-Step-Ahead Smoothing for State-Parameters Estimation of Contaminant Transport Models

KAUST Repository

Gharamti, M. E.

2015-05-11

The ensemble Kalman filter (EnKF) is a popular method for state-parameters estimation of subsurface flow and transport models based on field measurements. The common filtering procedure is to directly update the state and parameters as one single vector, which is known as the Joint-EnKF. In this study, we follow the one-step-ahead smoothing formulation of the filtering problem, to derive a new joint-based EnKF which involves a smoothing step of the state between two successive analysis steps. The new state-parameters estimation scheme is derived in a consistent Bayesian filtering framework and results in separate update steps for the state and the parameters. This new algorithm bears strong resemblance with the Dual-EnKF, but unlike the latter which first propagates the state with the model then updates it with the new observation, the proposed scheme starts by an update step, followed by a model integration step. We exploit this new formulation of the joint filtering problem and propose an efficient model-integration-free iterative procedure on the update step of the parameters only for further improved performances. Numerical experiments are conducted with a two-dimensional synthetic subsurface transport model simulating the migration of a contaminant plume in a heterogenous aquifer domain. Contaminant concentration data are assimilated to estimate both the contaminant state and the hydraulic conductivity field. Assimilation runs are performed under imperfect modeling conditions and various observational scenarios. Simulation results suggest that the proposed scheme efficiently recovers both the contaminant state and the aquifer conductivity, providing more accurate estimates than the standard Joint and Dual EnKFs in all tested scenarios. Iterating on the update step of the new scheme further enhances the proposed filter’s behavior. In term of computational cost, the new Joint-EnKF is almost equivalent to that of the Dual-EnKF, but requires twice more model

A method for model identification and parameter estimation

International Nuclear Information System (INIS)

Bambach, M; Heinkenschloss, M; Herty, M

2013-01-01

We propose and analyze a new method for the identification of a parameter-dependent model that best describes a given system. This problem arises, for example, in the mathematical modeling of material behavior where several competing constitutive equations are available to describe a given material. In this case, the models are differential equations that arise from the different constitutive equations, and the unknown parameters are coefficients in the constitutive equations. One has to determine the best-suited constitutive equations for a given material and application from experiments. We assume that the true model is one of the N possible parameter-dependent models. To identify the correct model and the corresponding parameters, we can perform experiments, where for each experiment we prescribe an input to the system and observe a part of the system state. Our approach consists of two stages. In the first stage, for each pair of models we determine the experiment, i.e. system input and observation, that best differentiates between the two models, and measure the distance between the two models. Then we conduct N(N − 1) or, depending on the approach taken, N(N − 1)/2 experiments and use the result of the experiments as well as the previously computed model distances to determine the true model. We provide sufficient conditions on the model distances and measurement errors which guarantee that our approach identifies the correct model. Given the model, we identify the corresponding model parameters in the second stage. The problem in the second stage is a standard parameter estimation problem and we use a method suitable for the given application. We illustrate our approach on three examples, including one where the models are elliptic partial differential equations with different parameterized right-hand sides and an example where we identify the constitutive equation in a problem from computational viscoplasticity. (paper)

Traveltime approximations and parameter estimation for orthorhombic media

KAUST Repository

Masmoudi, Nabil

2016-05-30

Building anisotropy models is necessary for seismic modeling and imaging. However, anisotropy estimation is challenging due to the trade-off between inhomogeneity and anisotropy. Luckily, we can estimate the anisotropy parameters Building anisotropy models is necessary for seismic modeling and imaging. However, anisotropy estimation is challenging due to the trade-off between inhomogeneity and anisotropy. Luckily, we can estimate the anisotropy parameters if we relate them analytically to traveltimes. Using perturbation theory, we have developed traveltime approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2, and Δχ in inhomogeneous background media. The parameter Δχ is related to Tsvankin-Thomsen notation and ensures easier computation of traveltimes in the background model. Specifically, our expansion assumes an inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. We have used the Shanks transform to enhance the accuracy of the formulas. A homogeneous medium simplification of the traveltime expansion provided a nonhyperbolic moveout description of the traveltime that was more accurate than other derived approximations. Moreover, the formulation provides a computationally efficient tool to solve the eikonal equation of an orthorhombic medium, without any constraints on the background model complexity. Although, the expansion is based on the factorized representation of the perturbation parameters, smooth variations of these parameters (represented as effective values) provides reasonable results. Thus, this formulation provides a mechanism to estimate the three effective parameters η1, η2, and Δχ. We have derived Dix-type formulas for orthorhombic medium to convert the effective parameters to their interval values.

Application of lumped-parameter models

DEFF Research Database (Denmark)

Ibsen, Lars Bo; Liingaard, Morten

This technical report concerns the lumped-parameter models for a suction caisson with a ratio between skirt length and foundation diameter equal to 1/2, embedded into an viscoelastic soil. The models are presented for three different values of the shear modulus of the subsoil (section 1.1). Subse...

Flare parameters inferred from a 3D loop model data base

Science.gov (United States)

Cuambe, Valente A.; Costa, J. E. R.; Simões, P. J. A.

2018-06-01

We developed a data base of pre-calculated flare images and spectra exploring a set of parameters which describe the physical characteristics of coronal loops and accelerated electron distribution. Due to the large number of parameters involved in describing the geometry and the flaring atmosphere in the model used, we built a large data base of models (˜250 000) to facilitate the flare analysis. The geometry and characteristics of non-thermal electrons are defined on a discrete grid with spatial resolution greater than 4 arcsec. The data base was constructed based on general properties of known solar flares and convolved with instrumental resolution to replicate the observations from the Nobeyama radio polarimeter spectra and Nobeyama radioheliograph (NoRH) brightness maps. Observed spectra and brightness distribution maps are easily compared with the modelled spectra and images in the data base, indicating a possible range of solutions. The parameter search efficiency in this finite data base is discussed. 8 out of 10 parameters analysed for 1000 simulated flare searches were recovered with a relative error of less than 20 per cent on average. In addition, from the analysis of the observed correlation between NoRH flare sizes and intensities at 17 GHz, some statistical properties were derived. From these statistics, the energy spectral index was found to be δ ˜ 3, with non-thermal electron densities showing a peak distribution ⪅107 cm-3, and Bphotosphere ⪆ 2000 G. Some bias for larger loops with heights as great as ˜2.6 × 109 cm, and looptop events were noted. An excellent match of the spectrum and the brightness distribution at 17 and 34 GHz of the 2002 May 31 flare is presented as well.

Construction of a voxel model from CT images with density derived from CT numbers

International Nuclear Information System (INIS)

Cheng Mengyun; Zeng Qin; Cao Ruifen; Li Gui; Zheng Huaqing; Huang Shanqing; Song Gang; Wu Yican

2010-01-01

The voxel models representing human anatomy have been developed to calculate dose distribution in human body, while the density is the most important physical property of voxel model. Traditionally, when creating the Monte Carlo input files, the average tissue parameters recommended in ICRP report were used to assign each voxel in the existing voxel models. However, as each tissue consists of many voxels in which voxels are different in their densities, the method of assigning average tissue parameters doesn't take account of the voxel's discrepancy, and can't represent human anatomy faithfully. To represent human anatomy more faithfully, a method was implemented to assign each voxel, the density of which was derived from CT number. In order to compare with the traditional method, we have constructed two models from a same cadaver specimen date set. A CT-based pelvic voxel model called Pelvis-CT model, was constructed, the densities of which were derived from the CT numbers. A color photograph-based pelvic voxel model called Pelvis-Photo model, was also constructed, the densities of which were taken from ICRP Publication. The CT images and color photographs were obtained from the same female cadaver specimen. The Pelvis-CT and Pelvis-Photo models were ported into Monte Carlo code MCNP to calculate the conversion coefficients from kerma free-in-air to absorbed dose for external monoenergetic photon beams with energies of 0.1, 1 and 10 MeV under anterior-posterior (AP) geometries. The results were compared with those of given in ICRP74. Differences of up to 50% were observed between conversion coefficients of Pelvis-CT and Pelvis-Photo models, moreover the discrepancies decreased for the photon beams with higher energies. The overall trend of conversion coefficients of the Pelvis-CT model were agreed well with that of ICRP74 data. (author)

A simulation of water pollution model parameter estimation

Science.gov (United States)

Kibler, J. F.

1976-01-01

A parameter estimation procedure for a water pollution transport model is elaborated. A two-dimensional instantaneous-release shear-diffusion model serves as representative of a simple transport process. Pollution concentration levels are arrived at via modeling of a remote-sensing system. The remote-sensed data are simulated by adding Gaussian noise to the concentration level values generated via the transport model. Model parameters are estimated from the simulated data using a least-squares batch processor. Resolution, sensor array size, and number and location of sensor readings can be found from the accuracies of the parameter estimates.

Seasonal variation of photosynthetic model parameters and leaf area index from global Fluxnet eddy covariance data

Science.gov (United States)

Groenendijk, M.; Dolman, A. J.; Ammann, C.; Arneth, A.; Cescatti, A.; Dragoni, D.; Gash, J. H. C.; Gianelle, D.; Gioli, B.; Kiely, G.; Knohl, A.; Law, B. E.; Lund, M.; Marcolla, B.; van der Molen, M. K.; Montagnani, L.; Moors, E.; Richardson, A. D.; Roupsard, O.; Verbeeck, H.; Wohlfahrt, G.

2011-12-01

Global vegetation models require the photosynthetic parameters, maximum carboxylation capacity (Vcm), and quantum yield (α) to parameterize their plant functional types (PFTs). The purpose of this work is to determine how much the scaling of the parameters from leaf to ecosystem level through a seasonally varying leaf area index (LAI) explains the parameter variation within and between PFTs. Using Fluxnet data, we simulate a seasonally variable LAIF for a large range of sites, comparable to the LAIM derived from MODIS. There are discrepancies when LAIF reach zero levels and LAIM still provides a small positive value. We find that temperature is the most common constraint for LAIF in 55% of the simulations, while global radiation and vapor pressure deficit are the key constraints for 18% and 27% of the simulations, respectively, while large differences in this forcing still exist when looking at specific PFTs. Despite these differences, the annual photosynthesis simulations are comparable when using LAIF or LAIM (r2 = 0.89). We investigated further the seasonal variation of ecosystem-scale parameters derived with LAIF. Vcm has the largest seasonal variation. This holds for all vegetation types and climates. The parameter α is less variable. By including ecosystem-scale parameter seasonality we can explain a considerable part of the ecosystem-scale parameter variation between PFTs. The remaining unexplained leaf-scale PFT variation still needs further work, including elucidating the precise role of leaf and soil level nitrogen.

Identification of ecosystem parameters by SDE-modelling

DEFF Research Database (Denmark)

Stochastic differential equations (SDEs) for ecosystem modelling have attracted increasing attention during recent years. The modelling has mostly been through simulation experiments in order to analyse how system noise propagates through the ordinary differential equation formulation of ecosystem...... models. Estimation of parameters in SDEs is, however, possible by combining Kalman filter techniques and likelihood estimation. By modelling parameters as random walks it is possible to identify linear as well as non-linear interactions between ecosystem components. By formulating a simple linear SDE...

Spatial probabilistic approach on landslide susceptibility assessment from high resolution sensors derived parameters

International Nuclear Information System (INIS)

Aman, S N A; Latif, Z Abd; Pradhan, B

2014-01-01

Landslide occurrence depends on various interrelating factors which consequently initiate to massive mass of soil and rock debris that move downhill due to the gravity action. LiDAR has come with a progressive approach in mitigating landslide by permitting the formation of more accurate DEM compared to other active space borne and airborne remote sensing techniques. The objective of this research is to assess the susceptibility of landslide in Ulu Klang area by investigating the correlation between past landslide events with geo environmental factors. A high resolution LiDAR DEM was constructed to produce topographic attributes such as slope, curvature and aspect. These data were utilized to derive second deliverables of landslide parameters such as topographic wetness index (TWI), surface area ratio (SAR) and stream power index (SPI) as well as NDVI generated from IKONOS imagery. Subsequently, a probabilistic based frequency ratio model was applied to establish the spatial relationship between the landslide locations and each landslide related factor. Factor ratings were summed up to obtain Landslide Susceptibility Index (LSI) to construct the landslide susceptibility map

Computational optogenetics: empirically-derived voltage- and light-sensitive channelrhodopsin-2 model.

Directory of Open Access Journals (Sweden)

John C Williams

Full Text Available Channelrhodospin-2 (ChR2, a light-sensitive ion channel, and its variants have emerged as new excitatory optogenetic tools not only in neuroscience, but also in other areas, including cardiac electrophysiology. An accurate quantitative model of ChR2 is necessary for in silico prediction of the response to optical stimulation in realistic tissue/organ settings. Such a model can guide the rational design of new ion channel functionality tailored to different cell types/tissues. Focusing on one of the most widely used ChR2 mutants (H134R with enhanced current, we collected a comprehensive experimental data set of the response of this ion channel to different irradiances and voltages, and used these data to develop a model of ChR2 with empirically-derived voltage- and irradiance- dependence, where parameters were fine-tuned via simulated annealing optimization. This ChR2 model offers: 1 accurate inward rectification in the current-voltage response across irradiances; 2 empirically-derived voltage- and light-dependent kinetics (activation, deactivation and recovery from inactivation; and 3 accurate amplitude and morphology of the response across voltage and irradiance settings. Temperature-scaling factors (Q10 were derived and model kinetics was adjusted to physiological temperatures. Using optical action potential clamp, we experimentally validated model-predicted ChR2 behavior in guinea pig ventricular myocytes. The model was then incorporated in a variety of cardiac myocytes, including human ventricular, atrial and Purkinje cell models. We demonstrate the ability of ChR2 to trigger action potentials in human cardiomyocytes at relatively low light levels, as well as the differential response of these cells to light, with the Purkinje cells being most easily excitable and ventricular cells requiring the highest irradiance at all pulse durations. This new experimentally-validated ChR2 model will facilitate virtual experimentation in neural and

Spatio-temporal modeling of nonlinear distributed parameter systems

CERN Document Server

Li, Han-Xiong

2011-01-01

The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches. In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein s

EVALUASI MODEL-MODEL PENDUGAAN UMUR SIMPAN PANGAN DARI DIFUSI HUKUM FICK UNIDIREKSIONAL EVALUATION OF SHELF-LIFE EQUATION MODELS DERIVED FROM UNIDIRECTIONAL FICK’S LAW

Directory of Open Access Journals (Sweden)

M. Arpah

2000-04-01

Full Text Available The aim of this research was to study the variation of shelf-life values, obtained in quantifying shelf-life of biscuits among models of accelerated storage studies (ASS from unidirectional Fick’S law. Shelf-life of biscuits is defined as the length of time of a packaged biscuits can be stored before the onset quality change appears.Four models: Heiss-Eichner (1971, Labuza (1983, Rudolph (1986 and Half Value Period or HVP model (Syarief, 1986 were evaluated. These models shared a common basic principle that they were all derived and developed from unidirectional Fick’s law. Therefore, each parameter of individual model can be compared to the athers. A semi empirical approach using reaction kinetics through Arrhenius plot was used as a real shelf-life values.Quantification resulted in two categories of shelf-life values, First those which higher than expected value and second, were lower than expected. Parameter evaluation of components of Heiss-Eichner and Labuza models clearly shown less in number than components of Rudolph and HVP models. This led to a conclusion that the more sophisticated models gave higher shelf-life values as compared to the Arhenius model.

Modeling parameters that characterize pacing of elite female 800-m freestyle swimmers.

Science.gov (United States)

Lipińska, Patrycja; Allen, Sian V; Hopkins, Will G

2016-01-01

Pacing offers a potential avenue for enhancement of endurance performance. We report here a novel method for characterizing pacing in 800-m freestyle swimming. Websites provided 50-m lap and race times for 192 swims of 20 elite female swimmers between 2000 and 2013. Pacing for each swim was characterized with five parameters derived from a linear model: linear and quadratic coefficients for effect of lap number, reductions from predicted time for first and last laps, and lap-time variability (standard error of the estimate). Race-to-race consistency of the parameters was expressed as intraclass correlation coefficients (ICCs). The average swim was a shallow negative quadratic with slowest time in the eleventh lap. First and last laps were faster by 6.4% and 3.6%, and lap-time variability was ±0.64%. Consistency between swimmers ranged from low-moderate for the linear and quadratic parameters (ICC = 0.29 and 0.36) to high for the last-lap parameter (ICC = 0.62), while consistency for race time was very high (ICC = 0.80). Only ~15% of swimmers had enough swims (~15 or more) to provide reasonable evidence of optimum parameter values in plots of race time vs. each parameter. The modest consistency of most of the pacing parameters and lack of relationships between parameters and performance suggest that swimmers usually compensated for changes in one parameter with changes in another. In conclusion, pacing in 800-m elite female swimmers can be characterized with five parameters, but identifying an optimal pacing profile is generally impractical.

GEMSFITS: Code package for optimization of geochemical model parameters and inverse modeling

International Nuclear Information System (INIS)

Miron, George D.; Kulik, Dmitrii A.; Dmytrieva, Svitlana V.; Wagner, Thomas

2015-01-01

Highlights: • Tool for generating consistent parameters against various types of experiments. • Handles a large number of experimental data and parameters (is parallelized). • Has a graphical interface and can perform statistical analysis on the parameters. • Tested on fitting the standard state Gibbs free energies of aqueous Al species. • Example on fitting interaction parameters of mixing models and thermobarometry. - Abstract: GEMSFITS is a new code package for fitting internally consistent input parameters of GEM (Gibbs Energy Minimization) geochemical–thermodynamic models against various types of experimental or geochemical data, and for performing inverse modeling tasks. It consists of the gemsfit2 (parameter optimizer) and gfshell2 (graphical user interface) programs both accessing a NoSQL database, all developed with flexibility, generality, efficiency, and user friendliness in mind. The parameter optimizer gemsfit2 includes the GEMS3K chemical speciation solver ( (http://gems.web.psi.ch/GEMS3K)), which features a comprehensive suite of non-ideal activity- and equation-of-state models of solution phases (aqueous electrolyte, gas and fluid mixtures, solid solutions, (ad)sorption. The gemsfit2 code uses the robust open-source NLopt library for parameter fitting, which provides a selection between several nonlinear optimization algorithms (global, local, gradient-based), and supports large-scale parallelization. The gemsfit2 code can also perform comprehensive statistical analysis of the fitted parameters (basic statistics, sensitivity, Monte Carlo confidence intervals), thus supporting the user with powerful tools for evaluating the quality of the fits and the physical significance of the model parameters. The gfshell2 code provides menu-driven setup of optimization options (data selection, properties to fit and their constraints, measured properties to compare with computed counterparts, and statistics). The practical utility, efficiency, and

Coastal aquifer management under parameter uncertainty: Ensemble surrogate modeling based simulation-optimization

Science.gov (United States)

Janardhanan, S.; Datta, B.

2011-12-01

saltwater intrusion are considered. The salinity levels resulting at strategic locations due to these pumping are predicted using the ensemble surrogates and are constrained to be within pre-specified levels. Different realizations of the concentration values are obtained from the ensemble predictions corresponding to each candidate solution of pumping. Reliability concept is incorporated as the percent of the total number of surrogate models which satisfy the imposed constraints. The methodology was applied to a realistic coastal aquifer system in Burdekin delta area in Australia. It was found that all optimal solutions corresponding to a reliability level of 0.99 satisfy all the constraints and as reducing reliability level decreases the constraint violation increases. Thus ensemble surrogate model based simulation-optimization was found to be useful in deriving multi-objective optimal pumping strategies for coastal aquifers under parameter uncertainty.

Identification of parameters of discrete-continuous models

International Nuclear Information System (INIS)

Cekus, Dawid; Warys, Pawel

2015-01-01

In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible

Identification of parameters of discrete-continuous models

Energy Technology Data Exchange (ETDEWEB)

Cekus, Dawid, E-mail: cekus@imipkm.pcz.pl; Warys, Pawel, E-mail: warys@imipkm.pcz.pl [Institute of Mechanics and Machine Design Foundations, Czestochowa University of Technology, Dabrowskiego 73, 42-201 Czestochowa (Poland)

2015-03-10

In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible.

The Effect of Metal Concentration on the Parameters Derived from Complexometric Titrations of Trace Elements in Seawater—A Model Study

Directory of Open Access Journals (Sweden)

Martha Gledhill

2017-08-01

Full Text Available In this study we examine the impact of dissolved metal concentrations on the parameters that are commonly determined from complexometric titrations in seawater. We use the non-ideal competitive adsorption (NICA model within the framework of the chemical speciation program visual MINTEQ with iron as a model metal. We demonstrate that dissolved iron concentrations effect the determined parameters for a heterogeneous binding site distribution with a fixed concentration of dissolved organic carbon. The commonly used terms “ligand concentration” and “binding constant” are therefore dependent on metal concentration, so we adopt the terminology suggested by Town and Filella (2000 and use the terms ligand quotient and stability quotient here. The systematic increase in the ligand quotient with dissolved iron concentration likely contributes toward the trend of increasing ligand quotient with dissolved iron concentration observed in field studies, and makes it hard to assign an objective meaning to the parameter. We suggest that calculation of the side reaction coefficient, a parameter that describes the probability that any added metal will be complexed, could be less prone to bias and misinterpretation than calculation of conditional stability and ligand quotients. We explore the impact of experimental design on side reaction coefficients by applying different detection windows, and multiwindow and reverse titration approaches. We identify the method that results in the best estimates of side reaction coefficients over a range of iron concentrations between 0.1 and 1.5 nmol L−1. We find that single window titrations can only reliably estimate side reaction coefficients over a limited range of iron concentrations. Multiwindow titrations provided estimates of side reaction coefficients within the 99% confidence interval of the values calculated directly from the NICA model at all iron concentrations examined here. We recommend that future

Agricultural and Environmental Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

Kaylie Rasmuson; Kurt Rautenstrauch

2003-01-01

This analysis is one of nine technical reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) biosphere model. It documents input parameters for the biosphere model, and supports the use of the model to develop Biosphere Dose Conversion Factors (BDCF). The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for the repository at Yucca Mountain. The ERMYN provides the TSPA with the capability to perform dose assessments. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships between the major activities and their products (the analysis and model reports) that were planned in the biosphere Technical Work Plan (TWP, BSC 2003a). It should be noted that some documents identified in Figure 1-1 may be under development and therefore not available at the time this document is issued. The ''Biosphere Model Report'' (BSC 2003b) describes the ERMYN and its input parameters. This analysis report, ANL-MGR-MD-000006, ''Agricultural and Environmental Input Parameters for the Biosphere Model'', is one of the five reports that develop input parameters for the biosphere model. This report defines and justifies values for twelve parameters required in the biosphere model. These parameters are related to use of contaminated groundwater to grow crops. The parameter values recommended in this report are used in the soil, plant, and carbon-14 submodels of the ERMYN

Verification Techniques for Parameter Selection and Bayesian Model Calibration Presented for an HIV Model

Science.gov (United States)

Wentworth, Mami Tonoe

Uncertainty quantification plays an important role when making predictive estimates of model responses. In this context, uncertainty quantification is defined as quantifying and reducing uncertainties, and the objective is to quantify uncertainties in parameter, model and measurements, and propagate the uncertainties through the model, so that one can make a predictive estimate with quantified uncertainties. Two of the aspects of uncertainty quantification that must be performed prior to propagating uncertainties are model calibration and parameter selection. There are several efficient techniques for these processes; however, the accuracy of these methods are often not verified. This is the motivation for our work, and in this dissertation, we present and illustrate verification frameworks for model calibration and parameter selection in the context of biological and physical models. First, HIV models, developed and improved by [2, 3, 8], describe the viral infection dynamics of an HIV disease. These are also used to make predictive estimates of viral loads and T-cell counts and to construct an optimal control for drug therapy. Estimating input parameters is an essential step prior to uncertainty quantification. However, not all the parameters are identifiable, implying that they cannot be uniquely determined by the observations. These unidentifiable parameters can be partially removed by performing parameter selection, a process in which parameters that have minimal impacts on the model response are determined. We provide verification techniques for Bayesian model calibration and parameter selection for an HIV model. As an example of a physical model, we employ a heat model with experimental measurements presented in [10]. A steady-state heat model represents a prototypical behavior for heat conduction and diffusion process involved in a thermal-hydraulic model, which is a part of nuclear reactor models. We employ this simple heat model to illustrate verification

Hot HB Stars in Globular Clusters: Physical Parameters and Consequences for Theory. VI; The Second Parameter Pair M 3 and M 13

Science.gov (United States)

Moehler, S.; Landsman, W. B.; Sweigart, A. V.; Grundahl, F.

2003-01-01

We present the results of spectroscopic analyses of hot horizontal branch (HB) stars in M 13 and M 3, which form a famous "second parameter" pair. F rom the spectra and Stromgren photometry we derived - for the first time in M 13 - atmospheric parameters (effective temperature and surface gravity). For stars with Stromgren temperatures between 10,000 and 12,000 K we found excellent agreement between the atmospheric parameters derived from Stromgren photometry and those derived from Balmer line profile fits. However, for cooler stars there is a disagreement in the parameters derived by the two methods, for which we have no satisfactory explanation. Stars hotter than 12,000 K show evidence for helium depletion and iron enrichment, both in M 3 and M 13. Accounting for the iron enrichment substantially improves the agreement with canonical evolutionary models, although the derived gravities and masses are still somewhat too low. This remaining discrepancy may be an indication that scaled-solar metal-rich model atmospheres do not adequately represent the highly non-solar abundance ratios found in blue HB stars affected by diffusion. We discuss the effects of an enhancement in the envelope helium abundance on the atmospheric parameters of the blue HB stars, as might be caused by deep mixing on the red giant branch or primordial pollution from an earlier generation of intermediate mass asymptotic giant branch stars. Key words. Stars: atmospheres - Stars: evolution - Stars: horizontal branch - Globular clusters: individual: M 3 - Globular clusters: individual: M 13

Zener Diode Compact Model Parameter Extraction Using Xyce-Dakota Optimization.

Energy Technology Data Exchange (ETDEWEB)

Buchheit, Thomas E. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wilcox, Ian Zachary [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandoval, Andrew J [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Reza, Shahed [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-12-01

This report presents a detailed process for compact model parameter extraction for DC circuit Zener diodes. Following the traditional approach of Zener diode parameter extraction, circuit model representation is defined and then used to capture the different operational regions of a real diode's electrical behavior. The circuit model contains 9 parameters represented by resistors and characteristic diodes as circuit model elements. The process of initial parameter extraction, the identification of parameter values for the circuit model elements, is presented in a way that isolates the dependencies between certain electrical parameters and highlights both the empirical nature of the extraction and portions of the real diode physical behavior which of the parameters are intended to represent. Optimization of the parameters, a necessary part of a robost parameter extraction process, is demonstrated using a 'Xyce-Dakota' workflow, discussed in more detail in the report. Among other realizations during this systematic approach of electrical model parameter extraction, non-physical solutions are possible and can be difficult to avoid because of the interdependencies between the different parameters. The process steps described are fairly general and can be leveraged for other types of semiconductor device model extractions. Also included in the report are recommendations for experiment setups for generating optimum dataset for model extraction and the Parameter Identification and Ranking Table (PIRT) for Zener diodes.

Brownian motion model with stochastic parameters for asset prices

Science.gov (United States)

Ching, Soo Huei; Hin, Pooi Ah

2013-09-01

The Brownian motion model may not be a completely realistic model for asset prices because in real asset prices the drift μ and volatility σ may change over time. Presently we consider a model in which the parameter x = (μ,σ) is such that its value x (t + Δt) at a short time Δt ahead of the present time t depends on the value of the asset price at time t + Δt as well as the present parameter value x(t) and m-1 other parameter values before time t via a conditional distribution. The Malaysian stock prices are used to compare the performance of the Brownian motion model with fixed parameter with that of the model with stochastic parameter.

Study on Parameters Modeling of Wind Turbines Using SCADA Data

Directory of Open Access Journals (Sweden)

Yonglong YAN

2014-08-01

Full Text Available Taking the advantage of the current massive monitoring data from Supervisory Control and Data Acquisition (SCADA system of wind farm, it is of important significance for anomaly detection, early warning and fault diagnosis to build the data model of state parameters of wind turbines (WTs. The operational conditions and the relationships between the state parameters of wind turbines are complex. It is difficult to establish the model of state parameter accurately, and the modeling method of state parameters of wind turbines considering parameter selection is proposed. Firstly, by analyzing the characteristic of SCADA data, a reasonable range of data and monitoring parameters are chosen. Secondly, neural network algorithm is adapted, and the selection method of input parameters in the model is presented. Generator bearing temperature and cooling air temperature are regarded as target parameters, and the two models are built and input parameters of the models are selected, respectively. Finally, the parameter selection method in this paper and the method using genetic algorithm-partial least square (GA-PLS are analyzed comparatively, and the results show that the proposed methods are correct and effective. Furthermore, the modeling of two parameters illustrate that the method in this paper can applied to other state parameters of wind turbines.

Checking the new IRI model: The bottomside B parameters

International Nuclear Information System (INIS)

Mosert, M.; Buresova, D.; Miro, G.; Lazo, B.; Ezquer, R.

2003-01-01

Electron density profiles obtained at Pruhonice (50.0, 15.0), El Arenosillo (37.1, 353.2) and Havana (23, 278) were used to check the bottom-side B parameters BO (thickness parameter) and B1 (shape parameter) predicted by the new IRI - 2000 version. The electron density profiles were derived from ionograms using the ARP technique. The data base includes daytime and nighttime ionograms recorded under different seasonal and solar activity conditions. Comparisons with IRI predictions were also done. The analysis shows that: a) The parameter B1 given by IRI 2000 reproduces better the observed ARP values than the IRI-90 version and b) The observed BO values are in general well reproduced by both IRI versions: IRI-90 and IRI-2000. (author)

Checking the new IRI model The bottomside B parameters

CERN Document Server

Mosert, M; Ezquer, R; Lazo, B; Miro, G

2002-01-01

Electron density profiles obtained at Pruhonice (50.0, 15.0), El Arenosillo (37.1, 353.2) and Havana (23, 278) were used to check the bottom-side B parameters BO (thickness parameter) and B1 (shape parameter) predicted by the new IRI - 2000 version. The electron density profiles were derived from ionograms using the ARP technique. The data base includes daytime and nighttime ionograms recorded under different seasonal and solar activity conditions. Comparisons with IRI predictions were also done. The analysis shows that: a) The parameter B1 given by IRI 2000 reproduces better the observed ARP values than the IRI-90 version and b) The observed BO values are in general well reproduced by both IRI versions: IRI-90 and IRI-2000.

A spatial structural derivative model for ultraslow diffusion

Directory of Open Access Journals (Sweden)

Xu Wei

2017-01-01

Full Text Available This study investigates the ultraslow diffusion by a spatial structural derivative, in which the exponential function ex is selected as the structural function to construct the local structural derivative diffusion equation model. The analytical solution of the diffusion equation is a form of Biexponential distribution. Its corresponding mean squared displacement is numerically calculated, and increases more slowly than the logarithmic function of time. The local structural derivative diffusion equation with the structural function ex in space is an alternative physical and mathematical modeling model to characterize a kind of ultraslow diffusion.

Seven-parameter statistical model for BRDF in the UV band.

Science.gov (United States)

Bai, Lu; Wu, Zhensen; Zou, Xiren; Cao, Yunhua

2012-05-21

A new semi-empirical seven-parameter BRDF model is developed in the UV band using experimentally measured data. The model is based on the five-parameter model of Wu and the fourteen-parameter model of Renhorn and Boreman. Surface scatter, bulk scatter and retro-reflection scatter are considered. An optimizing modeling method, the artificial immune network genetic algorithm, is used to fit the BRDF measurement data over a wide range of incident angles. The calculation time and accuracy of the five- and seven-parameter models are compared. After fixing the seven parameters, the model can well describe scattering data in the UV band.

Groundwater potentiality mapping using geoelectrical-based aquifer hydraulic parameters: A GIS-based multi-criteria decision analysis modeling approach

Directory of Open Access Journals (Sweden)

Kehinde Anthony Mogaji Hwee San Lim

2017-01-01

Full Text Available This study conducted a robust analysis on acquired 2D resistivity imaging data and borehole pumping test records to optimize groundwater potentiality mapping in Perak province, Malaysia using derived aquifer hydraulic properties. The transverse resistance (TR parameter was determined from the interpreted 2D resistivity imaging data by applying the Dar-Zarrouk parameter equation. Linear regression and GIS techniques were used to regress the estimated values for TR parameters with the aquifer transmissivity values extracted from the geospatially produced BPT records-based aquifer transmissivity map to develop the aquifer transmissivity parameter predictive (ATPP model. The reliability evaluated ATPP model using the Theil inequality coefficient measurement approach was used to establish geoelectrical-based hydraulic parameters (GHP modeling equations for the modeling of transmissivity (Tr, hydraulic conductivity (K, storativity (St, and hydraulic diffusivity (D properties. The applied GHP modeling equation results to the delineated aquifer media was used to produce aquifer potential conditioning factor maps for Tr, K, St, and D. The maps were modeled to develop an aquifer potential mapping index (APMI model via applying the multi-criteria decision analysis-analytic hierarchy process principle. The area groundwater reservoir productivity potential model map produced based on the processed APMI model estimates in the GIS environment was found to be 71% accurate. This study establishes a good alternative approach to determine aquifer hydraulic parameters even in areas where pumping test information is unavailable using a cost effective geophysical data. The produced map can be explored for hydrological decision making.

Derivation and application of mathematical model for well test analysis with variable skin factor in hydrocarbon reservoirs

Directory of Open Access Journals (Sweden)

Pengcheng Liu

2016-06-01

Full Text Available Skin factor is often regarded as a constant in most of the mathematical model for well test analysis in oilfields, but this is only a kind of simplified treatment with the actual skin factor changeable. This paper defined the average permeability of a damaged area as a function of time by using the definition of skin factor. Therefore a relationship between a variable skin factor and time was established. The variable skin factor derived was introduced into existing traditional models rather than using a constant skin factor, then, this newly derived mathematical model for well test analysis considering variable skin factor was solved by Laplace transform. The dimensionless wellbore pressure and its derivative changed with dimensionless time were plotted with double logarithm and these plots can be used for type curve fitting. The effects of all the parameters in the expression of variable skin factor were analyzed based on the dimensionless wellbore pressure and its derivative. Finally, actual well testing data were used to fit the type curves developed which validates the applicability of the mathematical model from Sheng-2 Block, Shengli Oilfield, China.

Selected examples of practical approaches for the assessment of model reliability - parameter uncertainty analysis

International Nuclear Information System (INIS)

Hofer, E.; Hoffman, F.O.

1987-02-01

The uncertainty analysis of model predictions has to discriminate between two fundamentally different types of uncertainty. The presence of stochastic variability (Type 1 uncertainty) necessitates the use of a probabilistic model instead of the much simpler deterministic one. Lack of knowledge (Type 2 uncertainty), however, applies to deterministic as well as to probabilistic model predictions and often dominates over uncertainties of Type 1. The term ''probability'' is interpreted differently in the probabilistic analysis of either type of uncertainty. After these discriminations have been explained the discussion centers on the propagation of parameter uncertainties through the model, the derivation of quantitative uncertainty statements for model predictions and the presentation and interpretation of the results of a Type 2 uncertainty analysis. Various alternative approaches are compared for a very simple deterministic model

Lumped-parameter Model of a Bucket Foundation

DEFF Research Database (Denmark)

Andersen, Lars; Ibsen, Lars Bo; Liingaard, Morten

2009-01-01

efficient model that can be applied in aero-elastic codes for fast evaluation of the dynamic structural response of wind turbines. The target solutions, utilised for calibration of the lumped-parameter models, are obtained by a coupled finite-element/boundaryelement scheme in the frequency domain......, and the quality of the models are tested in the time and frequency domains. It is found that precise results are achieved by lumped-parameter models with two to four internal degrees of freedom per displacement or rotation of the foundation. Further, coupling between the horizontal sliding and rocking cannot...

Source term modelling parameters for Project-90

International Nuclear Information System (INIS)

Shaw, W.; Smith, G.; Worgan, K.; Hodgkinson, D.; Andersson, K.

1992-04-01

This document summarises the input parameters for the source term modelling within Project-90. In the first place, the parameters relate to the CALIBRE near-field code which was developed for the Swedish Nuclear Power Inspectorate's (SKI) Project-90 reference repository safety assessment exercise. An attempt has been made to give best estimate values and, where appropriate, a range which is related to variations around base cases. It should be noted that the data sets contain amendments to those considered by KBS-3. In particular, a completely new set of inventory data has been incorporated. The information given here does not constitute a complete set of parameter values for all parts of the CALIBRE code. Rather, it gives the key parameter values which are used in the constituent models within CALIBRE and the associated studies. For example, the inventory data acts as an input to the calculation of the oxidant production rates, which influence the generation of a redox front. The same data is also an initial value data set for the radionuclide migration component of CALIBRE. Similarly, the geometrical parameters of the near-field are common to both sub-models. The principal common parameters are gathered here for ease of reference and avoidance of unnecessary duplication and transcription errors. (au)

Modeling bioaccumulation in humans using poly-parameter linear free energy relationships (PPLFERS)

Energy Technology Data Exchange (ETDEWEB)

Undeman, Emma, E-mail: emma.undeman@itm.su.se; Czub, Gertje; McLachlan, Michael S.

2011-04-01

Chemical partition coefficients between environmental media and biological tissues are a key component of bioaccumulation models. The single-parameter linear free energy relationships (spLFERs) commonly used for predicting partitioning are often derived using apolar chemicals and may not accurately capture polar chemicals. In this study, a poly-parameter LFER (ppLFER) based model of organic chemical bioaccumulation in humans is presented. Chemical partitioning was described by an air-body partition coefficient that was a volume weighted average of ppLFER based partition coefficients for the major organs and tissues constituting the human body. This model was compared to a spLFER model treating the body as a mixture of lipid ({approx} octanol) and water. Although model agreement was good for hydrophobic chemicals (average difference 15% for log K{sub OW} > 4 and log K{sub OA} > 8), the ppLFER model predicted {approx} 90% lower body burdens for hydrophilic chemicals (log K{sub OW} < 0). This was mainly due to lower predictions of muscle and adipose tissue sorption capacity for these chemicals. A comparison of the predicted muscle and adipose tissue sorption capacities of hydrophilic chemicals with measurements indicated that the ppLFER and spLFER models' uncertainties were similar. Consequently, little benefit from the implementation of ppLFERs in this model was identified. - Research Highlights: {yields}Implementation of ppLFERs resulted in on average 90% lower predicted body burdens. {yields}Uncertainties in spLFER and ppLFER predictions were similar. {yields}The benefit from implementation of ppLFERs in bioaccumulation models was limited.

Photometric parameter maps of the Moon derived from LROC WAC images

Science.gov (United States)

Sato, H.; Robinson, M. S.; Hapke, B. W.; Denevi, B. W.; Boyd, A. K.

2013-12-01

Spatially resolved photometric parameter maps were computed from 21 months of Lunar Reconnaissance Orbiter Camera (LROC) Wide Angle Camera (WAC) images. Due to a 60° field-of-view (FOV), the WAC achieves nearly global coverage of the Moon each month with more than 50% overlap from orbit-to-orbit. From the repeat observations at various viewing and illumination geometries, we calculated Hapke bidirectional reflectance model parameters [1] for 1°x1° "tiles" from 70°N to 70°S and 0°E to 360°E. About 66,000 WAC images acquired from February 2010 to October 2011 were converted from DN to radiance factor (I/F) though radiometric calibration, partitioned into gridded tiles, and stacked in a time series (tile-by-tile method [2]). Lighting geometries (phase, incidence, emission) were computed using the WAC digital terrain model (100 m/pixel) [3]. The Hapke parameters were obtained by model fitting against I/F within each tile. Among the 9 parameters of the Hapke model, we calculated 3 free parameters (w, b, and hs) by setting constant values for 4 parameters (Bco=0, hc=1, θ, φ=0) and interpolating 2 parameters (c, Bso). In this simplification, we ignored the Coherent Backscatter Opposition Effect (CBOE) to avoid competing CBOE and Shadow Hiding Opposition Effect (SHOE). We also assumed that surface regolith porosity is uniform across the Moon. The roughness parameter (θ) was set to an averaged value from the equator (× 3°N). The Henyey-Greenstein double lobe function (H-G2) parameter (c) was given by the 'hockey stick' relation [4] (negative correlation) between b and c based on laboratory measurements. The amplitude of SHOE (Bso) was given by the correlation between w and Bso at the equator (× 3°N). Single scattering albedo (w) is strongly correlated to the photometrically normalized I/F, as expected. The c shows an inverse trend relative to b due to the 'hockey stick' relation. The parameter c is typically low for the maria (0.08×0.06) relative to the

Agricultural and Environmental Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

Kaylie Rasmuson; Kurt Rautenstrauch

2003-06-20

This analysis is one of nine technical reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) biosphere model. It documents input parameters for the biosphere model, and supports the use of the model to develop Biosphere Dose Conversion Factors (BDCF). The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for the repository at Yucca Mountain. The ERMYN provides the TSPA with the capability to perform dose assessments. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships between the major activities and their products (the analysis and model reports) that were planned in the biosphere Technical Work Plan (TWP, BSC 2003a). It should be noted that some documents identified in Figure 1-1 may be under development and therefore not available at the time this document is issued. The ''Biosphere Model Report'' (BSC 2003b) describes the ERMYN and its input parameters. This analysis report, ANL-MGR-MD-000006, ''Agricultural and Environmental Input Parameters for the Biosphere Model'', is one of the five reports that develop input parameters for the biosphere model. This report defines and justifies values for twelve parameters required in the biosphere model. These parameters are related to use of contaminated groundwater to grow crops. The parameter values recommended in this report are used in the soil, plant, and carbon-14 submodels of the ERMYN.

Optimizing Performance Parameters of Chemically-Derived Graphene/p-Si Heterojunction Solar Cell.

Science.gov (United States)

Batra, Kamal; Nayak, Sasmita; Behura, Sanjay K; Jani, Omkar

2015-07-01

Chemically-derived graphene have been synthesized by modified Hummers method and reduced using sodium borohydride. To explore the potential for photovoltaic applications, graphene/p-silicon (Si) heterojunction devices were fabricated using a simple and cost effective technique called spin coating. The SEM analysis shows the formation of graphene oxide (GO) flakes which become smooth after reduction. The absence of oxygen containing functional groups, as observed in FT-IR spectra, reveals the reduction of GO, i.e., reduced graphene oxide (rGO). It was further confirmed by Raman analysis, which shows slight reduction in G-band intensity with respect to D-band. Hall effect measurement confirmed n-type nature of rGO. Therefore, an effort has been made to simu- late rGO/p-Si heterojunction device by using the one-dimensional solar cell capacitance software, considering the experimentally derived parameters. The detail analysis of the effects of Si thickness, graphene thickness and temperature on the performance of the device has been presented.

Quantitative predictions from competition theory with incomplete information on model parameters tested against experiments across diverse taxa

OpenAIRE

Fort, Hugo

2017-01-01

We derive an analytical approximation for making quantitative predictions for ecological communities as a function of the mean intensity of the inter-specific competition and the species richness. This method, with only a fraction of the model parameters (carrying capacities and competition coefficients), is able to predict accurately empirical measurements covering a wide variety of taxa (algae, plants, protozoa).

Pharmacological Classification and Activity Evaluation of Furan and Thiophene Amide Derivatives Applying Semi-Empirical ab initio Molecular Modeling Methods

Directory of Open Access Journals (Sweden)

Leszek Bober

2012-05-01

Full Text Available Pharmacological and physicochemical classification of the furan and thiophene amide derivatives by multiple regression analysis and partial least square (PLS based on semi-empirical ab initio molecular modeling studies and high-performance liquid chromatography (HPLC retention data is proposed. Structural parameters obtained from the PCM (Polarizable Continuum Model method and the literature values of biological activity (antiproliferative for the A431 cells expressed as LD₅₀ of the examined furan and thiophene derivatives was used to search for relationships. It was tested how variable molecular modeling conditions considered together, with or without HPLC retention data, allow evaluation of the structural recognition of furan and thiophene derivatives with respect to their pharmacological properties.

Multivariate analysis and extraction of parameters in resistive RAMs using the Quantum Point Contact model

Science.gov (United States)

Roldán, J. B.; Miranda, E.; González-Cordero, G.; García-Fernández, P.; Romero-Zaliz, R.; González-Rodelas, P.; Aguilera, A. M.; González, M. B.; Jiménez-Molinos, F.

2018-01-01

A multivariate analysis of the parameters that characterize the reset process in Resistive Random Access Memory (RRAM) has been performed. The different correlations obtained can help to shed light on the current components that contribute in the Low Resistance State (LRS) of the technology considered. In addition, a screening method for the Quantum Point Contact (QPC) current component is presented. For this purpose, the second derivative of the current has been obtained using a novel numerical method which allows determining the QPC model parameters. Once the procedure is completed, a whole Resistive Switching (RS) series of thousands of curves is studied by means of a genetic algorithm. The extracted QPC parameter distributions are characterized in depth to get information about the filamentary pathways associated with LRS in the low voltage conduction regime.

Band parameters of phosphorene

DEFF Research Database (Denmark)

Lew Yan Voon, L. C.; Wang, J.; Zhang, Y.

2015-01-01

Phosphorene is a two-dimensional nanomaterial with a direct band-gap at the Brillouin zone center. In this paper, we present a recently derived effective-mass theory of the band structure in the presence of strain and electric field, based upon group theory. Band parameters for this theory...... are computed using a first-principles theory based upon the generalized-gradient approximation to the density-functional theory. These parameters and Hamiltonian will be useful for modeling physical properties of phosphorene....

Bayesian estimation of parameters in a regional hydrological model

Directory of Open Access Journals (Sweden)

K. Engeland

2002-01-01

Full Text Available This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1 process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis

Toward a non-invasive screening tool for differentiation of pancreatic lesions based on intra-voxel incoherent motion derived parameters

Energy Technology Data Exchange (ETDEWEB)

Graf, Markus; Simon, Dirk; Mang, Sarah [Deutsches Krebsforschungszentrum (DKFZ), Heidelberg (Germany). Software Development for Integrated Therapy and Diagnostics; Lemke, Andreas [Heidelberg Univ., Mannheim (Germany). Dept. of Computer Assisted Clinical Medicine; Gruenberg, Katharina [Deutsches Krebsforschungszentrum (DKFZ), Heidelberg (Germany). Dept. of Radiology

2013-03-01

Early recognition of and differential diagnosis between pancreatic cancer and chronic pancreatitis is an important step in successful therapy. Parameters of the IVIM (intra-voxel incoherent motion) theory can be used to differentiate between those lesions. The objective of this work is to evaluate the effects of rigid image registration on IVIM derived parameters for differentiation of pancreatic lesions such as pancreatic cancer and solid mass forming pancreatitis. The effects of linear image registration methods on reproducibility and accuracy of IVIM derived parameters were quantified on MR images of ten volunteers. For this purpose, they were evaluated statistically by comparison of registered and unregistered parameter data. Further, the perfusion fraction f was used to differentiate pancreatic lesions on eleven previously diagnosed patient data sets. Its diagnostic power with and without rigid registration was evaluated using receiver operating curves (ROC) analysis. The pancreas was segmented manually on MR data sets of healthy volunteers as well as the patients showing solid pancreatic lesions. Diffusion weighted imaging was performed in 10 blocks of breath-hold phases. Linear registration of the weighted image stack leads to a 3.7% decrease in variability of the IVIM derived parameter f due to an improved anatomical overlap of 5%. Consequently, after registration the area under the curve in the ROC-analysis for the differentiation approach increased by 2.7%. In conclusion, rigid registration improves the differentiation process based on f-values. (orig.)

Toward a non-invasive screening tool for differentiation of pancreatic lesions based on intra-voxel incoherent motion derived parameters

International Nuclear Information System (INIS)

Graf, Markus; Simon, Dirk; Mang, Sarah; Lemke, Andreas; Gruenberg, Katharina

2013-01-01

Early recognition of and differential diagnosis between pancreatic cancer and chronic pancreatitis is an important step in successful therapy. Parameters of the IVIM (intra-voxel incoherent motion) theory can be used to differentiate between those lesions. The objective of this work is to evaluate the effects of rigid image registration on IVIM derived parameters for differentiation of pancreatic lesions such as pancreatic cancer and solid mass forming pancreatitis. The effects of linear image registration methods on reproducibility and accuracy of IVIM derived parameters were quantified on MR images of ten volunteers. For this purpose, they were evaluated statistically by comparison of registered and unregistered parameter data. Further, the perfusion fraction f was used to differentiate pancreatic lesions on eleven previously diagnosed patient data sets. Its diagnostic power with and without rigid registration was evaluated using receiver operating curves (ROC) analysis. The pancreas was segmented manually on MR data sets of healthy volunteers as well as the patients showing solid pancreatic lesions. Diffusion weighted imaging was performed in 10 blocks of breath-hold phases. Linear registration of the weighted image stack leads to a 3.7% decrease in variability of the IVIM derived parameter f due to an improved anatomical overlap of 5%. Consequently, after registration the area under the curve in the ROC-analysis for the differentiation approach increased by 2.7%. In conclusion, rigid registration improves the differentiation process based on f-values. (orig.)

Models and parameters for environmental radiological assessments

International Nuclear Information System (INIS)

Miller, C.W.

1983-01-01

This article reviews the forthcoming book Models and Parameters for Environmental Radiological Assessments, which presents a unified compilation of models and parameters for assessing the impact on man of radioactive discharges, both routine and accidental, into the environment. Models presented in this book include those developed for the prediction of atmospheric and hydrologic transport and deposition, for terrestrial and aquatic food-chain bioaccumulation, and for internal and external dosimetry. Summaries are presented for each of the transport and dosimetry areas previously for each of the transport and dosimetry areas previously mentioned, and details are available in the literature cited. A chapter of example problems illustrates many of the methodologies presented throughout the text. Models and parameters presented are based on the results of extensive literature reviews and evaluations performed primarily by the staff of the Health and Safety Research Division of Oak Ridge National Laboratory

Structure activity relationships of quinoxalin-2-one derivatives as platelet-derived growth factor-beta receptor (PDGFbeta R) inhibitors, derived from molecular modeling.

Science.gov (United States)

Mori, Yoshikazu; Hirokawa, Takatsugu; Aoki, Katsuyuki; Satomi, Hisanori; Takeda, Shuichi; Aburada, Masaki; Miyamoto, Ken-ichi

2008-05-01

We previously reported a quinoxalin-2-one compound (Compound 1) that had inhibitory activity equivalent to existing platelet-derived growth factor-beta receptor (PDGFbeta R) inhibitors. Lead optimization of Compound 1 to increase its activity and selectivity, using structural information regarding PDGFbeta R-ligand interactions, is urgently needed. Here we present models of the PDGFbeta R kinase domain complexed with quinoxalin-2-one derivatives. The models were constructed using comparative modeling, molecular dynamics (MD) and ligand docking. In particular, conformations derived from MD, and ligand binding site information presented by alpha-spheres in the pre-docking processing, allowed us to identify optimal protein structures for docking of target ligands. By carrying out molecular modeling and MD of PDGFbeta R in its inactive state, we obtained two structural models having good Compound 1 binding potentials. In order to distinguish the optimal candidate, we evaluated the structural activity relationships (SAR) between the ligand-binding free energies and inhibitory activity values (IC50 values) for available quinoxalin-2-one derivatives. Consequently, a final model with a high SAR was identified. This model included a molecular interaction between the hydrophobic pocket behind the ATP binding site and the substitution region of the quinoxalin-2-one derivatives. These findings should prove useful in lead optimization of quinoxalin-2-one derivatives as PDGFb R inhibitors.

Consequences of dark matter-dark energy interaction on cosmological parameters derived from type Ia supernova data

International Nuclear Information System (INIS)

Amendola, Luca; Campos, Gabriela Camargo; Rosenfeld, Rogerio

2007-01-01

Models where the dark matter component of the Universe interacts with the dark energy field have been proposed as a solution to the cosmic coincidence problem, since in the attractor regime both dark energy and dark matter scale in the same way. In these models the mass of the cold dark matter particles is a function of the dark energy field responsible for the present acceleration of the Universe, and different scenarios can be parametrized by how the mass of the cold dark matter particles evolves with time. In this article we study the impact of a constant coupling δ between dark energy and dark matter on the determination of a redshift dependent dark energy equation of state w DE (z) and on the dark matter density today from SNIa data. We derive an analytical expression for the luminosity distance in this case. In particular, we show that the presence of such a coupling increases the tension between the cosmic microwave background data from the analysis of the shift parameter in models with constant w DE and SNIa data for realistic values of the present dark matter density fraction. Thus, an independent measurement of the present dark matter density can place constraints on models with interacting dark energy

Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter

Science.gov (United States)

Simon, Ehouarn; Samuelsen, Annette; Bertino, Laurent; Mouysset, Sandrine

2015-12-01

A sequence of one-year combined state-parameter estimation experiments has been conducted in a North Atlantic and Arctic Ocean configuration of the coupled physical-biogeochemical model HYCOM-NORWECOM over the period 2007-2010. The aim is to evaluate the ability of an ensemble-based data assimilation method to calibrate ecosystem model parameters in a pre-operational setting, namely the production of the MyOcean pilot reanalysis of the Arctic biology. For that purpose, four biological parameters (two phyto- and two zooplankton mortality rates) are estimated by assimilating weekly data such as, satellite-derived Sea Surface Temperature, along-track Sea Level Anomalies, ice concentrations and chlorophyll-a concentrations with an Ensemble Kalman Filter. The set of optimized parameters locally exhibits seasonal variations suggesting that time-dependent parameters should be used in ocean ecosystem models. A clustering analysis of the optimized parameters is performed in order to identify consistent ecosystem regions. In the north part of the domain, where the ecosystem model is the most reliable, most of them can be associated with Longhurst provinces and new provinces emerge in the Arctic Ocean. However, the clusters do not coincide anymore with the Longhurst provinces in the Tropics due to large model errors. Regarding the ecosystem state variables, the assimilation of satellite-derived chlorophyll concentration leads to significant reduction of the RMS errors in the observed variables during the first year, i.e. 2008, compared to a free run simulation. However, local filter divergences of the parameter component occur in 2009 and result in an increase in the RMS error at the time of the spring bloom.

A variable-order fractal derivative model for anomalous diffusion

Directory of Open Access Journals (Sweden)

Liu Xiaoting

2017-01-01

Full Text Available This paper pays attention to develop a variable-order fractal derivative model for anomalous diffusion. Previous investigations have indicated that the medium structure, fractal dimension or porosity may change with time or space during solute transport processes, results in time or spatial dependent anomalous diffusion phenomena. Hereby, this study makes an attempt to introduce a variable-order fractal derivative diffusion model, in which the index of fractal derivative depends on temporal moment or spatial position, to characterize the above mentioned anomalous diffusion (or transport processes. Compared with other models, the main advantages in description and the physical explanation of new model are explored by numerical simulation. Further discussions on the dissimilitude such as computational efficiency, diffusion behavior and heavy tail phenomena of the new model and variable-order fractional derivative model are also offered.

Study of electroweak parameters at LEP

International Nuclear Information System (INIS)

Blum, W.

1991-10-01

The measurement of the line shape and asymmetry parameters of the Z 0 in its leptonic and hadronic decays are reviewed. Progress is reported about a considerable increase in measurement accuracy. Several tests of the Standard Model confirm it to better than one per cent. New values for the effective mixing parameter are derived from the line shape parameters averaged over the four LEP experiments. The corresponding limits on the top mass are presented. (orig.)

Aerodynamic Parameters of a UK City Derived from Morphological Data

Science.gov (United States)

Millward-Hopkins, J. T.; Tomlin, A. S.; Ma, L.; Ingham, D. B.; Pourkashanian, M.

2013-03-01

Detailed three-dimensional building data and a morphometric model are used to estimate the aerodynamic roughness length z 0 and displacement height d over a major UK city (Leeds). Firstly, using an adaptive grid, the city is divided into neighbourhood regions that are each of a relatively consistent geometry throughout. Secondly, for each neighbourhood, a number of geometric parameters are calculated. Finally, these are used as input into a morphometric model that considers the influence of height variability to predict aerodynamic roughness length and displacement height. Predictions are compared with estimations made using standard tables of aerodynamic parameters. The comparison suggests that the accuracy of plan-area-density based tables is likely to be limited, and that height-based tables of aerodynamic parameters may be more accurate for UK cities. The displacement heights in the standard tables are shown to be lower than the current predictions. The importance of geometric details in determining z 0 and d is then explored. Height variability is observed to greatly increase the predicted values. However, building footprint shape only has a significant influence upon the predictions when height variability is not considered. Finally, we develop simple relations to quantify the influence of height variation upon predicted z 0 and d via the standard deviation of building heights. The difference in these predictions compared to the more complex approach highlights the importance of considering the specific shape of the building-height distributions. Collectively, these results suggest that to accurately predict aerodynamic parameters of real urban areas, height variability must be considered in detail, but it may be acceptable to make simple assumptions about building layout and footprint shape.

Correlation studies for B-spline modeled F2 Chapman parameters obtained from FORMOSAT-3/COSMIC data

Directory of Open Access Journals (Sweden)

M. Limberger

2014-12-01

Full Text Available The determination of ionospheric key quantities such as the maximum electron density of the F2 layer NmF2, the corresponding F2 peak height hmF2 and the F2 scale height HF2 are of high relevance in 4-D ionosphere modeling to provide information on the vertical structure of the electron density (Ne. The Ne distribution with respect to height can, for instance, be modeled by the commonly accepted F2 Chapman layer. An adequate and observation driven description of the vertical Ne variation can be obtained from electron density profiles (EDPs derived by ionospheric radio occultation measurements between GPS and low Earth orbiter (LEO satellites. For these purposes, the six FORMOSAT-3/COSMIC (F3/C satellites provide an excellent opportunity to collect EDPs that cover most of the ionospheric region, in particular the F2 layer. For the contents of this paper, F3/C EDPs have been exploited to determine NmF2, hmF2 and HF2 within a regional modeling approach. As mathematical base functions, endpoint-interpolating polynomial B-splines are considered to model the key parameters with respect to longitude, latitude and time. The description of deterministic processes and the verification of this modeling approach have been published previously in Limberger et al. (2013, whereas this paper should be considered as an extension dealing with related correlation studies, a topic to which less attention has been paid in the literature. Relations between the B-spline series coefficients regarding specific key parameters as well as dependencies between the three F2 Chapman key parameters are in the main focus. Dependencies are interpreted from the post-derived correlation matrices as a result of (1 a simulated scenario without data gaps by taking dense, homogenously distributed profiles into account and (2 two real data scenarios on 1 July 2008 and 1 July 2012 including sparsely, inhomogeneously distributed F3/C EDPs. Moderate correlations between hmF2 and HF2 as

Learning about physical parameters: the importance of model discrepancy

International Nuclear Information System (INIS)

Brynjarsdóttir, Jenný; O'Hagan, Anthony

2014-01-01

Science-based simulation models are widely used to predict the behavior of complex physical systems. It is also common to use observations of the physical system to solve the inverse problem, that is, to learn about the values of parameters within the model, a process which is often called calibration. The main goal of calibration is usually to improve the predictive performance of the simulator but the values of the parameters in the model may also be of intrinsic scientific interest in their own right. In order to make appropriate use of observations of the physical system it is important to recognize model discrepancy, the difference between reality and the simulator output. We illustrate through a simple example that an analysis that does not account for model discrepancy may lead to biased and over-confident parameter estimates and predictions. The challenge with incorporating model discrepancy in statistical inverse problems is being confounded with calibration parameters, which will only be resolved with meaningful priors. For our simple example, we model the model-discrepancy via a Gaussian process and demonstrate that through accounting for model discrepancy our prediction within the range of data is correct. However, only with realistic priors on the model discrepancy do we uncover the true parameter values. Through theoretical arguments we show that these findings are typical of the general problem of learning about physical parameters and the underlying physical system using science-based mechanistic models. (paper)

Wind Farm Decentralized Dynamic Modeling With Parameters

DEFF Research Database (Denmark)

Soltani, Mohsen; Shakeri, Sayyed Mojtaba; Grunnet, Jacob Deleuran

2010-01-01

Development of dynamic wind flow models for wind farms is part of the research in European research FP7 project AEOLUS. The objective of this report is to provide decentralized dynamic wind flow models with parameters. The report presents a structure for decentralized flow models with inputs from...... local models. The results of this report are especially useful, but not limited, to design a decentralized wind farm controller, since in centralized controller design one can also use the model and update it in a central computing node.......Development of dynamic wind flow models for wind farms is part of the research in European research FP7 project AEOLUS. The objective of this report is to provide decentralized dynamic wind flow models with parameters. The report presents a structure for decentralized flow models with inputs from...

Optimizing incomplete sample designs for item response model parameters

NARCIS (Netherlands)

van der Linden, Willem J.

Several models for optimizing incomplete sample designs with respect to information on the item parameters are presented. The following cases are considered: (1) known ability parameters; (2) unknown ability parameters; (3) item sets with multiple ability scales; and (4) response models with

ESTIMATION OF CONSTANT AND TIME-VARYING DYNAMIC PARAMETERS OF HIV INFECTION IN A NONLINEAR DIFFERENTIAL EQUATION MODEL.

Science.gov (United States)

Liang, Hua; Miao, Hongyu; Wu, Hulin

2010-03-01

Modeling viral dynamics in HIV/AIDS studies has resulted in deep understanding of pathogenesis of HIV infection from which novel antiviral treatment guidance and strategies have been derived. Viral dynamics models based on nonlinear differential equations have been proposed and well developed over the past few decades. However, it is quite challenging to use experimental or clinical data to estimate the unknown parameters (both constant and time-varying parameters) in complex nonlinear differential equation models. Therefore, investigators usually fix some parameter values, from the literature or by experience, to obtain only parameter estimates of interest from clinical or experimental data. However, when such prior information is not available, it is desirable to determine all the parameter estimates from data. In this paper, we intend to combine the newly developed approaches, a multi-stage smoothing-based (MSSB) method and the spline-enhanced nonlinear least squares (SNLS) approach, to estimate all HIV viral dynamic parameters in a nonlinear differential equation model. In particular, to the best of our knowledge, this is the first attempt to propose a comparatively thorough procedure, accounting for both efficiency and accuracy, to rigorously estimate all key kinetic parameters in a nonlinear differential equation model of HIV dynamics from clinical data. These parameters include the proliferation rate and death rate of uninfected HIV-targeted cells, the average number of virions produced by an infected cell, and the infection rate which is related to the antiviral treatment effect and is time-varying. To validate the estimation methods, we verified the identifiability of the HIV viral dynamic model and performed simulation studies. We applied the proposed techniques to estimate the key HIV viral dynamic parameters for two individual AIDS patients treated with antiretroviral therapies. We demonstrate that HIV viral dynamics can be well characterized and

A 'simple' hybrid model for power derivatives

International Nuclear Information System (INIS)

Lyle, Matthew R.; Elliott, Robert J.

2009-01-01

This paper presents a method for valuing power derivatives using a supply-demand approach. Our method extends work in the field by incorporating randomness into the base load portion of the supply stack function and equating it with a noisy demand process. We obtain closed form solutions for European option prices written on average spot prices considering two different supply models: a mean-reverting model and a Markov chain model. The results are extensions of the classic Black-Scholes equation. The model provides a relatively simple approach to describe the complicated price behaviour observed in electricity spot markets and also allows for computationally efficient derivatives pricing. (author)

Deriving movement properties and the effect of the environment from the Brownian bridge movement model in monkeys and birds.

Science.gov (United States)

Buchin, Kevin; Sijben, Stef; van Loon, E Emiel; Sapir, Nir; Mercier, Stéphanie; Marie Arseneau, T Jean; Willems, Erik P

2015-01-01

The Brownian bridge movement model (BBMM) provides a biologically sound approximation of the movement path of an animal based on discrete location data, and is a powerful method to quantify utilization distributions. Computing the utilization distribution based on the BBMM while calculating movement parameters directly from the location data, may result in inconsistent and misleading results. We show how the BBMM can be extended to also calculate derived movement parameters. Furthermore we demonstrate how to integrate environmental context into a BBMM-based analysis. We develop a computational framework to analyze animal movement based on the BBMM. In particular, we demonstrate how a derived movement parameter (relative speed) and its spatial distribution can be calculated in the BBMM. We show how to integrate our framework with the conceptual framework of the movement ecology paradigm in two related but acutely different ways, focusing on the influence that the environment has on animal movement. First, we demonstrate an a posteriori approach, in which the spatial distribution of average relative movement speed as obtained from a "contextually naïve" model is related to the local vegetation structure within the monthly ranging area of a group of wild vervet monkeys. Without a model like the BBMM it would not be possible to estimate such a spatial distribution of a parameter in a sound way. Second, we introduce an a priori approach in which atmospheric information is used to calculate a crucial parameter of the BBMM to investigate flight properties of migrating bee-eaters. This analysis shows significant differences in the characteristics of flight modes, which would have not been detected without using the BBMM. Our algorithm is the first of its kind to allow BBMM-based computation of movement parameters beyond the utilization distribution, and we present two case studies that demonstrate two fundamentally different ways in which our algorithm can be applied to

Environmental Transport Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

M. Wasiolek

2004-01-01

This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the total system performance assessment for the license application (TSPA-LA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA-LA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]) (TWP). This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA). This report is one of the five reports that develop input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the conceptual model and the mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed description of the model input parameters. The output of this report is used as direct input in the ''Nominal Performance Biosphere Dose Conversion Factor Analysis'' and in the ''Disruptive Event Biosphere Dose Conversion Factor Analysis'' that calculate the values of biosphere dose conversion factors (BDCFs) for the groundwater and volcanic ash exposure scenarios, respectively. The purpose of this analysis was to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or in volcanic ash). The analysis was performed in accordance with the TWP (BSC 2004 [DIRS 169573])

Environmental Transport Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

M. Wasiolek

2004-09-10

This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the total system performance assessment for the license application (TSPA-LA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA-LA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]) (TWP). This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA). This report is one of the five reports that develop input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the conceptual model and the mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed description of the model input parameters. The output of this report is used as direct input in the ''Nominal Performance Biosphere Dose Conversion Factor Analysis'' and in the ''Disruptive Event Biosphere Dose Conversion Factor Analysis'' that calculate the values of biosphere dose conversion factors (BDCFs) for the groundwater and volcanic ash exposure scenarios, respectively. The purpose of this analysis was to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or in volcanic ash). The analysis

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

Science.gov (United States)

Wu, Yiping; Liu, Shuguang; Huang, Zhihong; Yan, Wende

2014-01-01

Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process-based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM-Auto, which incorporated a comprehensive R package—Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production-related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter-uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting-edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.

Estimating crop net primary production using inventory data and MODIS-derived parameters

Energy Technology Data Exchange (ETDEWEB)

Bandaru, Varaprasad; West, Tristram O.; Ricciuto, Daniel M.; Izaurralde, Roberto C.

2013-06-03

National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale and over national and continental extents. Existing satellite-based NPP products tend to underestimate NPP on croplands. A new Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP. The method is documented here and evaluated for corn and soybean crops in Iowa and Illinois in years 2006 and 2007. The method includes a crop-specific enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), shortwave radiation data estimated using Mountain Climate Simulator (MTCLIM) algorithm and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that correspond to the Cropland Data Layer (CDL) land cover product. The modeling framework represented well the gradient of NPP across Iowa and Illinois, and also well represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 980 g C m-2 yr-1 and 420 g C m-2 yr-1, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Estimated gross primary productivity (GPP) derived from AgI-LUE were in close agreement with eddy flux tower estimates. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.

Application of multi-parameter chorus and plasmaspheric hiss wave models in radiation belt modeling

Science.gov (United States)

Aryan, H.; Kang, S. B.; Balikhin, M. A.; Fok, M. C. H.; Agapitov, O. V.; Komar, C. M.; Kanekal, S. G.; Nagai, T.; Sibeck, D. G.

2017-12-01

Numerical simulation studies of the Earth's radiation belts are important to understand the acceleration and loss of energetic electrons. The Comprehensive Inner Magnetosphere-Ionosphere (CIMI) model along with many other radiation belt models require inputs for pitch angle, energy, and cross diffusion of electrons, due to chorus and plasmaspheric hiss waves. These parameters are calculated using statistical wave distribution models of chorus and plasmaspheric hiss amplitudes. In this study we incorporate recently developed multi-parameter chorus and plasmaspheric hiss wave models based on geomagnetic index and solar wind parameters. We perform CIMI simulations for two geomagnetic storms and compare the flux enhancement of MeV electrons with data from the Van Allen Probes and Akebono satellites. We show that the relativistic electron fluxes calculated with multi-parameter wave models resembles the observations more accurately than the relativistic electron fluxes calculated with single-parameter wave models. This indicates that wave models based on a combination of geomagnetic index and solar wind parameters are more effective as inputs to radiation belt models.

Modelling hydrodynamic parameters to predict flow assisted corrosion

International Nuclear Information System (INIS)

Poulson, B.; Greenwell, B.; Chexal, B.; Horowitz, J.

1992-01-01

During the past 15 years, flow assisted corrosion has been a worldwide problem in the power generating industry. The phenomena is complex and depends on environment, material composition, and hydrodynamic factors. Recently, modeling of flow assisted corrosion has become a subject of great importance. A key part of this effort is modeling the hydrodynamic aspects of this issue. This paper examines which hydrodynamic parameter should be used to correlate the occurrence and rate of flow assisted corrosion with physically meaningful parameters, discusses ways of measuring the relevant hydrodynamic parameter, and describes how the hydrodynamic data is incorporated into the predictive model

Input Uncertainty and its Implications on Parameter Assessment in Hydrologic and Hydroclimatic Modelling Studies

Science.gov (United States)

Chowdhury, S.; Sharma, A.

2005-12-01

Hydrological model inputs are often derived from measurements at point locations taken at discrete time steps. The nature of uncertainty associated with such inputs is thus a function of the quality and number of measurements available in time. A change in these characteristics (such as a change in the number of rain-gauge inputs used to derive spatially averaged rainfall) results in inhomogeneity in the associated distributional profile. Ignoring such uncertainty can lead to models that aim to simulate based on the observed input variable instead of the true measurement, resulting in a biased representation of the underlying system dynamics as well as an increase in both bias and the predictive uncertainty in simulations. This is especially true of cases where the nature of uncertainty likely in the future is significantly different to that in the past. Possible examples include situations where the accuracy of the catchment averaged rainfall has increased substantially due to an increase in the rain-gauge density, or accuracy of climatic observations (such as sea surface temperatures) increased due to the use of more accurate remote sensing technologies. We introduce here a method to ascertain the true value of parameters in the presence of additive uncertainty in model inputs. This method, known as SIMulation EXtrapolation (SIMEX, [Cook, 1994]) operates on the basis of an empirical relationship between parameters and the level of additive input noise (or uncertainty). The method starts with generating a series of alternate realisations of model inputs by artificially adding white noise in increasing multiples of the known error variance. The alternate realisations lead to alternate sets of parameters that are increasingly biased with respect to the truth due to the increased variability in the inputs. Once several such realisations have been drawn, one is able to formulate an empirical relationship between the parameter values and the level of additive noise

Band parameters of phosphorene

International Nuclear Information System (INIS)

Lew Yan Voon, L C; Wang, J; Zhang, Y; Willatzen, M

2015-01-01

Phosphorene is a two-dimensional nanomaterial with a direct band-gap at the Brillouin zone center. In this paper, we present a recently derived effective-mass theory of the band structure in the presence of strain and electric field, based upon group theory. Band parameters for this theory are computed using a first-principles theory based upon the generalized-gradient approximation to the density-functional theory. These parameters and Hamiltonian will be useful for modeling physical properties of phosphorene. (paper)

Deriving and Constraining 3D CME Kinematic Parameters from Multi-Viewpoint Coronagraph Images

Science.gov (United States)

Thompson, B. J.; Mei, H. F.; Barnes, D.; Colaninno, R. C.; Kwon, R.; Mays, M. L.; Mierla, M.; Moestl, C.; Richardson, I. G.; Verbeke, C.

2017-12-01

Determining the 3D properties of a coronal mass ejection using multi-viewpoint coronagraph observations can be a tremendously complicated process. There are many factors that inhibit the ability to unambiguously identify the speed, direction and shape of a CME. These factors include the need to separate the "true" CME mass from shock-associated brightenings, distinguish between non-radial or deflected trajectories, and identify asymmetric CME structures. Additionally, different measurement methods can produce different results, sometimes with great variations. Part of the reason for the wide range of values that can be reported for a single CME is due to the difficulty in determining the CME's longitude since uncertainty in the angle of the CME relative to the observing image planes results in errors in the speed and topology of the CME. Often the errors quoted in an individual study are remarkably small when compared to the range of values that are reported by different authors for the same CME. For example, two authors may report speeds of 700 +- 50 km/sec and 500+-50 km/sec for the same CME. Clearly a better understanding of the accuracy of CME measurements, and an improved assessment of the limitations of the different methods, would be of benefit. We report on a survey of CME measurements, wherein we compare the values reported by different authors and catalogs. The survey will allow us to establish typical errors for the parameters that are commonly used as inputs for CME propagation models such as ENLIL and EUHFORIA. One way modelers handle inaccuracies in CME parameters is to use an ensemble of CMEs, sampled across ranges of latitude, longitude, speed and width. The CMEs simulated in order to determine the probability of a "direct hit" and, for the cases with a "hit," derive a range of possible arrival times. Our study will provide improved guidelines for generating CME ensembles that more accurately sample across the range of plausible values.

Achieving scale-independent land-surface flux estimates - Application of the Multiscale Parameter Regionalization (MPR) to the Noah-MP land-surface model across the contiguous USA

Science.gov (United States)

Thober, S.; Mizukami, N.; Samaniego, L. E.; Attinger, S.; Clark, M. P.; Cuntz, M.

2016-12-01

Land-surface models use a variety of process representations to calculate terrestrial energy, water and biogeochemical fluxes. These process descriptions are usually derived from point measurements but are scaled to much larger resolutions in applications that range from about 1 km in catchment hydrology to 100 km in climate modelling. Both, hydrologic and climate models are nowadays run on different spatial resolutions, using the exact same land surface representations. A fundamental criterion for the physical consistency of land-surface simulations across scales is that a flux estimated over a given area is independent of the spatial model resolution (i.e., the flux-matching criterion). The Noah-MP land surface model considers only one soil and land cover type per model grid cell without any representation of subgrid variability, implying a weak flux-matching. A fractional approach simulates subgrid variability but it requires a higher computational demand than using effective parameters and it is used only for land cover in current land surface schemes. A promising approach to derive scale-independent parameters is the Multiscale Parameter Regionalization (MPR) technique, which consists of two steps: first, it applies transfer functions directly to high-resolution data (such as 100 m soil maps) to derive high-resolution model parameter fields, acknowledging the full subgrid variability. Second, it upscales these high-resolution parameter fields to the model resolution by using appropriate upscaling operators. MPR has shown to improve substantially the scalability of hydrologic models. Here, we apply the MPR technique to the Noah-MP land-surface model for a large sample of basins distributed across the contiguous USA. Specifically, we evaluate the flux-matching criterion for several hydrologic fluxes such as evapotranspiration and total runoff at scales ranging from 3 km to 48 km. We also investigate a p-norm scaling operator that goes beyond the current

WINKLER'S SINGLE-PARAMETER SUBGRADE MODEL FROM ...

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Page 1 ... corresponding single-parameter Winkler model presented in this work. Keywords: Heterogeneous subgrade, Reissner's simplified continuum, Shear interaction, Simplified continuum, Winkler ... model in practical applications and its long time familiarity among practical engineers, its usage has endured to this date ...

Parameter estimation in X-ray astronomy

International Nuclear Information System (INIS)

Lampton, M.; Margon, B.; Bowyer, S.

1976-01-01

The problems of model classification and parameter estimation are examined, with the objective of establishing the statistical reliability of inferences drawn from X-ray observations. For testing the validities of classes of models, the procedure based on minimizing the chi 2 statistic is recommended; it provides a rejection criterion at any desired significance level. Once a class of models has been accepted, a related procedure based on the increase of chi 2 gives a confidence region for the values of the model's adjustable parameters. The procedure allows the confidence level to be chosen exactly, even for highly nonlinear models. Numerical experiments confirm the validity of the prescribed technique.The chi 2 /sub min/+1 error estimation method is evaluated and found unsuitable when several parameter ranges are to be derived, because it substantially underestimates their joint errors. The ratio of variances method, while formally correct, gives parameter confidence regions which are more variable than necessary

Clinical validation of the LKB model and parameter sets for predicting radiation-induced pneumonitis from breast cancer radiotherapy

International Nuclear Information System (INIS)

Tsougos, Ioannis; Mavroidis, Panayiotis; Theodorou, Kyriaki; Rajala, J; Pitkaenen, M A; Holli, K; Ojala, A T; Hyoedynmaa, S; Jaervenpaeae, Ritva; Lind, Bengt K; Kappas, Constantin

2006-01-01

The choice of the appropriate model and parameter set in determining the relation between the incidence of radiation pneumonitis and dose distribution in the lung is of great importance, especially in the case of breast radiotherapy where the observed incidence is fairly low. From our previous study based on 150 breast cancer patients, where the fits of dose-volume models to clinical data were estimated (Tsougos et al 2005 Evaluation of dose-response models and parameters predicting radiation induced pneumonitis using clinical data from breast cancer radiotherapy Phys. Med. Biol. 50 3535-54), one could get the impression that the relative seriality is significantly better than the LKB NTCP model. However, the estimation of the different NTCP models was based on their goodness-of-fit on clinical data, using various sets of published parameters from other groups, and this fact may provisionally justify the results. Hence, we sought to investigate further the LKB model, by applying different published parameter sets for the very same group of patients, in order to be able to compare the results. It was shown that, depending on the parameter set applied, the LKB model is able to predict the incidence of radiation pneumonitis with acceptable accuracy, especially when implemented on a sub-group of patients (120) receiving D-bar-bar vertical bar EUD higher than 8 Gy. In conclusion, the goodness-of-fit of a certain radiobiological model on a given clinical case is closely related to the selection of the proper scoring criteria and parameter set as well as to the compatibility of the clinical case from which the data were derived. (letter to the editor)

Insight into model mechanisms through automatic parameter fitting: a new methodological framework for model development.

Science.gov (United States)

Tøndel, Kristin; Niederer, Steven A; Land, Sander; Smith, Nicolas P

2014-05-20

Striking a balance between the degree of model complexity and parameter identifiability, while still producing biologically feasible simulations using modelling is a major challenge in computational biology. While these two elements of model development are closely coupled, parameter fitting from measured data and analysis of model mechanisms have traditionally been performed separately and sequentially. This process produces potential mismatches between model and data complexities that can compromise the ability of computational frameworks to reveal mechanistic insights or predict new behaviour. In this study we address this issue by presenting a generic framework for combined model parameterisation, comparison of model alternatives and analysis of model mechanisms. The presented methodology is based on a combination of multivariate metamodelling (statistical approximation of the input-output relationships of deterministic models) and a systematic zooming into biologically feasible regions of the parameter space by iterative generation of new experimental designs and look-up of simulations in the proximity of the measured data. The parameter fitting pipeline includes an implicit sensitivity analysis and analysis of parameter identifiability, making it suitable for testing hypotheses for model reduction. Using this approach, under-constrained model parameters, as well as the coupling between parameters within the model are identified. The methodology is demonstrated by refitting the parameters of a published model of cardiac cellular mechanics using a combination of measured data and synthetic data from an alternative model of the same system. Using this approach, reduced models with simplified expressions for the tropomyosin/crossbridge kinetics were found by identification of model components that can be omitted without affecting the fit to the parameterising data. Our analysis revealed that model parameters could be constrained to a standard deviation of on

Modelling of intermittent microwave convective drying: parameter sensitivity

Directory of Open Access Journals (Sweden)

Zhang Zhijun

2017-06-01

Full Text Available The reliability of the predictions of a mathematical model is a prerequisite to its utilization. A multiphase porous media model of intermittent microwave convective drying is developed based on the literature. The model considers the liquid water, gas and solid matrix inside of food. The model is simulated by COMSOL software. Its sensitivity parameter is analysed by changing the parameter values by ±20%, with the exception of several parameters. The sensitivity analysis of the process of the microwave power level shows that each parameter: ambient temperature, effective gas diffusivity, and evaporation rate constant, has significant effects on the process. However, the surface mass, heat transfer coefficient, relative and intrinsic permeability of the gas, and capillary diffusivity of water do not have a considerable effect. The evaporation rate constant has minimal parameter sensitivity with a ±20% value change, until it is changed 10-fold. In all results, the temperature and vapour pressure curves show the same trends as the moisture content curve. However, the water saturation at the medium surface and in the centre show different results. Vapour transfer is the major mass transfer phenomenon that affects the drying process.

Level-density parameter of nuclei at finite temperature

International Nuclear Information System (INIS)

Gregoire, C.; Kuo, T.T.S.; Stout, D.B.

1991-01-01

The contribution of particle-particle (hole-hole) and of particle-hole ring diagrams to the nuclear level-density parameter at finite temperature is calculated. We first derive the correlated grand potential with the above ring diagrams included to all orders by way of a finite temperature RPA equation. An expression for the correlated level-density parameter is then obtained by differentiating the grand potential. Results obtained for the 40 Ca nucleus with realistic matrix elements derived from the Paris potential are presented. The contribution of the RPA correlations is found to be important, being significantly larger than typical Hartree-Fock results. The temperature dependence of the level-density parameter derived in the present work is generally similar to that obtained in a schematic model. Comparison with available experimental data is discussed. (orig.)

A generalized Fellner-Schall method for smoothing parameter optimization with application to Tweedie location, scale and shape models.

Science.gov (United States)

Wood, Simon N; Fasiolo, Matteo

2017-12-01

We consider the optimization of smoothing parameters and variance components in models with a regular log likelihood subject to quadratic penalization of the model coefficients, via a generalization of the method of Fellner (1986) and Schall (1991). In particular: (i) we generalize the original method to the case of penalties that are linear in several smoothing parameters, thereby covering the important cases of tensor product and adaptive smoothers; (ii) we show why the method's steps increase the restricted marginal likelihood of the model, that it tends to converge faster than the EM algorithm, or obvious accelerations of this, and investigate its relation to Newton optimization; (iii) we generalize the method to any Fisher regular likelihood. The method represents a considerable simplification over existing methods of estimating smoothing parameters in the context of regular likelihoods, without sacrificing generality: for example, it is only necessary to compute with the same first and second derivatives of the log-likelihood required for coefficient estimation, and not with the third or fourth order derivatives required by alternative approaches. Examples are provided which would have been impossible or impractical with pre-existing Fellner-Schall methods, along with an example of a Tweedie location, scale and shape model which would be a challenge for alternative methods, and a sparse additive modeling example where the method facilitates computational efficiency gains of several orders of magnitude. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2017, The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.

Retrospective forecast of ETAS model with daily parameters estimate

Science.gov (United States)

Falcone, Giuseppe; Murru, Maura; Console, Rodolfo; Marzocchi, Warner; Zhuang, Jiancang

2016-04-01

We present a retrospective ETAS (Epidemic Type of Aftershock Sequence) model based on the daily updating of free parameters during the background, the learning and the test phase of a seismic sequence. The idea was born after the 2011 Tohoku-Oki earthquake. The CSEP (Collaboratory for the Study of Earthquake Predictability) Center in Japan provided an appropriate testing benchmark for the five 1-day submitted models. Of all the models, only one was able to successfully predict the number of events that really happened. This result was verified using both the real time and the revised catalogs. The main cause of the failure was in the underestimation of the forecasted events, due to model parameters maintained fixed during the test. Moreover, the absence in the learning catalog of an event similar to the magnitude of the mainshock (M9.0), which drastically changed the seismicity in the area, made the learning parameters not suitable to describe the real seismicity. As an example of this methodological development we show the evolution of the model parameters during the last two strong seismic sequences in Italy: the 2009 L'Aquila and the 2012 Reggio Emilia episodes. The achievement of the model with daily updated parameters is compared with that of same model where the parameters remain fixed during the test time.

Modelling of parameters for asymmetric halo and symmetric DHDMG n-MOSFETs

Science.gov (United States)

De, Swapnadip; Sarkar, Angsuman; Sarkar, Chandan Kumar

2011-10-01

This article presents an analytical surface potential, threshold voltage and drain current model for asymmetric pocket-implanted, single-halo dual material gate and double-halo dual material gate (DHDMG) n-MOSFET (MOSFET, metal-oxide-semiconductor field-effect transistor) operating up to 40 nm regime. The model is derived by applying Gauss's law to a rectangular box, covering the entire depletion region. The asymmetric pocket-implanted model takes into account the effective doping concentration of the two linear pocket profiles at the source and the drain ends along with the inner fringing capacitances at both the source and the drain ends and the subthreshold drain and the substrate bias effect. Using the surface potential model, the threshold voltage and drain currents are estimated. The same model is used to find the characteristic parameters for dual-material gate (DMG) with halo implantations and double gate. The characteristic improvement is investigated. It is concluded that the DHDMG device structure exhibits better suppression of the short-channel effect (SCE) and the threshold voltage roll-off than DMG and double-gate MOSFET. The adequacy of the model is verified by comparing with two-dimensional device simulator DESSIS. A very good agreement of our model with DESSIS is obtained proving the validity of our model used in suppressing the SCEs.

Parameter identification in multinomial processing tree models

NARCIS (Netherlands)

Schmittmann, V.D.; Dolan, C.V.; Raijmakers, M.E.J.; Batchelder, W.H.

2010-01-01

Multinomial processing tree models form a popular class of statistical models for categorical data that have applications in various areas of psychological research. As in all statistical models, establishing which parameters are identified is necessary for model inference and selection on the basis

DESIGN OF LOW CYTOTOXICITY DIARYLANILINE DERIVATIVES BASED ON QSAR RESULTS: AN APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELLING

Directory of Open Access Journals (Sweden)

Ihsanul Arief

2016-11-01

Full Text Available Study on cytotoxicity of diarylaniline derivatives by using quantitative structure-activity relationship (QSAR has been done. The structures and cytotoxicities of  diarylaniline derivatives were obtained from the literature. Calculation of molecular and electronic parameters was conducted using Austin Model 1 (AM1, Parameterized Model 3 (PM3, Hartree-Fock (HF, and density functional theory (DFT methods.  Artificial neural networks (ANN analysis used to produce the best equation with configuration of input data-hidden node-output data = 5-8-1, value of r2 = 0.913; PRESS = 0.069. The best equation used to design and predict new diarylaniline derivatives.  The result shows that compound N1-(4′-Cyanophenyl-5-(4″-cyanovinyl-2″,6″-dimethyl-phenoxy-4-dimethylether benzene-1,2-diamine is the best-proposed compound with cytotoxicity value (CC50 of 93.037 μM.

Distributed parameter modeling to prevent charge cancellation for discrete thickness piezoelectric energy harvester

Science.gov (United States)

Krishnasamy, M.; Qian, Feng; Zuo, Lei; Lenka, T. R.

2018-03-01

The charge cancellation due to the change of strain along single continuous piezoelectric layer can remarkably affect the performance of a cantilever based harvester. In this paper, analytical models using distributed parameters are developed with some extent of averting the charge cancellation in cantilever piezoelectric transducer where the piezoelectric layers are segmented at strain nodes of concerned vibration mode. The electrode of piezoelectric segments are parallelly connected with a single external resistive load in the 1st model (Model 1). While each bimorph piezoelectric layers are connected in parallel to a resistor to form an independent circuit in the 2nd model (Model 2). The analytical expressions of the closed-form electromechanical coupling responses in frequency domain under harmonic base excitation are derived based on the Euler-Bernoulli beam assumption for both models. The developed analytical models are validated by COMSOL and experimental results. The results demonstrate that the energy harvesting performance of the developed segmented piezoelectric layer models is better than the traditional model of continuous piezoelectric layer.

Optimal parameters for the FFA-Beddoes dynamic stall model

Energy Technology Data Exchange (ETDEWEB)

Bjoerck, A; Mert, M [FFA, The Aeronautical Research Institute of Sweden, Bromma (Sweden); Madsen, H A [Risoe National Lab., Roskilde (Denmark)

1999-03-01

Unsteady aerodynamic effects, like dynamic stall, must be considered in calculation of dynamic forces for wind turbines. Models incorporated in aero-elastic programs are of semi-empirical nature. Resulting aerodynamic forces therefore depend on values used for the semi-empiricial parameters. In this paper a study of finding appropriate parameters to use with the Beddoes-Leishman model is discussed. Minimisation of the `tracking error` between results from 2D wind tunnel tests and simulation with the model is used to find optimum values for the parameters. The resulting optimum parameters show a large variation from case to case. Using these different sets of optimum parameters in the calculation of blade vibrations, give rise to quite different predictions of aerodynamic damping which is discussed. (au)

Predictive Models for Different Roughness Parameters During Machining Process of Peek Composites Using Response Surface Methodology

Directory of Open Access Journals (Sweden)

Mata-Cabrera Francisco

2013-10-01

Full Text Available Polyetheretherketone (PEEK composite belongs to a group of high performance thermoplastic polymers and is widely used in structural components. To improve the mechanical and tribological properties, short fibers are added as reinforcement to the material. Due to its functional properties and potential applications, it’s impor- tant to investigate the machinability of non-reinforced PEEK (PEEK, PEEK rein- forced with 30% of carbon fibers (PEEK CF30, and reinforced PEEK with 30% glass fibers (PEEK GF30 to determine the optimal conditions for the manufacture of the parts. The present study establishes the relationship between the cutting con- ditions (cutting speed and feed rate and the roughness (Ra , Rt , Rq , Rp , by develop- ing second order mathematical models. The experiments were planned as per full factorial design of experiments and an analysis of variance has been performed to check the adequacy of the models. These state the adequacy of the derived models to obtain predictions for roughness parameters within ranges of parameters that have been investigated during the experiments. The experimental results show that the most influence of the cutting parameters is the feed rate, furthermore, proved that glass fiber reinforcements produce a worse machinability.

Quantifying Correlation Uncertainty Risk in Credit Derivatives Pricing

Directory of Open Access Journals (Sweden)

Colin Turfus

2018-04-01

Full Text Available We propose a simple but practical methodology for the quantification of correlation risk in the context of credit derivatives pricing and credit valuation adjustment (CVA, where the correlation between rates and credit is often uncertain or unmodelled. We take the rates model to be Hull–White (normal and the credit model to be Black–Karasinski (lognormal. We summarise recent work furnishing highly accurate analytic pricing formulae for credit default swaps (CDS including with defaultable Libor flows, extending this to the situation where they are capped and/or floored. We also consider the pricing of contingent CDS with an interest rate swap underlying. We derive therefrom explicit expressions showing how the dependence of model prices on the uncertain parameter(s can be captured in analytic formulae that are readily amenable to computation without recourse to Monte Carlo or lattice-based computation. In so doing, we crucially take into account the impact on model calibration of the uncertain (or unmodelled parameters.

Resolving model parameter values from carbon and nitrogen stock measurements in a wide range of tropical mature forests using nonlinear inversion and regression trees

Science.gov (United States)

Shuguang Liua; Pamela Anderson; Guoyi Zhoud; Boone Kauffman; Flint Hughes; David Schimel; Vicente Watson; Joseph. Tosi

2008-01-01

Objectively assessing the performance of a model and deriving model parameter values from observations are critical and challenging in landscape to regional modeling. In this paper, we applied a nonlinear inversion technique to calibrate the ecosystem model CENTURY against carbon (C) and nitrogen (N) stock measurements collected from 39 mature tropical forest sites in...

Modeling of heat conduction via fractional derivatives

Science.gov (United States)

Fabrizio, Mauro; Giorgi, Claudio; Morro, Angelo

2017-09-01

The modeling of heat conduction is considered by letting the time derivative, in the Cattaneo-Maxwell equation, be replaced by a derivative of fractional order. The purpose of this new approach is to overcome some drawbacks of the Cattaneo-Maxwell equation, for instance possible fluctuations which violate the non-negativity of the absolute temperature. Consistency with thermodynamics is shown to hold for a suitable free energy potential, that is in fact a functional of the summed history of the heat flux, subject to a suitable restriction on the set of admissible histories. Compatibility with wave propagation at a finite speed is investigated in connection with temperature-rate waves. It follows that though, as expected, this is the case for the Cattaneo-Maxwell equation, the model involving the fractional derivative does not allow the propagation at a finite speed. Nevertheless, this new model provides a good description of wave-like profiles in thermal propagation phenomena, whereas Fourier's law does not.

An Iterative Method to Derive the Equivalent Centrifugal Compressor Performance at Various Operating Conditions: Part I: Modelling of Suction Parameters Impact

Directory of Open Access Journals (Sweden)

Waleed Albusaidi

2015-08-01

Full Text Available This paper introduces a new iterative method to predict the equivalent centrifugal compressor performance at various operating conditions. The presented theoretical analysis and empirical correlations provide a novel approach to derive the entire compressor map corresponding to various suction conditions without a prior knowledge of the detailed geometry. The efficiency model was derived to reflect the impact of physical gas properties, Mach number, and flow and work coefficients. One of the main features of the developed technique is the fact that it considers the variation in the gas properties and stage efficiency which makes it appropriate with hydrocarbons. This method has been tested to predict the performance of two multistage centrifugal compressors and the estimated characteristics are compared with the measured data. The carried comparison revealed a good matching with the actual values, including the stable operation region limits. Furthermore, an optimization study was conducted to investigate the influences of suction conditions on the stage efficiency and surge margin. Moreover, a new sort of presentation has been generated to obtain the equivalent performance characteristics for a constant discharge pressure operation at variable suction pressure and temperature working conditions. A further validation is included in part two of this study in order to evaluate the prediction capability of the derived model at various gas compositions.

Photoinjector optimization using a derivative-free, model-based trust-region algorithm for the Argonne Wakefield Accelerator

Science.gov (United States)

Neveu, N.; Larson, J.; Power, J. G.; Spentzouris, L.

2017-07-01

Model-based, derivative-free, trust-region algorithms are increasingly popular for optimizing computationally expensive numerical simulations. A strength of such methods is their efficient use of function evaluations. In this paper, we use one such algorithm to optimize the beam dynamics in two cases of interest at the Argonne Wakefield Accelerator (AWA) facility. First, we minimize the emittance of a 1 nC electron bunch produced by the AWA rf photocathode gun by adjusting three parameters: rf gun phase, solenoid strength, and laser radius. The algorithm converges to a set of parameters that yield an emittance of 1.08 μm. Second, we expand the number of optimization parameters to model the complete AWA rf photoinjector (the gun and six accelerating cavities) at 40 nC. The optimization algorithm is used in a Pareto study that compares the trade-off between emittance and bunch length for the AWA 70MeV photoinjector.

Lumped-parameters equivalent circuit for condenser microphones modeling.

Science.gov (United States)

Esteves, Josué; Rufer, Libor; Ekeom, Didace; Basrour, Skandar

2017-10-01

This work presents a lumped parameters equivalent model of condenser microphone based on analogies between acoustic, mechanical, fluidic, and electrical domains. Parameters of the model were determined mainly through analytical relations and/or finite element method (FEM) simulations. Special attention was paid to the air gap modeling and to the use of proper boundary condition. Corresponding lumped-parameters were obtained as results of FEM simulations. Because of its simplicity, the model allows a fast simulation and is readily usable for microphone design. This work shows the validation of the equivalent circuit on three real cases of capacitive microphones, including both traditional and Micro-Electro-Mechanical Systems structures. In all cases, it has been demonstrated that the sensitivity and other related data obtained from the equivalent circuit are in very good agreement with available measurement data.

Analysis of Modeling Parameters on Threaded Screws.

Energy Technology Data Exchange (ETDEWEB)

Vigil, Miquela S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Brake, Matthew Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vangoethem, Douglas [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-06-01

Assembled mechanical systems often contain a large number of bolted connections. These bolted connections (joints) are integral aspects of the load path for structural dynamics, and, consequently, are paramount for calculating a structure's stiffness and energy dissipation prop- erties. However, analysts have not found the optimal method to model appropriately these bolted joints. The complexity of the screw geometry cause issues when generating a mesh of the model. This paper will explore different approaches to model a screw-substrate connec- tion. Model parameters such as mesh continuity, node alignment, wedge angles, and thread to body element size ratios are examined. The results of this study will give analysts a better understanding of the influences of these parameters and will aide in finding the optimal method to model bolted connections.

Parameter Estimates in Differential Equation Models for Chemical Kinetics

Science.gov (United States)

Winkel, Brian

2011-01-01

We discuss the need for devoting time in differential equations courses to modelling and the completion of the modelling process with efforts to estimate the parameters in the models using data. We estimate the parameters present in several differential equation models of chemical reactions of order n, where n = 0, 1, 2, and apply more general…

Parameter identification in the logistic STAR model

DEFF Research Database (Denmark)

Ekner, Line Elvstrøm; Nejstgaard, Emil

We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter is that th...

Regionalization of SWAT Model Parameters for Use in Ungauged Watersheds

Directory of Open Access Journals (Sweden)

Indrajeet Chaubey

2010-11-01

Full Text Available There has been a steady shift towards modeling and model-based approaches as primary methods of assessing watershed response to hydrologic inputs and land management, and of quantifying watershed-wide best management practice (BMP effectiveness. Watershed models often require some degree of calibration and validation to achieve adequate watershed and therefore BMP representation. This is, however, only possible for gauged watersheds. There are many watersheds for which there are very little or no monitoring data available, thus the question as to whether it would be possible to extend and/or generalize model parameters obtained through calibration of gauged watersheds to ungauged watersheds within the same region. This study explored the possibility of developing regionalized model parameter sets for use in ungauged watersheds. The study evaluated two regionalization methods: global averaging, and regression-based parameters, on the SWAT model using data from priority watersheds in Arkansas. Resulting parameters were tested and model performance determined on three gauged watersheds. Nash-Sutcliffe efficiencies (NS for stream flow obtained using regression-based parameters (0.53–0.83 compared well with corresponding values obtained through model calibration (0.45–0.90. Model performance obtained using global averaged parameter values was also generally acceptable (0.4 ≤ NS ≤ 0.75. Results from this study indicate that regionalized parameter sets for the SWAT model can be obtained and used for making satisfactory hydrologic response predictions in ungauged watersheds.

Resonance parameter analysis with SAMMY

International Nuclear Information System (INIS)

Larson, N.M.; Perey, F.G.

1988-01-01

The multilevel R-matrix computer code SAMMY has evolved over the past decade to become an important analysis tool for neutron data. SAMMY uses the Reich-Moore approximation to the multilevel R-matrix and includes an optional logarithmic parameterization of the external R-function. Doppler broadening is simulated either by numerical integration using the Gaussian approximation to the free gas model or by a more rigorous solution of the partial differential equation equivalent to the exact free gas model. Resolution broadening of cross sections and derivatives also has new options that more accurately represent the experimental situation. SAMMY treats constant normalization and some types of backgrounds directly and treats other normalizations and/or backgrounds with the introduction of user-generated partial derivatives. The code uses Bayes' method as an efficient alternative to least squares for fitting experimental data. SAMMY allows virtually any parameter to be varied and outputs values, uncertainties, and covariance matrix for all varied parameters. Versions of SAMMY exist for VAX, FPS, and IBM computers

Soil-related Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

A. J. Smith

2003-01-01

This analysis is one of the technical reports containing documentation of the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the Total System Performance Assessment (TSPA) for the geologic repository at Yucca Mountain. The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A graphical representation of the documentation hierarchy for the ERMYN biosphere model is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (BSC 2003 [163602]). It should be noted that some documents identified in Figure 1-1 may be under development at the time this report is issued and therefore not available. This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. ''The Biosphere Model Report'' (BSC 2003 [160699]) describes in detail the conceptual model as well as the mathematical model and its input parameters. The purpose of this analysis was to develop the biosphere model parameters needed to evaluate doses from pathways associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation and ash

Anisotropic inflation with derivative couplings

Science.gov (United States)

Holland, Jonathan; Kanno, Sugumi; Zavala, Ivonne

2018-05-01

We study anisotropic power-law inflationary solutions when the inflaton and its derivative couple to a vector field. This type of coupling is motivated by D-brane inflationary models, in which the inflaton, and a vector field living on the D-brane, couple disformally (derivatively). We start by studying a phenomenological model where we show the existence of anisotropic solutions and demonstrate their stability via a dynamical system analysis. Compared to the case without a derivative coupling, the anisotropy is reduced and thus can be made consistent with current limits, while the value of the slow-roll parameter remains almost unchanged. We also discuss solutions for more general cases, including D-brane-like couplings.

Inhalation Exposure Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

K. Rautenstrauch

2004-09-10

This analysis is one of 10 reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN) biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. Inhalation Exposure Input Parameters for the Biosphere Model is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the Technical Work Plan for Biosphere Modeling and Expert Support (BSC 2004 [DIRS 169573]). This analysis report defines and justifies values of mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of ERMYN to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception.

Inhalation Exposure Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

K. Rautenstrauch

2004-01-01

This analysis is one of 10 reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN) biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. Inhalation Exposure Input Parameters for the Biosphere Model is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the Technical Work Plan for Biosphere Modeling and Expert Support (BSC 2004 [DIRS 169573]). This analysis report defines and justifies values of mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of ERMYN to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception

Modeling and Parameter Estimation of a Small Wind Generation System

Directory of Open Access Journals (Sweden)

Carlos A. Ramírez Gómez

2013-11-01

Full Text Available The modeling and parameter estimation of a small wind generation system is presented in this paper. The system consists of a wind turbine, a permanent magnet synchronous generator, a three phase rectifier, and a direct current load. In order to estimate the parameters wind speed data are registered in a weather station located in the Fraternidad Campus at ITM. Wind speed data were applied to a reference model programed with PSIM software. From that simulation, variables were registered to estimate the parameters. The wind generation system model together with the estimated parameters is an excellent representation of the detailed model, but the estimated model offers a higher flexibility than the programed model in PSIM software.

Parameters-related uncertainty in modeling sugar cane yield with an agro-Land Surface Model

Science.gov (United States)

Valade, A.; Ciais, P.; Vuichard, N.; Viovy, N.; Ruget, F.; Gabrielle, B.

2012-12-01

Agro-Land Surface Models (agro-LSM) have been developed from the coupling of specific crop models and large-scale generic vegetation models. They aim at accounting for the spatial distribution and variability of energy, water and carbon fluxes within soil-vegetation-atmosphere continuum with a particular emphasis on how crop phenology and agricultural management practice influence the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty in these models is related to the many parameters included in the models' equations. In this study, we quantify the parameter-based uncertainty in the simulation of sugar cane biomass production with the agro-LSM ORCHIDEE-STICS on a multi-regional approach with data from sites in Australia, La Reunion and Brazil. First, the main source of uncertainty for the output variables NPP, GPP, and sensible heat flux (SH) is determined through a screening of the main parameters of the model on a multi-site basis leading to the selection of a subset of most sensitive parameters causing most of the uncertainty. In a second step, a sensitivity analysis is carried out on the parameters selected from the screening analysis at a regional scale. For this, a Monte-Carlo sampling method associated with the calculation of Partial Ranked Correlation Coefficients is used. First, we quantify the sensitivity of the output variables to individual input parameters on a regional scale for two regions of intensive sugar cane cultivation in Australia and Brazil. Then, we quantify the overall uncertainty in the simulation's outputs propagated from the uncertainty in the input parameters. Seven parameters are identified by the screening procedure as driving most of the uncertainty in the agro-LSM ORCHIDEE-STICS model output at all sites. These parameters control photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), root

Holographic metal-insulator transition in higher derivative gravity

Energy Technology Data Exchange (ETDEWEB)

Ling, Yi, E-mail: lingy@ihep.ac.cn [Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049 (China); Shanghai Key Laboratory of High Temperature Superconductors, Shanghai, 200444 (China); Liu, Peng, E-mail: liup51@ihep.ac.cn [Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049 (China); Wu, Jian-Pin, E-mail: jianpinwu@mail.bnu.edu.cn [Institute of Gravitation and Cosmology, Department of Physics, School of Mathematics and Physics, Bohai University, Jinzhou 121013 (China); Shanghai Key Laboratory of High Temperature Superconductors, Shanghai, 200444 (China); Zhou, Zhenhua, E-mail: zhouzh@ihep.ac.cn [Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049 (China)

2017-03-10

We introduce a Weyl term into the Einstein–Maxwell-Axion theory in four dimensional spacetime. Up to the first order of the Weyl coupling parameter γ, we construct charged black brane solutions without translational invariance in a perturbative manner. Among all the holographic frameworks involving higher derivative gravity, we are the first to obtain metal-insulator transitions (MIT) when varying the system parameters at zero temperature. Furthermore, we study the holographic entanglement entropy (HEE) of strip geometry in this model and find that the second order derivative of HEE with respect to the axion parameter exhibits maximization behavior near quantum critical points (QCPs) of MIT. It testifies the conjecture in that HEE itself or its derivatives can be used to diagnose quantum phase transition (QPT).

Price models for oil derivates in Slovenia

International Nuclear Information System (INIS)

Nemac, F.; Saver, A.

1995-01-01

In Slovenia, a law is currently applied according to which any change in the price of oil derivatives is subject to the Governmental approval. Following the target of getting closer to the European Union, the necessity has arisen of finding ways for the introduction of liberalization or automated approach to price modifications depending on oscillations of oil derivative prices on the world market and the rate of exchange of the American dollar. It is for this reason that at the Agency for Energy Restructuring we made a study for the Ministry of Economic Affairs and Development regarding this issue. We analysed the possible models for the formation of oil derivative prices for Slovenia. Based on the assessment of experiences of primarily the west European countries, we proposed three models for the price formation for Slovenia. In future, it is expected that the Government of the Republic of Slovenia will make a selection of one of the proposed models to be followed by enforcement of price liberalization. The paper presents two representative models for price formation as used in Austria and Portugal. In the continuation the authors analyse the application of three models that they find suitable for the use in Slovenia. (author)

SAT-MAP-CLIMATE project results[SATellite base bio-geophysical parameter MAPping and aggregation modelling for CLIMATE models

Energy Technology Data Exchange (ETDEWEB)

Bay Hasager, C.; Woetmann Nielsen, N.; Soegaard, H.; Boegh, E.; Hesselbjerg Christensen, J.; Jensen, N.O.; Schultz Rasmussen, M.; Astrup, P.; Dellwik, E.

2002-08-01

Earth Observation (EO) data from imaging satellites are analysed with respect to albedo, land and sea surface temperatures, land cover types and vegetation parameters such as the Normalized Difference Vegetation Index (NDVI) and the leaf area index (LAI). The observed parameters are used in the DMI-HIRLAM-D05 weather prediction model in order to improve the forecasting. The effect of introducing actual sea surface temperatures from NOAA AVHHR compared to climatological mean values, shows a more pronounced land-sea breeze effect which is also observable in field observations. The albedo maps from NOAA AVHRR are rather similar to the climatological mean values so for the HIRLAM model this is insignicant, yet most likely of some importance in the HIRHAM regional climate model. Land cover type maps are assigned local roughness values determined from meteorological field observations. Only maps with a spatial resolution around 25 m can adequately map the roughness variations of the typical patch size distribution in Denmark. A roughness map covering Denmark is aggregated (ie area-average non-linearly) by a microscale aggregation model that takes the non-linear turbulent responses of each roughness step change between patches in an arbitrary pattern into account. The effective roughnesses are calculated into a 15 km by 15 km grid for the HIRLAM model. The effect of hedgerows is included as an added roughness effect as a function of hedge density mapped from a digital vector map. Introducing the new effective roughness maps into the HIRLAM model appears to remedy on the seasonal wind speed bias over land and sea in spring. A new parameterisation on the effective roughness for scalar surface fluxes is developed and tested on synthetic data. Further is a method for the estimation the evapotranspiration from albedo, surface temperatures and NDVI succesfully compared to field observations. The HIRLAM predictions of water vapour at 12 GMT are used for atmospheric correction of

Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

Science.gov (United States)

Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

2017-11-20

A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

Pseudo-dynamic source modelling with 1-point and 2-point statistics of earthquake source parameters

KAUST Repository

Song, S. G.

2013-12-24

Ground motion prediction is an essential element in seismic hazard and risk analysis. Empirical ground motion prediction approaches have been widely used in the community, but efficient simulation-based ground motion prediction methods are needed to complement empirical approaches, especially in the regions with limited data constraints. Recently, dynamic rupture modelling has been successfully adopted in physics-based source and ground motion modelling, but it is still computationally demanding and many input parameters are not well constrained by observational data. Pseudo-dynamic source modelling keeps the form of kinematic modelling with its computational efficiency, but also tries to emulate the physics of source process. In this paper, we develop a statistical framework that governs the finite-fault rupture process with 1-point and 2-point statistics of source parameters in order to quantify the variability of finite source models for future scenario events. We test this method by extracting 1-point and 2-point statistics from dynamically derived source models and simulating a number of rupture scenarios, given target 1-point and 2-point statistics. We propose a new rupture model generator for stochastic source modelling with the covariance matrix constructed from target 2-point statistics, that is, auto- and cross-correlations. Our sensitivity analysis of near-source ground motions to 1-point and 2-point statistics of source parameters provides insights into relations between statistical rupture properties and ground motions. We observe that larger standard deviation and stronger correlation produce stronger peak ground motions in general. The proposed new source modelling approach will contribute to understanding the effect of earthquake source on near-source ground motion characteristics in a more quantitative and systematic way.

SPOTting Model Parameters Using a Ready-Made Python Package.

Directory of Open Access Journals (Sweden)

Tobias Houska

Full Text Available The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool, an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI. We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.

Hydrological model parameter dimensionality is a weak measure of prediction uncertainty

Science.gov (United States)

Pande, S.; Arkesteijn, L.; Savenije, H.; Bastidas, L. A.

2015-04-01

This paper shows that instability of hydrological system representation in response to different pieces of information and associated prediction uncertainty is a function of model complexity. After demonstrating the connection between unstable model representation and model complexity, complexity is analyzed in a step by step manner. This is done measuring differences between simulations of a model under different realizations of input forcings. Algorithms are then suggested to estimate model complexity. Model complexities of the two model structures, SAC-SMA (Sacramento Soil Moisture Accounting) and its simplified version SIXPAR (Six Parameter Model), are computed on resampled input data sets from basins that span across the continental US. The model complexities for SIXPAR are estimated for various parameter ranges. It is shown that complexity of SIXPAR increases with lower storage capacity and/or higher recession coefficients. Thus it is argued that a conceptually simple model structure, such as SIXPAR, can be more complex than an intuitively more complex model structure, such as SAC-SMA for certain parameter ranges. We therefore contend that magnitudes of feasible model parameters influence the complexity of the model selection problem just as parameter dimensionality (number of parameters) does and that parameter dimensionality is an incomplete indicator of stability of hydrological model selection and prediction problems.

Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach

Directory of Open Access Journals (Sweden)

Xiao-meng Song

2013-01-01

Full Text Available Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1 a screening method (Morris for qualitative ranking of parameters, and (2 a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol. First, the Morris screening method was used to qualitatively identify the parameters' sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.

Updating parameters of the chicken processing line model

DEFF Research Database (Denmark)

Kurowicka, Dorota; Nauta, Maarten; Jozwiak, Katarzyna

2010-01-01

A mathematical model of chicken processing that quantitatively describes the transmission of Campylobacter on chicken carcasses from slaughter to chicken meat product has been developed in Nauta et al. (2005). This model was quantified with expert judgment. Recent availability of data allows...... updating parameters of the model to better describe processes observed in slaughterhouses. We propose Bayesian updating as a suitable technique to update expert judgment with microbiological data. Berrang and Dickens’s data are used to demonstrate performance of this method in updating parameters...... of the chicken processing line model....

Inference of directional selection and mutation parameters assuming equilibrium.

Science.gov (United States)

Vogl, Claus; Bergman, Juraj

2015-12-01

In a classical study, Wright (1931) proposed a model for the evolution of a biallelic locus under the influence of mutation, directional selection and drift. He derived the equilibrium distribution of the allelic proportion conditional on the scaled mutation rate, the mutation bias and the scaled strength of directional selection. The equilibrium distribution can be used for inference of these parameters with genome-wide datasets of "site frequency spectra" (SFS). Assuming that the scaled mutation rate is low, Wright's model can be approximated by a boundary-mutation model, where mutations are introduced into the population exclusively from sites fixed for the preferred or unpreferred allelic states. With the boundary-mutation model, inference can be partitioned: (i) the shape of the SFS distribution within the polymorphic region is determined by random drift and directional selection, but not by the mutation parameters, such that inference of the selection parameter relies exclusively on the polymorphic sites in the SFS; (ii) the mutation parameters can be inferred from the amount of polymorphic and monomorphic preferred and unpreferred alleles, conditional on the selection parameter. Herein, we derive maximum likelihood estimators for the mutation and selection parameters in equilibrium and apply the method to simulated SFS data as well as empirical data from a Madagascar population of Drosophila simulans. Copyright © 2015 Elsevier Inc. All rights reserved.

Intermediate modeling between kinetic equations and hydrodynamic limits: derivation, analysis and simulations

International Nuclear Information System (INIS)

Parisot, M.

2011-01-01

This work is dedicated study of a problem resulting from plasma physics: the thermal transfer of electrons in a plasma close to equilibrium Maxwellian. Firstly, a dimensional study of the Vlasov-Fokker-Planck-Maxwell system is performed, allowing one hand to identify a physically relevant parameter of scale and also to define mathematically the contours of validity domain. The asymptotic regime called Spitzer-Harm is studied for a relatively general class of collision operator. The following part of this work is devoted to the derivation and study of the hydrodynamic limit of the system of Vlasov-Maxwell-Landau outside the strictly asymptotic. A model proposed by Schurtz and Nicolais located in this context and analyzed. The particularity of this model lies in the application of a delocalization operation in the heat flux. The link with non-local models of Luciani and Mora is established as well as mathematics properties as the principle of maximum and entropy dissipation. Then a formal derivation from the Vlasov equations with a simplified collision operator, is proposed. The derivation, inspired by the recent work of D. Levermore, involves decomposition methods according to the spherical harmonics and methods of closing called diffusion methods. A hierarchy of intermediate models between the kinetic equations and the hydrodynamic limit is described. In particular a new hydrodynamic system integro-differential by nature, is proposed. The Schurtz and Nicolai model appears as a simplification of the system resulting from the derivation, assuming a steady flow of heat. The above results are then generalized to account for the internal energy dependence which appears naturally in the equation establishment. The existence and uniqueness of the solution of the nonstationary system are established in a simplified framework. The last part is devoted was the implementation of a specific numerical scheme to solve these models. We propose a finite volume approach can be

Using Whole-House Field Tests to Empirically Derive Moisture Buffering Model Inputs

Energy Technology Data Exchange (ETDEWEB)

Woods, J.; Winkler, J.; Christensen, D.; Hancock, E.

2014-08-01

Building energy simulations can be used to predict a building's interior conditions, along with the energy use associated with keeping these conditions comfortable. These models simulate the loads on the building (e.g., internal gains, envelope heat transfer), determine the operation of the space conditioning equipment, and then calculate the building's temperature and humidity throughout the year. The indoor temperature and humidity are affected not only by the loads and the space conditioning equipment, but also by the capacitance of the building materials, which buffer changes in temperature and humidity. This research developed an empirical method to extract whole-house model inputs for use with a more accurate moisture capacitance model (the effective moisture penetration depth model). The experimental approach was to subject the materials in the house to a square-wave relative humidity profile, measure all of the moisture transfer terms (e.g., infiltration, air conditioner condensate) and calculate the only unmeasured term: the moisture absorption into the materials. After validating the method with laboratory measurements, we performed the tests in a field house. A least-squares fit of an analytical solution to the measured moisture absorption curves was used to determine the three independent model parameters representing the moisture buffering potential of this house and its furnishings. Follow on tests with realistic latent and sensible loads showed good agreement with the derived parameters, especially compared to the commonly-used effective capacitance approach. These results show that the EMPD model, once the inputs are known, is an accurate moisture buffering model.

Diabatic models with transferrable parameters for generalized chemical reactions

International Nuclear Information System (INIS)

Reimers, Jeffrey R; McKemmish, Laura K; McKenzie, Ross H; Hush, Noel S

2017-01-01

Diabatic models applied to adiabatic electron-transfer theory yield many equations involving just a few parameters that connect ground-state geometries and vibration frequencies to excited-state transition energies and vibration frequencies to the rate constants for electron-transfer reactions, utilizing properties of the conical-intersection seam linking the ground and excited states through the Pseudo Jahn-Teller effect. We review how such simplicity in basic understanding can also be obtained for general chemical reactions. The key feature that must be recognized is that electron-transfer (or hole transfer) processes typically involve one electron (hole) moving between two orbitals, whereas general reactions typically involve two electrons or even four electrons for processes in aromatic molecules. Each additional moving electron leads to new high-energy but interrelated conical-intersection seams that distort the shape of the critical lowest-energy seam. Recognizing this feature shows how conical-intersection descriptors can be transferred between systems, and how general chemical reactions can be compared using the same set of simple parameters. Mathematical relationships are presented depicting how different conical-intersection seams relate to each other, showing that complex problems can be reduced into an effective interaction between the ground-state and a critical excited state to provide the first semi-quantitative implementation of Shaik’s “twin state” concept. Applications are made (i) demonstrating why the chemistry of the first-row elements is qualitatively so different to that of the second and later rows, (ii) deducing the bond-length alternation in hypothetical cyclohexatriene from the observed UV spectroscopy of benzene, (iii) demonstrating that commonly used procedures for modelling surface hopping based on inclusion of only the first-derivative correction to the Born-Oppenheimer approximation are valid in no region of the chemical

MEMS 3-DoF gyroscope design, modeling and simulation through equivalent circuit lumped parameter model

International Nuclear Information System (INIS)

Mian, Muhammad Umer; Khir, M. H. Md.; Tang, T. B.; Dennis, John Ojur; Riaz, Kashif; Iqbal, Abid; Bazaz, Shafaat A.

2015-01-01

Pre-fabrication, behavioural and performance analysis with computer aided design (CAD) tools is a common and fabrication cost effective practice. In light of this we present a simulation methodology for a dual-mass oscillator based 3 Degree of Freedom (3-DoF) MEMS gyroscope. 3-DoF Gyroscope is modeled through lumped parameter models using equivalent circuit elements. These equivalent circuits consist of elementary components which are counterpart of their respective mechanical components, used to design and fabricate 3-DoF MEMS gyroscope. Complete designing of equivalent circuit model, mathematical modeling and simulation are being presented in this paper. Behaviors of the equivalent lumped models derived for the proposed device design are simulated in MEMSPRO T-SPICE software. Simulations are carried out with the design specifications following design rules of the MetalMUMPS fabrication process. Drive mass resonant frequencies simulated by this technique are 1.59 kHz and 2.05 kHz respectively, which are close to the resonant frequencies found by the analytical formulation of the gyroscope. The lumped equivalent circuit modeling technique proved to be a time efficient modeling technique for the analysis of complex MEMS devices like 3-DoF gyroscopes. The technique proves to be an alternative approach to the complex and time consuming couple field analysis Finite Element Analysis (FEA) previously used

MEMS 3-DoF gyroscope design, modeling and simulation through equivalent circuit lumped parameter model

Energy Technology Data Exchange (ETDEWEB)

Mian, Muhammad Umer, E-mail: umermian@gmail.com; Khir, M. H. Md.; Tang, T. B. [Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Tronoh, Perak (Malaysia); Dennis, John Ojur [Department of Fundamental & Applied Sciences, Universiti Teknologi PETRONAS, Tronoh, Perak (Malaysia); Riaz, Kashif; Iqbal, Abid [Faculty of Electronics Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Khyber Pakhtunkhaw (Pakistan); Bazaz, Shafaat A. [Department of Computer Science, Center for Advance Studies in Engineering, Islamabad (Pakistan)

2015-07-22

Pre-fabrication, behavioural and performance analysis with computer aided design (CAD) tools is a common and fabrication cost effective practice. In light of this we present a simulation methodology for a dual-mass oscillator based 3 Degree of Freedom (3-DoF) MEMS gyroscope. 3-DoF Gyroscope is modeled through lumped parameter models using equivalent circuit elements. These equivalent circuits consist of elementary components which are counterpart of their respective mechanical components, used to design and fabricate 3-DoF MEMS gyroscope. Complete designing of equivalent circuit model, mathematical modeling and simulation are being presented in this paper. Behaviors of the equivalent lumped models derived for the proposed device design are simulated in MEMSPRO T-SPICE software. Simulations are carried out with the design specifications following design rules of the MetalMUMPS fabrication process. Drive mass resonant frequencies simulated by this technique are 1.59 kHz and 2.05 kHz respectively, which are close to the resonant frequencies found by the analytical formulation of the gyroscope. The lumped equivalent circuit modeling technique proved to be a time efficient modeling technique for the analysis of complex MEMS devices like 3-DoF gyroscopes. The technique proves to be an alternative approach to the complex and time consuming couple field analysis Finite Element Analysis (FEA) previously used.

Temporal variation and scaling of parameters for a monthly hydrologic model

Science.gov (United States)

Deng, Chao; Liu, Pan; Wang, Dingbao; Wang, Weiguang

2018-03-01

The temporal variation of model parameters is affected by the catchment conditions and has a significant impact on hydrological simulation. This study aims to evaluate the seasonality and downscaling of model parameter across time scales based on monthly and mean annual water balance models with a common model framework. Two parameters of the monthly model, i.e., k and m, are assumed to be time-variant at different months. Based on the hydrological data set from 121 MOPEX catchments in the United States, we firstly analyzed the correlation between parameters (k and m) and catchment properties (NDVI and frequency of rainfall events, α). The results show that parameter k is positively correlated with NDVI or α, while the correlation is opposite for parameter m, indicating that precipitation and vegetation affect monthly water balance by controlling temporal variation of parameters k and m. The multiple linear regression is then used to fit the relationship between ε and the means and coefficient of variations of parameters k and m. Based on the empirical equation and the correlations between the time-variant parameters and NDVI, the mean annual parameter ε is downscaled to monthly k and m. The results show that it has lower NSEs than these from model with time-variant k and m being calibrated through SCE-UA, while for several study catchments, it has higher NSEs than that of the model with constant parameters. The proposed method is feasible and provides a useful tool for temporal scaling of model parameter.

Threshold effects on renormalization group running of neutrino parameters in the low-scale seesaw model

International Nuclear Information System (INIS)

Bergstroem, Johannes; Ohlsson, Tommy; Zhang He

2011-01-01

We show that, in the low-scale type-I seesaw model, renormalization group running of neutrino parameters may lead to significant modifications of the leptonic mixing angles in view of so-called seesaw threshold effects. Especially, we derive analytical formulas for radiative corrections to neutrino parameters in crossing the different seesaw thresholds, and show that there may exist enhancement factors efficiently boosting the renormalization group running of the leptonic mixing angles. We find that, as a result of the seesaw threshold corrections to the leptonic mixing angles, various flavor symmetric mixing patterns (e.g., bi-maximal and tri-bimaximal mixing patterns) can be easily accommodated at relatively low energy scales, which is well within the reach of running and forthcoming experiments (e.g., the LHC).

Simple model for deriving sdg interacting boson model Hamiltonians: 150Nd example

Science.gov (United States)

Devi, Y. D.; Kota, V. K. B.

1993-07-01

A simple and yet useful model for deriving sdg interacting boson model (IBM) Hamiltonians is to assume that single-boson energies derive from identical particle (pp and nn) interactions and proton, neutron single-particle energies, and that the two-body matrix elements for bosons derive from pn interaction, with an IBM-2 to IBM-1 projection of the resulting p-n sdg IBM Hamiltonian. The applicability of this model in generating sdg IBM Hamiltonians is demonstrated, using a single-j-shell Otsuka-Arima-Iachello mapping of the quadrupole and hexadecupole operators in proton and neutron spaces separately and constructing a quadrupole-quadrupole plus hexadecupole-hexadecupole Hamiltonian in the analysis of the spectra, B(E2)'s, and E4 strength distribution in the example of 150Nd.

Simple model for deriving sdg interacting boson model Hamiltonians: 150Nd example

International Nuclear Information System (INIS)

Devi, Y.D.; Kota, V.K.B.

1993-01-01

A simple and yet useful model for deriving sdg interacting boson model (IBM) Hamiltonians is to assume that single-boson energies derive from identical particle (pp and nn) interactions and proton, neutron single-particle energies, and that the two-body matrix elements for bosons derive from pn interaction, with an IBM-2 to IBM-1 projection of the resulting p-n sdg IBM Hamiltonian. The applicability of this model in generating sdg IBM Hamiltonians is demonstrated, using a single-j-shell Otsuka-Arima-Iachello mapping of the quadrupole and hexadecupole operators in proton and neutron spaces separately and constructing a quadrupole-quadrupole plus hexadecupole-hexadecupole Hamiltonian in the analysis of the spectra, B(E2)'s, and E4 strength distribution in the example of 150 Nd

68Ga-PSMA-11 PET/CT-derived metabolic parameters for determination of whole-body tumor burden and treatment response in prostate cancer.

Science.gov (United States)

Schmidkonz, Christian; Cordes, Michael; Schmidt, Daniela; Bäuerle, Tobias; Goetz, Theresa Ida; Beck, Michael; Prante, Olaf; Cavallaro, Alexander; Uder, Michael; Wullich, Bernd; Goebell, Peter; Kuwert, Torsten; Ritt, Philipp

2018-05-03

We aimed at evaluating the role of 68 Ga-PSMA-11 PET/CT-derived metabolic parameters for assessment of whole-body tumor burden and its capability to determine therapeutic response in patients with prostate cancer. A total of 142 patients with biochemical recurrence of prostate cancer underwent PET/CT with [ 68 Ga]Ga-PSMA-HBED-CC ( 68 Ga-PSMA-11). Quantitative assessment of all 641 68 Ga-PSMA-11-positive lesions in the field of view was performed to calculate PSMA-derived parameters, including whole-body PSMA tumor volume (PSMA-TV) and whole-body total lesion PSMA (TL-PSMA), as well as the established SUVmax and SUVmean values. All PET-derived parameters were tested for correlation with serum PSA levels and for association with Gleason scores. In 23 patients who underwent 68 Ga-PSMA-11 PET/CT before and after therapy with either external beam radiation, androgen deprivation, or docetaxel chemotherapy, SUVmax and TL-PSMA were compared to radiographic response assessment of CT images based on RECIST 1.1 criteria and to biochemical response determined by changes of serum PSA levels. PSMA-TV and TL-PSMA demonstrated a significant correlation with serum PSA levels (P PET and biochemical response was 87% (95% confidence interval, 0.66-0.97; Cohen's κ = 0.78; P PET and CT were most likely due to limitations of CT and RECIST in rating small lymph nodes as metastases, as well as bone involvement, which was sometimes not detectable in CT. 68 Ga-PSMA-11 PET/CT-derived metabolic tumor parameters showed promising results for evaluation of treatment response. Especially, TL-PSMA demonstrated higher agreement rates with biochemical response compared to SUVmax. Larger, ideally prospective trials are needed to help to reveal the full potential of metabolic parameters derived from PET imaging with 68 Ga-PSMA-11.

An improved state-parameter analysis of ecosystem models using data assimilation

Science.gov (United States)

Chen, M.; Liu, S.; Tieszen, L.L.; Hollinger, D.Y.

2008-01-01

Much of the effort spent in developing data assimilation methods for carbon dynamics analysis has focused on estimating optimal values for either model parameters or state variables. The main weakness of estimating parameter values alone (i.e., without considering state variables) is that all errors from input, output, and model structure are attributed to model parameter uncertainties. On the other hand, the accuracy of estimating state variables may be lowered if the temporal evolution of parameter values is not incorporated. This research develops a smoothed ensemble Kalman filter (SEnKF) by combining ensemble Kalman filter with kernel smoothing technique. SEnKF has following characteristics: (1) to estimate simultaneously the model states and parameters through concatenating unknown parameters and state variables into a joint state vector; (2) to mitigate dramatic, sudden changes of parameter values in parameter sampling and parameter evolution process, and control narrowing of parameter variance which results in filter divergence through adjusting smoothing factor in kernel smoothing algorithm; (3) to assimilate recursively data into the model and thus detect possible time variation of parameters; and (4) to address properly various sources of uncertainties stemming from input, output and parameter uncertainties. The SEnKF is tested by assimilating observed fluxes of carbon dioxide and environmental driving factor data from an AmeriFlux forest station located near Howland, Maine, USA, into a partition eddy flux model. Our analysis demonstrates that model parameters, such as light use efficiency, respiration coefficients, minimum and optimum temperatures for photosynthetic activity, and others, are highly constrained by eddy flux data at daily-to-seasonal time scales. The SEnKF stabilizes parameter values quickly regardless of the initial values of the parameters. Potential ecosystem light use efficiency demonstrates a strong seasonality. Results show that the

Setting Parameters for Biological Models With ANIMO

NARCIS (Netherlands)

Schivo, Stefano; Scholma, Jetse; Karperien, Hermanus Bernardus Johannes; Post, Janine Nicole; van de Pol, Jan Cornelis; Langerak, Romanus; André, Étienne; Frehse, Goran

2014-01-01

ANIMO (Analysis of Networks with Interactive MOdeling) is a software for modeling biological networks, such as e.g. signaling, metabolic or gene networks. An ANIMO model is essentially the sum of a network topology and a number of interaction parameters. The topology describes the interactions

State-Space Modelling of Loudspeakers using Fractional Derivatives

DEFF Research Database (Denmark)

King, Alexander Weider; Agerkvist, Finn T.

2015-01-01

This work investigates the use of fractional order derivatives in modeling moving-coil loudspeakers. A fractional order state-space solution is developed, leading the way towards incorporating nonlinearities into a fractional order system. The method is used to calculate the response of a fractio......This work investigates the use of fractional order derivatives in modeling moving-coil loudspeakers. A fractional order state-space solution is developed, leading the way towards incorporating nonlinearities into a fractional order system. The method is used to calculate the response...... of a fractional harmonic oscillator, representing the mechanical part of a loudspeaker, showing the effect of the fractional derivative and its relationship to viscoelasticity. Finally, a loudspeaker model with a fractional order viscoelastic suspension and fractional order voice coil is fit to measurement data...

Constant-parameter capture-recapture models

Science.gov (United States)

Brownie, C.; Hines, J.E.; Nichols, J.D.

1986-01-01

Jolly (1982, Biometrics 38, 301-321) presented modifications of the Jolly-Seber model for capture-recapture data, which assume constant survival and/or capture rates. Where appropriate, because of the reduced number of parameters, these models lead to more efficient estimators than the Jolly-Seber model. The tests to compare models given by Jolly do not make complete use of the data, and we present here the appropriate modifications, and also indicate how to carry out goodness-of-fit tests which utilize individual capture history information. We also describe analogous models for the case where young and adult animals are tagged. The availability of computer programs to perform the analysis is noted, and examples are given using output from these programs.

Analysis of Drude model using fractional derivatives without singular kernels

Directory of Open Access Journals (Sweden)

Jiménez Leonardo Martínez

2017-11-01

Full Text Available We report study exploring the fractional Drude model in the time domain, using fractional derivatives without singular kernels, Caputo-Fabrizio (CF, and fractional derivatives with a stretched Mittag-Leffler function. It is shown that the velocity and current density of electrons moving through a metal depend on both the time and the fractional order 0 < γ ≤ 1. Due to non-singular fractional kernels, it is possible to consider complete memory effects in the model, which appear neither in the ordinary model, nor in the fractional Drude model with Caputo fractional derivative. A comparison is also made between these two representations of the fractional derivatives, resulting a considered difference when γ < 0.8.

Predicting plant biomass accumulation from image-derived parameters

Science.gov (United States)

Chen, Dijun; Shi, Rongli; Pape, Jean-Michel; Neumann, Kerstin; Graner, Andreas; Chen, Ming; Klukas, Christian

2018-01-01

Abstract Background Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently, and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologists. However, it is a great challenge to find a predictive biomass model across experiments. Results In the present study, we constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to 3 consecutive barley (Hordeum vulgare) experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model will contribute to relieving the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high across experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of the plant biomass outcome. Furthermore, methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass. Conclusions We have developed quantitative models to accurately predict plant biomass accumulation from image data. We anticipate that the analysis results will be useful to advance our views of the phenotypic determinants of plant biomass outcome, and the statistical methods can be broadly used for other plant species. PMID:29346559

Ground level enhancement (GLE) energy spectrum parameters model

Science.gov (United States)

Qin, G.; Wu, S.

2017-12-01

We study the ground level enhancement (GLE) events in solar cycle 23 with the four energy spectra parameters, the normalization parameter C, low-energy power-law slope γ 1, high-energy power-law slope γ 2, and break energy E0, obtained by Mewaldt et al. 2012 who fit the observations to the double power-law equation. we divide the GLEs into two groups, one with strong acceleration by interplanetary (IP) shocks and another one without strong acceleration according to the condition of solar eruptions. We next fit the four parameters with solar event conditions to get models of the parameters for the two groups of GLEs separately. So that we would establish a model of energy spectrum for GLEs for the future space weather prediction.

Parameter Estimation of Spacecraft Fuel Slosh Model

Science.gov (United States)

Gangadharan, Sathya; Sudermann, James; Marlowe, Andrea; Njengam Charles

2004-01-01

Fuel slosh in the upper stages of a spinning spacecraft during launch has been a long standing concern for the success of a space mission. Energy loss through the movement of the liquid fuel in the fuel tank affects the gyroscopic stability of the spacecraft and leads to nutation (wobble) which can cause devastating control issues. The rate at which nutation develops (defined by Nutation Time Constant (NTC can be tedious to calculate and largely inaccurate if done during the early stages of spacecraft design. Pure analytical means of predicting the influence of onboard liquids have generally failed. A strong need exists to identify and model the conditions of resonance between nutation motion and liquid modes and to understand the general characteristics of the liquid motion that causes the problem in spinning spacecraft. A 3-D computerized model of the fuel slosh that accounts for any resonant modes found in the experimental testing will allow for increased accuracy in the overall modeling process. Development of a more accurate model of the fuel slosh currently lies in a more generalized 3-D computerized model incorporating masses, springs and dampers. Parameters describing the model include the inertia tensor of the fuel, spring constants, and damper coefficients. Refinement and understanding the effects of these parameters allow for a more accurate simulation of fuel slosh. The current research will focus on developing models of different complexity and estimating the model parameters that will ultimately provide a more realistic prediction of Nutation Time Constant obtained through simulation.

Soil-Related Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

Smith, A. J.

2004-01-01

This report presents one of the analyses that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the details of the conceptual model as well as the mathematical model and the required input parameters. The biosphere model is one of a series of process models supporting the postclosure Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A schematic representation of the documentation flow for the Biosphere input to TSPA is presented in Figure 1-1. This figure shows the evolutionary relationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (TWP) (BSC 2004 [DIRS 169573]). This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil-Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. The purpose of this analysis was to develop the biosphere model parameters associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation or ash deposition and, as a direct consequence, radionuclide concentration in other environmental media that are affected by radionuclide concentrations in soil. The analysis was performed in accordance with the TWP (BSC 2004 [DIRS 169573]) where the governing procedure was defined as AP-SIII.9Q, ''Scientific Analyses''. This

Optimum Operational Parameters for Yawed Wind Turbines

Directory of Open Access Journals (Sweden)

David A. Peters

2011-01-01

Full Text Available A set of systematical optimum operational parameters for wind turbines under various wind directions is derived by using combined momentum-energy and blade-element-energy concepts. The derivations are solved numerically by fixing some parameters at practical values. Then, the interactions between the produced power and the influential factors of it are generated in the figures. It is shown that the maximum power produced is strongly affected by the wind direction, the tip speed, the pitch angle of the rotor, and the drag coefficient, which are specifically indicated by figures. It also turns out that the maximum power can take place at two different optimum tip speeds in some cases. The equations derived herein can also be used in the modeling of tethered wind turbines which can keep aloft and deliver energy.

Final Report for NFE-07-00912: Development of Model Fuels Experimental Engine Data Base & Kinetic Modeling Parameter Sets

Energy Technology Data Exchange (ETDEWEB)

Bunting, Bruce G [ORNL

2012-10-01

The automotive and engine industries are in a period of very rapid change being driven by new emission standards, new types of after treatment, new combustion strategies, the introduction of new fuels, and drive for increased fuel economy and efficiency. The rapid pace of these changes has put more pressure on the need for modeling of engine combustion and performance, in order to shorten product design and introduction cycles. New combustion strategies include homogeneous charge compression ignition (HCCI), partial-premixed combustion compression ignition (PCCI), and dilute low temperature combustion which are being developed for lower emissions and improved fuel economy. New fuels include bio-fuels such as ethanol or bio-diesel, drop-in bio-derived fuels and those derived from new crude oil sources such as gas-to-liquids, coal-to-liquids, oil sands, oil shale, and wet natural gas. Kinetic modeling of the combustion process for these new combustion regimes and fuels is necessary in order to allow modeling and performance assessment for engine design purposes. In this research covered by this CRADA, ORNL developed and supplied experimental data related to engine performance with new fuels and new combustion strategies along with interpretation and analysis of such data and consulting to Reaction Design, Inc. (RD). RD performed additional analysis of this data in order to extract important parameters and to confirm engine and kinetic models. The data generated was generally published to make it available to the engine and automotive design communities and also to the Reaction Design Model Fuels Consortium (MFC).

Turbulence modeling with fractional derivatives: Derivation from first principles and initial results

Science.gov (United States)

Epps, Brenden; Cushman-Roisin, Benoit

2017-11-01

Fluid turbulence is an outstanding unsolved problem in classical physics, despite 120+ years of sustained effort. Given this history, we assert that a new mathematical framework is needed to make a transformative breakthrough. This talk offers one such framework, based upon kinetic theory tied to the statistics of turbulent transport. Starting from the Boltzmann equation and ``Lévy α-stable distributions'', we derive a turbulence model that expresses the turbulent stresses in the form of a fractional derivative, where the fractional order is tied to the transport behavior of the flow. Initial results are presented herein, for the cases of Couette-Poiseuille flow and 2D boundary layers. Among other results, our model is able to reproduce the logarithmic Law of the Wall in shear turbulence.

Parameter sensitivity and uncertainty analysis for a storm surge and wave model

Directory of Open Access Journals (Sweden)

L. A. Bastidas

2016-09-01

Full Text Available Development and simulation of synthetic hurricane tracks is a common methodology used to estimate hurricane hazards in the absence of empirical coastal surge and wave observations. Such methods typically rely on numerical models to translate stochastically generated hurricane wind and pressure forcing into coastal surge and wave estimates. The model output uncertainty associated with selection of appropriate model parameters must therefore be addressed. The computational overburden of probabilistic surge hazard estimates is exacerbated by the high dimensionality of numerical surge and wave models. We present a model parameter sensitivity analysis of the Delft3D model for the simulation of hazards posed by Hurricane Bob (1991 utilizing three theoretical wind distributions (NWS23, modified Rankine, and Holland. The sensitive model parameters (of 11 total considered include wind drag, the depth-induced breaking γB, and the bottom roughness. Several parameters show no sensitivity (threshold depth, eddy viscosity, wave triad parameters, and depth-induced breaking αB and can therefore be excluded to reduce the computational overburden of probabilistic surge hazard estimates. The sensitive model parameters also demonstrate a large number of interactions between parameters and a nonlinear model response. While model outputs showed sensitivity to several parameters, the ability of these parameters to act as tuning parameters for calibration is somewhat limited as proper model calibration is strongly reliant on accurate wind and pressure forcing data. A comparison of the model performance with forcings from the different wind models is also presented.

Calibration of discrete element model parameters: soybeans

Science.gov (United States)

Ghodki, Bhupendra M.; Patel, Manish; Namdeo, Rohit; Carpenter, Gopal

2018-05-01

Discrete element method (DEM) simulations are broadly used to get an insight of flow characteristics of granular materials in complex particulate systems. DEM input parameters for a model are the critical prerequisite for an efficient simulation. Thus, the present investigation aims to determine DEM input parameters for Hertz-Mindlin model using soybeans as a granular material. To achieve this aim, widely acceptable calibration approach was used having standard box-type apparatus. Further, qualitative and quantitative findings such as particle profile, height of kernels retaining the acrylic wall, and angle of repose of experiments and numerical simulations were compared to get the parameters. The calibrated set of DEM input parameters includes the following (a) material properties: particle geometric mean diameter (6.24 mm); spherical shape; particle density (1220 kg m^{-3} ), and (b) interaction parameters such as particle-particle: coefficient of restitution (0.17); coefficient of static friction (0.26); coefficient of rolling friction (0.08), and particle-wall: coefficient of restitution (0.35); coefficient of static friction (0.30); coefficient of rolling friction (0.08). The results may adequately be used to simulate particle scale mechanics (grain commingling, flow/motion, forces, etc) of soybeans in post-harvest machinery and devices.

Bone marrow-derived mesenchymal stem cells influence early tendon-healing in a rabbit achilles tendon model.

Science.gov (United States)

Chong, Alphonsus K S; Ang, Abel D; Goh, James C H; Hui, James H P; Lim, Aymeric Y T; Lee, Eng Hin; Lim, Beng Hai

2007-01-01

A repaired tendon needs to be protected for weeks until it has accrued enough strength to handle physiological loads. Tissue-engineering techniques have shown promise in the treatment of tendon and ligament defects. The present study tested the hypothesis that bone marrow-derived mesenchymal stem cells can accelerate tendon-healing after primary repair of a tendon injury in a rabbit model. Fifty-seven New Zealand White rabbits were used as the experimental animals, and seven others were used as the source of bone marrow-derived mesenchymal stem cells. The injury model was a sharp complete transection through the midsubstance of the Achilles tendon. The transected tendon was immediately repaired with use of a modified Kessler suture and a running epitendinous suture. Both limbs were used, and each side was randomized to receive either bone marrow-derived mesenchymal stem cells in a fibrin carrier or fibrin carrier alone (control). Postoperatively, the rabbits were not immobilized. Specimens were harvested at one, three, six, and twelve weeks for analysis, which included evaluation of gross morphology (sixty-two specimens), cell tracing (twelve specimens), histological assessment (forty specimens), immunohistochemistry studies (thirty specimens), morphometric analysis (forty specimens), and mechanical testing (sixty-two specimens). There were no differences between the two groups with regard to the gross morphology of the tendons. The fibrin had degraded by three weeks. Cell tracing showed that labeled bone marrow-derived mesenchymal stem cells remained viable and present in the intratendinous region for at least six weeks, becoming more diffuse at later time-periods. At three weeks, collagen fibers appeared more organized and there were better morphometric nuclear parameters in the treatment group (p tendon repair can improve histological and biomechanical parameters in the early stages of tendon-healing.

On the soft supersymmetry-breaking parameters in gauge-mediated models

International Nuclear Information System (INIS)

Wagner, C.E.M.

1998-01-01

Gauge mediation of supersymmetry breaking in the observable sector is an attractive idea, which naturally alleviates the flavor changing neutral current problem of supersymmetric theories. Quite generally, however, the number and quantum number of the messengers are not known; nor is their characteristic mass scale determined by the theory. Using the recently proposed method to extract supersymmetry-breaking parameters from wave-function renormalization, we derived general formulae for the soft supersymmetry-breaking parameters in the observable sector, valid in the small and moderate tan β regimes, for the case of split messengers. The full leading-order effects of top Yukawa and gauge couplings on the soft supersymmetry-breaking parameters are included. We give a simple interpretation of the general formulae in terms of the renormalization group evolution of the soft supersymmetry-breaking parameters. As a by-product of this analysis, the one-loop renormalization group evolution of the soft supersymmetry-breaking parameters is obtained for arbitrary boundary conditions of the scalar and gaugino mass parameters at high energies. (orig.)

Vulnerable Derivatives and Good Deal Bounds: A Structural Model

DEFF Research Database (Denmark)

Murgoci, Agatha

2013-01-01

We price vulnerable derivatives -- i.e. derivatives where the counterparty may default. These are basically the derivatives traded on the over-the-counter (OTC) markets. Default is modeled in a structural framework. The technique employed for pricing is good deal bounds (GDBs). The method imposes...

A three-dimensional cohesive sediment transport model with data assimilation: Model development, sensitivity analysis and parameter estimation

Science.gov (United States)

Wang, Daosheng; Cao, Anzhou; Zhang, Jicai; Fan, Daidu; Liu, Yongzhi; Zhang, Yue

2018-06-01

Based on the theory of inverse problems, a three-dimensional sigma-coordinate cohesive sediment transport model with the adjoint data assimilation is developed. In this model, the physical processes of cohesive sediment transport, including deposition, erosion and advection-diffusion, are parameterized by corresponding model parameters. These parameters are usually poorly known and have traditionally been assigned empirically. By assimilating observations into the model, the model parameters can be estimated using the adjoint method; meanwhile, the data misfit between model results and observations can be decreased. The model developed in this work contains numerous parameters; therefore, it is necessary to investigate the parameter sensitivity of the model, which is assessed by calculating a relative sensitivity function and the gradient of the cost function with respect to each parameter. The results of parameter sensitivity analysis indicate that the model is sensitive to the initial conditions, inflow open boundary conditions, suspended sediment settling velocity and resuspension rate, while the model is insensitive to horizontal and vertical diffusivity coefficients. A detailed explanation of the pattern of sensitivity analysis is also given. In ideal twin experiments, constant parameters are estimated by assimilating 'pseudo' observations. The results show that the sensitive parameters are estimated more easily than the insensitive parameters. The conclusions of this work can provide guidance for the practical applications of this model to simulate sediment transport in the study area.

Uncertainty of Modal Parameters Estimated by ARMA Models

DEFF Research Database (Denmark)

Jensen, Jacob Laigaard; Brincker, Rune; Rytter, Anders

1990-01-01

In this paper the uncertainties of identified modal parameters such as eidenfrequencies and damping ratios are assed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty of the parameters...... by simulation study of a lightly damped single degree of freedom system. Identification by ARMA models has been choosen as system identification method. It is concluded that both the sampling interval and number of sampled points may play a significant role with respect to the statistical errors. Furthermore......, it is shown that the model errors may also contribute significantly to the uncertainty....

Consistent Stochastic Modelling of Meteocean Design Parameters

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Sterndorff, M. J.

2000-01-01

Consistent stochastic models of metocean design parameters and their directional dependencies are essential for reliability assessment of offshore structures. In this paper a stochastic model for the annual maximum values of the significant wave height, and the associated wind velocity, current...

Soil-Related Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

A. J. Smith

2004-09-09

This report presents one of the analyses that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the details of the conceptual model as well as the mathematical model and the required input parameters. The biosphere model is one of a series of process models supporting the postclosure Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A schematic representation of the documentation flow for the Biosphere input to TSPA is presented in Figure 1-1. This figure shows the evolutionary relationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (TWP) (BSC 2004 [DIRS 169573]). This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil-Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. The purpose of this analysis was to develop the biosphere model parameters associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation or ash deposition and, as a direct consequence, radionuclide concentration in other environmental media that are affected by radionuclide concentrations in soil. The analysis was performed in accordance with the TWP (BSC 2004 [DIRS 169573]) where the governing procedure