Power Prediction Model for Turning EN-31 Steel Using Response Surface Methodology

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M. Hameedullah

2010-01-01

Full Text Available Power consumption in turning EN-31 steel (a material that is most extensively used in automotive industry with tungstencarbide tool under different cutting conditions was experimentally investigated. The experimental runs were planned accordingto 24+8 added centre point factorial design of experiments, replicated thrice. The data collected was statisticallyanalyzed using Analysis of Variance technique and first order and second order power consumption prediction models weredeveloped by using response surface methodology (RSM. It is concluded that second-order model is more accurate than thefirst-order model and fit well with the experimental data. The model can be used in the automotive industries for decidingthe cutting parameters for minimum power consumption and hence maximum productivity

Solar Atmosphere to Earth's Surface: Long Lead Time dB/dt Predictions with the Space Weather Modeling Framework

Science.gov (United States)

Welling, D. T.; Manchester, W.; Savani, N.; Sokolov, I.; van der Holst, B.; Jin, M.; Toth, G.; Liemohn, M. W.; Gombosi, T. I.

2017-12-01

The future of space weather prediction depends on the community's ability to predict L1 values from observations of the solar atmosphere, which can yield hours of lead time. While both empirical and physics-based L1 forecast methods exist, it is not yet known if this nascent capability can translate to skilled dB/dt forecasts at the Earth's surface. This paper shows results for the first forecast-quality, solar-atmosphere-to-Earth's-surface dB/dt predictions. Two methods are used to predict solar wind and IMF conditions at L1 for several real-world coronal mass ejection events. The first method is an empirical and observationally based system to estimate the plasma characteristics. The magnetic field predictions are based on the Bz4Cast system which assumes that the CME has a cylindrical flux rope geometry locally around Earth's trajectory. The remaining plasma parameters of density, temperature and velocity are estimated from white-light coronagraphs via a variety of triangulation methods and forward based modelling. The second is a first-principles-based approach that combines the Eruptive Event Generator using Gibson-Low configuration (EEGGL) model with the Alfven Wave Solar Model (AWSoM). EEGGL specifies parameters for the Gibson-Low flux rope such that it erupts, driving a CME in the coronal model that reproduces coronagraph observations and propagates to 1AU. The resulting solar wind predictions are used to drive the operational Space Weather Modeling Framework (SWMF) for geospace. Following the configuration used by NOAA's Space Weather Prediction Center, this setup couples the BATS-R-US global magnetohydromagnetic model to the Rice Convection Model (RCM) ring current model and a height-integrated ionosphere electrodynamics model. The long lead time predictions of dB/dt are compared to model results that are driven by L1 solar wind observations. Both are compared to real-world observations from surface magnetometers at a variety of geomagnetic latitudes

Predictive Surface Roughness Model for End Milling of Machinable Glass Ceramic

Energy Technology Data Exchange (ETDEWEB)

Reddy, M Mohan; Gorin, Alexander [School of Engineering and Science, Curtin University of Technology, Sarawak (Malaysia); Abou-El-Hossein, K A, E-mail: mohan.m@curtin.edu.my [Mechanical and Aeronautical Department, Nelson Mandela Metropolitan University, Port Elegebeth, 6031 (South Africa)

2011-02-15

Advanced ceramics of Machinable glass ceramic is attractive material to produce high accuracy miniaturized components for many applications in various industries such as aerospace, electronics, biomedical, automotive and environmental communications due to their wear resistance, high hardness, high compressive strength, good corrosion resistance and excellent high temperature properties. Many research works have been conducted in the last few years to investigate the performance of different machining operations when processing various advanced ceramics. Micro end-milling is one of the machining methods to meet the demand of micro parts. Selecting proper machining parameters are important to obtain good surface finish during machining of Machinable glass ceramic. Therefore, this paper describes the development of predictive model for the surface roughness of Machinable glass ceramic in terms of speed, feed rate by using micro end-milling operation.

Predictive Surface Roughness Model for End Milling of Machinable Glass Ceramic

International Nuclear Information System (INIS)

Reddy, M Mohan; Gorin, Alexander; Abou-El-Hossein, K A

2011-01-01

Advanced ceramics of Machinable glass ceramic is attractive material to produce high accuracy miniaturized components for many applications in various industries such as aerospace, electronics, biomedical, automotive and environmental communications due to their wear resistance, high hardness, high compressive strength, good corrosion resistance and excellent high temperature properties. Many research works have been conducted in the last few years to investigate the performance of different machining operations when processing various advanced ceramics. Micro end-milling is one of the machining methods to meet the demand of micro parts. Selecting proper machining parameters are important to obtain good surface finish during machining of Machinable glass ceramic. Therefore, this paper describes the development of predictive model for the surface roughness of Machinable glass ceramic in terms of speed, feed rate by using micro end-milling operation.

Single-layer model for surface roughness.

Science.gov (United States)

Carniglia, C K; Jensen, D G

2002-06-01

Random roughness of an optical surface reduces its specular reflectance and transmittance by the scattering of light. The reduction in reflectance can be modeled by a homogeneous layer on the surface if the refractive index of the layer is intermediate to the indices of the media on either side of the surface. Such a layer predicts an increase in the transmittance of the surface and therefore does not provide a valid model for the effects of scatter on the transmittance. Adding a small amount of absorption to the layer provides a model that predicts a reduction in both reflectance and transmittance. The absorbing layer model agrees with the predictions of a scalar scattering theory for a layer with a thickness that is twice the rms roughness of the surface. The extinction coefficient k for the layer is proportional to the thickness of the layer.

Ocean surface waves in Hurricane Ike (2008) and Superstorm Sandy (2012): Coupled model predictions and observations

Science.gov (United States)

Chen, Shuyi S.; Curcic, Milan

2016-07-01

Forecasting hurricane impacts of extreme winds and flooding requires accurate prediction of hurricane structure and storm-induced ocean surface waves days in advance. The waves are complex, especially near landfall when the hurricane winds and water depth varies significantly and the surface waves refract, shoal and dissipate. In this study, we examine the spatial structure, magnitude, and directional spectrum of hurricane-induced ocean waves using a high resolution, fully coupled atmosphere-wave-ocean model and observations. The coupled model predictions of ocean surface waves in Hurricane Ike (2008) over the Gulf of Mexico and Superstorm Sandy (2012) in the northeastern Atlantic and coastal region are evaluated with the NDBC buoy and satellite altimeter observations. Although there are characteristics that are general to ocean waves in both hurricanes as documented in previous studies, wave fields in Ike and Sandy possess unique properties due mostly to the distinct wind fields and coastal bathymetry in the two storms. Several processes are found to significantly modulate hurricane surface waves near landfall. First, the phase speed and group velocities decrease as the waves become shorter and steeper in shallow water, effectively increasing surface roughness and wind stress. Second, the bottom-induced refraction acts to turn the waves toward the coast, increasing the misalignment between the wind and waves. Third, as the hurricane translates over land, the left side of the storm center is characterized by offshore winds over very short fetch, which opposes incoming swell. Landfalling hurricanes produce broader wave spectra overall than that of the open ocean. The front-left quadrant is most complex, where the combination of windsea, swell propagating against the wind, increasing wind-wave stress, and interaction with the coastal topography requires a fully coupled model to meet these challenges in hurricane wave and surge prediction.

Tuning and predicting the wetting of nanoengineered material surface

Science.gov (United States)

Ramiasa-MacGregor, M.; Mierczynska, A.; Sedev, R.; Vasilev, K.

2016-02-01

The wetting of a material can be tuned by changing the roughness on its surface. Recent advances in the field of nanotechnology open exciting opportunities to control macroscopic wetting behaviour. Yet, the benchmark theories used to describe the wettability of macroscopically rough surfaces fail to fully describe the wetting behaviour of systems with topographical features at the nanoscale. To shed light on the events occurring at the nanoscale we have utilised model gradient substrata where surface nanotopography was tailored in a controlled and robust manner. The intrinsic wettability of the coatings was varied from hydrophilic to hydrophobic. The measured water contact angle could not be described by the classical theories. We developed an empirical model that effectively captures the experimental data, and further enables us to predict the wetting of surfaces with nanoscale roughness by considering the physical and chemical properties of the material. The fundamental insights presented here are important for the rational design of advanced materials having tailored surface nanotopography with predictable wettability.The wetting of a material can be tuned by changing the roughness on its surface. Recent advances in the field of nanotechnology open exciting opportunities to control macroscopic wetting behaviour. Yet, the benchmark theories used to describe the wettability of macroscopically rough surfaces fail to fully describe the wetting behaviour of systems with topographical features at the nanoscale. To shed light on the events occurring at the nanoscale we have utilised model gradient substrata where surface nanotopography was tailored in a controlled and robust manner. The intrinsic wettability of the coatings was varied from hydrophilic to hydrophobic. The measured water contact angle could not be described by the classical theories. We developed an empirical model that effectively captures the experimental data, and further enables us to predict the

Semianalytical model predicting transfer of volatile pollutants from groundwater to the soil surface.

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Atteia, Olivier; Höhener, Patrick

2010-08-15

Volatilization of toxic organic contaminants from groundwater to the soil surface is often considered an important pathway in risk analysis. Most of the risk models use simplified linear solutions that may overpredict the volatile flux. Although complex numerical models have been developed, their use is restricted to experienced users and for sites where field data are known in great detail. We present here a novel semianalytical model running on a spreadsheet that simulates the volatilization flux and vertical concentration profile in a soil based on the Van Genuchten functions. These widely used functions describe precisely the gas and water saturations and movement in the capillary fringe. The analytical model shows a good accuracy over several orders of magnitude when compared to a numerical model and laboratory data. The effect of barometric pumping is also included in the semianalytical formulation, although the model predicts that barometric pumping is often negligible. A sensitivity study predicts significant fluxes in sandy vadose zones and much smaller fluxes in other soils. Fluxes are linked to the dimensionless Henry's law constant H for H < 0.2 and increase by approximately 20% when temperature increases from 5 to 25 degrees C.

A residual life prediction model based on the generalized σ -N curved surface

Directory of Open Access Journals (Sweden)

Zongwen AN

2016-06-01

Full Text Available In order to investigate change rule of the residual life of structure under random repeated load, firstly, starting from the statistic meaning of random repeated load, the joint probability density function of maximum stress and minimum stress is derived based on the characteristics of order statistic (maximum order statistic and minimum order statistic; then, based on the equation of generalized σ -N curved surface, considering the influence of load cycles number on fatigue life, a relationship among minimum stress, maximum stress and residual life, that is the σmin(n- σmax(n-Nr(n curved surface model, is established; finally, the validity of the proposed model is demonstrated by a practical case. The result shows that the proposed model can reflect the influence of maximum stress and minimum stress on residual life of structure under random repeated load, which can provide a theoretical basis for life prediction and reliability assessment of structure.

The prediction of BRDFs from surface profile measurements

International Nuclear Information System (INIS)

Church, E.L.; Takacs, P.Z.; Leonard, T.A.

1989-01-01

This paper discusses methods of predicting the BRDF of smooth surfaces from profile measurements of their surface finish. The conversion of optical profile data to the BRDF at the same wavelength is essentially independent of scattering models, while the conversion of mechanical measurements, and wavelength scaling in general, are model dependent. Procedures are illustrated for several surfaces, including two from the recent HeNe BRDF round robin, and results are compared with measured data. Reasonable agreement is found except for surfaces which involve significant scattering from isolated surface defects which are poorly sampled in the profile data

Prediction of reservoir compaction and surface subsidence

Energy Technology Data Exchange (ETDEWEB)

De Waal, J.A.; Smits, R.M.M.

1988-06-01

A new loading-rate-dependent compaction model for unconsolidated clastic reservoirs is presented that considerably improves the accuracy of predicting reservoir rock compaction and surface subsidence resulting from pressure depletion in oil and gas fields. The model has been developed on the basis of extensive laboratory studies and can be derived from a theory relating compaction to time-dependent intergranular friction. The procedure for calculating reservoir compaction from laboratory measurements with the new model is outlined. Both field and laboratory compaction behaviors appear to be described by one single normalized, nonlinear compaction curve. With the new model, the large discrepancies usually observed between predictions based on linear compaction models and actual (nonlinear) field behavior can be explained.

Translation of Land Surface Model Accuracy and Uncertainty into Coupled Land-Atmosphere Prediction

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Santanello, Joseph A.; Kumar, Sujay; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Zhou, Shuija

2012-01-01

Land-atmosphere (L-A) Interactions playa critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (US-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF Simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

Translation of Land Surface Model Accuracy and Uncertainty into Coupled Land-Atmosphere Prediction

Science.gov (United States)

Santanello, J. A.; Kumar, S.; Peters-Lidard, C. D.; Harrison, K. W.; Zhou, S.

2012-12-01

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (LIS-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

Molecular adsorption of alkanes on platinum surfaces: A predictive theoretical model

International Nuclear Information System (INIS)

Stinnett, J.A.; Madix, R.J.

1996-01-01

The adsorption probabilities of methane and propane on Pt(111), and propane on Pt(110)-(1x2) have been successfully predicted for a wide range of incident energies and angles with classical stochastic trajectory simulations, using a pairwise additive Morse methyl endash platinum potential previously developed from the measured trapping probabilities of ethane on Pt(111). These predictions, along with those for ethane adsorption on Pt(110)endash(1x2), comprise a unified model for the molecular adsorption of alkanes on platinum surfaces. The simulations show the initial trapping probabilities of methane and propane on Pt(111) are determined to within approximately 10% by the fate of the first bounce. They also indicate that at normal incidence on Pt(111) energy conversions from perpendicular translational motion to both cartwheeling rotation and lattice phonons play increasingly important roles in increasing the trapping probability as the alkane increases in size and molecular weight. For methane itself excitation of parallel translational momentum after the first bounce serves as the most effective energy storage mechanism which facilitates trapping, whereas for propane cartwheel rotational motion plays the dominant role. Excessive excitation of these modes of motion, however, can cause scattering on subsequent bounces by reconversion of the energy into perpendicular translational energy. Collisions of methane with the hollow and bridge sites on the Pt(111) surface appear less effective in trapping than do atop sites. The simulations also suggest excitation of the C endash C endash C bending mode of propane has little effect on the trapping of propane on platinum surfaces for beam energies below 55 kJ/mol. copyright 1996 American Institute of Physics

A robust empirical seasonal prediction of winter NAO and surface climate.

Science.gov (United States)

Wang, L; Ting, M; Kushner, P J

2017-03-21

A key determinant of winter weather and climate in Europe and North America is the North Atlantic Oscillation (NAO), the dominant mode of atmospheric variability in the Atlantic domain. Skilful seasonal forecasting of the surface climate in both Europe and North America is reflected largely in how accurately models can predict the NAO. Most dynamical models, however, have limited skill in seasonal forecasts of the winter NAO. A new empirical model is proposed for the seasonal forecast of the winter NAO that exhibits higher skill than current dynamical models. The empirical model provides robust and skilful prediction of the December-January-February (DJF) mean NAO index using a multiple linear regression (MLR) technique with autumn conditions of sea-ice concentration, stratospheric circulation, and sea-surface temperature. The predictability is, for the most part, derived from the relatively long persistence of sea ice in the autumn. The lower stratospheric circulation and sea-surface temperature appear to play more indirect roles through a series of feedbacks among systems driving NAO evolution. This MLR model also provides skilful seasonal outlooks of winter surface temperature and precipitation over many regions of Eurasia and eastern North America.

An Improved MUSIC Model for Gibbsite Surfaces

Energy Technology Data Exchange (ETDEWEB)

Mitchell, Scott C.; Bickmore, Barry R.; Tadanier, Christopher J.; Rosso, Kevin M.

2004-06-01

Here we use gibbsite as a model system with which to test a recently published, bond-valence method for predicting intrinsic pKa values for surface functional groups on oxides. At issue is whether the method is adequate when valence parameters for the functional groups are derived from ab initio structure optimization of surfaces terminated by vacuum. If not, ab initio molecular dynamics (AIMD) simulations of solvated surfaces (which are much more computationally expensive) will have to be used. To do this, we had to evaluate extant gibbsite potentiometric titration data that where some estimate of edge and basal surface area was available. Applying BET and recently developed atomic force microscopy methods, we found that most of these data sets were flawed, in that their surface area estimates were probably wrong. Similarly, there may have been problems with many of the titration procedures. However, one data set was adequate on both counts, and we applied our method of surface pKa int prediction to fitting a MUSIC model to this data with considerable success—several features of the titration data were predicted well. However, the model fit was certainly not perfect, and we experienced some difficulties optimizing highly charged, vacuum-terminated surfaces. Therefore, we conclude that we probably need to do AIMD simulations of solvated surfaces to adequately predict intrinsic pKa values for surface functional groups.

Surface Temperature Prediction of a Bridge for Tactical Decision Aide Modelling

Science.gov (United States)

1988-01-01

Roadway And Piling Surface Temperature Predictions (No Radiosity Incident on Lower Surface) Compared to Temperature Estimates...Heat gained from water = Heat lost by long wave radiosity radiation. Algebraically, with the conduction term expressed in the same manner as for...5 10 15 20 LOCAL TIME (hrs.) Figure 8. Effect of No Radiosity Incident on Lower Surface. 37 U 8a M OT U% 60-- 0- o.. 20- 0- 1 T I I 5 10 15 20 LOCAL

3D transient model to predict temperature and ablated areas during laser processing of metallic surfaces

Directory of Open Access Journals (Sweden)

Babak. B. Naghshine

2017-02-01

Full Text Available Laser processing is one of the most popular small-scale patterning methods and has many applications in semiconductor device fabrication and biomedical engineering. Numerical modelling of this process can be used for better understanding of the process, optimization, and predicting the quality of the final product. An accurate 3D model is presented here for short laser pulses that can predict the ablation depth and temperature distribution on any section of the material in a minimal amount of time. In this transient model, variations of thermal properties, plasma shielding, and phase change are considered. Ablation depth was measured using a 3D optical profiler. Calculated depths are in good agreement with measured values on laser treated titanium surfaces. The proposed model can be applied to a wide range of materials and laser systems.

Surface complexation modeling for predicting solid phase arsenic concentrations in the sediments of the Mississippi River Valley alluvial aquifer, Arkansas, USA

Science.gov (United States)

Sharif, M.S.U.; Davis, R.K.; Steele, K.F.; Kim, B.; Hays, P.D.; Kresse, T.M.; Fazio, J.A.

2011-01-01

The potential health impact of As in drinking water supply systems in the Mississippi River Valley alluvial aquifer in the state of Arkansas, USA is significant. In this context it is important to understand the occurrence, distribution and mobilization of As in the Mississippi River Valley alluvial aquifer. Application of surface complexation models (SCMs) to predict the sorption behavior of As and hydrous Fe oxides (HFO) in the laboratory has increased in the last decade. However, the application of SCMs to predict the sorption of As in natural sediments has not often been reported, and such applications are greatly constrained by the lack of site-specific model parameters. Attempts have been made to use SCMs considering a component additivity (CA) approach which accounts for relative abundances of pure phases in natural sediments, followed by the addition of SCM parameters individually for each phase. Although few reliable and internally consistent sorption databases related to HFO exist, the use of SCMs using laboratory-derived sorption databases to predict the mobility of As in natural sediments has increased. This study is an attempt to evaluate the ability of the SCMs using the geochemical code PHREEQC to predict solid phase As in the sediments of the Mississippi River Valley alluvial aquifer in Arkansas. The SCM option of the double-layer model (DLM) was simulated using ferrihydrite and goethite as sorbents quantified from chemical extractions, calculated surface-site densities, published surface properties, and published laboratory-derived sorption constants for the sorbents. The model results are satisfactory for shallow wells (10.6. m below ground surface), where the redox condition is relatively oxic or mildly suboxic. However, for the deep alluvial aquifer (21-36.6. m below ground surface) where the redox condition is suboxic to anoxic, the model results are unsatisfactory. ?? 2011 Elsevier Ltd.

On the predictability of land surface fluxes from meteorological variables

Science.gov (United States)

Haughton, Ned; Abramowitz, Gab; Pitman, Andy J.

2018-01-01

Previous research has shown that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

Response surface and neural network based predictive models of cutting temperature in hard turning

Directory of Open Access Journals (Sweden)

Mozammel Mia

2016-11-01

Full Text Available The present study aimed to develop the predictive models of average tool-workpiece interface temperature in hard turning of AISI 1060 steels by coated carbide insert. The Response Surface Methodology (RSM and Artificial Neural Network (ANN were employed to predict the temperature in respect of cutting speed, feed rate and material hardness. The number and orientation of the experimental trials, conducted in both dry and high pressure coolant (HPC environments, were planned using full factorial design. The temperature was measured by using the tool-work thermocouple. In RSM model, two quadratic equations of temperature were derived from experimental data. The analysis of variance (ANOVA and mean absolute percentage error (MAPE were performed to suffice the adequacy of the models. In ANN model, 80% data were used to train and 20% data were employed for testing. Like RSM, herein, the error analysis was also conducted. The accuracy of the RSM and ANN model was found to be ⩾99%. The ANN models exhibit an error of ∼5% MAE for testing data. The regression coefficient was found to be greater than 99.9% for both dry and HPC. Both these models are acceptable, although the ANN model demonstrated a higher accuracy. These models, if employed, are expected to provide a better control of cutting temperature in turning of hardened steel.

Comment on 'Modelling of surface energies of elemental crystals'

International Nuclear Information System (INIS)

Li Jinping; Luo Xiaoguang; Hu Ping; Dong Shanliang

2009-01-01

Jiang et al (2004 J. Phys.: Condens. Matter 16 521) present a model based on the traditional broken-bond model for predicting surface energies of elemental crystals. It is found that bias errors can be produced in calculating the coordination numbers of surface atoms, especially in the prediction of high-Miller-index surface energies. (comment)

Tectonic predictions with mantle convection models

Science.gov (United States)

Coltice, Nicolas; Shephard, Grace E.

2018-04-01

Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough

Predicting the Best Fit: A Comparison of Response Surface Models for Midazolam and Alfentanil Sedation in Procedures With Varying Stimulation.

Science.gov (United States)

Liou, Jing-Yang; Ting, Chien-Kun; Mandell, M Susan; Chang, Kuang-Yi; Teng, Wei-Nung; Huang, Yu-Yin; Tsou, Mei-Yung

2016-08-01

Selecting an effective dose of sedative drugs in combined upper and lower gastrointestinal endoscopy is complicated by varying degrees of pain stimulation. We tested the ability of 5 response surface models to predict depth of sedation after administration of midazolam and alfentanil in this complex model. The procedure was divided into 3 phases: esophagogastroduodenoscopy (EGD), colonoscopy, and the time interval between the 2 (intersession). The depth of sedation in 33 adult patients was monitored by Observer Assessment of Alertness/Scores. A total of 218 combinations of midazolam and alfentanil effect-site concentrations derived from pharmacokinetic models were used to test 5 response surface models in each of the 3 phases of endoscopy. Model fit was evaluated with objective function value, corrected Akaike Information Criterion (AICc), and Spearman ranked correlation. A model was arbitrarily defined as accurate if the predicted probability is effect-site concentrations tested ranged from 1 to 76 ng/mL and from 5 to 80 ng/mL for midazolam and alfentanil, respectively. Midazolam and alfentanil had synergistic effects in colonoscopy and EGD, but additivity was observed in the intersession group. Adequate prediction rates were 84% to 85% in the intersession group, 84% to 88% during colonoscopy, and 82% to 87% during EGD. The reduced Greco and Fixed alfentanil concentration required for 50% of the patients to achieve targeted response Hierarchy models performed better with comparable predictive strength. The reduced Greco model had the lowest AICc with strong correlation in all 3 phases of endoscopy. Dynamic, rather than fixed, γ and γalf in the Hierarchy model improved model fit. The reduced Greco model had the lowest objective function value and AICc and thus the best fit. This model was reliable with acceptable predictive ability based on adequate clinical correlation. We suggest that this model has practical clinical value for patients undergoing procedures

Experimental High-Resolution Land Surface Prediction System for the Vancouver 2010 Winter Olympic Games

Science.gov (United States)

Belair, S.; Bernier, N.; Tong, L.; Mailhot, J.

2008-05-01

The 2010 Winter Olympic and Paralympic Games will take place in Vancouver, Canada, from 12 to 28 February 2010 and from 12 to 21 March 2010, respectively. In order to provide the best possible guidance achievable with current state-of-the-art science and technology, Environment Canada is currently setting up an experimental numerical prediction system for these special events. This system consists of a 1-km limited-area atmospheric model that will be integrated for 16h, twice a day, with improved microphysics compared with the system currently operational at the Canadian Meteorological Centre. In addition, several new and original tools will be used to adapt and refine predictions near and at the surface. Very high-resolution two-dimensional surface systems, with 100-m and 20-m grid size, will cover the Vancouver Olympic area. Using adaptation methods to improve the forcing from the lower-resolution atmospheric models, these 2D surface models better represent surface processes, and thus lead to better predictions of snow conditions and near-surface air temperature. Based on a similar strategy, a single-point model will be implemented to better predict surface characteristics at each station of an observing network especially installed for the 2010 events. The main advantage of this single-point system is that surface observations are used as forcing for the land surface models, and can even be assimilated (although this is not expected in the first version of this new tool) to improve initial conditions of surface variables such as snow depth and surface temperatures. Another adaptation tool, based on 2D stationnary solutions of a simple dynamical system, will be used to produce near-surface winds on the 100-m grid, coherent with the high- resolution orography. The configuration of the experimental numerical prediction system will be presented at the conference, together with preliminary results for winter 2007-2008.

Internal Physical Features of a Land Surface Model Employing a Tangent Linear Model

Science.gov (United States)

Yang, Runhua; Cohn, Stephen E.; daSilva, Arlindo; Joiner, Joanna; Houser, Paul R.

1997-01-01

The Earth's land surface, including its biomass, is an integral part of the Earth's weather and climate system. Land surface heterogeneity, such as the type and amount of vegetative covering., has a profound effect on local weather variability and therefore on regional variations of the global climate. Surface conditions affect local weather and climate through a number of mechanisms. First, they determine the re-distribution of the net radiative energy received at the surface, through the atmosphere, from the sun. A certain fraction of this energy increases the surface ground temperature, another warms the near-surface atmosphere, and the rest evaporates surface water, which in turn creates clouds and causes precipitation. Second, they determine how much rainfall and snowmelt can be stored in the soil and how much instead runs off into waterways. Finally, surface conditions influence the near-surface concentration and distribution of greenhouse gases such as carbon dioxide. The processes through which these mechanisms interact with the atmosphere can be modeled mathematically, to within some degree of uncertainty, on the basis of underlying physical principles. Such a land surface model provides predictive capability for surface variables including ground temperature, surface humidity, and soil moisture and temperature. This information is important for agriculture and industry, as well as for addressing fundamental scientific questions concerning global and local climate change. In this study we apply a methodology known as tangent linear modeling to help us understand more deeply, the behavior of the Mosaic land surface model, a model that has been developed over the past several years at NASA/GSFC. This methodology allows us to examine, directly and quantitatively, the dependence of prediction errors in land surface variables upon different vegetation conditions. The work also highlights the importance of accurate soil moisture information. Although surface

Combined Molecular Dynamics Simulation-Molecular-Thermodynamic Theory Framework for Predicting Surface Tensions.

Science.gov (United States)

Sresht, Vishnu; Lewandowski, Eric P; Blankschtein, Daniel; Jusufi, Arben

2017-08-22

A molecular modeling approach is presented with a focus on quantitative predictions of the surface tension of aqueous surfactant solutions. The approach combines classical Molecular Dynamics (MD) simulations with a molecular-thermodynamic theory (MTT) [ Y. J. Nikas, S. Puvvada, D. Blankschtein, Langmuir 1992 , 8 , 2680 ]. The MD component is used to calculate thermodynamic and molecular parameters that are needed in the MTT model to determine the surface tension isotherm. The MD/MTT approach provides the important link between the surfactant bulk concentration, the experimental control parameter, and the surfactant surface concentration, the MD control parameter. We demonstrate the capability of the MD/MTT modeling approach on nonionic alkyl polyethylene glycol surfactants at the air-water interface and observe reasonable agreement of the predicted surface tensions and the experimental surface tension data over a wide range of surfactant concentrations below the critical micelle concentration. Our modeling approach can be extended to ionic surfactants and their mixtures with both ionic and nonionic surfactants at liquid-liquid interfaces.

Comparison on the forecast model of landfill surface

International Nuclear Information System (INIS)

Zhou Xiaozhi; Sang Shuxun; Cao Liwen; Ji Xiaoyan

2010-01-01

Using four large-scale simulated landfill equipments, indoor parallel simulation landfill experiment was carried out. By monitoring the cumulative settlement of MSW, comparable researches indicate the actual effects of 'empirical model' and 'biodegradation model' on landfill surface settlement forecast, and the optimization measures are proposed on the basis of model defects analysis. Research leaded to following results: To the short-term prediction of MSW settlement, two types of models all have satisfactory predictive validity. When performing medium and long-term prediction, 'empirical model' predicted a significant deviation from the actual, and the forecasting error of 'biodegradation model' is also gradually enlarge with the extending forecast period. For optimizing these two types of model, long-term surface settlement monitoring is fundamental method, and constantly modify the model parameters is the key according to the dynamic monitoring data. (authors)

A thermodynamic model for predicting surface melting and overheating of different crystal planes in BCC, FCC and HCP pure metallic thin films

International Nuclear Information System (INIS)

Jahangir, Vafa; Riahifar, Reza; Sahba Yaghmaee, Maziar

2016-01-01

In order to predict as well as study the surface melting phenomena in contradiction to surface overheating, a generalized thermodynamics model including the surface free energy of solid and the melt state along with the interfacial energy of solid–liquid (melt on substrate) has been introduced. In addition, the effect of different crystal structures of surfaces in fcc, bcc and hcp metals was included in surface energies as well as in the atomistic model. These considerations lead us to predict surface melting and overheating as two contradictory melting phenomena. The results of the calculation are demonstrated on the example of Pb and Al thin films in three groups of (100), (110) and (111) surface planes. Our conclusions show good agreement with experimental results and other theoretical investigations. Moreover, a computational algorithm has been developed which enables users to investigate the surface melt or overheating of single component metallic thin film with variable crystal structures and different crystalline planes. This model and developed software can be used for studying all related surface phenomena. - Highlights: • Investigating the surface melting and overheating phenomena • Effect of crystal orientations, surface energies, geometry and different atomic surface layers • Developing a computational algorithm and its related code (free-software SMSO-Ver1) • Thickness and orientation of surface plane dominate the surface melting or overheating. • Total excess surface energy as a function of thickness and temperature explains melting.

A thermodynamic model for predicting surface melting and overheating of different crystal planes in BCC, FCC and HCP pure metallic thin films

Energy Technology Data Exchange (ETDEWEB)

Jahangir, Vafa, E-mail: vafa.jahangir@yahoo.com; Riahifar, Reza, E-mail: reza_rfr@yahoo.com; Sahba Yaghmaee, Maziar, E-mail: fkmsahba@uni-miskolc.hu

2016-03-31

In order to predict as well as study the surface melting phenomena in contradiction to surface overheating, a generalized thermodynamics model including the surface free energy of solid and the melt state along with the interfacial energy of solid–liquid (melt on substrate) has been introduced. In addition, the effect of different crystal structures of surfaces in fcc, bcc and hcp metals was included in surface energies as well as in the atomistic model. These considerations lead us to predict surface melting and overheating as two contradictory melting phenomena. The results of the calculation are demonstrated on the example of Pb and Al thin films in three groups of (100), (110) and (111) surface planes. Our conclusions show good agreement with experimental results and other theoretical investigations. Moreover, a computational algorithm has been developed which enables users to investigate the surface melt or overheating of single component metallic thin film with variable crystal structures and different crystalline planes. This model and developed software can be used for studying all related surface phenomena. - Highlights: • Investigating the surface melting and overheating phenomena • Effect of crystal orientations, surface energies, geometry and different atomic surface layers • Developing a computational algorithm and its related code (free-software SMSO-Ver1) • Thickness and orientation of surface plane dominate the surface melting or overheating. • Total excess surface energy as a function of thickness and temperature explains melting.

A Model to Predict the Steady-State Concentration of Hydroxyl Radicals in the Surface Layer of Natural Waters

International Nuclear Information System (INIS)

Minero, C.; Lauri, V.; Maurino, V.; Pelizzetti, E.; Vione, D.

2007-01-01

A model was developed to predict the steady-state [·OH] in the surface layer of natural waters as a function of nitrate, inorganic carbon (IC) and dissolved organic matter (DOM). The parameter values were studied in the range detected in shallow high-mountain lakes, to which the model results are most relevant. Calculations indicate that [·OH] increases with increasing nitrate and decreasing IC, and conditions are also identified where [·OH] is directly proportional, inversely proportional or independent of DOM. Based on the model results it is possible to predict the half-life time, due to reaction with ·OH, of given dissolved compounds, including organic pollutants, from the water composition data

The CAnadian Surface Prediction ARchive (CaSPAr): A Platform to Enhance Environmental Modelling in Canada and Globally

Science.gov (United States)

Tolson, B.; Mai, J.; Kornelsen, K. C.; Coulibaly, P. D.; Anctil, F.; Fortin, V.; Leahy, M.; Hall, B.

2017-12-01

Environmental models are tools for the modern society for a wide range of applications such as flood and drought monitoring, carbon storage and release estimates, predictions of power generation amounts, or reservoir management amongst others. Environmental models differ in the types of processes they incorporate, where land surface models focus on the energy, water, and carbon cycle of the land and hydrological models concentrate mainly on the water cycle. All these models, however, have in common that they rely on environmental input data from ground observations such as temperature, precipitation and/or radiation to force the model. If the same model is run in forecast mode, numerical weather predictions (NWPs) are needed to replace these ground observations. Therefore, it is critical that NWP data be available to develop models and validate forecast performance. These data are provided by the Meteorological Service of Canada (MSC) on a daily basis. MSC provides multiple products ranging from large scale global models ( 33km/grid cell) to high resolution pan-Canadian models ( 2.5km/grid cell). Operational products providing forecasts in real-time are made publicly available only at the time of issue through various means with new forecasts issued 2-4 times per day. Unfortunately, long term storage of these data are offline and relatively inaccessible to the research and operational communities. The new Canadian Surface Prediction Archive (CaSPAr) platform is an accessible rolling archive of 10 of MSC's NWP products. The 500TB platform will allow users to extract specific time periods, regions of interest and variables of interest in an easy to access NetCDF format. CaSPAr and community contributed post-processing scripts and tools are being developed such that the users, for example, can interpolate the data due to their needs or auto-generate model forcing files. We will present the CaSPAr platform and provide some insights in the current development of the web

Using isotopes to improve impact and hydrological predictions of land-surface schemes in global climate models

International Nuclear Information System (INIS)

McGuffie, K.; Henderson-Sellers, A.

2002-01-01

Global climate model (GCM) predictions of the impact of large-scale land-use change date back to 1984 as do the earliest isotopic studies of large-basin hydrology. Despite this coincidence in interest and geography, with both papers focussed on the Amazon, there have been few studies that have tried to exploit isotopic information with the goal of improving climate model simulations of the land-surface. In this paper we analyze isotopic results from the IAEA global data base specifically with the goal of identifying signatures of potential value for improving global and regional climate model simulations of the land-surface. Evaluation of climate model predictions of the impacts of deforestation of the Amazon has been shown to be of significance by recent results which indicate impacts occurring distant from the Amazon i.e. tele-connections causing climate change elsewhere around the globe. It is suggested that these could be similar in magnitude and extent to the global impacts of ENSO events. Validation of GCM predictions associated with Amazonian deforestation are increasingly urgently required because of the additional effects of other aspects of climate change, particularly synergies occurring between forest removal and greenhouse gas increases, especially CO 2 . Here we examine three decades distributions of deuterium excess across the Amazon and use the results to evaluate the relative importance of the fractionating (partial evaporation) and non-fractionating (transpiration) processes. These results illuminate GCM scenarios of importance to the regional climate and hydrology: (i) the possible impact of increased stomatal resistance in the rainforest caused by higher levels of atmospheric CO2 [4]; and (ii) the consequences of the combined effects of deforestation and global warming on the regions climate and hydrology

Path Loss Prediction Over the Lunar Surface Utilizing a Modified Longley-Rice Irregular Terrain Model

Science.gov (United States)

Foore, Larry; Ida, Nathan

2007-01-01

This study introduces the use of a modified Longley-Rice irregular terrain model and digital elevation data representative of an analogue lunar site for the prediction of RF path loss over the lunar surface. The results are validated by theoretical models and past Apollo studies. The model is used to approximate the path loss deviation from theoretical attenuation over a reflecting sphere. Analysis of the simulation results provides statistics on the fade depths for frequencies of interest, and correspondingly a method for determining the maximum range of communications for various coverage confidence intervals. Communication system engineers and mission planners are provided a link margin and path loss policy for communication frequencies of interest.

AN ARTIFICIAL INTELLIGENCE APPROACH FOR THE PREDICTION OF SURFACE ROUGHNESS IN CO2 LASER CUTTING

Directory of Open Access Journals (Sweden)

MILOŠ MADIĆ

2012-12-01

Full Text Available In laser cutting, the cut quality is of great importance. Multiple non-linear effects of process parameters and their interactions make very difficult to predict cut quality. In this paper, artificial intelligence (AI approach was applied to predict the surface roughness in CO2 laser cutting. To this aim, artificial neural network (ANN model of surface roughness was developed in terms of cutting speed, laser power and assist gas pressure. The experimental results obtained from Taguchi’s L25 orthogonal array were used to develop ANN model. The ANN mathematical model of surface roughness was expressed as explicit nonlinear function of the selected input parameters. Statistical results indicate that the ANN model can predict the surface roughness with good accuracy. It was showed that ANNs may be used as a good alternative in analyzing the effects of cutting parameters on the surface roughness.

Comparisons Between Model Predictions and Spectral Measurements of Charged and Neutral Particles on the Martian Surface

Science.gov (United States)

Kim, Myung-Hee Y.; Cucinotta, Francis A.; Zeitlin, Cary; Hassler, Donald M.; Ehresmann, Bent; Rafkin, Scot C. R.; Wimmer-Schweingruber, Robert F.; Boettcher, Stephan; Boehm, Eckart; Guo, Jingnan;

Predictive occurrence models for coastal wetland plant communities: Delineating hydrologic response surfaces with multinomial logistic regression

Science.gov (United States)

Snedden, Gregg A.; Steyer, Gregory D.

2013-02-01

Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007-Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

Prediction of strong earthquake motions on rock surface using evolutionary process models

International Nuclear Information System (INIS)

Kameda, H.; Sugito, M.

1984-01-01

Stochastic process models are developed for prediction of strong earthquake motions for engineering design purposes. Earthquake motions with nonstationary frequency content are modeled by using the concept of evolutionary processes. Discussion is focused on the earthquake motions on bed rocks which are important for construction of nuclear power plants in seismic regions. On this basis, two earthquake motion prediction models are developed, one (EMP-IB Model) for prediction with given magnitude and epicentral distance, and the other (EMP-IIB Model) to account for the successive fault ruptures and the site location relative to the fault of great earthquakes. (Author) [pt

Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L.

Directory of Open Access Journals (Sweden)

J. Prakash Maran

2013-09-01

Full Text Available In this study, a comparative approach was made between artificial neural network (ANN and response surface methodology (RSM to predict the mass transfer parameters of osmotic dehydration of papaya. The effects of process variables such as temperature, osmotic solution concentration and agitation speed on water loss, weight reduction, and solid gain during osmotic dehydration were investigated using a three-level three-factor Box-Behnken experimental design. Same design was utilized to train a feed-forward multilayered perceptron (MLP ANN with back-propagation algorithm. The predictive capabilities of the two methodologies were compared in terms of root mean square error (RMSE, mean absolute error (MAE, standard error of prediction (SEP, model predictive error (MPE, chi square statistic (χ2, and coefficient of determination (R2 based on the validation data set. The results showed that properly trained ANN model is found to be more accurate in prediction as compared to RSM model.

Predicting surface fuel models and fuel metrics using lidar and CIR imagery in a dense mixed conifer forest

Science.gov (United States)

Marek K. Jakubowksi; Qinghua Guo; Brandon Collins; Scott Stephens; Maggi. Kelly

2013-01-01

We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m

Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing.

Science.gov (United States)

Rekik, Islem; Li, Gang; Lin, Weili; Shen, Dinggang

2016-02-01

Longitudinal neuroimaging analysis methods have remarkably advanced our understanding of early postnatal brain development. However, learning predictive models to trace forth the evolution trajectories of both normal and abnormal cortical shapes remains broadly absent. To fill this critical gap, we pioneered the first prediction model for longitudinal developing cortical surfaces in infants using a spatiotemporal current-based learning framework solely from the baseline cortical surface. In this paper, we detail this prediction model and even further improve its performance by introducing two key variants. First, we use the varifold metric to overcome the limitations of the current metric for surface registration that was used in our preliminary study. We also extend the conventional varifold-based surface registration model for pairwise registration to a spatiotemporal surface regression model. Second, we propose a morphing process of the baseline surface using its topographic attributes such as normal direction and principal curvature sign. Specifically, our method learns from longitudinal data both the geometric (vertices positions) and dynamic (temporal evolution trajectories) features of the infant cortical surface, comprising a training stage and a prediction stage. In the training stage, we use the proposed varifold-based shape regression model to estimate geodesic cortical shape evolution trajectories for each training subject. We then build an empirical mean spatiotemporal surface atlas. In the prediction stage, given an infant, we select the best learnt features from training subjects to simultaneously predict the cortical surface shapes at all later timepoints, based on similarity metrics between this baseline surface and the learnt baseline population average surface atlas. We used a leave-one-out cross validation method to predict the inner cortical surface shape at 3, 6, 9 and 12 months of age from the baseline cortical surface shape at birth. Our

Prediction of surface roughness in turning of Ti-6Al-4V using cutting parameters, forces and tool vibration

Science.gov (United States)

Sahu, Neelesh Kumar; Andhare, Atul B.; Andhale, Sandip; Raju Abraham, Roja

2018-04-01

Present work deals with prediction of surface roughness using cutting parameters along with in-process measured cutting force and tool vibration (acceleration) during turning of Ti-6Al-4V with cubic boron nitride (CBN) inserts. Full factorial design is used for design of experiments using cutting speed, feed rate and depth of cut as design variables. Prediction model for surface roughness is developed using response surface methodology with cutting speed, feed rate, depth of cut, resultant cutting force and acceleration as control variables. Analysis of variance (ANOVA) is performed to find out significant terms in the model. Insignificant terms are removed after performing statistical test using backward elimination approach. Effect of each control variables on surface roughness is also studied. Correlation coefficient (R2 pred) of 99.4% shows that model correctly explains the experiment results and it behaves well even when adjustment is made in factors or new factors are added or eliminated. Validation of model is done with five fresh experiments and measured forces and acceleration values. Average absolute error between RSM model and experimental measured surface roughness is found to be 10.2%. Additionally, an artificial neural network model is also developed for prediction of surface roughness. The prediction results of modified regression model are compared with ANN. It is found that RSM model and ANN (average absolute error 7.5%) are predicting roughness with more than 90% accuracy. From the results obtained it is found that including cutting force and vibration for prediction of surface roughness gives better prediction than considering only cutting parameters. Also, ANN gives better prediction over RSM models.

Decadal prediction skill in the ocean with surface nudging in the IPSL-CM5A-LR climate model

Science.gov (United States)

Mignot, Juliette; García-Serrano, Javier; Swingedouw, Didier; Germe, Agathe; Nguyen, Sébastien; Ortega, Pablo; Guilyardi, Eric; Ray, Sulagna

2016-08-01

Two decadal prediction ensembles, based on the same climate model (IPSL-CM5A-LR) and the same surface nudging initialization strategy are analyzed and compared with a focus on upper-ocean variables in different regions of the globe. One ensemble consists of 3-member hindcasts launched every year since 1961 while the other ensemble benefits from 9 members but with start dates only every 5 years. Analysis includes anomaly correlation coefficients and root mean square errors computed against several reanalysis and gridded observational fields, as well as against the nudged simulation used to produce the hindcasts initial conditions. The last skill measure gives an upper limit of the predictability horizon one can expect in the forecast system, while the comparison with different datasets highlights uncertainty when assessing the actual skill. Results provide a potential prediction skill (verification against the nudged simulation) beyond the linear trend of the order of 10 years ahead at the global scale, but essentially associated with non-linear radiative forcings, in particular from volcanoes. At regional scale, we obtain 1 year in the tropical band, 10 years at midlatitudes in the North Atlantic and North Pacific, and 5 years at tropical latitudes in the North Atlantic, for both sea surface temperature (SST) and upper-ocean heat content. Actual prediction skill (verified against observational or reanalysis data) is overall more limited and less robust. Even so, large actual skill is found in the extratropical North Atlantic for SST and in the tropical to subtropical North Pacific for upper-ocean heat content. Results are analyzed with respect to the specific dynamics of the model and the way it is influenced by the nudging. The interplay between initialization and internal modes of variability is also analyzed for sea surface salinity. The study illustrates the importance of two key ingredients both necessary for the success of future coordinated decadal

Predictive occurrence models for coastal wetland plant communities: delineating hydrologic response surfaces with multinomial logistic regression

Science.gov (United States)

Snedden, Gregg A.; Steyer, Gregory D.

2013-01-01

Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007–Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

Surface roughness prediction model in end milling of Al/SiCp MMC ...

African Journals Online (AJOL)

user

2 Department of Mechanical Engineering, Pondicherry Engineering College, ... Keywords: Surface roughness (Ra), Response surface method (RSM), End milling, .... To establish the initial model and refined model, a software package MiniTab ..... The After building the regression model, a numerical optimization technique ...

Sediment Transport Model for a Surface Irrigation System

Directory of Open Access Journals (Sweden)

Damodhara R. Mailapalli

2013-01-01

Full Text Available Controlling irrigation-induced soil erosion is one of the important issues of irrigation management and surface water impairment. Irrigation models are useful in managing the irrigation and the associated ill effects on agricultural environment. In this paper, a physically based surface irrigation model was developed to predict sediment transport in irrigated furrows by integrating an irrigation hydraulic model with a quasi-steady state sediment transport model to predict sediment load in furrow irrigation. The irrigation hydraulic model simulates flow in a furrow irrigation system using the analytically solved zero-inertial overland flow equations and 1D-Green-Ampt, 2D-Fok, and Kostiakov-Lewis infiltration equations. Performance of the sediment transport model was evaluated for bare and cropped furrow fields. The results indicated that the sediment transport model can predict the initial sediment rate adequately, but the simulated sediment rate was less accurate for the later part of the irrigation event. Sensitivity analysis of the parameters of the sediment module showed that the soil erodibility coefficient was the most influential parameter for determining sediment load in furrow irrigation. The developed modeling tool can be used as a water management tool for mitigating sediment loss from the surface irrigated fields.

Forced synchronization of large-scale circulation to increase predictability of surface states

Science.gov (United States)

Shen, Mao-Lin; Keenlyside, Noel; Selten, Frank; Wiegerinck, Wim; Duane, Gregory

2016-04-01

Numerical models are key tools in the projection of the future climate change. The lack of perfect initial condition and perfect knowledge of the laws of physics, as well as inherent chaotic behavior limit predictions. Conceptually, the atmospheric variables can be decomposed into a predictable component (signal) and an unpredictable component (noise). In ensemble prediction the anomaly of ensemble mean is regarded as the signal and the ensemble spread the noise. Naturally the prediction skill will be higher if the signal-to-noise ratio (SNR) is larger in the initial conditions. We run two ensemble experiments in order to explore a way to reduce the SNR of surface winds and temperature. One ensemble experiment is AGCM with prescribing sea surface temperature (SST); the other is AGCM with both prescribing SST and nudging the high-level temperature and winds to ERA-Interim. Each ensemble has 30 members. Larger SNR is expected and found over the tropical ocean in the first experiment because the tropical circulation is associated with the convection and the associated surface wind convergence as these are to a large extent driven by the SST. However, small SNR is found over high latitude ocean and land surface due to the chaotic and non-synchronized atmosphere states. In the second experiment the higher level temperature and winds are forced to be synchronized (nudged to reanalysis) and hence a larger SNR of surface winds and temperature is expected. Furthermore, different nudging coefficients are also tested in order to understand the limitation of both synchronization of large-scale circulation and the surface states. These experiments will be useful for the developing strategies to synchronize the 3-D states of atmospheric models that can be later used to build a super model.

Are we near the predictability limit of tropical Indo-Pacific sea surface temperatures?

Science.gov (United States)

Newman, Matthew; Sardeshmukh, Prashant D.

2017-08-01

The predictability of seasonal anomalies worldwide rests largely on the predictability of tropical sea surface temperature (SST) anomalies. Tropical forecast skill is also a key metric of climate models. We find, however, that despite extensive model development, the tropical SST forecast skill of the operational North American Multi-Model Ensemble (NMME) of eight coupled atmosphere-ocean models remains close both regionally and temporally to that of a vastly simpler linear inverse model (LIM) derived from observed covariances of SST, sea surface height, and wind fields. The LIM clearly captures the essence of the predictable SST dynamics. The NMME and LIM skills also closely track and are only slightly lower than the potential skill estimated using the LIM's forecast signal-to-noise ratios. This suggests that the scope for further skill improvement is small in most regions, except in the western equatorial Pacific where the NMME skill is currently much lower than the LIM skill.

Modeling uranium(VI) adsorption onto montmorillonite under varying carbonate concentrations: A surface complexation model accounting for the spillover effect on surface potential

Science.gov (United States)

Tournassat, C.; Tinnacher, R. M.; Grangeon, S.; Davis, J. A.

2018-01-01

The prediction of U(VI) adsorption onto montmorillonite clay is confounded by the complexities of: (1) the montmorillonite structure in terms of adsorption sites on basal and edge surfaces, and the complex interactions between the electrical double layers at these surfaces, and (2) U(VI) solution speciation, which can include cationic, anionic and neutral species. Previous U(VI)-montmorillonite adsorption and modeling studies have typically expanded classical surface complexation modeling approaches, initially developed for simple oxides, to include both cation exchange and surface complexation reactions. However, previous models have not taken into account the unique characteristics of electrostatic surface potentials that occur at montmorillonite edge sites, where the electrostatic surface potential of basal plane cation exchange sites influences the surface potential of neighboring edge sites ('spillover' effect). A series of U(VI) - Na-montmorillonite batch adsorption experiments was conducted as a function of pH, with variable U(VI), Ca, and dissolved carbonate concentrations. Based on the experimental data, a new type of surface complexation model (SCM) was developed for montmorillonite, that specifically accounts for the spillover effect using the edge surface speciation model by Tournassat et al. (2016a). The SCM allows for a prediction of U(VI) adsorption under varying chemical conditions with a minimum number of fitting parameters, not only for our own experimental results, but also for a number of published data sets. The model agreed well with many of these datasets without introducing a second site type or including the formation of ternary U(VI)-carbonato surface complexes. The model predictions were greatly impacted by utilizing analytical measurements of dissolved inorganic carbon (DIC) concentrations in individual sample solutions rather than assuming solution equilibration with a specific partial pressure of CO2, even when the gas phase was

Prediction of allosteric sites on protein surfaces with an elastic-network-model-based thermodynamic method.

Science.gov (United States)

Su, Ji Guo; Qi, Li Sheng; Li, Chun Hua; Zhu, Yan Ying; Du, Hui Jing; Hou, Yan Xue; Hao, Rui; Wang, Ji Hua

2014-08-01

Allostery is a rapid and efficient way in many biological processes to regulate protein functions, where binding of an effector at the allosteric site alters the activity and function at a distant active site. Allosteric regulation of protein biological functions provides a promising strategy for novel drug design. However, how to effectively identify the allosteric sites remains one of the major challenges for allosteric drug design. In the present work, a thermodynamic method based on the elastic network model was proposed to predict the allosteric sites on the protein surface. In our method, the thermodynamic coupling between the allosteric and active sites was considered, and then the allosteric sites were identified as those where the binding of an effector molecule induces a large change in the binding free energy of the protein with its ligand. Using the proposed method, two proteins, i.e., the 70 kD heat shock protein (Hsp70) and GluA2 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor, were studied and the allosteric sites on the protein surface were successfully identified. The predicted results are consistent with the available experimental data, which indicates that our method is a simple yet effective approach for the identification of allosteric sites on proteins.

Modeling of surface tension effects in venturi scrubbing

Science.gov (United States)

Ott, Robert M.; Wu, Tatsu K. L.; Crowder, Jerry W.

A modified model of venturi scrubber performance has been developed that addresses two effects of liquid surface tension: its effect on droplet size and its effect on particle penetration into the droplet. The predictions of the model indicate that, in general, collection efficiency increases with a decrease in liquid surface tension, but the range over which this increase is significant depends on the particle size and on the scrubber operating parameters. The predictions further indicate that the increases in collection efficiency are almost totally due to the effect of liquid surface tension on the mean droplet size, and that the collection efficiency is not significantly affected by the ability of the particle to penetrate the droplet.

Sensitivity of point scale surface runoff predictions to rainfall resolution

Directory of Open Access Journals (Sweden)

A. J. Hearman

2007-01-01

Full Text Available This paper investigates the effects of using non-linear, high resolution rainfall, compared to time averaged rainfall on the triggering of hydrologic thresholds and therefore model predictions of infiltration excess and saturation excess runoff at the point scale. The bounded random cascade model, parameterized to three locations in Western Australia, was used to scale rainfall intensities at various time resolutions ranging from 1.875 min to 2 h. A one dimensional, conceptual rainfall partitioning model was used that instantaneously partitioned water into infiltration excess, infiltration, storage, deep drainage, saturation excess and surface runoff, where the fluxes into and out of the soil store were controlled by thresholds. The results of the numerical modelling were scaled by relating soil infiltration properties to soil draining properties, and in turn, relating these to average storm intensities. For all soil types, we related maximum infiltration capacities to average storm intensities (k* and were able to show where model predictions of infiltration excess were most sensitive to rainfall resolution (ln k*=0.4 and where using time averaged rainfall data can lead to an under prediction of infiltration excess and an over prediction of the amount of water entering the soil (ln k*>2 for all three rainfall locations tested. For soils susceptible to both infiltration excess and saturation excess, total runoff sensitivity was scaled by relating drainage coefficients to average storm intensities (g* and parameter ranges where predicted runoff was dominated by infiltration excess or saturation excess depending on the resolution of rainfall data were determined (ln g*<2. Infiltration excess predicted from high resolution rainfall was short and intense, whereas saturation excess produced from low resolution rainfall was more constant and less intense. This has important implications for the accuracy of current hydrological models that use time

Integration of nitrogen dynamics into the Noah-MP land surface model v1.1 for climate and environmental predictions

International Nuclear Information System (INIS)

Cai, X.; Zhang, X.

2016-01-01

Climate and terrestrial biosphere models consider nitrogen an important factor in limiting plant carbon uptake, while operational environmental models view nitrogen as the leading pollutant causing eutrophication in water bodies. The community Noah land surface model with multi-parameterization options (Noah-MP) is unique in that it is the next-generation land surface model for the Weather Research and Forecasting meteorological model and for the operational weather/climate models in the National Centers for Environmental Prediction. Here in this study, we add a capability to Noah-MP to simulate nitrogen dynamics by coupling the Fixation and Uptake of Nitrogen (FUN) plant model and the Soil and Water Assessment Tool (SWAT) soil nitrogen dynamics. This model development incorporates FUN's state-of-the-art concept of carbon cost theory and SWAT's strength in representing the impacts of agricultural management on the nitrogen cycle. Parameterizations for direct root and mycorrhizal-associated nitrogen uptake, leaf retranslocation, and symbiotic biological nitrogen fixation are employed from FUN, while parameterizations for nitrogen mineralization, nitrification, immobilization, volatilization, atmospheric deposition, and leaching are based on SWAT. The coupled model is then evaluated at the Kellogg Biological Station – a Long Term Ecological Research site within the US Corn Belt. Results show that the model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). Furthermore, the addition of nitrogen dynamics improves the modeling of net primary productivity and evapotranspiration. The model improvement is expected to advance the capability of Noah-MP to simultaneously predict weather and water quality in fully coupled Earth system models.

Gamma-Ray Pulsars Models and Predictions

CERN Document Server

Harding, A K

2001-01-01

Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...

Prediction and Migration of Surface-related Resonant Multiples

KAUST Repository

Guo, Bowen

2015-08-19

Surface-related resonant multiples can be migrated to achieve better resolution than migrating primary reflections. We now derive the formula for migrating surface-related resonant multiples, and show its super-resolution characteristics. Moreover, a method is proposed to predict surface-related resonant multiples with zero-offset primary reflections. The prediction can be used to indentify and extract the true resonant multiple from other events. Both synthetic and field data are used to validate this prediction.

Prediction of viscosities and surface tensions of fuels using a new corresponding states model

DEFF Research Database (Denmark)

Queimada, A.J.; Rolo, L.I.; Caco, A.I.

2006-01-01

While some properties of diesels are cheap, easy and fast to measure, such as densities, others such as surface tensions and viscosities are expensive and time consuming. A new approach that uses some basic information such as densities to predict viscosities and surface tensions is here proposed......) 2005 Elsevier Ltd. All rights reserved....

The Large-scale Coronal Structure of the 2017 August 21 Great American Eclipse: An Assessment of Solar Surface Flux Transport Model Enabled Predictions and Observations

Science.gov (United States)

Nandy, Dibyendu; Bhowmik, Prantika; Yeates, Anthony R.; Panda, Suman; Tarafder, Rajashik; Dash, Soumyaranjan

2018-01-01

On 2017 August 21, a total solar eclipse swept across the contiguous United States, providing excellent opportunities for diagnostics of the Sun’s corona. The Sun’s coronal structure is notoriously difficult to observe except during solar eclipses; thus, theoretical models must be relied upon for inferring the underlying magnetic structure of the Sun’s outer atmosphere. These models are necessary for understanding the role of magnetic fields in the heating of the corona to a million degrees and the generation of severe space weather. Here we present a methodology for predicting the structure of the coronal field based on model forward runs of a solar surface flux transport model, whose predicted surface field is utilized to extrapolate future coronal magnetic field structures. This prescription was applied to the 2017 August 21 solar eclipse. A post-eclipse analysis shows good agreement between model simulated and observed coronal structures and their locations on the limb. We demonstrate that slow changes in the Sun’s surface magnetic field distribution driven by long-term flux emergence and its evolution governs large-scale coronal structures with a (plausibly cycle-phase dependent) dynamical memory timescale on the order of a few solar rotations, opening up the possibility for large-scale, global corona predictions at least a month in advance.

Prediction of incipient flow boiling from a uniformly heated surface

International Nuclear Information System (INIS)

Yin, S.T.; Abdelmessih, A.H.

1977-01-01

This study was undertaken to investigate the phenomenon of liquid superheat during incipient boiling in a uniformly heated forced convection channel. Experimental data were obtained using Freon 11 as the test medium. Based on existing theories, an analytical method was developed for predicting the point of termination of nucleate boiling, observed during a decreasing heat flux process with a nucleation activated surface. The method may also be used to predict the point of boiling incipience, observed during an increasing heat flux process with a non-activated surface; this point does not appear to have been treated analytically in previous work. It can be shown that some of the existing models are special cases of the present formulation

Predicting geometry of slip surfaces beneath landslides by fuzzy theory. Fuzzy riron wo riyoshita suberimen yosoku

Energy Technology Data Exchange (ETDEWEB)

Ono, K [Mie Univ., Mie (Japan). Faculty of Biological and Resources

1991-12-01

In case a landslide occurs on a slope, grasping the area of influence (location and shape of the slip surface) is required to take a countermeasure against landslides. This paper describes a method developed by the author for predicting a slip surface by utilizing fuzzy theory. The method predicts a slip surface from observations on ground surface displacement vectors, and the validity of the method has been verified through slip experiments conducted on slopes with a centrifugal model experiment device. The developed method for predicting the location of a slip surface well matches the experiment results, indicating the validity of the method. It has been found that the difference between the predicted and observed locations of a slip surface mainly is due to the error of the prediction in the starting and ending locations of the slip surface. It is also pointed out that, in order to improve the prediction of the shape of a slip surface, the observation density must be increased at the location where the shape of the slip surface strongly varies, since the direction of the slip surface is determined by the direction of the ground surface displacement vectors. 4 refs., 7 figs.

Prediction of Experimental Surface Heat Flux of Thin Film Gauges using ANFIS

Science.gov (United States)

Sarma, Shrutidhara; Sahoo, Niranjan; Unal, Aynur

2018-05-01

Precise quantification of surface heat fluxes in highly transient environment is of paramount importance from the design point of view of several engineering equipment like thermal protection or cooling systems. Such environments are simulated in experimental facilities by exposing the surface with transient heat loads typically step/impulsive in nature. The surface heating rates are then determined from highly transient temperature history captured by efficient surface temperature sensors. The classical approach is to use thin film gauges (TFGs) in which temperature variations are acquired within milliseconds, thereby allowing calculation of surface heat flux, based on the theory of one-dimensional heat conduction on a semi-infinite body. With recent developments in the soft computing methods, the present study is an attempt for the application of intelligent system technique, called adaptive neuro fuzzy inference system (ANFIS) to recover surface heat fluxes from a given temperature history recorded by TFGs without having the need to solve lengthy analytical equations. Experiments have been carried out by applying known quantity of `impulse heat load' through laser beam on TFGs. The corresponding voltage signals have been acquired and surface heat fluxes are estimated through classical analytical approach. These signals are then used to `train' the ANFIS model, which later predicts output for `test' values. Results from both methods have been compared and these surface heat fluxes are used to predict the non-linear relationship between thermal and electrical properties of the gauges that are exceedingly pertinent to the design of efficient TFGs. Further, surface plots have been created to give an insight about dimensionality effect of the non-linear dependence of thermal/electrical parameters on each other. Later, it is observed that a properly optimized ANFIS model can predict the impulsive heat profiles with significant accuracy. This paper thus shows the

Prediction of lateral surface, volume and sphericity of pomegranate using MLP artificial neural network

Directory of Open Access Journals (Sweden)

A Rohani

2015-09-01

Full Text Available Introduction: Fast and accurate determination of geometrical properties of agricultural products has many applications in agricultural operations like planting, cultivating, harvesting and post-harvesting. Calculations related to storing, shipping and storage-coating materials as well as peeling time and surface-microbial concentrations are some applications of estimating product volume and surface area. Sphericity is also a parameter by which the shape differences between fruits, vegetables, grains and seeds can be quantified. This parameter is important in grading systems and inspecting rolling capability of agricultural products. Bayram presented a new dimensional method and equation to calculate the sphericity of certain shapesand some granular food materials (Bayram, 2005. Kumar and Mathew proposed atheoretically soundmethod for estimating the surface area of ellipsoidal food materials (Kumar and Mathew, 2003. Clayton et al. used non-linear regression models for calculation of apple surface area using the fruit mass or volume (Clayton et al., 1995. Humeida and Hobani predicted surface area and volume of pomegranates based on the weight and geometrical diametermean (Humeida and Hobani, 1993. Wang and Nguang designeda low cost sensor system to automatically compute the volume and surface area of axi-symmetricagricultural products such as eggs, lemons, limes and tamarillos (Wang and Nguang, 2007. The main objective of this study was to investigate the potential of Artificial Neural Network (ANN technique as an alternative method to predict the volume, surface area and sphericity of pomegranates. Materials and methods: The water displacement method (WDM was used for measuring the actual volume of pomegranates. Also, the sphericity and surface area are computed by using analytical methods. In this study, the neural MLP models were designed based upon the three nominal diameters of pomegranatesas variable inputs, while the output model consisted

Finite element modeling of surface subsidence induced by underground coal mining

International Nuclear Information System (INIS)

Su, D.W.H.

1992-01-01

The ability to predict the effects of longwall mining on topography and surface structures is important for any coal company in making permit applications and anticipating potential mining problems. The sophisticated finite element model described and evaluated in this paper is based upon five years of underground and surface observations and evolutionary development of modeling techniques and attributes. The model provides a very powerful tool to address subsidence and other ground control questions. The model can be used to calculate postmining stress and strain conditions at any horizon between the mine and the ground surface. This holds the promise of assisting in the prediction of mining-related hydrological effects

A two-parameter model to predict fracture in the transition

International Nuclear Information System (INIS)

DeAquino, C.T.; Landes, J.D.; McCabe, D.E.

1995-01-01

A model is proposed that uses a numerical characterization of the crack tip stress field modified by the J - Q constraint theory and a weak link assumption to predict fracture behavior in the transition for reactor vessel steels. This model predicts the toughness scatter band for a component model from a toughness scatter band measured on a test specimen geometry. The model has been applied previously to two-dimensional through cracks. Many applications to actual components structures involve three-dimensional surface flaws. These cases require a more difficult level of analysis and need additional information. In this paper, both the current model for two-dimensional cracks and an approach needed to extend the model for the prediction of transition fracture behavior in three-dimensional surface flaws are discussed. Examples are presented to show how the model can be applied and in some cases to compare with other test results. (author). 13 refs., 7 figs

A micromechanical four-phase model to predict the compressive failure surface of cement concrete

Directory of Open Access Journals (Sweden)

A. Caporale,

2014-07-01

Full Text Available In this work, a micromechanical model is used in order to predict the failure surface of cement concrete subject to multi-axial compression. In the adopted model, the concrete material is schematised as a composite with the following constituents: coarse aggregate (gravel, fine aggregate (sand and cement paste. The cement paste contains some voids which grow during the loading process. In fact, the non-linear behavior of the concrete is attributed to the creation of cracks in the cement paste; the effect of the cracks is taken into account by introducing equivalent voids (inclusions with zero stiffness in the cement paste. The three types of inclusions (namely gravel, sand and voids have different scales, so that the overall behavior of the concrete is obtained by the composition of three different homogenizations; in the sense that the concrete is regarded as the homogenized material of the two-phase composite constituted of the gravel and the mortar; in turn, the mortar is the homogenized material of the two-phase composite constituted of the sand inclusions and a (porous cement paste matrix; finally, the (porous cement paste is the homogenized material of the two-phase composite constituted of voids and the pure paste. The pure paste represents the cement paste before the loading process, so that it does not contain voids or other defects due to the loading process. The abovementioned three homogenizations are realized with the predictive scheme of Mori-Tanaka in conjunction with the Eshelby method. The adopted model can be considered an attempt to find micromechanical tools able to capture peculiar aspects of the cement concrete in load cases of uni-axial and multi-axial compression. Attributing the non-linear behavior of concrete to the creation of equivalent voids in the cement paste provides correspondence with many phenomenological aspects of concrete behavior. Trying to improve this correspondence, the influence of the parameters of the

The "AQUASCOPE" simplified model for predicting 89, 90Sr, 131l and 134, 137Cs in surface waters after a large-scale radioactive fallout

NARCIS (Netherlands)

Smith, J.T.; Belova, N.V.; Bulgakov, A.A.; Comans, R.N.J.; Konoplev, A.V.; Kudelsky, A.V.; Madruga, M.J.; Voitsekhovitch, O.V.; Zibolt, G.

2005-01-01

Simplified dynamic models have been developed for predicting the concentrations of radiocesium, radiostrontium, and 131I in surface waters and freshwater fish following a large-scale radioactive fallout. The models are intended to give averaged estimates for radionuclides in water bodies and in fish

Novel structures of oxygen adsorbed on a Zr(0001) surface predicted from first principles

Energy Technology Data Exchange (ETDEWEB)

Gao, Bo [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); Beijing computational science research center, Beijing,100084 (China); Wang, Jianyun [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); Lv, Jian [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); College of Materials Science and Engineering, Jilin University, Changchun, 130012 (China); Gao, Xingyu [Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, 100088 (China); CAEP Software Center for High Performance Numerical Simulation, Beijing, 100088 (China); Zhao, Yafan [CAEP Software Center for High Performance Numerical Simulation, Beijing, 100088 (China); Wang, Yanchao, E-mail: wyc@calypso.cn [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); Beijing computational science research center, Beijing,100084 (China); College of Materials Science and Engineering, Jilin University, Changchun, 130012 (China); Song, Haifeng, E-mail: song_haifeng@iapcm.ac.cn [Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, 100088 (China); CAEP Software Center for High Performance Numerical Simulation, Beijing, 100088 (China); Ma, Yanming [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); Beijing computational science research center, Beijing,100084 (China)

2017-01-30

Highlights: • Two stable structures of O adsorbed on a Zr(0001) surface are predicted with SLAM. • A stable structure of O adsorbed on a Zr(0001) surface is proposed with MLAM. • The calculated work function change is agreement with experimental value. - Abstract: The structures of O atoms adsorbed on a metal surface influence the metal properties significantly. Thus, studying O chemisorption on a Zr surface is of great interest. We investigated O adsorption on a Zr(0001) surface using our newly developed structure-searching method combined with first-principles calculations. A novel structural prototype with a unique combination of surface face-centered cubic (SFCC) and surface hexagonal close-packed (SHCP) O adsorption sites was predicted using a single-layer adsorption model (SLAM) for a 0.5 and 1.0 monolayer (ML) O coverage. First-principles calculations based on the SLAM revealed that the new predicted structures are energetically favorable compared with the well-known SFCC structures for a low O coverage (0.5 and 1.0 ML). Furthermore, on basis of our predicted SFCC + SHCP structures, a new structure within multi-layer adsorption model (MLAM) was proposed to be more stable at the O coverage of 1.0 ML, in which adsorbed O atoms occupy the SFCC + SHCP sites and the substitutional octahedral sites. The calculated work functions indicate that the SFCC + SHCP configuration has the lowest work function of all known structures at an O coverage of 0.5 ML within the SLAM, which agrees with the experimental trend of work function with variation in O coverage.

Predictive Finite Rate Model for Oxygen-Carbon Interactions at High Temperature

Science.gov (United States)

Poovathingal, Savio

An oxidation model for carbon surfaces is developed to predict ablation rates for carbon heat shields used in hypersonic vehicles. Unlike existing empirical models, the approach used here was to probe gas-surface interactions individually and then based on an understanding of the relevant fundamental processes, build a predictive model that would be accurate over a wide range of pressures and temperatures, and even microstructures. Initially, molecular dynamics was used to understand the oxidation processes on the surface. The molecular dynamics simulations were compared to molecular beam experiments and good qualitative agreement was observed. The simulations reproduced cylindrical pitting observed in the experiments where oxidation was rapid and primarily occurred around a defect. However, the studies were limited to small systems at low temperatures and could simulate time scales only of the order of nanoseconds. Molecular beam experiments at high surface temperature indicated that a majority of surface reaction products were produced through thermal mechanisms. Since the reactions were thermal, they occurred over long time scales which were computationally prohibitive for molecular dynamics to simulate. The experiments provided detailed dynamical data on the scattering of O, O2, CO, and CO2 and it was found that the data from molecular beam experiments could be used directly to build a model. The data was initially used to deduce surface reaction probabilities at 800 K. The reaction probabilities were then incorporated into the direct simulation Monte Carlo (DSMC) method. Simulations were performed where the microstructure was resolved and dissociated oxygen convected and diffused towards it. For a gas-surface temperature of 800 K, it was found that despite CO being the dominant surface reaction product, a gas-phase reaction forms significant CO2 within the microstructure region. It was also found that surface area did not play any role in concentration of

Foundations of elastoplasticity subloading surface model

CERN Document Server

Hashiguchi, Koichi

2017-01-01

This book is the standard text book of elastoplasticity in which the elastoplasticity theory is comprehensively described from the conventional theory for the monotonic loading to the unconventional theory for the cyclic loading behavior. Explanations of vector-tensor analysis and continuum mechanics are provided first as a foundation for elastoplasticity theory, covering various strain and stress measures and their rates with their objectivities. Elastoplasticity has been highly developed by the creation and formulation of the subloading surface model which is the unified fundamental law for irreversible mechanical phenomena in solids. The assumption that the interior of the yield surface is an elastic domain is excluded in order to describe the plastic strain rate due to the rate of stress inside the yield surface in this model aiming at the prediction of cyclic loading behavior, although the yield surface enclosing the elastic domain is assumed in all the elastoplastic models other than the subloading surf...

Predicting nucleic acid binding interfaces from structural models of proteins.

Science.gov (United States)

Dror, Iris; Shazman, Shula; Mukherjee, Srayanta; Zhang, Yang; Glaser, Fabian; Mandel-Gutfreund, Yael

2012-02-01

The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Copyright © 2011 Wiley Periodicals, Inc.

Seasonal predictability of Kiremt rainfall in coupled general circulation models

Science.gov (United States)

Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen

2017-11-01

The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.

Prediction of Sea Surface Temperature Using Long Short-Term Memory

Science.gov (United States)

Zhang, Qin; Wang, Hui; Dong, Junyu; Zhong, Guoqiang; Sun, Xin

2017-10-01

This letter adopts long short-term memory(LSTM) to predict sea surface temperature(SST), which is the first attempt, to our knowledge, to use recurrent neural network to solve the problem of SST prediction, and to make one week and one month daily prediction. We formulate the SST prediction problem as a time series regression problem. LSTM is a special kind of recurrent neural network, which introduces gate mechanism into vanilla RNN to prevent the vanished or exploding gradient problem. It has strong ability to model the temporal relationship of time series data and can handle the long-term dependency problem well. The proposed network architecture is composed of two kinds of layers: LSTM layer and full-connected dense layer. LSTM layer is utilized to model the time series relationship. Full-connected layer is utilized to map the output of LSTM layer to a final prediction. We explore the optimal setting of this architecture by experiments and report the accuracy of coastal seas of China to confirm the effectiveness of the proposed method. In addition, we also show its online updated characteristics.

Prediction of water droplet evaporation on zircaloy surface

International Nuclear Information System (INIS)

Lee, Chi Young; In, Wang Kee

2014-01-01

In the present experimental study, the prediction of water droplet evaporation on a zircaloy surface was investigated using various initial droplet sizes. To the best of our knowledge, this may be the first valuable effort for understanding the details of water droplet evaporation on a zircaloy surface. The initial contact diameters of the water droplets tested ranged from 1.76 to 3.41 mm. The behavior (i.e., time-dependent droplet volume, contact angle, droplet height, and contact diameter) and mode-transition time of the water droplet evaporation were strongly influenced by the initial droplet size. Using the normalized contact angle (θ*) and contact diameter (d*), the transitions between evaporation modes were successfully expressed by a single curve, and their criteria were proposed. To predict the temporal droplet volume change and evaporation rate, the range of θ* > 0.25 and d* > 0.9, which mostly covered the whole evaporation period and the initial contact diameter remained almost constant during evaporation, was targeted. In this range, the previous contact angle functions for the evaporation model underpredicted the experimental data. A new contact angle function of a zircaloy surface was empirically proposed, which represented the present experimental data within a reasonable degree of accuracy. (author)

General predictive model of friction behavior regimes for metal contacts based on the formation stability and evolution of nanocrystalline surface films.

Energy Technology Data Exchange (ETDEWEB)

Argibay, Nicolas [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Cheng, Shengfeng [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Sawyer, W. G. [Univ. of Florida, Gainesville, FL (United States); Michael, Joseph R. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Chandross, Michael E. [Sandia National Lab. (SNL-CA), Livermore, CA (United States)

2015-09-01

The prediction of macro-scale friction and wear behavior based on first principles and material properties has remained an elusive but highly desirable target for tribologists and material scientists alike. Stochastic processes (e.g. wear), statistically described parameters (e.g. surface topography) and their evolution tend to defeat attempts to establish practical general correlations between fundamental nanoscale processes and macro-scale behaviors. We present a model based on microstructural stability and evolution for the prediction of metal friction regimes, founded on recently established microstructural deformation mechanisms of nanocrystalline metals, that relies exclusively on material properties and contact stress models. We show through complementary experimental and simulation results that this model overcomes longstanding practical challenges and successfully makes accurate and consistent predictions of friction transitions for a wide range of contact conditions. This framework not only challenges the assumptions of conventional causal relationships between hardness and friction, and between friction and wear, but also suggests a pathway for the design of higher performance metal alloys.

Decadal prediction skill in the ocean with surface nudging in the IPSL-CM5A-LR climate model

OpenAIRE

Mignot , Juliette; García-Serrano , Javier; Swingedouw , Didier; Germe , Agathe; Nguyen , Sébastien; Ortega , Pablo; Guilyardi , Éric; Ray , Sulagna

2016-01-01

International audience; Two decadal prediction ensembles, based on the same climate model (IPSL-CM5A-LR) and the same surface nudging initialization strategy are analyzed and compared with a focus on upper-ocean variables in different regions of the globe. One ensemble consists of 3-member hindcasts launched every year since 1961 while the other ensemble benefits from 9 members but with start dates only every 5 years. Analysis includes anomaly correlation coefficients and root mean square err...

Comparisons of predicted steady-state levels in rooms with extended- and local-reaction bounding surfaces

Science.gov (United States)

Hodgson, Murray; Wareing, Andrew

2008-01-01

A combined beam-tracing and transfer-matrix model for predicting steady-state sound-pressure levels in rooms with multilayer bounding surfaces was used to compare the effect of extended- and local-reaction surfaces, and the accuracy of the local-reaction approximation. Three rooms—an office, a corridor and a workshop—with one or more multilayer test surfaces were considered. The test surfaces were a single-glass panel, a double-drywall panel, a carpeted floor, a suspended-acoustical ceiling, a double-steel panel, and glass fibre on a hard backing. Each test surface was modeled as of extended or of local reaction. Sound-pressure levels were predicted and compared to determine the significance of the surface-reaction assumption. The main conclusions were that the difference between modeling a room surface as of extended or of local reaction is not significant when the surface is a single plate or a single layer of material (solid or porous) with a hard backing. The difference is significant when the surface consists of multilayers of solid or porous material and includes a layer of fluid with a large thickness relative to the other layers. The results are partially explained by considering the surface-reflection coefficients at the first-reflection angles.

Measuring and modeling surface sorption dynamics of organophosphate flame retardants on impervious surfaces

Data.gov (United States)

U.S. Environmental Protection Agency — The data presented in this data file is a product of a journal publication. The dataset contains measured and model predicted OPFRs gas-phase and surface-phase...

Using advanced surface complexation models for modelling soil chemistry under forests: Solling forest, Germany

Energy Technology Data Exchange (ETDEWEB)

Bonten, Luc T.C., E-mail: luc.bonten@wur.nl [Alterra-Wageningen UR, Soil Science Centre, P.O. Box 47, 6700 AA Wageningen (Netherlands); Groenenberg, Jan E. [Alterra-Wageningen UR, Soil Science Centre, P.O. Box 47, 6700 AA Wageningen (Netherlands); Meesenburg, Henning [Northwest German Forest Research Station, Abt. Umweltkontrolle, Sachgebiet Intensives Umweltmonitoring, Goettingen (Germany); Vries, Wim de [Alterra-Wageningen UR, Soil Science Centre, P.O. Box 47, 6700 AA Wageningen (Netherlands)

2011-10-15

Various dynamic soil chemistry models have been developed to gain insight into impacts of atmospheric deposition of sulphur, nitrogen and other elements on soil and soil solution chemistry. Sorption parameters for anions and cations are generally calibrated for each site, which hampers extrapolation in space and time. On the other hand, recently developed surface complexation models (SCMs) have been successful in predicting ion sorption for static systems using generic parameter sets. This study reports the inclusion of an assemblage of these SCMs in the dynamic soil chemistry model SMARTml and applies this model to a spruce forest site in Solling Germany. Parameters for SCMs were taken from generic datasets and not calibrated. Nevertheless, modelling results for major elements matched observations well. Further, trace metals were included in the model, also using the existing framework of SCMs. The model predicted sorption for most trace elements well. - Highlights: > Surface complexation models can be well applied in field studies. > Soil chemistry under a forest site is adequately modelled using generic parameters. > The model is easily extended with extra elements within the existing framework. > Surface complexation models can show the linkages between major soil chemistry and trace element behaviour. - Surface complexation models with generic parameters make calibration of sorption superfluous in dynamic modelling of deposition impacts on soil chemistry under nature areas.

Using advanced surface complexation models for modelling soil chemistry under forests: Solling forest, Germany

International Nuclear Information System (INIS)

Bonten, Luc T.C.; Groenenberg, Jan E.; Meesenburg, Henning; Vries, Wim de

2011-01-01

Various dynamic soil chemistry models have been developed to gain insight into impacts of atmospheric deposition of sulphur, nitrogen and other elements on soil and soil solution chemistry. Sorption parameters for anions and cations are generally calibrated for each site, which hampers extrapolation in space and time. On the other hand, recently developed surface complexation models (SCMs) have been successful in predicting ion sorption for static systems using generic parameter sets. This study reports the inclusion of an assemblage of these SCMs in the dynamic soil chemistry model SMARTml and applies this model to a spruce forest site in Solling Germany. Parameters for SCMs were taken from generic datasets and not calibrated. Nevertheless, modelling results for major elements matched observations well. Further, trace metals were included in the model, also using the existing framework of SCMs. The model predicted sorption for most trace elements well. - Highlights: → Surface complexation models can be well applied in field studies. → Soil chemistry under a forest site is adequately modelled using generic parameters. → The model is easily extended with extra elements within the existing framework. → Surface complexation models can show the linkages between major soil chemistry and trace element behaviour. - Surface complexation models with generic parameters make calibration of sorption superfluous in dynamic modelling of deposition impacts on soil chemistry under nature areas.

Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

Science.gov (United States)

Chow, R.; Bennett, J.; Dugge, J.; Wöhling, T.; Nowak, W.

2017-12-01

Hyporheic exchange is the interaction of water between rivers and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic fluxes have been attributed to the representation of heterogeneous subsurface properties. This research aims to evaluate which aspect of the subsurface representation - the spatial distribution of hydrofacies or the model for local-scale (within-facies) heterogeneity - most influences the predictive uncertainty. Also, we seek to identify data types that help reduce this uncertainty best. For this investigation, we conduct a modelling study of the Steinlach River meander, in Southwest Germany. The Steinlach River meander is an experimental site established in 2010 to monitor hyporheic exchange at the meander scale. We use HydroGeoSphere, a fully integrated surface water-groundwater model, to model hyporheic exchange and to assess the predictive uncertainty of hyporheic exchange transit times (HETT). A highly parameterized complex model is built and treated as `virtual reality', which is in turn modelled with simpler subsurface parameterization schemes (Figure). Then, we conduct Monte-Carlo simulations with these models to estimate the predictive uncertainty. Results indicate that: Uncertainty in HETT is relatively small for early times and increases with transit times. Uncertainty from local-scale heterogeneity is negligible compared to uncertainty in the hydrofacies distribution. Introducing more data to a poor model structure may reduce predictive variance, but does not reduce predictive bias. Hydraulic head observations alone cannot constrain the uncertainty of HETT, however an estimate of hyporheic exchange flux proves to be more effective at reducing this uncertainty. Figure: Approach for evaluating predictive model uncertainty. A conceptual model is first developed from the field investigations. A complex model (`virtual reality') is then developed based on that conceptual model

Advancing land surface model development with satellite-based Earth observations

Science.gov (United States)

Orth, Rene; Dutra, Emanuel; Trigo, Isabel F.; Balsamo, Gianpaolo

2017-04-01

The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help to improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology, but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability and understanding of climate system feedbacks. Orth, R., E. Dutra, I. F. Trigo, and G. Balsamo (2016): Advancing land surface model development with satellite-based Earth observations. Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-628

Radiation properties modeling for plasma-sprayed-alumina-coated rough surfaces for spacecrafts

International Nuclear Information System (INIS)

Li, R.M.; Joshi, Sunil C.; Ng, H.W.

2006-01-01

Spacecraft thermal control materials (TCMs) play a vital role in the entire service life of a spacecraft . Most of the conventional TCMs degrade in the harmful space environment . In the previous study, plasma sprayed alumina (PSA) coating was established as a new and better TCM for spacecrafts, in view of its stability and reliability compared to the traditional TCMs . During the investigation, the surface roughness of PSA was found important, because the roughness affects the radiative heat exchange between the surface and its surroundings. Parameters such as root-mean-square roughness cannot properly evaluate surface roughness effects on radiative properties of opaque surfaces . Some models have been developed earlier to predict the effects, such as Davies' model , Tang and Buckius's statistical geometric optics model . However, they are valid only in their own specific situations. In this paper, an energy absorption geometry model was developed and applied to investigate the roughness effects with the help of 2D surface profile of PSA coated substrate scanned at micron level. This model predicts effective normal solar absorptance (α ne ) and effective hemispherical infrared emittance (ε he ) of a rough PSA surface. These values, if used in the heat transfer analysis of an equivalent, smooth and optically flat surface, lead to the prediction of the same rate of heat exchange and temperature as that of for the rough PSA surface. The model was validated through comparison between a smooth and a rough PSA coated surfaces. Even though not tested for other types of materials, the model formulation is generic and can be used to incorporate the rough surface effects for other types of thermal coatings, provided the baseline values of normal solar absorptance (α n ) and hemispherical infrared emittance (ε h ) are available for a generic surface of the same material

Advancing land surface model development with satellite-based Earth observations

Science.gov (United States)

Orth, Rene; Dutra, Emanuel; Trigo, Isabel F.; Balsamo, Gianpaolo

2017-05-01

The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts, we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability, and understanding of climate system feedbacks.

Models for prediction of global solar radiation on horizontal surface ...

African Journals Online (AJOL)

The estimation of global solar radiation continues to play a fundamental role in solar engineering systems and applications. This paper compares various models for estimating the average monthly global solar radiation on horizontal surface for Akure, Nigeria, using solar radiation and sunshine duration data covering years ...

Modeling global distribution of agricultural insecticides in surface waters

International Nuclear Information System (INIS)

Ippolito, Alessio; Kattwinkel, Mira; Rasmussen, Jes J.; Schäfer, Ralf B.; Fornaroli, Riccardo; Liess, Matthias

2015-01-01

Agricultural insecticides constitute a major driver of animal biodiversity loss in freshwater ecosystems. However, the global extent of their effects and the spatial extent of exposure remain largely unknown. We applied a spatially explicit model to estimate the potential for agricultural insecticide runoff into streams. Water bodies within 40% of the global land surface were at risk of insecticide runoff. We separated the influence of natural factors and variables under human control determining insecticide runoff. In the northern hemisphere, insecticide runoff presented a latitudinal gradient mainly driven by insecticide application rate; in the southern hemisphere, a combination of daily rainfall intensity, terrain slope, agricultural intensity and insecticide application rate determined the process. The model predicted the upper limit of observed insecticide exposure measured in water bodies (n = 82) in five different countries reasonably well. The study provides a global map of hotspots for insecticide contamination guiding future freshwater management and conservation efforts. - Highlights: • First global map on insecticide runoff through modelling. • Model predicts upper limit of insecticide exposure when compared to field data. • Water bodies in 40% of global land surface may be at risk of adverse effects. • Insecticide application rate, terrain slope and rainfall main drivers of exposure. - We provide the first global map on insecticide runoff to surface water predicting that water bodies in 40% of global land surface may be at risk of adverse effects

A statistical model for predicting muscle performance

Science.gov (United States)

Byerly, Diane Leslie De Caix

The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

TH-CD-207A-05: Lung Surface Deformation Vector Fields Prediction by Monitoring Respiratory Surrogate Signals

International Nuclear Information System (INIS)

Nasehi Tehrani, J; Wang, J; McEwan, A

2016-01-01

Purpose: In this study, we developed and evaluated a method for predicting lung surface deformation vector fields (SDVFs) based on surrogate signals such as chest and abdomen motion at selected locations and spirometry measurements. Methods: A Patient-specific 3D triangular surface mesh of the lung region at end-expiration (EE) phase was obtained by threshold-based segmentation method. For each patient, a spirometer recorded the flow volume changes of the lungs; and 192 selected points at a regular spacing of 2cm X 2cm matrix points over a total area of 34cm X 24cm on the surface of chest and abdomen was used to detect chest wall motions. Preprocessing techniques such as QR factorization with column pivoting (QRCP) were employed to remove redundant observations of the chest and abdominal area. To create a statistical model between the lung surface and the corresponding surrogate signals, we developed a predictive model based on canonical ridge regression (CRR). Two unique weighting vectors were selected for each vertex on the surface of the lung, and they were optimized during the training process using the all other phases of 4D-CT except the end-inspiration (EI) phase. These parameters were employed to predict the vertices locations of a testing data set, which was the EI phase of 4D-CT. Results: For ten lung cancer patients, the deformation vector field of each vertex of lung surface mesh was estimated from the external motion at selected positions on the chest wall surface plus spirometry measurements. The average estimation of 98th percentile of error was less than 1 mm (AP= 0.85, RL= 0.61, and SI= 0.82). Conclusion: The developed predictive model provides a non-invasive approach to derive lung boundary condition. Together with personalized biomechanical respiration modelling, the proposed model can be used to derive the lung tumor motion during radiation therapy accurately from non-invasive measurements.

TH-CD-207A-05: Lung Surface Deformation Vector Fields Prediction by Monitoring Respiratory Surrogate Signals

Energy Technology Data Exchange (ETDEWEB)

Nasehi Tehrani, J; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States); McEwan, A [The University of Sydney, Sydney, New South Wales (Australia)

2016-06-15

Purpose: In this study, we developed and evaluated a method for predicting lung surface deformation vector fields (SDVFs) based on surrogate signals such as chest and abdomen motion at selected locations and spirometry measurements. Methods: A Patient-specific 3D triangular surface mesh of the lung region at end-expiration (EE) phase was obtained by threshold-based segmentation method. For each patient, a spirometer recorded the flow volume changes of the lungs; and 192 selected points at a regular spacing of 2cm X 2cm matrix points over a total area of 34cm X 24cm on the surface of chest and abdomen was used to detect chest wall motions. Preprocessing techniques such as QR factorization with column pivoting (QRCP) were employed to remove redundant observations of the chest and abdominal area. To create a statistical model between the lung surface and the corresponding surrogate signals, we developed a predictive model based on canonical ridge regression (CRR). Two unique weighting vectors were selected for each vertex on the surface of the lung, and they were optimized during the training process using the all other phases of 4D-CT except the end-inspiration (EI) phase. These parameters were employed to predict the vertices locations of a testing data set, which was the EI phase of 4D-CT. Results: For ten lung cancer patients, the deformation vector field of each vertex of lung surface mesh was estimated from the external motion at selected positions on the chest wall surface plus spirometry measurements. The average estimation of 98th percentile of error was less than 1 mm (AP= 0.85, RL= 0.61, and SI= 0.82). Conclusion: The developed predictive model provides a non-invasive approach to derive lung boundary condition. Together with personalized biomechanical respiration modelling, the proposed model can be used to derive the lung tumor motion during radiation therapy accurately from non-invasive measurements.

Calibration of a PHREEQC-based geochemical model to predict surface water discharge from an operating uranium mill in the Athabasca Basin

International Nuclear Information System (INIS)

Mahoney, J.; Ryan, F.

2014-01-01

A PHREEQC based geochemical model has been developed to predict impacts from the McClean Lake Mill discharges through three lakes in the Athabasca Basin, Saskatchewan, Canada. The model is primarily a mixing calculation that uses site specific water balances and water compositions from five sources: 1) two water treatment plants, 2) waters from pit dewatering wells, 3) run-off into the lakes from surface waters, 4) ambient lake compositions, and 5) precipitation (rain and snow) onto the pit lake surface. The model allows for the discharge of these waters into the first lake, which then flows into another nearby lake and finally into a third larger lake. Water losses through evaporation and the impact of subsequent evapoconcentration processes are included in the model. PHREEQC has numerous mass transfer options including mixing, user specified reactions, equilibration with gas and solid phases, and surface complexation. Thus this program is ideally suited to this application. Preparation of such a complicated model is facilitated by an EXCEL Spreadsheet, which converts the water balance into appropriately formatted mixing proportions and to prepare portions of the PHREEQC input file in a format directly useable by PHREEQC. This allows for a high level of flexibility, while reducing transcription errors. For each scenario, the model path involves mixing of the waters in the first lake, followed by evapoconcentration, equilibration of the resulting solution with gas phases, including carbon dioxide and oxygen and with minerals and surfaces. The resultant composition is mixed in the second lake with more surface water, lake water and precipitation, and then re-equilibrated. This water represents the flow into the final lake; further mixing/dilution is accommodated; chemical equilibration may also occur. Because of the numerous steps and processes that define the pathway, each annual step requires approximately 200 lines of input in PHREEQC. Models used in the initial

Predictive modeling of neuroanatomic structures for brain atrophy detection

Science.gov (United States)

Hu, Xintao; Guo, Lei; Nie, Jingxin; Li, Kaiming; Liu, Tianming

2010-03-01

In this paper, we present an approach of predictive modeling of neuroanatomic structures for the detection of brain atrophy based on cross-sectional MRI image. The underlying premise of applying predictive modeling for atrophy detection is that brain atrophy is defined as significant deviation of part of the anatomy from what the remaining normal anatomy predicts for that part. The steps of predictive modeling are as follows. The central cortical surface under consideration is reconstructed from brain tissue map and Regions of Interests (ROI) on it are predicted from other reliable anatomies. The vertex pair-wise distance between the predicted vertex and the true one within the abnormal region is expected to be larger than that of the vertex in normal brain region. Change of white matter/gray matter ratio within a spherical region is used to identify the direction of vertex displacement. In this way, the severity of brain atrophy can be defined quantitatively by the displacements of those vertices. The proposed predictive modeling method has been evaluated by using both simulated atrophies and MRI images of Alzheimer's disease.

An intermittency model for predicting roughness induced transition

Science.gov (United States)

Ge, Xuan; Durbin, Paul

2014-11-01

An extended model for roughness-induced transition is proposed based on an intermittency transport equation for RANS modeling formulated in local variables. To predict roughness effects in the fully turbulent boundary layer, published boundary conditions for k and ω are used, which depend on the equivalent sand grain roughness height, and account for the effective displacement of wall distance origin. Similarly in our approach, wall distance in the transition model for smooth surfaces is modified by an effective origin, which depends on roughness. Flat plate test cases are computed to show that the proposed model is able to predict the transition onset in agreement with a data correlation of transition location versus roughness height, Reynolds number, and inlet turbulence intensity. Experimental data for a turbine cascade are compared with the predicted results to validate the applicability of the proposed model. Supported by NSF Award Number 1228195.

Predicting acid dew point with a semi-empirical model

International Nuclear Information System (INIS)

Xiang, Baixiang; Tang, Bin; Wu, Yuxin; Yang, Hairui; Zhang, Man; Lu, Junfu

2016-01-01

Highlights: • The previous semi-empirical models are systematically studied. • An improved thermodynamic correlation is derived. • A semi-empirical prediction model is proposed. • The proposed semi-empirical model is validated. - Abstract: Decreasing the temperature of exhaust flue gas in boilers is one of the most effective ways to further improve the thermal efficiency, electrostatic precipitator efficiency and to decrease the water consumption of desulfurization tower, while, when this temperature is below the acid dew point, the fouling and corrosion will occur on the heating surfaces in the second pass of boilers. So, the knowledge on accurately predicting the acid dew point is essential. By investigating the previous models on acid dew point prediction, an improved thermodynamic correlation formula between the acid dew point and its influencing factors is derived first. And then, a semi-empirical prediction model is proposed, which is validated with the data both in field test and experiment, and comparing with the previous models.

A new MRI land surface model HAL

Science.gov (United States)

Hosaka, M.

2011-12-01

A land surface model HAL is newly developed for MRI-ESM1. It is used for the CMIP simulations. HAL consists of three submodels: SiByl (vegetation), SNOWA (snow) and SOILA (soil) in the current version. It also contains a land coupler LCUP which connects some submodels and an atmospheric model. The vegetation submodel SiByl has surface vegetation processes similar to JMA/SiB (Sato et al. 1987, Hirai et al. 2007). SiByl has 2 vegetation layers (canopy and grass) and calculates heat, moisture, and momentum fluxes between the land surface and the atmosphere. The snow submodel SNOWA can have any number of snow layers and the maximum value is set to 8 for the CMIP5 experiments. Temperature, SWE, density, grain size and the aerosol deposition contents of each layer are predicted. The snow properties including the grain size are predicted due to snow metamorphism processes (Niwano et al., 2011), and the snow albedo is diagnosed from the aerosol mixing ratio, the snow properties and the temperature (Aoki et al., 2011). The soil submodel SOILA can also have any number of soil layers, and is composed of 14 soil layers in the CMIP5 experiments. The temperature of each layer is predicted by solving heat conduction equations. The soil moisture is predicted by solving the Darcy equation, in which hydraulic conductivity depends on the soil moisture. The land coupler LCUP is designed to enable the complicated constructions of the submidels. HAL can include some competing submodels (precise and detailed ones, and simpler ones), and they can run at the same simulations. LCUP enables a 2-step model validation, in which we compare the results of the detailed submodels with the in-situ observation directly at the 1st step, and follows the comparison between them and those of the simpler ones at the 2nd step. When the performances of the detailed ones are good, we can improve the simpler ones by using the detailed ones as reference models.

Can foot anthropometric measurements predict dynamic plantar surface contact area?

Directory of Open Access Journals (Sweden)

Collins Natalie

2009-10-01

Full Text Available Abstract Background Previous studies have suggested that increased plantar surface area, associated with pes planus, is a risk factor for the development of lower extremity overuse injuries. The intent of this study was to determine if a single or combination of foot anthropometric measures could be used to predict plantar surface area. Methods Six foot measurements were collected on 155 subjects (97 females, 58 males, mean age 24.5 ± 3.5 years. The measurements as well as one ratio were entered into a stepwise regression analysis to determine the optimal set of measurements associated with total plantar contact area either including or excluding the toe region. The predicted values were used to calculate plantar surface area and were compared to the actual values obtained dynamically using a pressure sensor platform. Results A three variable model was found to describe the relationship between the foot measures/ratio and total plantar contact area (R2 = 0.77, p R2 = 0.76, p Conclusion The results of this study indicate that the clinician can use a combination of simple, reliable, and time efficient foot anthropometric measurements to explain over 75% of the plantar surface contact area, either including or excluding the toe region.

Building predictive models of soil particle-size distribution

Directory of Open Access Journals (Sweden)

Alessandro Samuel-Rosa

2013-04-01

Full Text Available Is it possible to build predictive models (PMs of soil particle-size distribution (psd in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index. The PMs explained more than half of the data variance. This performance is similar to (or even better than that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd of soils in regions of complex geology.

Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture

Directory of Open Access Journals (Sweden)

Akhilesh S. Nair

2016-11-01

Full Text Available In the present study, soil moisture assimilation is conducted over the Indian subcontinent, using the Noah Land Surface Model (LSM and the Soil Moisture Operational Products System (SMOPS observations by utilizing the Ensemble Kalman Filter. The study is conducted in two stages involving assimilation of soil moisture and simulation of brightness temperature (Tb using radiative transfer scheme. The results of data assimilation in the form of simulated Surface Soil Moisture (SSM maps are evaluated for the Indian summer monsoonal months of June, July, August, September (JJAS using the Land Parameter Retrieval Model (LPRM AMSR-E soil moisture as reference. Results of comparative analysis using the Global land Data Assimilation System (GLDAS SSM is also discussed over India. Data assimilation using SMOPS soil moisture shows improved prediction over the Indian subcontinent, with an average correlation of 0.96 and average root mean square difference (RMSD of 0.0303 m3/m3. The results are promising in comparison with the GLDAS SSM, which has an average correlation of 0.93 and average RMSD of 0.0481 m3/m3. In the second stage of the study, the assimilated soil moisture is used to simulate X-band brightness temperature (Tb at an incidence angle of 55° using the Community Microwave Emission Model (CMEM Radiative transfer Model (RTM. This is aimed to study the sensitivity of the parameterization scheme on Tb simulation over the Indian subcontinent. The result of Tb simulation shows that the CMEM parameterization scheme strongly influences the simulated top of atmosphere (TOA brightness temperature. Furthermore, the Tb simulations from Wang dielectric model and Kirdyashev vegetation model shows better similarity with the actual AMSR-E Tb over the study region.

Advances in land modeling of KIAPS based on the Noah Land Surface Model

Science.gov (United States)

Koo, Myung-Seo; Baek, Sunghye; Seol, Kyung-Hee; Cho, Kyoungmi

2017-08-01

As of 2013, the Noah Land Surface Model (LSM) version 2.7.1 was implemented in a new global model being developed at the Korea Institute of Atmospheric Prediction Systems (KIAPS). This land surface scheme is further refined in two aspects, by adding new physical processes and by updating surface input parameters. Thus, the treatment of glacier land, sea ice, and snow cover are addressed more realistically. Inconsistencies in the amount of absorbed solar flux at ground level by the land surface and radiative processes are rectified. In addition, new parameters are available by using 1-km land cover data, which had usually not been possible at a global scale. Land surface albedo/emissivity climatology is newly created using Moderate-Resolution Imaging Spectroradiometer (MODIS) satellitebased data and adjusted parameterization. These updates have been applied to the KIAPS-developed model and generally provide a positive impact on near-surface weather forecasting.

Olkiluoto surface and near-surface hydrological modelling in 2010

International Nuclear Information System (INIS)

Karvonen, T.

2011-08-01

The modeling approaches carried out with the Olkiluoto surface hydrological model (SHYD) include palaeohydrological evolution of the Olkiluoto Island, examination of the boundary condition at the geosphere-biosphere interface zone, simulations related to infiltration experiment, prediction of the influence of ONKALO on hydraulic head in shallow and deep bedrock and optimisation of the shallow monitoring network. A so called short-term prediction system was developed for continuous updating of the estimated drawdowns caused by ONKALO. The palaeohydrological simulations were computed for a period starting from the time when the highest hills on Olkiluoto Island rose above sea level around 2 500 years ago. The input data needed in the model were produced by the UNTAMO-toolbox. The groundwater flow evolution is primarily driven by the postglacial land uplift and the uncertainty in the land uplift model is the biggest single factor that influences the accuracy of the results. The consistency of the boundary condition at the geosphere-biosphere interface zone (GBIZ) was studied during 2010. The comparison carried out during 2010 showed that pressure head profiles computed with the SHYD model and deep groundwater flow model FEFTRA are in good agreement with each other in the uppermost 100 m of the bedrock. This implies that flux profiles computed with the two approaches are close to each other and hydraulic heads computed at level z=0 m with the SHYD can be used as head boundary condition in the deep groundwater flow model FEFTRA. The surface hydrological model was used to analyse the results of the infiltration experiment. Increase in bedrock recharge inside WCA explains around 60-63 % from the amount of water pumped from OL-KR14 and 37-40 % of the water pumped from OL-KR14 flows towards pumping section via the hydrogeological zones. Pumping from OL-KR14 has only a minor effect on heads and fluxes in zones HZ19A and HZ19C compared to responses caused by leakages into

Surface Ship Shock Modeling and Simulation: Two-Dimensional Analysis

Directory of Open Access Journals (Sweden)

Young S. Shin

1998-01-01

Full Text Available The modeling and simulation of the response of a surface ship system to underwater explosion requires an understanding of many different subject areas. These include the process of underwater explosion events, shock wave propagation, explosion gas bubble behavior and bubble-pulse loading, bulk and local cavitation, free surface effect, fluid-structure interaction, and structural dynamics. This paper investigates the effects of fluid-structure interaction and cavitation on the response of a surface ship using USA-NASTRAN-CFA code. First, the one-dimensional Bleich-Sandler model is used to validate the approach, and second, the underwater shock response of a two-dimensional mid-section model of a surface ship is predicted with a surrounding fluid model using a constitutive equation of a bilinear fluid which does not allow transmission of negative pressures.

Improvement of Surface Temperature Prediction Using SVR with MOGREPS Data for Short and Medium range over South Korea

Science.gov (United States)

Lim, S. J.; Choi, R. K.; Ahn, K. D.; Ha, J. C.; Cho, C. H.

2014-12-01

As the Korea Meteorology Administration (KMA) has operated Met Office Global and Regional Ensemble Prediction System (MOGREPS) with introduction of Unified Model (UM), many attempts have been made to improve predictability in temperature forecast in last years. In this study, post-processing method of MOGREPS for surface temperature prediction is developed with machine learning over 52 locations in South Korea. Past 60-day lag time was used as a training phase of Support Vector Regression (SVR) method for surface temperature forecast model. The selected inputs for SVR are followings: date and surface temperatures from Numerical Weather prediction (NWP), such as GDAPS, individual 24 ensemble members, mean and median of ensemble members for every 3hours for 12 days.To verify the reliability of SVR-based ensemble prediction (SVR-EP), 93 days are used (from March 1 to May 31, 2014). The result yielded improvement of SVR-EP by RMSE value of 16 % throughout entire prediction period against conventional ensemble prediction (EP). In particular, short range predictability of SVR-EP resulted in 18.7% better RMSE for 1~3 day forecast. The mean temperature bias between SVR-EP and EP at all test locations showed around 0.36°C and 1.36°C, respectively. SVR-EP is currently extending for more vigorous sensitivity test, such as increasing training phase and optimizing machine learning model.

Prediction of turbulent heat transfer with surface blowing using a non-linear algebraic heat flux model

International Nuclear Information System (INIS)

Bataille, F.; Younis, B.A.; Bellettre, J.; Lallemand, A.

2003-01-01

The paper reports on the prediction of the effects of blowing on the evolution of the thermal and velocity fields in a flat-plate turbulent boundary layer developing over a porous surface. Closure of the time-averaged equations governing the transport of momentum and thermal energy is achieved using a complete Reynolds-stress transport model for the turbulent stresses and a non-linear, algebraic and explicit model for the turbulent heat fluxes. The latter model accounts explicitly for the dependence of the turbulent heat fluxes on the gradients of mean velocity. Results are reported for the case of a heated boundary layer which is first developed into equilibrium over a smooth impervious wall before encountering a porous section through which cooler fluid is continuously injected. Comparisons are made with LDA measurements for an injection rate of 1%. The reduction of the wall shear stress with increase in injection rate is obtained in the calculations, and the computed rates of heat transfer between the hot flow and the wall are found to agree well with the published data

Stage I surface crack formation in thermal fatigue: A predictive multi-scale approach

International Nuclear Information System (INIS)

Osterstock, S.; Robertson, C.; Sauzay, M.; Aubin, V.; Degallaix, S.

2010-01-01

A multi-scale numerical model is developed, predicting the formation of stage I cracks, in thermal fatigue loading conditions. The proposed approach comprises 2 distinct calculation steps. Firstly, the number of cycles to micro-crack initiation is determined, in individual grains. The adopted initiation model depends on local stress-strain conditions, relative to sub-grain plasticity, grain orientation and grain deformation incompatibilities. Secondly, 2-4 grains long surface cracks (stage I) is predicted, by accounting for micro-crack coalescence, in 3 dimensions. The method described in this paper is applied to a 500 grains aggregate, loaded in representative thermal fatigue conditions. Preliminary results provide quantitative insight regarding position, density, spacing and orientations of stage I surface cracks and subsequent formation of crack networks. The proposed method is fully deterministic, provided all grain crystallographic orientations and micro-crack linking thresholds are specified. (authors)

Bootstrap prediction and Bayesian prediction under misspecified models

OpenAIRE

Fushiki, Tadayoshi

2005-01-01

We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

Surface drift prediction in the Adriatic Sea using hyper-ensemble statistics on atmospheric, ocean and wave models: Uncertainties and probability distribution areas

Science.gov (United States)

Rixen, M.; Ferreira-Coelho, E.; Signell, R.

2008-01-01

Despite numerous and regular improvements in underlying models, surface drift prediction in the ocean remains a challenging task because of our yet limited understanding of all processes involved. Hence, deterministic approaches to the problem are often limited by empirical assumptions on underlying physics. Multi-model hyper-ensemble forecasts, which exploit the power of an optimal local combination of available information including ocean, atmospheric and wave models, may show superior forecasting skills when compared to individual models because they allow for local correction and/or bias removal. In this work, we explore in greater detail the potential and limitations of the hyper-ensemble method in the Adriatic Sea, using a comprehensive surface drifter database. The performance of the hyper-ensembles and the individual models are discussed by analyzing associated uncertainties and probability distribution maps. Results suggest that the stochastic method may reduce position errors significantly for 12 to 72??h forecasts and hence compete with pure deterministic approaches. ?? 2007 NATO Undersea Research Centre (NURC).

The role of surface topography in predicting scattering at grazing incidence from optical surfaces

International Nuclear Information System (INIS)

Rehn, V.; Jones, V.O.; Elson, J.M.; Bennett, J.M.

1980-01-01

Monochromator design and the design of optical experiments at XUV and X-ray wavelengths are frequently limited by scattering from optical components, yet theoretical treatments are few and untested experimentally. This is partly due to the failure of scattering models used in the visible and near UV when the wavelength becomes comparable to, or smaller than, the topographic features on the surface, and partly it is due to the difficulty in measuring the topography on the required size scale. We briefly review the theoretical problems and prospects for accurately predicting both the magnitude and angular distribution of scattering at grazing incidence from optical surfaces. Experimental methods for determining and representing the surface topography are also reviewed, together with their limitations and ranges of applicability. Finally, the first results of our experiments, conducted recently at the Stanford Synchrotron Radiation Laboratory on the angular distribution of scattering by surfaces of known topography are presented and discussed, along with their potential implications for the theory of scattering, and for XUV and X-ray optical components. (orig.)

Predicting the Occurrence of Cave-Inhabiting Fauna Based on Features of the Earth Surface Environment.

Science.gov (United States)

Christman, Mary C; Doctor, Daniel H; Niemiller, Matthew L; Weary, David J; Young, John A; Zigler, Kirk S; Culver, David C

2016-01-01

One of the most challenging fauna to study in situ is the obligate cave fauna because of the difficulty of sampling. Cave-limited species display patchy and restricted distributions, but it is often unclear whether the observed distribution is a sampling artifact or a true restriction in range. Further, the drivers of the distribution could be local environmental conditions, such as cave humidity, or they could be associated with surface features that are surrogates for cave conditions. If surface features can be used to predict the distribution of important cave taxa, then conservation management is more easily obtained. We examined the hypothesis that the presence of major faunal groups of cave obligate species could be predicted based on features of the earth surface. Georeferenced records of cave obligate amphipods, crayfish, fish, isopods, beetles, millipedes, pseudoscorpions, spiders, and springtails within the area of Appalachian Landscape Conservation Cooperative in the eastern United States (Illinois to Virginia and New York to Alabama) were assigned to 20 x 20 km grid cells. Habitat suitability for these faunal groups was modeled using logistic regression with twenty predictor variables within each grid cell, such as percent karst, soil features, temperature, precipitation, and elevation. Models successfully predicted the presence of a group greater than 65% of the time (mean = 88%) for the presence of single grid cell endemics, and for all faunal groups except pseudoscorpions. The most common predictor variables were latitude, percent karst, and the standard deviation of the Topographic Position Index (TPI), a measure of landscape rugosity within each grid cell. The overall success of these models points to a number of important connections between the surface and cave environments, and some of these, especially soil features and topographic variability, suggest new research directions. These models should prove to be useful tools in predicting the

Sea surface salinity and temperature-based predictive modeling of southwestern US winter precipitation: improvements, errors, and potential mechanisms

Science.gov (United States)

Liu, T.; Schmitt, R. W.; Li, L.

2017-12-01

Using 69 years of historical data from 1948-2017, we developed a method to globally search for sea surface salinity (SSS) and temperature (SST) predictors of regional terrestrial precipitation. We then applied this method to build an autumn (SON) SSS and SST-based 3-month lead predictive model of winter (DJF) precipitation in southwestern United States. We also find that SSS-only models perform better than SST-only models. We previously used an arbitrary correlation coefficient (r) threshold, |r| > 0.25, to define SSS and SST predictor polygons for best subset regression of southwestern US winter precipitation; from preliminary sensitivity tests, we find that |r| > 0.18 yields the best models. The observed below-average precipitation (0.69 mm/day) in winter 2015-2016 falls within the 95% confidence interval of the prediction model. However, the model underestimates the anomalous high precipitation (1.78 mm/day) in winter 2016-2017 by more than three-fold. Moisture transport mainly attributed to "pineapple express" atmospheric rivers (ARs) in winter 2016-2017 suggests that the model falls short on a sub-seasonal scale, in which case storms from ARs contribute a significant portion of seasonal terrestrial precipitation. Further, we identify a potential mechanism for long-range SSS and precipitation teleconnections: standing Rossby waves. The heat applied to the atmosphere from anomalous tropical rainfall can generate standing Rossby waves that propagate to higher latitudes. SSS anomalies may be indicative of anomalous tropical rainfall, and by extension, standing Rossby waves that provide the long-range teleconnections.

Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms

Science.gov (United States)

Jian, Jhih-Wei; Elumalai, Pavadai; Pitti, Thejkiran; Wu, Chih Yuan; Tsai, Keng-Chang; Chang, Jeng-Yih; Peng, Hung-Pin; Yang, An-Suei

2016-01-01

Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites. PMID:27513851

Liquid surface model for carbon nanotube energetics

DEFF Research Database (Denmark)

Solov'yov, Ilia; Mathew, Maneesh; Solov'yov, Andrey V.

2008-01-01

an important insight in the energetics and stability of nanotubes of different chirality and might be important for the understanding of nanotube growth process. For the computations we use empirical Brenner and Tersoff potentials and discuss their applicability to the study of carbon nanotubes. From......In the present paper we developed a model for calculating the energy of single-wall carbon nanotubes of arbitrary chirality. This model, which we call as the liquid surface model, predicts the energy of a nanotube with relative error less than 1% once its chirality and the total number of atoms...... the calculated energies we determine the elastic properties of the single-wall carbon nanotubes (Young modulus, curvature constant) and perform a comparison with available experimental measurements and earlier theoretical predictions....

Predictive simulation of bidirectional Glenn shunt using a hybrid blood vessel model.

Science.gov (United States)

Li, Hao; Leow, Wee Kheng; Chiu, Ing-Sh

2009-01-01

This paper proposes a method for performing predictive simulation of cardiac surgery. It applies a hybrid approach to model the deformation of blood vessels. The hybrid blood vessel model consists of a reference Cosserat rod and a surface mesh. The reference Cosserat rod models the blood vessel's global bending, stretching, twisting and shearing in a physically correct manner, and the surface mesh models the surface details of the blood vessel. In this way, the deformation of blood vessels can be computed efficiently and accurately. Our predictive simulation system can produce complex surgical results given a small amount of user inputs. It allows the surgeon to easily explore various surgical options and evaluate them. Tests of the system using bidirectional Glenn shunt (BDG) as an application example show that the results produc by the system are similar to real surgical results.

Predictive Modelling and Time: An Experiment in Temporal Archaeological Predictive Models

OpenAIRE

David Ebert

2006-01-01

One of the most common criticisms of archaeological predictive modelling is that it fails to account for temporal or functional differences in sites. However, a practical solution to temporal or functional predictive modelling has proven to be elusive. This article discusses temporal predictive modelling, focusing on the difficulties of employing temporal variables, then introduces and tests a simple methodology for the implementation of temporal modelling. The temporal models thus created ar...

Durability and life prediction modeling in polyimide composites

Science.gov (United States)

Binienda, Wieslaw K.

1995-01-01

Sudden appearance of cracks on a macroscopically smooth surface of brittle materials due to cooling or drying shrinkage is a phenomenon related to many engineering problems. Although conventional strength theories can be used to predict the necessary condition for crack appearance, they are unable to predict crack spacing and depth. On the other hand, fracture mechanics theory can only study the behavior of existing cracks. The theory of crack initiation can be summarized into three conditions, which is a combination of a strength criterion and laws of energy conservation, the average crack spacing and depth can thus be determined. The problem of crack initiation from the surface of an elastic half plane is solved and compares quite well with available experimental evidence. The theory of crack initiation is also applied to concrete pavements. The influence of cracking is modeled by the additional compliance according to Okamura's method. The theoretical prediction by this structural mechanics type of model correlates very well with the field observation. The model may serve as a theoretical foundation for future pavement joint design. The initiation of interactive cracks of quasi-brittle material is studied based on a theory of cohesive crack model. These cracks may grow simultaneously, or some of them may close during certain stages. The concept of crack unloading of cohesive crack model is proposed. The critical behavior (crack bifurcation, maximum loads) of the cohesive crack model are characterized by rate equations. The post-critical behavior of crack initiation is also studied.

Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model

Directory of Open Access Journals (Sweden)

François Counillon

2016-12-01

Full Text Available We document a pilot stochastic re-analysis computed by assimilating sea surface temperature (SST anomalies into the ocean component of the coupled Norwegian Climate Prediction Model (NorCPM for the period 1950–2010 (doi: 10.11582/2016.00002. NorCPM is based on the Norwegian Earth System Model and uses the ensemble Kalman filter for data assimilation (DA. Here, we assimilate SST from the stochastic HadISST2 historical reconstruction. The accuracy, reliability and drift are investigated using both assimilated and independent observations. NorCPM is slightly overdispersive against assimilated observations but shows stable performance through the analysis period. It demonstrates skills against independent measurements: sea surface height, heat and salt content, in particular in the Equatorial and North Pacific, the North Atlantic Subpolar Gyre (SPG region and the Nordic Seas. Furthermore, NorCPM provides a reliable monitoring of the SPG index and represents the vertical temperature variability there, in good agreement with observations. The monitoring of the Atlantic meridional overturning circulation is also encouraging. The benefit of using a flow-dependent assimilation method and constructing the covariance in isopycnal coordinates are investigated in the SPG region. Isopycnal coordinates discretisation is found to better capture the vertical structure than standard depth-coordinate discretisation, because it leads to a deeper influence of the assimilated surface observations. The vertical covariance shows a pronounced seasonal and decadal variability that highlights the benefit of flow-dependent DA method. This study demonstrates the potential of NorCPM to compute an ocean re-analysis for the 19th and 20th centuries when SST observations are available.

[Fire behavior of ground surface fuels in Pinus koraiensis and Quercus mongolica mixed forest under no wind and zero slope condition: a prediction with extended Rothermel model].

Science.gov (United States)

Zhang, Ji-Li; Liu, Bo-Fei; Chu, Teng-Fei; Di, Xue-Ying; Jin, Sen

2012-06-01

A laboratory burning experiment was conducted to measure the fire spread speed, residual time, reaction intensity, fireline intensity, and flame length of the ground surface fuels collected from a Korean pine (Pinus koraiensis) and Mongolian oak (Quercus mongolica) mixed stand in Maoer Mountains of Northeast China under the conditions of no wind, zero slope, and different moisture content, load, and mixture ratio of the fuels. The results measured were compared with those predicted by the extended Rothermel model to test the performance of the model, especially for the effects of two different weighting methods on the fire behavior modeling of the mixed fuels. With the prediction of the model, the mean absolute errors of the fire spread speed and reaction intensity of the fuels were 0.04 m X min(-1) and 77 kW X m(-2), their mean relative errors were 16% and 22%, while the mean absolute errors of residual time, fireline intensity and flame length were 15.5 s, 17.3 kW X m(-1), and 9.7 cm, and their mean relative errors were 55.5%, 48.7%, and 24%, respectively, indicating that the predicted values of residual time, fireline intensity, and flame length were lower than the observed ones. These errors could be regarded as the lower limits for the application of the extended Rothermel model in predicting the fire behavior of similar fuel types, and provide valuable information for using the model to predict the fire behavior under the similar field conditions. As a whole, the two different weighting methods did not show significant difference in predicting the fire behavior of the mixed fuels by extended Rothermel model. When the proportion of Korean pine fuels was lower, the predicted values of spread speed and reaction intensity obtained by surface area weighting method and those of fireline intensity and flame length obtained by load weighting method were higher; when the proportion of Korean pine needles was higher, the contrary results were obtained.

Predicting wettability behavior of fluorosilica coated metal surface using optimum neural network

Science.gov (United States)

Taghipour-Gorjikolaie, Mehran; Valipour Motlagh, Naser

2018-02-01

The interaction between variables, which are effective on the surface wettability, is very complex to predict the contact angles and sliding angles of liquid drops. In this paper, in order to solve this complexity, artificial neural network was used to develop reliable models for predicting the angles of liquid drops. Experimental data are divided into training data and testing data. By using training data and feed forward structure for the neural network and using particle swarm optimization for training the neural network based models, the optimum models were developed. The obtained results showed that regression index for the proposed models for the contact angles and sliding angles are 0.9874 and 0.9920, respectively. As it can be seen, these values are close to unit and it means the reliable performance of the models. Also, it can be inferred from the results that the proposed model have more reliable performance than multi-layer perceptron and radial basis function based models.

Exploratory multivariate modeling and prediction of the physico-chemical properties of surface water and groundwater

Science.gov (United States)

Ayoko, Godwin A.; Singh, Kirpal; Balerea, Steven; Kokot, Serge

2007-03-01

SummaryPhysico-chemical properties of surface water and groundwater samples from some developing countries have been subjected to multivariate analyses by the non-parametric multi-criteria decision-making methods, PROMETHEE and GAIA. Complete ranking information necessary to select one source of water in preference to all others was obtained, and this enabled relationships between the physico-chemical properties and water quality to be assessed. Thus, the ranking of the quality of the water bodies was found to be strongly dependent on the total dissolved solid, phosphate, sulfate, ammonia-nitrogen, calcium, iron, chloride, magnesium, zinc, nitrate and fluoride contents of the waters. However, potassium, manganese and zinc composition showed the least influence in differentiating the water bodies. To model and predict the water quality influencing parameters, partial least squares analyses were carried out on a matrix made up of the results of water quality assessment studies carried out in Nigeria, Papua New Guinea, Egypt, Thailand and India/Pakistan. The results showed that the total dissolved solid, calcium, sulfate, sodium and chloride contents can be used to predict a wide range of physico-chemical characteristics of water. The potential implications of these observations on the financial and opportunity costs associated with elaborate water quality monitoring are discussed.

Modeling the Acid-Base Properties of Montmorillonite Edge Surfaces.

Science.gov (United States)

Tournassat, Christophe; Davis, James A; Chiaberge, Christophe; Grangeon, Sylvain; Bourg, Ian C

2016-12-20

The surface reactivity of clay minerals remains challenging to characterize because of a duality of adsorption surfaces and mechanisms that does not exist in the case of simple oxide surfaces: edge surfaces of clay minerals have a variable proton surface charge arising from hydroxyl functional groups, whereas basal surfaces have a permanent negative charge arising from isomorphic substitutions. Hence, the relationship between surface charge and surface potential on edge surfaces cannot be described using the Gouy-Chapman relation, because of a spillover of negative electrostatic potential from the basal surface onto the edge surface. While surface complexation models can be modified to account for these features, a predictive fit of experimental data was not possible until recently, because of uncertainty regarding the densities and intrinsic pK a values of edge functional groups. Here, we reexamine this problem in light of new knowledge on intrinsic pK a values obtained over the past decade using ab initio molecular dynamics simulations, and we propose a new formalism to describe edge functional groups. Our simulation results yield reasonable predictions of the best available experimental acid-base titration data.

Thermomechanical modelling of laser surface glazing for H13 tool steel

Science.gov (United States)

Kabir, I. R.; Yin, D.; Tamanna, N.; Naher, S.

2018-03-01

A two-dimensional thermomechanical finite element (FE) model of laser surface glazing (LSG) has been developed for H13 tool steel. The direct coupling technique of ANSYS 17.2 (APDL) has been utilised to solve the transient thermomechanical process. A H13 tool steel cylindrical cross-section has been modelled for laser power 200 W and 300 W at constant 0.2 mm beam width and 0.15 ms residence time. The model can predict temperature distribution, stress-strain increments in elastic and plastic region with time and space. The crack formation tendency also can be assumed by analysing the von Mises stress in the heat-concentrated zone. Isotropic and kinematic hardening models have been applied separately to predict the after-yield phenomena. At 200 W laser power, the peak surface temperature achieved is 1520 K which is below the melting point (1727 K) of H13 tool steel. For laser power 300 W, the peak surface temperature is 2523 K. Tensile residual stresses on surface have been found after cooling, which are in agreement with literature. Isotropic model shows higher residual stress that increases with laser power. Conversely, kinematic model gives lower residual stress which decreases with laser power. Therefore, both plasticity models could work in LSG for H13 tool steel.

Vertical dispersion from surface and elevated releases: An investigation of a Non-Gaussian plume model

International Nuclear Information System (INIS)

Brown, M.J.; Arya, S.P.; Snyder, W.H.

1993-01-01

The vertical diffusion of a passive tracer released from surface and elevated sources in a neutrally stratified boundary layer has been studied by comparing field and laboratory experiments with a non-Gaussian K-theory model that assumes power-law profiles for the mean velocity and vertical eddy diffusivity. Several important differences between model predictions and experimental data were discovered: (1) the model overestimated ground-level concentrations from surface and elevated releases at distances beyond the peak concentration; (2) the model overpredicted vertical mixing near elevated sources, especially in the upward direction; (3) the model-predicted exponent α in the exponential vertical concentration profile for a surface release [bar C(z)∝ exp(-z α )] was smaller than the experimentally measured exponent. Model closure assumptions and experimental short-comings are discussed in relation to their probable effect on model predictions and experimental measurements. 42 refs., 13 figs., 3 tabs

Specification for a surface-search radar-detection-range model

Science.gov (United States)

Hattan, Claude P.

1990-09-01

A model that predicts surface-search radar detection range versus a variety of combatants has been developed at the Naval Ocean Systems Center. This model uses a simplified ship radar cross section (RCS) model and the U.S. Navy Oceanographic and Atmospheric Mission Library Standard Electromagnetic Propagation Model. It provides the user with a method of assessing the effects of the environment of the performance of a surface-search radar system. The software implementation of the model is written in ANSI FORTRAN 77, with MIL-STD-1753 extensions. The program provides the user with a table of expected detection ranges when the model is supplied with the proper environmental radar system inputs. The target model includes the variation in RCS as a function of aspect angle and the distribution of reflected radar energy as a function of height above the waterline. The modeled propagation effects include refraction caused by a multisegmented refractivity profile, sea-surface roughness caused by local winds, evaporation ducting, and surface-based ducts caused by atmospheric layering.

Microwave Remote Sensing Modeling of Ocean Surface Salinity and Winds Using an Empirical Sea Surface Spectrum

Science.gov (United States)

Yueh, Simon H.

2004-01-01

Active and passive microwave remote sensing techniques have been investigated for the remote sensing of ocean surface wind and salinity. We revised an ocean surface spectrum using the CMOD-5 geophysical model function (GMF) for the European Remote Sensing (ERS) C-band scatterometer and the Ku-band GMF for the NASA SeaWinds scatterometer. The predictions of microwave brightness temperatures from this model agree well with satellite, aircraft and tower-based microwave radiometer data. This suggests that the impact of surface roughness on microwave brightness temperatures and radar scattering coefficients of sea surfaces can be consistently characterized by a roughness spectrum, providing physical basis for using combined active and passive remote sensing techniques for ocean surface wind and salinity remote sensing.

Geospatial application of the Water Erosion Prediction Project (WEPP) Model

Science.gov (United States)

D. C. Flanagan; J. R. Frankenberger; T. A. Cochrane; C. S. Renschler; W. J. Elliot

2011-01-01

The Water Erosion Prediction Project (WEPP) model is a process-based technology for prediction of soil erosion by water at hillslope profile, field, and small watershed scales. In particular, WEPP utilizes observed or generated daily climate inputs to drive the surface hydrology processes (infiltration, runoff, ET) component, which subsequently impacts the rest of the...

Analysis of Surface Heterogeneity Effects with Mesoscale Terrestrial Modeling Platforms

Science.gov (United States)

Simmer, C.

2015-12-01

An improved understanding of the full variability in the weather and climate system is crucial for reducing the uncertainty in weather forecasting and climate prediction, and to aid policy makers to develop adaptation and mitigation strategies. A yet unknown part of uncertainty in the predictions from the numerical models is caused by the negligence of non-resolved land surface heterogeneity and the sub-surface dynamics and their potential impact on the state of the atmosphere. At the same time, mesoscale numerical models using finer horizontal grid resolution [O(1)km] can suffer from inconsistencies and neglected scale-dependencies in ABL parameterizations and non-resolved effects of integrated surface-subsurface lateral flow at this scale. Our present knowledge suggests large-eddy-simulation (LES) as an eventual solution to overcome the inadequacy of the physical parameterizations in the atmosphere in this transition scale, yet we are constrained by the computational resources, memory management, big-data, when using LES for regional domains. For the present, there is a need for scale-aware parameterizations not only in the atmosphere but also in the land surface and subsurface model components. In this study, we use the recently developed Terrestrial Systems Modeling Platform (TerrSysMP) as a numerical tool to analyze the uncertainty in the simulation of surface exchange fluxes and boundary layer circulations at grid resolutions of the order of 1km, and explore the sensitivity of the atmospheric boundary layer evolution and convective rainfall processes on land surface heterogeneity.

A model to predict impervious surface for regional and municipal land use planning purposes

International Nuclear Information System (INIS)

Reilly, James; Maggio, Patricia; Karp, Steven

2004-01-01

The area of impervious surface in a watershed is a forcing variable in many hydrologic models and has been proposed as a policy variable surrogate for water quality. We report a new statistical model which will allow land use planners to estimate impervious surface given minimal, readily available information about future growth. Our model is suitable for master planning purposes. In more urbanized areas, it tends to produce quite accurate forecasts. However, in less developed, more rural places, forecast error will increase

Numerical Modelling and Prediction of Erosion Induced by Hydrodynamic Cavitation

Science.gov (United States)

Peters, A.; Lantermann, U.; el Moctar, O.

2015-12-01

The present work aims to predict cavitation erosion using a numerical flow solver together with a new developed erosion model. The erosion model is based on the hypothesis that collapses of single cavitation bubbles near solid boundaries form high velocity microjets, which cause sonic impacts with high pressure amplitudes damaging the surface. The erosion model uses information from a numerical Euler-Euler flow simulation to predict erosion sensitive areas and assess the erosion aggressiveness of the flow. The obtained numerical results were compared to experimental results from tests of an axisymmetric nozzle.

Verification of some numerical models for operationally predicting mesoscale winds aloft

International Nuclear Information System (INIS)

Cornett, J.S.; Randerson, D.

1977-01-01

Four numerical models are described for predicting mesoscale winds aloft for a 6 h period. These models are all tested statistically against persistence as the control forecast and against predictions made by operational forecasters. Mesoscale winds aloft data were used to initialize the models and to verify the predictions on an hourly basis. The model yielding the smallest root-mean-square vector errors (RMSVE's) was the one based on the most physics which included advection, ageostrophic acceleration, vertical mixing and friction. Horizontal advection was found to be the most important term in reducing the RMSVE's followed by ageostrophic acceleration, vertical advection, surface friction and vertical mixing. From a comparison of the mean absolute errors based on up to 72 independent wind-profile predictions made by operational forecasters, by the most complete model, and by persistence, we conclude that the model is the best wind predictor in the free air. In the boundary layer, the results tend to favor the forecaster for direction predictions. The speed predictions showed no overall superiority in any of these three models

Model error assessment of burst capacity models for energy pipelines containing surface cracks

International Nuclear Information System (INIS)

Yan, Zijian; Zhang, Shenwei; Zhou, Wenxing

2014-01-01

This paper develops the probabilistic characteristics of the model errors associated with five well-known burst capacity models/methodologies for pipelines containing longitudinally-oriented external surface cracks, namely the Battelle and CorLAS™ models as well as the failure assessment diagram (FAD) methodologies recommended in the BS 7910 (2005), API RP579 (2007) and R6 (Rev 4, Amendment 10). A total of 112 full-scale burst test data for cracked pipes subjected internal pressure only were collected from the literature. The model error for a given burst capacity model is evaluated based on the ratios of the test to predicted burst pressures for the collected data. Analysis results suggest that the CorLAS™ model is the most accurate model among the five models considered and the Battelle, BS 7910, API RP579 and R6 models are in general conservative; furthermore, the API RP579 and R6 models are markedly more accurate than the Battelle and BS 7910 models. The results will facilitate the development of reliability-based structural integrity management of pipelines. - Highlights: • Model errors for five burst capacity models for pipelines containing surface cracks are characterized. • Basic statistics of the model errors are obtained based on test-to-predicted ratios. • Results will facilitate reliability-based design and assessment of energy pipelines

Translational Modeling in Schizophrenia: Predicting Human Dopamine D2 Receptor Occupancy.

Science.gov (United States)

Johnson, Martin; Kozielska, Magdalena; Pilla Reddy, Venkatesh; Vermeulen, An; Barton, Hugh A; Grimwood, Sarah; de Greef, Rik; Groothuis, Geny M M; Danhof, Meindert; Proost, Johannes H

2016-04-01

To assess the ability of a previously developed hybrid physiology-based pharmacokinetic-pharmacodynamic (PBPKPD) model in rats to predict the dopamine D2 receptor occupancy (D2RO) in human striatum following administration of antipsychotic drugs. A hybrid PBPKPD model, previously developed using information on plasma concentrations, brain exposure and D2RO in rats, was used as the basis for the prediction of D2RO in human. The rat pharmacokinetic and brain physiology parameters were substituted with human population pharmacokinetic parameters and human physiological information. To predict the passive transport across the human blood-brain barrier, apparent permeability values were scaled based on rat and human brain endothelial surface area. Active efflux clearance in brain was scaled from rat to human using both human brain endothelial surface area and MDR1 expression. Binding constants at the D2 receptor were scaled based on the differences between in vitro and in vivo systems of the same species. The predictive power of this physiology-based approach was determined by comparing the D2RO predictions with the observed human D2RO of six antipsychotics at clinically relevant doses. Predicted human D2RO was in good agreement with clinically observed D2RO for five antipsychotics. Models using in vitro information predicted human D2RO well for most of the compounds evaluated in this analysis. However, human D2RO was under-predicted for haloperidol. The rat hybrid PBPKPD model structure, integrated with in vitro information and human pharmacokinetic and physiological information, constitutes a scientific basis to predict the time course of D2RO in man.

Model for the prediction of subsurface strata movement due to underground mining

Science.gov (United States)

Cheng, Jianwei; Liu, Fangyuan; Li, Siyuan

2017-12-01

The problem of ground control stability due to large underground mining operations is often associated with large movements and deformations of strata. It is a complicated problem, and can induce severe safety or environmental hazards either at the surface or in strata. Hence, knowing the subsurface strata movement characteristics, and making any subsidence predictions in advance, are desirable for mining engineers to estimate any damage likely to affect the ground surface or subsurface strata. Based on previous research findings, this paper broadly applies a surface subsidence prediction model based on the influence function method to subsurface strata, in order to predict subsurface stratum movement. A step-wise prediction model is proposed, to investigate the movement of underground strata. The model involves a dynamic iteration calculation process to derive the movements and deformations for each stratum layer; modifications to the influence method function are also made for more precise calculations. The critical subsidence parameters, incorporating stratum mechanical properties and the spatial relationship of interest at the mining level, are thoroughly considered, with the purpose of improving the reliability of input parameters. Such research efforts can be very helpful to mining engineers’ understanding of the moving behavior of all strata over underground excavations, and assist in making any damage mitigation plan. In order to check the reliability of the model, two methods are carried out and cross-validation applied. One is to use a borehole TV monitor recording to identify the progress of subsurface stratum bedding and caving in a coal mine, the other is to conduct physical modelling of the subsidence in underground strata. The results of these two methods are used to compare with theoretical results calculated by the proposed mathematical model. The testing results agree well with each other, and the acceptable accuracy and reliability of the

A physically based model of global freshwater surface temperature

Science.gov (United States)

van Beek, Ludovicus P. H.; Eikelboom, Tessa; van Vliet, Michelle T. H.; Bierkens, Marc F. P.

2012-09-01

Temperature determines a range of physical properties of water and exerts a strong control on surface water biogeochemistry. Thus, in freshwater ecosystems the thermal regime directly affects the geographical distribution of aquatic species through their growth and metabolism and indirectly through their tolerance to parasites and diseases. Models used to predict surface water temperature range between physically based deterministic models and statistical approaches. Here we present the initial results of a physically based deterministic model of global freshwater surface temperature. The model adds a surface water energy balance to river discharge modeled by the global hydrological model PCR-GLOBWB. In addition to advection of energy from direct precipitation, runoff, and lateral exchange along the drainage network, energy is exchanged between the water body and the atmosphere by shortwave and longwave radiation and sensible and latent heat fluxes. Also included are ice formation and its effect on heat storage and river hydraulics. We use the coupled surface water and energy balance model to simulate global freshwater surface temperature at daily time steps with a spatial resolution of 0.5° on a regular grid for the period 1976-2000. We opt to parameterize the model with globally available data and apply it without calibration in order to preserve its physical basis with the outlook of evaluating the effects of atmospheric warming on freshwater surface temperature. We validate our simulation results with daily temperature data from rivers and lakes (U.S. Geological Survey (USGS), limited to the USA) and compare mean monthly temperatures with those recorded in the Global Environment Monitoring System (GEMS) data set. Results show that the model is able to capture the mean monthly surface temperature for the majority of the GEMS stations, while the interannual variability as derived from the USGS and NOAA data was captured reasonably well. Results are poorest for

Predicting daylight illuminance on inclined surfaces using sky luminance data

Energy Technology Data Exchange (ETDEWEB)

Li, D.H.W.; Lau, C.C.S.; Lam, J.C. [City University of Hong Kong, Kowloon (China). Dept. of Building and Construction

2005-07-01

Daylight illuminance, particularly on vertical surfaces, plays a major role in determining and evaluating the daylighting performance of a building. In many parts of the world, however, the basic daylight illuminance data for various vertical planes are not always readily available. The usual method to obtain diffuse illuminance on tilted planes would be based on inclined surface models using data from the horizontal measurements. Alternatively, the diffuse illuminance on a sloping plane can be computed by integrating the luminance distribution of the sky 'seen' by the plane. This paper presents an approach to estimate the vertical outdoor illuminance from sky luminance data and solar geometry. Sky luminance data recorded from January 1999 to December 2001 in Hong Kong and generated by two well-known sky luminance models (Kittler and Perez) were used to compute the outdoor illuminance for the four principal vertical planes (N, E, S and W). The performance of this approach was evaluated against data measured in the same period. Statistical analysis indicated that using sky luminance distributions to predict outdoor illuminance can give reasonably good agreement with measured data for all vertical surfaces. The findings provide an accurate alternative to determine the amount of daylight on vertical as well as other inclined surfaces when sky luminance data are available. (author)

Seasonal Prediction of Regional Surface Air Temperature and First-flowering Date in South Korea using Dynamical Downscaling

Science.gov (United States)

Ahn, J. B.; Hur, J.

2015-12-01

The seasonal prediction of both the surface air temperature and the first-flowering date (FFD) over South Korea are produced using dynamical downscaling (Hur and Ahn, 2015). Dynamical downscaling is performed using Weather Research and Forecast (WRF) v3.0 with the lateral forcing from hourly outputs of Pusan National University (PNU) coupled general circulation model (CGCM) v1.1. Gridded surface air temperature data with high spatial (3km) and temporal (daily) resolution are obtained using the physically-based dynamical models. To reduce systematic bias, simple statistical correction method is then applied to the model output. The FFDs of cherry, peach and pear in South Korea are predicted for the decade of 1999-2008 by applying the corrected daily temperature predictions to the phenological thermal-time model. The WRF v3.0 results reflect the detailed topographical effect, despite having cold and warm biases for warm and cold seasons, respectively. After applying the correction, the mean temperature for early spring (February to April) well represents the general pattern of observation, while preserving the advantages of dynamical downscaling. The FFD predictabilities for the three species of trees are evaluated in terms of qualitative, quantitative and categorical estimations. Although FFDs derived from the corrected WRF results well predict the spatial distribution and the variation of observation, the prediction performance has no statistical significance or appropriate predictability. The approach used in the study may be helpful in obtaining detailed and useful information about FFD and regional temperature by accounting for physically-based atmospheric dynamics, although the seasonal predictability of flowering phenology is not high enough. Acknowledgements This work was carried out with the support of the Rural Development Administration Cooperative Research Program for Agriculture Science and Technology Development under Grant Project No. PJ009953 and

A poroelastic reservoir model for predicting subsidence and mapping subsurface pressure fronts

International Nuclear Information System (INIS)

Du, J.; Olson, J.E.

2001-01-01

A forward model was constructed to numerically predict surface subsidence and reservoir compaction following the approach of Segall [Pure Appl. Phys. 139 (1992) 536]. A nucleus of poroelastic strain is numerically integrated over a rectangular prism assuming constant pressure change. This fundamental geometry allows a reservoir to be divided into many small cubic blocks in a manner similar to reservoir simulation. The subsidence and compaction effects of the pressure change throughout the reservoir are calculated by the superposition of results from each individual block. Using forward modeling, pressure boundary conditions can be acquired from pressure test data or reservoir simulation predictions. An inversion model also was developed that can track pressure fronts in a subsurface reservoir using surface displacements. The capability of the inversion model was demonstrated using synthetic examples of one-well and four-well cases with different layouts of surface observation locations. The impact of noise on the inversion result is also included

Modeling Surface Roughness to Estimate Surface Moisture Using Radarsat-2 Quad Polarimetric SAR Data

Science.gov (United States)

Nurtyawan, R.; Saepuloh, A.; Budiharto, A.; Wikantika, K.

2016-08-01

Microwave backscattering from the earth's surface depends on several parameters such as surface roughness and dielectric constant of surface materials. The two parameters related to water content and porosity are crucial for estimating soil moisture. The soil moisture is an important parameter for ecological study and also a factor to maintain energy balance of land surface and atmosphere. Direct roughness measurements to a large area require extra time and cost. Heterogeneity roughness scale for some applications such as hydrology, climate, and ecology is a problem which could lead to inaccuracies of modeling. In this study, we modeled surface roughness using Radasat-2 quad Polarimetric Synthetic Aperture Radar (PolSAR) data. The statistical approaches to field roughness measurements were used to generate an appropriate roughness model. This modeling uses a physical SAR approach to predicts radar backscattering coefficient in the parameter of radar configuration (wavelength, polarization, and incidence angle) and soil parameters (surface roughness and dielectric constant). Surface roughness value is calculated using a modified Campbell and Shepard model in 1996. The modification was applied by incorporating the backscattering coefficient (σ°) of quad polarization HH, HV and VV. To obtain empirical surface roughness model from SAR backscattering intensity, we used forty-five sample points from field roughness measurements. We selected paddy field in Indramayu district, West Java, Indonesia as the study area. This area was selected due to intensive decreasing of rice productivity in the Northern Coast region of West Java. Third degree polynomial is the most suitable data fitting with coefficient of determination R2 and RMSE are about 0.82 and 1.18 cm, respectively. Therefore, this model is used as basis to generate the map of surface roughness.

Why Did the Bear Cross the Road? Comparing the Performance of Multiple Resistance Surfaces and Connectivity Modeling Methods

Directory of Open Access Journals (Sweden)

Samuel A. Cushman

2014-12-01

Full Text Available There have been few assessments of the performance of alternative resistance surfaces, and little is known about how connectivity modeling approaches differ in their ability to predict organism movements. In this paper, we evaluate the performance of four connectivity modeling approaches applied to two resistance surfaces in predicting the locations of highway crossings by American black bears in the northern Rocky Mountains, USA. We found that a resistance surface derived directly from movement data greatly outperformed a resistance surface produced from analysis of genetic differentiation, despite their heuristic similarities. Our analysis also suggested differences in the performance of different connectivity modeling approaches. Factorial least cost paths appeared to slightly outperform other methods on the movement-derived resistance surface, but had very poor performance on the resistance surface obtained from multi-model landscape genetic analysis. Cumulative resistant kernels appeared to offer the best combination of high predictive performance and sensitivity to differences in resistance surface parameterization. Our analysis highlights that even when two resistance surfaces include the same variables and have a high spatial correlation of resistance values, they may perform very differently in predicting animal movement and population connectivity.

Developing Metamodels for Fast and Accurate Prediction of the Draping of Physical Surfaces

DEFF Research Database (Denmark)

Christensen, Esben Toke; Forrester, AIJ.; Lund, Erik

2018-01-01

In this paper, the use of methods from the meta- or surrogate modeling literature, for building models predicting the draping of physical surfaces, is examined. An example application concerning modeling of the behavior of a variable shape mold is treated. Four different methods are considered...... and local variants, are compared in terms of accuracy and numerical efficiency on data sets of different sizes for the treated application. It is shown that the POD-based methods are vastly superior to models based on kriging alone, and that the use of a difference model structure is advantageous...

The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia

Science.gov (United States)

Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.

2013-10-01

Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

Evaluation of Deep Learning Models for Predicting CO2 Flux

Science.gov (United States)

Halem, M.; Nguyen, P.; Frankel, D.

2017-12-01

Artificial neural networks have been employed to calculate surface flux measurements from station data because they are able to fit highly nonlinear relations between input and output variables without knowing the detail relationships between the variables. However, the accuracy in performing neural net estimates of CO2 flux from observations of CO2 and other atmospheric variables is influenced by the architecture of the neural model, the availability, and complexity of interactions between physical variables such as wind, temperature, and indirect variables like latent heat, and sensible heat, etc. We evaluate two deep learning models, feed forward and recurrent neural network models to learn how they each respond to the physical measurements, time dependency of the measurements of CO2 concentration, humidity, pressure, temperature, wind speed etc. for predicting the CO2 flux. In this paper, we focus on a) building neural network models for estimating CO2 flux based on DOE data from tower Atmospheric Radiation Measurement data; b) evaluating the impact of choosing the surface variables and model hyper-parameters on the accuracy and predictions of surface flux; c) assessing the applicability of the neural network models on estimate CO2 flux by using OCO-2 satellite data; d) studying the efficiency of using GPU-acceleration for neural network performance using IBM Power AI deep learning software and packages on IBM Minsky system.

Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality

Science.gov (United States)

Bradford, S. A.; Liang, J.; Li, W.; Murata, T.; Simunek, J.

2017-12-01

Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models, which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with physically-based models, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. In this presentation we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport (the HYDRUS-1D overland flow module). A large number of numerical simulations were carried out to develop a database containing information about the impact of various input parameters (weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices) on runoff water quantity and quality outputs. This database was used to train data-driven models. Three different methods (Neural Networks, Support Vector Machines, and Recurrence Neural Networks) were explored to prepare input- output functional relations. Results demonstrate the ability and limitations of machine learning and deep learning models to predict runoff water quantity and quality.

Prediction of the surface roughness of AA6082 flow-formed tubes by design of experiments

International Nuclear Information System (INIS)

Srinivasulu, M.; Komaraiah, M.; Rao, C. S. Krishna Prasada

2013-01-01

Flow forming is a modern, chipless metal forming process that is employed for the production of thin-walled seamless tubes. Experiments are conducted on AA6082 alloy pre-forms to flow form into thin-walled tubes on a CNC flow-forming machine with a single roller. Design of experiments is used to predict the surface roughness of flow-formed tubes. The process parameters selected for this study are the roller axial feed, mandrel speed, and roller radius. A standard response surface methodology (RSM) called the Box Behnken design is used to perform the experimental runs. The regression model developed by RSM successfully predicts the surface roughness of AA6082 flow-formed tubes within the range of the selected process parameters.

Prediction of the surface roughness of AA6082 flow-formed tubes by design of experiments

Energy Technology Data Exchange (ETDEWEB)

Srinivasulu, M. [Government Polytechnic for Women Badangpet, Hyderabad (India); Komaraiah, M. [Sreenidhi Institute of Science and Technology, Hyderabad (India); Rao, C. S. Krishna Prasada [Bharat Dynamics Limited, Hyderabad (India)

2013-06-15

Flow forming is a modern, chipless metal forming process that is employed for the production of thin-walled seamless tubes. Experiments are conducted on AA6082 alloy pre-forms to flow form into thin-walled tubes on a CNC flow-forming machine with a single roller. Design of experiments is used to predict the surface roughness of flow-formed tubes. The process parameters selected for this study are the roller axial feed, mandrel speed, and roller radius. A standard response surface methodology (RSM) called the Box Behnken design is used to perform the experimental runs. The regression model developed by RSM successfully predicts the surface roughness of AA6082 flow-formed tubes within the range of the selected process parameters.

A surface-renewal model of cross-flow microfiltration

Directory of Open Access Journals (Sweden)

A. Hasan

2013-03-01

Full Text Available A mathematical model using classical cake-filtration theory and the surface-renewal concept is formulated for describing cross-flow microfiltration under dynamic and steady-state conditions. The model can predict the permeate flux and cake buildup in the filter. The three basic parameters of the model are the membrane resistance, specific cake resistance and rate of surface renewal. The model is able to correlate experimental permeate flow rate data in the microfiltration of fermentation broths in laboratory- and pilot-scale units with an average root-mean-square (RMS error of 4.6%. The experimental data are also compared against the critical-flux model of cross-flow microfiltration, which has average RMS errors of 6.3, 5.5 and 6.1% for the cases of cake filtration, intermediate blocking and complete blocking mechanisms, respectively.

Assessing the phototransformation of diclofenac, clofibric acid and naproxen in surface waters: Model predictions and comparison with field data.

Science.gov (United States)

Avetta, Paola; Fabbri, Debora; Minella, Marco; Brigante, Marcello; Maurino, Valter; Minero, Claudio; Pazzi, Marco; Vione, Davide

2016-11-15

Phototransformation is important for the fate in surface waters of the pharmaceuticals diclofenac (DIC) and naproxen (NAP) and for clofibric acid (CLO), a metabolite of the drug clofibrate. The goal of this paper is to provide an overview of the prevailing photochemical processes, which these compounds undergo in the different conditions found in freshwater environments. The modelled photochemical half-life times of NAP and DIC range from a few days to some months, depending on water conditions (chemistry and depth) and on the season. The model indicates that direct photolysis is the dominant degradation pathway of DIC and NAP in sunlit surface waters, and potentially toxic cyclic amides were detected as intermediates of DIC direct phototransformation. With modelled half-life times in the month-year range, CLO is predicted to be more photostable than DIC or NAP and to be degraded mainly by reaction with the • OH radical and with the triplet states of chromophoric dissolved organic matter ( 3 CDOM*). The CLO intermediates arising from these processes and detected in this study (hydroquinone and 4-chlorophenol) are, respectively, a chronic toxicant to aquatic organisms and a possible carcinogen for humans. Hydroquinone is formed with only ∼5% yield upon CLO triplet-sensitised transformation, but it is highly toxic for algae and crustaceans. In contrast, the formation yield of 4-chlorophenol reaches ∼50% upon triplet sensitisation and ∼10% by · OH reaction. The comparison of model predictions with field data from a previous study yielded a very good agreement in the case of DIC and, when using 4-carboxybenzophenone as proxy for triplet sensitisation by CDOM, a good agreement was found for CLO as well. In the case of NAP, the comparison with field data suggests that its direct photolysis quantum yield approaches or even falls below the lower range of literature values. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting knee replacement damage in a simulator machine using a computational model with a consistent wear factor.

Science.gov (United States)

Zhao, Dong; Sakoda, Hideyuki; Sawyer, W Gregory; Banks, Scott A; Fregly, Benjamin J

2008-02-01

Wear of ultrahigh molecular weight polyethylene remains a primary factor limiting the longevity of total knee replacements (TKRs). However, wear testing on a simulator machine is time consuming and expensive, making it impractical for iterative design purposes. The objectives of this paper were first, to evaluate whether a computational model using a wear factor consistent with the TKR material pair can predict accurate TKR damage measured in a simulator machine, and second, to investigate how choice of surface evolution method (fixed or variable step) and material model (linear or nonlinear) affect the prediction. An iterative computational damage model was constructed for a commercial knee implant in an AMTI simulator machine. The damage model combined a dynamic contact model with a surface evolution model to predict how wear plus creep progressively alter tibial insert geometry over multiple simulations. The computational framework was validated by predicting wear in a cylinder-on-plate system for which an analytical solution was derived. The implant damage model was evaluated for 5 million cycles of simulated gait using damage measurements made on the same implant in an AMTI machine. Using a pin-on-plate wear factor for the same material pair as the implant, the model predicted tibial insert wear volume to within 2% error and damage depths and areas to within 18% and 10% error, respectively. Choice of material model had little influence, while inclusion of surface evolution affected damage depth and area but not wear volume predictions. Surface evolution method was important only during the initial cycles, where variable step was needed to capture rapid geometry changes due to the creep. Overall, our results indicate that accurate TKR damage predictions can be made with a computational model using a constant wear factor obtained from pin-on-plate tests for the same material pair, and furthermore, that surface evolution method matters only during the initial

Surface Energy and Mass Balance Model for Greenland Ice Sheet and Future Projections

Science.gov (United States)

Liu, Xiaojian

The Greenland Ice Sheet contains nearly 3 million cubic kilometers of glacial ice. If the entire ice sheet completely melted, sea level would raise by nearly 7 meters. There is thus considerable interest in monitoring the mass balance of the Greenland Ice Sheet. Each year, the ice sheet gains ice from snowfall and loses ice through iceberg calving and surface melting. In this thesis, we develop, validate and apply a physics based numerical model to estimate current and future surface mass balance of the Greenland Ice Sheet. The numerical model consists of a coupled surface energy balance and englacial model that is simple enough that it can be used for long time scale model runs, but unlike previous empirical parameterizations, has a physical basis. The surface energy balance model predicts ice sheet surface temperature and melt production. The englacial model predicts the evolution of temperature and meltwater within the ice sheet. These two models can be combined with estimates of precipitation (snowfall) to estimate the mass balance over the Greenland Ice Sheet. We first compare model performance with in-situ observations to demonstrate that the model works well. We next evaluate how predictions are degraded when we statistically downscale global climate data. We find that a simple, nearest neighbor interpolation scheme with a lapse rate correction is able to adequately reproduce melt patterns on the Greenland Ice Sheet. These results are comparable to those obtained using empirical Positive Degree Day (PDD) methods. Having validated the model, we next drove the ice sheet model using the suite of atmospheric model runs available through the CMIP5 atmospheric model inter-comparison, which in turn built upon the RCP 8.5 (business as usual) scenarios. From this exercise we predict how much surface melt production will increase in the coming century. This results in 4-10 cm sea level equivalent, depending on the CMIP5 models. Finally, we try to bound melt water

Using field data to assess model predictions of surface and ground fuel consumption by wildfire in coniferous forests of California

Science.gov (United States)

Lydersen, Jamie M.; Collins, Brandon M.; Ewell, Carol M.; Reiner, Alicia L.; Fites, Jo Ann; Dow, Christopher B.; Gonzalez, Patrick; Saah, David S.; Battles, John J.

2014-03-01

Inventories of greenhouse gas (GHG) emissions from wildfire provide essential information to the state of California, USA, and other governments that have enacted emission reductions. Wildfires can release a substantial amount of GHGs and other compounds to the atmosphere, so recent increases in fire activity may be increasing GHG emissions. Quantifying wildfire emissions however can be difficult due to inherent variability in fuel loads and consumption and a lack of field data of fuel consumption by wildfire. We compare a unique set of fuel data collected immediately before and after six wildfires in coniferous forests of California to fuel consumption predictions of the first-order fire effects model (FOFEM), based on two different available fuel characterizations. We found strong regional differences in the performance of different fuel characterizations, with FOFEM overestimating the fuel consumption to a greater extent in the Klamath Mountains than in the Sierra Nevada. Inaccurate fuel load inputs caused the largest differences between predicted and observed fuel consumption. Fuel classifications tended to overestimate duff load and underestimate litter load, leading to differences in predicted emissions for some pollutants. When considering total ground and surface fuels, modeled consumption was fairly accurate on average, although the range of error in estimates of plot level consumption was very large. These results highlight the importance of fuel load input to the accuracy of modeled fuel consumption and GHG emissions from wildfires in coniferous forests.

Comparison of microfacet BRDF model to modified Beckmann-Kirchhoff BRDF model for rough and smooth surfaces.

Science.gov (United States)

Butler, Samuel D; Nauyoks, Stephen E; Marciniak, Michael A

2015-11-02

A popular class of BRDF models is the microfacet models, where geometric optics is assumed. In contrast, more complex physical optics models may more accurately predict the BRDF, but the calculation is more resource intensive. These seemingly disparate approaches are compared in detail for the rough and smooth surface approximations of the modified Beckmann-Kirchhoff BRDF model, assuming Gaussian surface statistics. An approximation relating standard Fresnel reflection with the semi-rough surface polarization term, Q, is presented for unpolarized light. For rough surfaces, the angular dependence of direction cosine space is shown to be identical to the angular dependence in the microfacet distribution function. For polished surfaces, the same comparison shows a breakdown in the microfacet models. Similarities and differences between microfacet BRDF models and the modified Beckmann-Kirchhoff model are identified. The rationale for the original Beckmann-Kirchhoff F(bk)(2) geometric term relative to both microfacet models and generalized Harvey-Shack model is presented. A modification to the geometric F(bk)(2) term in original Beckmann-Kirchhoff BRDF theory is proposed.

Improving Permafrost Hydrology Prediction Through Data-Model Integration

Science.gov (United States)

Wilson, C. J.; Andresen, C. G.; Atchley, A. L.; Bolton, W. R.; Busey, R.; Coon, E.; Charsley-Groffman, L.

2017-12-01

The CMIP5 Earth System Models were unable to adequately predict the fate of the 16GT of permafrost carbon in a warming climate due to poor representation of Arctic ecosystem processes. The DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic project aims to reduce uncertainty in the Arctic carbon cycle and its impact on the Earth's climate system by improved representation of the coupled physical, chemical and biological processes that drive how much buried carbon will be converted to CO2 and CH4, how fast this will happen, which form will dominate, and the degree to which increased plant productivity will offset increased soil carbon emissions. These processes fundamentally depend on permafrost thaw rate and its influence on surface and subsurface hydrology through thermal erosion, land subsidence and changes to groundwater flow pathways as soil, bedrock and alluvial pore ice and massive ground ice melts. LANL and its NGEE colleagues are co-developing data and models to better understand controls on permafrost degradation and improve prediction of the evolution of permafrost and its impact on Arctic hydrology. The LANL Advanced Terrestrial Simulator was built using a state of the art HPC software framework to enable the first fully coupled 3-dimensional surface-subsurface thermal-hydrology and land surface deformation simulations to simulate the evolution of the physical Arctic environment. Here we show how field data including hydrology, snow, vegetation, geochemistry and soil properties, are informing the development and application of the ATS to improve understanding of controls on permafrost stability and permafrost hydrology. The ATS is being used to inform parameterizations of complex coupled physical, ecological and biogeochemical processes for implementation in the DOE ACME land model, to better predict the role of changing Arctic hydrology on the global climate system. LA-UR-17-26566.

Three-model ensemble wind prediction in southern Italy

Science.gov (United States)

Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo

2016-03-01

Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

Application of Artificial Neural Network and Response Surface Methodology in Modeling of Surface Roughness in WS2 Solid Lubricant Assisted MQL Turning of Inconel 718

Science.gov (United States)

Maheshwera Reddy Paturi, Uma; Devarasetti, Harish; Abimbola Fadare, David; Reddy Narala, Suresh Kumar

2018-04-01

In the present paper, the artificial neural network (ANN) and response surface methodology (RSM) are used in modeling of surface roughness in WS2 (tungsten disulphide) solid lubricant assisted minimal quantity lubrication (MQL) machining. The real time MQL turning of Inconel 718 experimental data considered in this paper was available in the literature [1]. In ANN modeling, performance parameters such as mean square error (MSE), mean absolute percentage error (MAPE) and average error in prediction (AEP) for the experimental data were determined based on Levenberg–Marquardt (LM) feed forward back propagation training algorithm with tansig as transfer function. The MATLAB tool box has been utilized in training and testing of neural network model. Neural network model with three input neurons, one hidden layer with five neurons and one output neuron (3-5-1 architecture) is found to be most confidence and optimal. The coefficient of determination (R2) for both the ANN and RSM model were seen to be 0.998 and 0.982 respectively. The surface roughness predictions from ANN and RSM model were related with experimentally measured values and found to be in good agreement with each other. However, the prediction efficacy of ANN model is relatively high when compared with RSM model predictions.

Predictability of the recent slowdown and subsequent recovery of large-scale surface warming using statistical methods

Science.gov (United States)

Mann, Michael E.; Steinman, Byron A.; Miller, Sonya K.; Frankcombe, Leela M.; England, Matthew H.; Cheung, Anson H.

2016-04-01

The temporary slowdown in large-scale surface warming during the early 2000s has been attributed to both external and internal sources of climate variability. Using semiempirical estimates of the internal low-frequency variability component in Northern Hemisphere, Atlantic, and Pacific surface temperatures in concert with statistical hindcast experiments, we investigate whether the slowdown and its recent recovery were predictable. We conclude that the internal variability of the North Pacific, which played a critical role in the slowdown, does not appear to have been predictable using statistical forecast methods. An additional minor contribution from the North Atlantic, by contrast, appears to exhibit some predictability. While our analyses focus on combining semiempirical estimates of internal climatic variability with statistical hindcast experiments, possible implications for initialized model predictions are also discussed.

Modelling the growth of Listeria monocytogenes on the surface of smear- or mould-ripened cheese

Directory of Open Access Journals (Sweden)

Sol eSchvartzman

2014-07-01

Full Text Available Surface-ripened cheeses are matured by means of manual or mechanical technologies posing a risk of cross-contamination, if any cheeses are contaminated with Listeria monocytogenes. In predictive microbiology, primary models are used to describe microbial responses, such as growth rate over time and secondary models explain how those responses change with environmental factors. In this way, primary models were used to assess the growth rate of L. monocytogenes during ripening of the cheeses and the secondary models to test how much the growth rate was affected by either the pH and/or the water activity (aw of the cheeses. The two models combined can be used to predict outcomes. The purpose of these experiments was to test three primary (the modified Gompertz equation, the Baranyi and Roberts model and the Logistic model and three secondary (the Cardinal model, the Ratowski model and the Presser model mathematical models in order to define which combination of models would best predict the growth of L. monocytogenes on the surface of artificially contaminated surface-ripened cheeses. Growth on the surface of the cheese was assessed and modelled. The primary models were firstly fitted to the data and the effects of pH and aw on the growth rate (μmax were incorporated and assessed one by one with the secondary models. The Logistic primary model by itself did not show a better fit of the data among the other primary models tested, but the inclusion of the Cardinal secondary model improved the final fit. The aw was not related to the growth of Listeria. This study suggests that surface-ripened cheese should be separately regulated within EU microbiological food legislation and results expressed as counts per surface area rather than per gram.

Modeling the growth of Listeria monocytogenes on the surface of smear- or mold-ripened cheese.

Science.gov (United States)

Schvartzman, M Sol; Gonzalez-Barron, Ursula; Butler, Francis; Jordan, Kieran

2014-01-01

Surface-ripened cheeses are matured by means of manual or mechanical technologies posing a risk of cross-contamination, if any cheeses are contaminated with Listeria monocytogenes. In predictive microbiology, primary models are used to describe microbial responses, such as growth rate over time and secondary models explain how those responses change with environmental factors. In this way, primary models were used to assess the growth rate of L. monocytogenes during ripening of the cheeses and the secondary models to test how much the growth rate was affected by either the pH and/or the water activity (aw) of the cheeses. The two models combined can be used to predict outcomes. The purpose of these experiments was to test three primary (the modified Gompertz equation, the Baranyi and Roberts model, and the Logistic model) and three secondary (the Cardinal model, the Ratowski model, and the Presser model) mathematical models in order to define which combination of models would best predict the growth of L. monocytogenes on the surface of artificially contaminated surface-ripened cheeses. Growth on the surface of the cheese was assessed and modeled. The primary models were firstly fitted to the data and the effects of pH and aw on the growth rate (μmax) were incorporated and assessed one by one with the secondary models. The Logistic primary model by itself did not show a better fit of the data among the other primary models tested, but the inclusion of the Cardinal secondary model improved the final fit. The aw was not related to the growth of Listeria. This study suggests that surface-ripened cheese should be separately regulated within EU microbiological food legislation and results expressed as counts per surface area rather than per gram.

Quantifying the predictive consequences of model error with linear subspace analysis

Science.gov (United States)

White, Jeremy T.; Doherty, John E.; Hughes, Joseph D.

2014-01-01

All computer models are simplified and imperfect simulators of complex natural systems. The discrepancy arising from simplification induces bias in model predictions, which may be amplified by the process of model calibration. This paper presents a new method to identify and quantify the predictive consequences of calibrating a simplified computer model. The method is based on linear theory, and it scales efficiently to the large numbers of parameters and observations characteristic of groundwater and petroleum reservoir models. The method is applied to a range of predictions made with a synthetic integrated surface-water/groundwater model with thousands of parameters. Several different observation processing strategies and parameterization/regularization approaches are examined in detail, including use of the Karhunen-Loève parameter transformation. Predictive bias arising from model error is shown to be prediction specific and often invisible to the modeler. The amount of calibration-induced bias is influenced by several factors, including how expert knowledge is applied in the design of parameterization schemes, the number of parameters adjusted during calibration, how observations and model-generated counterparts are processed, and the level of fit with observations achieved through calibration. Failure to properly implement any of these factors in a prediction-specific manner may increase the potential for predictive bias in ways that are not visible to the calibration and uncertainty analysis process.

Numerically predicting horizontally oriented spent fuel rod surface temperatures

International Nuclear Information System (INIS)

Wix, S.D.; Koski, J.A.

1993-01-01

A comparison between numerical calculations with use of commercial thermal analysis software packages and experimental data simulating a horizontally oriented spent fuel rod array was performed. Twelve cases were analyzed using air and helium for the fill gas, with three different heat dissipation levels. The numerically predicted temperatures are higher than the experimental data for all levels of heat dissipation with air as the fill gas. The temperature differences are 4 degrees C and 23 degrees C for the low heat dissipation and high dissipation, respectively. The temperature predictions using helium as a fill gas are lower than the experimental data for the low and medium heat dissipation levels. The temperature predictions are 1 degrees C and 6 degrees C lower than the experimental data for the low and medium heat dissipation, respectively. For the high heat dissipation level, the temperature predictions are 16 degrees C higher than the experimental data. Differences between the predicted and experimental temperatures can be attributed to several factors. These factors include a experimental uncertainity in the temperature and heat dissipation measurements, actual convection effects not included in the model, and axial heat flow in the experimental data. This works demonstrates that horizontally oriented spent fuel rod surface temperature predictions can be made using existing commercial software packages. This work also shows that end effects, such as axial heat transfer through the spent fuel rods, will be increasingly important as the amount of dissipated heat increases

Numerically predicting horizontally oriented spent fuel rod surface temperatures

International Nuclear Information System (INIS)

Wix, S.D.; Koski, J.A.

1992-01-01

A comparison between numerical calculations with use of commercial thermal analysis software packages and experimental data simulating a horizontally oriented spent fuel rod array was performed. Twelve cases were analyzed using air and helium for the fill gas, with three different heat dissipation levels. The numerically predicted temperatures are higher than the experimental data for all levels of heat dissipation with air as the fill gas. The temperature differences are 4 degree C and 23 degree C for the low heat dissipation and high heat dissipation, respectively. The temperature predictions using helium as a fill gas are lower than the experimental data for the low and medium heat dissipation levels. The temperature predictions are 1 degree C and 6 degree C lower than the experimental data for the low and medium heat dissipation, respectively. For the high heat dissipation level, the temperature predictions are 16 degree C higher than the experimental data. Differences between the predicted and experimental temperatures can be attributed to several factors. These factors include experimental uncertainty in the temperature and heat dissipation measurements, actual convection effects not included in the model, and axial heat flow in the experimental data. This work demonstrates that horizontally oriented spent fuel rod surface temperature predictions can be made using existing commercial software packages. This work also shows that end effects, such as axial heat transfer through the spent fuel rods, will be increasingly important as the amount of dissipated heat increases

Prediction of lake surface temperature using the air2water model: guidelines, challenges, and future perspectives

Directory of Open Access Journals (Sweden)

Sebastiano Piccolroaz

2016-04-01

Full Text Available Water temperature plays a primary role in controlling a wide range of physical, geochemical and ecological processes in lakes, with considerable influences on lake water quality and ecosystem functioning. Being able to reliably predict water temperature is therefore a desired goal, which stimulated the development of models of different type and complexity, ranging from simple regression-based models to more sophisticated process-based numerical models. However, both types of models suffer of some limitations: the first are not able to address some fundamental physical processes as e.g., thermal stratification, while the latter generally require a large amount of data in input, which are not always available. In this work, lake surface temperature is simulated by means of air2water, a hybrid physically-based/statistical model, which is able to provide a robust, predictive understanding of LST dynamics knowing air temperature only. This model showed performances that are comparable with those obtained by using process based models (a root mean square error on the order of 1°C, at daily scale, while retaining the simplicity and parsimony of regression-based models, thus making it a good candidate for long-term applications.The aim of the present work is to provide the reader with useful and practical guidelines for proper use of the air2water model and for critical analysis of results. Two case studies have been selected for the analysis: Lake Superior and Lake Erie. These are clear and emblematic examples of a deep and a shallow temperate lake characterized by markedly different thermal responses to external forcing, thus are ideal for making the results of the analysis the most general and comprehensive. Particular attention is paid to assessing the influence of missing data on model performance, and to evaluating when an observed time series is sufficiently informative for proper model calibration or, conversely, data are too scarce thus

Surface Temperature Variation Prediction Model Using Real-Time Weather Forecasts

Science.gov (United States)

Karimi, M.; Vant-Hull, B.; Nazari, R.; Khanbilvardi, R.

2015-12-01

Combination of climate change and urbanization are heating up cities and putting the lives of millions of people in danger. More than half of the world's total population resides in cities and urban centers. Cities are experiencing urban Heat Island (UHI) effect. Hotter days are associated with serious health impacts, heart attaches and respiratory and cardiovascular diseases. Densely populated cities like Manhattan, New York can be affected by UHI impact much more than less populated cities. Even though many studies have been focused on the impact of UHI and temperature changes between urban and rural air temperature, not many look at the temperature variations within a city. These studies mostly use remote sensing data or typical measurements collected by local meteorological station networks. Local meteorological measurements only have local coverage and cannot be used to study the impact of UHI in a city and remote sensing data such as MODIS, LANDSAT and ASTER have with very low resolution which cannot be used for the purpose of this study. Therefore, predicting surface temperature in urban cities using weather data can be useful.Three months of Field campaign in Manhattan were used to measure spatial and temporal temperature variations within an urban setting by placing 10 fixed sensors deployed to measure temperature, relative humidity and sunlight. Fixed instrument shelters containing relative humidity, temperature and illumination sensors were mounted on lampposts in ten different locations in Manhattan (Vant-Hull et al, 2014). The shelters were fixed 3-4 meters above the ground for the period of three months from June 23 to September 20th of 2013 making measurements with the interval of 3 minutes. These high resolution temperature measurements and three months of weather data were used to predict temperature variability from weather forecasts. This study shows that the amplitude of spatial and temporal variation in temperature for each day can be predicted

Modelling bankruptcy prediction models in Slovak companies

Directory of Open Access Journals (Sweden)

Kovacova Maria

2017-01-01

Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

Finite Element Simulation of Shot Peening: Prediction of Residual Stresses and Surface Roughness

Science.gov (United States)

Gariépy, Alexandre; Perron, Claude; Bocher, Philippe; Lévesque, Martin

Shot peening is a surface treatment that consists of bombarding a ductile surface with numerous small and hard particles. Each impact creates localized plastic strains that permanently stretch the surface. Since the underlying material constrains this stretching, compressive residual stresses are generated near the surface. This process is commonly used in the automotive and aerospace industries to improve fatigue life. Finite element analyses can be used to predict residual stress profiles and surface roughness created by shot peening. This study investigates further the parameters and capabilities of a random impact model by evaluating the representative volume element and the calculated stress distribution. Using an isotropic-kinematic hardening constitutive law to describe the behaviour of AA2024-T351 aluminium alloy, promising results were achieved in terms of residual stresses.

Application of a novel cellular automaton porosity prediction model to aluminium castings

International Nuclear Information System (INIS)

Atwood, R.C.; Chirazi, A.; Lee, P.D.

2002-01-01

A multiscale model was developed to predict the formation of porosity within a solidifying aluminium-silicon alloy. The diffusion of silicon and dissolved gas was simulated on a microscopic scale combined with cellular automaton models of gas porosity formation within the growing three-dimensional solidification microstructure. However, due to high computational cost, the modelled volume is limited to the millimetre range. This renders the application of direct modelling of complex shape castings unfeasible. Combining the microstructural modelling with a statistical response-surface prediction method allows application of the microstructural model results to industrial scale casts by incorporating them in commercial solidification software. (author)

Comparison of Predictive Modeling Methods of Aircraft Landing Speed

Science.gov (United States)

Diallo, Ousmane H.

2012-01-01

Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

Coupled assimilation for an intermediated coupled ENSO prediction model

Science.gov (United States)

Zheng, Fei; Zhu, Jiang

2010-10-01

The value of coupled assimilation is discussed using an intermediate coupled model in which the wind stress is the only atmospheric state which is slavery to model sea surface temperature (SST). In the coupled assimilation analysis, based on the coupled wind-ocean state covariance calculated from the coupled state ensemble, the ocean state is adjusted by assimilating wind data using the ensemble Kalman filter. As revealed by a series of assimilation experiments using simulated observations, the coupled assimilation of wind observations yields better results than the assimilation of SST observations. Specifically, the coupled assimilation of wind observations can help to improve the accuracy of the surface and subsurface currents because the correlation between the wind and ocean currents is stronger than that between SST and ocean currents in the equatorial Pacific. Thus, the coupled assimilation of wind data can decrease the initial condition errors in the surface/subsurface currents that can significantly contribute to SST forecast errors. The value of the coupled assimilation of wind observations is further demonstrated by comparing the prediction skills of three 12-year (1997-2008) hindcast experiments initialized by the ocean-only assimilation scheme that assimilates SST observations, the coupled assimilation scheme that assimilates wind observations, and a nudging scheme that nudges the observed wind stress data, respectively. The prediction skills of two assimilation schemes are significantly better than those of the nudging scheme. The prediction skills of assimilating wind observations are better than assimilating SST observations. Assimilating wind observations for the 2007/2008 La Niña event triggers better predictions, while assimilating SST observations fails to provide an early warning for that event.

Investigation and modelling of rubber stationary friction on rough surfaces

International Nuclear Information System (INIS)

Le Gal, A; Klueppel, M

2008-01-01

This paper presents novel aspects regarding the physically motivated modelling of rubber stationary sliding friction on rough surfaces. The description of dynamic contact is treated within the framework of a generalized Greenwood-Williamson theory for rigid/soft frictional pairings. Due to the self-affinity of rough surfaces, both hysteresis and adhesion friction components arise from a multi-scale excitation of surface roughness. Beside a complete analytical formulation of contact parameters, the morphology of macrotexture is considered via the introduction of a second scaling range at large length scales which mostly contribute to hysteresis friction. Moreover, adhesion friction is related to the real area of contact combined with the kinetics of interfacial peeling effects. Friction experiments carried out with different rubbers on rough granite and asphalt point out the relevance of hysteresis and adhesion friction concepts on rough surfaces. The two scaling ranges approach significantly improves the description of wet and dry friction behaviour within the range of low sliding velocity. In addition, material and surface effects are predicted and understood on a physical basis. The applicability of such modelling is of high interest for materials developers and road constructors regarding the prediction of wet grip performance of tyres on road tracks

Investigation and modelling of rubber stationary friction on rough surfaces

Energy Technology Data Exchange (ETDEWEB)

Le Gal, A; Klueppel, M [Deutsches Institut fuer Kautschuktechnologie, Eupener Strasse 33, D-30519 Hannover (Germany)

2008-01-09

This paper presents novel aspects regarding the physically motivated modelling of rubber stationary sliding friction on rough surfaces. The description of dynamic contact is treated within the framework of a generalized Greenwood-Williamson theory for rigid/soft frictional pairings. Due to the self-affinity of rough surfaces, both hysteresis and adhesion friction components arise from a multi-scale excitation of surface roughness. Beside a complete analytical formulation of contact parameters, the morphology of macrotexture is considered via the introduction of a second scaling range at large length scales which mostly contribute to hysteresis friction. Moreover, adhesion friction is related to the real area of contact combined with the kinetics of interfacial peeling effects. Friction experiments carried out with different rubbers on rough granite and asphalt point out the relevance of hysteresis and adhesion friction concepts on rough surfaces. The two scaling ranges approach significantly improves the description of wet and dry friction behaviour within the range of low sliding velocity. In addition, material and surface effects are predicted and understood on a physical basis. The applicability of such modelling is of high interest for materials developers and road constructors regarding the prediction of wet grip performance of tyres on road tracks.

Multi-year predictability in a coupled general circulation model

Energy Technology Data Exchange (ETDEWEB)

Power, Scott; Colman, Rob [Bureau of Meteorology Research Centre, Melbourne, VIC (Australia)

2006-02-01

Multi-year to decadal variability in a 100-year integration of a BMRC coupled atmosphere-ocean general circulation model (CGCM) is examined. The fractional contribution made by the decadal component generally increases with depth and latitude away from surface waters in the equatorial Indo-Pacific Ocean. The relative importance of decadal variability is enhanced in off-equatorial ''wings'' in the subtropical eastern Pacific. The model and observations exhibit ''ENSO-like'' decadal patterns. Analytic results are derived, which show that the patterns can, in theory, occur in the absence of any predictability beyond ENSO time-scales. In practice, however, modification to this stochastic view is needed to account for robust differences between ENSO-like decadal patterns and their interannual counterparts. An analysis of variability in the CGCM, a wind-forced shallow water model, and a simple mixed layer model together with existing and new theoretical results are used to improve upon this stochastic paradigm and to provide a new theory for the origin of decadal ENSO-like patterns like the Interdecadal Pacific Oscillation and Pacific Decadal Oscillation. In this theory, ENSO-driven wind-stress variability forces internal equatorially-trapped Kelvin waves that propagate towards the eastern boundary. Kelvin waves can excite reflected internal westward propagating equatorially-trapped Rossby waves (RWs) and coastally-trapped waves (CTWs). CTWs have no impact on the off-equatorial sub-surface ocean outside the coastal wave guide, whereas the RWs do. If the frequency of the incident wave is too high, then only CTWs are excited. At lower frequencies, both CTWs and RWs can be excited. The lower the frequency, the greater the fraction of energy transmitted to RWs. This lowers the characteristic frequency of variability off the equator relative to its equatorial counterpart. Both the eastern boundary interactions and the accumulation of

An analytical model for force prediction in ball nose micro milling of inclined surfaces

DEFF Research Database (Denmark)

Bissacco, Giuliano; Hansen, Hans Nørgaard; De Chiffre, Leonardo

2010-01-01

Ball nose micro milling is a key process for the generation of free form surfaces and inclined surfaces often present in mould inserts for micro replication. This paper presents a new cutting force model for ball nose micro milling that is capable of taking into account the effect of the edge...

Quantitative relationships for the prediction of the vapor pressure of some hydrocarbons from the van der Waals molecular surface

Directory of Open Access Journals (Sweden)

Olariu Tudor

2015-01-01

Full Text Available A quantitative structure - property relationship (QSPR modeling of vapor pressure at 298.15 K, expressed as log (VP / Pa was performed for a series of 84 hydrocarbons (63 alkanes and 21 cycloalkanes using the van der Waals (vdW surface area, SW/Å2, calculated by the Monte Carlo method, as the molecular descriptor. The QSPR model developed from the subset of 63 alkanes (C1-C16, deemed as the training set, was successfully used for the prediction of the log (VP / Pa values of the 21 cycloalkanes, which was the external prediction (test subset. A QSPR model was also developed for a series composed of all 84 hydrocarbons. Both QSPR models were statistically tested for their ability to fit the data and for prediction. The results showed that the vdW molecular surface used as molecular descriptor (MD explains the variance of the majority of the log (VP / Pa values in this series of 84 hydrocarbons. This MD describes very well the intermolecular forces that hold neutral molecules together. The clear physical meaning of the molecular surface values, SW/Å2, could explain the success of the QSPR models obtained with a single structural molecular descriptor.

Detailed physical properties prediction of pure methyl esters for biodiesel combustion modeling

International Nuclear Information System (INIS)

An, H.; Yang, W.M.; Maghbouli, A.; Chou, S.K.; Chua, K.J.

2013-01-01

Highlights: ► Group contribution methods from molecular level have been used for the prediction. ► Complete prediction of the physical properties for 5 methyl esters has been done. ► The predicted results can be very useful for biodiesel combustion modeling. ► Various models have been compared and the best model has been identified. ► Predicted properties are over large temperature ranges with excellent accuracies. -- Abstract: In order to accurately simulate the fuel spray, atomization, combustion and emission formation processes of a diesel engine fueled with biodiesel, adequate knowledge of biodiesel’s physical properties is desired. The objective of this work is to do a detailed physical properties prediction for the five major methyl esters of biodiesel for combustion modeling. The physical properties considered in this study are: normal boiling point, critical properties, vapor pressure, and latent heat of vaporization, liquid density, liquid viscosity, liquid thermal conductivity, gas diffusion coefficients and surface tension. For each physical property, the best prediction model has been identified, and very good agreements have been obtained between the predicted results and the published data where available. The calculated results can be used as key references for biodiesel combustion modeling.

Sorption of uranium (VI) on homoionic sodium smectite experimental study and surface complexation modeling.

Science.gov (United States)

Korichi, Smain; Bensmaili, Aicha

2009-09-30

This paper is an extension of a previous paper where the natural and purified clay in the homoionic Na form were physico-chemically characterized (doi:10.1016/j.clay.2008.04.014). In this study, the adsorption behavior of U (VI) on a purified Na-smectite suspension is studied using batch adsorption experiments and surface complexation modeling (double layer model). The sorption of uranium was investigated as a function of pH, uranium concentration, solid to liquid ratio, effect of natural organic matter (NOM) and NaNO(3) background electrolyte concentration. Using the MINTEQA2 program, the speciation of uranium was calculated as a function of pH and uranium concentration. Model predicted U (VI) aqueous speciation suggests that important aqueous species in the [U (VI)]=1mg/L and pH range 3-7 including UO(2)(2+), UO(2)OH(+), and (UO(2))(3)(OH)(5)(+). The concentration of UO(2)(2+) decreased and that of (UO(2))(3)(OH)(5)(+) increased with increasing pH. The potentiometric titration values and uptake of uranium in the sodium smectite suspension were simulated by FITEQL 4.0 program using a two sites model, which is composed of silicate and aluminum reaction sites. We compare the acidity constants values obtained by potentiometric titration from the purified sodium smectite with those obtained from single oxides (quartz and alpha-alumina), taking into account the surface heterogeneity and the complex nature of natural colloids. We investigate the uranium sorption onto purified Na-smectite assuming low, intermediate and high edge site surfaces which are estimated from specific surface area percentage. The sorption data is interpreted and modeled as a function of edge site surfaces. A relationship between uranium sorption and total site concentration was confirmed and explained through variation in estimated edge site surface value. The modeling study shows that, the convergence during DLM modeling is related to the best estimation of the edge site surface from the N(2

A new class of actuator surface models for wind turbines

Science.gov (United States)

Yang, Xiaolei; Sotiropoulos, Fotis

2018-05-01

Actuator line model has been widely employed in wind turbine simulations. However, the standard actuator line model does not include a model for the turbine nacelle which can significantly impact turbine wake characteristics as shown in the literature. Another disadvantage of the standard actuator line model is that more geometrical features of turbine blades cannot be resolved on a finer mesh. To alleviate these disadvantages of the standard model, we develop a new class of actuator surface models for turbine blades and nacelle to take into account more geometrical details of turbine blades and include the effect of turbine nacelle. In the actuator surface model for blade, the aerodynamic forces calculated using the blade element method are distributed from the surface formed by the foil chords at different radial locations. In the actuator surface model for nacelle, the forces are distributed from the actual nacelle surface with the normal force component computed in the same way as in the direct forcing immersed boundary method and the tangential force component computed using a friction coefficient and a reference velocity of the incoming flow. The actuator surface model for nacelle is evaluated by simulating the flow over periodically placed nacelles. Both the actuator surface simulation and the wall-resolved large-eddy simulation are carried out. The comparison shows that the actuator surface model is able to give acceptable results especially at far wake locations on a very coarse mesh. It is noted that although this model is employed for the turbine nacelle in this work, it is also applicable to other bluff bodies. The capability of the actuator surface model in predicting turbine wakes is assessed by simulating the flow over the MEXICO (Model experiments in Controlled Conditions) turbine and a hydrokinetic turbine.

Surface roughness prediction of particulate composites using artificial neural networks in turning operation

Directory of Open Access Journals (Sweden)

Mohammad Ramezani

2015-07-01

Full Text Available A number of factors, e.g. cutting speed and feed rate, affect the surface roughness in machining process. In this paper, an Artificial Neural Network model was used to forecast surface roughness with related inputs, including cutting speed and feed rate. The output of the ANN model input parameters related to the machined surface roughness parameters. In this research, twelve samples of experimental data were used to train the network. Moreover, four other experimental tests were implemented to test the network. The study concludes that ANN was a reliable and accurate method for predicting machining parameters in CNC turning operation of Particulate Reinforced Aluminum Matrix Composites (PAMCs specimens with 0%, 5%, 10% and 15% filler. The aim of this work is to decrease the production cost and consequently increase the production rate of these materials for industry without any trial and error method procedure.

Predictive modelling of fatigue failure in concentrated lubricated contacts.

Science.gov (United States)

Evans, H P; Snidle, R W; Sharif, K J; Bryant, M J

2012-01-01

Reducing frictional losses in response to the energy agenda will require use of less viscous lubricants causing hydrodynamically-lubricated bearings to operate with thinner films leading to "mixed lubrication" conditions in which a degree of direct interaction occurs between surfaces protected only by boundary tribofilms. The paper considers the consequences of thinner films and mixed lubrication for concentrated contacts such as those occurring between the teeth of power transmission gears and in rolling element bearings. Surface fatigue in gears remains a serious problem in demanding applications, and its solution will become more pressing with the tendency towards thinner oils. The particular form of failure examined here is micropitting, which is identified as a fatigue phenomenon occurring at the scale of the surface roughness asperities. It has emerged recently as a systemic difficulty in the operation of large scale wind turbines where it occurs in both power transmission gears and their support bearings. Predictive physical modelling of these contacts requires a transient mixed lubrication analysis for conditions in which the predicted lubricant film thickness is of the same order or significantly less than the height of surface roughness features. Numerical solvers have therefore been developed which are able to deal with situations in which transient solid contacts occur between surface asperity features under realistic engineering conditions. Results of the analysis, which reveal the detailed time-varying behaviour of pressure and film clearance, have been used to predict fatigue and damage accumulation at the scale of surface asperity features with the aim of improving understanding of the micropitting phenomenon. The possible consequences on fatigue of residual stress fields resulting from plastic deformation of surface asperities is also considered.

Why did the bear cross the road? Comparing the performance of multiple resistance surfaces and connectivity modeling methods

Science.gov (United States)

Samuel A. Cushman; Jesse S. Lewis; Erin L. Landguth

2014-01-01

There have been few assessments of the performance of alternative resistance surfaces, and little is known about how connectivity modeling approaches differ in their ability to predict organism movements. In this paper, we evaluate the performance of four connectivity modeling approaches applied to two resistance surfaces in predicting the locations of highway...

Impact of Soil Moisture Assimilation on Land Surface Model Spin-Up and Coupled LandAtmosphere Prediction

Science.gov (United States)

Santanello, Joseph A., Jr.; Kumar, Sujay V.; Peters-Lidard, Christa D.; Lawston, P.

2016-01-01

Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASAs Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land-atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS-WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spin-up can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux- PBL-ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research.

Improvement of gas entrainment prediction method. Introduction of surface tension effect

International Nuclear Information System (INIS)

Ito, Kei; Sakai, Takaaki; Ohshima, Hiroyuki; Uchibori, Akihiro; Eguchi, Yuzuru; Monji, Hideaki; Xu, Yongze

2010-01-01

A gas entrainment (GE) prediction method has been developed to establish design criteria for the large-scale sodium-cooled fast reactor (JSFR) systems. The prototype of the GE prediction method was already confirmed to give reasonable gas core lengths by simple calculation procedures. However, for simplification, the surface tension effects were neglected. In this paper, the evaluation accuracy of gas core lengths is improved by introducing the surface tension effects into the prototype GE prediction method. First, the mechanical balance between gravitational, centrifugal, and surface tension forces is considered. Then, the shape of a gas core tip is approximated by a quadratic function. Finally, using the approximated gas core shape, the authors determine the gas core length satisfying the mechanical balance. This improved GE prediction method is validated by analyzing the gas core lengths observed in simple experiments. Results show that the analytical gas core lengths calculated by the improved GE prediction method become shorter in comparison to the prototype GE prediction method, and are in good agreement with the experimental data. In addition, the experimental data under different temperature and surfactant concentration conditions are reproduced by the improved GE prediction method. (author)

A kinetic model for borosilicate glass dissolution based on the dissolution affinity of a surface alteration layer

International Nuclear Information System (INIS)

Bourcier, W.L.; Peiffer, D.W.; Knauss, K.G.; McKeegan, K.D.; Smith, D.K.

1989-11-01

A kinetic model for the dissolution of borosilicate glass is used to predict the dissolution rate of a nuclear waste glass. In the model, the glass dissolution rate is controlled by the rate of dissolution of an alkali-depleted amorphous surface (gel) layer. Our model predicts that all components concentrated in the surface layer, affect glass dissolution rates. The good agreement between predicted and observed elemental dissolution rates suggests that the dissolution rate of the gel layer limits the overall rate of glass dissolution. The model predicts that the long-term rate of glass dissolution will depend mainly on ion concentrations in solution, and therefore on the secondary phases which precipitate and control ion concentrations. 10 refs., 5 figs., 1 tab

[Application of three compartment model and response surface model to clinical anesthesia using Microsoft Excel].

Science.gov (United States)

Abe, Eiji; Abe, Mari

2011-08-01

With the spread of total intravenous anesthesia, clinical pharmacology has become more important. We report Microsoft Excel file applying three compartment model and response surface model to clinical anesthesia. On the Microsoft Excel sheet, propofol, remifentanil and fentanyl effect-site concentrations are predicted (three compartment model), and probabilities of no response to prodding, shaking, surrogates of painful stimuli and laryngoscopy are calculated using predicted effect-site drug concentration. Time-dependent changes in these calculated values are shown graphically. Recent development in anesthetic drug interaction studies are remarkable, and its application to clinical anesthesia with this Excel file is simple and helpful for clinical anesthesia.

A mechanical model for surface layer formation on self-lubricating ceramic composites

NARCIS (Netherlands)

Song, Jiupeng; Valefi, Mahdiar; de Rooij, Matthias B.; Schipper, Dirk J.

2010-01-01

To predict the thickness of a self-lubricating layer on the contact surface of ceramic composite material containing a soft phase during dry sliding test, a mechanical model was built to calculate the material transfer of the soft second phase in the composite to the surface. The tribological test,

Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study

Directory of Open Access Journals (Sweden)

Abdel Badie Sharkawy

2011-01-01

Full Text Available A study is presented to model surface roughness in end milling process. Three types of intelligent networks have been considered. They are (i radial basis function neural networks (RBFNs, (ii adaptive neurofuzzy inference systems (ANFISs, and (iii genetically evolved fuzzy inference systems (G-FISs. The machining parameters, namely, the spindle speed, feed rate, and depth of cut have been used as inputs to model the workpiece surface roughness. The goal is to get the best prediction accuracy. The procedure is illustrated using experimental data of end milling 6061 aluminum alloy. The three networks have been trained using experimental training data. After training, they have been examined using another set of data, that is, validation data. Results are compared with previously published results. It is concluded that ANFIS networks may suffer the local minima problem, and genetic tuning of fuzzy networks cannot insure perfect optimality unless suitable parameter setting (population size, number of generations etc. and tuning range for the FIS, parameters are used which can be hardly satisfied. It is shown that the RBFN model has the best performance (prediction accuracy in this particular case.

Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum

Science.gov (United States)

Wagle, Pradeep; Bhattarai, Nishan; Gowda, Prasanna H.; Kakani, Vijaya G.

2017-06-01

Robust evapotranspiration (ET) models are required to predict water usage in a variety of terrestrial ecosystems under different geographical and agrometeorological conditions. As a result, several remote sensing-based surface energy balance (SEB) models have been developed to estimate ET over large regions. However, comparison of the performance of several SEB models at the same site is limited. In addition, none of the SEB models have been evaluated for their ability to predict ET in rain-fed high biomass sorghum grown for biofuel production. In this paper, we evaluated the performance of five widely used single-source SEB models, namely Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET with Internalized Calibration (METRIC), Surface Energy Balance System (SEBS), Simplified Surface Energy Balance Index (S-SEBI), and operational Simplified Surface Energy Balance (SSEBop), for estimating ET over a high biomass sorghum field during the 2012 and 2013 growing seasons. The predicted ET values were compared against eddy covariance (EC) measured ET (ETEC) for 19 cloud-free Landsat image. In general, S-SEBI, SEBAL, and SEBS performed reasonably well for the study period, while METRIC and SSEBop performed poorly. All SEB models substantially overestimated ET under extremely dry conditions as they underestimated sensible heat (H) and overestimated latent heat (LE) fluxes under dry conditions during the partitioning of available energy. METRIC, SEBAL, and SEBS overestimated LE regardless of wet or dry periods. Consequently, predicted seasonal cumulative ET by METRIC, SEBAL, and SEBS were higher than seasonal cumulative ETEC in both seasons. In contrast, S-SEBI and SSEBop substantially underestimated ET under too wet conditions, and predicted seasonal cumulative ET by S-SEBI and SSEBop were lower than seasonal cumulative ETEC in the relatively wetter 2013 growing season. Our results indicate the necessity of inclusion of soil moisture or plant water stress

Estimation and prediction under local volatility jump-diffusion model

Science.gov (United States)

Kim, Namhyoung; Lee, Younhee

2018-02-01

Volatility is an important factor in operating a company and managing risk. In the portfolio optimization and risk hedging using the option, the value of the option is evaluated using the volatility model. Various attempts have been made to predict option value. Recent studies have shown that stochastic volatility models and jump-diffusion models reflect stock price movements accurately. However, these models have practical limitations. Combining them with the local volatility model, which is widely used among practitioners, may lead to better performance. In this study, we propose a more effective and efficient method of estimating option prices by combining the local volatility model with the jump-diffusion model and apply it using both artificial and actual market data to evaluate its performance. The calibration process for estimating the jump parameters and local volatility surfaces is divided into three stages. We apply the local volatility model, stochastic volatility model, and local volatility jump-diffusion model estimated by the proposed method to KOSPI 200 index option pricing. The proposed method displays good estimation and prediction performance.

Simulation of infiltration and redistribution of intense rainfall using Land Surface Models

Science.gov (United States)

Mueller, Anna; Verhoef, Anne; Cloke, Hannah

2016-04-01

Flooding from intense rainfall (FFIR) can cause widespread damage and disruption. Numerical Weather Prediction (NWP) models provide distributed information about atmospheric conditions, such as precipitation, that can lead to a flooding event. Short duration, high intensity rainfall events are generally poorly predicted by NWP models, because of the high spatiotemporal resolution required and because of the way the convective rainfall is described in the model. The resolution of NWP models is ever increasing. Better understanding of complex hydrological processes and the effect of scale is important in order to improve the prediction of magnitude and duration of such events, in the context of disaster management. Working as part of the NERC SINATRA project, we evaluated how the Land Surface Model (LSM) components of NWP models cope with high intensity rainfall input and subsequent infiltration problems. Both in terms of the amount of water infiltrated in the soil store, as well as the timing and the amount of surface and subsurface runoff generated. The models investigated are SWAP (Soil Water Air Plant, Alterra, the Netherlands, van Dam 1997), JULES (Joint UK Land Environment Simulator a component of Unified Model in UK Met Office, Best et al. 2011) and CHTESSEL (Carbon and Hydrology- Tiled ECMWF Scheme for Surface Exchanges over Land, Balsamo et al. 2009) We analysed the numerical aspects arising from discontinuities (or sharp gradients) in forcing and/or the model solution. These types of infiltration configurations were tested in the laboratory (Vachaud 1971), for some there are semi-analytical solutions (Philip 1957, Parlange 1972, Vanderborght 2005) or reference numerical solutions (Haverkamp 1977, van Dam 2000, Vanderborght 2005). The maximum infiltration by the surface, Imax, is in general dependent on atmospheric conditions, surface type, soil type, soil moisture content θ, and surface orographic factor σ. The models used differ in their approach to

Hybrid Prediction Model of the Temperature Field of a Motorized Spindle

Directory of Open Access Journals (Sweden)

Lixiu Zhang

2017-10-01

Full Text Available The thermal characteristics of a motorized spindle are the main determinants of its performance, and influence the machining accuracy of computer numerical control machine tools. It is important to accurately predict the thermal field of a motorized spindle during its operation to improve its thermal characteristics. This paper proposes a model to predict the temperature field of a high-speed and high-precision motorized spindle under different working conditions using a finite element model and test data. The finite element model considers the influence of the parameters of the cooling system and the lubrication system, and that of environmental conditions on the coefficient of heat transfer based on test data for the surface temperature of the motorized spindle. A genetic algorithm is used to optimize the coefficient of heat transfer of the spindle, and its temperature field is predicted using a three-dimensional model that employs this optimal coefficient. A prediction model of the 170MD30 temperature field of the motorized spindle is created and simulation data for the temperature field are compared with the test data. The results show that when the speed of the spindle is 10,000 rpm, the relative mean prediction error is 1.5%, and when its speed is 15,000 rpm, the prediction error is 3.6%. Therefore, the proposed prediction model can predict the temperature field of the motorized spindle with high accuracy.

Predictive Modeling in Actinide Chemistry and Catalysis

Energy Technology Data Exchange (ETDEWEB)

Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2016-05-16

These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.

Predicting Nanocrystal Shape through Consideration of Surface-Ligand Interactions

KAUST Repository

Bealing, Clive R.

2012-03-27

Density functional calculations for the binding energy of oleic acid-based ligands on Pb-rich {100} and {111} facets of PbSe nanocrystals determine the surface energies as a function of ligand coverage. Oleic acid is expected to bind to the nanocrystal surface in the form of lead oleate. The Wulff construction predicts the thermodynamic equilibrium shape of the PbSe nanocrystals. The equilibrium shape is a function of the ligand surface coverage, which can be controlled by changing the concentration of oleic acid during synthesis. The different binding energy of the ligand on the {100} and {111} facets results in different equilibrium ligand coverages on the facets, and a transition in the equilibrium shape from octahedral to cubic is predicted when increasing the ligand concentration during synthesis. © 2012 American Chemical Society.

Model for prediction of strip temperature in hot strip steel mill

International Nuclear Information System (INIS)

Panjkovic, Vladimir

2007-01-01

Proper functioning of set-up models in a hot strip steel mill requires reliable prediction of strip temperature. Temperature prediction is particularly important for accurate calculation of rolling force because of strong dependence of yield stress and strip microstructure on temperature. A comprehensive model was developed to replace an obsolete model in the Western Port hot strip mill of BlueScope Steel. The new model predicts the strip temperature evolution from the roughing mill exit to the finishing mill exit. It takes into account the radiative and convective heat losses, forced flow boiling and film boiling of water at strip surface, deformation heat in the roll gap, frictional sliding heat, heat of scale formation and the heat transfer between strip and work rolls through an oxide layer. The significance of phase transformation was also investigated. Model was tested with plant measurements and benchmarked against other models in the literature, and its performance was very good

Model for prediction of strip temperature in hot strip steel mill

Energy Technology Data Exchange (ETDEWEB)

Panjkovic, Vladimir [BlueScope Steel, TEOB, 1 Bayview Road, Hastings Vic. 3915 (Australia)]. E-mail: Vladimir.Panjkovic@BlueScopeSteel.com

2007-10-15

Proper functioning of set-up models in a hot strip steel mill requires reliable prediction of strip temperature. Temperature prediction is particularly important for accurate calculation of rolling force because of strong dependence of yield stress and strip microstructure on temperature. A comprehensive model was developed to replace an obsolete model in the Western Port hot strip mill of BlueScope Steel. The new model predicts the strip temperature evolution from the roughing mill exit to the finishing mill exit. It takes into account the radiative and convective heat losses, forced flow boiling and film boiling of water at strip surface, deformation heat in the roll gap, frictional sliding heat, heat of scale formation and the heat transfer between strip and work rolls through an oxide layer. The significance of phase transformation was also investigated. Model was tested with plant measurements and benchmarked against other models in the literature, and its performance was very good.

Generic global regression models for growth prediction of Salmonella in ground pork and pork cuts

DEFF Research Database (Denmark)

Buschhardt, Tasja; Hansen, Tina Beck; Bahl, Martin Iain

2017-01-01

Introduction and Objectives Models for the prediction of bacterial growth in fresh pork are primarily developed using two-step regression (i.e. primary models followed by secondary models). These models are also generally based on experiments in liquids or ground meat and neglect surface growth....... It has been shown that one-step global regressions can result in more accurate models and that bacterial growth on intact surfaces can substantially differ from growth in liquid culture. Material and Methods We used a global-regression approach to develop predictive models for the growth of Salmonella....... One part of obtained logtransformed cell counts was used for model development and another for model validation. The Ratkowsky square root model and the relative lag time (RLT) model were integrated into the logistic model with delay. Fitted parameter estimates were compared to investigate the effect...

Surface complexation modeling of zinc sorption onto ferrihydrite.

Science.gov (United States)

Dyer, James A; Trivedi, Paras; Scrivner, Noel C; Sparks, Donald L

2004-02-01

A previous study involving lead(II) [Pb(II)] sorption onto ferrihydrite over a wide range of conditions highlighted the advantages of combining molecular- and macroscopic-scale investigations with surface complexation modeling to predict Pb(II) speciation and partitioning in aqueous systems. In this work, an extensive collection of new macroscopic and spectroscopic data was used to assess the ability of the modified triple-layer model (TLM) to predict single-solute zinc(II) [Zn(II)] sorption onto 2-line ferrihydrite in NaNO(3) solutions as a function of pH, ionic strength, and concentration. Regression of constant-pH isotherm data, together with potentiometric titration and pH edge data, was a much more rigorous test of the modified TLM than fitting pH edge data alone. When coupled with valuable input from spectroscopic analyses, good fits of the isotherm data were obtained with a one-species, one-Zn-sorption-site model using the bidentate-mononuclear surface complex, (triple bond FeO)(2)Zn; however, surprisingly, both the density of Zn(II) sorption sites and the value of the best-fit equilibrium "constant" for the bidentate-mononuclear complex had to be adjusted with pH to adequately fit the isotherm data. Although spectroscopy provided some evidence for multinuclear surface complex formation at surface loadings approaching site saturation at pH >/=6.5, the assumption of a bidentate-mononuclear surface complex provided acceptable fits of the sorption data over the entire range of conditions studied. Regressing edge data in the absence of isotherm and spectroscopic data resulted in a fair number of surface-species/site-type combinations that provided acceptable fits of the edge data, but unacceptable fits of the isotherm data. A linear relationship between logK((triple bond FeO)2Zn) and pH was found, given by logK((triple bond FeO)2Znat1g/l)=2.058 (pH)-6.131. In addition, a surface activity coefficient term was introduced to the model to reduce the ionic strength

Predictive modeling of complications.

Science.gov (United States)

Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

2016-09-01

Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

Prediction of health effects of cross-border atmospheric pollutants using an aerosol forecast model.

Science.gov (United States)

Onishi, Kazunari; Sekiyama, Tsuyoshi Thomas; Nojima, Masanori; Kurosaki, Yasunori; Fujitani, Yusuke; Otani, Shinji; Maki, Takashi; Shinoda, Masato; Kurozawa, Youichi; Yamagata, Zentaro

2018-08-01

Health effects of cross-border air pollutants and Asian dust are of significant concern in Japan. Currently, models predicting the arrival of aerosols have not investigated the association between arrival predictions and health effects. We investigated the association between subjective health symptoms and unreleased aerosol data from the Model of Aerosol Species in the Global Atmosphere (MASINGAR) acquired from the Japan Meteorological Agency, with the objective of ascertaining if these data could be applied to predicting health effects. Subjective symptom scores were collected via self-administered questionnaires and, along with modeled surface aerosol concentration data, were used to conduct a risk evaluation using generalized estimating equations between October and November 2011. Altogether, 29 individuals provided 1670 responses. Spearman's correlation coefficients were determined for the relationship between the proportion of the participants reporting the maximum score of two or more for each symptom and the surface concentrations for each considered aerosol species calculated using MASINGAR; the coefficients showed significant intermediate correlations between surface sulfate aerosol concentration and respiratory, throat, and fever symptoms (R = 0.557, 0.454, and 0.470, respectively; p < 0.01). In the general estimation equation (logit link) analyses, a significant linear association of surface sulfate aerosol concentration, with an endpoint determined by reported respiratory symptom scores of two or more, was observed (P trend = 0.001, odds ratio [OR] of the highest quartile [Q4] vs. the lowest [Q1] = 5.31, 95% CI = 2.18 to 12.96), with adjustment for potential confounding. The surface sulfate aerosol concentration was also associated with throat and fever symptoms. In conclusion, our findings suggest that modeled data are potentially useful for predicting health risks of cross-border aerosol arrivals. Copyright © 2018 Elsevier Ltd

Predicting future glacial lakes in Austria using different modelling approaches

Science.gov (United States)

Otto, Jan-Christoph; Helfricht, Kay; Prasicek, Günther; Buckel, Johannes; Keuschnig, Markus

2017-04-01

Glacier retreat is one of the most apparent consequences of temperature rise in the 20th and 21th centuries in the European Alps. In Austria, more than 240 new lakes have formed in glacier forefields since the Little Ice Age. A similar signal is reported from many mountain areas worldwide. Glacial lakes can constitute important environmental and socio-economic impacts on high mountain systems including water resource management, sediment delivery, natural hazards, energy production and tourism. Their development significantly modifies the landscape configuration and visual appearance of high mountain areas. Knowledge on the location, number and extent of these future lakes can be used to assess potential impacts on high mountain geo-ecosystems and upland-lowland interactions. Information on new lakes is critical to appraise emerging threads and potentials for society. The recent development of regional ice thickness models and their combination with high resolution glacier surface data allows predicting the topography below current glaciers by subtracting ice thickness from glacier surface. Analyzing these modelled glacier bed surfaces reveals overdeepenings that represent potential locations for future lakes. In order to predict the location of future glacial lakes below recent glaciers in the Austrian Alps we apply different ice thickness models using high resolution terrain data and glacier outlines. The results are compared and validated with ice thickness data from geophysical surveys. Additionally, we run the models on three different glacier extents provided by the Austrian Glacier Inventories from 1969, 1998 and 2006. Results of this historical glacier extent modelling are compared to existing glacier lakes and discussed focusing on geomorphological impacts on lake evolution. We discuss model performance and observed differences in the results in order to assess the approach for a realistic prediction of future lake locations. The presentation delivers

A Methylmercury Prediction Too For Surface Waters Across The Contiguous United States (Invited)

Science.gov (United States)

Krabbenhoft, D. P.; Booth, N.; Lutz, M.; Fienen, M. N.; Saltman, T.

2009-12-01

About 20 years ago, researchers at a few locations across the globe discovered high levels of mercury in fish from remote settings lacking any obvious mercury source. We now know that for most locations atmospheric deposition is the dominant mercury source, and that mercury methylation is the key process that translates low mercury loading rates into relatively high levels in top predators of aquatic food webs. Presently, almost all US states have advisories for elevated levels of mercury in sport fish, and as a result there is considerable public awareness and concern for this nearly ubiquitous contaminant issue. In some states, “statewide” advisories have been issued because elevated fish mercury levels are so common, or the state has no effective way to monitor thousands of lakes, reservoirs, wetlands, and streams. As such, resource managers and public health officials have limited options for informing the public on of where elevated mercury concentrations in sport fish are more likely to occur than others. This project provides, for the first time, a national map of predicted (modeled) methylmercury concentrations in surface waters, which is the most toxic and bioaccumulative form of mercury in the environment. The map is the result of over two decades of research that resulted in the formulation of conceptual models of the mercury methylation process, which is strongly governed by environmental conditions - specifically hydrologic landscapes and water quality. The resulting predictive map shows clear regional trends in the distribution of methylmercury concentrations in surface waters. East of the Mississippi, the Gulf and southeastern Atlantic coast, the northeast, the lower Mississippi valley, and Great Lakes area are predicted to have generally higher environmental methylmercury levels. Higher-elevation, well-drained areas of Appalachia are predicted to have relatively lower methylmercury abundance. Other than the prairie pothole region, in the western

Modeling heat efficiency, flow and scale-up in the corotating disc scraped surface heat exchanger

DEFF Research Database (Denmark)

Friis, Alan; Szabo, Peter; Karlson, Torben

2002-01-01

A comparison of two different scale corotating disc scraped surface heat exchangers (CDHE) was performed experimentally. The findings were compared to predictions from a finite element model. We find that the model predicts well the flow pattern of the two CDHE's investigated. The heat transfer...... performance predicted by the model agrees well with experimental observations for the laboratory scale CDHE whereas the overall heat transfer in the scaled-up version was not in equally good agreement. The lack of the model to predict the heat transfer performance in scale-up leads us to identify the key...

Modeling Fate and Transport of Rotavirus in Surface Flow by Integrating WEPP and a Pathogen Transport Model

Science.gov (United States)

Bhattarai, R.; Kalita, P. K.; Davidson, P. C.; Kuhlenschmidt, M. S.

2012-12-01

More than 3.5 million people die each year from a water related diseases in this world. Every 20 seconds, a child dies from a water-related illness. Even in a developed country like the United States, there have been at least 1870 outbreaks associated with drinking water during the period of 1920 to 2002, causing 883,806 illnesses. Most of these outbreaks are resulted due to the presence of microbial pathogens in drinking water. Rotavirus infection has been recognized as the most common cause of diarrhea in young children throughout the world. Laboratory experiments conducted at the University of Illinois have demonstrated that recovery of rotavirus has been significantly affected by climatic and soil-surface conditions like slope, soil types, and ground cover. The objective of this study is to simulate the fate and transport of Rotavirus in overland and near-surface flow using a process-based model. In order to capture the dynamics of sediment-bound pathogens, the Water Erosion Prediction Project (WEPP) is coupled with the pathogen transport model. Transport of pathogens in overland flow can be simulated mathematically by including terms for the concentration of the pathogens in the liquid phase (in suspension or free-floating) and the solid phase (adsorbed to the fine solid particles like clay and silt). Advection, adsorption, and decay processes are considered. The mass balance equations are solved using numerical technique to predict spatial and temporal changes in pathogen concentrations in two phases. Outputs from WEPP simulations (flow velocity, depth, saturated conductivity and the soil particle fraction exiting in flow) are transferred as input for the pathogen transport model. Three soil types and three different surface cover conditions have been used in the experimental investigations. Results from these conditions have been used in calibrating and validating the simulation results. Bare surface conditions have produced very good agreement between

Modelling episodic acidification of surface waters: the state of science.

Science.gov (United States)

Eshleman, K N; Wigington, P J; Davies, T D; Tranter, M

1992-01-01

Field studies of chemical changes in surface waters associated with rainfall and snowmelt events have provided evidence of episodic acidification of lakes and streams in Europe and North America. Modelling these chemical changes is particularly challenging because of the variability associated with hydrological transport and chemical transformation processes in catchments. This paper provides a review of mathematical models that have been applied to the problem of episodic acidification. Several empirical approaches, including regression models, mixing models and time series models, support a strong hydrological interpretation of episodic acidification. Regional application of several models has suggested that acidic episodes (in which the acid neutralizing capacity becomes negative) are relatively common in surface waters in several regions of the US that receive acid deposition. Results from physically based models have suggested a lack of understanding of hydrological flowpaths, hydraulic residence times and biogeochemical reactions, particularly those involving aluminum. The ability to better predict episodic chemical responses of surface waters is thus dependent upon elucidation of these and other physical and chemical processes.

Study of Model Predictive Control for Path-Following Autonomous Ground Vehicle Control under Crosswind Effect

Directory of Open Access Journals (Sweden)

Fitri Yakub

2016-01-01

Full Text Available We present a comparative study of model predictive control approaches of two-wheel steering, four-wheel steering, and a combination of two-wheel steering with direct yaw moment control manoeuvres for path-following control in autonomous car vehicle dynamics systems. Single-track mode, based on a linearized vehicle and tire model, is used. Based on a given trajectory, we drove the vehicle at low and high forward speeds and on low and high road friction surfaces for a double-lane change scenario in order to follow the desired trajectory as close as possible while rejecting the effects of wind gusts. We compared the controller based on both simple and complex bicycle models without and with the roll vehicle dynamics for different types of model predictive control manoeuvres. The simulation result showed that the model predictive control gave a better performance in terms of robustness for both forward speeds and road surface variation in autonomous path-following control. It also demonstrated that model predictive control is useful to maintain vehicle stability along the desired path and has an ability to eliminate the crosswind effect.

Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology.

Science.gov (United States)

Huang, Jen-Ching; Chang, Ho; Kuo, Chin-Guo; Li, Jeen-Fong; You, Yong-Chin

2015-12-04

Atomic force microscopy (AFM) was used for visualization of a nano-oxidation technique performed on diamond-like carbon (DLC) thin film. Experiments of the nano-oxidation technique of the DLC thin film include those on nano-oxidation points and nano-oxidation lines. The feature sizes of the DLC thin film, including surface morphology, depth, and width, were explored after application of a nano-oxidation technique to the DLC thin film under different process parameters. A databank for process parameters and feature sizes of thin films was then established, and multiple regression analysis (MRA) and a back-propagation neural network (BPN) were used to carry out the algorithm. The algorithmic results are compared with the feature sizes acquired from experiments, thus obtaining a prediction model of the nano-oxidation technique of the DLC thin film. The comparative results show that the prediction accuracy of BPN is superior to that of MRA. When the BPN algorithm is used to predict nano-point machining, the mean absolute percentage errors (MAPE) of depth, left side, and right side are 8.02%, 9.68%, and 7.34%, respectively. When nano-line machining is being predicted, the MAPEs of depth, left side, and right side are 4.96%, 8.09%, and 6.77%, respectively. The obtained data can also be used to predict cross-sectional morphology in the DLC thin film treated with a nano-oxidation process.

Binding Ligand Prediction for Proteins Using Partial Matching of Local Surface Patches

Directory of Open Access Journals (Sweden)

Lee Sael

2010-12-01

Full Text Available Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group.

Binding ligand prediction for proteins using partial matching of local surface patches.

Science.gov (United States)

Sael, Lee; Kihara, Daisuke

2010-01-01

Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group.

Core surface flow modelling from high-resolution secular variation

DEFF Research Database (Denmark)

Holme, R.; Olsen, Nils

2006-01-01

-flux hypothesis, but the spectrum of the SV implies that a conclusive test of frozen-flux is not possible. We parametrize the effects of diffusion as an expected misfit in the flow prediction due to departure from the frozen-flux hypothesis; at low spherical harmonic degrees, this contribution dominates...... the expected departure of the SV predictions from flow to the observed SV, while at high degrees the SV model uncertainty is dominant. We construct fine-scale core surface flows to model the SV. Flow non-uniqueness is a serious problem because the flows are sufficiently small scale to allow flow around non......-series of magnetic data and better parametrization of the external magnetic field....

Wake-Model Effects on Induced Drag Prediction of Staggered Boxwings

Directory of Open Access Journals (Sweden)

Julian Schirra

2018-01-01

Full Text Available For staggered boxwings the predictions of induced drag that rely on common potential-flow methods can be of limited accuracy. For example, linear, freestream-fixed wake models cannot resolve effects related to wake deflection and roll-up, which can have significant affects on the induced drag projection of these systems. The present work investigates the principle impact of wake modelling on the accuracy of induced drag prediction of boxwings with stagger. The study compares induced drag predictions of a higher-order potential-flow method that uses fixed and relaxed-wake models, and of an Euler-flow method. Positive-staggered systems at positive angles of attack are found to be particularly prone to higher-order wake effects due to vertical contraction of wakes trajectories, which results in smaller effective height-to-span ratios than compared with negative stagger and thus closer interactions between trailing wakes and lifting surfaces. Therefore, when trying to predict induced drag of positive staggered boxwings, only a potential-flow method with a fully relaxed-wake model will provide the high-degree of accuracy that rivals that of an Euler method while being computationally significantly more efficient.

Estimating surface water concentrations of “down-the-drain” chemicals in China using a global model

International Nuclear Information System (INIS)

Whelan, M.J.; Hodges, J.E.N.; Williams, R.J.; Keller, V.D.J.; Price, O.R.; Li, M.

2012-01-01

Predictions of surface water exposure to “down-the-drain” chemicals are presented which employ grid-based spatially-referenced data on average monthly runoff, population density, country-specific per capita domestic water and substance use rates and sewage treatment provision. Water and chemical load are routed through the landscape using flow directions derived from digital elevation data, accounting for in-stream chemical losses using simple first order kinetics. Although the spatial and temporal resolution of the model are relatively coarse, the model still has advantages over spatially inexplicit “unit-world” approaches, which apply arbitrary dilution factors, in terms of predicting the location of exposure hotspots and the statistical distribution of concentrations. The latter can be employed in probabilistic risk assessments. Here the model was applied to predict surface water exposure to “down-the-drain” chemicals in China for different levels of sewage treatment provision. Predicted spatial patterns of concentration were consistent with observed water quality classes for China. - Highlights: ► A global-scale model of “down-the-drain” chemical concentrations is presented. ► The model was used to predict spatial patterns of exposure in China. ► Predictions were consistent with observed water quality classes. ► The model can identify hotspots and statistical distributions of concentrations. - A global-scale model was used to predict spatial patterns of “down-the-drain” chemical concentrations in China. Predictions were consistent with observed water quality classes, demonstrating the potential value of the model.

A coupled mass transfer and surface complexation model for uranium (VI) removal from wastewaters

International Nuclear Information System (INIS)

Lenhart, J.; Figueroa, L.A.; Honeyman, B.D.

1994-01-01

A remediation technique has been developed for removing uranium (VI) from complex contaminated groundwater using flake chitin as a biosorbent in batch and continuous flow configurations. With this system, U(VI) removal efficiency can be predicted using a model that integrates surface complexation models, mass transport limitations and sorption kinetics. This integration allows the reactor model to predict removal efficiencies for complex groundwaters with variable U(VI) concentrations and other constituents. The system has been validated using laboratory-derived kinetic data in batch and CSTR systems to verify the model predictions of U(VI) uptake from simulated contaminated groundwater

Prediction of antigenic epitopes on protein surfaces by consensus scoring

Directory of Open Access Journals (Sweden)

Zhang Chi

2009-09-01

Full Text Available Abstract Background Prediction of antigenic epitopes on protein surfaces is important for vaccine design. Most existing epitope prediction methods focus on protein sequences to predict continuous epitopes linear in sequence. Only a few structure-based epitope prediction algorithms are available and they have not yet shown satisfying performance. Results We present a new antigen Epitope Prediction method, which uses ConsEnsus Scoring (EPCES from six different scoring functions - residue epitope propensity, conservation score, side-chain energy score, contact number, surface planarity score, and secondary structure composition. Applied to unbounded antigen structures from an independent test set, EPCES was able to predict antigenic eptitopes with 47.8% sensitivity, 69.5% specificity and an AUC value of 0.632. The performance of the method is statistically similar to other published methods. The AUC value of EPCES is slightly higher compared to the best results of existing algorithms by about 0.034. Conclusion Our work shows consensus scoring of multiple features has a better performance than any single term. The successful prediction is also due to the new score of residue epitope propensity based on atomic solvent accessibility.

One-level modeling for diagnosing surface winds over complex terrain. II - Applicability to short-range forecasting

Science.gov (United States)

Alpert, P.; Getenio, B.; Zak-Rosenthal, R.

1988-01-01

The Alpert and Getenio (1988) modification of the Mass and Dempsey (1985) one-level sigma-surface model was used to study four synoptic events that included two winter cases (a Cyprus low and a Siberian high) and two summer cases. Results of statistical verification showed that the model is not only capable of diagnosing many details of surface mesoscale flow, but might also be useful for various applications which require operative short-range prediction of the diurnal changes of high-resolution surface flow over complex terrain, for example, in locating wildland fires, determining the dispersion of air pollutants, and predicting changes in wind energy or of surface wind for low-level air flights.

Developing an Effective Model for Predicting Spatially and Temporally Continuous Stream Temperatures from Remotely Sensed Land Surface Temperatures

Directory of Open Access Journals (Sweden)

Kristina M. McNyset

2015-12-01

Full Text Available Although water temperature is important to stream biota, it is difficult to collect in a spatially and temporally continuous fashion. We used remotely-sensed Land Surface Temperature (LST data to estimate mean daily stream temperature for every confluence-to-confluence reach in the John Day River, OR, USA for a ten year period. Models were built at three spatial scales: site-specific, subwatershed, and basin-wide. Model quality was assessed using jackknife and cross-validation. Model metrics for linear regressions of the predicted vs. observed data across all sites and years: site-specific r2 = 0.95, Root Mean Squared Error (RMSE = 1.25 °C; subwatershed r2 = 0.88, RMSE = 2.02 °C; and basin-wide r2 = 0.87, RMSE = 2.12 °C. Similar analyses were conducted using 2012 eight-day composite LST and eight-day mean stream temperature in five watersheds in the interior Columbia River basin. Mean model metrics across all basins: r2 = 0.91, RMSE = 1.29 °C. Sensitivity analyses indicated accurate basin-wide models can be parameterized using data from as few as four temperature logger sites. This approach generates robust estimates of stream temperature through time for broad spatial regions for which there is only spatially and temporally patchy observational data, and may be useful for managers and researchers interested in stream biota.

An Anisotropic Hardening Model for Springback Prediction

Science.gov (United States)

Zeng, Danielle; Xia, Z. Cedric

2005-08-01

As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test.

An Anisotropic Hardening Model for Springback Prediction

International Nuclear Information System (INIS)

Zeng, Danielle; Xia, Z. Cedric

2005-01-01

As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

Science.gov (United States)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

Candidate Prediction Models and Methods

DEFF Research Database (Denmark)

Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

2005-01-01

This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....

The Prediction of Surface Tension of Ternary Mixtures at Different Temperatures Using Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Ali Khazaei

2014-07-01

Full Text Available In this work, artificial neural network (ANN has been employed to propose a practical model for predicting the surface tension of multi-component mixtures. In order to develop a reliable model based on the ANN, a comprehensive experimental data set including 15 ternary liquid mixtures at different temperatures was employed. These systems consist of 777 data points generally containing hydrocarbon components. The ANN model has been developed as a function of temperature, critical properties, and acentric factor of the mixture according to conventional corresponding-state models. 80% of the data points were employed for training ANN and the remaining data were utilized for testing the generated model. The average absolute relative deviations (AARD% of the model for the training set, the testing set, and the total data points were obtained 1.69, 1.86, and 1.72 respectively. Comparing the results with Flory theory, Brok-Bird equation, and group contribution theory has proved the high prediction capability of the attained model.

Uncertainty-based calibration and prediction with a stormwater surface accumulation-washoff model based on coverage of sampled Zn, Cu, Pb and Cd field data

DEFF Research Database (Denmark)

Lindblom, Erik Ulfson; Ahlman, S.; Mikkelsen, Peter Steen

2011-01-01

allows identifying a range of behavioral model parameter sets. The small catchment size and nearness of the rain gauge justified excluding the hydrological model parameters from the uncertainty assessment. Uniform, closed prior distributions were heuristically specified for the dry and wet removal...... of accumulated metal available on the conceptual catchment surface. Forward Monte Carlo analysis based on the posterior parameter sets covered 95% of the observed event mean concentrations, and 95% prediction quantiles for site mean concentrations were estimated to 470 μg/l ±20% for Zn, 295 μg/l ±40% for Cu, 20...

Assessment of structures and stabilities of defect clusters and surface energies predicted by nine interatomic potentials for UO{sub 2}

Energy Technology Data Exchange (ETDEWEB)

Taller, Stephen A. [School of Nuclear Engineering, Purdue University, West Lafayette, IN 47907 (United States); Bai, Xian-Ming, E-mail: xianming.bai@inl.gov [Fuels Modeling and Simulation Department, Idaho National Laboratory, Idaho Falls, ID 83415 (United States)

2013-11-15

The irradiation in nuclear reactors creates many point defects and defect clusters in uranium dioxide (UO{sub 2}) and their evolution severely degrades the thermal and mechanical properties of the nuclear fuels. Previously many empirical interatomic potentials have been developed for modeling defect production and evolution in UO{sub 2}. However, the properties of defect clusters and extended defects are usually not fitted into these potentials. In this work nine interatomic potentials for UO{sub 2} are examined by using molecular statics and molecular dynamics to assess their applicability in predicting the properties of various types of defect clusters in UO{sub 2}. The binding energies and structures for these defect clusters have been evaluated for each potential. In addition, the surface energies of voids of different radii and (1 1 0) flat surfaces predicted by these potentials are also evaluated. It is found that both good agreement and significant discrepancies exist for these potentials in predicting these properties. For oxygen interstitial clusters, these potentials predict significantly different defect cluster structures and stabilities; For defect clusters consisting of both uranium and oxygen defects, the prediction is in better agreement; The surface energies predicted by these potentials have significant discrepancies, and some of them are much higher than the experimentally measured values. The results from this work can provide insight on interpreting the outcome of atomistic modeling of defect production using these potentials and may provide guidelines for choosing appropriate potential models to study problems of interest in UO{sub 2}.

Predictive multiscale computational model of shoe-floor coefficient of friction.

Science.gov (United States)

Moghaddam, Seyed Reza M; Acharya, Arjun; Redfern, Mark S; Beschorner, Kurt E

2018-01-03

Understanding the frictional interactions between the shoe and floor during walking is critical to prevention of slips and falls, particularly when contaminants are present. A multiscale finite element model of shoe-floor-contaminant friction was developed that takes into account the surface and material characteristics of the shoe and flooring in microscopic and macroscopic scales. The model calculates shoe-floor coefficient of friction (COF) in boundary lubrication regime where effects of adhesion friction and hydrodynamic pressures are negligible. The validity of model outputs was assessed by comparing model predictions to the experimental results from mechanical COF testing. The multiscale model estimates were linearly related to the experimental results (p < 0.0001). The model predicted 73% of variability in experimentally-measured shoe-floor-contaminant COF. The results demonstrate the potential of multiscale finite element modeling in aiding slip-resistant shoe and flooring design and reducing slip and fall injuries. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Prediction of surface tension of binary mixtures with the parachor method

Directory of Open Access Journals (Sweden)

Němec Tomáš

2015-01-01

Full Text Available The parachor method for the estimation of the surface tension of binary mixtures is modified by considering temperature-dependent values of the parachor parameters. The temperature dependence is calculated by a least-squares fit of pure-solvent surface tension data to the binary parachor equation utilizing the Peng-Robinson equation of state for the calculation of equilibrium densities. A very good agreement between experimental binary surface tension data and the predictions of the modified parachor method are found for the case of the mixtures of carbon dioxide and butane, benzene, and cyclohexane, respectively. The surface tension is also predicted for three refrigerant mixtures, i.e. propane, isobutane, and chlorodifluoromethane, with carbon dioxide.

Wind power prediction models

Science.gov (United States)

Levy, R.; Mcginness, H.

1976-01-01

Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

Improving the Yule-Nielsen modified Neugebauer model by dot surface coverages depending on the ink superposition conditions

Science.gov (United States)

Hersch, Roger David; Crete, Frederique

2005-01-01

Dot gain is different when dots are printed alone, printed in superposition with one ink or printed in superposition with two inks. In addition, the dot gain may also differ depending on which solid ink the considered halftone layer is superposed. In a previous research project, we developed a model for computing the effective surface coverage of a dot according to its superposition conditions. In the present contribution, we improve the Yule-Nielsen modified Neugebauer model by integrating into it our effective dot surface coverage computation model. Calibration of the reproduction curves mapping nominal to effective surface coverages in every superposition condition is carried out by fitting effective dot surfaces which minimize the sum of square differences between the measured reflection density spectra and reflection density spectra predicted according to the Yule-Nielsen modified Neugebauer model. In order to predict the reflection spectrum of a patch, its known nominal surface coverage values are converted into effective coverage values by weighting the contributions from different reproduction curves according to the weights of the contributing superposition conditions. We analyze the colorimetric prediction improvement brought by our extended dot surface coverage model for clustered-dot offset prints, thermal transfer prints and ink-jet prints. The color differences induced by the differences between measured reflection spectra and reflection spectra predicted according to the new dot surface estimation model are quantified on 729 different cyan, magenta, yellow patches covering the full color gamut. As a reference, these differences are also computed for the classical Yule-Nielsen modified spectral Neugebauer model incorporating a single halftone reproduction curve for each ink. Taking into account dot surface coverages according to different superposition conditions considerably improves the predictions of the Yule-Nielsen modified Neugebauer model. In

Inverse and Predictive Modeling

Energy Technology Data Exchange (ETDEWEB)

Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-09-27

The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

Archaeological predictive model set.

Science.gov (United States)

2015-03-01

This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

Statistical analysis and modelling of surface runoff from arable fields in central Europe

Directory of Open Access Journals (Sweden)

P. Fiener

2013-10-01

Full Text Available Surface runoff generation on arable fields is an important driver of flooding, on-site and off-site damages by erosion, and of nutrient and agrochemical transport. In general, three different processes generate surface runoff (Hortonian runoff, saturation excess runoff, and return of subsurface flow. Despite the developments in our understanding of these processes it remains difficult to predict which processes govern runoff generation during the course of an event or throughout the year, when soil and vegetation on arable land are passing many states. We analysed the results from 317 rainfall simulations on 209 soils from different landscapes with a resolution of 14 286 runoff measurements to determine temporal and spatial differences in variables governing surface runoff, and to derive and test a statistical model of surface runoff generation independent from an a priori selection of modelled process types. Measured runoff was related to 20 time-invariant soil properties, three variable soil properties, four rain properties, three land use properties and many derived variables describing interactions and curvilinear behaviour. In an iterative multiple regression procedure, six of these properties/variables best described initial abstraction and the hydrograph. To estimate initial abstraction, the percentages of stone cover above 10% and of sand content in the bulk soil were needed, while the hydrograph could be predicted best from rain depth exceeding initial abstraction, rainfall intensity, soil organic carbon content, and time since last tillage. Combining the multiple regressions to estimate initial abstraction and surface runoff allowed modelling of event-specific hydrographs without an a priori assumption of the underlying process. The statistical model described the measured data well and performed equally well during validation. In both cases, the model explained 71 and 58% of variability in accumulated runoff volume and instantaneous

Ion mobilities in diatomic gases: measurement versus prediction with non-specular scattering models.

Science.gov (United States)

Larriba, Carlos; Hogan, Christopher J

2013-05-16

Ion/electrical mobility measurements of nanoparticles and polyatomic ions are typically linked to particle/ion physical properties through either application of the Stokes-Millikan relationship or comparison to mobilities predicted from polyatomic models, which assume that gas molecules scatter specularly and elastically from rigid structural models. However, there is a discrepancy between these approaches; when specular, elastic scattering models (i.e., elastic-hard-sphere scattering, EHSS) are applied to polyatomic models of nanometer-scale ions with finite-sized impinging gas molecules, predictions are in substantial disagreement with the Stokes-Millikan equation. To rectify this discrepancy, we developed and tested a new approach for mobility calculations using polyatomic models in which non-specular (diffuse) and inelastic gas-molecule scattering is considered. Two distinct semiempirical models of gas-molecule scattering from particle surfaces were considered. In the first, which has been traditionally invoked in the study of aerosol nanoparticles, 91% of collisions are diffuse and thermally accommodating, and 9% are specular and elastic. In the second, all collisions are considered to be diffuse and accommodating, but the average speed of the gas molecules reemitted from a particle surface is 8% lower than the mean thermal speed at the particle temperature. Both scattering models attempt to mimic exchange between translational, vibrational, and rotational modes of energy during collision, as would be expected during collision between a nonmonoatomic gas molecule and a nonfrozen particle surface. The mobility calculation procedure was applied considering both hard-sphere potentials between gas molecules and the atoms within a particle and the long-range ion-induced dipole (polarization) potential. Predictions were compared to previous measurements in air near room temperature of multiply charged poly(ethylene glycol) (PEG) ions, which range in morphology from

ECMWF seasonal forecast system 3 and its prediction of sea surface temperature

Energy Technology Data Exchange (ETDEWEB)

Stockdale, Timothy N.; Anderson, David L.T.; Balmaseda, Magdalena A.; Ferranti, Laura; Mogensen, Kristian; Palmer, Timothy N.; Molteni, Franco; Vitart, Frederic [ECMWF, Reading (United Kingdom); Doblas-Reyes, Francisco [ECMWF, Reading (United Kingdom); Institut Catala de Ciencies del Clima (IC3), Barcelona (Spain)

2011-08-15

The latest operational version of the ECMWF seasonal forecasting system is described. It shows noticeably improved skill for sea surface temperature (SST) prediction compared with previous versions, particularly with respect to El Nino related variability. Substantial skill is shown for lead times up to 1 year, although at this range the spread in the ensemble forecast implies a loss of predictability large enough to account for most of the forecast error variance, suggesting only moderate scope for improving long range El Nino forecasts. At shorter ranges, particularly 3-6 months, skill is still substantially below the model-estimated predictability limit. SST forecast skill is higher for more recent periods than earlier ones. Analysis shows that although various factors can affect scores in particular periods, the improvement from 1994 onwards seems to be robust, and is most plausibly due to improvements in the observing system made at that time. The improvement in forecast skill is most evident for 3-month forecasts starting in February, where predictions of NINO3.4 SST from 1994 to present have been almost without fault. It is argued that in situations where the impact of model error is small, the value of improved observational data can be seen most clearly. Significant skill is also shown in the equatorial Indian Ocean, although predictive skill in parts of the tropical Atlantic are relatively poor. SST forecast errors can be especially high in the Southern Ocean. (orig.)

A local leaky-box model for the local stellar surface density-gas surface density-gas phase metallicity relation

Science.gov (United States)

Zhu, Guangtun Ben; Barrera-Ballesteros, Jorge K.; Heckman, Timothy M.; Zakamska, Nadia L.; Sánchez, Sebastian F.; Yan, Renbin; Brinkmann, Jonathan

2017-07-01

We revisit the relation between the stellar surface density, the gas surface density and the gas-phase metallicity of typical disc galaxies in the local Universe with the SDSS-IV/MaNGA survey, using the star formation rate surface density as an indicator for the gas surface density. We show that these three local parameters form a tight relationship, confirming previous works (e.g. by the PINGS and CALIFA surveys), but with a larger sample. We present a new local leaky-box model, assuming star-formation history and chemical evolution is localized except for outflowing materials. We derive closed-form solutions for the evolution of stellar surface density, gas surface density and gas-phase metallicity, and show that these parameters form a tight relation independent of initial gas density and time. We show that, with canonical values of model parameters, this predicted relation match the observed one well. In addition, we briefly describe a pathway to improving the current semi-analytic models of galaxy formation by incorporating the local leaky-box model in the cosmological context, which can potentially explain simultaneously multiple properties of Milky Way-type disc galaxies, such as the size growth and the global stellar mass-gas metallicity relation.

Water Resources Research Program. Surface thermal plumes: evaluation of mathematical models for the near and complete field

International Nuclear Information System (INIS)

Dunn, W.E.; Policastro, A.J.; Paddock, R.A.

1975-05-01

This report evaluates mathematical models that may be used to predict the flow and temperature distributions resulting from heated surface discharges from power-plant outfalls. Part One discusses the basic physics of surface-plume dispersion and provides a critical review of 11 of the most popular and promising plume models developed to predict the near- and complete-field plume. The principal conclusion of the report is that the available models, in their present stage of development, may be used to give only general estimates of plume characteristics; precise predictions are not currently possible. The Shirazi-Davis and Pritchard (No. 1) models appear superior to the others tested and are capable of correctly predicting general plume characteristics. (The predictions show roughly factor-of-two accuracy in centerline distance to a given isotherm, factor-of-two accuracy in plume width, and factor-of-five accuracy in isotherm areas.) The state of the art can best be improved by pursuing basic laboratory studies of plume dispersion along with further development of numerical-modeling techniques

Mapping the global depth to bedrock for land surface modelling

Science.gov (United States)

Shangguan, W.; Hengl, T.; Yuan, H.; Dai, Y. J.; Zhang, S.

2017-12-01

Depth to bedrock serves as the lower boundary of land surface models, which controls hydrologic and biogeochemical processes. This paper presents a framework for global estimation of Depth to bedrock (DTB). Observations were extracted from a global compilation of soil profile data (ca. 130,000 locations) and borehole data (ca. 1.6 million locations). Additional pseudo-observations generated by expert knowledge were added to fill in large sampling gaps. The model training points were then overlaid on a stack of 155 covariates including DEM-based hydrological and morphological derivatives, lithologic units, MODIS surfacee reflectance bands and vegetation indices derived from the MODIS land products. Global spatial prediction models were developed using random forests and Gradient Boosting Tree algorithms. The final predictions were generated at the spatial resolution of 250m as an ensemble prediction of the two independently fitted models. The 10-fold cross-validation shows that the models explain 59% for absolute DTB and 34% for censored DTB (depths deep than 200 cm are predicted as 200 cm). The model for occurrence of R horizon (bedrock) within 200 cm does a good job. Visual comparisons of predictions in the study areas where more detailed maps of depth to bedrock exist show that there is a general match with spatial patterns from similar local studies. Limitation of the data set and extrapolation in data spare areas should not be ignored in applications. To improve accuracy of spatial prediction, more borehole drilling logs will need to be added to supplement the existing training points in under-represented areas.

Modeling and Prediction of Krueger Device Noise

Science.gov (United States)

Guo, Yueping; Burley, Casey L.; Thomas, Russell H.

2016-01-01

This paper presents the development of a noise prediction model for aircraft Krueger flap devices that are considered as alternatives to leading edge slotted slats. The prediction model decomposes the total Krueger noise into four components, generated by the unsteady flows, respectively, in the cove under the pressure side surface of the Krueger, in the gap between the Krueger trailing edge and the main wing, around the brackets supporting the Krueger device, and around the cavity on the lower side of the main wing. For each noise component, the modeling follows a physics-based approach that aims at capturing the dominant noise-generating features in the flow and developing correlations between the noise and the flow parameters that control the noise generation processes. The far field noise is modeled using each of the four noise component's respective spectral functions, far field directivities, Mach number dependencies, component amplitudes, and other parametric trends. Preliminary validations are carried out by using small scale experimental data, and two applications are discussed; one for conventional aircraft and the other for advanced configurations. The former focuses on the parametric trends of Krueger noise on design parameters, while the latter reveals its importance in relation to other airframe noise components.

A level set methodology for predicting the effect of mask wear on surface evolution of features in abrasive jet micro-machining

International Nuclear Information System (INIS)

Burzynski, T; Papini, M

2012-01-01

A previous implementation of narrow-band level set methodology developed by the authors was extended to allow for the modelling of mask erosive wear in abrasive jet micro-machining (AJM). The model permits the prediction of the surface evolution of both the mask and the target simultaneously, by representing them as a hybrid and continuous mask–target surface. The model also accounts for the change in abrasive mass flux incident to both the target surface and, for the first time, the eroding mask edge, that is brought about by the presence of the mask edge itself. The predictions of the channel surface and eroded mask profiles were compared with measurements on channels machined in both glass and poly-methyl-methacrylate (PMMA) targets at both normal and oblique incidence, using tempered steel and elastomeric masks. A much better agreement between the predicted and measured profiles was found when mask wear was taken into account. Mask wear generally resulted in wider and deeper glass target profiles and wider PMMA target profiles, respectively, when compared to cases where no mask wear was present. This work has important implications for the AJM of complex MEMS and microfluidic devices that require longer machining times. (paper)

Predictive Multiscale Modeling of Nanocellulose Based Materials and Systems

International Nuclear Information System (INIS)

Kovalenko, Andriy

2014-01-01

Cellulose Nanocrysals (CNC) is a renewable biodegradable biopolymer with outstanding mechanical properties made from highly abundant natural source, and therefore is very attractive as reinforcing additive to replace petroleum-based plastics in biocomposite materials, foams, and gels. Large-scale applications of CNC are currently limited due to its low solubility in non-polar organic solvents used in existing polymerization technologies. The solvation properties of CNC can be improved by chemical modification of its surface. Development of effective surface modifications has been rather slow because extensive chemical modifications destabilize the hydrogen bonding network of cellulose and deteriorate the mechanical properties of CNC. We employ predictive multiscale theory, modeling, and simulation to gain a fundamental insight into the effect of CNC surface modifications on hydrogen bonding, CNC crystallinity, solvation thermodynamics, and CNC compatibilization with the existing polymerization technologies, so as to rationally design green nanomaterials with improved solubility in non-polar solvents, controlled liquid crystal ordering and optimized extrusion properties. An essential part of this multiscale modeling approach is the statistical- mechanical 3D-RISM-KH molecular theory of solvation, coupled with quantum mechanics, molecular mechanics, and multistep molecular dynamics simulation. The 3D-RISM-KH theory provides predictive modeling of both polar and non-polar solvents, solvent mixtures, and electrolyte solutions in a wide range of concentrations and thermodynamic states. It properly accounts for effective interactions in solution such as steric effects, hydrophobicity and hydrophilicity, hydrogen bonding, salt bridges, buffer, co-solvent, and successfully predicts solvation effects and processes in bulk liquids, solvation layers at solid surface, and in pockets and other inner spaces of macromolecules and supramolecular assemblies. This methodology

Predictive Multiscale Modeling of Nanocellulose Based Materials and Systems

Science.gov (United States)

Kovalenko, Andriy

2014-08-01

Cellulose Nanocrysals (CNC) is a renewable biodegradable biopolymer with outstanding mechanical properties made from highly abundant natural source, and therefore is very attractive as reinforcing additive to replace petroleum-based plastics in biocomposite materials, foams, and gels. Large-scale applications of CNC are currently limited due to its low solubility in non-polar organic solvents used in existing polymerization technologies. The solvation properties of CNC can be improved by chemical modification of its surface. Development of effective surface modifications has been rather slow because extensive chemical modifications destabilize the hydrogen bonding network of cellulose and deteriorate the mechanical properties of CNC. We employ predictive multiscale theory, modeling, and simulation to gain a fundamental insight into the effect of CNC surface modifications on hydrogen bonding, CNC crystallinity, solvation thermodynamics, and CNC compatibilization with the existing polymerization technologies, so as to rationally design green nanomaterials with improved solubility in non-polar solvents, controlled liquid crystal ordering and optimized extrusion properties. An essential part of this multiscale modeling approach is the statistical- mechanical 3D-RISM-KH molecular theory of solvation, coupled with quantum mechanics, molecular mechanics, and multistep molecular dynamics simulation. The 3D-RISM-KH theory provides predictive modeling of both polar and non-polar solvents, solvent mixtures, and electrolyte solutions in a wide range of concentrations and thermodynamic states. It properly accounts for effective interactions in solution such as steric effects, hydrophobicity and hydrophilicity, hydrogen bonding, salt bridges, buffer, co-solvent, and successfully predicts solvation effects and processes in bulk liquids, solvation layers at solid surface, and in pockets and other inner spaces of macromolecules and supramolecular assemblies. This methodology

Water Resources Research Program. Surface thermal plumes: evaluation of mathematical models for the near and complete field

International Nuclear Information System (INIS)

Dunn, W.E.; Policastro, A.J.; Paddock, R.A.

1975-08-01

This report evaluates mathematical models that may be used to predict the flow and temperature distributions resulting from heated surface discharges from power-plant outfalls. Part One discusses the basic physics of surface-plume dispersion and provides a critical review of 11 of the most popular and promising plume models developed to predict the near- and complete-field plume. Part Two compares predictions from the models to prototype data, laboratory data, or both. Part Two also provides a generic discussion of the issues surrounding near- and complete-field modeling. The principal conclusion of the report is that the available models, in their present stage of development, may be used to give only general estimates of plume characteristics; precise predictions are not currently possible. The Shirazi-Davis and Pritchard (No. 1) models appear superior to the others tested and are capable of correctly predicting general plume characteristics. (The predictions show roughly factor-of-two accuracy in centerline distance to a given isotherm, factor-of-two accuracy in plume width, and factor-of-five accuracy in isotherm areas.) The state of the art can best be improved by pursuing basic laboratory studies of plume dispersion along with further development of numerical-modeling techniques

Prediction of material removal rate and surface roughness for wire electrical discharge machining of nickel using response surface methodology

Directory of Open Access Journals (Sweden)

Thangam Chinnadurai

2016-12-01

Full Text Available This study focuses on investigating the effects of process parameters, namely, Peak current (Ip, Pulse on time (Ton, Pulse off time (Toff, Water pressure (Wp, Wire feed rate (Wf, Wire tension (Wt, Servo voltage (Sv and Servo feed setting (Sfs, on the Material Removal Rate (MRR and Surface Roughness (SR for Wire electrical discharge machining (Wire-EDM of nickel using Taguchi method. Response Surface Methodology (RSM is adopted to evolve mathematical relationships between the wire cutting process parameters and the output variables of the weld joint to determine the welding input parameters that lead to the desired optimal wire cutting quality. Besides, using response surface plots, the interaction effects of process parameters on the responses are analyzed and discussed. The statistical software Mini-tab is used to establish the design and to obtain the regression equations. The developed mathematical models are tested by analysis-of-variance (ANOVA method to check their appropriateness and suitability. Finally, a comparison is made between measured and calculated results, which are in good agreement. This indicates that the developed models can predict the responses accurately and precisely within the limits of cutting parameter being used.

Prediction of material removal rate and surface roughness for wire electrical discharge machining of nickel using response surface methodology

International Nuclear Information System (INIS)

Chinnadurai, T.; Vendan, S.A.

2016-01-01

This study focuses on investigating the effects of process parameters, namely, Peak current (Ip), Pulse on time (Ton), Pulse off time (Toff), Water pressure (Wp), Wire feed rate (Wf), Wire tension (Wt), Servo voltage (Sv) and Servo feed setting (Sfs), on the Material Removal Rate (MRR) and Surface Roughness (SR) for Wire electrical discharge machining (Wire-EDM) of nickel using Taguchi method. Response Surface Methodology (RSM) is adopted to evolve mathematical relationships between the wire cutting process parameters and the output variables of the weld joint to determine the welding input parameters that lead to the desired optimal wire cutting quality. Besides, using response surface plots, the interaction effects of process parameters on the responses are analyzed and discussed. The statistical software Mini-tab is used to establish the design and to obtain the regression equations. The developed mathematical models are tested by analysis-of-variance (ANOVA) method to check their appropriateness and suitability. Finally, a comparison is made between measured and calculated results, which are in good agreement. This indicates that the developed models can predict the responses accurately and precisely within the limits of cutting parameter being used. (Author)

Prediction of material removal rate and surface roughness for wire electrical discharge machining of nickel using response surface methodology

Energy Technology Data Exchange (ETDEWEB)

Chinnadurai, T.; Vendan, S.A.

2016-07-01

This study focuses on investigating the effects of process parameters, namely, Peak current (Ip), Pulse on time (Ton), Pulse off time (Toff), Water pressure (Wp), Wire feed rate (Wf), Wire tension (Wt), Servo voltage (Sv) and Servo feed setting (Sfs), on the Material Removal Rate (MRR) and Surface Roughness (SR) for Wire electrical discharge machining (Wire-EDM) of nickel using Taguchi method. Response Surface Methodology (RSM) is adopted to evolve mathematical relationships between the wire cutting process parameters and the output variables of the weld joint to determine the welding input parameters that lead to the desired optimal wire cutting quality. Besides, using response surface plots, the interaction effects of process parameters on the responses are analyzed and discussed. The statistical software Mini-tab is used to establish the design and to obtain the regression equations. The developed mathematical models are tested by analysis-of-variance (ANOVA) method to check their appropriateness and suitability. Finally, a comparison is made between measured and calculated results, which are in good agreement. This indicates that the developed models can predict the responses accurately and precisely within the limits of cutting parameter being used. (Author)

Topological characterization of antireflective and hydrophobic rough surfaces: are random process theory and fractal modeling applicable?

Science.gov (United States)

Borri, Claudia; Paggi, Marco

2015-02-01

The random process theory (RPT) has been widely applied to predict the joint probability distribution functions (PDFs) of asperity heights and curvatures of rough surfaces. A check of the predictions of RPT against the actual statistics of numerically generated random fractal surfaces and of real rough surfaces has been only partially undertaken. The present experimental and numerical study provides a deep critical comparison on this matter, providing some insight into the capabilities and limitations in applying RPT and fractal modeling to antireflective and hydrophobic rough surfaces, two important types of textured surfaces. A multi-resolution experimental campaign using a confocal profilometer with different lenses is carried out and a comprehensive software for the statistical description of rough surfaces is developed. It is found that the topology of the analyzed textured surfaces cannot be fully described according to RPT and fractal modeling. The following complexities emerge: (i) the presence of cut-offs or bi-fractality in the power-law power-spectral density (PSD) functions; (ii) a more pronounced shift of the PSD by changing resolution as compared to what was expected from fractal modeling; (iii) inaccuracy of the RPT in describing the joint PDFs of asperity heights and curvatures of textured surfaces; (iv) lack of resolution-invariance of joint PDFs of textured surfaces in case of special surface treatments, not accounted for by fractal modeling.

Modeling and Prediction of Soil Water Vapor Sorption Isotherms

DEFF Research Database (Denmark)

Arthur, Emmanuel; Tuller, Markus; Moldrup, Per

2015-01-01

Soil water vapor sorption isotherms describe the relationship between water activity (aw) and moisture content along adsorption and desorption paths. The isotherms are important for modeling numerous soil processes and are also used to estimate several soil (specific surface area, clay content.......93) for a wide range of soils; and (ii) develop and test regression models for estimating the isotherms from clay content. Preliminary results show reasonable fits of the majority of the investigated empirical and theoretical models to the measured data although some models were not capable to fit both sorption...... directions accurately. Evaluation of the developed prediction equations showed good estimation of the sorption/desorption isotherms for tested soils....

Evaluation of an atmospheric model with surface and ABL meteorological data for energy applications in structured areas

Science.gov (United States)

Triantafyllou, A. G.; Kalogiros, J.; Krestou, A.; Leivaditou, E.; Zoumakis, N.; Bouris, D.; Garas, S.; Konstantinidis, E.; Wang, Q.

2018-03-01

This paper provides the performance evaluation of the meteorological component of The Air Pollution Model (TAPM), a nestable prognostic model, in predicting meteorological variables in urban areas, for both its surface layer and atmospheric boundary layer (ABL) turbulence parameterizations. The model was modified by incorporating four urban land surface types, replacing the existing single urban surface. Control runs were carried out over the wider area of Kozani, an urban area in NW Greece. The model was evaluated for both surface and ABL meteorological variables by using measurements of near-surface and vertical profiles of wind and temperature. The data were collected by using monitoring surface stations in selected sites as well as an acoustic sounder (SOnic Detection And Ranging (SODAR), up to 300 m above ground) and a radiometer profiler (up to 600 m above ground). The results showed the model demonstrated good performance in predicting the near-surface meteorology in the Kozani region for both a winter and a summer month. In the ABL, the comparison showed that the model's forecasts generally performed well with respect to the thermal structure (temperature profiles and ABL height) but overestimated wind speed at the heights of comparison (mostly below 200 m) up to 3-4 ms-1.

A Novel Hybrid Model for Drawing Trace Reconstruction from Multichannel Surface Electromyographic Activity.

Science.gov (United States)

Chen, Yumiao; Yang, Zhongliang

2017-01-01

Recently, several researchers have considered the problem of reconstruction of handwriting and other meaningful arm and hand movements from surface electromyography (sEMG). Although much progress has been made, several practical limitations may still affect the clinical applicability of sEMG-based techniques. In this paper, a novel three-step hybrid model of coordinate state transition, sEMG feature extraction and gene expression programming (GEP) prediction is proposed for reconstructing drawing traces of 12 basic one-stroke shapes from multichannel surface electromyography. Using a specially designed coordinate data acquisition system, we recorded the coordinate data of drawing traces collected in accordance with the time series while 7-channel EMG signals were recorded. As a widely-used time domain feature, Root Mean Square (RMS) was extracted with the analysis window. The preliminary reconstruction models can be established by GEP. Then, the original drawing traces can be approximated by a constructed prediction model. Applying the three-step hybrid model, we were able to convert seven channels of EMG activity recorded from the arm muscles into smooth reconstructions of drawing traces. The hybrid model can yield a mean accuracy of 74% in within-group design (one set of prediction models for all shapes) and 86% in between-group design (one separate set of prediction models for each shape), averaged for the reconstructed x and y coordinates. It can be concluded that it is feasible for the proposed three-step hybrid model to improve the reconstruction ability of drawing traces from sEMG.

Deterministic prediction of surface wind speed variations

Directory of Open Access Journals (Sweden)

G. V. Drisya

2014-11-01

Full Text Available Accurate prediction of wind speed is an important aspect of various tasks related to wind energy management such as wind turbine predictive control and wind power scheduling. The most typical characteristic of wind speed data is its persistent temporal variations. Most of the techniques reported in the literature for prediction of wind speed and power are based on statistical methods or probabilistic distribution of wind speed data. In this paper we demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data, at locations where the wind dynamics exhibit chaotic behaviour. The predictions are remarkably accurate up to 1 h with a normalised RMSE (root mean square error of less than 0.02 and reasonably accurate up to 3 h with an error of less than 0.06. Repeated application of these methods at 234 different geographical locations for predicting wind speeds at 30-day intervals for 3 years reveals that the accuracy of prediction is more or less the same across all locations and time periods. Comparison of the results with f-ARIMA model predictions shows that the deterministic models with suitable parameters are capable of returning improved prediction accuracy and capturing the dynamical variations of the actual time series more faithfully. These methods are simple and computationally efficient and require only records of past data for making short-term wind speed forecasts within practically tolerable margin of errors.

A hybrid model to predict the entrainment and subsurface transport of oil

International Nuclear Information System (INIS)

Spaulding, M.L.; Odulo, A.; Kolluru, V.S.

1992-01-01

The entrainment of surface oil into the water column and its subsequent subsurface transport and dispersion are predicted by a hybrid analytic-numerical solution to the advective diffusion equation. Total oil or selected hydrocarbon component concentrations in the water column are predicted. Assuming that the principal mechanism for entrainment is due to breaking waves, the oil entrainment rate is specified using the empirically based algorithm of Delvigne and Sweeney (1988). The subsurface transport model explicitly accounts for buoyant forces on dispersed oil by droplet size. Application of the model to an analytic test case and several hypothetical scenarios illustrates the model's utility. 35 refs., 8 figs., 2 tabs

Confidence scores for prediction models

DEFF Research Database (Denmark)

Gerds, Thomas Alexander; van de Wiel, MA

2011-01-01

In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation,...

A review of measurement and modelling results of particle atmosphere-surface exchange

DEFF Research Database (Denmark)

Pryor, Sara; Gallagher, M.; Sievering, H.

2008-01-01

Atmosphere-surface exchange represents one mechanism by which atmospheric particle mass and number size distributions are modified. Deposition velocities (upsilon(d)) exhibit a pronounced dependence on surface type, due in part to turbulence structure (as manifest in friction velocity), with minima...... agreement between models and observations is found over less-rough surfaces though those data also imply substantially higher surface collection efficiencies than were originally proposed and are manifest in current models. We review theorized dependencies for particle fluxes, describe and critique model...... of approximately 0.01 and 0.2 cm s(-1) over grasslands and 0.1-1 cm s(-1) over forests. However, as noted over 20 yr ago, observations over forests generally do not support the pronounced minimum of deposition velocity (upsilon(d)) for particle diameters of 0.1-2 mu m as manifest in theoretical predictions. Closer...

Comprehensive and critical review of the predictive properties of the various mass models

International Nuclear Information System (INIS)

Haustein, P.E.

1984-01-01

Since the publication of the 1975 Mass Predictions approximately 300 new atomic masses have been reported. These data come from a variety of experimental studies using diverse techniques and they span a mass range from the lightest isotopes to the very heaviest. It is instructive to compare these data with the 1975 predictions and several others (Moeller and Nix, Monahan, Serduke, Uno and Yamada which appeared latter. Extensive numerical and graphical analyses have been performed to examine the quality of the mass predictions from the various models and to identify features in these models that require correction. In general, there is only rough correlation between the ability of a particular model to reproduce the measured mass surface which had been used to refine its adjustable parameters and that model's ability to predict correctly the new masses. For some models distinct systematic features appear when the new mass data are plotted as functions of relevant physical variables. Global intercomparisons of all the models are made first, followed by several examples of types of analysis performed with individual mass models

Rainfall-runoff model for prediction of waterborne viral contamination in a small river catchment

Science.gov (United States)

Gelati, E.; Dommar, C.; Lowe, R.; Polcher, J.; Rodó, X.

2013-12-01

We present a lumped rainfall-runoff model aimed at providing useful information for the prediction of waterborne viral contamination in small rivers. Viral contamination of water bodies may occur because of the discharge of sewage effluents and of surface runoff over areas affected by animal waste loads. Surface runoff is caused by precipitation that cannot infiltrate due to its intensity and to antecedent soil water content. It may transport animal feces to adjacent water bodies and cause viral contamination. We model streamflow by separating it into two components: subsurface flow, which is produced by infiltrated precipitation; and surface runoff. The model estimates infiltrated and non-infiltrated precipitation and uses impulse-response functions to compute the corresponding fractions of streamflow. The developed methodologies are applied to the Glafkos river, whose catchment extends for 102 km2 and includes the city of Patra. Streamflow and precipitation observations are available at a daily time resolution. Waterborne virus concentration measurements were performed approximately every second week from the beginning of 2011 to mid 2012. Samples were taken at several locations: in river water upstream of Patras and in the urban area; in sea water at the river outlet and approximately 2 km south-west of Patras; in sewage effluents before and after treatment. The rainfall-runoff model was calibrated and validated using observed streamflow and precipitation data. The model contribution to waterborne viral contamination prediction was benchmarked by analyzing the virus concentration measurements together with the estimated surface runoff values. The presented methodology may be a first step towards the development of waterborne viral contamination alert systems. Predicting viral contamination of water bodies would benefit sectors such as water supply and tourism.

Optical Properties of Three Beach Waters: Implications for Predictive Modeling of Enterococci

Science.gov (United States)

Sunlight plays an important role in the inactivation of fecal indicator bacteria in recreational waters. Solar radiation can explain temporal trends in bacterial counts and is commonly used as an explanatory variable in predictive models. Broadband surface radiation provides a ba...

Ability of One-Dimensional Hairsine-Rose Erosion Model to Predict Sediment Transport over a Soil with Significant Surface Stones

Science.gov (United States)

Jomaa, S.; Barry, D. A.; Sander, G. C.; Parlange, J.-Y.; Heng, B. C. P.; Tromp-van Meerveld, H. J.

2010-05-01

Surface stones affect erosion rates by reducing raindrop-driven detachment and protecting the original soil against overland flow induced-hydraulic stress. Numerous studies have shown that the effect of surface stones on erosion depends on both the stone characteristics (e.g., size, distribution) and the soil properties. The aim of this study was (i) to quantify how the stone characteristics can affect the total sediment concentration and the concentrations of the individual size classes, (ii) to test if stones affect preferentially a particular size class within the eroded sediment and (iii) to determine whether the 1D Hairsine-Rose (H-R) erosion model can represent the experimental data. A series of laboratory experiments were conducted using the 2 m × 6 m EPFL erosion flume for a high rainfall intensity (60 mm/h) event on a gentle slope (2.2%). The flume was divided into two identical 1-m wide flumes. This separation was done to allow simultaneous replicate experiments. Experiments were conducted with different configurations and scenarios (stone coverage, size and emplacement). Three coverage proportions (20%, 40%, and 70%), two stone diameters (3-4 and 6-7 cm) and two emplacement types (topsoil and partially embedded) were tested. For each experiment, the total sediment concentration, the concentration for the individual size classes, and the flume discharge were measured. Infiltration rates were measured at different depths and locations. A high resolution laser scanner provided details of the surface change due to erosion during the experiments. This technique allowed us to quantify the spatial distribution of eroded soil and to understand better if sediment transport is 1D or rather 2D over the flumes. The one-dimensional Hairsine-Rose (H-R) erosion model was used to fit the integrated data and to provide estimates of the parameters. The ability of the 1D H-R model to predict the measured sediment concentrations in the presence of stones in the soil matrix

Assessment of prediction skill in equatorial Pacific Ocean in high resolution model of CFS

Science.gov (United States)

Arora, Anika; Rao, Suryachandra A.; Pillai, Prasanth; Dhakate, Ashish; Salunke, Kiran; Srivastava, Ankur

2018-01-01

The effect of increasing atmospheric resolution on prediction skill of El Niño southern oscillation phenomenon in climate forecast system model is explored in this paper. Improvement in prediction skill for sea surface temperature (SST) and winds at all leads compared to low resolution model in the tropical Indo-Pacific basin is observed. High resolution model is able to capture extreme events reasonably well. As a result, the signal to noise ratio is improved in the high resolution model. However, spring predictability barrier (SPB) for summer months in Nino 3 and Nino 3.4 region is stronger in high resolution model, in spite of improvement in overall prediction skill and dynamics everywhere else. Anomaly correlation coefficient of SST in high resolution model with observations in Nino 3.4 region targeting boreal summer months when predicted at lead times of 3-8 months in advance decreased compared its lower resolution counterpart. It is noted that higher variance of winds predicted in spring season over central equatorial Pacific compared to observed variance of winds results in stronger than normal response on subsurface ocean, hence increases SPB for boreal summer months in high resolution model.

Tyre noise predictions from computed road surface texture induced contact pressure; Romen no outotsu ni kiinsuru sesshoku atsuryoku ni yoru tire soon no suitei

Energy Technology Data Exchange (ETDEWEB)

Mikami, T. [Japan Automobile Research Institute Inc., Tsukuba (Japan)

1999-07-01

A method for tire/road noise prediction is studied based on the result of road surface profile measurement (horizontal direction measurement interval 3mm, horizontal direction measurement accuracy 8{mu}m, distance measured 1655m, using a laser-aided profile meter). The obtained road surface profile is used for the calculation of contact pressure that occurs between the tire tread and road surface (using the 2-dimensional calculation model of Clapp et al.). For the examination of the relationship between the contact pressure and generated noise, tire noise is measured using a microphone array provided near the tire circumference. The frequency spectral ratio between the generated noise and contact pressure is determined as a transmission function. It may be said that the transmission function is unique to the tire, not dependent on the road surface profile. The road surface profile is determined by use of the transmission function, and this enables the prediction of the noise from the tire. Noises were measured on several kinds of road surfaces different in coarseness for a passenger car and truck, and the values from these actual measurements are compared with the predicted values, and then it is found that the prediction model is valid. (NEDO)

Modelling Monsoons: Understanding and Predicting Current and Future Behaviour

Energy Technology Data Exchange (ETDEWEB)

Turner, A; Sperber, K R; Slingo, J M; Meehl, G A; Mechoso, C R; Kimoto, M; Giannini, A

2008-09-16

The global monsoon system is so varied and complex that understanding and predicting its diverse behaviour remains a challenge that will occupy modellers for many years to come. Despite the difficult task ahead, an improved monsoon modelling capability has been realized through the inclusion of more detailed physics of the climate system and higher resolution in our numerical models. Perhaps the most crucial improvement to date has been the development of coupled ocean-atmosphere models. From subseasonal to interdecadal timescales, only through the inclusion of air-sea interaction can the proper phasing and teleconnections of convection be attained with respect to sea surface temperature variations. Even then, the response to slow variations in remote forcings (e.g., El Nino-Southern Oscillation) does not result in a robust solution, as there are a host of competing modes of variability that must be represented, including those that appear to be chaotic. Understanding the links between monsoons and land surface processes is not as mature as that explored regarding air-sea interactions. A land surface forcing signal appears to dominate the onset of wet season rainfall over the North American monsoon region, though the relative role of ocean versus land forcing remains a topic of investigation in all the monsoon systems. Also, improved forecasts have been made during periods in which additional sounding observations are available for data assimilation. Thus, there is untapped predictability that can only be attained through the development of a more comprehensive observing system for all monsoon regions. Additionally, improved parameterizations - for example, of convection, cloud, radiation, and boundary layer schemes as well as land surface processes - are essential to realize the full potential of monsoon predictability. Dynamical considerations require ever increased horizontal resolution (probably to 0.5 degree or higher) in order to resolve many monsoon features

Comparison of CFD Predictions with Shuttle Global Flight Thermal Imagery and Discrete Surface Measurements

Science.gov (United States)

Wood, William A.; Kleb, William L.; Tang, chun Y.; Palmer, Grant E.; Hyatt, Andrew J.; Wise, Adam J.; McCloud, Peter L.

2010-01-01

Surface temperature measurements from the STS-119 boundary-layer transition experiment on the space shuttle orbiter Discovery provide a rare opportunity to assess turbulent CFD models at hypersonic flight conditions. This flight data was acquired by on-board thermocouples and by infrared images taken off-board by the Hypersonic Thermodynamic Infrared Measurements (HYTHIRM) team, and is suitable for hypersonic CFD turbulence assessment between Mach 6 and 14. The primary assessment is for the Baldwin-Lomax and Cebeci-Smith algebraic turbulence models in the DPLR and LAURA CFD codes, respectively. A secondary assessment is made of the Shear-Stress Transport (SST) two-equation turbulence model in the DPLR code. Based upon surface temperature comparisons at eleven thermocouple locations, the algebraic-model turbulent CFD results average 4% lower than the measurements for Mach numbers less than 11. For Mach numbers greater than 11, the algebraic-model turbulent CFD results average 5% higher than the three available thermocouple measurements. Surface temperature predictions from the two SST cases were consistently 3 4% higher than the algebraic-model results. The thermocouple temperatures exhibit a change in trend with Mach number at about Mach 11; this trend is not reflected in the CFD results. Because the temperature trends from the turbulent CFD simulations and the flight data diverge above Mach 11, extrapolation of the turbulent CFD accuracy to higher Mach numbers is not recommended.

Coupled hygrothermal, electrochemical, and mechanical modelling for deterioration prediction in reinforced cementitious materials

DEFF Research Database (Denmark)

Michel, Alexander; Geiker, Mette Rica; Lepech, M.

2017-01-01

In this paper a coupled hygrothermal, electrochemical, and mechanical modelling approach for the deterioration prediction in cementitious materials is briefly outlined. Deterioration prediction is thereby based on coupled modelling of (i) chemical processes including among others transport of hea......, i.e. information, such as such as corrosion current density, damage state of concrete cover, etc., are constantly exchanged between the models....... and matter as well as phase assemblage on the nano and micro scale, (ii) corrosion of steel including electrochemical processes at the reinforcement surface, and (iii) material performance including corrosion- and load-induced damages on the meso and macro scale. The individual FEM models are fully coupled...

A new constitutive model for prediction of springback in sheet metal forming

International Nuclear Information System (INIS)

Appiah, E.; Jain, M.

2004-01-01

With advances in computer capabilities, cost of sheet metal forming has being reducing mainly due to the reduction of trial and error approaches. At the moment, a complete process can be simulated on computer and appropriate forming conditions optimized before actual industrial forming process is carried out. While formability predictions have improved, the problem of springback exhibited by most metal, including aluminum alloy AA6111-T4, after forming persist and often leads to significant part fit-up problems during assembly. There are a number of factors that affect springback and perhaps the most significant one is constitutive equation. In this paper springback predicted by six advanced kinematic models are evaluated. In addition an improved constitutive kinematic model is presented. It is shown that by adding stress correction term (SCT) to Armstrong-Frederick model a relatively simple and yet accurate stress prediction could be obtained. The SCT was developed with the assumption that the yield surface remains convex, yield center depends on translation, size and shape variations of the yield surface. The model is implemented in a commercial finite element code (ABAQUS/Standard) via its user material interface (UMAT). Numerical simulations of U-bending were performed using automotive aluminum sheet material (AA6111-T4). It was noted that springback has inverse relationship with residual stress

The Community Surface Dynamics Modeling System: Experiences on Building a Collaborative Modeling Platform

Science.gov (United States)

Overeem, I.; Hutton, E.; Kettner, A.; Peckham, S. D.; Syvitski, J. P.

2012-12-01

The Community Surface Dynamics Modeling System - CSDMS- develops a software platform with shared and coupled modules for modeling earth surface processes as a community resource. The framework allows prediction of water, sediment and nutrient transport through the landscape and seacape. The underlying paradigm is that the Earth surface we live on is a dynamic system; topography changes with seasons, with landslides and earthquakes, with erosion and deposition. The Earth Surface changes due to storms and floods, and important boundaries, like the coast, are ever-moving features. CSDMS sets out to make better predictions of these changes. Earth surface process modeling bridges the terrestrial, coastal and marine domains and requires understanding of the system over a range of time scales, which inherently needs interdisciplinarity. Members of CSDMS (~830 in July 2012) are largely from academic institutions (˜75%), followed by federal agencies (˜17%), and oil and gas companies (˜5%). Members and governmental bodies meet once annually and rely additionally on web-based information for communication. As an organization that relies on volunteer participation, CSDMS faces challenges to scientific collaboration. Encouraging volunteerism among its members to provide and adapt metadata and model code to be sufficiently standardized for coupling is crucial to building an integrated community modeling system. We here present CSDMS strategies aimed at providing the appropriate technical tools and cyberinfrastructure to support a variety of user types, ranging from advanced to novice modelers. Application of these advances in science is key, both into the educational realm and for managers and decision-makers. We discuss some of the implemented ideas to further organizational transparency and user engagement in small-scale governance, such as advanced trackers and voting systems for model development prioritization through the CSDMS wiki. We analyzed data on community

The Martian surface radiation environment – a comparison of models and MSL/RAD measurements

Directory of Open Access Journals (Sweden)

Matthiä Daniel

2016-01-01

Full Text Available Context: The Radiation Assessment Detector (RAD on the Mars Science Laboratory (MSL has been measuring the radiation environment on the surface of Mars since August 6th 2012. MSL-RAD is the first instrument to provide detailed information about charged and neutral particle spectra and dose rates on the Martian surface, and one of the primary objectives of the RAD investigation is to help improve and validate current radiation transport models. Aims: Applying different numerical transport models with boundary conditions derived from the MSL-RAD environment the goal of this work was to both provide predictions for the particle spectra and the radiation exposure on the Martian surface complementing the RAD sensitive range and, at the same time, validate the results with the experimental data, where applicable. Such validated models can be used to predict dose rates for future manned missions as well as for performing shield optimization studies. Methods: Several particle transport models (GEANT4, PHITS, HZETRN/OLTARIS were used to predict the particle flux and the corresponding radiation environment caused by galactic cosmic radiation on Mars. From the calculated particle spectra the dose rates on the surface are estimated. Results: Calculations of particle spectra and dose rates induced by galactic cosmic radiation on the Martian surface are presented. Although good agreement is found in many cases for the different transport codes, GEANT4, PHITS, and HZETRN/OLTARIS, some models still show large, sometimes order of magnitude discrepancies in certain particle spectra. We have found that RAD data is helping to make better choices of input parameters and physical models. Elements of these validated models can be applied to more detailed studies on how the radiation environment is influenced by solar modulation, Martian atmosphere and soil, and changes due to the Martian seasonal pressure cycle. By extending the range of the calculated particle

Predicting outdoor sound

CERN Document Server

Attenborough, Keith; Horoshenkov, Kirill

2014-01-01

1. Introduction  2. The Propagation of Sound Near Ground Surfaces in a Homogeneous Medium  3. Predicting the Acoustical Properties of Outdoor Ground Surfaces  4. Measurements of the Acoustical Properties of Ground Surfaces and Comparisons with Models  5. Predicting Effects of Source Characteristics on Outdoor Sound  6. Predictions, Approximations and Empirical Results for Ground Effect Excluding Meteorological Effects  7. Influence of Source Motion on Ground Effect and Diffraction  8. Predicting Effects of Mixed Impedance Ground  9. Predicting the Performance of Outdoor Noise Barriers  10. Predicting Effects of Vegetation, Trees and Turbulence  11. Analytical Approximations including Ground Effect, Refraction and Turbulence  12. Prediction Schemes  13. Predicting Sound in an Urban Environment.

A Theoretical Model for the Prediction of Siphon Breaking Phenomenon

International Nuclear Information System (INIS)

Bae, Youngmin; Kim, Young-In; Seo, Jae-Kwang; Kim, Keung Koo; Yoon, Juhyeon

2014-01-01

A siphon phenomenon or siphoning often refers to the movement of liquid from a higher elevation to a lower one through a tube in an inverted U shape (whose top is typically located above the liquid surface) under the action of gravity, and has been used in a variety of reallife applications such as a toilet bowl and a Greedy cup. However, liquid drainage due to siphoning sometimes needs to be prevented. For example, a siphon breaker, which is designed to limit the siphon effect by allowing the gas entrainment into a siphon line, is installed in order to maintain the pool water level above the reactor core when a loss of coolant accident (LOCA) occurs in an open-pool type research reactor. In this paper, we develop a theoretical model to predict the siphon breaking phenomenon. In this paper, a theoretical model to predict the siphon breaking phenomenon is developed. It is shown that the present model predicts well the fundamental features of the siphon breaking phenomenon and undershooting height

A Theoretical Model for the Prediction of Siphon Breaking Phenomenon

Energy Technology Data Exchange (ETDEWEB)

Bae, Youngmin; Kim, Young-In; Seo, Jae-Kwang; Kim, Keung Koo; Yoon, Juhyeon [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2014-10-15

A siphon phenomenon or siphoning often refers to the movement of liquid from a higher elevation to a lower one through a tube in an inverted U shape (whose top is typically located above the liquid surface) under the action of gravity, and has been used in a variety of reallife applications such as a toilet bowl and a Greedy cup. However, liquid drainage due to siphoning sometimes needs to be prevented. For example, a siphon breaker, which is designed to limit the siphon effect by allowing the gas entrainment into a siphon line, is installed in order to maintain the pool water level above the reactor core when a loss of coolant accident (LOCA) occurs in an open-pool type research reactor. In this paper, we develop a theoretical model to predict the siphon breaking phenomenon. In this paper, a theoretical model to predict the siphon breaking phenomenon is developed. It is shown that the present model predicts well the fundamental features of the siphon breaking phenomenon and undershooting height.

Prediction of Sliding Friction Coefficient Based on a Novel Hybrid Molecular-Mechanical Model.

Science.gov (United States)

Zhang, Xiaogang; Zhang, Yali; Wang, Jianmei; Sheng, Chenxing; Li, Zhixiong

2018-08-01

Sliding friction is a complex phenomenon which arises from the mechanical and molecular interactions of asperities when examined in a microscale. To reveal and further understand the effects of micro scaled mechanical and molecular components of friction coefficient on overall frictional behavior, a hybrid molecular-mechanical model is developed to investigate the effects of main factors, including different loads and surface roughness values, on the sliding friction coefficient in a boundary lubrication condition. Numerical modelling was conducted using a deterministic contact model and based on the molecular-mechanical theory of friction. In the contact model, with given external loads and surface topographies, the pressure distribution, real contact area, and elastic/plastic deformation of each single asperity contact were calculated. Then asperity friction coefficient was predicted by the sum of mechanical and molecular components of friction coefficient. The mechanical component was mainly determined by the contact width and elastic/plastic deformation, and the molecular component was estimated as a function of the contact area and interfacial shear stress. Numerical results were compared with experimental results and a good agreement was obtained. The model was then used to predict friction coefficients in different operating and surface conditions. Numerical results explain why applied load has a minimum effect on the friction coefficients. They also provide insight into the effect of surface roughness on the mechanical and molecular components of friction coefficients. It is revealed that the mechanical component dominates the friction coefficient when the surface roughness is large (Rq > 0.2 μm), while the friction coefficient is mainly determined by the molecular component when the surface is relatively smooth (Rq < 0.2 μm). Furthermore, optimal roughness values for minimizing the friction coefficient are recommended.

Predicting supramolecular self-assembly on reconstructed metal surfaces

Science.gov (United States)

Roussel, Thomas J.; Barrena, Esther; Ocal, Carmen; Faraudo, Jordi

2014-06-01

The prediction of supramolecular self-assembly onto solid surfaces is still challenging in many situations of interest for nanoscience. In particular, no previous simulation approach has been capable to simulate large self-assembly patterns of organic molecules over reconstructed surfaces (which have periodicities over large distances) due to the large number of surface atoms and adsorbing molecules involved. Using a novel simulation technique, we report here large scale simulations of the self-assembly patterns of an organic molecule (DIP) over different reconstructions of the Au(111) surface. We show that on particular reconstructions, the molecule-molecule interactions are enhanced in a way that long-range order is promoted. Also, the presence of a distortion in a reconstructed surface pattern not only induces the presence of long-range order but also is able to drive the organization of DIP into two coexisting homochiral domains, in quantitative agreement with STM experiments. On the other hand, only short range order is obtained in other reconstructions of the Au(111) surface. The simulation strategy opens interesting perspectives to tune the supramolecular structure by simulation design and surface engineering if choosing the right molecular building blocks and stabilising the chosen reconstruction pattern.The prediction of supramolecular self-assembly onto solid surfaces is still challenging in many situations of interest for nanoscience. In particular, no previous simulation approach has been capable to simulate large self-assembly patterns of organic molecules over reconstructed surfaces (which have periodicities over large distances) due to the large number of surface atoms and adsorbing molecules involved. Using a novel simulation technique, we report here large scale simulations of the self-assembly patterns of an organic molecule (DIP) over different reconstructions of the Au(111) surface. We show that on particular reconstructions, the molecule

A DOUBLE-RING ALGORITHM FOR MODELING SOLAR ACTIVE REGIONS: UNIFYING KINEMATIC DYNAMO MODELS AND SURFACE FLUX-TRANSPORT SIMULATIONS

International Nuclear Information System (INIS)

Munoz-Jaramillo, Andres; Martens, Petrus C. H.; Nandy, Dibyendu; Yeates, Anthony R.

2010-01-01

The emergence of tilted bipolar active regions (ARs) and the dispersal of their flux, mediated via processes such as diffusion, differential rotation, and meridional circulation, is believed to be responsible for the reversal of the Sun's polar field. This process (commonly known as the Babcock-Leighton mechanism) is usually modeled as a near-surface, spatially distributed α-effect in kinematic mean-field dynamo models. However, this formulation leads to a relationship between polar field strength and meridional flow speed which is opposite to that suggested by physical insight and predicted by surface flux-transport simulations. With this in mind, we present an improved double-ring algorithm for modeling the Babcock-Leighton mechanism based on AR eruption, within the framework of an axisymmetric dynamo model. Using surface flux-transport simulations, we first show that an axisymmetric formulation-which is usually invoked in kinematic dynamo models-can reasonably approximate the surface flux dynamics. Finally, we demonstrate that our treatment of the Babcock-Leighton mechanism through double-ring eruption leads to an inverse relationship between polar field strength and meridional flow speed as expected, reconciling the discrepancy between surface flux-transport simulations and kinematic dynamo models.

Prediction of abrupt reservoir compaction and surface subsidence due to pore collapse in carbonates

Energy Technology Data Exchange (ETDEWEB)

Smits, R.M.M.; de Waal, A.; van Kooten, J.F.C.

1986-01-01

A new procedure has been developed to predict the abrupt in-situ compaction and the associated surface subsidence above high-porosity carbonate fields showing pore collapse. The approach is based on an extensive laboratory compaction study in which the effects of carbonate type, porosity, core preparation, pore saturant, horizontal to vertical stress ratio and loading rate on the pore collapse behaviour were investigated. For each carbonate type a trendline was established describing the relationship between the porosity after collapse and the vertical effective stress. This trendline concept, in combination with existing subsidence models, enables reservoir compaction and surface subsidence to be predicted on the basis of wireline porosity logs. Static and dynamic elastic constants were found to be uncorrelated during pore collapse. The position of the trendline depends strongly on carbonate type, pore saturant, loading rate and stress ratio. Therefore procedures are given to derive the correct in-situ trendline from laboratory compaction experiments.

Prediction of abrupt reservoir compaction and surface subsidence caused by pore collapse in carbonates

Energy Technology Data Exchange (ETDEWEB)

Smits, R.M.M.; De Waal, J.A.; Van Kootan, J.F.C.

1988-06-01

A new procedure has been developed to predict the abrupt in-situ compaction and the associated surface subsidence above high-porosity carbonate fields that show pore collapse. The approach is based on an extensive laboratory compaction study in which the effects of carbonate type, porosity, core preparation, pore saturant, horizontal/vertical stress ratio, and loading rate on pore-collapse behavior were investigated. For a number of carbonate types, a trendline was established that describes the relationship between the porosity after collapse and the vertical effective stress. This trendline concept, in combination with existing subsidence models, enables reservoir compaction and surface subsidence to be predicted on the basis of wireline porosity logs. Static and dynamic elastic constants were found to be uncorrelated during pore collapse. The position of the trendline depends strongly on carbonate type, pore saturant, loading rate, and stress ratio. Therefore, procedures are given to derive the correct in-situ trendline from laboratory compaction experiments.

Land Surface Data Assimilation

Science.gov (United States)

Houser, P. R.

2012-12-01

Information about land surface water, energy and carbon conditions is of critical importance to real-world applications such as agricultural production, water resource management, flood prediction, water supply, weather and climate forecasting, and environmental preservation. While ground-based observational networks are improving, the only practical way to observe these land surface states on continental to global scales is via satellites. Remote sensing can make spatially comprehensive measurements of various components of the terrestrial system, but it cannot provide information on the entire system (e.g. evaporation), and the observations represent only an instant in time. Land surface process models may be used to predict temporal and spatial terrestrial dynamics, but these predictions are often poor, due to model initialization, parameter and forcing, and physics errors. Therefore, an attractive prospect is to combine the strengths of land surface models and observations (and minimize the weaknesses) to provide a superior terrestrial state estimate. This is the goal of land surface data assimilation. Data Assimilation combines observations into a dynamical model, using the model's equations to provide time continuity and coupling between the estimated fields. Land surface data assimilation aims to utilize both our land surface process knowledge, as embodied in a land surface model, and information that can be gained from observations. Both model predictions and observations are imperfect and we wish to use both synergistically to obtain a more accurate result. Moreover, both contain different kinds of information, that when used together, provide an accuracy level that cannot be obtained individually. Model biases can be mitigated using a complementary calibration and parameterization process. Limited point measurements are often used to calibrate the model(s) and validate the assimilation results. This presentation will provide a brief background on land

An Extended Assessment of Fluid Flow Models for the Prediction of Two-Dimensional Steady-State Airfoil Aerodynamics

Directory of Open Access Journals (Sweden)

José F. Herbert-Acero

2015-01-01

Full Text Available This work presents the analysis, application, and comparison of thirteen fluid flow models in the prediction of two-dimensional airfoil aerodynamics, considering laminar and turbulent subsonic inflow conditions. Diverse sensitivity analyses of different free parameters (e.g., the domain topology and its discretization, the flow model, and the solution method together with its convergence mechanisms revealed important effects on the simulations’ outcomes. The NACA 4412 airfoil was considered throughout the work and the computational predictions were compared with experiments conducted under a wide range of Reynolds numbers (7e5≤Re≤9e6 and angles-of-attack (-10°≤α≤20°. Improvements both in modeling accuracy and processing time were achieved by considering the RS LP-S and the Transition SST turbulence models, and by considering finite volume-based solution methods with preconditioned systems, respectively. The RS LP-S model provided the best lift force predictions due to the adequate modeling of the micro and macro anisotropic turbulence at the airfoil’s surface and at the nearby flow field, which in turn allowed the adequate prediction of stall conditions. The Transition-SST model provided the best drag force predictions due to adequate modeling of the laminar-to-turbulent flow transition and the surface shear stresses. Conclusions, recommendations, and a comprehensive research agenda are presented based on validated computational results.

Pavement Aging Model by Response Surface Modeling

Directory of Open Access Journals (Sweden)

Manzano-Ramírez A.

2011-10-01

Full Text Available In this work, surface course aging was modeled by Response Surface Methodology (RSM. The Marshall specimens were placed in a conventional oven for time and temperature conditions established on the basis of the environment factors of the region where the surface course is constructed by AC-20 from the Ing. Antonio M. Amor refinery. Volatilized material (VM, load resistance increment (ΔL and flow resistance increment (ΔF models were developed by the RSM. Cylindrical specimens with real aging were extracted from the surface course pilot to evaluate the error of the models. The VM model was adequate, in contrast (ΔL and (ΔF models were almost adequate with an error of 20 %, that was associated with the other environmental factors, which were not considered at the beginning of the research.

Accurate and dynamic predictive model for better prediction in medicine and healthcare.

Science.gov (United States)

Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

2018-05-01

Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

Demonstration of Linked UAV Observations and Atmospheric Model Predictions in Chem/Bio Attack Response

National Research Council Canada - National Science Library

Davidson, Kenneth

2003-01-01

... meteorological data, and the means for linking the UAV data to real-time dispersion prediction. The primary modeling effort focused on an adaptation of the 'Wind On Constant Streamline Surfaces...

Continuous agglomerate model for identifying the solute- indifferent part of colloid nanoparticle's surface charge

International Nuclear Information System (INIS)

Alfimov, A V; Aryslanova, E M; Chivilikhin, S A

2016-01-01

This work proposes an explicit analytical model for the surface potential of a colloidal nano-agglomerate. The model predicts that when an agglomerate reaches a certain critical size, its surface potential becomes independent of the agglomerate radius. The model also provides a method for identifying and quantifying the solute-indifferent charge in nanocolloids, that allows to assess the stability of toxicologically significant parameters of the system. (paper)

Estimation of the solubility parameters of model plant surfaces and agrochemicals: a valuable tool for understanding plant surface interactions.

Science.gov (United States)

Khayet, Mohamed; Fernández, Victoria

2012-11-14

Most aerial plant parts are covered with a hydrophobic lipid-rich cuticle, which is the interface between the plant organs and the surrounding environment. Plant surfaces may have a high degree of hydrophobicity because of the combined effects of surface chemistry and roughness. The physical and chemical complexity of the plant cuticle limits the development of models that explain its internal structure and interactions with surface-applied agrochemicals. In this article we introduce a thermodynamic method for estimating the solubilities of model plant surface constituents and relating them to the effects of agrochemicals. Following the van Krevelen and Hoftyzer method, we calculated the solubility parameters of three model plant species and eight compounds that differ in hydrophobicity and polarity. In addition, intact tissues were examined by scanning electron microscopy and the surface free energy, polarity, solubility parameter and work of adhesion of each were calculated from contact angle measurements of three liquids with different polarities. By comparing the affinities between plant surface constituents and agrochemicals derived from (a) theoretical calculations and (b) contact angle measurements we were able to distinguish the physical effect of surface roughness from the effect of the chemical nature of the epicuticular waxes. A solubility parameter model for plant surfaces is proposed on the basis of an increasing gradient from the cuticular surface towards the underlying cell wall. The procedure enabled us to predict the interactions among agrochemicals, plant surfaces, and cuticular and cell wall components, and promises to be a useful tool for improving our understanding of biological surface interactions.

Assessing modeled Greenland surface mass balance in the GISS Model E2 and its sensitivity to surface albedo

Science.gov (United States)

Alexander, Patrick; LeGrande, Allegra N.; Koenig, Lora S.; Tedesco, Marco; Moustafa, Samiah E.; Ivanoff, Alvaro; Fischer, Robert P.; Fettweis, Xavier

2016-04-01

The surface mass balance (SMB) of the Greenland Ice Sheet (GrIS) plays an important role in global sea level change. Regional Climate Models (RCMs) such as the Modèle Atmosphérique Régionale (MAR) have been employed at high spatial resolution with relatively complex physics to simulate ice sheet SMB. Global climate models (GCMs) incorporate less sophisticated physical schemes and provide outputs at a lower spatial resolution, but have the advantage of modeling the interaction between different components of the earth's oceans, climate, and land surface at a global scale. Improving the ability of GCMs to represent ice sheet SMB is important for making predictions of future changes in global sea level. With the ultimate goal of improving SMB simulated by the Goddard Institute for Space Studies (GISS) Model E2 GCM, we compare simulated GrIS SMB against the outputs of the MAR model and radar-derived estimates of snow accumulation. In order to reproduce present-day climate variability in the Model E2 simulation, winds are constrained to match the reanalysis datasets used to force MAR at the lateral boundaries. We conduct a preliminary assessment of the sensitivity of the simulated Model E2 SMB to surface albedo, a parameter that is known to strongly influence SMB. Model E2 albedo is set to a fixed value of 0.8 over the entire ice sheet in the initial configuration of the model (control case). We adjust this fixed value in an ensemble of simulations over a range of 0.4 to 0.8 (roughly the range of observed summer GrIS albedo values) to examine the sensitivity of ice-sheet-wide SMB to albedo. We prescribe albedo from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 v6 to examine the impact of a more realistic spatial and temporal variations in albedo. An age-dependent snow albedo parameterization is applied, and its impact on SMB relative to observations and the RCM is assessed.

Surface Complexation Modeling of Calcite Zeta Potential Measurement in Mixed Brines for Carbonate Wettability Characterization

Science.gov (United States)

Song, J.; Zeng, Y.; Biswal, S. L.; Hirasaki, G. J.

2017-12-01

We presents zeta potential measurements and surface complexation modeling (SCM) of synthetic calcite in various conditions. The systematic zeta potential measurement and the proposed SCM provide insight into the role of four potential determining cations (Mg2+, SO42- , Ca2+ and CO32-) and CO2 partial pressure in calcite surface charge formation and facilitate the revealing of calcite wettability alteration induced by brines with designed ionic composition ("smart water"). Brines with varying potential determining ions (PDI) concentration in two different CO2 partial pressure (PCO2) are investigated in experiments. Then, a double layer SCM is developed to model the zeta potential measurements. Moreover, we propose a definition for contribution of charged surface species and quantitatively analyze the variation of charged species contribution when changing brine composition. After showing our model can accurately predict calcite zeta potential in brines containing mixed PDIs, we apply it to predict zeta potential in ultra-low and pressurized CO2 environments for potential applications in carbonate enhanced oil recovery including miscible CO2 flooding and CO2 sequestration in carbonate reservoirs. Model prediction reveals that pure calcite surface will be positively charged in all investigated brines in pressurized CO2 environment (>1atm). Moreover, the sensitivity of calcite zeta potential to CO2 partial pressure in the various brine is found to be in the sequence of Na2CO3 > Na2SO4 > NaCl > MgCl2 > CaCl2 (Ionic strength=0.1M).

Simultaneously estimation for surface heat fluxes of steel slab in a reheating furnace based on DMC predictive control

International Nuclear Information System (INIS)

Li, Yanhao; Wang, Guangjun; Chen, Hong

2015-01-01

The predictive control theory is utilized for the research of a simultaneous estimation of heat fluxes through the upper, side and lower surface of a steel slab in a walking beam type rolling steel reheating furnace. An inverse algorithm based on dynamic matrix control (DMC) is established. That is, each surface heat flux of a slab is simultaneously estimated through rolling optimization on the basis of temperature measurements in selected points of its interior by utilizing step response function as predictive model of a slab's temperature. The reliability of the DMC results is enhanced without prior assuming specific functions of heat fluxes over a period of future time. The inverse algorithm proposed a respective regularization to effectively improve the stability of the estimated results by considering obvious strength differences between the upper as well as lower and side surface heat fluxes of the slab. - Highlights: • The predictive control theory is adopted. • An inversion scheme based on DMC is established. • Upper, side and lower surface heat fluxes of slab are estimated based DMC. • A respective regularization is proposed to improve the stability of results

A Modified Approach in Modeling and Calculation of Contact Characteristics of Rough Surfaces

Directory of Open Access Journals (Sweden)

J.A. Abdo

2005-12-01

Full Text Available A mathematical formulation for the contact of rough surfaces is presented. The derivation of the contact model is facilitated through the definition of plastic asperities that are assumed to be embedded at a critical depth within the actual surface asperities. The surface asperities are assumed to deform elastically whereas the plastic asperities experience only plastic deformation. The deformation of plastic asperities is made to obey the law of conservation of volume. It is believed that the proposed model is advantageous since (a it provides a more accurate account of elasticplastic behavior of surfaces in contact and (b it is applicable to model formulations that involve asperity shoulder-to shoulder contact. Comparison of numerical results for estimating true contact area and contact force using the proposed model and the earlier methods suggest that the proposed approach provides a more realistic prediction of elastic-plastic contact behavior.

Modeling the effects of ultrasound power and reactor dimension on the biodiesel production yield: Comparison of prediction abilities between response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS)

International Nuclear Information System (INIS)

Mostafaei, Mostafa; Javadikia, Hossein; Naderloo, Leila

2016-01-01

Biodiesel is as an alternative petro-diesel fuel produced from the renewable resources. The use of novel technologies such as ultrasound technology for biodiesel production intensifies the reaction and reduces the process cost. The present study is aimed to evaluate and compare the prediction and simulating efficiency of the response surface methodology (RSM) and adaptive Neuro-fuzzy inference system (ANFIS) approaches for modeling the transesterification yield achieved in ultrasonic reactor. The influence of independent variables (reactor diameter, liquid height and ultrasound intensity) on the conversion of fatty acid methyl esters (FAME) was investigated by Box-Behnken design of RSM and two ANFIS approaches (hybrid and back-propagation optimization methods). All models were compared statistically based on the training and validation data set by the coefficient of determination (R2), root mean squares error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean relative percent deviation (MRPD). The calculated R2 for RSM and two ANFIS models were 0.9669, 0.9812 and 0.9808, respectively. All models indicated good predictions, however, the ANFIS models were more precise compared to the RSM model, which proves that the ANFIS is a powerful tool for modeling and optimizing FAME production in ultrasound reactor. - Highlights: • The ultrasound assisted FAME conversion was modelled using RSM and ANFIS approaches. • The scatter diagrams indicate the models accurately predicted the reaction yield. • The ANFIS model (hybrid) has higher R"2 (0.9812) compared to the RSM model. • The predicted deviations and residual values are relatively small for ANFIS model. • ANFIS model was more accurate for predicting ultrasound assisted FAME conversion.

Modeling Water-Surface Elevations and Virtual Shorelines for the Colorado River in Grand Canyon, Arizona

Science.gov (United States)

Magirl, Christopher S.; Breedlove, Michael J.; Webb, Robert H.; Griffiths, Peter G.

2008-01-01

Using widely-available software intended for modeling rivers, a new one-dimensional hydraulic model was developed for the Colorado River through Grand Canyon from Lees Ferry to Diamond Creek. Solving one-dimensional equations of energy and continuity, the model predicts stage for a known steady-state discharge at specific locations, or cross sections, along the river corridor. This model uses 2,680 cross sections built with high-resolution digital topography of ground locations away from the river flowing at a discharge of 227 m3/s; synthetic bathymetry was created for topography submerged below the 227 m3/s water surface. The synthetic bathymetry was created by adjusting the water depth at each cross section up or down until the model?s predicted water-surface elevation closely matched a known water surface. This approach is unorthodox and offers a technique to construct one-dimensional hydraulic models of bedrock-controlled rivers where bathymetric data have not been collected. An analysis of this modeling approach shows that while effective in enabling a useful model, the synthetic bathymetry can differ from the actual bathymetry. The known water-surface profile was measured using elevation data collected in 2000 and 2002, and the model can simulate discharges up to 5,900 m3/s. In addition to the hydraulic model, GIS-based techniques were used to estimate virtual shorelines and construct inundation maps. The error of the hydraulic model in predicting stage is within 0.4 m for discharges less than 1,300 m3/s. Between 1,300-2,500 m3/s, the model accuracy is about 1.0 m, and for discharges between 2,500-5,900 m3/s, the model accuracy is on the order of 1.5 m. In the absence of large floods on the flow-regulated Colorado River in Grand Canyon, the new hydraulic model and the accompanying inundation maps are a useful resource for researchers interested in water depths, shorelines, and stage-discharge curves for flows within the river corridor with 2002 topographic

Surface magnetic field strengths: New tests of magnetoconvective models of M dwarfs

International Nuclear Information System (INIS)

MacDonald, James; Mullan, D. J.

2014-01-01

Precision modeling of M dwarfs has become worthwhile in recent years due to the increasingly precise values of masses and radii which can be obtained from eclipsing binary studies. In a recent paper, Torres has identified four prime M dwarf pairs with the most precise empirical determinations of masses and radii. The measured radii are consistently larger than standard stellar models predict by several percent. These four systems potentially provide the most challenging tests of precision evolutionary models of cool dwarfs at the present time. We have previously modeled M dwarfs in the context of a criterion due to Gough and Tayler in which magnetic fields inhibit the onset of convection according to a physics-based prescription. In the present paper, we apply our magnetoconvective approach to the four prime systems in the Torres list. Going a step beyond what we have already modeled in CM Dra (one of the four Torres systems), we note that new constraints on magnetoconvective models of M dwarfs are now available from empirical estimates of magnetic field strengths on the surfaces of these stars. In the present paper, we consider how well our magnetoconvective models succeed when confronted with this new test of surface magnetic field strengths. Among the systems listed by Torres, we find that plausible magnetic models work well for CM Dra, YY Gem, and CU Cnc. (The fourth system in Torres's list does not yet have enough information to warrant magnetic modeling.) Our magnetoconvection models of CM Dra, YY Gem, and CU Cnc yield predictions of the magnetic fluxes on the stellar surface which are consistent with the observed correlation between magnetic flux and X-ray luminosity.

Surface Magnetic Field Strengths: New Tests of Magnetoconvective Models of M Dwarfs

Science.gov (United States)

MacDonald, James; Mullan, D. J.

2014-05-01

Precision modeling of M dwarfs has become worthwhile in recent years due to the increasingly precise values of masses and radii which can be obtained from eclipsing binary studies. In a recent paper, Torres has identified four prime M dwarf pairs with the most precise empirical determinations of masses and radii. The measured radii are consistently larger than standard stellar models predict by several percent. These four systems potentially provide the most challenging tests of precision evolutionary models of cool dwarfs at the present time. We have previously modeled M dwarfs in the context of a criterion due to Gough & Tayler in which magnetic fields inhibit the onset of convection according to a physics-based prescription. In the present paper, we apply our magnetoconvective approach to the four prime systems in the Torres list. Going a step beyond what we have already modeled in CM Dra (one of the four Torres systems), we note that new constraints on magnetoconvective models of M dwarfs are now available from empirical estimates of magnetic field strengths on the surfaces of these stars. In the present paper, we consider how well our magnetoconvective models succeed when confronted with this new test of surface magnetic field strengths. Among the systems listed by Torres, we find that plausible magnetic models work well for CM Dra, YY Gem, and CU Cnc. (The fourth system in Torres's list does not yet have enough information to warrant magnetic modeling.) Our magnetoconvection models of CM Dra, YY Gem, and CU Cnc yield predictions of the magnetic fluxes on the stellar surface which are consistent with the observed correlation between magnetic flux and X-ray luminosity.

LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation

Directory of Open Access Journals (Sweden)

Schroeder Michael

2006-09-01

Full Text Available Abstract Background Identifying pockets on protein surfaces is of great importance for many structure-based drug design applications and protein-ligand docking algorithms. Over the last ten years, many geometric methods for the prediction of ligand-binding sites have been developed. Results We present LIGSITEcsc, an extension and implementation of the LIGSITE algorithm. LIGSITEcsc is based on the notion of surface-solvent-surface events and the degree of conservation of the involved surface residues. We compare our algorithm to four other approaches, LIGSITE, CAST, PASS, and SURFNET, and evaluate all on a dataset of 48 unbound/bound structures and 210 bound-structures. LIGSITEcsc performs slightly better than the other tools and achieves a success rate of 71% and 75%, respectively. Conclusion The use of the Connolly surface leads to slight improvements, the prediction re-ranking by conservation to significant improvements of the binding site predictions. A web server for LIGSITEcsc and its source code is available at scoppi.biotec.tu-dresden.de/pocket.

Influence of surface nudging on climatological mean and ENSO feedbacks in a coupled model

Science.gov (United States)

Zhu, Jieshun; Kumar, Arun

2018-01-01

Studies have suggested that surface nudging could be an efficient way to reconstruct the subsurface ocean variability, and thus a useful method for initializing climate predictions (e.g., seasonal and decadal predictions). Surface nudging is also the basis for climate models with flux adjustments. In this study, however, some negative aspects of surface nudging on climate simulations in a coupled model are identified. Specifically, a low-resolution version of the NCEP Climate Forecast System, version 2 (CFSv2L) is used to examine the influence of nudging on simulations of climatological mean and on the coupled feedbacks during ENSO. The effect on ENSO feedbacks is diagnosed following a heat budget analysis of mixed layer temperature anomalies. Diagnostics of the climatological mean state indicates that, even though SST biases in all ocean basins, as expected, are eliminated, the fidelity of climatological precipitation, surface winds and subsurface temperature (or the thermocline depth) could be highly ocean basin dependent. This is exemplified by improvements in the climatology of these variables in the tropical Atlantic, but degradations in the tropical Pacific. Furthermore, surface nudging also distorts the dynamical feedbacks during ENSO. For example, while the thermocline feedback played a critical role during the evolution of ENSO in a free simulation, it only played a minor role in the nudged simulation. These results imply that, even though the simulation of surface temperature could be improved in a climate model with surface nudging, the physics behind might be unrealistic.

Multi-model analysis in hydrological prediction

Science.gov (United States)

Lanthier, M.; Arsenault, R.; Brissette, F.

2017-12-01

Hydrologic modelling, by nature, is a simplification of the real-world hydrologic system. Therefore ensemble hydrological predictions thus obtained do not present the full range of possible streamflow outcomes, thereby producing ensembles which demonstrate errors in variance such as under-dispersion. Past studies show that lumped models used in prediction mode can return satisfactory results, especially when there is not enough information available on the watershed to run a distributed model. But all lumped models greatly simplify the complex processes of the hydrologic cycle. To generate more spread in the hydrologic ensemble predictions, multi-model ensembles have been considered. In this study, the aim is to propose and analyse a method that gives an ensemble streamflow prediction that properly represents the forecast probabilities and reduced ensemble bias. To achieve this, three simple lumped models are used to generate an ensemble. These will also be combined using multi-model averaging techniques, which generally generate a more accurate hydrogram than the best of the individual models in simulation mode. This new predictive combined hydrogram is added to the ensemble, thus creating a large ensemble which may improve the variability while also improving the ensemble mean bias. The quality of the predictions is then assessed on different periods: 2 weeks, 1 month, 3 months and 6 months using a PIT Histogram of the percentiles of the real observation volumes with respect to the volumes of the ensemble members. Initially, the models were run using historical weather data to generate synthetic flows. This worked for individual models, but not for the multi-model and for the large ensemble. Consequently, by performing data assimilation at each prediction period and thus adjusting the initial states of the models, the PIT Histogram could be constructed using the observed flows while allowing the use of the multi-model predictions. The under-dispersion has been

Multi-model assessment of the impact of soil moisture initialization on mid-latitude summer predictability

Science.gov (United States)

Ardilouze, Constantin; Batté, L.; Bunzel, F.; Decremer, D.; Déqué, M.; Doblas-Reyes, F. J.; Douville, H.; Fereday, D.; Guemas, V.; MacLachlan, C.; Müller, W.; Prodhomme, C.

2017-12-01

Land surface initial conditions have been recognized as a potential source of predictability in sub-seasonal to seasonal forecast systems, at least for near-surface air temperature prediction over the mid-latitude continents. Yet, few studies have systematically explored such an influence over a sufficient hindcast period and in a multi-model framework to produce a robust quantitative assessment. Here, a dedicated set of twin experiments has been carried out with boreal summer retrospective forecasts over the 1992-2010 period performed by five different global coupled ocean-atmosphere models. The impact of a realistic versus climatological soil moisture initialization is assessed in two regions with high potential previously identified as hotspots of land-atmosphere coupling, namely the North American Great Plains and South-Eastern Europe. Over the latter region, temperature predictions show a significant improvement, especially over the Balkans. Forecast systems better simulate the warmest summers if they follow pronounced dry initial anomalies. It is hypothesized that models manage to capture a positive feedback between high temperature and low soil moisture content prone to dominate over other processes during the warmest summers in this region. Over the Great Plains, however, improving the soil moisture initialization does not lead to any robust gain of forecast quality for near-surface temperature. It is suggested that models biases prevent the forecast systems from making the most of the improved initial conditions.

Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces.

Directory of Open Access Journals (Sweden)

Ching-Tai Chen

Full Text Available Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins and were tested on an independent dataset (consisting of 142 proteins. The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Science.gov (United States)

Chen, Ching-Tai; Peng, Hung-Pin; Jian, Jhih-Wei; Tsai, Keng-Chang; Chang, Jeng-Yih; Yang, Ei-Wen; Chen, Jun-Bo; Ho, Shinn-Ying; Hsu, Wen-Lian; Yang, An-Suei

2012-01-01

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with

Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast.

Science.gov (United States)

Ceglar, Andrej; Toreti, Andrea; Prodhomme, Chloe; Zampieri, Matteo; Turco, Marco; Doblas-Reyes, Francisco J

2018-01-22

Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, while it fosters the use of climate information favouring adaptation strategies. As climate variability and extremes have significant influence on agricultural production, the early prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.

Investigating the need for complex vs. simple scenarios to improve predictions of aquatic ecosystem exposure with the SoilPlus model

International Nuclear Information System (INIS)

Ghirardello, Davide; Morselli, Melissa; Otto, Stefan; Zanin, Giuseppe; Di Guardo, Antonio

2014-01-01

A spatially-explicit version of the recent multimedia fate model SoilPlus was developed and applied to predict the runoff of three pesticides in a small agricultural watershed in north-eastern Italy. In order to evaluate model response to increasing spatial resolution, a tiered simulation approach was adopted, also using a dynamic model for surface water (DynA model), to predict the fate of pesticides in runoff water and sediment, and concentrations in river water. Simulation outputs were compared to water concentrations measured in the basin. Results showed that a high spatial resolution and scenario complexity improved model predictions of metolachlor and terbuthylazine in runoff to an acceptable performance (R 2 = 0.64–0.70). The importance was also shown of a field-based database of properties (i.e. soil texture and organic carbon, rainfall and water flow, pesticides half-life in soil) in reducing the distance between predicted and measured surface water concentrations and its relevance for risk assessment. Highlights: • A GIS based model was developed to predict pesticide fate in soil and water. • Spatial scenario was obtained at field level for a small agricultural basin. • A tiered strategy was applied to test the performance gain with complexity. • Increased details of scenario as well as the role of surface water are relevant. -- In order to obtain more ecologically realistic predictions of pulse exposure in aquatic ecosystems detailed information about the scenario is required

A mathematical look at a physical power prediction model

DEFF Research Database (Denmark)

Landberg, L.

1998-01-01

This article takes a mathematical look at a physical model used to predict the power produced from wind farms. The reason is to see whether simple mathematical expressions can replace the original equations and to give guidelines as to where simplifications can be made and where they cannot....... The article shows that there is a linear dependence between the geostrophic wind and the local wind at the surface, but also that great care must be taken in the selection of the simple mathematical models, since physical dependences play a very important role, e.g. through the dependence of the turning...

Surface Complexation Modeling in Variable Charge Soils: Charge Characterization by Potentiometric Titration

Directory of Open Access Journals (Sweden)

Giuliano Marchi

2015-10-01

Full Text Available ABSTRACT Intrinsic equilibrium constants of 17 representative Brazilian Oxisols were estimated from potentiometric titration measuring the adsorption of H+ and OH− on amphoteric surfaces in suspensions of varying ionic strength. Equilibrium constants were fitted to two surface complexation models: diffuse layer and constant capacitance. The former was fitted by calculating total site concentration from curve fitting estimates and pH-extrapolation of the intrinsic equilibrium constants to the PZNPC (hand calculation, considering one and two reactive sites, and by the FITEQL software. The latter was fitted only by FITEQL, with one reactive site. Soil chemical and physical properties were correlated to the intrinsic equilibrium constants. Both surface complexation models satisfactorily fit our experimental data, but for results at low ionic strength, optimization did not converge in FITEQL. Data were incorporated in Visual MINTEQ and they provide a modeling system that can predict protonation-dissociation reactions in the soil surface under changing environmental conditions.

Application of turbulence modeling to predict surface heat transfer in stagnation flow region of circular cylinder

Science.gov (United States)

Wang, Chi R.; Yeh, Frederick C.

1987-01-01

A theoretical analysis and numerical calculations for the turbulent flow field and for the effect of free-stream turbulence on the surface heat transfer rate of a stagnation flow are presented. The emphasis is on the modeling of turbulence and its augmentation of surface heat transfer rate. The flow field considered is the region near the forward stagnation point of a circular cylinder in a uniform turbulent mean flow. The free stream is steady and incompressible with a Reynolds number of the order of 10 to the 5th power and turbulence intensity of less than 5 percent. For this analysis, the flow field is divided into three regions: (1) a uniform free-stream region where the turbulence is homogeneous and isotropic; (2) an external viscid flow region where the turbulence is distorted by the variation of the mean flow velocity; and, (3) an anisotropic turbulent boundary layer region over the cylinder surface. The turbulence modeling techniques used are the kappa-epsilon two-equation model in the external flow region and the time-averaged turbulence transport equation in the boundary layer region. The turbulence double correlations, the mean velocity, and the mean temperature within the boundary layer are solved numerically from the transport equations. The surface heat transfer rate is calculated as functions of the free-stream turbulence longitudinal microlength scale, the turbulence intensity, and the Reynolds number.

Climate Prediction for Brazil's Nordeste: Performance of Empirical and Numerical Modeling Methods.

Science.gov (United States)

Moura, Antonio Divino; Hastenrath, Stefan

2004-07-01

Comparisons of performance of climate forecast methods require consistency in the predictand and a long common reference period. For Brazil's Nordeste, empirical methods developed at the University of Wisconsin use preseason (October January) rainfall and January indices of the fields of meridional wind component and sea surface temperature (SST) in the tropical Atlantic and the equatorial Pacific as input to stepwise multiple regression and neural networking. These are used to predict the March June rainfall at a network of 27 stations. An experiment at the International Research Institute for Climate Prediction, Columbia University, with a numerical model (ECHAM4.5) used global SST information through February to predict the March June rainfall at three grid points in the Nordeste. The predictands for the empirical and numerical model forecasts are correlated at +0.96, and the period common to the independent portion of record of the empirical prediction and the numerical modeling is 1968 99. Over this period, predicted versus observed rainfall are evaluated in terms of correlation, root-mean-square error, absolute error, and bias. Performance is high for both approaches. Numerical modeling produces a correlation of +0.68, moderate errors, and strong negative bias. For the empirical methods, errors and bias are small, and correlations of +0.73 and +0.82 are reached between predicted and observed rainfall.

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

Science.gov (United States)

Duda, David P.; Minnis, Patrick

2009-01-01

Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data.

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Federico Nuñez-Piña

2018-01-01

Full Text Available The problem of assigning buffers in a production line to obtain an optimum production rate is a combinatorial problem of type NP-Hard and it is known as Buffer Allocation Problem. It is of great importance for designers of production systems due to the costs involved in terms of space requirements. In this work, the relationship among the number of buffer slots, the number of work stations, and the production rate is studied. Response surface methodology and artificial neural network were used to develop predictive models to find optimal throughput values. 360 production rate values for different number of buffer slots and workstations were used to obtain a fourth-order mathematical model and four hidden layers’ artificial neural network. Both models have a good performance in predicting the throughput, although the artificial neural network model shows a better fit (R=1.0000 against the response surface methodology (R=0.9996. Moreover, the artificial neural network produces better predictions for data not utilized in the models construction. Finally, this study can be used as a guide to forecast the maximum or near maximum throughput of production lines taking into account the buffer size and the number of machines in the line.

Spatial and temporal patterns of land surface fluxes from remotely sensed surface temperatures within an uncertainty modelling framework

Directory of Open Access Journals (Sweden)

M. F. McCabe

2005-01-01

Full Text Available Characterising the development of evapotranspiration through time is a difficult task, particularly when utilising remote sensing data, because retrieved information is often spatially dense, but temporally sparse. Techniques to expand these essentially instantaneous measures are not only limited, they are restricted by the general paucity of information describing the spatial distribution and temporal evolution of evaporative patterns. In a novel approach, temporal changes in land surface temperatures, derived from NOAA-AVHRR imagery and a generalised split-window algorithm, are used as a calibration variable in a simple land surface scheme (TOPUP and combined within the Generalised Likelihood Uncertainty Estimation (GLUE methodology to provide estimates of areal evapotranspiration at the pixel scale. Such an approach offers an innovative means of transcending the patch or landscape scale of SVAT type models, to spatially distributed estimates of model output. The resulting spatial and temporal patterns of land surface fluxes and surface resistance are used to more fully understand the hydro-ecological trends observed across a study catchment in eastern Australia. The modelling approach is assessed by comparing predicted cumulative evapotranspiration values with surface fluxes determined from Bowen ratio systems and using auxiliary information such as in-situ soil moisture measurements and depth to groundwater to corroborate observed responses.

Predictive Modeling in Race Walking

Directory of Open Access Journals (Sweden)

Krzysztof Wiktorowicz

2015-01-01

Full Text Available This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers’ training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors.

Model parameter-related optimal perturbations and their contributions to El Niño prediction errors

Science.gov (United States)

Tao, Ling-Jiang; Gao, Chuan; Zhang, Rong-Hua

2018-04-01

Errors in initial conditions and model parameters (MPs) are the main sources that limit the accuracy of ENSO predictions. In addition to exploring the initial error-induced prediction errors, model errors are equally important in determining prediction performance. In this paper, the MP-related optimal errors that can cause prominent error growth in ENSO predictions are investigated using an intermediate coupled model (ICM) and a conditional nonlinear optimal perturbation (CNOP) approach. Two MPs related to the Bjerknes feedback are considered in the CNOP analysis: one involves the SST-surface wind coupling ({α _τ } ), and the other involves the thermocline effect on the SST ({α _{Te}} ). The MP-related optimal perturbations (denoted as CNOP-P) are found uniformly positive and restrained in a small region: the {α _τ } component is mainly concentrated in the central equatorial Pacific, and the {α _{Te}} component is mainly located in the eastern cold tongue region. This kind of CNOP-P enhances the strength of the Bjerknes feedback and induces an El Niño- or La Niña-like error evolution, resulting in an El Niño-like systematic bias in this model. The CNOP-P is also found to play a role in the spring predictability barrier (SPB) for ENSO predictions. Evidently, such error growth is primarily attributed to MP errors in small areas based on the localized distribution of CNOP-P. Further sensitivity experiments firmly indicate that ENSO simulations are sensitive to the representation of SST-surface wind coupling in the central Pacific and to the thermocline effect in the eastern Pacific in the ICM. These results provide guidance and theoretical support for the future improvement in numerical models to reduce the systematic bias and SPB phenomenon in ENSO predictions.

Adding propensity scores to pure prediction models fails to improve predictive performance

Directory of Open Access Journals (Sweden)

Amy S. Nowacki

2013-08-01

Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

Model-free and model-based reward prediction errors in EEG.

Science.gov (United States)

Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

2018-05-24

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

Predicting the minimum liquid surface tension activity of pseudomonads expressing biosurfactants.

Science.gov (United States)

Mohammed, I U; Deeni, Y; Hapca, S M; McLaughlin, K; Spiers, A J

2015-01-01

Bacteria produce a variety of biosurfactants capable of significantly reducing liquid (aqueous) surface tension (γ) with a range of biological roles and biotechnological uses. To determine the lowest achievable surface tension (γMin ), we tested a diverse collection of Pseudomonas-like isolates from contaminated soil and activated sludge and identified those expressing biosurfactants by drop-collapse assay. Liquid surface tension-reducing ability was quantitatively determined by tensiometry, with 57 isolates found to significantly lower culture supernatant surface tensions to 24·5-49·1 mN m(-1) . Differences in biosurfactant behaviour determined by foaming, emulsion and oil-displacement assays were also observed amongst isolates producing surface tensions of 25-27 mN m(-1) , suggesting that a range of structurally diverse biosurfactants were being expressed. Individual distribution identification (IDI) analysis was used to identify the theoretical probability distribution that best fitted the surface tension data, which predicted a γMin of 24·24 mN m(-1) . This was in agreement with predictions based on earlier work of published mixed bacterial spp. data, suggesting a fundamental limit to the ability of bacterial biosurfactants to reduce surface tensions in aqueous systems. This implies a biological restriction on the synthesis and export of these agents or a physical-chemical restriction on their functioning once produced. Numerous surveys of biosurfactant-producing bacteria have been conducted, but only recently has an attempt been made to predict the minimum liquid surface tension these surface-active agents can achieve. Here, we determine a theoretical minimum of 24 mN m(-1) by statistical analysis of tensiometry data, suggesting a fundamental limit for biosurfactant activity in bacterial cultures incubated under standard growth conditions. This raises a challenge to our understanding of biosurfactant expression, secretion and function, as well as

Incorporating human-water dynamics in a hyper-resolution land surface model

Science.gov (United States)

Vergopolan, N.; Chaney, N.; Wanders, N.; Sheffield, J.; Wood, E. F.

2017-12-01

The increasing demand for water, energy, and food is leading to unsustainable groundwater and surface water exploitation. As a result, the human interactions with the environment, through alteration of land and water resources dynamics, need to be reflected in hydrologic and land surface models (LSMs). Advancements in representing human-water dynamics still leave challenges related to the lack of water use data, water allocation algorithms, and modeling scales. This leads to an over-simplistic representation of human water use in large-scale models; this is in turn leads to an inability to capture extreme events signatures and to provide reliable information at stakeholder-level spatial scales. The emergence of hyper-resolution models allows one to address these challenges by simulating the hydrological processes and interactions with the human impacts at field scales. We integrated human-water dynamics into HydroBlocks - a hyper-resolution, field-scale resolving LSM. HydroBlocks explicitly solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs); and its HRU-based model parallelization allows computationally efficient long-term simulations as well as ensemble predictions. The implemented human-water dynamics include groundwater and surface water abstraction to meet agricultural, domestic and industrial water demands. Furthermore, a supply-demand water allocation scheme based on relative costs helps to determine sectoral water use requirements and tradeoffs. A set of HydroBlocks simulations over the Midwest United States (daily, at 30-m spatial resolution for 30 years) are used to quantify the irrigation impacts on water availability. The model captures large reductions in total soil moisture and water table levels, as well as spatiotemporal changes in evapotranspiration and runoff peaks, with their intensity related to the adopted water management strategy. By incorporating human-water dynamics in

InterProSurf: a web server for predicting interacting sites on protein surfaces

Science.gov (United States)

Negi, Surendra S.; Schein, Catherine H.; Oezguen, Numan; Power, Trevor D.; Braun, Werner

2009-01-01

Summary A new web server, InterProSurf, predicts interacting amino acid residues in proteins that are most likely to interact with other proteins, given the 3D structures of subunits of a protein complex. The prediction method is based on solvent accessible surface area of residues in the isolated subunits, a propensity scale for interface residues and a clustering algorithm to identify surface regions with residues of high interface propensities. Here we illustrate the application of InterProSurf to determine which areas of Bacillus anthracis toxins and measles virus hemagglutinin protein interact with their respective cell surface receptors. The computationally predicted regions overlap with those regions previously identified as interface regions by sequence analysis and mutagenesis experiments. PMID:17933856

Direct measurements of surface scattering in Si nanosheets using a microscale phonon spectrometer: implications for Casimir-limit predicted by Ziman theory.

Science.gov (United States)

Hertzberg, Jared B; Aksit, Mahmut; Otelaja, Obafemi O; Stewart, Derek A; Robinson, Richard D

2014-02-12

Thermal transport in nanostructures is strongly affected by phonon-surface interactions, which are expected to depend on the phonon's wavelength and the surface roughness. Here we fabricate silicon nanosheets, measure their surface roughness (∼ 1 nm) using atomic force microscopy (AFM), and assess the phonon scattering rate in the sheets with a novel technique: a microscale phonon spectrometer. The spectrometer employs superconducting tunnel junctions (STJs) to produce and detect controllable nonthermal distributions of phonons from ∼ 90 to ∼ 870 GHz. This technique offers spectral resolution nearly 10 times better than a thermal conductance measurement. We compare measured phonon transmission rates to rates predicted by a Monte Carlo model of phonon trajectories, assuming that these trajectories are dominated by phonon-surface interactions and using the Ziman theory to predict phonon-surface scattering rates based on surface topology. Whereas theory predicts a diffuse surface scattering probability of less than 40%, our measurements are consistent with a 100% probability. Our nanosheets therefore exhibit the so-called "Casimir limit" at a much lower frequency than expected if the phonon scattering rates follow the Ziman theory for a 1 nm surface roughness. Such a result holds implications for thermal management in nanoscale electronics and the design of nanostructured thermoelectrics.

Nonlinear chaotic model for predicting storm surges

Directory of Open Access Journals (Sweden)

M. Siek

2010-09-01

Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.

Use of Ocean Remote Sensing Data to Enhance Predictions with a Coupled General Circulation Model

Science.gov (United States)

Rienecker, Michele M.

1999-01-01

Surface height, sea surface temperature and surface wind observations from satellites have given a detailed time sequence of the initiation and evolution of the 1997/98 El Nino. The data have beet complementary to the subsurface TAO moored data in their spatial resolution and extent. The impact of satellite observations on seasonal prediction in the tropical Pacific using a coupled ocean-atmosphere general circulation model will be presented.

Modeling nanostructural surface modifications in metal cutting by an approach of thermodynamic irreversibility: Derivation and experimental validation

Science.gov (United States)

Buchkremer, S.; Klocke, F.

2017-01-01

Performance and operational safety of many metal parts in engineering depend on their surface integrity. During metal cutting, large thermomechanical loads and high gradients of the loads concerning time and location act on the surfaces and may yield significant structural material modifications, which alter the surface integrity. In this work, the derivation and validation of a model of nanostructural surface modifications in metal cutting are presented. For the first time in process modeling, initiation and kinetics of these modifications are predicted using a thermodynamic potential, which considers the interdependent developments of plastic work, dissipation, heat conduction and interface energy as well as the associated productions and flows of entropy. The potential is expressed based on the free Helmholtz energy. The irreversible thermodynamic state changes in the workpiece surface are homogenized over the volume in order to bridge the gap between discrete phenomena involved with the initiation and kinetics of dynamic recrystallization and its macroscopic implications for surface integrity. The formulation of the thermodynamic potential is implemented into a finite element model of orthogonal cutting of steel AISI 4140. Close agreement is achieved between predicted nanostructures and those obtained in transmission electron microscopical investigations of specimen produced in cutting experiments.

Departure Queue Prediction for Strategic and Tactical Surface Scheduler Integration

Science.gov (United States)

Zelinski, Shannon; Windhorst, Robert

2016-01-01

A departure metering concept to be demonstrated at Charlotte Douglas International Airport (CLT) will integrate strategic and tactical surface scheduling components to enable the respective collaborative decision making and improved efficiency benefits these two methods of scheduling provide. This study analyzes the effect of tactical scheduling on strategic scheduler predictability. Strategic queue predictions and target gate pushback times to achieve a desired queue length are compared between fast time simulations of CLT surface operations with and without tactical scheduling. The use of variable departure rates as a strategic scheduler input was shown to substantially improve queue predictions over static departure rates. With target queue length calibration, the strategic scheduler can be tuned to produce average delays within one minute of the tactical scheduler. However, root mean square differences between strategic and tactical delays were between 12 and 15 minutes due to the different methods the strategic and tactical schedulers use to predict takeoff times and generate gate pushback clearances. This demonstrates how difficult it is for the strategic scheduler to predict tactical scheduler assigned gate delays on an individual flight basis as the tactical scheduler adjusts departure sequence to accommodate arrival interactions. Strategic/tactical scheduler compatibility may be improved by providing more arrival information to the strategic scheduler and stabilizing tactical scheduler changes to runway sequence in response to arrivals.

Mathematical model for biomolecular quantification using large-area surface-enhanced Raman spectroscopy mapping

DEFF Research Database (Denmark)

Palla, Mirkó; Bosco, Filippo; Yang, Jaeyoung

2015-01-01

Surface-enhanced Raman spectroscopy (SERS) based on nanostructured platforms is a promising technique for quantitative and highly sensitive detection of biomolecules in the field of analytical biochemistry. Here, we report a mathematical model to predict experimental SERS signal (or hotspot) inte...

A Predictive Model of Surface Warfare Officer Retention: Factors Affecting Turnover

National Research Council Canada - National Science Library

Gjurich, Gregory

1999-01-01

Junior Surface Warfare Officer retention is in a crisis. The Surface Warfare Officer community anticipates an inability to fill Department Head billets due to the number of junior Surface Warfare Officers leaving military service...

Extracting falsifiable predictions from sloppy models.

Science.gov (United States)

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

2007-12-01

Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.

Assembling three-dimensional nanostructures on metal surfaces with a reversible vertical single-atom manipulation: A theoretical modeling

International Nuclear Information System (INIS)

Yang Tianxing; Ye Xiang; Huang Lei; Xie Yiqun; Ke Sanhuang

2012-01-01

Highlights: ► We simulate the reversible vertical single-atom manipulations on several metal surfaces. ► We propose a method to predict whether a reversible vertical single-atom manipulation can be successful on several metal surfaces. ► A 3-dimensional Ni nanocluster is assembled on the Ni(1 1 1) surface using a Ni trimer-apex tip. - Abstract: We propose a theoretical model to show that pulling up an adatom from an atomic step requires a weaker force than from the flat surfaces of Al(0 0 1), Ni(1 1 1), Pt(1 1 0) and Au(1 1 0). Single adatom in the atomic step can be extracted vertically by a trimer-apex tip while can be released to the flat surface. This reversible vertical manipulation can then be used to fabricate a supported three-dimensional (3D) nanostructure on the Ni(1 1 1) surface. The present modeling can be used to predict whether the reversible vertical single-atom manipulation and thus the assembling of 3D nanostructures can be achieved on a metal surface.

Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

Science.gov (United States)

Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

2012-01-01

The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

Method of predicting surface deformation in the form of sinkholes

Energy Technology Data Exchange (ETDEWEB)

Chudek, M.; Arkuszewski, J.

1980-06-01

Proposes a method for predicting probability of sinkhole shaped subsidence, number of funnel-shaped subsidences and size of individual funnels. The following factors which influence the sudden subsidence of the surface in the form of funnels are analyzed: geologic structure of the strata between mining workings and the surface, mining depth, time factor, and geologic disolocations. Sudden surface subsidence is observed only in the case of workings situated up to a few dozen meters from the surface. Using the proposed method is explained with some examples. It is suggested that the method produces correct results which can be used in coal mining and in ore mining. (1 ref.) (In Polish)

Using Flux Site Observations to Calibrate Root System Architecture Stencils for Water Uptake of Plant Functional Types in Land Surface Models.

Science.gov (United States)

Bouda, M.

2017-12-01

Root system architecture (RSA) can significantly affect plant access to water, total transpiration, as well as its partitioning by soil depth, with implications for surface heat, water, and carbon budgets. Despite recent advances in land surface model (LSM) descriptions of plant hydraulics, RSA has not been included because of its three-dimensional complexity, which makes RSA modelling generally too computationally costly. This work builds upon the recently introduced "RSA stencil," a process-based 1D layered model that captures the dynamic shifts in water potential gradients of 3D RSA in response to heterogeneous soil moisture profiles. In validations using root systems calibrated to the rooting profiles of four plant functional types (PFT) of the Community Land Model, the RSA stencil predicts plant water potentials within 2% of the outputs of full 3D models, despite its trivial computational cost. In transient simulations, the RSA stencil yields improved predictions of water uptake and soil moisture profiles compared to a 1D model based on root fraction alone. Here I show how the RSA stencil can be calibrated to time-series observations of soil moisture and transpiration to yield a water uptake PFT definition for use in terrestrial models. This model-data integration exercise aims to improve LSM predictions of soil moisture dynamics and, under water-limiting conditions, surface fluxes. These improvements can be expected to significantly impact predictions of downstream variables, including surface fluxes, climate-vegetation feedbacks and soil nutrient cycling.