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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

FY2021 Status Report on the Computing Systems for the Yucca Mountain Project TSPA-LA Models and Testing of Selected Process Models

Sandia National Laboratories continued evaluation of the total system performance assessment (TSPA) for License Application (LA) computing systems for the previously considered Yucca Mountain Project (YMP). This was done to maintain the operational readiness of the computing infrastructure (computer hardware and software) and knowledge capability for total system performance assessment) type analysis, as directed by the National Nuclear Security Administration (NNSA), DOE 2010. The FY21 task included continued operation of the cluster; maintenance of the TSPA-LA models (with GoldSim 9.60.300); continued assessment of the status of the Infiltration Model; (a process model that feeds the TSP -LA) and preliminary assessments of the Unsaturated Zone Flow Model and the Saturated Zone Flow and Transport Model Abstraction (process models that feed the TSPA-LA). The 2014 cluster and supporting software systems are currently fully operational to support TSPA-LA type analyses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2010

Regression Model Term Selection for the Analysis of Strain-Gage Balance Calibration Data

The paper discusses the selection of regression model terms for the analysis of wind tunnel strain-gage balance calibration data. Different function class combinations are presented that may be used to analyze calibration data using either a non-iterative or an iterative method. The role of the intercept term in a regression model of calibration data is reviewed. In addition, useful algorithms and metrics originating from linear algebra and statistics are recommended that will help an analyst (i) to identify and avoid both linear and near-linear dependencies between regression model terms and (ii) to make sure that the selected regression model of the calibration data uses only statistically significant terms. Three different tests are suggested that may be used to objectively assess the predictive capability of the final regression model of the calibration data. These tests use both the original data points and regression model independent confirmation points. Finally, data from a simplified manual calibration of the Ames MK40 balance is used to illustrate the application of some of the metrics and tests to a realistic calibration data set.

Ulbrich, Norbert Manfred↗
DOE OSTI2021

High Strain-Rate Strength Experiments on Molybdenum for Constitutive Model Parameter Selection

The constitutive model parameters for molybdenum obtained from the Plansee supplier have been constrained via Strength-Stabilized Rayleigh-Taylor experiments performed at the Los Alamos Neutron Science Center. Previous Hopkinson-bar measurements have produced a degenerate family of constitutive model parameters proven in the low strain rate regime. The experiments presented here provide a constraint at strain rates from 10 4 –10 6 s -1 which is used to select a preferred set of parameters for high strain-rate applications. Further analysis is warranted including determining a global fit in parameter space using all available high and low strain-rate data available.

36 MATERIALS SCIENCE↗
DOE OSTI2020

FY19 Status Report on the Computing Systems for the Yucca Mountain Project TSPA-LA Models and Preliminary Testing of a Selected Process Model

Sandia National Laboratories continued evaluation of total system performance assessment (TSPA) computing systems for the previously considered Yucca Mountain Project. This was done to maintain the operational readiness of the computing infrastructure (computer hardware and software) and knowledge capability for total system performance assessment) type analysis, as directed by the National Nuclear Security Administration (NNSA), DOE 2010. The FY19 task included continued operation of the cluster; maintenance of the TSPA-LA models (with Gold Sim 9.60.300); preliminary assessment of the status of the Infiltration Model (a process model that feeds the TSPA-LA). In addition, precautionary actions were needed to extend the life of the cluster hardware. To do that, three new nodes were added to the cluster. In the event any of the original nodes fail they will be replaced with the new nodes, thereby maintaining the core capability. The 2014 cluster and supporting software systems are currently fully operational to support TSPA-LA type analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Behavior of the maximum likelihood in quantum state tomography

Quantum state tomography on a d-dimensional system demands resources that grow rapidly with d. They may be reduced by using model selection to tailor the number of parameters in the model (i.e., the size of the density matrix). Most model selection methods typically rely on a test statistic and a null theory that describes its behavior when two models are equally good. Here, we consider the loglikelihood ratio. Because of the positivity constraint ρ ≥ 0, quantum state space does not generally satisfy local asymptotic normality (LAN), meaning the classical null theory for the loglikelihood ratio (the Wilks theorem) should not be used. Thus, understanding and quantifying how positivity affects the null behavior of this test statistic is necessary for its use in model selection for state tomography. We define a new generalization of LAN, metric-projected LAN, show that quantum state space satisfies it, and derive a replacement for the Wilks theorem. In addition to enabling reliable model selection, our results shed more light on the qualitative effects of the positivity constraint on state tomography.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering

One of the longstanding open problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. Here, we propose automated model order selection (AMOS), a solution to the SGC model selection problem under a random interconnection model (RIM) using a novel selection criterion that is based on an asymptotic phase transition analysis. AMOS can more generally be applied to discovering hidden block diagonal structure in symmetric non-negative matrices. Numerical experiments on simulated graphs validate the phase transition analysis, and real-world network data is used to validate the performance of the proposed model selection procedure.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

A Rapid Approach to Modeling Species-Habitat Relationships

A growing number of species require conservation or management efforts. Success of these activities requires knowledge of the species' occurrence pattern. Species-habitat models developed from GIS data sources are commonly used to predict species occurrence but commonly used data sources are often developed for purposes other than predicting species occurrence and are of inappropriate scale and the techniques used to extract predictor variables are often time consuming and cannot be repeated easily and thus cannot efficiently reflect changing conditions. We used digital orthophotographs and a grid cell classification scheme to develop an efficient technique to extract predictor variables. We combined our classification scheme with a priori hypothesis development using expert knowledge and a previously published habitat suitability index and used an objective model selection procedure to choose candidate models. We were able to classify a large area (57,000 ha) in a fraction of the time that would be required to map vegetation and were able to test models at varying scales using a windowing process. Interpretation of the selected models confirmed existing knowledge of factors important to Florida scrub-jay habitat occupancy. The potential uses and advantages of using a grid cell classification scheme in conjunction with expert knowledge or an habitat suitability index (HSI) and an objective model selection procedure are discussed.

Carter, Geoffrey M.↗
DOE OSTI2019

Selective Oxidation of Acetaldehyde to Acetic Acid on Pd–Au Bimetallic Model Catalysts

Acetic acid is a widely employed reactant in the chemical industry, and it is also used as a food ingredient. Here, we report a catalytic reaction pathway for the gas-phase selective oxidation of acetaldehyde to acetic acid on a Pd–Au(111) heterogeneous model catalyst. On an oxygen precovered Pd–Au surface, acetaldehyde is selectively oxidized to acetic acid from 250 to 340 K. Using FT-IR, the formation of acetate species is detected from 160 to 260 K on this surface, which is a widely known adsorbate derived from acetic acid on metal surfaces. With higher Pd coverages, the acetaldehyde is less selectively oxidized to acetic acid, and near 375 K, CO 2 , H 2 O, CH 4 , and H 2 are evolved, evidence for the decarboxylation of acetate. Finally, in our density functional theory calculations, we confirm that the relative energy difference between the acetate state and the decarboxylated state decreases as Pd ensemble size increases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Evaluation of a Traditional Regression Model Term Selection Process for Balance Calibration Data Sets

Key elements of a traditional regression model term selection process for the analysis of wind tunnel strain-gage balance calibration data are discussed. The traditional process is sometimes applied when regression models of the gage outputs are needed and an analyst does not have access to a term selection algorithm. In principle, the traditional process identifies supported regression model terms of a gage output by combining an analyst's subject- matter knowledge with a visual examination of selected plots of the calibration loads. Twelve empirical rules are listed that may be used to select regression model terms. Data from the manual calibration of a force balance is used to illustrate the application of the traditional term selection process. The data example shows that the traditional process can become very complex. Its successful application highly depends on an analyst's experience and skills. Therefore, it is suggested to replace the traditional term selection process with an automated term selection algorithm. Then, objective metrics are systematically applied during the term selection that make the identification of the regression model more repeatable and less dependent on an analyst's personal skills.

strain-gage balance↗
DOE OSTI2023

Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize

Root-associated microbiomes in the rhizosphere (rhizobiomes) are increasingly known to play an important role in nutrient acquisition, stress tolerance, and disease resistance of plants. However, it remains largely unclear to what extent these rhizobiomes contribute to trait variation for different genotypes and if their inclusion in the genomic selection protocol can enhance prediction accuracy. To address these questions, we developed a microbiome-enabled genomic selection method that incorporated host SNPs and amplicon sequence variants from plant rhizobiomes in a maize diversity panel under high and low nitrogen (N) field conditions. Our cross-validation results showed that the microbiome-enabled genomic selection model significantly outperformed the conventional genomic selection model for nearly all time-series traits related to plant growth and N responses, with an average relative improvement of 3.7%. The improvement was more pronounced under low N conditions (8.4–40.2% of relative improvement), consistent with the view that some beneficial microbes can enhance N nutrient uptake, particularly in low N fields. However, our study could not definitively rule out the possibility that the observed improvement is partially due to the amplicon sequence variants being influenced by microenvironments. Using a high-dimensional mediation analysis method, our study has also identified microbial mediators that establish a link between plant genotype and phenotype. Some of the detected mediator microbes were previously reported to promote plant growth. The enhanced prediction accuracy of the microbiome-enabled genomic selection models, demonstrated in a single environment, serves as a proof-of-concept for the potential application of microbiome-enabled plant breeding for sustainable agriculture.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Automatic Selection of High-Fidelity Models and Surrogates for Uncertainty Analysis

Surrogate models (SMs) with distinct features developed in the last decade provide an efficient substitute to the high-fidelity physical simulations. However, it is generally challenging to quantify the accuracy of surrogate models in a region of interest and examine reliability in the overall domain without additional system evaluations. This paper introduces a new scheme, automated adaptive model selection, to adaptively construct and quantify a set of surrogate models and automatically switch between the surrogates and a high-fidelity code. Cross-validation approaches, such as KFold, Leave One Out (LOO) and Leave P Out (LPO), are employed to assess the global accuracy of the surrogates. The switching strategy is based on local validation metrics, crowding distance, that are able to provide an assessment on the goodness of the prediction made by the surrogates. This decision metric decides whether the surrogates are good enough for a certain realization or whether the high-fidelity code needs to be executed. This new scheme has been implemented in Risk Analysis in Virtual ENvironment (RAVEN) software, which can be employed to accelerate the Probabilistic Risk Assessment (PRA) and Uncertainty Quantification (UQ) processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Improved Nested Sampling and Surrogate-Enabled Comparison With Other Marginal Likelihood Estimators

Estimating marginal likelihood is of central importance to Bayesian model selection and/or model averaging. The nested sampling method has been recently used together with the Metropolis-Hasting (M-H) sampling algorithm for estimating marginal likelihood. This study develops a new implementation of nested sampling by using the DiffeRential Evolution Adaptive Metropolis (DREAMzs) sampling algorithm. The two implementations of nested sampling are evaluated for four models of a synthetic groundwater flow modeling. The DREAMzs-based nested sampling outperforms the M-H-based nested sampling in terms of their accuracy, convergence, efficiency, and stability, which is attributed to the fact that DREAMzs is more robust than M-H for parameter sampling. The nested sampling method is also compared with four other methods (arithmetic mean, harmonic mean, stabilized harmonic mean, and thermodynamic integration) commonly used for estimating marginal likelihood and posterior probability of the four groundwater models. The comparative study requires hundreds of millions of model executions, which would not be possible without using surrogate models to replace the original models. Using the arithmetic mean estimator as the reference, the comparison reveals that thermodynamic integration outperforms nested sampling in terms of accuracy and stability, whereas nested sampling is more computationally efficient to reach to convergence. Here, the harmonic mean and stabilized harmonic mean methods give the worst marginal likelihood estimation. Accurate marginal likelihood estimation is important for accurate estimation of posterior model probability and better predictive performance of Bayesian model averaging.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Massive fields as systematics for single field inflation

During inflation, massive fields can contribute to the power spectrum of curvature perturbation via a dimension-5 operator. This contribution can be considered as a bias for the program of using n {sub s} and r to select inflation models. Even the dimension-5 operator is suppressed by Λ = M {sub p} , there is still a significant shift on the n {sub s} - r diagram if the massive fields have m ∼ H . On the other hand, if the heavy degree of freedom appears only at the same energy scale as the suppression scale of the dimension-5 operator, then significant shift on the n {sub s} - r diagram takes place at m =Λ ∼ 70 H , which is around the inflationary time-translation symmetry breaking scale. Hence, the systematics from massive fields pose a greater challenge for future high precision experiments for inflationary model selection. This result can be thought of as the impact of UV sensitivity to inflationary observables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Model Test Bed for Evaluating Wave Models and Best Practices for Resource Assessment and Characterization

A wave model test bed is established to benchmark, test and evaluate spectral wave models and modeling methodologies (i.e., best practices) for predicting the wave energy resource parameters recommended by the International Electrotechnical Commission, IEC TS 62600-101Ed. 1.0 ©2015. Among other benefits, the model test bed can be used to investigate the suitability of different models, specifically what source terms should be included in spectral wave models under different wave climate conditions and for different classes of resource assessment. The overarching goal is to use these investigations to provide industry guidance for model selection and modeling best practices depending on the wave site conditions and desired class of resource assessment. Modeling best practices are reviewed, and limitations and knowledge gaps in predicting wave energy resource parameters are identified.

13 HYDRO ENERGY↗
DOE OSTI2022

Methods for Computing Physically Realistic Estimates of Electric Water Heater Demand Response Resource Suitable for Bulk Power System Planning Models

Demand response is commonly called on to reduce load during system peak times or to respond to contingency events. In future power systems with higher shares of wind and solar generation (which we describe together as variable generation [VG]), demand response could have more opportunities to provide energy shifting or operating reserve services. This report evaluates the ability of residential electric water heaters, both electric resistance water heaters (ERWHs) and heat pump water heaters (HPWHs), to provide such services starting from detailed whole-building energy models that realistically represent New England single family home stock. We use a parsimonious surrogate model to represent operational flexibility in a form suitable for linear and mixed integer programming. This enables relatively fast determination of aggregate contingency reserve resource, price-taking energy shifting outcomes, and in some cases the determination of aggregate models at the megawatt (MW) scale that can be directly included in large-scale grid models. After selecting modeling methods and parameters through various computational experiments, we find interquartile ranges of contingency reserve resource in ISO-NE for about 603,400 ERWHs of 45 MW - 69 MW for Claim10 (50 minute responses provided with 10 minutes of advanced notification) and 65 MW - 102 MW for Claim30 (30 minute responses provided with 30 minutes of advanced notification), and for about 619,000 HPWHs of 48 MW - 88 MW for Claim10 and 52 MW - 90 MW for Claim30. The overall reserve resource is up to 32% of total load for ERWHs providing Claim10 service, 47% for ERWHs providing Claim30 service, 93% for HPWHs providing Claim10 service, and 97% for HPWHs providing Claim30 service. More work is required to determine if HPWHs are inherently more suitable than ERWHs for providing contingency reserve or if these results reflect idiosyncrasies of the single family home stock model used in this study. The value of this contingency resource in a Near-term VG model of ISO-NE is $\$ 0.40$ to $\$1.20$ per water heater-year, and significantly larger, $\$ 3.80$ to $\$ 5.30$ per water heater-year in a Mid-term VG model of ISONE. Aggregating surrogate models to the MW-scale for energy shifting service is more challenging than for contingency service and we only present such results for ERWHs, because we were unable to determine satisfactory ways to deal with HPWHs' time-varying and path dependent operational characteristics. Individual surrogate models suitable for evaluating the energy shifting resource from both ERWHs and HPWHs are created, however, and dispatched against day-ahead prices from the Near-Term VG and Mid-Term VG models of ISO-NE. The individual surrogate models are able to access and potentially shift all 640 GWh of HPWH load and 1,547 GWh of ERWH load we modeled in two different single family home stock models. In contrast, the most effective model of aggregate ERWH shifting resource we created only captured 34.7% of the total ERWH load. Energy shifting affected by price-taking dispatch against modeled day-ahead energy prices produces per water heater year profits of $\$19.44$ - $\$22.93$ for individual HPWHs, $\$39.11$ - $\$40.54$ for individual ERWHs, and up to $\$4.00$ - $\$4.24$ for aggregated ERWHs, with the variations mainly due to grid conditions (more or less VG). When the supply-side response to these changes is accounted for, the per water heater year production cost savings for ISO-NE are $\$7.50$ to $\$17.70$ for the most effective set of endogenously dispatched aggregate ERWHs, $\$15.60$ to $\$15.70$ for individual ERWHs dispatched against the DA prices, and $\$10.70$ to $\$11.20$ for individual HPWHs dispatched against DA prices. Those ranges primarily represent the difference between Near-Term VG and Mid-Term VG grid conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1985

Generation of Surface Coordinates by Elliptic Partial Differential Equations

The problem of generating spatial coordinates by numerical methods through carefully selected mathematical models is of current interest both in mechanics and physics. The problem of generation of a desired system of coordinates in a given surface was considered, which essentially is an effort directed to the problem of grid generation in a two-dimensional non-Euclidean space. The mathematical model selected for this purpose is based on the formulae of Gauss for a surface. The proposed equations can be used to generate a new coordinate system from the data of an already given coordinate system in a surface. If the coefficients of the first and second fundamental forms have been given, then the proposed equations can be used to generate a surface satisfying the given data (surface fitting). The proposed equations can also be used to generate surfaces in the space between two arbitrary given surfaces, thus providing 3D grids in an Euclidean space.

Warsi, Z. U. A.↗