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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

MLMOD: Machine Learning Methods for Data-Driven Modeling in LAMMPS

MLMOD is a software package for incorporating machine learning approaches and models into simulations of microscale mechanics and molecular dynamics in LAMMPS. Recent machine learning approaches provide promising data-driven approaches for learning representations for system behaviors from experimental data and high fidelity simulations. The package facilitates learning and using data-driven models for (i) dynamics of the system at larger spatial-temporal scales (ii) interactions between system components, (iii) features yielding coarser degrees of freedom, and (iv) features for new quantities of interest characterizing system behaviors. MLMOD provides hooks in LAMMPS for (i) modeling dynamics and time-step integration, (ii) modeling interactions, and (iii) computing quantities of interest characterizing system states. The package allows for use of machine learning methods with general model classes including Neural Networks, Gaussian Process Regression, Kernel Models, and other approaches. Here we discuss our prototype C++/Python package, aims, and example usage. For related papers, examples, updates, and additional information see https://github.com/atzberg/mlmod and http://atzberger.org/.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Qualitative Strategy for Fusion of Physics into Empirical Models for Process Anomaly Detection

To facilitate the automated online monitoring of power plants, a systematic and qualitative strategy for anomaly detection is presented. This strategy is essential to provide credible reasoning on why and when an empirical versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. Empirical methods are usually based on pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The presented strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability, and suitability for training and testing, cause-effect, entropy inference, and model fitting. The strategy is demonstrated through a pilot use case for the application of anomaly detection to capture a valve packing leak at the high-pressure coolant injection system of a nuclear power plant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2002

A Comparison of the Age-Spectra from Data Assimilation Models

We use kinematic and diabatic back trajectory calculations, driven by winds from a general circulation model (GCM) and two different data assimilation systems (DAS), to compute the age spectrum at three latitudes in the lower stratosphere. The age-spectra are compared to chemical transport model (CTM) calculations, and the mean ages from all of these studies are compared to observations. The age spectra computed using the GCM winds show a reasonably well-isolated tropics in good agreement with observations; however, the age spectra determined from the DAS differ from the GCM spectra. For the diabatic trajectory calculations, the age spectrum is too broad as a result of too much exchange between the tropics and mid-latitudes. The age spectrum determined using the kinematic trajectory calculation is less broad and lacks an age offset; both of these features are due to excessive vertical dispersion of parcels. The tropical and mid-latitude mean age difference between the diabatically and kinematically determined age-spectra is about one year, the former being older. The CTM calculation of the age spectrum using the DAS winds shows the same dispersive characteristics of the kinematic trajectory calculation. These results suggest that the current DAS products will not give realistic trace gas distributions for long integrations; they also help explain why the mean ages determined in a number of previous DAS driven CTM's are too young compared with observations. Finally, we note trajectory-generated age spectra show significant age anomalies correlated with the seasonal cycles, and these anomalies can be linked to year-to-year variations in the tropical heating rate. These anomalies are suppressed in the CTM spectra suggesting that the CTM transport is too diffusive.

Schoeberl, Mark R.↗
NASA NTRS2010

Eight Year Climatologies from Observational (AIRS) and Model (MERRA) Data

We examine climatologies derived from eight years of temperature, water vapor, cloud, and trace gas observations made by the Atmospheric Infrared Sounder (AIRS) instrument flying on the Aqua satellite and compare them to similar climatologies constructed with data from a global assimilation model, the Modern Era Retrospective-Analysis for Research and Applications (MERRA). We use the AIRS climatologies to examine anomalies and trends in the AIRS data record. Since sampling can be an issue for infrared satellites in low earth orbit, we also use the MERRA data to examine the AIRS sampling biases. By sampling the MERRA data at the AIRS space-time locations both with and without the AIRS quality control we estimate the sampling bias of the AIRS climatology and the atmospheric conditions where AIRS has a lower sampling rate. While the AIRS temperature and water vapor sampling biases are small at low latitudes, they can be more than a few degrees in temperature or 10 percent in water vapor at higher latitudes. The largest sampling biases are over desert. The AIRS and MERRA data are available from the Goddard Earth Sciences Data and Information Services Center (GES DISC). The AIRS climatologies we used are available for analysis with the GIOVANNI data exploration tool. (see, http://disc.gsfc.nasa.gov).

Hearty, Thomas↗
NASA NTRS1997

Analysis of Forest Foliage Using a Multivariate Mixture Model

Data with wet chemical measurements and near infrared spectra of ground leaf samples were analyzed to test a multivariate regression technique for estimating component spectra which is based on a linear mixture model for absorbance. The resulting unmixed spectra for carbohydrates, lignin, and protein resemble the spectra of extracted plant starches, cellulose, lignin, and protein. The unmixed protein spectrum has prominent absorption spectra at wavelengths which have been associated with nitrogen bonds.

Hlavka, C. A.↗
DOE OSTI2020

Data Integration Aggregated Model For Nuclear Deployment

The data of a nuclear utility or plant are stored in various isolated forms in different systems. These data have different structures and tools and are, therefore, used independently. The integration of data sources is often needed and is performed manually and on an as-needed basis. The integration of the data would enable creating a data warehouse for a single nuclear power plant (NPP) or multiple plants. A data model contains objects that make up a given domain along with their attributes and relationships and could be used as a standard for exchange between applications for simple integration and data aggregation. After a survey of existing models, it was determined that a new model was required to meet the scope of this problem. DIAMOND has been developed in the form of an ontology to meet these needs. An ontology is simply a data model that also acts as a taxonomy for the domain, organizing the objects in a hierarchy with the most general objects at the top.

Browning, JerenM↗
DOE OSTI2013

Better models by discarding data?

Making the most of hard-won data in protein crystallography: to keep or not to keep, that is the question. In macromolecular X-ray crystallography, typical data sets have substantial multiplicity. This can be used to calculate the consistency of repeated measurements and thereby assess data quality. Recently, the properties of a correlation coefficient, CC{sub 1/2}, that can be used for this purpose were characterized and it was shown that CC{sub 1/2} has superior properties compared with ‘merging’ R values. A derived quantity, CC*, links data and model quality. Using experimental data sets, the behaviour of CC{sub 1/2} and the more conventional indicators were compared in two situations of practical importance: merging data sets from different crystals and selectively rejecting weak observations or (merged) unique reflections from a data set. In these situations controlled ‘paired-refinement’ tests show that even though discarding the weaker data leads to improvements in the merging R values, the refined models based on these data are of lower quality. These results show the folly of such data-filtering practices aimed at improving the merging R values. Interestingly, in all of these tests CC{sub 1/2} is the one data-quality indicator for which the behaviour accurately reflects which of the alternative data-handling strategies results in the best-quality refined model. Its properties in the presence of systematic error are documented and discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Tularosa Basin Play Fairway Analysis Data and Models

This submission includes raster datasets for each layer of evidence used for weights of evidence analysis as well as the deterministic play fairway analysis (PFA). Data representative of heat, permeability and groundwater comprises some of the raster datasets. Additionally, the final deterministic PFA model is provided along with a certainty model. All of these datasets are best used with an ArcGIS software package, specifically Spatial Data Modeler.

15 GEOTHERMAL ENERGY↗
NASA NTRS2017

Analyzing System on A Chip Single Event Upset Responses using Single Event Upset Data, Classical Reliability Models, and Space Environment Data

We are investigating the application of classical reliability performance metrics combined with standard single event upset (SEU) analysis data. We expect to relate SEU behavior to system performance requirements. Our proposed methodology will provide better prediction of SEU responses in harsh radiation environments with confidence metrics. single event upset (SEU), single event effect (SEE), field programmable gate array devises (FPGAs)

single event effect (SEE)↗
NASA NTRS1974

Analysis of Skylab/Apollo Telescope Mount S-056 observations based on a force-free magnetic field model

Data obtained from the S-056 X-ray experiment on Skylab/ATM have been analyzed based on the assumption that the magnetic fields in the chromosphere and lower corona are force-free. Underlying the analysis is the hypothesis that the observed X-ray filaments coincide with magnetic field lines. The photographic recording of the filaments can then be compared with the projection along the line of sight of the computed magnetic field lines of the model. Ground-based observations of the longitudinal magnetic field component complement the X-ray data and are used in the theoretical interpretation.

Meyer, R. X.↗
DOE OSTI2012

Design of Accelerator Online Simulator Server Using Structured Data

Model based control plays an important role for a modern accelerator during beam commissioning, beam study, and even daily operation. With a realistic model, beam behaviour can be predicted and therefore effectively controlled. The approach used by most current high level application environments is to use a built-in simulation engine and feed a realistic model into that simulation engine. Instead of this traditional monolithic structure, a new approach using a client-server architecture is under development. An on-line simulator server is accessed via network accessible structured data. With this approach, a user can easily access multiple simulation codes. This paper describes the design, implementation, and current status of PVData, which defines the structured data, and PVAccess, which provides network access to the structured data.

43 PARTICLE ACCELERATORS↗
NASA NTRS2010

Analysis of Sting Balance Calibration Data Using Optimized Regression Models

Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.

Ulbrich, N.↗
DOE OSTI2017

Data from: "Declines in low-elevation subalpine tree populations outpace growth in high-elevation populations with warming"

This data package contains model data that were used to support conclusions drawn in “Declines in low-elevation subalpine populations outpace growth in high-elevation populations with warming”, by Conlisk et al. 2017. Experimental data collected at field sites within the Alpine Treeline Warming Experiment (ATWE), and data from long-term observational plots were collected on Niwot Ridge, Colorado, USA and used to formulate models contained within the folder “Model_archive” in the zipped folder “Conlisk_etal_JEcol2017_model_archive12022020.zip”. The contents of this compressed folder are described in the data user's guide attached to this archive.There are two folders within the zipped folder - “EngelmannSpruce” and “LimberPine” - for each of the two species in the paper. Models are stored as text files and .sch files can also be opened as text files. However, please note that all these files are specific to the RAMAS Metapop population modeling software, and you will need the program in order to be able to run these models. There are two separate documents, both named “Conlisk_JofEcology_SI_01262017” within “Model_archive”. One is a Microsoft Word file, and the other is a PDF. The former can be opened with Microsoft Word, and the latter can be opened by Adobe Acrobat Reader, or any software compatible with a PDF.------------------1. Species distribution shifts in response to climate change require that recruitment increase beyond current range boundaries. For trees with long lifespans, the importance of climate-sensitive seedling establishment to the pace of range shifts has not been demonstrated quantitatively. 2. Using spatially explicit, stochastic population models combined with data from long-term forest surveys, we explored whether the climate-sensitivity of recruitment observed in climate manipulation experiments was sufficient to alter populations and elevation ranges of two widely distributed, high-elevation North American conifers. 3. Empirically observed, warming-driven declines in recruitment led to rapid modeled population declines at the low-elevation, “warm edge” of subalpine forest and slow emergence of populations beyond the high-elevation, “cool edge”. Because population declines in the forest occurred much faster than population emergence in the alpine, we observed range contraction for both species. For Engelmann spruce, this contraction was permanent over the modeled time horizon, even in the presence of increased moisture. For limber pine, lower sensitivity to warming may facilitate persistence at low elevations – especially in the presence of increased moisture – and rapid establishment above treeline, and, ultimately, expansion into the alpine. 4. Synthesis. Assuming 21st century warming and no additional moisture, population dynamics in high-elevation forests led to transient range contractions for limber pine and potentially permanent range contractions for Engelmann spruce. Thus, limitations to seedling recruitment with warming can constrain the pace of subalpine tree range shifts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

An Integrated Modeling and Data Management Strategy for Cellulosic Biomass Production Decisions

Emerging cellulosic bioenergy markets can provide land managers with additional options for crop production decisions. Integrating dedicated bioenergy crops such as perennial grasses and short rotation woody species within the agricultural landscape can have positive impacts on several environmental processes including increased soil organic matter in degraded soils, reduced sediment loading in watersheds, lower green house gas (GHG) fluxes, and reduced nutrient loading in watersheds. Implementing this type of diverse bioenergy production system in a way that maximizes potential environmental benefits requires a dynamic integrated modeling and data management strategy. This paper presents a strategy for designing diverse bioenergy cropping systems within the existing row crop production landscape in the midwestern United States. The integrated model developed quantifies a wide range environmental processes including soil erosion from wind and water, soil organic matter changes, and soil GHG fluxes within a geospatial data management framework. This framework assembles and formats information from multiple spatial and temporal scales. The data assembled includes yield and productivity data from harvesting equipment at the 1m scale, surface topography data from LiDAR mapping at the less than 1m scale, soil data from US soil survey databases at the 10m to 100m scale, and climate data at the county scale. These models and data tools are assembled into an integrated computational environment that is used to determine sustainable removal rates for agricultural residues for bioenergy production at the sub-field scale under a wide range of land management practices. Using this integrated model, innovative management practices including cover cropping are then introduced and evaluated for their impact on bioenergy production and important environmental processes. The impacts of introducing dedicated energy crops onto high-risk landscape positions currently being manage in row crop production are also investigated.

09 BIOMASS FUELS↗
DOE OSTI2014

Generation IV benchmarking of TRISO fuel performance models under accident conditions: Modeling input data

This document presents the benchmark plan for the calculation of particle fuel performance on safety testing experiments that are representative of operational accidental transients. The benchmark is dedicated to the modeling of fission product release under accident conditions by fuel performance codes from around the world, and the subsequent comparison to post-irradiation experiment (PIE) data from the modeled heating tests. The accident condition benchmark is divided into three parts: the modeling of a simplified benchmark problem to assess potential numerical calculation issues at low fission product release; the modeling of the AGR-1 and HFR-EU1bis safety testing experiments; and, the comparison of the AGR-1 and HFR-EU1bis modeling results with PIE data. The simplified benchmark case, thereafter named NCC (Numerical Calculation Case), is derived from ''Case 5'' of the International Atomic Energy Agency (IAEA) Coordinated Research Program (CRP) on coated particle fuel technology [IAEA 2012]. It is included so participants can evaluate their codes at low fission product release. ''Case 5'' of the IAEA CRP-6 showed large code-to-code discrepancies in the release of fission products, which were attributed to ''effects of the numerical calculation method rather than the physical model''[IAEA 2012]. The NCC is therefore intended to check if these numerical effects subsist. The first two steps imply the involvement of the benchmark participants with a modeling effort following the guidelines and recommendations provided by this document. The third step involves the collection of the modeling results by Idaho National Laboratory (INL) and the comparison of these results with the available PIE data. The objective of this document is to provide all necessary input data to model the benchmark cases, and to give some methodology guidelines and recommendations in order to make all results suitable for comparison with each other. The participants should read this document thoroughly to make sure all the data needed for their calculations is provided in the document. Missing data will be added to a revision of the document if necessary.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

GEN-IV Benchmarking of Triso Fuel Performance Models under accident conditions modeling input data

This document presents the benchmark plan for the calculation of particle fuel performance on safety testing experiments that are representative of operational accidental transients. The benchmark is dedicated to the modeling of fission product release under accident conditions by fuel performance codes from around the world, and the subsequent comparison to post-irradiation experiment (PIE) data from the modeled heating tests. The accident condition benchmark is divided into three parts: • The modeling of a simplified benchmark problem to assess potential numerical calculation issues at low fission product release. • The modeling of the AGR-1 and HFR-EU1bis safety testing experiments. • The comparison of the AGR-1 and HFR-EU1bis modeling results with PIE data. The simplified benchmark case, thereafter named NCC (Numerical Calculation Case), is derived from “Case 5” of the International Atomic Energy Agency (IAEA) Coordinated Research Program (CRP) on coated particle fuel technology [IAEA 2012]. It is included so participants can evaluate their codes at low fission product release. “Case 5” of the IAEA CRP-6 showed large code-to-code discrepancies in the release of fission products, which were attributed to “effects of the numerical calculation method rather than the physical model” [IAEA 2012]. The NCC is therefore intended to check if these numerical effects subsist. The first two steps imply the involvement of the benchmark participants with a modeling effort following the guidelines and recommendations provided by this document. The third step involves the collection of the modeling results by Idaho National Laboratory (INL) and the comparison of these results with the available PIE data. The objective of this document is to provide all necessary input data to model the benchmark cases, and to give some methodology guidelines and recommendations in order to make all results suitable for comparison with each other. The participants should read this document thoroughly to make sure all the data needed for their calculations is provided in the document. Missing data will be added to a revision of the document if necessary. 09/2016: Tables 6 and 8 updated. AGR-2 input data added

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Machine Learning-Enhanced Multiphase CFD for Carbon Capture Modeling Run Data

Repository for the data generated as part of the 2023-2024 ALCC project "Machine Learning-Enhanced Multiphase CFD for Carbon Capture Modeling." The data was generated with MFIX-Exa's CFD-DEM model. The problem of interest is gravity driven, particle-laden, gas-solid flow in a triply-periodic domain of length 2048 particle diameters with an aspect ratio of 4. The mean particle concentration ranges from 1% to 40% and the Archimedes number ranges from 18 to 90. The particle-to-fluid density ratio, particle-particle restitution and friction coefficients and domain aspect ratio are held constant at values of 1000, 0.9, 0.25 and 4, respectively. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award ALCC-ERCAP0025948.

AMReX↗