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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Kinetic model for electric-field induced point defect redistribution near semiconductor surfaces

The spatial distribution of point defects near semiconductor surfaces affects the efficiency of devices. Near-surface band bending generates electric fields that influence the spatial redistribution of charged mobile defects that exchange infrequently with the lattice, as recently demonstrated for pile-up of isotopic oxygen near rutile TiO{sub 2} (110). The present work derives a mathematical model to describe such redistribution and establishes its temporal dependence on defect injection rate and band bending. The model shows that band bending of only a few meV induces significant redistribution, and that the direction of the electric field governs formation of either a valley or a pile-up.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Decayheatml

This code is designed to predict and analyze the decay heat generated in molten salt reactors (MSRs) using a hybrid approach that combines machine learning and segmented polynomial fitting. The accurate prediction of decay heat is essential for reactor safety and the optimization of spent fuel storage. The code operates through several key components: 1) Data Architecture: It incorporates a modular data architecture that handles various MSR-specific operational parameters such as power density, humidity content, and air ingress. These parameters are sampled using Sobol sequences to ensure comprehensive coverage of operational uncertainties. 2) Machine Learning Framework: The code employs a diverse set of machine learning models, including polynomial regression, decision trees, random forests, gradient boosting, support vector regression, k-nearest neighbors, multi-layer perceptrons, and symbolic regression. These models are trained to predict decay heat over a wide temporal range, from immediate shutdown up to 10,000 years. 3) Region-Optimized Training: The temporal domain is divided into multiple regions, each modeled separately to capture distinct decay heat characteristics across different time scales. This approach significantly improves the accuracy and interpretability of predictions. 4) Segmented Polynomial Interpretation (SPI): The SPI method translates machine learning predictions into piecewise polynomial equations. These equations are physically interpretable and can be directly integrated into existing engineering workflows and safety analyses. 5) Front-End Interfaces: The code includes both a Jupyter notebook interface for research development and a Streamlit web application for operational deployment. These interfaces allow users to interactively explore decay heat predictions, adjust operational parameters, and visualize results in real-time. 6) Applications: The framework supports various applications, including safety system validation and spent fuel container optimization. It enables real-time evaluation of worst-case decay heat scenarios, informing the design of passive safety systems and optimizing container designs for long-term storage. Overall, this code provides a robust, accurate, and user-friendly tool for predicting decay heat in MSRs, enhancing reactor safety, and optimizing spent fuel management.

Retamales, Mauricio Eduardo Tano [Idaho National L↗
NASA NTRS1988

Dynamics Explorer guest investigator

The research has focused on using the SAI auroral images as a high resolution auroral precipitation input to the USU global scale ionospheric model. From the global scale modeling viewpoint, these images offer unique spatial and temporal resolution since all prior studies have used empirical auroral models. These latter models are devoid of storm, substorm, or discrete oval features. The research focused on the problems in converting images to energy flux; using LAPU data to calibrate these energy fluxes; using the USU Time Dependent Ionospheric Model (TDIM) to look at the ionospheric consequences of this structure; and then using DE-2 in-situ observations to compare with the TDIM ionospheric parameters. In carrying out these studies, several additional investigations cropped up which were pursued to help meet the overall goals. The foremost difficulty in carrying out the TDIM modeling in conjunction with the high resolution DE auroral model was that of defining an appropriate ionospheric convection pattern. Under northward conditions this pattern is very complex. In order to study Theta aurora or in general northward IMF conditions, a new model was required. Hence, a study was completed to supply this new model to drive the TDIM as a function of the IMF. With the DE auroral model having adequate resolution to show structure on the 100's of km and all model electric fields being devoid of such structure, an investigation was pursued to find out the effects of structures in the electric field on the F-region.

Sojka, J. J.↗
NASA NTRS2005

A Comparison of Geographic Information Systems, Complex Networks, and Other Models for Analyzing Transportation Network Topologies

This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.

Alexandrov, Natalia↗
NASA NTRS2007

Optical and X-Ray Observations of GRB 060526: A Complex Afterglow Consistent with an Achromatic Jet Break

We obtained 98 R-band and 18 B, r', i' images of the optical afterglow of GRB 060526 (z = 3.21) with the MDM 1.3 m, 2.4 m, and the PROMPT telescopes at CTIO over the five nights following the burst trigger. Combining these data with other optical observations reported in GCN and the Swift XRT observations, we compare the optical and X-ray afterglow light curves of GRB 060526. Both the optical and X-ray afterglow light curves show rich features, such as flares and breaks. The densely sampled optical observations provide very good coverage at T > 10(exp 4) s. We observed a break at 2.4 x 10(exp 5) sin the optical afterglow light curve. Compared with the X-ray afterglow light curve, the break is consistent with an achromatic break supporting the beaming models of GRBs. However, the prebreak and postbreak temporal decay slopes are difficult to explain in simple afterglow models. We estimated a jet angle of theta(sub j) approx. 7deg and a prompt emission size of R(sub prompt) approx. 2 x 10(exp 14) cm. In addition, we detected several optical flares with amplitudes of (Delta)m approx. 0.2,0.6, and 0.2 mag. The X-ray afterglows detected by Swift have shown complicated decay patterns. Recently, many well-sampled optical afterglows also show decays with flares and multiple breaks. GRB 060526 provides an additional case of such a complex, well-observed optical afterglow. The accumulated well-sampled afterglows indicate that most of the optical afterglows are complex.

Dai, X.↗
DOE OSTI2026

Analytical Models of Frequency and Voltage in Large-Scale All-Inverter Power Systems

Low-order frequency response models for power systems have a decades-long history in optimization and control problems such as unit commitment, economic dispatch, and wide-area control. With a few exceptions, these models are built upon the Newtonian mechanics of synchronous generators, assuming that the frequency dynamics across a system are approximately homogeneous, and assume the dynamics of nodal voltages for most operating conditions are negligible, and thus are not directly computed at all buses. As a result, the use of system frequency models results in the systematic underestimation of frequency minimum nadir and maximum RoCoF, and provides no insight into the reactive power-voltage dynamics. This paper proposes a low-order model of both frequency and voltage response in grid-forming inverter-dominated power systems. The proposed model accounts for spatial-temporal variations in frequency and voltage behavior across a system and as a result, demonstrates the heterogeneity of frequency response in future renewable power systems. Electromagnetic transient (EMT) simulations are used to validate the utility, accuracy, and computational efficiency of these models, setting the basis for them to serve as fast, scalable alternatives to EMT simulation, especially when dealing with very large-scale systems, for both planning and operational studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1981

Propagation in a shearing plasma. III - Magnetic field effects and pulsar microstructure periods

Effects of a magnetic field on wave and pulse propagation in a shearing plasma are investigated. Equations for the wave fields are derived from Maxwell's equations and the Minkowski relations for an anisotropic medium, when the magnetic field is parallel to the shearing velocity. The independent propagation modes are elliptically polarized in the stationary frame and vary with the velocity of the medium. Numerical calculations are presented which show the propagation of waves and pulses through a plane-parallel shearing plasma in the case of weak and strong fields. It is found that in a weak field, propagation effects are very similar to those previously found in field-free models: the transmitted pulses acquire temporal modulations whose period is frequency dependent. In a strong field, the period of the temporal modulations is independent of frequency. Transmitted pulses can also undergo polarization changes as a result of phase differences between modes. These results are discussed in relation to their significance to propagation in a pulsar magnetosphere and, in particular, to a model for pulse microstructure.

Harding, A. K.↗
NASA NTRS2001

International Research Workshop on Integrating GIS and Environmental Modeling: Problems, Prospects, and Research Needs

The 4th International Conference on Integrating GIS and Environmental Modeling (GIS/EM4) was convened in Banff, Canada, September 2-8, 2000 at The Banff Centre for Conferences. The meeting's purpose, like it's predecessors was to reformulate, each three to four years, the collaborative research agenda for integrating spatio-temporal analysis with environmental simulation modeling.

Parks, Bradley↗
NASA NTRS2001

International Research Workshop on Integrating GIS and Environmental Modeling: Problems, Prospects, and Research Needs

The 4th International Conference on Integrating GIS and Environmental Modeling (GIS/EM4) was convened in Banff, Canada, September 2-8, 2000 at The Banff Centre for Conferences. The meeting's purpose, like it's predecessors was to reformulate, each three to four years, the collaborative research agenda for integrating spatio-temporal analysis with environmental simulation modeling.

Parks, Bradley↗
DOE OSTI2012

US Department of Energy National Lab Activities in Marine Hydrokinetics: Machine Performance Testing

Marine and hydrokinetic (MHK) technology performance testing in the laboratory and field supports the US Department of Energy s MHK program goals to advance the technology readiness levels of MHK machines, to ensure environmentally responsible designs, to identify key cost drivers, and to reduce the cost of energy of MHK technologies. Laboratory testing results from scaled model machine testing at the University of Minnesota s St. Anthony Falls Laboratory (SAFL) main channel flume are presented, including simultaneous machine power and inflow measurements for a 1:10 scale three-bladed axial flow turbine used to assess machine performance in turbulent flows, and detailed measurements of inflow and wake flow velocity and turbulence, including the assessment of the effects of large energetic organized vortex shedding on machine performance and wake turbulence downstream. Scaled laboratory testing provides accurate data sets for near- and far-field hydrodynamic models, and useful information on technology and environmental readiness levels before full-scale testing and demonstration in open water. This study validated turbine performance for a technology in order to advance its technology readiness level. Synchronized ADV measurements to calculate spatio-temporal characteristics of turbulence supported model development of the inflow turbulence model, Hydro-TurbSim, developed by the National Renewable Energy Laboratory (NREL) to evaluate unsteady loading on MHK machines. Wake flow measurements supported model development of the far-field model, SNL-EFDC, developed by Sandia National Laboratory (SNL) to optimize spacing for MHK machine arrays.

acoustic Doppler profiler↗
DOE OSTI2024

LSTM-Based Data Integration to Improve Snow Water Equivalent Prediction and Diagnose Error Sources

Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Recently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydrologic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western United States to integrate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to improve future predictions. SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow- and deep-snow sites. The median Nash–Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE values d max were reduced by 41% and 57%, respectively. DI effectively mitigated accumulated model and forcing errors that would otherwise be persistent. Moreover, by applying DI to different observations (30-day-lagged, 7-day-lagged), we revealed the spatial distribution of errors with different persistent lengths. For example, integrating 30-day-lagged SWE was ineffective for ephemeral snow sites in the southwestern United States, but significantly reduced monthly-scale biases for regions with stable seasonal snowpack such as high-elevation sites in California. These biases are likely attributable to large interannual variability in snowfall or site-specific snow redistribution patterns that can accumulate to impactful levels over time for nonephemeral sites. These results set up benchmark levels and provide guidance for future model improvement strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Physics-based control-oriented modeling of the current density profile evolution in NSTX-Upgrade

Active control of the toroidal current density profile is among those plasma control milestones that the National Spherical Torus eXperiment-Upgrade (NSTX-U) program must achieve to realize its next-step operational goals. Motivated by the coupled, nonlinear, multivariable, distributed-parameter plasma dynamics, the first step towards control design is the development of a physics-based, control-oriented model for the current profile evolution in response to non-inductive current drives and heating systems. The evolution of the toroidal current density profile is closely related to the evolution of the poloidal magnetic flux profile, whose dynamics is modeled by a nonlinear partial differential equation (PDE) referred to as the magnetic-flux diffusion equation (MDE). The proposed control-oriented model predicts the spatial-temporal evolution of the current density profile by combining the nonlinear MDE with physics-based correlations obtained at NSTX-U for the electron density, electron temperature, and non-inductive current drives (neutral beams). The resulting first-principles-driven, control-oriented model is tailored for NSTX-U based on predictions by the time-dependent transport code TRANSP. Furthermore, main objectives and possible challenges associated with the use of the developed model for the design of both feedforward and feedback controllers are also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Evaluating three evapotranspiration estimates from model of different complexity over China using the ILAMB benchmarking system

The land surface models range in complexity of terrestrial evapotranspiration, yet it is unknown how model complexity translates to accuracy of modeled evapotranspiration estimates. Here, we use the International Land Model Benchmarking system to assess ET estimates from three models of varying complexity driven by the same forcing datasets: an earth system model, a terrestrial biosphere model, and a stand-alone ET model. The performance assessment includes both temporal and spatial evaluation, and different plant functional types across China. Our results indicate that the most complex model, an earth system model, performed best against the benchmarking datasets and metrics. Terrestrial biosphere model performed best in simulating inter-annual variability of ET, while earth system model performed best in simulating the seasonal cycle. The more complex models (earth system model and terrestrial biosphere model) perform better in forest, shrub and crop ecosystems, while the simpler model (stand-alone ET model) perform better in grass ecosystems. Our study demonstrates the impact of model complexity on ET estimates and highlights directions for future ET model improvements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Interstellar hydrogen fluxes measured by IBEX-LO in 2009: Numerical modeling and comparison with the data

In this paper, we perform numerical modeling of the interstellar hydrogen fluxes measured by IBEX-Lo during orbit 23 (spring 2009) using a state-of-the-art kinetic model of the interstellar neutral hydrogen distribution in the heliosphere. This model takes into account the temporal and heliolatitudinal variations of the solar parameters as well as the non-Maxwellian kinetic properties of the hydrogen distribution due to charge exchange in the heliospheric interface. We found that there is a qualitative difference between the IBEX-Lo data and the modeling results obtained with the three-dimensional, time-dependent model. Namely, the model predicts a larger count rate in energy bin 2 (20–41 eV) than in energy bin 1 (11–21 eV), while the data shows the opposite case. We perform study of the model parameter effects on the IBEX-Lo fluxes and the ratio of fluxes in two energy channels. We show that the most important parameter, which has a major influence on the ratio of the fluxes in the two energy bins, is the solar radiation pressure. The parameter fitting procedure shows that the best agreement between the model result and the data occurs when the ratio of the solar radiation pressure to the solar gravitation, μ{sub 0}, is 1.26{sub −0.076}{sup +0.06}, and the total ionization rate of hydrogen at 1 AU is β{sub E,0}=3.7{sub −0.35}{sup +0.39}×10{sup −7} s{sup −1}. We have found that the value of μ{sub 0} is much larger than μ{sub 0} = 0.89, which is the value derived from the integrated solar Lyα flux data for the period of time studied. We discuss possible reasons for the differences.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods

Long short-term memory (LSTM) networks have demonstrated successful applications in accurately and efficiently predicting reservoir releases from hydrometeorological drivers including reservoir storage, inflow, precipitation, and temperature. However, due to its black-box nature and lack of process-based implementation, we are unsure whether LSTM makes good predictions for the right reasons. In this work, we use an explainable machine learning (ML) method, called SHapley Additive exPlanations (SHAP), to evaluate the variable importance and variable-wise temporal importance in the LSTM model prediction. In application to 30 reservoirs over the Upper Colorado River Basin, United States, we show that LSTM can accurately predict the reservoir releases with NSE ≥ 0.69 for all the considered reservoirs despite of their diverse storage sizes, functionality, elevations, etc. Additionally, SHAP indicates that storage and inflow are more influential than precipitation and temperature. Moreover, the storage and inflow show a relatively long-term influence on the release up to 7 days and this influence decreases as the lag time increases for most reservoirs. These findings from SHAP are consistent with our physical understanding. However, in a few reservoirs, SHAP gives some temporal importances that are difficult to interpret from a hydrological point of view, probably because of its ignorance of the variable interactions. SHAP is a useful tool for black-box ML model explanations, but the hydrological processes inferred from its results should be interpreted cautiously. More investigations of SHAP and its applications in hydrological modeling is needed and will be pursued in our future study.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Aspects of spatial and temporal aggregation in estimating regional carbon dioxide fluxes from temperate forest soils

We examine the influence of aggregation errors on developing estimates of regional soil-CO2 flux from temperate forests. We find daily soil-CO2 fluxes to be more sensitive to changes in soil temperatures (Q(sub 10) = 3.08) than air temperatures (Q(sub 10) = 1.99). The direct use of mean monthly air temperatures with a daily flux model underestimates regional fluxes by approximately 4%. Temporal aggregation error varies with spatial resolution. Overall, our calibrated modeling approach reduces spatial aggregation error by 9.3% and temporal aggregation error by 15.5%. After minimizing spatial and temporal aggregation errors, mature temperate forest soils are estimated to contribute 12.9 Pg C/yr to the atmosphere as carbon dioxide. Georeferenced model estimates agree well with annual soil-CO2 fluxes measured during chamber studies in mature temperate forest stands around the globe.

Kicklighter, David W.↗
DOE OSTI2022

Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the southeastern Atlantic

Biomass burning smoke is advected over the southeastern Atlantic Ocean between July and October of each year. This smoke plume overlies and mixes into a region of persistent low marine clouds. Model calculations of climate forcing by this plume vary significantly in both magnitude and sign. NASA EVS-2 (Earth Venture Suborbital-2) ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) had deployments for field campaigns off the west coast of Africa in 3 consecutive years (September 2016, August 2017, and October 2018) with the goal of better characterizing this plume as a function of the monthly evolution by measuring the parameters necessary to calculate the direct aerosol radiative effect. Here, this dataset and satellite retrievals of cloud properties are used to test the representation of the smoke plume and the underlying cloud layer in two regional models (WRF-CAM5 and CNRM-ALADIN) and two global models (GEOS and UM-UKCA). The focus is on the comparisons of those aerosol and cloud properties that are the primary determinants of the direct aerosol radiative effect and on the vertical distribution of the plume and its properties. The representativeness of the observations to monthly averages are tested for each field campaign, with the sampled mean aerosol light extinction generally found to be within 20 % of the monthly mean at plume altitudes. When compared to the observations, in all models, the simulated plume is too vertically diffuse and has smaller vertical gradients, and in two of the models (GEOS and UM-UKCA), the plume core is displaced lower than in the observations. Plume carbon monoxide, black carbon, and organic aerosol masses indicate underestimates in modeled plume concentrations, leading, in general, to underestimates in mid-visible aerosol extinction and optical depth. Biases in mid-visible single scatter albedo are both positive and negative across the models. Observed vertical gradients in single scatter albedo are not captured by the models, but the models do capture the coarse temporal evolution, correctly simulating higher values in October (2018) than in August (2017) and September (2016). Uncertainties in the measured absorption Ångstrom exponent were large but propagate into a negligible (<4 %) uncertainty in integrated solar absorption by the aerosol and, therefore, in the aerosol direct radiative effect. Model biases in cloud fraction, and, therefore, the scene albedo below the plume, vary significantly across the four models. The optical thickness of clouds is, on average, well simulated in the WRF-CAM5 and ALADIN models in the stratocumulus region and is underestimated in the GEOS model; UM-UKCA simulates cloud optical thickness that is significantly too high. Overall, the study demonstrates the utility of repeated, semi-random sampling across multiple years that can give insights into model biases and how these biases affect modeled climate forcing. The combined impact of these aerosol and cloud biases on the direct aerosol radiative effect (DARE) is estimated using a first-order approximation for a subset of five comparison grid boxes. A significant finding is that the observed grid box average aerosol and cloud properties yield a positive (warming) aerosol direct radiative effect for all five grid boxes, whereas DARE using the grid-box-averaged modeled properties ranges from much larger positive values to small, negative values. It is shown quantitatively how model biases can offset each other, so that model improvements that reduce biases in only one property (e.g., single scatter albedo but not cloud fraction) would lead to even greater biases in DARE. Across the models, biases in aerosol extinction and in cloud fraction and optical depth contribute the largest biases in DARE, with aerosol single scatter albedo also making a significant contribution.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Temporal and spatial variations observed in the ionospheric composition of Venus - Implication for empirical modelling

The distributions of several ion species in a data base obtained by in situ measurements of the thermal ion composition of the ionosphere of Venus by the Pioneer Venus Orbiter have been sorted to identify temporal and spatial variations and determine the feasibility of an analytical representation of the experimental results. The first results from the sorting of several prominent ions including O(+), O2(+), and H(+) and several minor ions including CO2(+), C(+), and H2(+) reveal significant diurnal variations which consist of strong day to night contrast in the ion concentrations, with differences of one to two orders of magnitude, depending upon ion mass and altitude. It is suggested that repeatable day to night gradients in the ion distribution are adaptable to parametric modeling.

Taylor, H. A., Jr.↗