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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Occupant Response Analysis of a Full-Scale Crash Test of a Fokker F28 Fellowship Aircraft

In this report, the capability of component fuselage testing and finite element models (FEMs) to predict occupant injury risk when applied to full-scale aircraft crash testing was evaluated. Component level and full-scale crash tests of a Fokker-F28 aircraft were performed in conjunction with representative FEM simulations. Lumbar spine injury risk, calculated through Anthropomorphic Test Device (ATD) outputs measured in the full-scale test, was compared with predictions made by component level testing and simulations. A quantitative assessment of FEM prediction accuracy was made using the International Organization for Standardization (ISO) Technical Report (TR) 16250 curve comparison methodology. Lumbar load injury risk measured in the full-scale test configuration was found to be significantly higher than in the component tests performed. The FEM provided a closer prediction of occupant injury risk than component testing but exhibited limitations in the multi-axis load environment. This work will help inform the application of component testing and FEM simulation when used to evaluate aircraft crashworthiness.

Jacob B Putnam↗
NASA NTRS2017

Characterizing DebriSat Fragments: So Many Fragments, So Much Data, and So Little Time

To improve prediction accuracy, the DebriSat project was conceived by NASA and DoD to update existing standard break-up models. Updating standard break-up models require detailed fragment characteristics such as physical size, material properties, bulk density, and ballistic coefficient. For the DebriSat project, a representative modern LEO spacecraft was developed and subjected to a laboratory hypervelocity impact test and all generated fragments with at least one dimension greater than 2 mm are collected, characterized and archived. Since the beginning of the characterization phase of the DebriSat project, over 130,000 fragments have been collected and approximately 250,000 fragments are expected to be collected in total, a three-fold increase over the 85,000 fragments predicted by the current break-up model. The challenge throughout the project has been to ensure the integrity and accuracy of the characteristics of each fragment. To this end, the post hypervelocity-impact test activities, which include fragment collection, extraction, and characterization, have been designed to minimize handling of the fragments. The procedures for fragment collection, extraction, and characterization were painstakingly designed and implemented to maintain the post-impact state of the fragments, thus ensuring the integrity and accuracy of the characterization data. Each process is designed to expedite the accumulation of data, however, the need for speed is restrained by the need to protect the fragments. Methods to expedite the process such as parallel processing have been explored and implemented while continuing to maintain the highest integrity and value of the data. To minimize fragment handling, automated systems have been developed and implemented. Errors due to human inputs are also minimized by the use of these automated systems. This paper discusses the processes and challenges involved in the collection, extraction, and characterization of the fragments as well as the time required to complete the processes. The objective is to provide the orbital debris community an understanding of the scale of the effort required to generate and archive high quality data and metadata for each debris fragment 2 mm or larger generated by the DebriSat project.

Shiotani, B.↗
DOE OSTI2022

Performance and power modeling and prediction using MuMMI and 10 machine learning methods

Energy-efficient scientific applications require insight into how high performance computing system features impact the applications' power and performance. This insight can result from the development of performance and power models. Here, in this article, we use the modeling and prediction tool MuMMI (Multiple Metrics Modeling Infrastructure) and 10 machine learning methods to model and predict performance and power consumption and compare their prediction error rates. We use an algorithm-based fault-tolerant linear algebra code and a multilevel checkpointing fault-tolerant heat distribution code to conduct our modeling and prediction study on the Cray XC40 Theta and IBM BG/Q Mira at Argonne National Laboratory and the Intel Haswell cluster Shepard at Sandia National Laboratories. Our experimental results show that the prediction error rates in performance and power using MuMMI are less than 10% for most cases. By utilizing the models for runtime, node power, CPU power, and memory power, we identify the most significant performance counters for potential application optimizations, and we predict theoretical outcomes of the optimizations. Based on two collected datasets, we analyze and compare the prediction accuracy in performance and power consumption using MuMMI and 10 machine learning methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multivariate degradation modeling using generalized cauchy process and application in life prediction of dye-sensitized solar cells

Recently, the Generalized Cauchy (GC) process has been applied to capture a Long Memory (LM) phenomenon in product degradation modeling and life prediction. Compared with the traditional fractional Brownian motion that captures the LM using a single Hurst parameter, the GC process has two free parameters (Hurst and fractal dimension parameters) that flexibly capture both global LM and local irregularity. However, all existing GC-based degradation models are for a single Degradation Characteristic (DC). In this article, motivated by a real degradation problem of dye-sensitized solar cells that jointly exhibits multiple DCs, global LM, local irregularity and DC-wise cross-correlation, we propose a novel GC-based Multivariate Degradation Model (GC-MDM) to simultaneously capture the aforementioned effects. A maximum likelihood estimation approach is developed to estimate parameters of the GC-MDM. Subsequently, product life prediction based on the GC-MDM is developed. The proposed GC-MDM is validated through a simulation study and a physical experiment of dye-sensitized solar cells. Furthermore, results show that the proposed GC-MDM fundamentally improves the life prediction accuracy in comparison with conventional degradation models which significantly misestimate the uncertainty of product life.

14 SOLAR ENERGY↗
DOE OSTI2026

Development of local hybrid density functionals to treat self-interaction error and many-electron effects

The goal of this project was to improve density functional theory (DFT) for systems where conventional semilocal approximations are limited by self-interaction error (SIE) and by near-degeneracy or strong many-electron effects. While DFT remains the only broadly practical first-principles framework for large-scale materials simulations, its predictive accuracy is often challenged in situations involving stretched bonds, charge transfer, transition-metal chemistry, magnetic couplings, band gaps, and correlated electronic states. This project addressed these limitations by developing physically grounded and numerically robust exchange–correlation functionals that retain the efficiency of modern DFT while extending its predictive scope.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework

Infectious diseases (IDs) have a significant detrimental impact on global health. Timely and accurate ID forecasting can result in more informed implementation of control measures and prevention policies. To meet the operational decision-making needs of real-world circumstances, we aimed to build a standardized, reliable, and trustworthy ID forecasting pipeline and visualization dashboard that is generalizable across a wide range of modeling techniques, IDs, and global locations. We forecasted 6 diverse, zoonotic diseases (brucellosis, campylobacteriosis, Middle East respiratory syndrome, Q fever, tick-borne encephalitis, and tularemia) across 4 continents and 8 countries. We included a wide range of statistical, machine learning, and deep learning models (n=9) and trained them on a multitude of features (average n=2326) within the One Health landscape, including demography, landscape, climate, and socioeconomic factors. The pipeline and dashboard were created in consideration of crucial operational metrics—prediction accuracy, computational efficiency, spatiotemporal generalizability, uncertainty quantification, and interpretability—which are essential to strategic data-driven decisions. While no single best model was suitable for all disease, region, and country combinations, our ensemble technique selects the best-performing model for each given scenario to achieve the closest prediction. For new or emerging diseases in a region, the ensemble model can predict how the disease may behave in the new region using a pretrained model from a similar region with a history of that disease. The data visualization dashboard provides a clean interface of important analytical metrics, such as ID temporal patterns, forecasts, prediction uncertainties, and model feature importance across all geographic locations and disease combinations. As the need for real-time, operational ID forecasting capabilities increases, this standardized and automated platform for data collection, analysis, and reporting is a major step forward in enabling evidence-based public health decisions and policies for the prevention and mitigation of future ID outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A Novel Modeling Framework for Computationally Efficient and Accurate Real-Time Ensemble Flood Forecasting With Uncertainty Quantification

A novel modeling framework that simultaneously improves accuracy, predictability, and computational efficiency is presented. It embraces the benefits of three modeling techniques integrated together for the first time: surrogate modeling, parameter inference, and data assimilation. The use of polynomial chaos expansion (PCE) surrogates significantly decreases computational time. Parameter inference allows for model faster convergence, reduced uncertainty, and superior accuracy of simulated results. Ensemble Kalman filters assimilate errors that occur during forecasting. To examine the applicability and effectiveness of the integrated framework, we developed 18 approaches according to how surrogate models are constructed, what type of parameter distributions are used as model inputs, and whether model parameters are updated during the data assimilation procedure. We conclude that (1) PCE must be built over various forcing and flow conditions, and in contrast to previous studies, it does not need to be rebuilt at each time step; (2) model parameter specification that relies on constrained, posterior information of parameters (so-called Selected specification) can significantly improve forecasting performance and reduce uncertainty bounds compared to Random specification using prior information of parameters; and (3) no substantial differences in results exist between single and dual ensemble Kalman filters, but the latter better simulates flood peaks. The use of PCE effectively compensates for the computational load added by the parameter inference and data assimilation (up to ~80 times faster). Therefore, the presented approach contributes to a shift in modeling paradigm arguing that complex, high-fidelity hydrologic and hydraulic models should be increasingly adopted for real-time and ensemble flood forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2022

A Spatiotemporal-Aware Weighting Scheme for Improving Climate Model Ensemble Predictions

Multimodel ensembling has been widely used to improve climate model predictions, and the improvement strongly depends on the ensembling scheme. In this work, we propose a Bayesian neural network (BNN) ensembling method, which combines climate models within a Bayesian model averaging framework, to improve the predictive capability of model ensembles. Our proposed BNN approach calculates spatiotemporally varying model weights and biases by leveraging individual models' simulation skill, calibrates the ensemble prediction against observations by considering observation data uncertainty, and quantifies epistemic uncertainty when extrapolating to new conditions. More importantly, the BNN method provides interpretability about which climate model contributes more to the ensemble prediction at which locations and times. Thus, beyond its predictive capability, the method also brings insights and understanding of the models to guide further model and data development. In this study, we design experiments using an ensemble of CMIP6 climate model simulations to illustrate the BNN ensembling method's capability with respect to prediction accuracy, interpretability, and uncertainty quantification (UQ). We demonstrate that BNN can correctly assign larger weights to the regions and seasons where the individual model fits the observation better. Moreover, its offered interpretability is consistent with our understanding of localized climate model performance. Additionally, BNN shows an increasing uncertainty when the prediction is farther away from the period with constrained data, which appropriately reflects our trustworthiness of the models in the changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Extend an innovative HPC-Compatible Multiple Temporal-spatial Resolution Concurrent Finite Element Modeling Approach to Guide Laser Powder Bed Fusion Additive

Laser power bed fusing (PBF) additive manufacturing is a key enabling technology to manufacture highly complex and integrated automotive structures. However, the geometric complexity of PBF-AM technique also leads to highly non-uniform heating and cooling rate in the manufactured part, which may cause flaw formation and produce excessive and nonuniform residual stresses, which increase quality uncertainties and manufacture issues, leading to increases in cost and energy consumption in the form of rejected parts. In this research project, we developed an innovative Multi-Spatial-Temporal-Resolution Finite Element (MUST-FE) method and completed the corresponding high performance computation (HPC) platform-based in-house code, which enables high accuracy prediction of temperature and residual stress fields for component-scale PBF-AM manufacture in efficient computation time. The MUST-FE model is calibrated and validated with a “2D pad” AlSi10Mg experiments by matching the melt pool shape and dimension, and with a “XY-cross” AlSi10Mg experiment by matching the thermal distortion and residual stress. The innovative multi-resolution and concurrent modeling approach adopted in this code ensures accuracy and computational efficiency, which will enable energy-efficient and high-yield, low-cost manufacturing of optimized, qualifiable automotive structures and contribute towards reaching technical targets outlined in AMO’s Program Plan to develop additive manufacturing systems that deliver consistently reliable parts with predictable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Molecular dipole moment learning via rotationally equivariant derivative kernels in molecular-orbital-based machine learning

This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree–Fock computations. A MOB pairwise decomposition of the correlation part of the dipole moment is applied, and these pair dipole moments could be further regressed as a universal function of MOs. The dipole MOB features consist of the energy MOB features and their responses to electric fields. An interpretable and rotationally equivariant derivative kernel for Gaussian process regression (GPR) is introduced to learn the dipole moment more efficiently. The proposed problem setup, feature design, and ML algorithm are shown to provide highly accurate models for both dipole moments and energies on water and 14 small molecules. To demonstrate the ability of MOB-ML to function as generalized density-matrix functionals for molecular dipole moments and energies of organic molecules, we further apply the proposed MOB-ML approach to train and test the molecules from the QM9 dataset. The application of local scalable GPR with Gaussian mixture model unsupervised clustering GPR scales up MOB-ML to a large-data regime while retaining the prediction accuracy. In addition, compared with the literature results, MOB-ML provides the best test mean absolute errors of 4.21 mD and 0.045 kcal/mol for dipole moment and energy models, respectively, when training on 110 000 QM9 molecules. The excellent transferability of the resulting QM9 models is also illustrated by the accurate predictions for four different series of peptides.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2018

Application of Lattice Boltzmann and Navier-Stokes Methods to NASA's Wall Mounted Hump

Lattice Boltzmann (LB) based Large Eddy Simulation (LES), Reynolds-averaged Navier-Stokes (RANS) as well as hybrid RANS/LES methods within the Launch Ascent and Vehicle Aerodynamics (LAVA) solver framework are applied to NASA's wall-mounted hump. Computational results are compared with experiments performed by Greenblatt et al. A detailed comparison between the accuracy and resolution requirements of the two approaches for turbulence resolving simulations, as well as the suitability of different grid paradigms (body-fitted curvilinear and block structured Cartesian) are presented. This test case is part of NASA's Revolutionary Computational Aerosciences (RCA) sub-project which addresses the technical challenge of predicting flow separation and reattachment accurately. Improvements in predictive accuracy by as much as 90% are demonstrated using LB as well as hybrid RANS/LES approaches compared to state-of-the-art steady state RANS simulations.

Lattice Boltzmann↗
DOE OSTI2024

Evaluation and development of flow condensation correlations using the data from low GWP refrigerants in an axial micro-fin aluminum tube

To mitigate global warming, the world is transitioning to refrigerants with low global warming potential (GWP). Supporting this shift requires a model that can accurately predict the heat transfer and pressure drop of new refrigerants, crucial for designing efficient heat exchangers. Existing models, however, are largely based on currently deployed refrigerants and primarily developed for unexpanded micro-fin tubes with spiral angles of 6° to 30°. Their applicability to new refrigerants, especially in expanded micro-fin tubes, is uncertain. This study assesses the performance of four well-known condensation models for six emerging refrigerants—R-32, R-454B, R-454C, R-455A, R-1234yf, and R-1234ze(E)—against experimental data. Initially, the Han and Lee (2005) model shows the best prediction accuracy with a mean absolute deviation (MAD) of 22.1 %. To enhance the accuracy of heat transfer models for new refrigerants and geometries with large temperature glides, two approaches are proposed. Here, the first approach applies a simple correction factor, reducing the MAD of the Cavallini et al. (2009) model from 68.2 % to 15.4 %. The second approach uses the variable metric method for minimization, fitting new constants to the data. This optimization results in the Kedzierski and Goncalves (1997) model achieving the highest accuracy, with a MAD of 13.1 %. For pressure drop models, the Cavallini et al. (1997) model is the most accurate with a MAD of 6.4 %, followed by the Haraguchi et al. (1993) model with a MAD of 9.4 %. Due to its simplicity, the Haraguchi et al. (1993) model is a practical option for predicting frictional pressure drop.

Axial micro-fin tubes↗
NASA NTRS1998

Experiments to Determine Whether Recursive Partitioning (CART) or an Artificial Neural Network Overcomes Theoretical Limitations of Cox Proportional Hazards Regression

New computationally intensive tools for medical survival analyses include recursive partitioning (also called CART) and artificial neural networks. A challenge that remains is to better understand the behavior of these techniques in effort to know when they will be effective tools. Theoretically they may overcome limitations of the traditional multivariable survival technique, the Cox proportional hazards regression model. Experiments were designed to test whether the new tools would, in practice, overcome these limitations. Two datasets in which theory suggests CART and the neural network should outperform the Cox model were selected. The first was a published leukemia dataset manipulated to have a strong interaction that CART should detect. The second was a published cirrhosis dataset with pronounced nonlinear effects that a neural network should fit. Repeated sampling of 50 training and testing subsets was applied to each technique. The concordance index C was calculated as a measure of predictive accuracy by each technique on the testing dataset. In the interaction dataset, CART outperformed Cox (P less than 0.05) with a C improvement of 0.1 (95% Cl, 0.08 to 0.12). In the nonlinear dataset, the neural network outperformed the Cox model (P less than 0.05), but by a very slight amount (0.015). As predicted by theory, CART and the neural network were able to overcome limitations of the Cox model. Experiments like these are important to increase our understanding of when one of these new techniques will outperform the standard Cox model. Further research is necessary to predict which technique will do best a priori and to assess the magnitude of superiority.

Kattan, Michael W.↗
DOE OSTI2026

Non -degenerate marginal-likelihood calibration with application to quantum characterization

Here, we propose a marginal likelihood strategy within the Kennedy-O’Hagan (KOH) Bayesian framework, where a Gaussian process (GP) models the discrepancy between a physical system and its simulator. Our approach introduces a novel marginalized likelihood by integrating out the degenerate eigenspace of the covariance matrix, rather than approximating the original likelihood. Unlike approximation methods that compromise accuracy for computational efficiency, our method defines an exact likelihood—distinct from the original but preserving all relevant information. This formulation achieves computational efficiency and stability, even for large datasets where the covariance matrix nears degeneracy. Applied to the characterization of a superconducting quantum device at Lawrence Livermore National Laboratory, the approach enhances the predictive accuracy of the Lindblad master equations for modeling Ramsey measurement data by effectively quantifying uncertainties consistent with the quantum data.

general physics↗
DOE OSTI2024

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of Diff-Mix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

25 - ENERGY STORAGE↗
DOE OSTI2025

Artificial intelligence time series forecasting for feed-forward laser stabilization

Laser plasma accelerators, typically operating at 1–10 Hz repetition rates, have the ability to produce high-quality electron beams in compact, all-optical-driven configurations, with the electron beams uniquely suited for a wide variety of accelerator-based applications. However, fluctuations and drifts in the laser delivery to the meter-scaled and below plasma target (the electron beam source) will translate into electron beam source variations that can limit their utility for demanding applications like light sources or linear colliders. Commercially available active feedback laser stabilization systems are intrinsically bandwidth limited due to their integration with multi-inch corrective mirror mounts which minimizes their effectiveness. In this manuscript, we present a Neural Network time series forecaster that can predict laser position fluctuations of the laser delivery to the final target well ahead of a future laser shot. The Root-Mean-Square-Error (RMSE) of the prediction accuracy was < 2 μ m for a 1 / e 2 beam radius of 34 μ m . Our feed-forward approach serves as a first-step in circumventing the bandwidth limitations imposed by the currently available stabilization systems since it allows for mirrors to be moved into position ahead of time to offset the predicted future position drift. This will help advance laser plasma accelerator research by providing greater robustness and stability needed for its applications.

Berger, Curtis↗
NASA NTRS1981

The Theory and Practice of Estimating the Accuracy of Dynamic Flight-Determined Coefficients

Means of assessing the accuracy of maximum likelihood parameter estimates obtained from dynamic flight data are discussed. The most commonly used analytical predictors of accuracy are derived and compared from both statistical and simplified geometrics standpoints. The accuracy predictions are evaluated with real and simulated data, with an emphasis on practical considerations, such as modeling error. Improved computations of the Cramer-Rao bound to correct large discrepancies due to colored noise and modeling error are presented. The corrected Cramer-Rao bound is shown to be the best available analytical predictor of accuracy, and several practical examples of the use of the Cramer-Rao bound are given. Engineering judgement, aided by such analytical tools, is the final arbiter of accuracy estimation.

Maine, R. E.↗