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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Soil Modeling Collaboration for Specific Soil Models in Support of Corn Stover Harvest

The objective of this milestone is to develop an integrated decision support platform for determining the impacts of agricultural and forestry management practices on the environmental and economic performance of a landscape. Vast datasets and many dynamic simulation models are available to evaluate environmental performance parameters, including soil erosion from wind and water, soil carbon dynamics, nitrogen cycles, greenhouse gas fluxes, nutrient runoff, and sediment runoff. The challenges are that these tools (1) are typically designed to consider a limited subset of critical environmental parameters, (2) often operate at different spatial and temporal scales, (3) are built using inconsistent computational approaches, and (4) are often not applicable across all regions and agronomic management systems. The Landscape Environmental Assessment Framework (LEAF) overcomes these challenges and integrates these varied tools into an effective, single computational platform. For the first time, because of LEAF, disparate models and datasets can seamlessly and comprehensively work together to provide decision makers with reliable, site-specific environmental performance assessments at the subfield scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

SST-TG-P1F4R3200: Decaying Stably-Stratified Turbulence (SST), Initialized Using Taylor-Green Vortices (TG) at Prandtl Number Pr=1, Froude Number Fr=4, Reynolds Number Re=3200

This dataset comprises direct numerical simulations (DNS) of decaying stably-stratified turbulence influenced by a linear background density gradient, initialized using an array of Taylor-Green vortices, as described in [Riley & de Bruyn Kops (2003)](https://doi.org/10.1063/1.1578077). The initial Prandtl, Froude, and Reynolds numbers are (Pr, Fr, Re) = (1, 4, 3200). A total of 15,000 snapshots are recorded at uniform time intervals, each with a spatial resolution of 512x512x256 grid points. Four flow variables are associated with each snapshot: the three velocity components (u,v,w) and the perturbed density field (rho) away from the background gradient. All fields are stored in binary format (32-bit little-endian), each with a size of 255 MB, yielding a total dataset size of 15.3 TB. Further details are referenced in the attached README file, and a current list of publications and associated analysis tools are provided at https://stratified-turbulence.github.io/web/.

42 ENGINEERING↗
DOE OSTI2025

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer Based Hydrogen Production Facility

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at NREL's Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗
DOE OSTI2026

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer-Based Hydrogen Production Facility: Preprint

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at the National Renewable Energy Laboratory (NREL)'s Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗
DOE OSTI2025

GRUMDN: A Multi-Task Model for Predicting Human Patterns-of-Life from Stay Transition Data

Understanding human patterns-of-life (PoL) is essential towards ensuring safe and secure indoor facility environment as well as outdoor urban environment. Prediction of human movement in between places of interest is vital in understanding human PoL. Movement between spaces maybe represented and detected in one of the two forms: 1) trajectories: locations measured at regular time intervals by mobile sensors, bluetooth or GPS sensors; or 2) stay transitions: semantic PoI (points of interest) and stay duration data measurable by eventbased sensors that collect data when a check-in or check-out event is detected. Stay transition data provides a more compressed data format compared to trajectories data, especially in situations with longer stay durations, while preserving the information necessary for PoL analysis. Now as introduced briefly in the paper, our deployed end application (Digital Twin of a facility with non-player characters, besides the interactive user in virtual reality) needed a well-performing and validated AI/ML model for simulating high quality stay transitions behavior. In this study we thus primarily present our findings with developing and validating that model, which is a multi-task neural network for stay transition prediction. The neural network consists of two heads, for corresponding two tasks of stay category prediction and stay duration prediction. We evaluated gated recurrent units and multi-layer perceptrons of varying network sizes for stay category prediction; while mixture density networks, noisy generator-only networks, and generative adversarial networks of varying network sizes for stay duration prediction. We have then evaluated four multi-task models, constructed by combining these specialized models, on their ability to predict stay transition data. We tested our models on datasets from two different cases: 1) a simulation-generated dataset of indoor movement within the HFIR (high flux isotope reactor) nuclear reactor facility at Oak Ridge National Laboratory (ORNL); and 2) the GeoLife human mobility dataset of outdoor urban movement available in literature. Our results indicate that GRUMDN, which combines gated recurrent units (GRU) for stay category prediction task, and mixture density networks (MDN) for stay duration prediction task, did overall outperform other multitask models and the current state-of-the-art.

Gunaratne, Chathika [ORNL] (ORCID:0000000225088745↗
DOE OSTI2022

Latent Space Simulation for Carbon Capture Design Optimization

The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational challenge in CCS design. While the IA associated with a particular CCS design can be estimated via a computational fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with numerous CCS designs is prohibitively costly. However, previous works such as Deep Fluids (Kim et al., 2019) show that large simulation speed-ups and low error are achievable by replacing CFD simulators with neural-network (NN) surrogates that mimic the CFD simulation process. This raises the possibility for a fast, accurate replacement for a CFD simulator, and thus computationally feasible IA-based CCS-design optimization. As such, here, we explore whether an existing NN-surrogate approach (and variants we develop) can successfully be applied to our complex carbon-capture CFD simulations, with the ultimate goal of obtaining a fast and accurate simulator for our CCS-design application. Our experiments build on the Deep Fluids approach and find that resulting surrogates can produce large speed ups (4000x) while maintaining IA relative errors as low as 4% on unseen CCS configurations (interpolating between configurations seen during training). Thus, despite less faithfulness to the underlying physics of the problem, NN surrogates may be a promising tool for our CCS design optimization problem. Notably, though, the Deep Fluids approach has limitations for our application (such as model non-transferability to CCS-packing changes), which we discuss. We conclude with potential directions for future work that may, like innovations we introduced here (e.g., transformer-based dynamics prediction), improve performance on our complicated dataset of CCS CFD simulations.

AI Surrogate Simulation, Carbon Capture, design op↗
DOE OSTI2013

Individually optimized uniform contrast enhancement in CT angiography for the diagnosis of pulmonary thromboembolic disease—A simulation study

Purpose: To improve the diagnostic quality of CT pulmonary angiography (CTPA) by individually optimizing a biphasic contrast injection function to achieve targeted uniform contrast enhancement. To compare the results against a previously reported discrete Fourier transform (DFT) approach. Methods: This simulation study used the CTPA datasets of 27 consecutive patients with pulmonary thromboembolic disease (PE). An optimization approach was developed consisting of (1) computation of the impulse enhancement function (IEF) based on a test bolus scan, and (2) optimization of a biphasic contrast injection function using the IEF in order to achieve targeted uniform enhancement. The injection rates and durations of a biphasic contrast injection function are optimized by minimizing the difference between the resulting contrast enhancement curve and the targeted uniform enhancement curve, while conforming to the clinical constraints of injection rate and total contrast volume. The total contrast volume was limited first to the clinical standard of 65 ml, and then to the same amount used in the DFT approach for comparison. The optimization approach and the DFT approach were compared in terms of the root mean square error (RMSE) and total contrast volume used. Results: When the total contrast volume was limited to 65 ml, the optimization approach produced significantly better contrast enhancement (closer to the targeted uniform contrast enhancement) than the DFT approach (RMSE 17 HU vs 56 HU,p < 0.00001). On average, the optimization approach used 63 ml contrast, while the DFT approach used 50 ml with four patients exceeding 65 ml. When equivalent total contrast volume was used for individual patient, the optimization approach still generated significantly better contrast enhancement (RMSE 44 HU vs 56 HU, p < 0.01). Constraints for the injection function could be easily accommodated into the optimization process when searching for the optimal biphasic injection function. Conclusions: The optimization approach generated individually optimized biphasic injection functions yielding significantly better contrast enhancement compared to the DFT approach. This new approach has the potential to improve the diagnostic quality of CTPA for PE.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Identification of key parameters controlling demographically structured vegetation dynamics in a Land Surface Model

This dataset includes outputs of 5000 simulations of CLM4.5(ED) to quantify the sensitivity of the model outputs to changes in model parameters using the Fourier Amplitude Sensitivity Test (FAST). Each simulation is generated by simultaneously sampling from 15% deviations of the default values of >80 vegetation parameters. This dataset includes 1) model codes of CLM4.5(ED) used for the sensitivity analysis; 2) the parameter samples; and 3) the corresponding model outputs of vegetation status (e.g., Gross Primary Production, Leaf Area Index and Biomass) and demography (e.g., diameter growth and mortality rates). The outputs are organized to the format of the FAST toolbox (https://sites.google.com/site/xuchongang/uasatoolbox).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Simulation and Application of Bayesian Dose Uncertainty Modeling for Low-Boom Community Noise Surveys

In dose-response modeling, failing to account for dose uncertainty can cause artificial flattening of the estimated slope of the dose-response curve. Previous analyses of NASA sonic boom community noise survey data utilized a Bayesian multilevel logistic regression model, which did not account for dose uncertainty. The current work extends the model to account for either classical or Berkson dose uncertainty. The extended model is applied to two simulated dose-response datasets to illustrate conditions under which the dose uncertainty term does and does not correct for the artificial flattening introduced by dose uncertainty. Finally, the extended model is applied to two previous NASA sonic boom community noise surveys. The resulting dose-response curve slope for the average participant is 5 to 10% steeper, but the difference in the noise dose that elicits a 5% highly annoyed response is small (less than 1 dB). The difference remains insignificant when producing population summary dose-response curves. Commentary is included on applicability to future X-59 low-boom community noise survey data modeling and analysis.

X-59↗
DOE OSTI2022

The “good,” the “bad,” and the “hidden” in neutron scattering and molecular dynamics of ionic aqueous solutions

We characterize a concentrated 7.3 m CaCl 2 solution, combining neutron diffraction with chloride isotopic substitution (Cl-NDIS) in null water and molecular dynamics (MD) simulations. We elucidate the solution structure, thermodynamic properties, and extent of ion pairing previously suggested as concentration-dependent and often not observed at lower concentrations. Our Cl-NDIS measurements designate the solvent-shared ion pairing as dominant and the contact ion pairing (CIP) as insignificant even under conditions close to the solubility limit. The MD models parameterized against neutron diffraction with calcium isotopic substitution (Ca-NDIS) overestimate CIP despite successfully reproducing most of the Cl-NDIS signal. This drawback originates from the fact that Ca 2+ –Cl - interactions were primarily “hidden” in the Ca-NDIS signal due to overlapping with Ca 2+ –O w and Ca 2+ –H w contributions to the total scattering. Contrary, MD models with moderate CIP and possessing generally good performance at high concentrations fail to reproduce the NDIS measurements accurately. Therefore, the electronic polarization, introduced in most of the recent MD models via scaling ionic charges, resolves some but not all parameterization drawbacks. We conclude that despite improving the quality of MD models “on average,” the question “which model is the best” has not been answered but replaced by the question “which model is better for a given research.” An overall “good” model can still be inappropriate or, in some instances, “bad” and, unfortunately, produce erroneous results. Additionally, the accurate interpretation of several NDIS datasets, complemented by MD simulations, can prevent such mistakes and help identify the strengths, weaknesses, and convenient applications for corresponding computational models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Validation of the Community Land Model Version 5 over the Contiguous United States (CONUS) using in-situ and remote sensing datasets

The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

An efficient approach for prediction of Warburg-type resistance under working currents

In the present paper, an efficient approach for the prediction of Warburg-type element is proposed via the analysis of the anode-supported solid oxide fuel cell (SOFC) performance under various working conditions. The details of the performance, polarization curves, impedance behavior, and species distribution profiles within the electrode are investigated via the combination of equivalent circuit model (ECM) analysis and multiphysics numerical simulations. The multiphysics simulation is developed and calibrated with experimental results of SOFC button cells under various working currents. With the complete datasets generated from the calibrated simulations, the trends of the element parameters involved in equivalent circuit model are analyzed. Generalized empirical functions are proposed as well as the procedures of prediction of performance under different conditions. The verification cases show good agreement between the predicted results from proposed model and the reference results. This proposed approach can be utilized to quickly predict the properties for desired performance in the manufacturing processes, and it also has the potential of reducing the computational cost in the simulation of large SOFCs.

25 ENERGY STORAGE↗
DOE OSTI2023

Diffusion Probabilistic Modeling for Video Generation

Denoising diffusion probabilistic models are a promising new class of generative models that mark a milestone in high-quality image generation. This paper showcases their ability to sequentially generate video, surpassing prior methods in perceptual and probabilistic forecasting metrics. We propose an autoregressive, end-to-end optimized video diffusion model inspired by recent advances in neural video compression. The model successively generates future frames by correcting a deterministic next-frame prediction using a stochastic residual generated by an inverse diffusion process. We compare this approach against six baselines on four datasets involving natural and simulation-based videos. We find significant improvements in terms of perceptual quality and probabilistic frame forecasting ability for all datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

JHTDB-wind: a web-accessible large-eddy simulation database of a wind farm with virtual sensor querying

This paper introduces JHTDB-wind (https://turbulence.idies.jhu.edu/datasets/windfarms, last access: 11 November 2025), a publicly accessible database containing large-eddy simulation (LES) data from wind farms. Building on the framework of the Johns Hopkins Turbulence Database (JHTDB), which hosts direct numerical simulation (DNS) and some LES datasets of canonical turbulent flows, JHTDB-wind stores the 4D space–time history of the flow and provides users the ability to access and query the data via a web-based virtual sensor interface. The initial dataset comprises LES results from a large wind farm with 10×6 turbines, modeled using a filtered actuator line method, under conventionally neutral atmospheric conditions. These data comprise 1 h (hour) of flow field data (velocity, pressure, potential temperature deviation, subgrid-scale (SGS) eddy viscosity, and turbine forces, approximately 15 TB (terabytes) and wind turbine data – including both turbine-level operational quantities and blade-level aerodynamic quantities (approximately 1.3 TB) – stored in Zarr and Parquet formats, respectively. Data retrieval is facilitated by the giverny Python package, allowing remote users to query the database in Python or MATLAB (C and Fortran support are available for flow field data). This paper details the simulation setup and demonstrates data access through examples that analyze wind farm flow structures and turbine performance. The framework is extensible to future datasets, including the JHTDB-wind diurnal cycle simulation analyzed in Xiao et al. (2025).

17 WIND ENERGY↗
DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗
DOE OSTI2021

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Abstract Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.

25 ENERGY STORAGE↗
DOE OSTI2025

Automated and highly parallelized Bayesian optimization scheme for direct drive fusion experiments on OMEGA

Finding the optimal implosion design on existing experimental facilities for inertial confinement fusion requires an exhaustive search of the vast design parameter space. This is infeasible both with experiments and with simulations. Consequently, a large fraction of the experimentally realizable design space remains unexplored, and new design schemes are challenging to optimize in a reasonable time frame. On the OMEGA laser facility, predictive machine learning models have been developed to accurately forecast the result of an experiment using only inexpensive simulations and the large dataset of prior experimental data. However, the full design space remains vast enough to be unassailable with simple optimization techniques. Here we develop an automated and optimally parallel Bayesian optimization algorithm that can entirely optimize the target and pulse shape of a direct-drive ICF implosion under a given design paradigm. We use this algorithm to find a markedly improved design for the performance implosions on OMEGA that is predicted to hydroequivalently scale to ignition at 2.15 MJ.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗