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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modeling the Sources and Transport Processes During Extreme Ammonia Episodes in the U.S. Corn Belt

Abstract Atmospheric ammonia (NH 3 ) is the primary form of reactive nitrogen (N r ) and a precursor of ammonium (NH 4 + ) aerosols. Ammonia has been linked to adverse impacts on human health, the loss of ecosystem biodiversity, and plays a key role in aerosol radiative forcing. The midwestern United States is the major NH 3 source in North America because of dense livestock operations and the high use of synthetic nitrogen fertilizers. Here, we combine tall‐tower (100 m) observations in Minnesota and Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) modeling to investigate high and low NH 3 emission episodes within the U.S. Corn Belt to improve our understanding of the distribution of emission sources and transport processes. We examined observations and performed model simulations for cases in February through November of 2017 and 2018. The results showed the following: (1) Peak emissions in November 2017 were enhanced by above‐normal air temperatures, implying a Q 10 (i.e., the change in NH 3 emissions for a temperature increase of 10°C) of 2.5 for emissions. (2) The intensive livestock emissions rom northern Iowa, approximately 400 km away from the tall tower, accounted for 17.6% of the abundance in tall‐tower NH 3 mixing ratios. (3) Ammonia mixing ratios in the innermost domain 3 frequently (i.e., 336 hr, 48% of November 2017) exceeded 5.3 ppb, an important air quality health standard. (4) In November 2017, simulated NH 3 net ecosystem exchange (the difference between NH 3 emissions and dry deposition) accounted for 60–65% of gross NH 3 emissions for agricultural areas and was 2.8–3.1 times the emissions of forested areas. (5) We estimated a mean annual NH 3 net ecosystem exchange of 1.60 ± 0.06 nmol · m −2 · s −1 for agricultural lands and −0.07 ± 0.02 nmol · m −2 · s −1 for forested lands. These results imply that future warmer fall temperatures will enhance agricultural NH 3 emissions, increase the frequency of dangerous NH 3 episodes, and enhance dry NH 3 deposition in adjacent forested lands.

Hu, Cheng↗
DOE OSTI2021

DEPRECATED AI-Batt-OS (Autonomous Identification of Battery Life Models - Open Source) [SWR 21-17]

DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.

Gasper, Paul↗
DOE OSTI2024

Applied radiation tags for warhead dismantlement transparency

Here, this study explores the feasibility of applying radiation tags as a Chain of Custody tool to confirm the dismantlement of individual warheads by investigating time dependent emissions from special nuclear material. Precedents of photon and neutron sources that have been used to irradiate warhead components are presented. Measurements of irradiated Highly Enriched Uranium (HEU) are used to benchmark simulations with the CINDER90 library for MCNP. Delayed gamma emissions are simulated as a function of time since irradiation of moderated HEU and Weapons Grade Plutonium with photon and neutron sources. Models of commercial 9 MV and 15 MV linacs were found to induce ~10 9 fissions/g and ~10 11 fissions/g, respectively, creating 3 to 4 orders of magnitude more delayed gammas than neutron sources, modeled at 10 10 n/s. However, a DT neutron source was found to induce sufficient fissions, ~10 7 fissions/g, to create quantifiable signatures relative to intrinsic emissions. Applied radiation tagging is determined to be technically feasible, but its operational context and practical implementation requires significant development.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Factors That Influence Variability in Stress-Drop Measurements Using Spectral Decomposition and Spectral-Ratio Methods for the 2019 Ridgecrest Earthquake Sequence

Stress drop is a fundamental parameter related to earthquake source physics, but is hard to measure accurately. To better understand how different factors influence stress-drop measurements, we compare two different methods using the Ridgecrest stress-drop validation data set: spectral decomposition (SD) and spectral ratio (SR), each with different processing options. Here, we also examine the influence of spectral complexity on source parameter measurement. Applying the SD method, we find that frequency bandwidth and time-window length could influence spectral magnitude calibration, while depth-dependent attenuation is important to correctly map stress-drop variations. For the SR method, we find that the selected source model has limited influence on the measurements; however, the Boatwright model tends to produce smaller standard deviation and larger magnitude dependence than the Brune model. Variance reduction threshold, frequency bandwidth, and time-window length, if chosen within an appropriate parameter range, have limited influence on source parameter measurement. For both methods, wave type, attenuation correction, and spectral complexity strongly influence the result. The scale factor that quantifies the magnitude dependence of stress drop show large variations with different processing options, and earthquakes with complex source spectra deviating from the Brune-type source models tend to have larger scale factor than earthquakes without complexity. Based on these detailed comparisons, we make a few specific suggestions for data processing workflows that could help future studies of source parameters and interpretations.

58 GEOSCIENCES↗
DOE OSTI2024

Open‐Source Anaerobic Digestion Modeling Platform, Anaerobic Digestion Model No. 1 Fast (ADM1F)

An open‐source modeling platform, called Anaerobic Digestion Model No. 1 Fast (ADM1F), is introduced to achieve fast and numerically stable simulations of anaerobic digestion processes. ADM1F is compatible with an iPython interface to facilitate model configuration, simulation, data analysis, and visualization. Faster simulations and more stable results are accomplished by implementing an advanced open‐source library of numerical methods called Portable Extensive Toolkit for Scientific Computation (PETSc) to solve the ADM1 system of equations. Leveraging PETSc, ADM1F can consistently complete a steady‐state simulation under 0.2 s, over 99% faster than a benchmark ADM1 model implemented with MATLAB while achieving agreement of model outputs within 1% of those obtained with the benchmark model. For dynamic simulations, however, ADM1F has a computational speed advantage only when the influent characteristics update more frequently than every 4 h. The ability of ADM1F to be useful as a tool to study anaerobic digestion systems is demonstrated through two example implementations of ADM1F: (1) a two‐phase co‐digestion scenario evaluating the impact of the organic loading rate and the substrate composition on reactor performance and stability, and (2) a conventional digester scenario assessing the effectiveness of recovery strategies after disruptions that led to instability. These examples demonstrate how the high simulation speed and the convenience of the iPython interface allow ADM1F to complete complex analyses within minutes, much faster than computational strategies currently reported in the literature.

anaerobic co-digestion↗
DOE OSTI2024

Cognitive Grid Optimization

This project laid the foundation to include a security constrained economic dispatch (SCED) within one of the leading simulators which is used to train system operators who keep the lights on for over 150 million people in USA. The SCED is designed to handle very high penetrations of renewable generation as well as battery storage. As a follow on the this project, a Trusted Source Model of the North American Electric Interconnections will be built from public GIS data. The various North American Markets will be emulated. This Trusted Source Model will grow the software developers for the next generation of power applications that are needed to transition to all green generation while everything is electrified.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

An infrasound source analysis of the OSIRIS-REx sample return capsule hypersonic re-entry

The OSIRIS-REx sample return capsule's hypersonic re-entry into the atmosphere is a rare opportunity to test a variety of sonic boom source models since the projectile dimensions are well characterized. While the as-flown flight path is unknown, the predicted flight path enables a rough approximation of the source Mach number and location. Six infrasound microphones deployed in the boom carpet along the predicted flight path recorded impulsive signals from the OSIRIS-REx re-entry. Using a suite of atmosphere profiles and the geometric acoustics approximation, we estimate locations with uncertainty estimates along the flight path from which the signals were emitted. Acoustic overpressure and signal duration predictions from Whitham's far field theory, Carlson's simplified sonic boom prediction method, and a drag-dominated hypersonic model are analyzed with uncertainty estimates from the location estimate. While the Carlson simplified sonic boom prediction method could be accurate, our preference is for the drag-dominated source model. Using this source model with an inviscid Burgers's equation solver for propagation, we obtained an excellent match to the recorded data. In conclusion, these results will help better inform future sample return capsule re-entry observation campaigns as well as contribute to a better understanding of high altitude infrasonic sources.

58 GEOSCIENCES↗
DOE OSTI2020

Zonal-Based Emission Source Term Model for Predicting Particulate Emission Factors in Wildfire Simulations

A physics/chemistry-based numerical model for predicting the emission of fine particles from wildfires is proposed. This model implements the fundamental mechanisms of soot formation in a combustion environment: soot nucleation, surface growth, agglomeration, oxidation, and particle fragmentation. These mechanisms occur on a scale too fine for the discretization of most wildfire models, which need to simulate landscape-scale dynamics. As a result this model implements a zonal approach, where the computed soot particle distribution is partitioned into process zones within a single resolved grid cell. These process zones include: an inception zone (for nucleation), a heating zone (for coagulation, surface growth, and fragmentation), a reaction zone (for oxidation), and a quenched zone (for atmospheric processes). Governing mechanisms are applied to the appropriate zones to predict total particle growth and emission. Additionally, the proposed model is implemented into HIGRAD/FIRETEC, a physics-based wildfire simulation code which couples interactions between fire, fuels, atmosphere, and topography on a landscape scale. Fire simulations among grasslands and conifer forests are performed and compared against experimental data for emission factors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Inverting infrasound data for the seismoacoustic source time functions and surface spall at the Source Physics Experiments Phase II: Dry Alluvium Geology

This report presents the infrasound data recorded as part of the Source Physics Experiment - Phase 2, Dry Alluvium Geology. This experiment, also known colloquially as DAG, consisted of four underground chemical explosions at the Nevada National Security Site. We focus our analysis on only the fourth explosion (DAG-4) as we determined that this was the only event that produced clear source-generated infrasound energy as recorded by the DAG sensors. We analyze the data using two inversion methods. The first method is designed to estimate the point-source seismoacoustic source time functions, and the second inversion method is designed to estimate the first-order characteristics (e.g. horizontal dimensions and maximum amplitude) of the actual spall surface. For both analysis methods, we are able to fit the data reasonably well, with various assumptions of the source model. The estimated seismoacoustic source appears to be a combination of a buried, isotropic explosion with a maximum amplitude of ~2 x 10 9 Nm and a vertically oriented force, applied to the Earth's surface with a maximum amplitude of 4 x 10 7 N. We use the vertically oriented force to simulate surface spall. The estimated spall surface has an approximate radius of ~40 m with a maximum acceleration magnitude in the range of 0.8 to 1.5 m/s/s. These estimates are approximately similar to the measured surface acceleration at the site.

58 GEOSCIENCES↗
DOE OSTI2025

STARTR: An Open-Source MARVEL model for the NRIC Virtual Test Bed [Poster]

The National Reactor Innovation Center (NRIC) seeks to improve the understanding of microreactor physics in industry and academia through the development of a Microreactor Applications Research Validation and Evaluation (MARVEL) reactor-based model, published on the Virtual Test Bed (VTB). To achieve this goal, the Sodium-cooled Thermal-spectrum Advanced Research Test Reactor (STARTR) model was built using publicly available MARVEL specifications where possible and approximations where applicable, and was optimized for fast runtimes for researchers to receive rapid simulation feedback. STARTR will fill a gap between stakeholder interest and available models, as the first Sodium-cooled Thermal Reactor (STR) hosted on the VTB with baseline performance sanctioned by INL. This project involved the definition of all materials used in the reactor, geometry and all reactor subcomponents, and assertion of tallies and simulation settings within OpenMC 0.13.3. This poster details a small subset of the overall reactor physics testing: the two-dimensional power peaking factors and the flux energy spectrum, as well as plots of the created geometry. Future work includes code-to-code verification between the OpenMC-based model and a separately designed MCNP 6.2-based model.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2020

AGR-3/4 Modeling and Transport Parameters

Outline AGR-3/4 experiment and objectives Description of AGR-3/4 modeling approach and inputs Source term (release from particles) Temperature profiles Diffusion through concentric rings Adsorption across gaps between rings Simplified Source Model and updated model results Reconstruction of fission product distributions from gamma tomography data

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Small-Signal Stability Impacts of Load and Network Dynamics on Grid-Forming Inverters

This paper presents several stability analyses for grid-forming inverters and synchronous generators considering the dynamics of transmission lines and different load models. Load models are usually of secondary importance compared to generation source models, but as the results show, they play a crucial role in stability studies with the introduction of inverter-based resources. Given inverter control time scales, the implications of considering or neglecting electromagnetic transients of the network are very relevant in the stability assessments. In this paper, we perform eigenvalue analyses for inverter-based resources and synchronous machines connected to a load and explore the effects of multiples models under different network representations. We explore maximum loadability of inverter-based resources and synchronous machines, while analyzing the effects of load and network dynamic models on small-signal stability. The results show that the network representation plays a fundamental role in the stability of the system of different load models. The resulting stability regions are significantly different depending on the source and load model considered.

grid forming inverters↗
DOE OSTI2023

Nonlinear models for coupling the effects of stimulated Raman scattering to inertial confinement fusion codes

Laser plasma instabilities (LPI) reduce driver-target coupling, alter implosion symmetry, and therefore can fundamentally limit fusion performance in inertial confinement fusion (ICF). Developing a predictive modeling capability for LPI effects can critically advance the success of the field. We perform vector particle-in-cell simulations of multi-speckled laser beams undergoing stimulated Raman scattering (SRS) at various densities and intensities relevant to mainly indirectly driven and a subset of parameter space for directly driven ICF systems, focusing on the regimes with intensities above the onset of electron trapping. Based on the wavenumber of the SRS daughter electron plasma wave, we identify several regions with underpinning SRS saturation physics: the electron-trapping dominated region with intermediate kλD values, the strong Landau damping region at larger kλD values, and the region where the Langmuir decay instability arises at lower kλD values. We develop a nonlinear SRS reflectivity model that features the base trapping-dominated scaling of (kλD)−4 and its modifications. Electron trapping deforms the initialized electron distribution functions, and we have developed a new δf-Gaussian-mixture algorithm for an accurate characterization of the trapped hot electron population. With this SRS hot electron description, we construct a nonlinear energy deposition model and a hot electron source model—based on a modified Manley–Rowe relation—suitable for including SRS effects as a sub-grid module in a high-fidelity ICF design code.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Digital twin framework for PIP-II linac: AI-driven multi-scale modeling from ion source to 800 MeV

The PIP-II superconducting linac at Fermilab is designed to deliver multi-megawatt proton beams for neutrino physics and other high-intensity applications. To expedite commissioning and enhance operational reliability, we have developed an EPICS-based data flow framework that seamlessly integrates digital twins (DT) with physical twins (PT). These digital twins comprise high-fidelity beam dynamics models or data-driven surrogate models connected to their physical counterparts through real-time diagnostics and advanced machine-learning algorithms.Central to this framework is Linac_Gen, an accelerated simulation tool that incorporates convolutional neural networks, random forests, and genetic algorithms to provide up to a tenfold speedup in optimizing the accelerator geometry model. An EPICS translator layer ensures interoperability by efficiently mapping lattice parameters across diverse simulation platforms.Our EPICS-based framework supports multiple operational modes—monitoring, passive learning, closed-loop control, and online learning—covering the entire machine lifecycle. By leveraging HPC resources and multi-objective optimization techniques, the digital twin enables adaptive trajectory correction, real-time fault detection, and predictive modeling of beam stability. This comprehensive approach paves the way for robust, high-intensity operation and data-driven accelerator R&D at Fermilab.

Pathak, Abhishek [Fermilab]↗
DOE OSTI2022

First Sagittarius A* Event Horizon Telescope Results. IV. Variability, Morphology, and Black Hole Mass

In this paper we quantify the temporal variability and image morphology of the horizon-scale emission from Sgr A*, as observed by the EHT in 2017 April at a wavelength of 1.3 mm. We find that the Sgr A* data exhibit variability that exceeds what can be explained by the uncertainties in the data or by the effects of interstellar scattering. The magnitude of this variability can be a substantial fraction of the correlated flux density, reaching ~100% on some baselines. Through an exploration of simple geometric source models, we demonstrate that ring-like morphologies provide better fits to the Sgr A* data than do other morphologies with comparable complexity. We develop two strategies for fitting static geometric ring models to the time-variable Sgr A* data; one strategy fits models to short segments of data over which the source is static and averages these independent fits, while the other fits models to the full data set using a parametric model for the structural variability power spectrum around the average source structure. Both geometric modeling and image-domain feature extraction techniques determine the ring diameter to be 51.8 ± 2.3 μas (68% credible intervals), with the ring thickness constrained to have an FWHM between ~30% and 50% of the ring diameter. To bring the diameter measurements to a common physical scale, we calibrate them using synthetic data generated from GRMHD simulations. This calibration constrains the angular size of the gravitational radius to be ${4.8}_{-0.7}^{+1.4}$ μas, which we combine with an independent distance measurement from maser parallaxes to determine the mass of Sgr A* to be ${4.0}_{-0.6}^{+1.1}\times {10}^{6}$ M⊙.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Multi-task Parallelism for Robust Pre-training of Graph Foundation Models on Multi-source, Multi-fidelity Atomistic Modeling Data

Graph foundation models using graph neural networks promise sustainable, efficient atomistic modeling. To tackle challenges of processing multi-source, multi-fidelity data during pre-training, recent studies employ multi-task learning, in which shared message passing layers initially process input atomistic structures regardless of source, then route them to multiple decoding heads that predict data-specific outputs. This approach stabilizes pre-training and enhances a model’s transferability to unexplored chemical regions. Preliminary results on approximately four million structures are encouraging, yet questions remain about generalizability to larger, more diverse datasets and scalability on supercomputers. We propose a multi-task parallelism method that distributes each head across computing resources with GPU acceleration. Implemented in the open-source HydraGNN architecture, our method was trained on over 24 million structures from five datasets and tested on the Perlmutter, Aurora, and Frontier supercomputers, demonstrating efficient scaling on all three highly heterogeneous super-computing architectures.

Lupo Pasini, Massimiliano [ORNL] (ORCID:0000000249↗