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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 667 records · Page 37

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NASA NTRS1993

Improved Performance and Safety for High Energy Batteries Through Use of Hazard Anticipation and Capacity Prediction

Prediction of the capacity remaining in used high rate, high energy batteries is important information to the user. Knowledge of the capacity remaining in used batteries results in better utilization. This translates into improved readiness and cost savings due to complete, efficient use. High rate batteries, due to their chemical nature, are highly sensitive to misuse (i.e., over discharge or very high rate discharge). Battery failure due to misuse or manufacturing defects could be disastrous. Since high rate, high energy batteries are expensive and energetic, a reliable method of predicting both failures and remaining energy has been actively sought. Due to concerns over safety, the behavior of lithium/sulphur dioxide cells at different temperatures and current drains was examined. The main thrust of this effort was to determine failure conditions for incorporation in hazard anticipation circuitry. In addition, capacity prediction formulas have been developed from test data. A process that performs continuous, real-time hazard anticipation and capacity prediction was developed. The introduction of this process into microchip technology will enable the production of reliable, safe, and efficient high energy batteries.

Atwater, Terrill↗

DOE OSTI2025

Toward accelerating rare-earth metal extraction using equivariant neural networks

The separation of rare-earth metals, vital for numerous advanced technologies, is hampered by their similar chemical properties, making ligand discovery a significant challenge. Traditional experimental and quantum chemistry approaches for identifying effective ligands are often resource-intensive. We introduce a machine learning protocol based on an equivariant neural network, Allegro, for the rapid and accurate prediction of binding energies in rare-earth complexes. Key to this work is our newly curated dataset of rare-earth metal complexes—made publicly available to foster further research—systematically generated using the Architector program. This dataset distinctively features functionalized derivatives of proven rare-earth-chelating scaffolds, hydroxypyridinone (HOPO), catecholamide (CAM), and their thio-analogues, selected for their established efficacy in binding these elements. Trained on this valuable resource, our Allegro models demonstrate excellent performance, particularly when trained to directly predict DFT-level binding energies, yielding highly accurate results that closely correlate with theoretical calculations on a diverse test set. Furthermore, this strategy exhibited strong out-of-sample generalization, accurately predicting binding energies for an isomeric HOPO-derivative ligand not seen during training. By substantially reducing computational demands, this machine learning framework, alongside the provided dataset, represent powerful tools to accelerate the high-throughput screening and rational design of novel ligands for efficient rare-earth metal separation.

Gupta, Ankur K. [Lawrence Berkeley National Labora↗

DOE OSTI2024

Predictive model using artificial neural network to design phase change material-based ocean thermal energy harvesting systems for powering uncrewed underwater vehicles

Uncrewed Underwater Vehicles (UUVs) are a major beneficiary of the phase change material (PCM)-based ocean thermal energy harvesting technology for their mission needs. However, this technology relies on different parameters and energy conversion steps that could be critical to the general energy generation efficiency. Sea trials showed that the design performed lower than their laboratory design specifications. This underperformance results from different factors, mainly the UUV’s trajectory, travel time, underwater ocean currents, temperature fluctuations, and biofouling on the heat exchanger due to long term underwater operations. Therefore, there exists a need to continuously monitor the ambient energy harvesting system and predict system performance, for mission planning purposes. Two major parameters influencing the energy harvesting system include the final pressure inside the hydraulic energy storage vessel or accumulator, and the electrical load value. Here, this work focuses on the hydraulic to electric energy conversion system. Therefore, a combination of numerical model and experimental testing is used to develop a predictive model using artificial neural network using MATLAB. After validation with experimental testing, 1000 data samples obtained from the numerical model are used to train the ANN. Compared to the experimental results, the developed ANN model can predict in less than a second the designed benchtop system’s total efficiency with less than 15 percent maximum error range. This predictive model development represents a cost-effective way for optimization and a computational energy efficient mode aboard UUVs for mission planning for deployed UUVs using PCM-based ocean thermal energy harvesting technology.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2026

HydraGNN v5.0

HydraGNN v5.0 expands the code base into a more portable, scalable, and flexible framework for scientific graph learning, with particular strength in atomistic machine-learning interatomic potentials and large-scale distributed training. The release adds Fully Sharded Data Parallel (FSDP) support alongside existing DDP and DeepSpeed paths, including FSDP-aware checkpointing and optimizer integration, and introduces a configurable multi-precision training workflow supporting FP32, BF16, and FP64 across GPUs and Intel XPUs. For atomistic modeling, HydraGNN v5.0 strengthens its MLIP capabilities through dynamic graph construction at every forward pass, energy-conserving force prediction via automatic differentiation, and per-atom energy loss formulations, while extending EGNN models to properly handle periodic boundary conditions. The release also broadens model expressiveness through graph-level attribute conditioning, adds new multi-task and model-parallel extensions such as MACE support and encoder/decoder branch optimization, and expands application coverage with integrated examples for datasets including OC25, Nabla2-DFT, QCML, Open Polymers 2026, and OPF. In parallel, HydraGNN v5.0 improves production readiness through performance optimizations for large-scale runs, stratified sampling and linear-regression preprocessing utilities, and tested installation scripts for DOE supercomputers including Frontier, Aurora, Perlmutter, and Andes. Overall, the release advances HydraGNN as a robust software platform for scalable graph neural networks across materials science, chemistry, and scientific machine learning workflows

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗

NASA NTRS1975

High efficiency graded band-gap Al/x/Ga/1-x/As-GaAs p-on-n solar cell

A theoretical analysis of p-on-n (p/n) graded band-gap Al/x/Ga/1-x/As-GaAs solar cells including all practical energy loss mechanisms predicts air mass zero efficiencies of 17.3%. The energy losses include those due to spectral reflection, surface, bulk and junction recombination currents, and series resistance. The device consists of a layer of p-type Al/x/Ga/1-x/As 3.5-micron thick on top of an n-type GaAs substrate. The graded band-gap is achieved by decreasing x from 0.35 at the surface to zero at the junction. The same structure without the graded Al concentration, no longer an optimum device, has an efficiency of 10.4%. The primary function of the graded band-gap material is the reduction of surface and bulk recombination losses in the surface layer by a built-in electric field. A comparison of the p/n structure with an optimized n/p structure indicates that the latter has a slightly higher efficiency (17.7%) for assumed minority carrier diffusion lengths and surface recombination velocity.

Hutchby, J. A.↗

NASA NTRS2017

MMS Observation of Inverse Energy Dispersion in Shock Drift Accelerated Ions

The four Magnetospheric Multiscale (MMS) spacecraft observed a ∼1 min burst of energetic ions (50–1000 keV) in the region upstream from the subsolar quasi-perpendicular bow shock on 6 December 2015. The composition, flux levels, and spectral indices of these energetic protons, helium, and oxygen ions greatly resemble those seen in the outer magnetosphere earlier while MMS crossed the magnetopause and differ significantly from those simultaneously observed far upstream by Advanced Composition Explorer (ACE). However, the event cannot be explained solely in terms of leakage from the magnetosphere. The strongly southward orientation of the interplanetary magnetic field (IMF) lines at the time of the event precludes any connection to the magnetosphere. This point is confirmed by the presence of energetic electrons, known to occur on magnetic field lines that graze the bow shock rather than connect to the magnetosphere. We suggest that the ions gradient drifted out of the nearby quasi-parallel foreshock and into the quasi-perpendicular bow shock. Each of the ion species exhibited an inverse energy dispersion. As predicted by models for shock drift acceleration, the energies of the ions increased as 𝜃(sub Bn), the angle between the IMF and the shock normal, increased. Finally, we note that a similar event was observed a few minutes later in the subsolar magnetosheath, indicating that such events can be swept downstream of the bow shock.

Lee, S. H.↗

NASA NTRS1997

Possible Evidence for Relativistic Shocks in Gamma-Ray Bursts

Relativistic shock models of gamma-ray bursts may be tested by comparing their predicted low-energy asymptotic spectral indices s to observations. Synchrotron radiation theory predicts that the instantaneous spectrum has s = 1/3, and the spectrum integrated over the radiative decay of the electrons' energies has s = 1/2 with other cases lying between these limits. We examine the spectra of 11 bursts obtained by the Large Area Detectors on BATSE. One agrees with the predicted instantaneous spectrum, as does the initial portion of a second, and three are close to the predicted integrated spectrum. All of the observed asymptotic spectral slopes lie in the predicted range. This evidence for relativistic shocks is independent of detailed models of bursts and of assumptions about their distances. Radiation observed with the predicted instantaneous spectrum has a comparatively smooth time dependence, consistent with the necessarily long radiation time, while radiation observed with the predicted integrated spectrum has a spiky time dependence, consistent with the necessarily short radiation time.

Cohen, E.↗

DOE OSTI2020

Computationally Predicted High-Throughput Free-Energy Phase Diagrams for the Discovery of Solid-State Hydrogen Storage Reactions

The design of multinary solid-state material systems that undergo reversible phase changes via changes in temperature and pressure provides a potential means of safely storing hydrogen. However, fully mapping the stabilities of known or newly targeted compounds relative to competing phases at reaction conditions has previously required many stringent experiments or computationally demanding calculations of each compound’s change in Gibbs energy with respect to temperature, G(T). Here, we have extended the approach of constructing chemical potential phase diagrams based on ΔG f (T) to enable the analysis of phase stability at non-zero temperatures. We first performed density functional theory calculations to compute the formation enthalpies of binary, ternary, and quaternary compounds within several compositional spaces of current interest for solid-state hydrogen storage. Temperature effects on solid compound stability were then accounted for using our recently introduced machine learned descriptor for the temperature-dependent contribution G δ (T) to the Gibbs energy G(T). From these Gibbs energies, we evaluated each compound’s stability relative to competing compounds over a wide range of conditions and show using chemical potential and composition phase diagrams that the predicted stable phases and H2 release reactions are consistent with experimental observations. This demonstrates that our approach rapidly computes the thermochemistry of hydrogen release reactions for compounds at sufficiently high accuracy relative to experiment to provide a powerful framework for analyzing hydrogen storage materials. This framework based on G(T) enables the accelerated discovery of active materials for a variety of technologies that rely on solid-state reactions involving these materials.

08 HYDROGEN↗

NASA NTRS2019

Casper-1, Part 6: Uncertainty Quantification, Factor Effects, and Outlier Analysis for an On-Board Airplane Trajectory Prediction Function

This report presents data analysis results for a simulation-based approach named CASPEr (Characterization of Airplane State Prediction Error) to characterize the performance of onboard energy state and automation mode prediction functions for terminal area arrival and approach phases of flight over a wide range of conditions. In particular, the results include quantification of energy state (i.e., altitude and airspeed) prediction performance, models for prediction performance as a function of initial energy state (i.e., initial altitude, airspeed, and weight) and weather factors, and analysis of outlier prediction performance. Wind speed, wind direction, and wind gradient were found to be major factors in energy state prediction performance. Initial energy and gust intensity were also significant factors in airspeed prediction performance. Furthermore, the results suggest that errors in automation mode prediction may be a major contributor to outlier prediction performance.

Torres-Pomales, Wilfredo↗

NASA NTRS1989

The dissociation energy of N2

The requirements for very accurate ab initio quantum chemical prediction of dissociation energies are examined using a detailed investigation of the nitrogen molecule. Although agreement with experiment to within 1 kcal/mol is not achieved even with the most elaborate multireference CI (configuration interaction) wave functions and largest basis sets currently feasible, it is possible to obtain agreement to within about 2 kcal/mol, or 1 percent of the dissociation energy. At this level it is necessary to account for core-valence correlation effects and to include up to h-type functions in the basis. The effect of i-type functions, the use of different reference configuration spaces, and basis set superposition error were also investigated. After discussing these results, the remaining sources of error in our best calculations are examined.

Almloef, Jan↗

NASA NTRS1989

The dissociation energy of N2

Almlof, Jan↗

DOE OSTI2022

Predicting the Cetane Number, Sooting Tendency, and Energy Density of Terpene Fuel Additives

Abstract Discovering renewable fuels and fuel additives is paramount in reducing carbon emissions from internal combustion engines. Terpenes, a group of compounds that can be synthesized from plant matter and microorganisms, have gained significant interest in recent years as promising candidates for fuels/additives. Terpenes are a diverse class of compounds that contain rings and methyl branches, resulting in high energy densities and optimal cold weather behavior. Their variation in bond order, carbon chains, and functional groups lead to varying degrees of soot formation and performance in existing engines. The present work leverages predictive models, namely artificial neural networks, to predict the cetane number (CN), sooting tendency (quantified with yield sooting index, YSI), and energy density (quantified with lower heating value, LHV) of terpenes and hydrogenated terpenes whose sooting propensities were previously determined through experimental means. Predicted sooting propensities of these terpenes are compared with experimental values, and predicted cetane numbers and energy densities are used to comment on the compounds’ ability to act as fuels/additives. Expected prediction errors for CN, YSI, and LHV, defined by blind test set median absolute error, are within 5.56 cetane units, 3.63 yield sooting index units, and 0.77 MJ/kg respectively. Additionally, the present work investigates a variety of correlation/dependence metrics for property-property relationships, furthering our understanding of how combustion-relevant properties are related.

09 BIOMASS FUELS↗

DOE OSTI2026

Surrogate model evaluation and building energy benchmarking for commercial buildings

Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio TM /EnergyPlus TM to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Functional Connectivity of Red Chlorophylls in Cyanobacterial Photosystem I Revealed by Fluence-Dependent Transient Absorption

External stressors modulate the oligomerization state of photosystem I (PSI) in cyanobacteria. The number of red chlorophylls (Chls), pigments lower in energy than the P700 reaction center, depends on the oligomerization state of PSI. Here, we use ultrafast transient absorption spectroscopy to interrogate the effective connectivity of the red Chls in excitonic energy pathways in trimeric PSI in native thylakoid membranes of the model cyanobacterium Synechocystis sp. PCC 6803, including emergent dynamics, as red Chls increase in number and proximity. Fluence-dependent dynamics indicate singlet–singlet annihilation within energetically connected red Chl sites in the PSI antenna but not within bulk Chl sites on the picosecond time scale. These data support picosecond energy transfer between energetically connected red Chl sites as the physical basis of singlet–singlet annihilation. The time scale of this energy transfer is faster than predicted by Förster resonance energy transfer calculations, raising questions about the physical mechanism of the process. Our results indicate distinct strategies to steer excitations through the PSI antenna; the red Chls present a shallow reservoir that direct excitations away from P 700 , extending the time to trapping by the reaction center.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

A DYNAMIC ENERGY MODEL FOR DISPLAY CASES CONSIDERING FAULTS IN SUPERMARKETS FOR LOW GWP REFRIGERANTION

Display cases are widely used in supermarkets to demonstrate commercial goods for customers, like milk, frozen food, ice-cream, etc. There are two types of display case: open case and closed case. The goods inside display cases need to stay within a stable temperature range to meet the quality standards. However, due to variety of faults (e.g., leaving door opening), it is hard to maintain a stable temperature. The associated energy consumption from display cases is extremely high due to faults. There is a need for a dynamic energy model to accurately predict the good temperature and energy consumption under faults. A dynamic model is developed based on operation data collected through field tests in a low global warming potential (GWP) refrigeration system, at Oak Ridge National Laboratory.

Li, Yanfei↗

NASA NTRS1995

Solar flares controlled by helicity conservation

The energy release in a class of solar flares is studied on the assumption that during burst events in highly conducting plasma the magnetic helicity of plasma is approximately conserved. The available energy release under a solar flare controlled by the helicity conservation is shown to be defined by the magnetic structure of the associated prominence. The approach throws light on some solar flare enigmas: the role of the associated prominence. The approach throws light on some solar flare enigmas: the role of the associated prominences; the discontinuation of the reconnection of magnetic lines long before the complete reconnection of participated fields occurs; the existence of quiet prominences which, in spite of their usual optical appearance, do not initiate any flare events; the small energy release under a solar flare in comparison with the stockpile of magnetic energy in surrounding fields. The predicted scale of the energy release is in a fair agreement with observations.

Gliner, Erast B.↗

NASA NTRS2001

Advanced Thin Ionization Calorimeter (ATIC) Balloon Experiment

During grant NAG5-5064, Louisiana State University (LSU) led the ATIC team in the development, construction, testing, accelerator validation, pre-deployment integration and flight operations of the Advanced Thin Ionization Calorimeter (ATIC) Balloon Experiment. This involved interfacing among the ATIC collaborators (UMD, NRL/MSFC, SU, MSU, WI, SNU) to develop a new balloon payload based upon a fully active calorimeter, a carbon target, a scintillator strip hodoscope and a pixilated silicon solid state detector for a detailed investigation of the very high energy cosmic rays to energies beyond 10(exp 14) eV/nucleus. It is in this very high energy region that theory predicts changes in composition and energy spectra related to the Supernova Remnant Acceleration model for cosmic rays below the "knee" in the all-particle spectrum. This report provides a documentation list, details the anticipated ATIC science return, describes the particle detection principles on which the experiment is based, summarizes the simulation results for the system, describes the validation work at the CERN SPS accelerator and details the balloon flight configuration. The ATIC experiment had a very successful LDB flight from McMurdo, Antarctica in 12/00 - 1/01. The instrument performed well for the entire 15 days. Preliminary data analysis shows acceptable charge resolution and an all-particle power law energy deposition distribution not inconsistent with previous measurements. Detailed analysis is underway and will result in new data on the cosmic ray charge and energy spectra in the GeV - TeV energy range. ATIC is currently being refurbished in anticipation of another LDB flight in the 2002-03 period.

Wefel, John P.↗

DOE OSTI2023

The Symmetry Energy: Current Status of Ab Initio Predictions vs. Empirical Constraints

Infinite nuclear matter is a suitable laboratory to learn about nuclear forces in many-body systems. In particular, modern theoretical predictions of neutron-rich matter are timely because of recent and planned experiments aimed at constraining the equation of state of isospin-asymmetric matter. For these reasons, we have taken a broad look at the equation of state of neutron-rich matter and the closely related symmetry energy, which is the focal point of this article. Its density dependence is of paramount importance for a number of nuclear and astrophysical systems, ranging from neutron skins to the structure of neutron stars. We review and discuss ab initio predictions in relation to recent empirical constraints. We emphasize and demonstrate that free-space nucleon–nucleon data pose stringent constraints on the density dependence of the neutron matter equation of state, which essentially determines the slope of the symmetry energy at saturation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗