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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The role of Northeast Pacific meltwater events in deglacial climate change

Columbia River megafloods occurred repeatedly during the last deglaciation, but the impacts of this fresh water on Pacific hydrography are largely unknown. To reconstruct changes in ocean circulation during this period, we used a numerical model to simulate the flow trajectory of Columbia River megafloods and compiled records of sea surface temperature, paleo-salinity, and deep-water radiocarbon from marine sediment cores in the Northeast Pacific. The North Pacific sea surface cooled and freshened during the early deglacial (19.0-16.5 ka) and Younger Dryas (12.9-11.7 ka) intervals, coincident with the appearance of subsurface water masses depleted in radiocarbon relative to the sea surface. We infer that Pacific meltwater fluxes contributed to net Northern Hemisphere cooling prior to North Atlantic Heinrich Events, and again during the Younger Dryas stadial. Abrupt warming in the Northeast Pacific similarly contributed to hemispheric warming during the Bølling and Holocene transitions. These findings underscore the importance of changes in North Pacific freshwater fluxes and circulation in deglacial climate events.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

S-band transponder experiment

The purpose of this experiment was to measure the variations in the lunar gravitational field near the trajectory of orbiting space vehicles (the command and service module (CSM) and the small particles and fields subsatellites ejected from the Apollo 15 and 16 spacecraft). New information has been obtained from all Apollo orbiting spacecraft; however, this report shall be limited to the results from the Apollo 17 CSM and the Apollo 16 subsatellite. The data acquired are precise speed measurements of the orbiting spacecraft from which accelerations or gravity profiles may be inferred. Feature resolution is controlled by the spacecraft altitude and is almost a direct relationship (i.e., data taken from a 50-km altitude will resolve approximately a 50-km feature). Therefore, revolutions 3 to 12, when the CSM was in the low-altitude orbits, provided the clearest information.

Sjogren, W. L.↗
DOE OSTI2024

The influence of microbial colonization on inflammatory versus pro-healing trajectories in combat extremity wounds

A combination of improved body armor, medical transportation, and treatment has led to the increased survival of warfighters from combat extremity injuries predominantly caused by blasts in modern conflicts. Despite advances, a high rate of complications such as wound infections, wound failure, amputations, and a decreased quality of life exist. To study the molecular underpinnings of wound failure, wound tissue biopsies from combat extremity injuries had RNA extracted and sequenced. Wounds were classified by colonization (colonized vs. non-colonized) and outcome (healed vs. failed) status. Differences in gene expression were investigated between timepoints at a gene level, and longitudinally by multi-gene networks, inferred proportions of immune cells, and expression of healing-related functions. Differences between wound outcomes in colonized wounds were more apparent than in non-colonized wounds. Colonized/healed wounds appeared able to mount an adaptive immune response to infection and progress beyond the inflammatory stage of healing, while colonized/failed wounds did not. Although, both colonized and non-colonized failed wounds showed increasing inferred immune and inflammatory programs, non-colonized/failed wounds progressed beyond the inflammatory stage, suggesting different mechanisms of failure dependent on colonization status. Overall, these data reveal gene expression profile differences in healing wounds that may be utilized to improve clinical treatment paradigms.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A Quantum-Based Approach to Predict Primary Radiation Damage in Polymeric Networks

Initial atomistic-level radiation damage in chemically reactive materials is thought to induce reaction cascades that can result in undesirable degradation of macroscale properties. Ensembles of quantum-based molecular dynamics (QMD) simulations can accurately predict these cascades, but extracting chemical insights from the many underlying trajectories is a labor-intensive process that can require substantial a priori intuition. We develop here a general and automated graph-based approach to extract all chemically distinct structures sampled in QMD simulations and apply our approach to predict primary radiation damage of polydimethylsiloxane (PDMS), the main constituent of silicones. A postprocessing protocol is developed to identify underlying polymer backbone structures as connected components in QMD trajectories. Furthermore, these backbones form a repository of radiation-damaged structures. A scheme for extracting and updating a library of isomorphically distinct structures is proposed to identify the spanning set and aid chemical interpretation of the repository. The analyses are applied to ensembles of cascade QMD simulations in which the four element types in PDMS are selectively excited in primary knock-on atom events. Our approach reveals a much higher degree of combinatorial complexity in this system than was inferred through radiolysis experiments. Probabilities are extracted for radiation-induced network changes including formation of branch points, carbon linkages, cycles, bond scissions, and carbon uptake into the Si–O siloxane backbone network. The general analysis framework presented here is readily extendable to modeling chemical degradation of other polymers and molecular materials and provides a basis for future quantum-informed multiscale modeling of radiation damage.

36 MATERIALS SCIENCE↗
NASA NTRS1998

Synthesis of Contributed Simulations for OREX Test Cases

A synthesis is presented of the computer simulations of the flow over the Orbital Reentry Vehicle (ORV) at the 92.8 km and 63.6 km Earth altitude trajectory points that were discussed at the First Europe-U.S. High Speed Flow Field Database Workshop Part 2, Napoli, Italy, November 1997. For the materials used on the surface of ORV, the non-catalytic wall condition is appropriate at 92.8 km and the finite-rate catalytic wall condition at 63.6 km. Additional simulations are required for establishing the independency of the discussed results from numerics. The proper modeling of natural phenomena needs further sensitivity studies. The uncertainties of inferred flight data are lacking for a proper evaluation of the presented results.

Mehta, Unmeel B.↗
DOE OSTI2026

Extracting the Breakout Distance from the ECOT Trajectories: Gaussian Process Regression Approach

Enhanced Corner Turning (ECOT) experiments provide an important metric of performance of high explosive (HE) formulations. The breakout distance is a single scalar value that characterizes the corner turning efficiency of an HE. Extracting the breakout distance from the raw ECOT results, whether experimental or simulated, is a conceptually straightforward procedure which, however, is non-unique, especially in the presence of noise. More specifically, this procedure involves numerical smoothing and selecting particular values for parameters of this smoothing introduces human bias. In this work, we propose to use the Gaussian process regression to analyze ECOT results. This analysis involves the effective smoothing of the data, thus allowing for accurate extraction of the breakout distance. Most importantly, the parameters of this smoothing can be inferred from the ECOT data itself, rendering the approach effectively parameter-free and thus diminishing the human bias. An additional benefit of the Gaussian process regression, being a statistical inference method, is that not just the value of the breakout distance, but also its confidence interval can be extracted from the data. This report introduces the Gaussian process regression, as applied to ECOT, and demonstrates its usefulness by extracting the breakout distances for a selection of experimental and simulated data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS1994

Stationary premixed flames in spherical and cylindrical geometries

Stationary source-free spherical flames ('flame balls') in premixed combustible gases were studied by employing low-gravity (micro-g) environments in a drop tower and an aircraft flying parabolic trajectories to diminish the impact of buoyancy-induced convective flow. Flame balls were found in all mixture families tested when: (1) the Lewis number Le of the deficient reactant was sufficiently low; and (2) the compositions were sufficiently close to the flammability limits. Probably as a consequence of the reduction in buoyant convection, the flammability limits at micro-g were significantly more dilute than those at Earth gravity; for example, 3.35% H2 vs 4.0% H2 in lean H2-air mixtures. By comparison with analytical and computational models, it is inferred that the phenomenon is probably related to diffusive-thermal effects in low-Le mixtures in conjunction with flame-front curvature and radiative heat losses from the combustion products. The chemical reaction mechanism appears to play no qualitative role. In the aircraft experiments, the gravity levels (approximately equal 10(exp -2)g(sub 0)) were found to cause noticeable motion of flame balls due to buoyancy, which in turn influenced the behavior of flame balls. At these g levels, a new type of transient, nearly cylindrical flame structure, termed 'flame strings,' was observed.

Ronney, P. D.↗
DOE OSTI2025

Digital Twin for Chemical Science: a case study on water interactions on the Ag(111) surface

Directly visualizing chemical trajectories offers insights into catalysis, gas-phase reactions and photoinduced dynamics. Tracking the transformation of chemical species is best achieved by coupling theory and experiment. Here we developed Digital Twin for Chemical Science (DTCS) v.01, which integrates theory, experiment and their bidirectional feedback loops into a unified platform for chemical characterization. DTCS addresses a core question: given a set of experimental conditions, what is the expected outcome and why? It consists of a forward solver that takes a chemical reaction network and predicts spectra under experimental conditions, and an inverse solver that infers kinetics from measured spectra. We applied DTCS to ambient-pressure X-ray photoelectron spectroscopy measurements of the Ag–H2O interface as an example. This approach enables real-time knowledge extraction and guides experiments until a stopping condition is met based on accuracy and degeneracy. As a step toward autonomous chemical characterization, DTCS provides mechanistic knowledge in a verified, standardized manner.

Chemistry↗
NASA NTRS1997

Nitrogen Oxides and Ozone from B-747 Measurements (NOXAR) During POLINAT-2 and SONEX: Overview and Case Studies on Continental and Marine Convection

In the framework of the project POLINAT 2 (Pollution in the North Atlantic Flight Corridor) we measured NO(x) (NO and NO2) and ozone on 85 flights through the North Atlantic Flight Corridor (NAFC) with a fully automated system permanently installed aboard an in-service Swissair B-747 airliner in the period of August to November 1997. The averaged NO(x) concentrations both in the NAFC and at the U.S. east coast were similar to that measured in autumn 1995 with the same system. The patchy occurrence of NO(x) enhancements up to 3000 pptv over several hundred kilometers (plumes), predominately found over the U.S. east coast lead to a log-normal NO(x) probability density function. In three case studies we examine the origins of such plumes by combining back-trajectories with brightness temperature enhanced (IR) satellite imagery, lightning observations from the U.S. National Lightning Detection Network (NLDN) and the Optical Transient Detector (OTD) satellite. We demonstrate that the location of NO(x) plumes can be well explained with maps of convective influence. We show that the number of lightning flashes in cluster of marine thunderstorms is proportional to the NO(x) concentrations observed several hundred kilometers downwind of the anvil outflows. From the fact that in autumn the NO(x) maximum was found several hundred kilometers off the U.S. east coast, it can be inferred that thunderstorms triggered over the warm Gulf Stream current are major sources for the regional upper tropospheric NO(x) budget in autumn.

Jeker, Dominique P.↗
DOE OSTI2023

Multiscale Reactive Model for 1,3,5-Triamino-2,4,6-trinitrobenzene Inferred by Reactive MD Simulations and Unsupervised Learning

When high-energy-density materials are subjected to thermal or mechanical insults at extreme conditions (shock loading), a coupled response between the thermo-mechanical and chemical behaviors is systematically induced. Herein we develop a reaction model for the fast chemistry of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) at the mesoscopic scale, where the chemical behavior is determined by underlying microscopic reactive simulations. The slow carbon cluster formation is not discussed in the present work. All-atom reactive molecular dynamics (MD) simulations are performed with the ReaxFF potential, and a reduced-order chemical kinetics model for TATB is fitted to isothermal and adiabatic simulations of single crystal chemical decomposition. Unsupervised machine learning techniques based on non-negative matrix factorization are applied to MD trajectories to model the decomposition kinetics of TATB in terms of a four-component model. The associated heats of reaction are fit to the temperature evolution from adiabatic decomposition trajectories. Using a chemical species analysis, we show that non-negative matrix factorization captures the main chemical decomposition steps of TATB and provides an accurate estimation of their evolution with temperature. The final analytical formulation, coupled to a diffusion term, is incorporated into a continuum formalism, and simulation results are compared one-to-one against MD simulations of 1D reaction propagation along different crystallographic directions and with different initial temperatures. A good agreement is found for both the temporal and spatial evolution of the temperature field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Efficient Subset Simulation using Hamiltonian Neural Network enhanced Markov Chain Monte Carlo Methods

The Monte Carlo method delivers an unbiased estimate of the probability of failure. However, the variance of the estimate depends on the number of evaluated samples. This number must be very large for estimations of a low probability of failure. If the evaluation of each sample is computationally expensive, the crude Monte Carlo simulation strategy is impracticable. Therefore, subset simulations are used to reduce the required number of evaluations. Subset simulations require a Markov Chain Monte Carlo sampler, such as the random walk Metropolis-Hastings algorithm. The algorithm, however, struggles with sampling in low-probability regions, especially if they are narrow. As a consequence, advanced Markov Chain Monte Carlo simulations have been developed. In particular, the Hamiltonian Monte Carlo method explores the target distribution rapidly. Driven by the idea of Hamiltonian dynamics, this sampler provides a non-random walk through the target distribution. The incorporation of subset simulation and Hamiltonian Monte Carlo methods has shown promising results for reliability analysis. One downside of the Hamiltonian Monte Carlo method is that gradient evaluations are computationally expensive, especially when dealing with high-dimensional problems and evaluating long trajectories. We show that integrating Hamiltonian neural networks in Hamiltonian Monte Carlo simulations significantly speeds up the sampling task. Furthermore, the enhancement of adaptive trajectory length within the Hamiltonian Monte Carlo results in the efficient proposal of the following states. Based on this recent enhancement, we provide a fast sampling strategy for subset simulations using Hamiltonian neural networks to replace the evaluation of the gradient and significantly speed up the Hamiltonian Monte Carlo simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The Potential Energy Hotspot: Effects of Impact Velocity, Defect Geometry, and Crystallographic Orientation

In energetic materials, the localization of energy into “hotspots” is known to dictate the initiation of chemical reactions and detonation. Recent all-atom simulations have shown that more energy is localized as internal potential energy (PE) than can be inferred from the kinetic energy (KE) alone. The mechanisms associated with pore collapse and hotspot formation are known to depend on pore geometry and dynamic material response such as plasticity. Therefore, we use molecular dynamics (MD) simulations to characterize shock-induced pore collapse and the subsequent formation of hotspots in 1,3,5-triamino-2,4,6-trinitrobenzene (TATB), a highly anisotropic molecular crystal, for various defect shapes, shock strengths, and crystallographic orientations. We find that the localization of energy as PE is consistently larger than the KE in cases with significant plastic deformation. Additionally, an analysis of MD trajectories reveals the underlying molecular- and crystal-level processes that govern the effect of orientation and pore shape on PE localization. We find that the regions of highest PE relate to the areas of maximum plastic deformation, while KE is maximized at the point of impact. Comparisons against octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) reveal less energy localization in TATB, which could be a contributing factor to the latter’s insensitivity.

36 MATERIALS SCIENCE↗
DOE OSTI2025

GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Large language models (LLMs) are increasingly adapted to downstream tasks via reinforcement learning (RL) methods like Group Relative Policy Optimization (GRPO), which often require thousands of rollouts to learn new tasks. We argue that the interpretable nature of language often provides a much richer learning medium for LLMs, compared to policy gradients derived from sparse, scalar rewards. To test this, we introduce GEPA (Genetic-Pareto), a prompt optimizer that thoroughly incorporates natural language reflection to learn high-level rules from trial and error. Given any AI system containing one or more LLM prompts, GEPA samples trajectories (e.g., reasoning, tool calls, and tool outputs) and reflects on them in natural language to diagnose problems, propose and test prompt updates, and combine complementary lessons from the Pareto frontier of its own attempts. As a result of GEPA's design, it can often turn even just a few rollouts into a large quality gain. Across six tasks, GEPA outperforms GRPO by 6% on average and by up to 20%, while using up to 35x fewer rollouts. GEPA also outperforms the leading prompt optimizer, MIPROv2, by over 10% (e.g., +12% accuracy on AIME-2025), and demonstrates promising results as an inference-time search strategy for code optimization. We release our code at https://github.com/gepa-ai/gepa.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

In-Situ and Remote-Sensing Data Fusion Using Machine Learning Techniques to Infer Urban and Fire Related Pollution Plumes

Airmass type characterization is key in understanding the relative contribution of various emission sources to atmospheric composition and air quality and can be useful in bottom-up model validation and emission inventories. However, classification of pollution plumes from space is often not trivial. Sub-orbital campaigns, such as SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) give us a unique opportunity to study atmospheric composition in detail, by using a vast suite of in-situ instruments for the detection of trace gases and aerosols. These measurements allow identification of spatial and temporal atmospheric composition changes due to various pollution plumes resulting from urban, biogenic and smoke emissions. Nevertheless, to transfer the knowledge gathered from such campaigns into a global spatial and temporal context, there is a need to develop workflow that can be applicable to measurements from space. In this work we rely on sub-orbital in-situ and total column remote sensing measurements of various pollution plumes taken aboard the NASA DC-8 during 2013 SEAC4RS campaign, linking them through a neural-network (NN) algorithm to allow inference of pollution plume types by input of columnar aerosol and trace-gas measurements. In particular, we use the 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) airborne measurements of wavelength dependent aerosol optical depth (AOD), particle size proxies, O3, NO2 and water vapor to classify different pollution plumes. Our method relies on assigning a-priori ground-truth labeling to the various plumes, which include urban pollution, different fire types (i.e. forest and agriculture) and fire stage (i.e. fresh and aged) using cluster analysis of aerosol and trace-gases in-situ and auxiliary (e.g. trajectory) data and the training of a NN scheme to fit the best prediction parameters using 4STAR measurements as input. We explore our misclassification rates as related to our ground-truth labels, and with multi-layered pollution plume cases. The next step in our analysis is to optimize parameter selection for a scheme that can be applied to space-borne aerosol and trace-gas observation platforms such as OMI, and future geostationary satellites such as TEMPO and GEO-CAPE.

Neural-network↗
NASA NTRS2010

Thermal History and Fragmentation of Ureilitic Asteroids: Insights from the Almahata Sitta Fall

Prior to recovery the Almahata Sitta fall was observed as the asteroid 2008 TC3 on an Earth-bound trajectory, providing a unique link between spectral data and ureilite composition. The event has also provided insight into the nature of ureilitic objects in space. In particular, the large size (4 m3) and low density (2.2 g/cm3) of the object combined with near-complete disintegration upon entry suggest a porous and loosely-consolidated body [1]. Accordingly, recovered fragments are small in size (1.5-283g) and represent several different ureilite lithologies. Some recovered fragments appear brecciated while others do not. We use chemical and mineralogic data to dissect the thermal history of this new ureilite, then use this information to compare the inferred size of fragments within the asteroid to those initially dislodged from a common ureilite parent body (UPB).

Herrin, J. S.↗
NASA NTRS2011

Analysis of ALTAIR 1998 Meteor Radar Data

We describe a new analysis of a set of 32 UHF meteor radar traces recorded with the 422 MHz ALTAIR radar facility in November 1998. Emphasis is on the velocity measurements, and on inferences that can be drawn from them regarding the meteor masses and mass densities. We find that the velocity vs altitude data can be fitted as quadratic functions of the path integrals of the atmospheric densities vs distance, and deceleration rates derived from those fits all show the expected behavior of increasing with decreasing altitude. We also describe a computer model of the coupled processes of collisional heating, radiative cooling, evaporative cooling and ablation, and deceleration - for meteors composed of defined mixtures of mineral constituents. For each of the cases in the data set we ran the model starting with the measured initial velocity and trajectory inclination, and with various trial values of the quantity mPs 2 (the initial mass times the mass density squared), and then compared the computed deceleration vs altitude curves vs the measured ones. In this way we arrived at the best-fit values of the mPs 2 for each of the measured meteor traces. Then further, assuming various trial values of the density Ps, we compared the computed mass vs altitude curves with similar curves for the same set of meteors determined previously from the measured radar cross sections and an electrostatic scattering model. In this way we arrived at estimates of the best-fit mass densities Ps for each of the cases. Keywords meteor ALTAIR radar analysis 1 Introduction This paper describes a new analysis of a set of 422 MHz meteor scatter radar data recorded with the ALTAIR High-Power-Large-Aperture radar facility at Kwajalein Atoll on 18 November 1998. The exceptional accuracy/precision of the ALTAIR tracking data allow us to determine quite accurate meteor trajectories, velocities and deceleration rates. The measurements and velocity/deceleration data analysis are described in Sections II and III. The main point of this paper is to use these deceleration rate data, together with results from a computer model, to determine values of the quantities mPs 2 (the meteor mass times its material density squared); and further, by combining these m s 2 values with meteor mass estimates for the same set of meteors determined separately from measured radar scattering

Zinn, J.↗
DOE OSTI2024

Terrestrial photosynthesis inferred from plant carbonyl sulfide uptake

Terrestrial photosynthesis, or gross primary production (GPP), is the largest carbon flux in the biosphere, but its global magnitude and spatiotemporal dynamics remain uncertain. The global annual mean GPP is historically thought to be around 120 PgC yr −1 , which is about 30–50 PgC yr −1 lower than GPP inferred from the oxygen-18 ( 18 O) isotope and soil respiration. This disparity is a source of uncertainty in predicting climate–carbon cycle feedbacks. Here, in this study, we infer GPP from carbonyl sulfide, an innovative tracer for CO 2 diffusion from ambient air to leaf chloroplasts through stomata and mesophyll layers. We demonstrate that explicitly representing mesophyll diffusion is important for accurately quantifying the spatiotemporal dynamics of carbonyl sulfide uptake by plants. From the estimate of carbonyl sulfide uptake by plants, we infer a global contemporary GPP of 157 (±8.5) PgC yr −1 , which is consistent with estimates from 18 O (150–175 PgC yr −1 ) and soil respiration ($149^{+29}_{-23}$) PgC yr −1 ), but with an improved confidence level. Our global GPP is higher than satellite optical observation-driven estimates (120–140 PgC yr –1 ) that are used for Earth system model benchmarking. This difference predominantly occurs in the pan-tropical rainforests and is corroborated by ground measurements, suggesting a more productive tropics than satellite-based GPP products indicated. As GPP is a primary determinant of terrestrial carbon sinks and may shape climate trajectories, our findings lay a physiological foundation on which the understanding and prediction of carbon–climate feedbacks can be advanced.

Lai, Jiameng↗
DOE OSTI2025

Real-Time event reconstruction for Nuclear Physics Experiments using Artificial Intelligence

Charged track reconstruction is a critical task in nuclear physics experiments, enabling the identification and analysis of particles produced in high-energy collisions. Machine learning (ML) has emerged as a powerful tool for this purpose, addressing the challenges posed by complex detector geometries, high event multiplicities, and noisy data. Traditional methods rely on pattern recognition algorithms like the Kalman filter, but ML techniques, such as neural networks, graph neural networks (GNNs), and recurrent neural networks (RNNs), offer improved accuracy and scalability. By learning from simulated and real detector data, ML models can identify and classify tracks, predict trajectories, and handle ambiguities caused by overlapping or missing hits. Moreover, ML-based approaches can process data in near-real-time, enhancing the efficiency of experiments at large-scale facilities like the Large Hadron Collider (LHC) and Jefferson Lab (JLAB). As detector technologies and computational resources evolve, ML-driven charged track reconstruction continues to push the boundaries of precision and discovery in nuclear physics. In these proceedings, we highlight advancements in charged track identification leveraging Artificial Intelligence within the CLAS12 detector, achieving a notable enhancement in experimental statistics compared to traditional methods. Additionally, we showcase real-time event reconstruction capabilities, including the inference of charged particle properties, such as momentum, direction, and species identification, at speeds matching data acquisition rates. These innovations enable the extraction of physics observables directly from the experiment in real-time.

Gavalian, Gagik (ORCID:0000000267385457)↗