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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Modeling and Characterizing the electron backscatter in a cylindrical anode-based distributed X-ray source

Upcoming advancements in computed tomography architectures warrants the investigation of new X-ray source designs and the impacts that electron backscatter can have on these designs. One such design being investigated is a distributed, cylindrical anode-based X-ray source. For such a distributed X-ray source, we developed a modeling pipeline for simulating electron optics and transport to characterize the quality of the primary X-ray beam and the electron backscatter behavior. We report our results on the energy distributions of the bremsstrahlung spectra; electron backscatter ratio; and spatial, temporal, and energy distributions of backscattered electrons that return to the anode.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2021

23.4% monolithic epitaxial GaAsP/Si tandem solar cells and quantification of losses from threading dislocations

A 2-terminal, dual-junction, epitaxially integrated, GaAsP/Si tandem solar cell with an 3rd party certified efficiency of 23.4 % was fabricated via MOCVD growth on an ex-situ produced Si sub cell. The drastic efficiency improvement over the authors previous peer-reviewed demonstration of such a device architecture is examined. Critical advancements in top cell design to maximize short wavelength response were critical in enabling improved top cell response. An in-depth analysis of this champion tandem cell has identified key loss mechanisms which elucidate the pathway for further efficiency gains. First, voltage dependent collection efficiency in the GaAsP top cell is the primary cause of fill factor losses currently limiting efficiency. Analysis of spectrally resolved I–V measurements and analytical device modeling and indicate poor diffusion length due to elevated dislocation densities as the likely cause for the voltage dependent collection efficiency. Second, modeling for the GaAs 0.75 P 0.25 top cell, using experimental data at multiple dislocation densities, provides quantitative understanding of the current and voltage losses associated with threading dislocations providing a clear efficiency pathway with reduction in dislocation density. Finally, Si subcell modeling identifies the pathway for further Si subcell advances over the present, simplistic design, which has yet to employ the known benefits of rear surface texture or dielectric passivation.

14 SOLAR ENERGY↗
DOE OSTI2022

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature’s own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and “smart” architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Furthermore, continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Mass Conservation Relaxed (MCR) LSTM Model for Streamflow Simulation Across CONUS

The recent development of the physics-aware Mass-Conserving Long Short-Term Memory network (MC-LSTM) provides an alternative to other data-driven Deep Learning (DL) models in hydrology. Mass-Conserving Long Short-Term Memory incorporates mass conservation directly into the LSTM architecture. Despite the theoretical advancements, studies have reported a surprisingly limited performance of the MC-LSTM in streamflow simulation. We hypothesize that such a limitation is due to the unrealistic mass conservation scheme in MC-LSTM, which overlooks unobserved incoming water fluxes beyond precipitation. As an attempt to verify this hypothesis, we propose a Mass Conservation Relaxed LSTM (MCR-LSTM), which incorporates a bi-directional mass relaxation (MR) component to account for potential incoming water fluxes beyond precipitation. We train and test the proposed MCR-LSTM model across 531 watersheds in the contiguous United States (CONUS) against three baseline models: the Sacramento Soil Moisture Accounting, LSTM, and MC-LSTM. Our results show that MCR-LSTM outperforms MC-LSTM despite its underperformance compared to LSTM. Specifically, MCR-LSTM's advantage over MC-LSTM is mainly seen in the Plains and Western U.S., where the newly incorporated MR component better simulates water loss and suggests the likely existence of additional incoming water fluxes beyond precipitation, respectively. The novelty and contribution of this study are twofold: firstly, it introduces an alternative physics-aware DL tool (i.e., MCR-LSTM) in hydrology with higher accuracy in specific regions compared to MC-LSTM. Secondly, it provides a diagnosis of regions where strict, precipitation-based mass conservation constraints may be unrealistic in streamflow simulation.

deep learning↗
DOE OSTI2014

Fractal design concepts for stretchable electronics

Stretchable electronics provide a foundation for applications that exceed the scope of conventional wafer and circuit board technologies due to their unique capacity to integrate with soft materials and curvilinear surfaces. The range of possibilities is predicated on the development of device architectures that simultaneously offer advanced electronic function and compliant mechanics. Here we report that thin films of hard electronic materials patterned in deterministic fractal motifs and bonded to elastomers enable unusual mechanics with important implications in stretchable device design. In particular, we demonstrate the utility of Peano, Greek cross, Vicsek and other fractal constructs to yield space-filling structures of electronic materials, including monocrystalline silicon, for electrophysiological sensors, precision monitors and actuators, and radio frequency antennas. These devices support conformal mounting on the skin and have unique properties such as invisibility under magnetic resonance imaging. Here, the results suggest that fractal-based layouts represent important strategies for hard-soft materials integration.

42 ENGINEERING↗
DOE OSTI2023

Structures of honeybee-infecting Lake Sinai virus reveal domain functions and capsid assembly with dynamic motions

Understanding the structural diversity of honeybee-infecting viruses is critical to maintain pollinator health and manage the spread of diseases in ecology and agriculture. We determine cryo-EM structures of $T$ = 4 and $T$ = 3 capsids of virus-like particles (VLPs) of Lake Sinai virus (LSV) 2 and delta-N48 LSV1, belonging to tetraviruses, at resolutions of 2.3–2.6 Å in various pH environments. Structural analysis shows that the LSV2 capsid protein (CP) structural features, particularly the protruding domain and C-arm, differ from those of other tetraviruses. The anchor loop on the central β-barrel domain interacts with the neighboring subunit to stabilize homo-trimeric capsomeres during assembly. Delta-N48 LSV1 CP interacts with ssRNA via the rigid helix α1’, α1’–α1 loop, β-barrel domain, and C-arm. Cryo-EM reconstructions, combined with X-ray crystallographic and small-angle scattering analyses, indicate that pH affects capsid conformations by regulating reversible dynamic particle motions and sizes of LSV2 VLPs. C-arms exist in all LSV2 and delta-N48 LSV1 VLPs across varied pH conditions, indicating that autoproteolysis cleavage is not required for LSV maturation. The observed linear domino-scaffold structures of various lengths, made up of trapezoid-shape capsomeres, provide a basis for icosahedral $T$ = 4 and $T$ = 3 architecture assemblies. These findings advance understanding of honeybee-infecting viruses that can cause Colony Collapse Disorder.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Decoupling electrode kinetics to elucidate reaction mechanisms in alkaline water electrolysis

Alkaline water electrolysis (AWE) presents key advantages, including reduced material costs, enhanced operational stability, and compatibility with non-precious metal catalysts, positioning it as a scalable route for hydrogen production. In this study, we introduce a minimally invasive single-cell configuration incorporating a reference electrode via diaphragm extension to form an internal ion channel. This setup, combined with an interfaced potentiostat and auxiliary electrometer, enables real-time, independent monitoring of anode and cathode behavior, offering high-resolution electrochemical diagnostics. While it is well established that the hydrogen evolution reaction (HER) exhibits sluggish kinetics in alkaline media, our study reveals that this limitation persists even in practical AWE systems where nickel-based substrates are used as electrodes. This observation is supported by both experimental data and voltage breakdown modeling. Arrhenius-type analysis reveals that localized electric fields induced by catalysts shift the reaction kinetics from classical Butler–Volmer behavior toward a Marcus-like regime, where interfacial molecular dynamics and bimolecular charge transfer dominate. We propose a semi-empirical model and a surficial reaction mechanism to describe these dynamics. This work underscores the critical need for cathode innovation and provides a rational framework for designing advanced catalysts and electrode architectures to optimize AWE performance.

08 HYDROGEN↗
DOE OSTI2024

Enhancing Discoverability and Management of Atmospheric Data at Scale: Solutions from the ARM Data Center

The Atmospheric Radiation Measurement (ARM) is a multi-laboratory and multi-institutional U.S. Department of Energy (DOE) Office of Science National User Facility. The ARM Data Center (ADC), located at Oak Ridge National Laboratory, collects, archives, and shares vast atmospheric data crucial for climate research. The ADC manages over 7 PB of data from 460 instruments worldwide, processing it into more than 11,000 diverse data products using the Network Common Data Form (NetCDF) for machine-independent accessibility. The primary challenge addressed in this paper is the efficient management and distribution of vast and diverse datasets essential for the climate research community, enhancing accessibility through advanced tools like Data Discovery. The ADC has developed advanced infrastructure and software architecture to handle the continuous influx of heterogeneous data to enhance data discoverability, resulting in increased scientific collaboration. In 2023, users from over 34 countries downloaded and utilized ARM data, resulting in 1,455 publications. The ADC’s efforts have significantly improved the discoverability and usability of atmospheric data, fostering extensive scientific research and collaboration. This paper details the solutions implemented by the ADC team for efficient data discovery and distribution, and it demonstrates ARM’s capability of staging processed data for scientific analysis.

Shah, Chirag [ORNL] (ORCID:0000000203145737)↗
DOE OSTI2019

How lithium dendrites form in liquid batteries

Conventional rechargeable lithium (Li)–ion batteries generally use graphite as the anode, where Li ions are stored in the layered graphite. However, the use of Li metal as the anode is now being reconsidered. These next-generation battery technologies could potentially double the cell energy of conventional Li-ion batteries. Rechargeable Li metal batteries were commercialized more than four decades ago but were in use only briefly because of safety concerns. With the advancements of electrolyte, electrode architecture, and characterization techniques in recent years, a better fundamental understanding of the interfacial reactions during charging and discharging that dictate cell performance has developed and inspired a reevaluation of the use of Li metal anodes in rechargeable batteries.

Xiao, Jie↗
DOE OSTI2025

Plant Reload Optimization (prlo)

The PRLO framework is built on a modular and extensible architecture that tightly couples advanced evolutionary optimization algorithms with nuclear fuel depletion solvers (i.e., nuclear physics neutronics code). It supports exploring complex, high-dimensional design spaces constrained by user-specified operational, safety, and economic constraints. Objectives such as minimizing fresh fuel enrichment, flattening radial and axial power distributions, and maximizing discharge burnup are evaluated. PRLO’s equilibrium cycle optimization capability enables the identification of core configurations that maintain fuel cycle sustainability over extended planning horizons. Its integration with the RAVEN platform facilitates optimization of loading patterns or fuel shuffling schemes across multiple cycles. The interface with SIMULATE, a licensed industry-standard nodal code developed by Studsvik, ensures accurate neutronic and thermal-hydraulic feedback for reactor core design. PRLO’s automated workflow engine supports iterative design refinement, enabling utilities to streamline core design processes and meet evolving performance and regulatory targets.

Kim, Junyung [Idaho National Laboratory] (00090005↗
DOE OSTI2017

EV Everywhere Sandia Appendix

Sandia National Laboratories has a large cybersecurity research program supporting the Department of Energy as well as other federal departments and agencies. Fundamental research helps us stay ahead of threats. Sandia’s research efforts in cybersecurity are focused in three broad areas: 1. Trusted hardware, software, and systems 2. Networks and systems architectures and analysis 3. Effective cyber defense systems Research is conducted in state-of-the-art facilities with extensive computing and information science capabilities, which range from architectures and algorithms to advanced modeling and simulation. Three areas of active research most applicable to connected vehicle security are: Red Team Assessments, Emulytics™, and Supply Chain Risk Management.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

The Development of the Generalized Spallation Model [Thesis]

The generalized spallation model (GSM) is a general spallation physics event generator with a physics-rich history dating back to the early 1970s during the development of the CEM. GSM estimates the outcome of high-energy hadron-nucleus and nucleus-nucleus collisions, where collision energies often exceed hundreds of MeV. Such a collision may include a neutron incident on carbon, for example. The thesis presented here outlines the continued development and modernization of GSM, whereby it was migrated from a legacy Fortran code to utilize an advanced and modern Fortran architecture. Verification and validation of GSM also took place throughout the continued development of GSM. The results of this work have transformed GSM into a highly modern and powerful event generator, where numerous benefits have been fostered and precipitated, including but not limited to portability, modularization, flexibility, robustness, and scalability. GSM is a robust and well developed software library that clients and end-users may confidently use, within the scope outlined here, as a result of this work.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks

Focal Area: This white paper responds to Focal area III by exploring data fusion, learning and explainable AI methods in characterizing hydrological extremes and interconnections. It also addresses Focal area II by using probabilistic AI and ensemble ML for predicting extremes and compound extremes. Science Challenge: A key question associated with the integrated water (or hydrological) cycle grand challenge in the Earth and Environmental Systems Sciences Division (EESSD) strategic plan, is how the frequency and intensity of hydrological events will change. Prediction of the tail behavior (extremes) of the hydrological cycle is especially challenging, because of their stochasticity and low probability. These extreme events and their compound impacts have significant societal and economic consequences. It is anticipated for the next-generation Earth System models (ESMs), that model predictability of the water cycle will improve with increased resolution (e.g., regionally refined E3SM), advanced software and computational architectures, and improved model physics based on the data from ARM measurements and high-fidelity models. However, the challenges for predictability of low-probability high-impact extreme events will unlikely be alleviated with conventional modeling and data-driven approaches, as ESMs are calibrated largely for capturing the high-frequency mean climate states. Recent AI and ML applications have shown great potential in quantifying well-defined climate extremes (e.g., supervised learning of tropical cyclones/atmospheric rivers by ClimateNet1) but few efforts are dedicated to compound events, extreme drivers and uncertainty estimation. We envision the opportunity to develop and apply ML and interpretable AI methods extended on the existing efforts, specifically, for: (1) identification of compound extremes, (2) diagnosing drivers of extremes, (3) bias correction in extreme predictions and (4) probabilistic modeling of extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Next Gen High Efficiency Boosted Engine Development

This work represents an advanced engineering research project partially funded by the U.S. Department of Energy (DOE). Ford Motor Company, FEV North America, and Oak Ridge National Laboratory collaborated to develop a next generation boosted spark ignited engine concept. The project goals, specified by the DOE, were 23% improved fuel economy and 15% reduced weight relative to a 2015 or newer light-duty vehicle. The fuel economy goal was achieved by designing an engine incorporating high geometric compression ratio, high dilution tolerance, low pumping work, and low friction. The increased tendency for knock with high compression ratio was addressed using early intake valve closing (EIVC), cooled exhaust gas recirculation (EGR), an active pre-chamber ignition system, and careful management of the fresh charge temperature. Engine weight reduction measures were implemented throughout the engine system making use of composite materials, advanced manufacturing techniques, and architectural choices. This report outlines the analytical, design, fabrication, and test work conducted for the duration of the project. The combustion system stability, EGR tolerance, and knock resistance were validated on a single cylinder engine. An inline six-cylinder engine was then designed targeting application in the Ford F150. Multi-cylinder engines were produced and tested achieving the target vehicle fuel economy improvement of 23% assessed using measured engine fuel consumption combined with a vehicle drive cycle simulation. Actions were identified and designs were demonstrated to achieve the 15% weight reduction target. This project included items covering a range of technology readiness levels. Some of the technologies explored are production ready, while others were investigated to understand the limitations for what can be achieved in a stoichiometric, gasoline-fueled, spark-ignited internal combustion engine.

42 ENGINEERING↗
DOE OSTI2020

The Architectural Dynamics of the Bacterial Flagellar Motor Switch

The rotary bacterial flagellar motor is remarkable in biochemistry for its highly synchronized operation and amplification during switching of rotation sense. The motor is part of the flagellar basal body, a complex multi-protein assembly. Sensory and energy transduction depends on a core of six proteins that are adapted in different species to adjust torque and produce diverse switches. Motor response to chemotactic and environmental stimuli is driven by interactions of the core with small signal proteins. The initial protein interactions are propagated across a multi-subunit cytoplasmic ring to switch torque. Torque reversal triggers structural transitions in the flagellar filament to change motile behavior. Subtle variations in the core components invert or block switch operation. The mechanics of the flagellar switch have been studied with multiple approaches, from protein dynamics to single molecule and cell biophysics. The architecture, driven by recent advances in electron cryo-microscopy, is available for several species. Computational methods have correlated structure with genetic and biochemical databases. The design principles underlying the basis of switch ultra-sensitivity and its dependence on motor torque remain elusive, but tantalizing clues have emerged. This review aims to consolidate recent knowledge into a unified platform that can inspire new research strategies.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1982

Advances in information extraction techniques

Sundry recent developments are presented which show some potential for affecting the automatic extraction of information from remotely sensed data. Pattern representations more abstract than Euclidean vector spaces offer some hope of unifying structural and decision theoretical approaches. The estimation of expected classification error rates is becoming more sophisticated and rigorous, but useful finite-sample results for nonparametric distributions appear unobtainable. Focus on computational complexity allows comparison of algorithms, while software engineering techniques reduce the effort necessary to develop and maintain complex image processing systems. Advances in computer systems architecture, commercial database technology, and man-machine communications should be closely monitored by the remote sensing community. A NASA-sponsored recommendation for research directions in mathematical pattern recognition are offered.

Nagy, G.↗
NASA NTRS1990

Engine selection for the Space Exploration Initiative

The process used by NASA in the selection of the engine for the Space Exploration Initiative mission is described. The major propulsion requirements of the engine are addressed along with the engine options and key drivers and trades. Special attention is given to the requirements of the propellant feed system and the reaction control system. As a result of the 90-study, four moderate chamber pressure expander-cycle oxygen/hydrogen engines with a thrust level of 20,000 lbf each were selected for the lunar transfer vehicle. The paper also presents results of architecture studies and of advanced engine test bed studies.

Jones, Lee W.↗