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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Reduced-order modeling for efficient cross section library development in high-temperature gas reactor pebble-bed depletion analysis

Accurate modeling of running-in and equilibrium conditions in pebble-bed reactors (PBRs) requires precise microscopic multigroup neutron cross sections. In Griffin, deterministic neutronics calculations rely on multivariate interpolation over large cross section libraries, resulting in significant memory usage and performance bottlenecks. This work, together with a companion paper on Griffin integration, explores reduced-order models (ROMs) to replace interpolation with lightweight surrogates. Several ROM techniques are benchmarked, with deep neural networks (DNNs) demonstrating superior memory efficiency, scalability, and predictive accuracy. A total of 295 DNNs were trained to build a comprehensive isotope library, integrated into Griffin through a custom LibTorch interface for depletion analysis. Initial results demonstrate that DNN-based ROMs drastically reduce memory demands while preserving accuracy, enabling finer tabulations and additional state variables without overhead. In conclusion, the framework also supports online cross section generation and real-time DNN updates through transfer learning, improving fidelity by capturing self-shielding and evolving nuclide compositions during burnup.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

In the Mix : A Workshop Merging Computational Chemistry and Electrochemistry Alongside Data Science

As chemistry expands to more complex and interdisciplinary areas, a new generation of diverse researchers must engage with science and learn effective cross-disciplinary collaboration and communication. To these ends, we designed and implemented In the Mix, a graduate student-led, two-day workshop for undergraduate students promoting collaborative science in the context of energy storage innovations. Here, the interactive workshop was designed for future and emerging researchers to gain hands-on experience with data science, computational chemistry, and electrochemistry techniques that are critical for developing materials for battery technologies. Participants also visited commercial renewable energy facilities to help them connect discovery-based research with industry and broader societal considerations. The workshop content and structure ensured that participants experienced the interrelatedness of the fields and understood the importance of collaborative research to yield scientific advances with real-world applications. An external team evaluated the workshop and participants’ perceptions of their experiences. While our research context was energy storage, the workshop goals and outcomes are applicable to other contexts. Interdisciplinary, experiential workshops are a key avenue to broadening participation in science and research, and the ideas presented here can be readily modified for other scientific contexts and/or incorporated as broader impact activities.

25 ENERGY STORAGE↗
DOE OSTI2025

Convergent Protocols for Computing Protein–Ligand Interaction Energies Using Fragment-Based Quantum Chemistry

Fragment-based quantum chemistry methods offer a way to sidestep the steep nonlinear scaling of electronic structure calculations so that large molecular systems can be investigated using high-level methods. Here, we use fragmentation to compute protein–ligand interaction energies in systems with several thousand atoms, using a new software platform for managing fragment-based calculations that implements a screened many-body expansion. Convergence tests using a minimal-basis semiempirical method (HF-3c) indicate that two-body calculations, with single-residue fragments and simple hydrogen caps, are sufficient to reproduce interaction energies obtained using conventional supramolecular electronic structure calculations, to within 1 kcal/mol at about 1% of the computational cost. We also demonstrate that the HF-3c results are illustrative of trends obtained with density functional theory in basis sets up to augmented quadruple-ζ quality. Strategic deployment of fragmentation facilitates the use of converged biomolecular model systems alongside high-quality electronic structure methods and basis sets, bringing ab initio quantum chemistry to systems of hitherto unimaginable size. This will be useful for generation of high-quality training data for machine learning applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Moment extraction using an unfolding protocol without binning

Deconvolving (“unfolding”) detector distortions is a critical step in the comparison of cross-section measurements with theoretical predictions in particle and nuclear physics. However, most existing approaches require histogram binning while many theoretical predictions are at the level of statistical moments. We develop a new approach to directly unfold distribution moments as a function of another observable without having to first discretize the data. Our moment unfolding technique uses machine learning and is inspired by Boltzmann weight factors and generative adversarial networks (GANs). We demonstrate the performance of this approach using jet substructure measurements in collider physics. With this illustrative example, we find that our moment unfolding protocol is more precise than bin-based approaches and is as or more precise than completely unbinned methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Learning genetic perturbation effects with variational causal inference

Advances in sequencing technologies have enhanced the understanding of gene regulation in cells. In particular, Perturb-seq has enabled high-resolution profiling of the transcriptomic response to genetic perturbations at the single-cell level. This understanding has implications in functional genomics and potentially for identifying therapeutic targets. Various computational models have been developed to predict perturbational effects. While deep learning models excel at interpolating observed perturbational data, they tend to overfit in the lack of enough data and may not generalize well to unseen perturbations. In contrast, mechanistic models, such as linear causal models based on gene regulatory networks, hold greater potential for extrapolation, as they encapsulate regulatory information that can predict responses to unseen perturbations. However, their application has been limited to small studies due to overly simplistic assumptions, making them less effective in handling noisy, large-scale single-cell data. We propose a hybrid approach that combines a mechanistic causal model with variational deep learning, termed Single Cell Causal Variational Autoencoder (SCCVAE). The mechanistic model employs a learned regulatory network to represent perturbational changes as shift interventions that propagate through the learned network. SCCVAE integrates this mechanistic causal model into a variational autoencoder, generating rich, comprehensive transcriptomic responses. Our results indicate that SCCVAE exhibits superior performance over current state-of-the-art baselines for extrapolating to predict unseen perturbational responses. Additionally, for the observed perturbations, the latent space learned by SCCVAE allows for the identification of functional perturbation modules and simulation of single-gene knockdown experiments of varying penetrance, presenting a robust tool for interpreting and interpolating perturbational responses at the single-cell level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Assessing shellfish water exposure to fecal bacteria pollution in Salish Sea: three-dimensional modeling and implications for monitoring

Fecal bacteria (FB) contamination poses significant risks to shellfish safety and management in coastal and estuarine waters. Despite extensive pollution identification and correction efforts, FB contamination in shellfish-growing areas persists in the Salish Sea, highlighting the need to identify overlooked sources and better understand FB transport from riverine and shoreline inputs to shellfish beds. To address this, a high-resolution three-dimensional hydrodynamic model coupled with FB kinetics was developed and applied to a case study site in Salish Sea—Portage Bay—to simulate freshwater plume circulation, flushing dynamics, and bacterial transport. Daily FB loading from the major freshwater inflow—Nooksack River was generated by both linear interpolation and integrating a machine learning approach (XGBoost), trained on historical hydrological and meteorological data. The model successfully reproduced both the magnitude and seasonal variation of FB concentrations in Portage Bay for the year of 2021, demonstrating that simplified FB kinetics with first-order decay due to mortality was effective in this dynamic coastal environment with short flushing time. Model results identified the Nooksack River as the dominant far-field FB source, while scenario simulations showed that near-field coastal stormwater outfalls elevated local FB levels following rainfall, particularly under low-flow conditions. The XGBoost prediction provided comparable or superior accuracy to linear interpolation, particularly during periods of missing observational data, by capturing short-term variability and event-driven loading more effectively. Integrating data-driven riverine FB inputs with mechanistic coastal numerical modeling provides a robust framework for operational forecasting of shellfish bed exposure risk and supports adaptive monitoring and management of shellfish growing areas in the Salish Sea and similar coastal systems.

Salish Sea↗
NASA NTRS1992

An expert system for monitoring dynamic aspects of RCS

An expert system (ARC) based on the MIMIC(2) framework is being developed for assistance in monitoring the Space Shuttle reaction control system (RCS) and in training RCS mission controllers. ARC has four components: a hypothesis generator, a model-constructor, a model-simulator and a matcher. The hypothesis generator examines current readings of sensors to generate a set of hypotheses. The model constructor generates models from these hypotheses. The simulator predicts future behaviors of the system. The matcher validates hypotheses by testing the predicted states against real data. The hypothesis generator postulates faults using model-based reasoning (MBR), learned rules from real data, and expert-defined rules. The simulator uses a qualitative QSIM model of the RCS which can handle both nominal modes as well as a variety of fault models. This allows the simulator to simulate the system even when one or more components have degraded. Finally, the matcher is responsible for comparing readings against predictions and calculating confidence in a particular hypothesis.

Kulkarni, Deepak↗
NASA NTRS1993

New Technologies for Space Avionics, 1993

The report reviews a 1993 effort that investigated issues associated with the development of requirements, with the practice of concurrent engineering and with rapid prototyping, in the development of a next-generation Reaction Jet Drive Controller. This report details lessons learned, the current status of the prototype, and suggestions for future work. The report concludes with a discussion of the vision of future avionics architectures based on the principles associated with open architectures and integrated vehicle health management.

Aibel, David W.↗
NASA NTRS1994

NASA's Hypersonic Research Engine Project: A review

The goals of the NASA Hypersonic Research Engine (HRE) Project, which began in 1964, were to design, develop, and construct a high-performance hypersonic research ramjet/scramjet engine for flight tests of the developed concept over the speed range of Mach 4 to 8. The project was planned to be accomplished in three phases: project definition, research engine development, and flight test using the X-15A-2 research airplane, which was modified to carry hydrogen fuel for the research engine. The project goal of an engine flight test was eliminated when the X-15 program was canceled in 1968. Ground tests of full-scale engine models then became the focus of the project. Two axisymmetric full-scale engine models, having 18-inch-diameter cowls, were fabricated and tested: a structural model and combustion/propulsion model. A brief historical review of the project, with salient features, typical data results, and lessons learned, is presented. An extensive number of documents were generated during the HRE Project and are listed.

Andrews, Earl H.↗
NASA NTRS1994

New technologies for space avionics

This report reviews a 1994 effort that continued 1993 investigations into issues associated with the definition of requirements, with the practice concurrent engineering and rapid prototyping in the context of the development of a prototyping of a next-generation reaction jet driver controller. This report discusses lessons learned, the testing of the current prototype, the details of the current design, and the nature and performance of a mathematical model of the life cycle of a pilot operated valve solenoid.

Aibel, David W.↗
NASA NTRS1992

Toward Agent Programs with Circuit Semantics

New ideas are presented for computing and organizing actions for autonomous agents in dynamic environments-environments in which the agent's current situation cannot always be accurately discerned and in which the effects of actions cannot always be reliably predicted. The notion of 'circuit semantics' for programs based on 'teleo-reactive trees' is introduced. Program execution builds a combinational circuit which receives sensory inputs and controls actions. These formalisms embody a high degree of inherent conditionality and thus yield programs that are suitably reactive to their environments. At the same time, the actions computed by the programs are guided by the overall goals of the agent. The paper also speculates about how programs using these ideas could be automatically generated by artificial intelligence planning systems and adapted by learning methods.

ELECTRICAL CIRCUITS↗
NASA NTRS2003

The NASA Education Enterprise: Inspiring the Next Generation of Explorers

On April 12, 2002, NASA Administrator Sean O Keefe opened a new window to the future of space exploration with these words in his Pioneering the Future address. Thus began the conceptual framework for structuring the new Education Enterprise. The Agency s mission is to understand and protect our home planet; to explore the universe in search for life; and to inspire the next generation of explorers as only NASA can. In adopting this mission, education became a core element and is now a vital part of every major NASA research and development mission. NASA s call to inspire the next generation of explorers is now resounding throughout the NASA community and schools of all levels all around the country. The goal is to capture student interest, nurture their natural curiosities, and intrigue their minds with new and exciting scientific research; as well as to provide educators with the creative tools they need to improve America s scientific literacy. The future of NASA begins with America s youngest scholars. According to Administrator O Keefe s address, if NASA does not motivate the youngest generation now, there is little prospect this generation will choose to pursue scientific disciplines later. Since embracing Administrator O Keefe s educational mandate over a year ago, NASA has been fully devoted to broadening its roadmap to motivation. The efforts have generated a whole new showcase of thoughtprovoking and fun learning opportunities, through printed material, Web sites and Webcasts, robotics, rocketry, aerospace design contests, and various other resources as only NASA can.

Source record↗
NASA NTRS2008

Aerosol Profile Measurements from the NASA Langley Research Center Airborne High Spectral Resolution Lidar

Since achieving first light in December of 2005, the NASA Langley Research Center (LaRC) Airborne High Spectral Resolution Lidar (HSRL) has been involved in seven field campaigns, accumulating over 450 hours of science data across more than 120 flights. Data from the instrument have been used in a variety of studies including validation and comparison with the Cloud- Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite mission, aerosol property retrievals combining passive and active instrument measurements, aerosol type identification, aerosol-cloud interactions, and cloud top and planetary boundary layer (PBL) height determinations. Measurements and lessons learned from the HSRL are leading towards next-generation HSRL instrument designs that will enable even further studies of aerosol intensive and extensive parameters and the effects of aerosols on the climate system. This paper will highlight several of the areas in which the NASA Airborne HSRL is making contributions to climate science.

Obland, Michael D.↗
NASA NTRS2009

Implementation of an Online Database for Chemical Propulsion Systems

The Johns Hopkins University, Chemical Propulsion Information Analysis Center (CPIAC) has been working closely with NASA Goddard Space Flight Center (GSFC); NASA Marshall Space Flight Center (MSFC); the University of Alabama at Huntsville (UAH); The Johns Hopkins University, Applied Physics Laboratory (APL); and NASA Jet Propulsion Laboratory (JPL) to capture satellite and spacecraft propulsion system information for an online database tool. The Spacecraft Chemical Propulsion Database (SCPD) is a new online central repository containing general and detailed system and component information on a variety of spacecraft propulsion systems. This paper only uses data that have been approved for public release with unlimited distribution. The data, supporting documentation, and ability to produce reports on demand, enable a researcher using SCPD to compare spacecraft easily, generate information for trade studies and mass estimates, and learn from the experiences of others through what has already been done. This paper outlines the layout and advantages of SCPD, including a simple example application with a few chemical propulsion systems from various NASA spacecraft.

David B. Owen, II↗
NASA NTRS2009

Parametric Analysis of a Hover Test Vehicle using Advanced Test Generation and Data Analysis

Large complex aerospace systems are generally validated in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. This is due to the large parameter space, and complex, highly coupled nonlinear nature of the different systems that contribute to the performance of the aerospace system. We have addressed the factors deterring such an analysis by applying a combination of technologies to the area of flight envelop assessment. We utilize n-factor (2,3) combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. The data generated is automatically analyzed through a combination of unsupervised learning using a Bayesian multivariate clustering technique (AutoBayes) and supervised learning of critical parameter ranges using the machine-learning tool TAR3, a treatment learner. Covariance analysis with scatter plots and likelihood contours are used to visualize correlations between simulation parameters and simulation results, a task that requires tool support, especially for large and complex models. We present results of simulation experiments for a cold-gas-powered hover test vehicle.

Gundy-Burlet, Karen↗
NASA NTRS2016

MoonRIDERS: NASA and Hawaiis Innovative Lunar Surface Flight Experiment for Landing in Late 2017

Recently, NASA Kennedy Space Center, Hawaii's state aerospace agency PISCES, and two Hawaii high schools Iolani and Kealakehe have come together in a unique collaboration called MoonRIDERS. This strategic partnership will allow Hawaii students to participate directly in sending a science experiment to the surface of the moon. The MoonRIDERS project started in the spring of 2014, with each institution responsible for its own project costs and activities. PISCES, given its legislative direction in advancing planetary surface systems, saw this collaboration as an important opportunity to inspire a young generation and encourage STEM (Science, Technology, Engineering, and Mathematics) learning. Under the guidance of PISCES and NASA, the students will be involved hands-on from start to finish in the engineering, testing, and validation of a space technology called the Electrodynamic Dust Shield (EDS). Dust is a critical issue for space exploration, as evidenced by the Apollo lunar missions and Mars rovers and landers. Dust creates a number of problems for humans and hardware, including inhalation, mechanical interference, wear and tear on spacesuits, inhibition of heat transfer on radiators, and reduced efficiency of solar panels. To address this, the EDS is designed to work on a variety of materials, and functions by generatingelectrodynamic fields to clear away the dust. The Google Lunar XPRIZE (GLXP), a space competition "designed to inspire pioneers to do robotic space transport on a budget," serves as a likely method for the MoonRIDERS to get their project to the moon. The EDS would potentially be flown as a hosted payload on a competitor's lander (still to be chosen). This briefing will provide an overview of the technology, the unique partnership, progress update and testing leading to this flight opportunity.

Payload experiment↗
NASA NTRS2004

Mars Exploration Rover (MER) project environmental assurance program

This paper presents a comprehensive summary of the environmental assurance program for the MER project. A series of test and analysis metrics are generated. Selections of the numerous lessons that have been learned from implementation of the MER environmental assurance program are documented in this paper. They include both technical and programmatic lessons that would be helpful in improving implementation of the environmental program for future projects.

Hoffman, Alan R↗
NASA NTRSDate not supplied

Concept of Operations and Checkout Testing for Green Propellant Loading System

Low toxicity propulsion, or Green Propulsion, is increasingly being considered for use on spacecraft following the successful demonstration during the Green Propellant Infusion Mission in 2020. One of these missions is JPL’s Lunar Flashlight (LF), a polar orbiting 12U CubeSat that uses lasers to investigate water ice deposits on the moon’s surface. For the past 3 years, Marshall Space Flight Center has developed the ASCENT propulsion system for LF, along with a green propellant loading system to service the spacecraft. The Propellant Loading System (PLS) is composed of 3 mobile carts designed to operate at a variety of launch facilities and handle the 2 primary green propellants, LMP-103S and ASCENT. In anticipation of LF propellant loading, the PLS was recently installed into an MSFC facility for checkout operations. In the winter of 2020, a simulant spacecraft was successfully loaded with water, pressurized to flight pressure, and drained back into the system. These checkouts drove the concept of operations for final spacecraft loading and informed minor modifications to the PLS for better operability. Additionally, a parallel effort was started to design the next generation of the PLS based on water flow lessons learned. With water flow testing complete, propellant checkouts and wet dress rehearsals with spacecraft pathfinder hardware are anticipated to begin in late Spring 2021.

Green Propulsion↗