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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Leveraging High-throughput Computation and Machine Learning to Discover and Understand Low-Temperature Fast Oxygen Conductors (Final Technical Report)

The major goals of this work are twofold: (1) to enable transformative basic understanding of structure-property-performance relationships governing oxygen transport in oxygen-active materials and (2) facilitate the discovery and rational design of new oxygen-active materials which transport oxygen efficiently at low temperature. Transformative understanding and materials design will be accomplished by synergistically combining materials data mining, machine learning, high-throughput computation and targeted experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MSU IETC LSTM Ethernet Decode (AN EDGE)

This research explores the ability of machine learning to perform signal separation of an Ethernet style encoded, full-duplex communication. Typical signal separation currently requires an active tap of the communication line, followed by a recombination and retransmission of the data. The purpose of this research is to study a passive approach to data acquisition from a full-duplex signal. The machine learning model used in this research is a long-short-term memory recurrent neural network (LSTM-RNN). The results show that the LSTM was largely successful in recreating the transmission signal from the measured data points, though the separated signals have not yet been tested using a decoding method.

Full Duplex Signals↗
DOE OSTI2026

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗
DOE OSTI2023

Investigating permafrost carbon dynamics in Alaska with artificial intelligence

Abstract Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integratesin-situmeasurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO 2 ), and methane flux (CH 4 )—with high precision and minimal loss (i.e. ALT RMSE : 1.327 cm [1969–2022]; CO 2 RMSE : 0.697µmolCO 2 m −2 s −1 [2003–2021]; CH 4 RMSE : 0.715 nmolCH 4 m −2 s −1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

Environmental Sciences & Ecology↗
DOE OSTI2025

DoE as a “Digital Innovation” Sponsor of the WCRP OSC2023 (Final Report)

The WCRP Open Science Conference (https://wcrp-osc2023.org/) was a once-in-a-decade opportunity to jointly explore the transformative actions urgently needed to ensure a sustainable future. Held in Kigali, Rwanda on October 23 -27, 2023, it showcased advances in climate science, helped identify gaps and opportunities, and provided a forum for communities to jointly develop future activities. Scientists, practitioners, politicians, policy makers, intergovernmental agencies and NGOs showcased their work, learned from each other, and explored new ways to work together.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1987

Technology advancements for servicing of future spacecraft systems

Problems associated with the in-orbit repair and maintenance of spacecraft systems are examined with reference to experience gained from three servicing missions: in-orbit capture, repair, and reflight of the Solar Maximum Mission satellite, capture and return to earth of the Palapa and Westar communications satellites, and in-orbit repair of Syncom 3. It is then shown how the lessons learned from the three servicing missions are applied to current and future servicing activities. In particular, planned servicing missions for the Hubble Space Telescope, tools and servicing facilities development, and the development of the Explorer Platform are discussed.

Cepollina, F. J.↗
NASA NTRS1989

Evaluation and analysis of the orbital maneuvering vehicle video system

The work accomplished in the summer of 1989 in association with the NASA/ASEE Summer Faculty Research Fellowship Program at Marshall Space Flight Center is summarized. The task involved study of the Orbital Maneuvering Vehicle (OMV) Video Compression Scheme. This included such activities as reviewing the expected scenes to be compressed by the flight vehicle, learning the error characteristics of the communication channel, monitoring the CLASS tests, and assisting in development of test procedures and interface hardware for the bit error rate lab being developed at MSFC to test the VCU/VRU. Numerous comments and suggestions were made during the course of the fellowship period regarding the design and testing of the OMV Video System. Unfortunately from a technical point of view, the program appears at this point in time to be trouble from an expense prospective and is in fact in danger of being scaled back, if not cancelled altogether. This makes technical improvements prohibitive and cost-reduction measures necessary. Fortunately some cost-reduction possibilities and some significant technical improvements that should cost very little were identified.

Moorhead, Robert J., II↗
NASA NTRS2017

The Relationship Between Cosmic-Ray Exposure Ages And Mixing Of CM Chondrite Lithologies

Carbonaceous (C) chondrites are primitive materials probably deriving from C, P and D asteroids, and as such potentially include samples and analogues of the target asteroids of the Dawn, Hayabusa2 and OSIRIS-Rex missions. Foremost among the C chondrites are the CM chondrites, the most common type, and which have experienced the widest range of early solar system processes including oxidation, hydration, metamorphism, and impact shock deformation, often repeatedly or cyclically [1]. To track the activity of these processes in the early solar system, it is critical to learn how many separate bodies are represented by the CMs. Nishiizumi and Caffee [2] have reported that the CMs are unique in displaying several distinct peaks for cosmic-ray exposure (CRE) age groups, and that excavation from significant depth and exposure as small entities in space is the best explanation for the observed radionuclide data. There are either 3 or 4 CRE groups for CMs (Fig.1). We decided to systematically characterize the petrography in each of the CRE age groups to determine whether the groups have significant petrographic differences with these reflecting different parent asteroid geological processing or multiple original bodies. We previously re-ported preliminary results of our work [3], however we have now reexamined these meteorites from the perspective of brecciation, with interesting new results.

Zolensky, M. E.↗
NASA NTRSDate not supplied

AEROKATS and ROVER Education Network (AREN) – Exploring Through Teamwork

Exploring our home planet and other venues, requires a broad range of talents and contributions. The NASA SciAct AEROKATS and ROVER Education Network (AREN) project aims to bring an array of roles and responsibilities to a project-based learning environment focused on exploring the world around us. Teamwork is critical, and activities range from artistic, fabrication, technical design, data analysis, project planning, communications, and structured field activities. The objective is to create these teams highlighting individual skills and interests combined to form unique and productive environments.

Geoffrey L Bland↗
NASA NTRS2021

Spacecraft Line-Of-Sight Jitter Management and Mitigation Lessons Learned And Engineering Best Practices

Predicting, managing, controlling, and testing spacecraft line-of-sight (LoS) jitter caused by micro-vibrations due to on-board internal disturbance sources is a formidable multidisciplinary engineering task. It is especially challenging for those missions hosting high-performance (e.g., nano-radian/milli-arcsecond class), vibration-sensitive optical sensor payloads with stringent pointing stability requirements. The Nation Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) are planning technically aggressive spaceflight missions that include ultra-high-performance optical payloads with delicate, highly vibration-sensitive scientific and observational instruments. The guidance, navigation, and control community of practice will need to leverage collective experiences and document their best practices and lessons learned to address future micro-vibration challenges. To identify lessons learned and best practices the NASA Engineering & Safety Center sponsored a 2-day Spacecraft LoS Jitter Workshop in late 2019. The workshop’s goal was to provide a multidisciplinary forum to elicit deeper understanding of the issues related to addressing the spacecraft LoS jitter/micro-vibration problem. The primary objective was to identify, document, and share lessons learned, best practices, and preferred options for jitter-related analysis and test activities. Representatives from NASA, ESA, along with NASA’s industrial partners, independent consultant subject matter experts, and members of academia participated in the workshop. This paper will describe the motivation for the workshop and summarize the identified findings and recommendations.

Swanson, Davin K.↗
NASA NTRS2022

TruePAL – An AI Assistant for First Responder Safety

This paper presents the development of an AI assistant, Trusted and Explainable Artificial Intelligence for Saving Lives (TruePAL), to provide real-time warning of risks of potential crashes to the first responders. The TruePAL system employs an AI and deep learning technology for saving first responders and roadside crews lives in and around active traffic. A deep neural network (DNN) and a Non-Axiomatic Reasoning System (NARS) are implemented as an AI system. A mobile app with AI interface is developed to perform verbal communication with the first responders. The TruePAL team has developed an explainable AI approach by opening up the DNN blackbox to extract the activation filters of various features and parts of the targeted objects. The combination of DNN and NARS makes the TruePAL system explainable to the users. TruePAL ingests on-board cameras, radar, and other sensor signals, analyzes the environment and traffic patterns to generate timely warning to drivers and roadside crews to avoid crashes. The TruePAL team, in collaboration with the Miami/Dade Police Dept., has designed five use cases and multiple sub-scenarios in a CARLA driving simulator to test the capability of TruePAL in timely warning to the first responder drivers in potential crash scenarios. We have successfully demonstrated its capability of timely warning in over a dozen scenarios based on the use cases. The preliminary test simulation results show that TruePAL could provide the drivers and crew members advanced warning before a crash occurs.

Chow, Edward↗
DOE OSTI2026

Multireference Methods for Chemistry and Materials Science: Automated Active Spaces, Efficient Dynamic Correlation, and Extended Systems

While multiconfigurational approaches have long been relegated to expert practitioners working on a case-by-case basis, recent developments have increasingly made these methods more routine and applicable to broader sets of systems. This article outlines the state-of-the-art in multiconfigurational approaches, with an emphasis on moving from delicate hand-selected pathways through configuration space toward more robust and efficient approaches to treating a host of challenging chemical systems accurately. First, we overview recent work in automated active-space selection, which has enabled increasingly large-scale applications of multireference methods to modeling vertical excitations and reactivity. Second, we highlight the increasingly efficient methods for recovering correlation energy beyond the active space, as headlined by extensions of pair-density functional theory and its role in accurate and efficient treatment of excited-state dynamics and its utilization to train machine-learned potentials. Finally, we highlight recent efforts to treat extended systems that until recently have lied beyond the traditional limits of active-space methods, giving center stage to product-form wave functions of the localized active space family of methods that allow for the computation of multiconfigurational band structures. These recent advancements point to a broader use of multireference approaches for high-impact chemical and materials science applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Leveraging the 2024 Solar Eclipse to Enhance Science Learning through Citizen Science

The solar eclipse on April 8, 2024, provided a rare and compelling opportunity for educators and students to engage in hands-on citizen science. Two NASA Science Activation projects GLOBE Mission Earth (GME) and NASA Earth Science Education Collaborative (NESEC) partnered together to provide a unique professional development experience for educators interested in the eclipse. They invited educators to participate in a specialized 5-week online workshop designed to engage the educators in Global Learning and Observations to Benefit the Environment (GLOBE) citizen science about the eclipse. The workshop was designed to support their certification as GLOBE educators and develop their knowledge, skills, and confidence in conducting a GLOBE investigation. Over 60 educators from across the United States took part in this workshop, which focused on investigating the eclipse by collecting and analyzing atmospheric data. The GLOBE Program promotes environmental and scientific literacy by enabling participants to collect Earth science data and contribute it to a global database accessible for research. The 2024 GLOBE Eclipse workshop trained educators in specific GLOBE protocols—Clouds, Air Temperature, and Surface Temperature—using the GLOBE Eclipse tool integrated into the GLOBE Observer app. This training was supplemented with guidance on engaging students in authentic scientific research and creating research posters to present their findings. The workshop aimed to enhance educators’ skills and confidence in integrating GLOBE protocols into their teaching practices. It addressed several key areas outlined in the National Academies' report on Learning through Citizen Science, including scientific context, nature of participation, and project infrastructure. Educators learned about the atmospheric effects of solar eclipses and were trained to use scientific tools and data analysis methods relevant to their research. They also engaged in live sessions and asynchronous activities, practicing data collection and analysis with real-time feedback. Survey results from the workshop highlighted that participants were primarily motivated by the desire to better use data in their classrooms and improve their proficiency with the GLOBE Observer app. Post-workshop evaluations showed significant increases in educators' confidence regarding their ability to conduct and guide scientific research. Participants reported feeling well-prepared to use the GLOBE Observer app for data collection and were successful in integrating their eclipse observations into classroom activities. Participants also valued the opportunity to contribute to authentic science through GLOBE, which involved observing atmospheric changes such as air temperature fluctuations and cloud cover alterations during the eclipse. The success of the workshop is evident in the increased confidence and skill levels of educators, as well as the publication of research posters on the GLOBE Mission Earth Student Research webpage.This session will discuss how the GLOBE Eclipse workshop series effectively utilized the unique context of the solar eclipse to enhance science education through citizen science. By adhering to the principles outlined in the Learning through Citizen Science framework, the workshop successfully engaged educators and students in meaningful scientific practices, demonstrating the potential of citizen science to enrich science education and foster a deeper understanding of Earth systems.

Jessica Taylor↗
DOE OSTI2024

Machine Learning Decoding of Full Duplex Signals

A full duplex signal is when two endpoints (server one and server two) transmit on a single conductor pair simultaneously and with the same frequency. This results in the waveforms created from each server to be merged with one another when observed at any point along the transmission line making physical analysis of the wave unobtainable. This project was orchestrated to find the means to separate the merged signal into two separate signals which represent the signals originally sent from each server without an active tap.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning

Abstract mRNA degradation is a central process that affects all gene expression levels, though it remains challenging to predict the stability of a mRNA from its sequence, due to the many coupled interactions that control degradation rate. Here, we carried out massively parallel kinetic decay measurements on over 50,000 bacterial mRNAs, using a learn-by-design approach to develop and validate a predictive sequence-to-function model of mRNA stability. mRNAs were designed to systematically vary translation rates, secondary structures, sequence compositions, G-quadruplexes, i-motifs, and RppH activity, resulting in mRNA half-lives from about 20 seconds to 20 minutes. We combined biophysical models and machine learning to develop steady-state and kinetic decay models of mRNA stability with high accuracy and generalizability, utilizing transcription rate models to identify mRNA isoforms and translation rate models to calculate ribosome protection. Overall, the developed model quantifies the key interactions that collectively control mRNA stability in bacterial operons and predicts how changing mRNA sequence alters mRNA stability, which is important when studying and engineering bacterial genetic systems.

Cetnar, Daniel P.↗
NASA NTRS2007

Ares Launch Vehicles Lean Practices Case Study

The Ares launch vehicles team, managed by the Ares Projects Office (APO) at NASA Marshall Space Flight Center, has completed the Ares I Crew Launch Vehicle System Requirements Review and System Definition Review and early design work for the Ares V Cargo Launch Vehicle. This paper provides examples of how Lean Manufacturing, Kaizen events, and Six Sigma practices are helping APO deliver a new space transportation capability on time and within budget, while still meeting stringent technical requirements. For example, Lean philosophies have been applied to numerous process definition efforts and existing process improvement activities, including the Ares I-X test flight Certificate of Flight Readiness (CoFR) process, risk management process, and review board organization and processes. Ares executives learned Lean practices firsthand, making the team "smart buyers" during proposal reviews and instilling the team with a sense of what is meant by "value-added" activities. Since the goal of the APO is to field launch vehicles at a reasonable cost and on an ambitious schedule, adopting Lean philosophies and practices will be crucial to the Ares Project's long-term SUCCESS.

Doreswamy, Rajiv↗
NASA NTRS1995

Looking at Earth from Space: Teacher's Guide with Activities for Earth and Space Science

The Maryland Pilot Earth Science and Technology Education Network (MAPS-NET) project was sponsored by the National Aeronautics and Space Administration (NASA) to enrich teacher preparation and classroom learning in the area of Earth system science. This publication includes a teacher's guide that replicates material taught during a graduate-level course of the project and activities developed by the teachers. The publication was developed to provide teachers with a comprehensive approach to using satellite imagery to enhance science education. The teacher's guide is divided into topical chapters and enables teachers to expand their knowledge of the atmosphere, common weather patterns, and remote sensing. Topics include: weather systems and satellite imagery including mid-latitude weather systems; wave motion and the general circulation; cyclonic disturbances and baroclinic instability; clouds; additional common weather patterns; satellite images and the internet; environmental satellites; orbits; and ground station set-up. Activities are listed by suggested grade level and include the following topics: using weather symbols; forecasting the weather; cloud families and identification; classification of cloud types through infrared Automatic Picture Transmission (APT) imagery; comparison of visible and infrared imagery; cold fronts; to ski or not to ski (imagery as a decision making tool), infrared and visible satellite images; thunderstorms; looping satellite images; hurricanes; intertropical convergence zone; and using weather satellite images to enhance a study of the Chesapeake Bay. A list of resources is also included.

Steele, Colleen↗
DOE OSTI2025

Active oversight and quality control in standard Bayesian optimization for autonomous experiments

The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.

36 MATERIALS SCIENCE↗