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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Building Datasets and Training Methods for ML Based Magnet Quench Detection

Detecting quenches in superconducting (SC) magnets during training is a challenging process that involves capturing physical events that occur at different frequencies and appear as various signal features. These events may be correlated across instrumentation type, thermal cycle, and ramp. These events together build a more complete picture of continuous processes occurring in the magnet, and may allow us to flag potential precursors for quench detection. We present our work on building an automatic machine learning (ML) based quench detection system. We build upon our existing work on unsupervised auto-encoders for acoustic sensors and quench antenna (QA) by first establishing a supervised ML training pipeline. We show the results of an event tagging, analysis, and simulation framework on our QA and acoustic data which are used concurrently to build a training dataset for a supervised implementation. We then show how this supervised training can be used as a prior in a semi-supervised framework and compare this to the unsupervised neural network auto-encoder performance.This allows us to have a more concrete understanding of the performance of our algorithms relative to physical events occurring in the magnet, and also provides a baseline software tool to generically evaluate our quench prediction autoencoders under completely unsupervised, supervised, and semi-supervised training conditions.

Khan, Maira [Fermilab]↗
DOE OSTI2025

MLCommons Science Benchmarks

Benchmarks are a cornerstone of modern machine learning practice, providing standardized eval- uations that enable reproducibility, comparison, and scientific progress. Yet, as AI systems particularly deep learning models become increasingly dynamic, traditional static benchmarking approaches are losing their relevance. Models rapidly evolve in architecture, scale, and capability; datasets shift; and deployment contexts continuously change, creating a moving target for evaluation. Without adaptive benchmarking frame- works, both scientific assessment and real-world de- ployment risk becoming misaligned with actual system behavior. Drawing on our experience from MLCommons, educa- tional initiatives, and government programs such as the DOE s Million Parameter Consortium, we identify key barriers that hinder the broader adoption and utility of benchmarking in AI. These include substantial resource demands, limited access to specialized hardware, lack of expertise in benchmark design, and uncertainty among practitioners about how to relate benchmark results to their own application domains. Moreover, current benchmarks often emphasize peak performance on leadership-class hardware, offering limited guidance for more diverse, real-world deployment scenarios. We argue that benchmarking itself must become dy- namic in order to incorporate evolving models, updated data, and heterogeneous computational platforms while maintaining transparency, reproducibility, and inter- pretability. Democratizing this process requires not only technical innovation, but also systematic educational efforts spanning undergraduate to professional levels to develop sustained expertise in benchmark design and use. Finally, benchmarks should be framed and com- municated to support application-relevant comparisons, enabling both developers and users to make informed, context-sensitive decisions. Advancing dynamic and inclusive benchmarking practices will be essential to ensure that evaluation keeps pace with the evolving AI landscape and supports responsible, reproducible, and accessible AI deployment.

Hawks, Benjamin G. [Fermilab]↗
NASA NTRS1983

Meteorological and dynamical requirements for MST radar networks: Waves

Studies of wave motions using the MST radar have concentrated on single station time series analyses of gravity waves and tides. Since these radars collect high time resolution data they have the potential to become a significant tool for mesoscale research. In addition, radars are operated almost continuously unattended and, consequently, data sets are available for analyzing longer period wave motions such as tides and planetary scale waves. Although there is much to learn from single station data, the possibilities of new knowledge from a network of radars is exciting. The scales of wave motions in the atmosphere cover a broad range. Consequently the choice of a radar network depends to a large extent on the types of wave motions that are studied. There are many outstanding research problems that would benefit from observations from a MST radar network. In particular, there is a strong need for measurements of gravity wave parameters and equatorial wave motions. Some of the current problems in wave dynamics are discussed.

Avery, S. K.↗
NASA NTRS1987

The Jupiter-Io connection - An Alfven engine in space

Much has been learned about the electromagnetic interaction between Jupiter and its satellite Io from in situ observations. Io, in its motion through the Io plasma torus at Jupiter, continuously generates an Alfven wing that carries two billion kilowatts of power into the jovian ionosphere. Concurrently, Io is acted upon by a J x B force tending to propel it out of the jovian system. The energy source for these processes is the rotation of Jupiter. This unusual planet-satellite coupling serves as an archetype for the interaction of a large moving conductor with a magnetized plasma, a problem of general space and astrophysical interest.

Belcher, John W.↗
NASA NTRS2003

Standards Advisor-Advanced Information Technology for Advanced Information Delivery

Developers of space systems must deal with an increasing amount of information in responding to extensive requirements and standards from numerous sources. Accessing these requirements and standards, understanding them, comparing them, negotiating them and responding to them is often an overwhelming task. There are resources to aid the space systems developer, such as lessons learned and best practices. Again, though, accessing, understanding, and using this information is often more difficult than helpful. This results in space systems that: 1. Do not meet all their requirements. 2. Do not incorporate prior engineering experience. 3. Cost more to develop. 4. Take longer to develop. The NASA Technical Standards Program (NTSP) web site at http://standards.nasa.gov has made significant improvements in making standards, lessons learned, and related material available to space systems developers agency-wide. The Standards Advisor was conceived to take the next steps beyond the current product, continuing to apply evolving information technology that continues to improve information delivery to space systems developers. This report describes the features of the Standards Advisor and suggests a technical approach to its development.

Hawker, J. Scott↗
NASA NTRS2016

MOPITT Mechanisms 16 Years In-Orbit Operation on TERRA

The 16th anniversary of the launch of NASA's Terra Spacecraft was marked on December 18, 2015, with the Measurements of Pollution in the Troposphere (MOPITT) instrument being a successful contributor to the NASA EOS flagship. MOPITT has been enabled by a large suite of mechanisms, allowing the instrument to perform long-duration monitoring of atmospheric carbon monoxide, providing global measurements of this important greenhouse gas for 16 years. Mechanisms have been successfully employed for scanning, cooling of detectors, and to optically modulate the gas path length within the instrument by means of pressure and gas cell length variation. The instrument utilizes these devices to perform correlation spectroscopy, enabling measurements with vertical resolution from the nadir view, and has thereby furthered understanding of source and global transport effects of carbon monoxide. Given the design requirement for a 5.25-year lifetime, the stability and performance of the majority of mechanisms have far surpassed design goals. With 16 continuously operating mechanisms in service on MOPITT, including 12 rotating mechanisms and 4 with linear drive elements, the instrument was an ambitious undertaking. The long life requirements combined with demands for cleanliness and optical stability made for difficult design choices including that of the selection of new lubrication processes. Observations and lessons learned with regards to many aspects of the mechanisms and associated monitoring devices are discussed here. Mechanism behaviors are described, including anomalies, long-term drive current/power, fill pressure, vibration and cold-tip temperature trends. The effectiveness of particular lubrication formulations and the screening method implemented is discussed in relation to continuous rotating mechanisms and stepper motors, which have exceeded 15 billon rotations and 2.5 billion steps respectively. Aspects of gas cell hermeticity, optical cleanliness, heater problems and SEU effects on accelerometers are also discussed.

Gibson, Andrew S.↗
NASA NTRS2014

On-Orbit Performance of MODIS Solar Diffuser Stability Monitor

Moderate Resolution Imaging Spectroradiometer (MODIS) is currently operated on both the Terra and Aqua spacecraft. It collects data in 20 reflective solar bands (RSB) and 16 thermal emissive bands. MODIS RSB calibration is reflectance based via an on-board solar diffuser (SD). On-orbit changes in the SD bidirectional reflectance factor are tracked by an onboard solar diffuser stability monitor (SDSM). The SDSM functions as an independent ratioing radiometer with nine filtered detectors, covering wavelengths in the visible (VIS) and near-infrared (NIR) spectral regions. A brief overview of SDSM design functions, on-orbit operations, and performance for both Terra and Aqua MODIS is provided. In addition to the SD on-orbit degradation at different wavelengths, the changes in SDSM detector responses and their potential impact on tracking SD on-orbit degradation are examined. After more than 12 years of on-orbit operation, Aqua MODIS SD has shown degradation varying from 0.6% at 0.94 microns to 19.0% at 0.41 microns. Due to more frequent solar exposure and longer operation time, the Terra MODIS SD has experienced a much larger degradation, varying from 2.3% at 0.94 microns to 48.0% at 0.41 microns. For both Terra and Aqua MODIS, the SD has experienced more degradation at shorter wavelengths. Meanwhile, the SDSM detector responses have also experienced wavelength-dependent degradation. The largest change in the SDSM detector responses, however, occurred at longer NIR wavelengths. Since launch, the SDSM systems on both Terra and Aqua MODIS have continued their nominal operations, enabling critical parameters to be derived in support of the RSB on-orbit calibration. The calibration strategies developed for and lessons learned from MODIS SDSM operations, and a preliminary performance comparison with the S-NPP VIIRS SDSM are discussed.

MODIS↗
NASA NTRSDate not supplied

Mapping National Forest Aboveground Biomass in Mexico By Integrating GEDI and Landsat Times Series Data

Mexico is one of the countries with great potential for the UN's Reducing Emissions from Deforestation and Forest Degradation (REDD+) program, a key nature-based solution for the forest sector. To monitor carbon stock changes, there is a growing demand for unbiased Monitoring Reporting Verification (MRV) systems to facilitate effective forest management and climate change mitigation strategies. Remote sensing-based national aboveground biomass density (AGBD) estimation over Mexico is scarce and often limited to one-time static mapping, leading to spatiotemporal inconsistency in inputs. As an effort under NASA's Carbon Monitoring System (CMS) program, we have developed a remote sensing-based approach to create consistent historical AGBD maps of Mexico using multi-stream remote sensing data, including spaceborne lidar GEDI and long-term Landsat time series, as well as topographic information. We employ the continuous change detection and classification (CCDC) algorithm for temporal modeling of Landsat surface reflectance, followed by the inference of forest AGBD using a random forest machine learning algorithm with the temporal information of land surface dynamics extracted by the CCDC as input. GEDI provides unprecedented forest structure and AGBD sampling datasets for model training and validation practices. In this presentation, we share the progress made in developing a spatially explicit mapping of historical AGBD changes associated with land surface changes and post-disturbance landscapes.

Taejin Park↗
DOE OSTI2025

Monitoring river flow status using low-cost wildlife camera and image segmentation artificial intelligence

Continuous measurement and monitoring of surface water coverage in non-perennial streams are essential for understanding the exchange fluxes between surface and subsurface waters under both inundated and non-inundated conditions. In this study, a wildlife camera photo-based framework was developed to monitor small stream water inundation, depth, discharge, and velocity. Two advanced machine learning models, YOLOv8 and Mask2Former, were utilized to efficiently analyze images captured by wildlife cameras. The accuracy of the framework was validated against on-site depth measurements at six sites in the Yakima River Basin, along with the gage height, discharge, and velocity data from four USGS sites. This approach facilitates long-term, continuous monitoring and quantification of river intermittency and water availability with high precision and low cost, thereby advancing river ecosystem research and management.

machine learning↗
NASA NTRSDate not supplied

Open-Source Data Engineering at NASA: CCMC's Approach to Managing Petabyte-Scale Heliophysics Data

The Community Coordinated Modeling Center (CCMC) at NASA Goddard Space Flight Center (GSFC) leads heliophysics research by providing open access to numerous models and their outputs. Our resources are available on-demand and continuously updated with real-time data, covering sun-earth interactions across multiple domains. These domains include coronal, heliosphere, inner and global magnetosphere, ionosphere, thermosphere, and lower atmosphere interactions. Operating in a hybrid environment, CCMC utilizes both self-owned hardware and Amazon Web Services (AWS) cloud infrastructure. Managing petabytes of data across multiple locations necessitates robust data engineering solutions. To address this challenge, CCMC has adopted industry-standard and open-source tools. We use Apache Airflow as our primary data engineering platform, Python for scripting and data processing, and GitLab for version control and CI/CD. Additionally, we employ Kubernetes for containerized services, Grafana and Prometheus for metrics and monitoring, and Terraform and Puppet for reproducible infrastructure as code. This presentation will discuss lessons learned from our data engineering experiences, platforms evaluated but found unsuitable for our scientific data requirements, and specific techniques developed to enhance data transfer speed and reliability. By using these technologies effectively, CCMC continues to advance heliophysics research through efficient data management and open-access modeling.

space weather↗
NASA NTRS2006

Intelligence for Human-Assistant Planetary Surface Robots

The central premise in developing effective human-assistant planetary surface robots is that robotic intelligence is needed. The exact type, method, forms and/or quantity of intelligence is an open issue being explored on the ERA project, as well as others. In addition to field testing, theoretical research into this area can help provide answers on how to design future planetary robots. Many fundamental intelligence issues are discussed by Murphy [2], including (a) learning, (b) planning, (c) reasoning, (d) problem solving, (e) knowledge representation, and (f) computer vision (stereo tracking, gestures). The new "social interaction/emotional" form of intelligence that some consider critical to Human Robot Interaction (HRI) can also be addressed by human assistant planetary surface robots, as human operators feel more comfortable working with a robot when the robot is verbally (or even physically) interacting with them. Arkin [3] and Murphy are both proponents of the hybrid deliberative-reasoning/reactive-execution architecture as the best general architecture for fully realizing robot potential, and the robots discussed herein implement a design continuously progressing toward this hybrid philosophy. The remainder of this chapter will describe the challenges associated with robotic assistance to astronauts, our general research approach, the intelligence incorporated into our robots, and the results and lessons learned from over six years of testing human-assistant mobile robots in field settings relevant to planetary exploration. The chapter concludes with some key considerations for future work in this area.

Hirsh, Robert↗
DOE OSTI2025

Best of both worlds: Enforcing detailed balance in machine learning models of transition rates

The slow microstructural evolution of materials often plays a key role in determining material properties. When the unit steps of the evolution process are slow, direct simulation approaches such as molecular dynamics become prohibitive and Kinetic Monte-Carlo (kMC) algorithms, where the state-to-state evolution of the system is represented in terms of a continuous-time Markov chain, are instead frequently relied upon to efficiently predict long-time evolution. The accuracy of kMC simulations however relies on the complete and accurate knowledge of reaction pathways and corresponding kinetics. This requirement becomes extremely stringent in complex systems such as concentrated alloys where the astronomical number of local atomic configurations makes the a priori tabulation of all possible transitions impractical. Machine learning models of transition kinetics have been used to mitigate this problem by enabling the efficient on-the-fly prediction of kinetic parameters. While conventional KMC methods based on transition state theory naturally yield reversible dynamics that exactly obey the detailed balance criterion, providing strong guarantees on the properties of the stationary distribution, many recently-proposed ML-based approaches to barrier predictions provide no such guarantees. In this study, we derive conditions under which physics-informed ML architectures exactly enforce the detailed balance condition by construction, even when relying on non-extensive descriptions of states in terms of local environments around mobile defects. In conclusion, using the diffusion of a vacancy in a concentrated alloy as an example, we show that such ML architectures also exhibit superior performance in terms of prediction accuracy, demonstrating that the imposition of physical constraints can facilitate the accurate learning of barriers at no increase in computational cost.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Benchmark for two-dimensional large scale coherent structures in partially magnetized E × B plasmas—community collaboration & lessons learned

Low-temperature plasmas (LTPs) are essential to both fundamental scientific research and critical industrial applications. As in many areas of science, numerical simulations have become a vital tool for uncovering new physical phenomena and guiding technological development. Code benchmarking remains crucial for verifying implementations and evaluating performance. This work continues the Landmark benchmark initiative, a series specifically designed to support the verification of LTP codes. In this study, seventeen simulation codes from a collaborative community of nineteen international institutions modeled a partially magnetized E × B Penning discharge. The emergence of large scale coherent structures, or rotating plasma spokes, endows this configuration with an enormous range of time scales, making it particularly challenging to simulate. The codes showed excellent agreement on the rotation frequency of the spoke as well as key plasma properties, including time-averaged ion density, plasma potential, and electron temperature profiles. Achieving this level of agreement came with challenges, and we share lessons learned on how to conduct future benchmarking campaigns. Comparing code implementations, computational hardware, and simulation runtimes also revealed interesting trends, which are summarized with the aim of guiding future plasma simulation software development.

benchmarking↗
NASA NTRS1993

Genetic learning in rule-based and neural systems

The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

Smith, Robert E.↗
NASA NTRS2018

Stirling Convertor Controller Development at NASA Glenn Research Center

For nearly two decades, NASA Glenn Research Center has been supporting the development of radioisotope power systems (RPS). NASA desires higher conversion efficiency RPS options that are reliable and robust with long-life design. Dynamic conversion, such as Stirling and Brayton, offer the potential for higher conversion efficiencies than current RPS but have yet to be demonstrated in a flight application. The RPS program sent out a solicitation to investigate options for dynamic conversion technologies. As a result of this solicitation, four dynamic power convertor (DPC) technologies were selected for design and three are proceeding to the fabrication phase of prototype dynamic convertors. One lesson learned from the Advanced Stirling Radioisotope Generator (ASRG) project is that controller development should be coordinated with the development of a dynamic convertor. As a result of this, Glenn has been utilizing hardware from past Stirling convertor projects, including that of the ASRG, to support controller development for the DPCs. Glenn has developed a strong knowledge base on both analog and digital Stirling DPC controllers and will continue to expand and apply that knowledge to the DPCs. Over the past 15 years, controllers were developed at Glenn, at Lockheed Martin (LM), and by the Johns Hopkins University Applied Physics Laboratory (APL). Various generations of the controllers have been developed as lessons were learned through various component- and system-level tests. Some of the tests performed were fault tolerance, flight acceptance vibration, electromagnetic interference (EMI), spacecraft integration, and extended operation. The fault tolerance test characterized the controller’s ability to handle various fault conditions, including high or low bus power consumption, total open load or short circuit, and replacing a failed controller card while the backup maintains control of the Stirling convertor. The vibration test confirms the controller’s ability to control an Advanced Stirling Convertor (ASC) during launch. The EMI test characterized the alternating-current (AC) and direct-current (DC) magnetic and electric fields emitted by the single ASC and if the controller has an impact on the radiated EMI. Spacecraft integration testing in the Radioisotope Power Systems (RPS), System Integration Laboratory (RSIL) provided insight into the electrical interactions between the representative RPS, its associated control schemes, and realistic electric system loads. The extended operation test allows data to be collected over a period of thousands of hours to obtain long-term performance data of the system. This paper describes the history of controller development at Glenn, tests performed on these controllers, and lessons learned.

Dugala, Gina M.↗
DOE OSTI2025

High entropy powering green energy: hydrogen, batteries, electronics, and catalysis

A reformation in energy is underway to replace fossil fuels with renewable sources, driven by the development of new, robust, and multi-functional materials. High-entropy materials (HEMs) have emerged as promising candidates for various green energy applications, having unusual chemistries that give rise to remarkable functionalities. This review examines recent innovations in HEMs, focusing on hydrogen generation/storage, fuel cells, batteries, semiconductors/electronics, and catalysis—where HEMs have demonstrated the ability to outperform state-of-the-art materials. We present new master plots that illustrate the superior performance of HEMs compared to conventional systems for hydrogen generation/storage and heat-to-electricity conversion. We highlight the role of computational methods, such as density functional theory and machine learning, in accelerating the discovery and optimization of HEMs. The review also presents current challenges and proposes future directions for the field. We emphasize the need for continued integration of modeling, data, and experiments to investigate and leverage the underlying mechanisms of the HEMs that are powering progress in sustainable energy.

batteries↗
NASA NTRS1988

Simple explanations and reasoning: From philosophy of science to expert systems

A preliminary prototype of a simple explanation system was constructed. Although the system, based on the idea of storytelling, did not incorporate all of the principles of simple explanation, it did demonstrate the potential of the approach. The system incorporated a hypertext system, an inference engine, and facilities for constructing contrast type explanations. The continued development of such a system should prove to be valuable. By extending the resources of the expert system paradigm, the knowledge engineer is not forced to learn a new set of skills, and the domain knowledge already acquired by him is not lost. Further, both the beginning user and the more advanced user can be accommodated. For the beginning user, corrective explanations and ES explanations provide facilities for more clearly understanding the way in which the system is functioning. For the more advanced user, the instance and state explanations allow him to focus on the issues at hand. The simple model of explanation attempts to exploit and show how the why and how facilities of the expert system paradigm can be extended by attending to the pragmatics of explanation and adding texture to the ordinary pattern of reasoning in a rule based system.

Rochowiak, Daniel↗
NASA NTRS2012

Integrating Engineering Data Systems for NASA Spaceflight Projects

NASA has a large range of custom-built and commercial data systems to support spaceflight programs. Some of the systems are re-used by many programs and projects over time. Management and systems engineering processes require integration of data across many of these systems, a difficult problem given the widely diverse nature of system interfaces and data models. This paper describes an ongoing project to use a central data model with a web services architecture to support the integration and access of linked data across engineering functions for multiple NASA programs. The work involves the implementation of a web service-based middleware system called Data Aggregator to bring together data from a variety of systems to support space exploration. Data Aggregator includes a central data model registry for storing and managing links between the data in disparate systems. Initially developed for NASA's Constellation Program needs, Data Aggregator is currently being repurposed to support the International Space Station Program and new NASA projects with processes that involve significant aggregating and linking of data. This change in user needs led to development of a more streamlined data model registry for Data Aggregator in order to simplify adding new project application data as well as standardization of the Data Aggregator query syntax to facilitate cross-application querying by client applications. This paper documents the approach from a set of stand-alone engineering systems from which data are manually retrieved and integrated, to a web of engineering data systems from which the latest data are automatically retrieved and more quickly and accurately integrated. This paper includes the lessons learned through these efforts, including the design and development of a service-oriented architecture and the evolution of the data model registry approaches as the effort continues to evolve and adapt to support multiple NASA programs and priorities.

Carvalho, Robert E.↗