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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Development of nickel hydrogen battery expert system

The Hubble Telescope Battery Testbed employs the nickel-cadmium battery expert system (NICBES-2) which supports the evaluation of performances of Hubble Telescope spacecraft batteries and provides alarm diagnosis and action advice. NICBES-2 also provides a reasoning system along with a battery domain knowledge base to achieve this battery health management function. An effort to modify NICBES-2 to accommodate nickel-hydrogen battery environment in testbed is described.

Shiva, Sajjan G.↗
DOE OSTI2024

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Corn grown on dredged sediments alters rat behavior in the elevated plus maze via the gut mycobiome

The use of marginal lands for growing food has increased worldwide. Many of these lands are supplemented with soil amendments to enhance their productivity, but they may also introduce contaminants depending on their origin. The use of sediments dredged from commercial waterways as a soil amendment has increased in recent years. However, there is significant concern regarding their potential to negatively affect public health by transferring contaminants, like metals, to the food supply. This study examined the behavior, microbiome and physiology of rats fed corn grown on dredged sediments with rats fed commercial feed corn and related those metrics to kernel metal content. Metal content did not differ between kernels grown on dredged sediments and feed corn for most metals except iron which was greater in commercial feed corn. Although there is some variability by animal response, animals fed corn grown on dredged sediments had a gut microbial community that generally promoted anxiety-like behavior while animals fed feed corn had a gut microbial community that promoted a more typical response. These findings suggest the effect of corn grown on dredged sediments on public health is complex and highlights the need for further investigation.

Rua, Megan [Wright State University, Dayton, OH]↗
NASA NTRS2025

Improving Adhesive Bondline Time of Flight Predictions During Autoclave Cure Utilizing Machine Learning

Composite materials are increasingly being used in aerospace applications due to their superior strength-to-weight ratio compared to commonly used metals. A current limitation to widespread adoption is the certification of adhesively bonded joints. One approach to improving adhesive bonding in composites is accurately measuring the thickness of adhesive bondlines in composite laminates. Precise bondline thickness control is essential for aerospace applications where adhesive layer thickness directly affects joint fracture properties and structural performance. This study focused on implementing machine learning techniques to determine the ultrasonic time of flight (directly correlated to thickness) in adhesive bondlines throughout autoclave cure cycles. A high-temperature (use up to 180°C) ultrasonic scanning system was deployed in an autoclave to provide time of flight data through composite panels. Three experiments were conducted on the curing of 305 mm × 305 mm unidirectional composite panels. In the first experiment, a piecewise function was fit for the temperature correction factor to account for changing autoclave temperatures. Due to deficiencies in the first calibration experiment, a second experiment was run, and the results were used to train a machine learning model. The revised experiment, in combination with the machine learning model, significantly increased the accuracy of the bondline time of flight predictions (~14% error reduced to <1%). Data was processed using the Regression Learner Application in MATLAB®, with a Support Vector Machine selected for the model. The result was a machine learning algorithm capable of reliably quantifying ultrasonic time of flight through adhesive bondlines. The third experiment provided independent test data for the machine learning model, demonstrating that the model produces accurate predictions from data beyond its training set.

Machine Learning↗
NASA NTRS2006

Data Mining for ISHM of Liquid Rocket Propulsion Status Update

This document consists of presentation slides that review the current status of data mining to support the work with the Integrated Systems Health Management (ISHM) for the systems associated with Liquid Rocket Propulsion. The aim of this project is to have test stand data from Rocketdyne to design algorithms that will aid in the early detection of impending failures during operation. These methods will be extended and improved for future platforms (i.e., CEV/CLV).

Ashok Srivastava↗
DOE OSTI2025

3D-Printed mmWave Quasi-Holographic Antenna for 2-D Beamforming

This paper presents the design and manufacturing of a novel 2D-scanning antenna that integrates a 3D-printed Rotman lens with a quasi-holographic leaky-wave antenna (HLWA). The proposed design achieved beam-scanning capabilities by leveraging the beamforming of the Rotman lens and the high-gain directive properties of the quasi-HLWA. The Rotman lens (RL) enables beam steering in the elevation plane by switching between input ports. The quasi-HLWA, designed using holographic principles, achieves frequency-controlled beam scanning in the azimuth plane. The entire antenna structure was fabricated using additive manufacturing with an Ink1092 substrate and silver ink for the conductive traces. This approach provides greater control over material placement and design freedom compared to traditional methods. A 25° transmission linear substrate taper was used to ensure good impedance matching between the Rotman lens and the quasi-HLWA, allowing greater gain while maintaining a good scanning range. The experimental results validate the 2D scanning capability of the proposed antenna. The antenna system provides coverage from −54° to 54° in the elevation θ plane and −28° to 28° in the azimuth plane ϕ , with a maximum measured gain of 21.3 dBi at 28 GHz with an average radiation efficiency η=60 %. The fabricated prototype was tested, and the performance was in good agreement with the simulated performance.

Rotman lens↗
NASA NTRS2010

Ares I-X Ground Diagnostic Prototype

Automating prelaunch diagnostics for launch vehicles offers three potential benefits. First, it potentially improves safety by detecting faults that might otherwise have been missed so that they can be corrected before launch. Second, it potentially reduces launch delays by more quickly diagnosing the cause of anomalies that occur during prelaunch processing. Reducing launch delays will be critical to the success of NASA's planned future missions that require in-orbit rendezvous. Third, it potentially reduces costs by reducing both launch delays and the number of people needed to monitor the prelaunch process. NASA is currently developing the Ares I launch vehicle to bring the Orion capsule and its crew of four astronauts to low-earth orbit on their way to the moon. Ares I-X will be the first unmanned test flight of Ares I. It is scheduled to launch on October 27, 2009. The Ares I-X Ground Diagnostic Prototype is a prototype ground diagnostic system that will provide anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage thrust vector control (TVC) and for the associated ground hydraulics while it is in the Vehicle Assembly Building (VAB) at John F. Kennedy Space Center (KSC) and on the launch pad. It will serve as a prototype for a future operational ground diagnostic system for Ares I. The prototype combines three existing diagnostic tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool that is commercially produced by Qualtech Systems, Inc. It uses a qualitative model of failure propagation to perform fault isolation and diagnostics. We adapted an existing TEAMS model of the TVC to use for diagnostics and developed a TEAMS model of the ground hydraulics. The second tool, Spacecraft Health Inference Engine (SHINE), is a rule-based expert system developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification. The prototype uses the outputs of SHINE as inputs to TEAMS. The third tool, the Inductive Monitoring System (IMS), is an anomaly detection tool developed at NASA Ames Research Center and is currently used to monitor the International Space Station Control Moment Gyroscopes. IMS automatically "learns" a model of historical nominal data in the form of a set of clusters and signals an alarm when new data fails to match this model. IMS offers the potential to detect faults that have not been modeled. The three tools have been integrated and deployed to Hangar AE at KSC where they interface with live data from the Ares I-X vehicle and from the ground hydraulics. The outputs of the tools are displayed on a console in Hangar AE, one of the locations from which the Ares I-X launch will be monitored. In a previous publication, we discussed how we selected the three tools based primarily on their ability to be certified for human spaceflight and described our plans for the prototype. This abstract is due October 23, 2009, and the Ares I-X launch is currently scheduled for October 27, 2009. If this abstract is accepted, then the full paper will describe how the prototype performed before the launch. It will include an analysis of the prototype's accuracy, including false-positive rates, false-negative rates, and receiver operating characteristics (ROC) curves. It will also include a description of the prototype's computational requirements, including CPU usage, main memory usage, and disk usage. If the prototype detects any faults during the prelaunch period then the paper will include a description of those faults. Similarly, if the prototype has any false alarms then the paper will describe them and will attempt to explain their causes. Also, the paper will describe the three tools and how they are used in the prototype. It will include a description of the TEAMS models of the Ares I-X first-stage TVC and associated ground hydraulics and how we adapted the TVC model for use in real-time diagnostics. It will describe the SHINE rules used for fault detection and mode identification and the software architecture that interfaces the various pieces of existing software that are part of the prototype to one another. It will describe how we selected the sensor values and commands that were used to train the IMS model and how we optimized the number of clusters in the IMS model. It will include screen shots of the graphical display that we developed in Java to display the outputs of the three tools. Because Ares I-X data was not yet available to us while we were developing the prototype, we used historical data from the Space Shuttle's Solid Rocket Booster (SRB) TVCs and the associated ground hydraulics to train IMS and to test the entire prototype. Because most of the failure modes that we modeled have never occurred in the Shuttle we inserted simulated failures into the Shuttle data. The Ares I-X first-stage TVC is very similar to the SRB TVC and we expect the data will be very similar. After the launch, we will determine how similar the data actually is and report how any differences in the data affected the diagnostic accuracy of the prototype. Finally, although we did not get the prototype certified, we designed it in a way that it could be certified and wrote a preliminary certification plan. The paper will include a brief summary of how we considered the need for certification in the design of the prototype, how we tested the prototype before deploying it to Hangar AE, and how we would propose to get it certified if it were deployed as an operational system. The paper will conclude with a description of some of the challenges we faced and some of the lessons learned in developing and deploying the prototype.

International Space Station↗
DOE OSTI1989

Nuclear Safety [Vol. 30, No. 1, January-March 1989]

Nuclear Safety is a review journal that covers significant developments in the field of nuclear safety. Its scope includes the analysis and control of hazards associated with nuclear energy, operations involving fissionable materials, and the products of nuclear fission and their effects on the environment. Primary emphasis is on safety in reactor design, construction, and operation; however, the safety aspects of the entire fuel cycle, including fuel fabrication, spent-fuel processing, nuclear waste disposal, handling of radioisotopes, and environmental effects of these operations, are also treated. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 1 International Conference on Thermal Reactor Safety: NUCSAFE 88 by A. P. Malinauskas and J. G. Pruett; ACCIDENT ANALYSIS: 18 Chernobyl Today: State of Research by A. Yu. Gagarinskii; CONTROL AND INSTRUMENTATION: 23 Problems Associated with Routine In-Plant Radioactive Effluent Monitoring Systems at U.S. Light-Water Reactors by Barry G. Wahlig, David M. Walker, Mahmoud R. Ghavi, and John M. Palms; DESIGN FEATURES: 34 Twentieth DOE/NRC Nuclear Air-Cleaning Conference by R. R. Bellamy, D. W. Moeller, and M. W. First, 46 Design Options for the Core Support Structure for Liquid-Metal-Cooled Reactor Plants by J. P. Burelbach, W. J. Kann, J. G. Saiveau, and R. W. Seidensticker; ENVIRONMENTAL EFFECTS: 53 The Radiological Consequences of the Chernobyl Accident by L. V. Konstantinov and A. J. González; WASTE AND SPENT FUEL MANAGEMENT: 70 Activities Related to Waste Management Compiled by E. G. Silver; OPERATING EXPERIENCES: 80 Fuel Performance Annual Report for 1986 by W. J. Bailey, 103 Safety Considerations Related to 12-Hour Shift Schedules by S. Gould, 105 Reactor Shutdown Experience Compiled by J. W. Cletcher, 108 Operating U.S. Power Reactors Compiled by E. G. Silver; RECENT DEVELOPMENTS: 130 General Administrative Activities Compiled by E. G. Silver, 140 Reports, Standards, and Safety Guides by D. S. Queener, 145 Status of Power-Reactor Licensing Activities Compiled by E. G. Silver, 153 Proposed Rule Changes as of Sept. 30, 1988; ANNOUNCEMENTS: 69 Northwestern University Short Course on Radiation Safety, 79 International Seminar on Fission Product Transport Processes in Reactor Accidents, 129 Harvard School of Public Health Offers Short Courses, 152 MIT Offers Summer Short Courses on Reactor Safety and Nodal Analysis, 152 Seminar on Use of PCs in Probabilistic Safety Analysis, 157 The Authors, 160 Indexes to Nuclear Safety, Volume 29.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1970

A battery simulator

Simulator verifies proper operation of a battery cell voltage-monitoring device. It also contains variable ac voltage to ascertain that a battery scanner will perform its function at all possible ac voltages.

Ferrell, S., Jr.↗
DOE OSTI2026

Environmental Performance Report 2025: Annual Site Environmental Report per the U.S. Department of Energy Order 231.1B Chg 1

The National Laboratory of the Rockies (NLR) Environmental Performance Report provides a description of the laboratory's environmental management activities in calendar year 2025, including information on environmental performance, environmental compliance activities and status, and environmental protection programs, highlights, and successes. The purpose of this report is to ensure that the U.S. Department of Energy (DOE) and the public receive timely, accurate information about events that have positively affected or could adversely affect the health and safety of the public or workers, the environment, or the operations of DOE facilities. This report meets the DOE requirements of the Annual Site Environmental Report (ASER) and has been prepared in accordance with DOE Order 231.1B Chg 1, Environment, Safety and Health Reporting.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRSDate not supplied

In-Situ Scanning Electron Microscope Experiments for Microscale Mechanical Testing and Validated Modeling of Fiber Reinforced Thermoplastics

A novel, in-situ, scanning electron microscope (SEM) mechanical testing capability for materials at the microscale which provides experimental validation to a machine learning (ML) toolset for full-field validation of physics-based micromechanics models is being developed by researchers at NASA Glenn Research Center. These are enabling technologies for the integration of multiscale digital twins for materials into system level models which will result in the improved performance, material discovery, reduced production cost and time, rapid characterization, and prognostic structural health monitoring (SHM) for materials and structures for extreme environments in support of NASA space exploration missions. In order to bridge the material structure-to-system gap for digital twins, physics-based models must be experimentally validated at multiple length scales. Seminal microscale experiments, conducted at the Air Force Research Laboratory (AFRL), were limited to transverse compression of single-layer, unidirectional thermoset polymer matrix composite (PMC) micropillar specimens [1]. The early phases of the current project followed those initial results and setup to reproduce the compression testing of PMC material on the custom-built piezoelectric actuated micromechanical testing rig built by MicroTesting Solutions LLC. In this work, samples of thermoplastic PMC material were first machined into 3 mm cubes, and then further machining and final milling was done using a Focused Ion Beam (FIB). The initial experiment was done on a pillar roughly 20 µm x 20 µm x 40 µm tall. Additional pillars were milled with final sizes ranging from 20 µm x 20 µm x 40 µm tall to 40 µm x 40 µm x 65 µm tall. A speckle pattern for in-situ full-field measurements using Digital Image Correlation (DIC) was applied with platinum, which was coated on the surface, and then the FIB was used to mill away some of the coating to produce an irregular pattern of Pt on the pillar surface. The samples were loaded into the custom testing rig and placed into the SEM and loaded under compression until failure. Images were collected in the SEM during testing. Post-processing of the images was conducted using DIC to obtain full-field displacement and strain measurements elucidating the role of the matrix as well as fiber-fiber interaction at the microscale within the composite subjected to compression loading well into the non-linear regime of the material. Moreover, the evolution of fiber-matrix debonding and matrix cracking is observed in-situ at the microscale. This data, along with images segmented with a newly developed ML toolset [2], was used to create and validate physics-based micromechanics models. An image of the failed micropillar is shown in Figure 1. The techniques developed in the initial compression experiment was tailored to the validation needs of the models and expanded to include different sized samples as well as possibly tension and fatigue.

Laura Wilson↗
NASA NTRS2006

Lithium-Ion Small Cell Battery Shorting Study

Positive Temperature Coefficient (PTC) provides adequate sustained hard short protection for AEA batteries with up to 8 cells in series. PTC cannot protect against sustained hard short in AEA batteries with 10 cells or more in series. Protective fused connector is a proven way to protect larger batteries from hard short damage: a) Hard short not credible in unmanned missions; b) However, recommended during ground handling; c) Inexpensive item. Preliminary diode protection scheme has passed manned space safety requirements for high voltage batteries. SCM confirmed fused connector did not affect battery health, however, this affect of hard short on the its long calendar and cycle life performance needs to be verified.

Pearson, Chris↗
NASA NTRS1980

NASA/Marshall's lithium battery applications

A general lithium battery is described and a summary of lithium battery applications is presented. Four aspects of a particular lithium battery, the inducement environmental contamination monitoring battery, are discussed-design and construction details, thermal vacuum tests, projection tests, and acceptance tests.

Paschal, L. E.↗
NASA NTRS1994

Extended Duration Orbiter Medical Project Microbial Air Sampler (STS-50/USML-1)

The Microbial Air Sampler was used on mission days 1, 7, and 13 in the Spacelab during STS-50/USML-1. Microbial air samples were collected using two types of media strips containing agar (Rose Bengal for yeast and molds, TSA for bacteria). The bacterial level found on day 1 was lower than experienced on previous Spacelab missions. A high level of fungi was present on day 1, however subsequent samples on days 7 and 13 did not indicate fungal growth. Bacterial growth was also minimized in this microgravity environment as the mission progressed. No pathogenic microorganisms were isolated, and the health risk from airborne microbes was minimal throughout the mission.

Duane L Pierson↗
NASA NTRS2026

The Operational NASA Anomaly Gas Analyzer and Exploration Follow-Ons

The NASA Anomaly Gas Analyzer (AGA) is the culmination of nearly ten years of advancement of core technology originally developed by Vista Photonics through the Small Business Innovation Research (SBIR) program and expanded using NASA program funding. The AGA is a portable, battery operated, optical gas detection instrument for continuous monitoring of H 2 O, CO 2 , O 2 , CO, NH 3 , HCN, HF, HCl in the spacecraft environment. Real time, in-situ monitored, gas concentrations are communicated at 1 Hz through a built-in display on-orbit and additionally through a serial interface on the ground. The instruments function over the typical range of temperatures and pressures required for the spacecraft environment. A ten-year operational life is targeted. Seven AGA instruments are currently deployed on the International Space Station and two Orion-specific units are manifested for Artemis 2. A variation of the operational AGA is under development for lunar Human Landing Systems. A wider calibrated operating pressure envelope is required in this application. Otherwise, the instruments straightforwardly address lessons learned from the original AGA by improving aspects of thermal control, ruggedness, and ease-of-use. A subset of AGA-developed sensed gases (H 2 O, CO 2 , O 2 ) is the subject of the Primary Constituent Monitor (PCM) development for the Habitation and Logistics Outpost (HALO) destined for lunar orbit. Two PCMs will be installed in HALO as part of the environmental control loop where they will interface with vehicle power and communications. While the basic sensor techniques are unchanged from the AGA, these instruments have been radiation hardened to survive for many years in the harsh target environment. Current development status for these instruments will be presented along with recent developments of a naval submarine variant.

ECLSS↗
NASA NTRSDate not supplied

Numerical Investigation of Heat Transfer and Fluid Flow within Electrochemical Hydrogen Peroxide Generation Unit

Long-term manned space missions require the onboard production of disinfectants essential for maintaining crew health and supporting life systems. Currently, disinfection aboard the International Space Station (ISS) relies on disposable wetted wipes, which are regularly resupplied from Earth. This approach imposes a significant burden on resupply logistics, storage, and waste management. To address these challenges and support future missions, efforts are underway to develop an in-situ solution that electrochemically generates hydrogen peroxide disinfectant using onboard resources. In collaboration with NASA, Faraday Technology, Inc. has advanced this concept through a series of Small Business Innovation Research (SBIR) projects, resulting in the development of a Peroxide Generation Unit (PGU). The PGU can produce up to 3 wt.% hydrogen peroxide on-demand at a rate of 1 liter per day, providing a sustainable alternative to Earth-dependent supplies. The resulting aqueous hydrogen peroxide (H₂O₂) is an effective disinfectant, safe for crew use, compatible with spacecraft systems, and free from volatiles, off-gassing, or residues. This innovation offers a reliable, efficient solution for onboard disinfection, reducing dependence on Earth-based resupply while ensuring the health and safety of space crews. Generating hydrogen peroxide at the required rate needs high voltages and currents, exceeding 20V and 2A respectively, which leads to significant heat generation from Joule heating. This temperature rise poses a risk to sensitive system components, especially critical and expensive membranes that can degrade under thermal stress. To mitigate this risk, the thermal, fluid, and electrical flows within the system are modeled computationally using the commercial software COMSOL. The numerical simulations are validated against experimental data from both sub-scale and alpha-scale systems. Once verified, the model is employed to identify thermal hotspots, investigate their underlying causes, and explore solutions to prevent them.

Life Support Systems (LSS)↗