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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

A small satellite design for deep space network testing and training

With the continuing exploration of the Solar System and the reemphasis on Earth focused missions, the need for faster data transmission rates has grown. Ka-band could allow a higher data delivery rate over the current X-band, however the adverse effects of the Earth's atmosphere on Ka are as yet unknown. The Deep Space Network and Jet Propulsion Lab have proposed to launch a small satellite that would simultaneously transmit X and Ka signals to test the viability of switching to Ka-band. The Mockingbird Design Team at the University of Texas at Austin applied small satellite design principles to achieve this objective. The Mockingbird design, named BATSAT, incorporates simple, low-cost systems designed for university production and testing. The BATSAT satellite is a 0.64 m diameter, spherical panel led satellite, mounted with solar cells and omni-directional antennae. The antennae configuration negates the need for active attitude control or spin stabilization. The space-frame truss structure was designed for 11 g launch loads while allowing for easy construction and solar-panel mounting. The communication system transmits at 1 mW by carrying the required Ka and X-band transmitters, as well as an S band transmitter used for DSN training. The power system provides the 8.6 W maximum power requirements via silicon solar arrays and nickel-cadmium batteries. The BATSAT satellite will be lofted into an 1163 km, 70 deg orbit by the Pegasus launch system. This orbit fulfills DSN dish slew rate requirements while keeping the satellite out of the heaviest regions of the Van Allen radiation belts. Each of the three DSN stations capable of receiving Ka-band (Goldstone, Canberra, and Madrid) will have an average of 85 minutes of view-time per day over the satellites ten year design life. Mockingbird Designs hopes that its small satellite design will not only be applicable to this specific mission scenario, but that it could easily be modified for instrument capability for university, government, and/or commercial research.

Mcwilliams, Dennis↗
NASA NTRSDate not supplied

Evaluating the Efficacy of Conditional Variational Autoencoders in Generating Synthetic Single Nuclei RNA-Seq Data for Space Biology Research

Astronauts are subject to unique stressors during spaceflight, leading to changes in their cellular function. However, neither astronauts nor model organisms respond the same to spaceflight, and research implicates a contribution of omics components in differential responses. Understanding how gene expression affects astronaut health is critical for the success of long-term space missions, prompting interest in developing personalized predictive models leveraging artificial intelligence (AI) and machine learning (ML) techniques. Developing such models requires extensive data, which is challenging to obtain and share. This study explores the use of conditional variational autoencoders (CVAEs) to synthetically generate single-nuclei RNA-seq (snRNA-seq) data. CVAEs build on standard variational autoencoders (VAEs) by conditioning data generation on covariates like sample identity and mission parameters, enhancing the relevance of generated data for specific contexts. For our work, we built two CVAEs with varying degrees of sparsity to optimize both interpretability and generative power. We train and validate models on existing snRNA-seq data collected from the brain tissue of mice subjected to spaceflight conditions and their ground control counterparts. We evaluate model performance using statistical tests and visualizations to compare synthetic data to real data. We aim to demonstrate that these prototype CVAE architectures could be used in future space biology work and that this is a method worth further exploring.

Sarah Golts↗
DOE OSTI2024

Physics-informed heterogeneous graph neural networks for DC blocker placement

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of these devices and the sparsity of transformers that experience high GICs during GMD events, however, calls for a sparse placement strategy that involves high computational cost. To address this challenge, we developed a physics-informed heterogeneous graph neural network (PIHGNN) for solving the graph-based dc-blocker placement problem. Our approach combines a heterogeneous graph neural network (HGNN) with a physics-informed neural network (PINN) to capture the diverse types of nodes and edges in ac/dc networks and incorporates the physical laws of the power grid. We train the PIHGNN model using a surrogate power flow model and validate it using case studies. Results demonstrate that PIHGNN can effectively and efficiently support the deployment of GIC dc-current blockers, ensuring the continued supply of electricity to meet societal demands. Furthermore, our approach has the potential to contribute to the development of more reliable and resilient power grids capable of withstanding the growing threat that GMDs pose.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Tailoring coherent beam combined laser pulse trains for high peak and average power applications

Laser systems based on coherent beam combination (CBC) that rely on tiled pupil architecture intrinsically carry digital capabilities independently applicable to all three essential characteristics of a laser pulse: amplitude, phase and polarization. Those capabilities allow the far-field energy distribution to be flexibly tailored in real time. Operation in the femtosecond regime at high repetition rates gives access to a wide range of applications requiring both high peak and average powers. We address the task of independent peak versus average power adjustment needed for applications seeking to decouple nonlinear phenomena associated with GW peak power from the thermal load inherent to kW average power operation. The technical solutions proposed are presented in the framework of the Ecole Polytechnique XCAN CBC laser platform (61 independent channels) with an emphasis on thermal management measures implemented to ensure its nominal operation.

47 OTHER INSTRUMENTATION↗
NASA NTRS1991

Pulsed response of a TWT

The consequence of frequency domain multiple access channelization in a satellite communications system is that the ground- and space-based components often are required to operate in a linear region to prevent the generation of distortion signals. Components of a time division multiple access (TDMA) satellite system, such as a traveling-wave tube (TWT), can operate in the highest output power state because the channelization technique is relatively insensitive to the distortions resulting from saturated operation. A 30 GHz TWT was tested to determine the suitability of such a device in a TDMA system. Testing was focused on the ability of the TWT's output signal to rise up to full power at the leading edge of TDMA bursts, simulated by a pulse train. A peak power meter was used to display and measure the pulsed signal waveform. Measurements on the TWT output pulse rise time indicate that the TWT lengthened the rise time by 10 to 20 ns. Imposing modulator turn on timing that precedes the data burst by the TWT rise time is a logical approach to coordination of the two subsystem specification.

May, Brian D.↗
DOE OSTI2024

REDLY

SAND2025-11838O REDLY provides a framework for designing, training, and validating physics-informed neural networks for power system applications. It trains surrogates for Alternating Current Power Flow prediction and general economic dispatch problems, such as Alternating Current Optimal Power Flow and Direct Current Power Flow. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Blakely, Logan↗
NASA NTRS2012

Component Repair Experiment-1: An Experiment Evaluating Electronic Component-Level Repair During Spaceflight

The Component Repair Experiment-1 (CRE-1) examines the capability for astronauts to perform electronics repair tasks in space. The goal is to determine the current capabilities and limits for the crew, and to make recommendations to improve and expand the range of work that astronauts may perform. CRE-1 provided two-layer, functional circuit boards and replacement components, a small tool kit, written and video training materials, and 1 hr of hands on training for the crew slated to perform the experiment approximately 7 months prior to the mission. Astronauts Michael Fincke and Sandra Magnus performed the work aboard the International Space Station (ISS) in February and March 2009. The astronauts were able to remove and replace components successfully, demonstrating the feasibility of performing component-level electronics repairs within a spacecraft. Several unsuccessful tasks demonstrated areas in need of improvement. These include improved and longer training prior to a mission, an improved soldering iron with a higher operating temperature and steady power source, video training and practice boards for refresher work or practice before a repair, and improved and varied hand tools and containment system.

Easton, John W.↗
NASA NTRS1994

Turbine design and application volumes 1, 2, and 3

NASA has an interest in turbines related primarily to aeronautics and space applications. Airbreathing turbine engines provide jet and turboshaft propulsion, as well as auxiliary power for aircraft. Propellant-driven turbines provide rocket propulsion and auxiliary power for spacecraft. Closed-cycle turbine engines using inert gases, organic fluids, and metal fluids have been studied for providing long-duration electric power for spacecraft. Other applications of interest for turbine engines include land-vehicle (cars, trucks, buses, trains, etc.) propulsion power and ground-based electrical power. In view of the turbine-system interest and efforts at Lewis Research Center, a course entitled 'Turbine Design and Application' was presented during 1968-69 as part of the In-house Graduate Study Program. The course was somewhat revised and again presented in 1972-73. Various aspects of turbine technology were covered including thermodynamic and fluid-dynamic concepts, fundamental turbine concepts, velocity diagrams, losses, blade aerodynamic design, blade cooling, mechanical design, operation, and performance. The notes written and used for the course have been revised and edited for publication. Such a publication can serve as a foundation for an introductory turbine course, a means for self-study, or a reference for selected topics. Any consistent set of units will satisfy the equations presented. Two commonly used consistent sets of units and constant values are given after the symbol definitions. These are the SI units and the U.S. customary units. A single set of equations covers both sets of units by including all constants required for the U.S. customary units and defining as unity those not required for the SI units. Three volumes are compiled into one.

Engine design↗
DOE OSTI2025

Enhancing Autonomous Control of Microreactors Using Multi-Agent Reinforcement Learning

In order for microreactors to be economically competitive, operation costs will need to be minimized through some degree of autonomous control. Previous work has demonstrated the effectiveness of reinforcement learning (RL) for load-following control in a drum-controlled microreactor. This study extends that work by exploring the potential of RL to independently control each of the reactor’s drums. We compare a single-agent RL approach with a multi-agent RL (MARL) framework, testing them for generalization across different load-following power profiles and control timescales, and for robustness in cases of randomly disabled control drums. Since the point kinetics simulation environment used in this study cannot resolve spatial effects, we assume that in the absence of spatially localized disturbances, optimal drum movements should be symmetrical. We demonstrate that single-agent RL is able to achieve accurate performance only when symmetric actions are ignored; otherwise, it fails to train a useful controller. Meanwhile, the MARL framework performs symmetric actions by design and trains a robust, accurate agent, as evidenced by mean absolute errors in power matching of 0.41% for the training power profile, 0.68% for a profile with half the drums disabled, and 0.21% for a profile on a realistic load-following time horizon.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1971

Further observations of the pulsating X-ray source Cygnus X-1 from Uhuru.

Since the discovery of the pulsations from the X-ray source Cygnus X-1 more data from Uhuru have been analyzed. The conclusions reached about the temporal behavior of the source are described. Large fluctuations of intensity exist on all observed time scales ranging from 50 msec to 10 sec containing up to 50 per cent of the power. Periodic pulse trains with periods from 0.3 sec to over 10 sec exist containing 10-25 per cent of the power; however, they persist only for several seconds or several tens of seconds. No single period is consistently present. This behavior is shown to reconcile the apparently contradictory results obtained by other observers. The energy spectrum of the source is best fitted by a power law, but with great variability in the spectral index and with an excess of flux at higher energies.

Schreier, E.↗
NASA NTRS1973

Turbine Design and Application, Volume 2

NASA has an interest in turbines related primarily to aeronautics and space applications. Airbreathing turbine engines provide jet and turboshaft propulsion, as well as auxiliary power for aircraft. Propellant -driven turbines provide rocket propulsion and auxiliary power for spacecraft. Closed-cycle turbine engines using inert gases, organic fluids, and metal fluids have been studied for providing long-duration electric power for spacecraft. Other applications of current interest for turbine engines include land-vehicle (cars, trucks, buses, trains, etc.) propulsion power and ground-based electrical power. In view of the turbine-system interest and efforts at Lewis Research Center, a course entitled "Turbine Design and Application" was presented during 1968-69 as part of the In-House Graduate Study Program. The course was somewhat revised and again presented in 1972-73. Various aspects of turbine technology were covered including thermodynamic and fluid-dynamic concepts, fundamental turbine concepts, velocity diagrams, losses, blade aerodynamic design, blade cooling, mechanical design, operation, and performance. The notes written and used for the course have been revised and edited for publication. Such a publication can serve as a foundation for an introductory turbine course, a means for self-study, or a reference for selected topics. The first volume presented the material covering thermodynamic and fluid-dynamic concepts, fundamental turbine concepts, and velocity diagram design. This second volume presents the material related to blade aerodynamic design and turbine energy losses.

Arthur J Glassman↗
NASA NTRS1998

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Trejo, Leonard J.↗
NASA NTRS1999

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Evoked Potentials↗
NASA NTRS1997

Countermeasures for Maintenance of Cardiovascular and Muscle Function in Space Flight

In this session, Session FA2, the discussion focuses on the following topics: Effects of Repeated Long Duration +2Gz Load on Man's Cardiovascular Function; Certain Approaches to the Development of On-Board Automated Training System; Cardiac, Arterial, and Venous Adaptation to Og during 6 Month MIR-Spaceflights with and without "Thigh Cuffs" (93-95); Space Cycle(TM) Induced Physiologic Responses; Muscular Deconditioning During Long-term Spaceflight Exercise Recommendations to Optimize Crew Performance; Structure And Function of Knee Extensors After Long-Duration Spaceflight in Man, Effects of Countermeasure Exercise Training; Force and power characteristics of an exercise ergometer designed for use in space; and The simulating of overgravity conditions for astronauts' motor apparatus at the conditions of the training for orbital flights.

Source record↗
NASA NTRS2001

ISS Microgravity Research Payload Training Methodology

The NASA Microgravity Research Discipline has multiple categories of science payloads that are being planned and currently under development to operate on various ISS on-orbit increments. The current program includes six subdisciplines; Materials Science, Fluids Physics, Combustion Science, Fundamental Physics, Cellular Biology and Macromolecular Biotechnology. All of these experiment payloads will require the astronaut various degrees of crew interaction and science observation. With the current programs planning to build various facility class science racks, the crew will need to be trained on basic core operations as well as science background. In addition, many disciplines will use the Express Rack and the Microgravity Science Glovebox (MSG) to utilize the accommodations provided by these facilities for smaller and less complex type hardware. The Microgravity disciplines will be responsible to have a training program designed to maximize the experiment and hardware throughput as well as being prepared for various contingencies both with anomalies as well as unexpected experiment observations. The crewmembers will need various levels of training from simple tasks as power on and activate to extensive training on hardware mode change out to observing the cell growth of various types of tissue cultures. Sample replacement will be required for furnaces and combustion type modules. The Fundamental Physics program will need crew EVA support to provide module change out of experiment. Training will take place various research centers and hardware development locations. It is expected that onboard training through various methods and video/digital technology as well as limited telecommunication interaction. Since hardware will be designed to operate from a few weeks to multiple research increments, flexibility must be planned in the training approach and procedure skills to optimize the output as well as the equipment maintainability. Early increment lessons learned will be addressed.

Schlagheck, Ronald↗
NASA NTRS1982

Clutchless multiple drive source for output shaft

A first shaft is connected to a source of rotational power and has a gear fixedly mounted on the shaft. A second gear is fixedly mounted on a gear shaft that is parallel to the first shaft. A third gear, also fixedly mounted on gear shaft, meshes with a fourth gear fixedly mounted on the output shaft. The first input shaft and output shaft are rotatably mounted through a housing that is itself rotatable with respect to a support. Both shafts are coaxial and in end-to-end relationship. A second input shaft is connected to a second source of rotational power. A fifth gear, fixedly mounted on second input shaft, meshes with a sixth gear, which is fixedly mounted on rotatable housing and in coaxial relationship with first input shaft. In operation, the first drive source and gear train provide rotational power in a first direction to drive the output shaft in a given direction of rotation. The second source of rotational power may be operated either to decrease the rate of rotation imparted to the output shaft by the first source of rotational power, or to increase that rate of rotation, depending on which direction the housing is rotated by the second source of rotational power.

Webster, L. D.↗
DOE OSTI2025

An Open-Access Repository of Synchrophasor Data Quality Examples: Curation and Example Applications

Synchrophasor measurements are critical in providing wide-area situational awareness to power system operators. However, data artifacts may be introduced due to various issues such as loss of communication, loss of GPS signal, internal clock error, and vendor-specific implementation of phasor estimation algorithms. Tools designed to provide actionable insights from synchrophasor data, hence, must be designed to be robust to these data quality issues. In this work, two years of synchrophasor data sourced from multiple electric utilities in the United States were analyzed to identify examples of data quality problems. These examples were then labeled and published in the Grid Event Signature Library, a publicly available repository of power system measurements hosted by the Oak Ridge National Laboratory. This paper describes the data curation process, and illustrates two application use cases where the dataset can be valuable to the research community. In the first use case, a random forest classifier is trained to distinguish power system disturbance signatures from data anomalies introduced in synchrophasor measurements due to clock errors. The second use case studies the impact of data quality issues on an example synchrophasor application (specifically, event start time determination). The choice of data quality problems investigated is informed by the examples in the repository curated in this work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Improving Trustworthiness of Data-Driven Power Grid Contingency Analysis With Bayesian Residual Graph Neural Networks

The evolving energy landscape requires novel tools to efficiently perform contingency analysis and reliability assessment of power grids, potentially in real-time. The high computational cost of traditional power flow solvers limits their applicability in practice. Machine learning (ML) surrogates such as deep neural networks (NNs) accelerate power flow solvers computations, enabling high-order contingency analysis and real-time decision-making by learning highly nonlinear functions and integrating grid topology via graph architectures. However, (graph) NNs lack predictive power away from training data and do not provide predictive confidence estimates. Here, we present a Bayesian residual graph NN that integrates knowledge from low-fidelity data via residual training and embeds granular quantification of uncertainties, improving trustworthiness critical for high-consequence decision-making. Applying Bayesian concepts to NNs is challenging due to the high-dimensionality of both the parameter space, complicating derivation of a meaningful prior, and the output space in large grid systems, requiring enhanced techniques to assess the predicted high-dimensional uncertainties. Our contributions include: (1) Deriving a prior for fully connected and graph NNs that leverages low-fidelity data to guide mean predictions and appropriately control prior predictive uncertainty. (2) Integrating this prior within an ensembling with anchoring scheme for efficient approximate posterior inference. (3) Deriving enhanced metrics to assess accuracy of both the mean and uncertainty predictions in high dimensions, appropriately accounting for correlations propagated through graph layers. The resulting Bayesian residual graph NN is tested on a contingency analysis task for 14-bus and 118-bus grids.

24 - POWER TRANSMISSION AND DISTRIBUTION↗