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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Optimization, Design, and Demonstration of 1 kW Stirling Controller Using Capacitor-based Power Factor Correction

Free-piston Stirling convertors provide efficient, reliable thermal to electric power conversion, provided they are paired with a reliable controller. This paper outlines work underway on design concepts for kilowatt-class controllers. Using passive power factor correction (PFC) this design explores the possibility of reducing programmatic risk through system simplification. Efficiency is maximized through incorporating wide-bandgap gallium nitride (GaN) switches, and passive PFC volume is minimized through the use of polymer multi-layer capacitors. This work is aimed at exploring new approaches for future Stirling controllers.

Christopher Barth↗
NASA NTRS2019

Design and optimization of radiation-hardened isolated converters for Jovian environments

This paper presents the design and optimization of a class of flexible, high efficiency, multiple output capable, radiation-hardened isolated DC/DC converters for Jovian environments. The proposed converters achieve lower electromagnetic emission, better thermal management and higher efficiency. A new series of radiation-hardened isolated GaN gate driver circuit is proposed for space applications. Experimental results based on two prototype units implemented in the laboratory verify the performance of the proposed isolated converters.

Carr, Gregory↗
NASA NTRS2021

Spectral Synthesis for Geostationary Satellite-to-Satellite Translation

Earth-observing satellites carrying multispectral sensors are widely used to monitor the physical and biological states of the atmosphere, land, and oceans. These satellites have different vantage points above the Earth and different spectral imaging bands resulting in inconsistent imagery from one to another. This presents challenges in building downstream applications. What if we could generate synthetic bands for existing satellites from the union of all domains? We tackle the problem of generating synthetic spectral imagery for multispectral sensors as an unsupervised image-to-image translation problem modeled with a variational autoencoder (VAE) and generative adversarial network (GAN) architecture. Our approach introduces a novel shared spectral reconstruction loss to constrain the high-dimensional feature space of multispectral images. Simulated experiments performed by dropping one or more spectral bands show that cross-domain reconstruction outperforms measurements obtained from a second vantage point. Our proposed approach enables the synchronization of multispectral data and provides a basis for more homogeneous remote sensing datasets.

Geostationary satellites↗
NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗
NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗
NASA NTRS2021

Infrared Imaging using Sparse Microresonator Arrays and Compressed Sensing

In this work, we present an uncooled infrared (IR) thermal imager featuring a sparse array of resonant pixels that can be monolithically integrated with the readout electronics for long-term operation in high-temperature environments. Each of the detector pixels is made of an IR-sensitive GaN microresonator developed to be operated at up to 500 0C. The pixels are employed in sparse array configuration to allow for routing the signal carrying traces and to ease the routing from pixel to circuit, as well as to reduce the power consumption at the expense of moderate post-processing time and increased complexity to recover the image. Three 128 by 128 sparse resonator arrays with the array densities of 10%, 25% and 37.5% are designed, and used in conjunction with the Compressed Sensing (CS) method to reconstruct complex test images from highly incomplete data. Capabilities and performance metrics of the recovery technique are explored.

Rais-Zadeh, Mina↗
NASA NTRS2021

Thermal Infrared Detector Sparse Array for NASA Planetary Applications

In this work, we present an uncooled infrared (IR) thermal imager featuring a sparse array of resonant pixels that can be monolithically integrated with the readout electronics for long-term operation in hightemperature environments. Each of the detector pixels is made of an IR-sensitive GaN microresonator developed to be operated at up to 500 0C. The pixels are employed in sparse array configuration to allow for routing the signal carrying traces and to ease the routing from pixel to circuit, as well as to reduce the power consumption. A 128´128 sparse resonator array with the array density of 37.5% is designed and used in conjunction with the Compressed Sensing method to reconstruct complex test images from highly incomplete data. Capabilities and performance metrics of the recovery technique are explored.

Rais-Zadeh, Mina↗
NASA NTRS2023

Forward for the new edition of: “What Every Engineer Should Know About Risk Engineering and Management"

In aerospace and engineering in general, the major metrics are capability, cost, and safety/risk. With the increasingly rapid emergence and utilization of new technologies and systems of increasing complexity, ensuring safety becomes more difficult. The technology and practice/applications of safety/risk technologies require updating in concert with capability and systems technology changes. Hence the new, updated edition of this risk engineering book. Major changes in technology and applications, now and going forward, increasingly involve artificial intelligence (AI)/autonomy and complex systems, which are rapidly developing and moving targets when it comes to risk/safety analysis. These introduce both new risks and safety issues, and they alter more usual ones. The current reality is that often the best AI is when it is “Black Boxed”, developed “independently”, without detailed human understanding of how and by what processes decisions and results are produced. There are ongoing efforts to make the AI processes more understandable by humans, with results to be determined. Also, trusted and true autonomy is free of human intervention, which would require machines to ideate to solve in real time issues that arise due to unknown unknowns and even known unknowns. Machines using generative adversarial network (GANs) and other approaches are beginning to ideate. Overall, the capabilities and practice of AI/autonomy utilization is a work in progress with the rate of progress substantial and the impacts upon system risk/safety major.

Dennis M. Bushnell↗
NASA NTRSDate not supplied

Super-Resolution from Space: Using MERRA-2 and MAIAC Satellite Imagery to Produce Daily Continuous 1 km PM2.5 Estimates

PM2.5 measurements from ground stations are the gold standard when available, but the expense and coverage of such stations limits widespread monitoring. Having accurate PM2.5 estimates outside the range of these stations is important for monitoring this crucial aspect of air quality. The goal of this project is to produce daily 1 km continuous PM2.5 estimates for the contiguous US relying primarily on satellite-derived data sources. This is important because models based on such data can be more easily expanded outside the study area and produce global estimates as well. The temporal availability of such data products is often weekly/daily, unlike land-use products with are often available at a yearly or worse temporal resolution. To achieve our goal, we use a couple of different deep neural network architectures to produce PM2.5 measurements at 10 km and 1 km resolution. We use two model architectures, a UNET-like model and a GAN-based model. We train both models using MERRA-2 data and MAIAC AOD data scaled to 10 km and 1 km for the two different prediction resolutions. MERRA-2 imagery is data rich with a wide range of geospatial variables at 50 km and has long historical availability (beginning in 1980). We’re also using higher spatial resolution MAIAC data at 1 km to provide finer resolution spatial context. This essentially leverages the spatial resolution of MAIAC data and the “wider” information of MERRA-2 data to predict PM2.5. For the target data we’re using a modeled 1 km PM2.5 dataset produced by Harvard to pre-train our models and then fine-tune our models using ground station measurements. Not only are our results comparable with the performance of the Harvard dataset, but can be generalized to any area or time where MERRA-2 and MAIAC data is available.

satellite imagery↗
NASA NTRSDate not supplied

The Doppler Wind Temperature Sensor (DWTS) Flight Evaluation and Experiments (TES-16,17)

The Doppler Wind Temperature Sensor instrument (DWTS), developed by Global Atmospheric Technologies and Sciences (GATS), is a powerful yet simple tool with the potential of becoming a new window to upper atmosphere dynamics. Based around a defense-grade infrared camera peering through a static gas cell used as a scanning spectral filter, the DWTS is designed to measure the fingerprints of wind and thermal waves as they propagate to the mesosphere and lower thermosphere. DWTS achieves this scanning by measuring the induced change in the doppler shift of the emission as it passes through the DWTS field of view. DWTS holds promise to aid in more accurate weather determination (Gordley, et al), and the core technology can be easily adapted to study other atmospheres of interest. In partnership with GATS, NOAA, and other collaborators, NASA Ames and the Nano Orbital Workshop group have been working to evaluate the DWTS instrument on orbit and optimize it as a flexible cube satellite payload. The first mission selected for DWTS evaluation is preparing for flight in early 2024, followed by a more capable science mission in 2025, with both missions part of the TES-n/NOW flight series. The first flight, TES-16/DWTS-A, will incorporate a single DWTS instrument in an approximately 2U payload volume with the imaging aperture perpendicular to the flight velocity vector. With an estimated power consumption of 20 watts, supplied via a custom NOW-designed GaN converter, the instrument will maintain the imaging sensor plane at 80K using an integrated pulse-tube cryocooler during instrument evaluation periods. Data from DWTS will be captured via a NOW-designed custom data processor before being transmitted via S-band radio back to select ground stations, with instrument command and control operated via L-band global-coverage radio. After the TES-16/DWTS-A initial demonstration mission, the subsequent TES-17/DWTS-B mission will be a dedicated science mission equipped with three DWTS instruments, each hosting a different gas cell to obtain full altitude coverage from 20 to 200 km both day and night from a single cube satellite. The intention of the flight series, and one of NASA’s interests in the instrument, is to advance a Martian atmospheric instrument (Colaprete, Gordley, et al) which, if successful, would greatly further understanding of Martian atmospheric behavior. The proposed paper will review the flight series in detail, including challenges from the TES-16 flight tests and the projected challenges and application to Mars study. Additional detail regarding the possible applications of a Cognitive Communication technique in current flight development by NOW collaborators at the NASA Glenn Research Center will also be discussed, including the implications of using an automated User Initiated Service (UIS) protocol to maximize the data collected per orbit.

Doppler Wind Temperature Sensor↗
NASA NTRS2023

Extended Temperature Operation of Isolated, Precision Gate Driver, ADuM4120

Electronics used in space missions encounter exposure to extreme temperatures and operation under wide thermal swings. Most commercial-off-the-shelf (COTS) devices are not designed to function under such extreme conditions and very little data exist on their performance outside their specified range of operation. Extending the temperature range at which electronic parts can operate reliably leads to potential benefits resulting in improving reliability, reducing mass/weight, and simplifying thermal design due to elimination of thermal control elements and fewer parts count. In this work, the performance of an isolated, precision gate driver for controlling power-level transistors, including gallium nitride (GaN) and silicon carbide (SiC) devices, was evaluated under extreme temperatures and thermal cycling. The investigations were carried out to assess performance for potential use of this device in space exploration missions under extreme temperature conditions.

Wide Bandgap Power Electronics↗
NASA NTRS2022

A High Voltage Tethered Power System for Planetary Surface Applications

In the pursuit of human and robotic expansion of theLunar surface and other planetary bodies, power conversionand transmission over long distances has emerged as a distinctneed. The ability to transmit power over kilometers at highefficiency enables human settlement, in-situ resource utilization,and extreme terrain robotic exploration, through the ability toreach previously unreachable places. The critical buildingblocks of such a system are being developed by the TetheredPower System for Lunar Mobility and Power Transmission(TYMPO) project, a NASA project aimed at reaching flightreadiness by 2024. This paper presents an architectural andrequirements overview of the TYMPO project, along withtechnology discussions on the GaN-based multilevel DCXconverters, dual fiber optics and power-line communicationssystem, and high-voltage tether. Finally, a set of proposed futuremissions will be presented, mapping the capabilities of theTYMPO system to missions that may soon explore planetarysurfaces throughout the solar system.

Yang, Summer↗