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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Fire Detection Tradeoffs as a Function of Vehicle Parameters

Fire survivability depends on the detection of and response to a fire before it has produced an unacceptable environment in the vehicle. This detection time is the result of interplay between the fire burning and growth rates; the vehicle size; the detection system design; the transport time to the detector (controlled by the level of mixing in the vehicle); and the rate at which the life support system filters the atmosphere, potentially removing the detected species or particles. Given the large differences in critical vehicle parameters (volume, mixing rate and filtration rate) the detection approach that works for a large vehicle (e.g. the ISS) may not be the best choice for a smaller crew capsule. This paper examines the impact of vehicle size and environmental control and life support system parameters on the detectability of fires in comparison to the hazard they present. A lumped element model was developed that considers smoke, heat, and toxic product release rates in comparison to mixing and filtration rates in the vehicle. Recent work has quantified the production rate of smoke and several hazardous species from overheated spacecraft polymers. These results are used as the input data set in the lumped element model in combination with the transport behavior of major toxic products released by overheating spacecraft materials to evaluate the necessary alarm thresholds to enable appropriate response to the fire hazard.

microgravity↗
NASA NTRS2017

Radar Ambiguities and Signal Processing Design Tradeoffs in the REASON Radar Sounder

NASA’s planned Europa Clipper mission would carry a radar sounder which operates at HF and VHF frequencies that can penetrate the icy shell of Europa. The sounder would be used to characterize subsurface bodies of liquid water and to measure the thickness of the icy shell. The spacecraft would make observations during flyby to reduce radiation exposure. The varying geometry during the flyby is expected to cause varying radar ambiguity and surface clutter contamination. At the same time, data down-link restrictions would require on-board pulse summation to reduce data volume which would require on-board Doppler filtering to control clutter and ambiguity contamination levels. This paper will study some of the design trade-offs involved in specifying the on-board Doppler filtering.

Hawkins, D.↗
NASA NTRS2021

LEDs for Extraterrestrial Agriculture: Tradeoffs between Color Perception and Photon Efficacy

Growing food on extraterrestrial surfaces requires the development of efficient lighting technologies to provide photons for photosynthesis. Here we discuss the development and demonstration of LED panels with a high color fidelity index that can achieve a photon efficacy of 3.6 µmol of photons per joule of input electrical energy. As of June 2021, this is higher than commercial LED fixtures on Earth. This high efficacy makes LED technology a preferred option to solar fiber optics for extraterrestrial applications. Increasing the fraction of red LEDs and photons increases the efficacy but decreases the perception of color.

Bioregenerative↗
NASA NTRS2022

Simulation-based Study of Single-Event Burnout in 4H-SiC High-Voltage Vertical Superjunction DMOSFET: Physical Failure Mechanism and Robustness vs. Performance Tradeoffs

We explore and elucidate physical failure mechanisms in a 4H-SiC, high voltage, superjunction (SJ) vertical DMOSFET from a single heavy ion strike using three-dimensional electro-thermal transient simulations. The single-event burnout (SEB) failure is thermal runaway from second breakdown, initiated by impact ionization and terminated with mesoplasma formation, at the center of the P-pillar/N+ substrate interface. We also demonstrate that the SEB performance of this SiC SJ DMOSFET is insensitive to the pillar width but sensitive to the strike location with ion strike at the P-pillar causing SEB at a lower blocking voltage than at the N-pillar. Compared to commercially available 1.2 kV blocking-rated non-SJ DMOSFETs, which have been demonstrated to survive SEB up to 525 V, the SJ DMOSFET increases SEB survival threshold voltage (VSEB) by a factor of 2.2, making it close to 1200 V, while the on-resistance is increased by only 11%. Using our recently developed figure of merit (FoM), which considers the trade-off between VSEB and on-state performance, we find that the SiC SJ DMOSFET achieves a FoM that is 14 times better, making it superior to conventional 1.2 kV SiC DMOSFETs for long-term radiation-tolerant operation in space applications.

Silicon Carbide↗
NASA NTRSDate not supplied

Effect of Nuclear Thermal Propulsion Engine Thrust and Isp Tradeoffs and Alternate Propellants on ΔV Budget and Architecture Mass for Crewed Mars Vehicles

This paper will provide an overview of a spacecraft architecture model developed at AMA as well as an insight into some of the results provided. Currently, preliminary analysis has been completed with this model on evaluating different thrust to weight ratios of Nuclear Thermal Propulsion based vehicles using hydrogen as the propellant by altering the number of engines in the engine block. Gravity losses are incorporated into this analysis as different thrust to weight ratios will provide different burn times. So far, this analysis has shown that in terms of the vehicle mass, it is best to trade the gravity losses for lower vehicle mass as the engines considered add significant dry mass to the vehicle. The final version of this paper will compare gravity losses and vehicle masses for vehicles using different engine masses as well as the ammonia propellant.

Master equipment list↗
NASA NTRSDate not supplied

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.

machine learningautonomous systems; flight simulat↗
NASA NTRSDate not supplied

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.

machine learning↗
NASA NTRSDate not supplied

Effect of Engine Thrust and Isp Tradeoffs and Alternate Propellants on ΔV Budget and Architecture Mass for 1st Generation Nuclear Thermal Propulsion Flight Test Systems

Following the first Nuclear Thermal Propulsion (NTP) system test, also known as DRACO, the next NTP system to be developed would be the 1st Generation NTP system. An analysis was conducted to determine the performance of different vehicle configurations utilizing hydrogen (H-NTP), ammonia (A-NTP), and methane (M-NTP) as propellants launched onboard commercial launch vehicles. This was enabled by a quasi-steady-state power balance engine model and vehicle component physics that sized the vehicle system using Master Equipment List (MEL) parameters. The analysis considered configuration cases outlined by a matrix of different mission classes and vehicle configurations that covered the design space of the 1st Generation NTP system to explore various propellant options, engine architectures, and mission scopes. Four mission classes were evaluated which included single burn missions performing maneuvers having a ΔV of 1 km/s and 2.5 km/s followed by 2-burn and 4-burn missions that aimed to exhaust the launch vehicles volume or mass limitations. In all cases, the NTP systems with the lowest thrust class had the longest burn time of which A-NTP and M-NTP systems provided the longest and shortest burn times depending on the launch vehicle used while H-NTP systems tended to cluster together in the middle. Longer burn times could be useful as a testing platform to increase the time for data accumulation. Across the multi-burn cases, H-NTP systems were found to be volume limited while A-NTP and M-NTP systems were mass limited. Both single burn missions showed that A-NTP configurations provided the lowest dry mass given that ammonia had the highest density with comparable performance to M-NTP systems and no requirement for cryocoolers. The results showed that beyond the propellant selection type, the launch vehicle selection, which included Starship, New Glenn, Vulcan, Falcon 9 (recoverable), and Falcon Heavy (recoverable), was a primary driving factor in the test vehicle’s capabilities. Trends were determined based on a set of dimensionless parameters that included the mass ratio of inert mass to initial wetted mass, ratio of specific impulse to burn time, and a dimensionless engine parameter (ratio of impulse to engine momentum). These relationships found the “knees-in-the-curves” that could be a significant point of reference for the designer as they indicate a change in the trend which is located at a specific impulse to burn time ratio of 1, a mass ratio of 0.6, and an engine performance parameter of 4. This study did not attempt to make a recommendation rather provide the tools for the reader to select their own configuration based on their needs.

Propellant↗
NASA NTRSDate not supplied

Effect of Engine Thrust and Isp Tradeoffs and Alternate Propellants on ΔV Budget and Architecture Mass for 1st Generation Nuclear Thermal Propulsion Flight Test Systems

Following the first Nuclear Thermal Propulsion (NTP) system test, also known as DRACO, the next NTP system to be developed would be the 1st Generation NTP system. An analysis was conducted to determine the performance of different vehicle configurations utilizing hydrogen (H-NTP), ammonia (A-NTP), and methane (M-NTP) as propellants launched onboard commercial launch vehicles. This was enabled by a quasi-steady-state power balance engine model and vehicle component physics that sized the vehicle system using Master Equipment List (MEL) parameters. The analysis considered configuration cases outlined by a matrix of different mission classes and vehicle configurations that covered the design space of the 1st Generation NTP system to explore various propellant options, engine architectures, and mission scopes. Four mission classes were evaluated which included single burn missions performing maneuvers having a ΔV of 1 km/s and 2.5 km/s followed by 2-burn and 4-burn missions that aimed to exhaust the launch vehicles volume or mass limitations. In all cases, the NTP systems with the lowest thrust class had the longest burn time of which A-NTP and M-NTP systems provided the longest and shortest burn times depending on the launch vehicle used while H-NTP systems tended to cluster together in the middle. Longer burn times could be useful as a testing platform to increase the time for data accumulation. Across the multi-burn cases, H-NTP systems were found to be volume limited while A-NTP and M-NTP systems were mass limited. Both single burn missions showed that A-NTP configurations provided the lowest dry mass given that ammonia had the highest density with comparable performance to M-NTP systems and no requirement for cryocoolers. The results showed that beyond the propellant selection type, the launch vehicle selection, which included Starship, New Glenn, Vulcan, Falcon 9 (recoverable), and Falcon Heavy (recoverable), was a primary driving factor in the test vehicle’s capabilities. Trends were determined based on a set of dimensionless parameters that included the mass ratio of inert mass to initial wetted mass, ratio of specific impulse to burn time, and a dimensionless engine parameter (ratio of impulse to engine momentum). These relationships found the “knees-in-the-curves” that could be a significant point of reference for the designer as they indicate a change in the trend which is located at a specific impulse to burn time ratio of 1, a mass ratio of 0.6, and an engine performance parameter of 4. This study did not attempt to make a recommendation rather provide the tools for the reader to select their own configuration based on their needs.

Propellant↗
NASA NTRSDate not supplied

Effect of Engine Thrust and Isp Tradeoffs and Alternate Propellants on ΔV Budget and Architecture Mass for 1st Generation Nuclear Thermal Propulsion Flight Test Systems

Following the first Nuclear Thermal Propulsion (NTP) system test, also known as DRACO, the next NTP system to be developed would be the 1st Generation NTP system. An analysis was conducted to determine the performance of different vehicle configurations utilizing hydrogen (H-NTP), ammonia (A-NTP), and methane (M-NTP) as propellants launched onboard commercial launch vehicles. This was enabled by a quasi-steady-state power balance engine model and vehicle component physics that sized the vehicle system using Master Equipment List (MEL) parameters. The analysis considered configuration cases outlined by a matrix of different mission classes and vehicle configurations that covered the design space of the 1st Generation NTP system to explore various propellant options, engine architectures, and mission scopes. Four mission classes were evaluated which included single burn missions performing maneuvers having a ΔV of 1 km/s and 2.5 km/s followed by 2-burn and 4-burn missions that aimed to exhaust the launch vehicles volume or mass limitations. In all cases, the NTP systems with the lowest thrust class had the longest burn time of which A-NTP and M-NTP systems provided the longest and shortest burn times depending on the launch vehicle used while H-NTP systems tended to cluster together in the middle. Longer burn times could be useful as a testing platform to increase the time for data accumulation. Across the multi-burn cases, H-NTP systems were found to be volume limited while A-NTP and M-NTP systems were mass limited. Both single burn missions showed that A-NTP configurations provided the lowest dry mass given that ammonia had the highest density with comparable performance to M-NTP systems and no requirement for cryocoolers. The results showed that beyond the propellant selection type, the launch vehicle selection, which included Starship, New Glenn, Vulcan, Falcon 9 (recoverable), and Falcon Heavy (recoverable), was a primary driving factor in the test vehicle’s capabilities. Trends were determined based on a set of dimensionless parameters that included the mass ratio of inert mass to initial wetted mass, ratio of specific impulse to burn time, and a dimensionless engine parameter (ratio of impulse to engine momentum). These relationships found the “knees-in-the-curves” that could be a significant point of reference for the designer as they indicate a change in the trend which is located at a specific impulse to burn time ratio of 1, a mass ratio of 0.6, and an engine performance parameter of 4. This study did not attempt to make a recommendation rather provide the tools for the reader to select their own configuration based on their needs.

Propellant↗
NASA NTRS1982

Design trade-offs for a multispectral linear array (MLA) instrument

The NASA Landsat program with imagery produced by the Multispectral Scanner has been thoroughly successful. The second generation of Landsat is represented by the Thematic Mapper, while the MLA sensor belongs to a third generation. It is pointed out that the strength of the MLA concept emanates from the pushbroom image-formation approach, which offers some fundamental improvements over opto-mechanically scanned instruments. The increased dwell time of the MLA sensor can be employed to improve signal-to-noise, spectral resolution, and spatial resolution simultaneously. Problems regarding design tradeoffs are considered, taking into account instrument sizing, configuration tradeoffs, optical design tradeoffs, aspects of spectral separation and registration, and radiometric accuracy and calibration tradeoffs. A description is provided of the instrument concept to which the design tradeoffs have led.

Mika, A. M.↗