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Vulnerability of a variety of candidate spacecraft electronics to total ionizing dose and displacement damage is studied. Devices tested include optoelectronics, digital, analog, linear bipolar devices, and hybrid devices.
Total ionizing dose, displacement damage dose, and single-event effect testing were performed to characterize and determine the suitability of candidate electronics for NASA space utilization. Devices tested include optoelectronics, digital, analog, bipolar devices, and FPGAs.
The Mojave Volatiles Prospector (MVP) project is a science-driven field program with the goal to produce critical knowledge for conducting robotic exploration of the Moon. MVP will feed science, payload, and operational lessons learned to the development of a real-time, short-duration lunar polar volatiles prospecting mission. MVP achieves these goals through a simulated lunar rover mission to investigate the composition and distribution of surface and subsurface volatiles in a natural and a priori unknown environment within the Mojave Desert, improving our understanding of how to find, characterize, and access volatiles on the Moon. The MVP field site is the Mojave Desert, selected for its low, naturally occurring water abundance. The Mojave typically has on the order of 2-6% water, making it a suitable lunar analog for this field test. MVP uses the Near Infrared and Visible Spectrometer Subsystem (NIRVSS), Neutron Spectrometer Subsystem (NSS), and a downward facing GroundCam camera on the KREX-2 rover to investigate the relationship between the distribution of volatiles and soil crust variation. Through this investigation, we mature robotic in situ instruments and concepts of instrument operations, improve ground software tools for real time science, and carry out publishable research on the water cycle and its connection to geomorphology and mineralogy in desert environments. A lunar polar rover mission is unlike prior space missions and requires a new concept of operations. The rover must navigate 3-5 km of terrain and examine multiple sites in in just ~6 days. Operational decisions must be made in real time, requiring constant situational awareness, data analysis and rapid turnaround decision support tools. This presentation will focus on the first science results and operational architecture findings from the MVP field deployment relevant to a lunar polar rover mission.
Total ionizing dose, displacement damage dose, and single-event effect testing were performed to characterize and determine the suitability of candidate electronics for NASA space utilization. Devices tested include optoelectronics, digital, analog, bipolar devices, and FPGAs.
The purpose of this test was to validate the Analog Devices (ADI) RH1021BMH-10 flight lot for use in the fabrication of Europa Clipper Propulsion subsystem flight hardware. This test shall serve as the radiation lot acceptance test (RLAT) for this flight lot with wafer lot number 769658.1 and lot date code (LDC) 1430A. Low dose rate (LDR) irradiations were performed in this test so that the device susceptibility to enhanced low dose rate sensitivity (ELDRS) could be determined.
Total ionizing dose and displacement damage testing were performed to characterize and determine the suitability of candidate electronics for NASA space utilization. Devices tested include optoelectronics, digital, analog, linear bipolar devices, and hybrid devices.
The PACE-MAPP algorithm, under development for combined SPEXone, HARP2 and OCI observations from NASA’s future Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission, directly inverts the coupled atmosphere-ocean system to retrieve aerosol optical and microphysical properties and ocean optical properties simultaneously. PACE-MAPP thus retrieves the spectrally-resolved inherent optical properties of the Earth’s waters: the spectral particulate scattering coefficient, b_p (λ), the spectral absorption coefficient for particulates and color-dissolved matter, a_tot (λ), and the spectral particulate backscatter efficiency, b ̃_"bp". From these three spectrally-resolved components, we can derive the spectral particulate hemispherical backscattering coefficient b_bp (λ) and the spectral diffuse attenuation coefficient K_"d" (λ). Direct comparisons of b_"bp" (532) and K_"d" (532) are made to collocated High-Spectral Resolution Lidar (HSRL) in-water measurements. The PACE-MAPP algorithm was tested using PACE-analog datasets collected by Research Scanning Polarimeter (RSP) observations during the NASA NAAMES (The North Atlantic Aerosols and Marine Ecosystems Study) and SABOR (Ship-Aircraft Bio-Optical Research) airborne campaigns, both of which also include ship-based in situ measurements.
The purpose of this testing was to characterize Analog Devices’ AD8065 FastFET amplifier for low-dose rate (LDR) total ionizing dose (TID) response based on the Lucy project requirements and will be used on the L’ORRI instrument being built by Johns Hopkins University Applied Physics Lab. The AD8065 is a voltage feedback amplifier with FET inputs developed with the proprietary eXtra Fast Complementary Bipolar (XFCB) process. The amplifier boasts low noise operation and very high input impedance with a wide supply voltage range.
In human space flight, crew preparation is paramount. NASA employs various tools in order to best prepare its crews; simulation and simulators, training for specific extravehicular activity (EVA) tasks in its Neutral Buoyancy Laboratory, and survival training. Apollo astronauts participated in desert wilderness survival to hone their skills. NASA has a long history of using terrestrial based activities as space flight analogs to advance science, test hardware and techniques, and develop crew leadership skills and team solidarity
Total ionizing dose, displacement damage dose, and single event effects testing were performed to characterize and determine the suitability of candidate electronics for NASA space utilization. Devices tested include optoelectronics, digital, analog, and bipolar devices.
Total ionizing dose, displacement damage dose, and single event effects testing were performed to characterize and determine the suitability of candidate electronics for NASA space utilization. Devices tested include optoelectronics, digital, analog, and bipolar devices.
KNaCK (Kinematic Navigation and Cartography Knapsack) is a backpack-mounted mobile mapping system. It can map its surroundings in 3 dimensions and localize itself in space using a LiDAR (Light Detection and Ranging) sensor and SLAM (Simultaneous Localization and Mapping) algorithm. The KNaCK team is leveraging caves as a proving ground to refine technology for mapping and navigation on other worlds while simultaneously advancing the State of the Art for terrestrial cave exploration and study.
Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.
The Kinematic Navigation and Cartography Knapsack (KNaCK) team at NASA’s Marshall Space Flight Center in Huntsville, Alabama is developing tools that enable ultra-high resolution terrain mapping and navigation using mobile LiDAR (Light Detection and Ranging) and SLAM (Simultaneous Localization and Mapping) algorithms in fully GPS-denied and unilluminated environments [1,2]. The backpack mounted LiDAR instrument under development by our team demonstrates the potential of mobile SLAM LiDAR for use in challenging lunar and planetary surface environments as well as for lunar, planetary, and terrestrial cave exploration, study, and utilization.
Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.
The Kinematic Navigation and Cartography Knapsack (KNaCK) team at NASA’s Marshall Space Flight Center in Huntsville, Alabama is developing tools that enable ultra-high resolution terrain mapping and navigation using mobile LiDAR (Light Detection and Ranging) and SLAM (Simultaneous Localization and Mapping) algorithms in fully GPS-denied and unilluminated environments. The backpack mounted LiDAR instrument under development by our team demonstrates the potential of mobile SLAM LiDAR for use in challenging lunar and planetary surface environments as well as for lunar, planetary, and terrestrial cave exploration, study, and utilization.
Supersonic free-flight test of delta boost-glide figure with analog study of coupled motions
The ablation mechanisms of stony meteorite material and a terrestrial analog were investigated at the Hypersonic Materials Environmental Test System (HyMETS) facility at NASA’s Langley Research Center. The primary analyte (Tamdakht-H5) and arc-jet test conditions were chosen to simulate the entry of an ordinary chondrite through the Earth’s mesosphere. Basalt was selected to compare the meteorite response against material with enhanced polymerization and an elevated concentration of volatile species. The initial ablation process for each material commences with the vaporization of volatile species and germination of a viscous melt layer on the stagnation surface. However, each material exhibited distinct mass loss mechanisms, with basalt recessing at twice the rate of Tamdakht. Inspection of high-speed video and chemical analysis of the post-test melt layer suggests the dominant mode of mass loss of Tamdakht is expressed through the vaporization of volatile compounds. Vaporization rates of Tamdakht are 18x higher than basalt under the most severe test conditions which provides enhanced thermal shielding. Furthermore, Tamdakht produces a relatively stable melt flow, as evidenced by perpetual growth at the sidewall and the exiguous detachment of molten material into the flow. The ablation mechanism of basalt is markedly different from Tamdakht and is governed by volatile species in the virgin material. The dominant mode of mass loss for basalt is attributed to the detachment of large sections of the stagnation surface near the edge radius. An additional but subordinate mode of mass loss is assigned to the removal of melt flow as it passes over the edge radius, where shear forces overcome the rheological properties of the molten matter. Finally, mechanistic details will be discussed in terms of designing future test campaigns, developing meteorite-based materials response models, and impacting the broader scope of the Asteroid Threat Assessment Project (ATAP).