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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

A Novel High-Performance Mission-Enabling Multi-Purpose Radioisotope Heat Source

Recent studies indicate science mission concepts targeting access to the sub-surface oceans of icy moons require ice-penetrating cryobots powered by advanced Radioisotope Power Systems (RPS). These systems would deliver waste heat for ice-melting in the range of 10 kW. Minimizing the transit time through the kilometers-thick ice shells to just a few years requires these RPS to utilize heat sources having a higher thermal energy volumetric density than the existing flight-qualified General-Purpose Heat Source (GPHS). A Compact Heat Source (CPHS)has been conceptualized in which the graphite impact shells(GIS) of the existing GPHS are rearranged in a hexagonal aeroshell containing seven GIS per module, as opposed to the standard two per module; offering a thermal energy density of 0.57 W/cm3versus 0.29 W/cm3 offered by the GPHS simply from the repackaging of Technology Readiness Level (TRL)9 subassemblies. Preliminary thermal modeling of the CPHS integrated into a notional radioisotope thermoelectric generator(RTG) structure further suggests that centerline temperatures are well within allowable limits during nominal operation. Given the need for the CPHS for a subset of missions, it is worth exploring the applicability of the CPHS for more general RTG purposes. We discuss herein how the CPHS may be implemented with either heritage or in-development thermoelectric converter technologies into a Next-Generation RTG concept. Due to a higher energy density, the legacy heat rejection fin arrangement must be modified to permit a sufficiently low cold-side temperature. Preliminary finite element analysis suggests fin-root temperatures can be kept as low as 520 K while allowing the generator to fit within the usable dimensions of currently available United States Department of Energy shipping containers. Such temperatures would certainly be compatible with the use of high temperature thermoelectric converter technologies.. A prime candidate is the heritage silicon-germanium (SiGe) unicouple, whose design could be adapted by approximately halving the leg-length, but without changes in hot and cold junction interfaces, which are features critical to the proven performance and reliability of these devices. The estimated Beginning of Life power for a SiGe-based CPHS-RTG using 12 CPHS for a thermal inventory of 10.5 kW is greater than 600 W under deep space operating conditions. Using higher performance segmented couples currently in development that are based on skutterudite,La3−xTe4and 14-1-11 Zintl thermoelectric materials in lieu of the SiGe unicouples would increase the power level to more than1 kW. The high specific power (We/kg) attribute of CPHS-RTGs found in this study could potentially enable Radioisotope Electric Propulsion (REP) mission concepts. Past NASA REP mission concept studies identified specific power needs in excess of 6to 8 We/kg. Based on a GPHS-RTG-like system configuration, we show that at fin root temperatures between 530 K and 570K (deep space environment), specific powers exceeding 10 We/kg are achievable using high performance segmented thermoelectric converters. The compact sizing and power density of the CPHS-RTG would constitute a significant step upgrade in specific power when compared to heritage GPHS-RTG (approximately5.1 We/kg) and off-the-shelf Multi-Mission RTG (approximately 2.6 We/kg).

Nesmith, Bill J.↗
NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗
NASA NTRS2006

Exploring Europa with an RPS-Powered Orbiter Spacecraft

This paper describes a conceptual flagship-class Europa orbiter concept that was assumed to launch as early as 2012, arriving at Europa approximately 8 years later using inner solar system gravity assists to reach Jupiter. Jupiter's intense radiation environment limits the mission duration at Europa to 30 days for this study, though the duration is a result of multiple trades and is by no means fixed. The Europa Subgroup of the Outer Planets Assessment Group identified six primary science objectives for this concept.

Europa↗
NASA NTRS2011

Mars Science Laboratory Launch Pad Thermal Control

This paper will describe the challenges faced in accommodating the warm Multi Mission Radioisotope Thermoelectric Generator (MMRTG) during the pre-launch phases of integration, launch pad operations as well as during launch. Predictions of temperatures during these phases will be presented when all the cooling systems (HRS and A/C) are operational. In-air tests conducted on the spacecraft in December 2008 to simulate the launch conditions were very successful and showed that all components would be within their allowable limits during these phases. Results of these tests will be shared in this paper.

Radioisotope Thermoelectric Generator (RTG)↗
NASA NTRS2012

MSL DAN Science Investigation: Physical Simulation of DAN

The main objective of the proposed investigation is to study the characteristics (i.e., hydrogen content, soil composition, layer-structure, etc.) of sub-surface and the surface radiation (neutron in particular) environment.

Multi-Mission Radioisotope Thermoelectric Generato↗