MétaCan
Menu
Back to cohort
Record W3115904600 · doi:10.1149/ma2020-022227mtgabs

High Temperature Batteries for Venus Surface Missions

2020· article· en· W3115904600 on OpenAlexaff
Dean E. Glass, John‐Paul Jones, Abhijit V. Shevade, E. Raub, D. Bhakta, Ratnakumar Bugga

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsEaglePicher (Canada)
Fundersnot available
KeywordsVenusAtmosphere of VenusAstrobiologyAerospace engineeringEnvironmental scienceGeologyPhysicsEngineering

Abstract

fetched live from OpenAlex

In-situ exploration of Venus is seriously hampered by its severe environment, which is benign (28 o C) at an altitude of 55 km, but rapidly becomes hostile, with increasing temperature and CO 2 pressure at lower altitudes, eventually reaching ~465°C and 90 bars at the surface. 1 These challenging conditions have limited the previous Venus surface missions, e.g., the Russian Venera series and Vega-2 Landers, 2 to barely two hours after deployment with lithium-primary batteries, despite the use of considerable insulation, phase-change materials, and similar heat sinks to isolate batteries and avionics from high surface temperatures. The recent decadal survey, ‘Vision and Voyages for Planetary Science in the Decade as well as the more recent Venus Exploration and Analysis Group (VEXAG) study 3 emphasized the need to gather basic information on the crust, mantle, core, atmosphere/exosphere, and bulk composition of Venus, to understand the evolutionary paths of Venus in relation to Earth and recommended long-duration landers and probes for future missions. In order to enable extended surface missions on Venus, e.g., landers, probes and seismometers, NASA has initiated the development of high temperature electronics and power technologies, under its ‘Hot Operating Temperature Technology’ (HOTTech) program. Under this program, we have been developing advanced primary batteries resilient to the hostile conditions on the Venus surface and operational for several days with high specific energy (>100 Wh/kg) and energy density (>150 Wh/l). Here, we will describe the development of high temperature batteries based on lithium alloy (e.g., Li-Al) anodes, molten salt electrolytes containing binary/ternary mixtures of alkali metal halides, cathodes consisting of transition metal sulfides, and designs similar to the aerospace thermal batteries. 4 With FeS cathode and appropriate changes in the electrolyte, binder and active material ratios, we have demonstrated the operation of the high temperature battery in prototype cells for 30 days in primary mode, and >150 days in rechargeable mode at 475 o C. Further, with suitable thin coatings of inorganic compounds, e.g., Al 2 O 3 , AlF 3 and AlBO 3 on the cathode particles, the utilization of the cathode, and hence the operational life of the cells have been improved by another 50%. Acknowledgements The work described here was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration (NASA) and supported by the NASA’s HOTTech project. The information in this document is pre-decisional and is provided for planning and discussion only. References Basilevsky, J. W. Head, "The surface of Venus". Rep. Prog. Phys . 66, 1699 (2003). Gilmore, et al., “Venus Surface Composition Constrained by Observation and Experiment”, Space Sci. Rev . 212, 1511–1540 (2017); doi:10.1007/s11214-017-0370-8. A. Bullock, et al., “A Venus Flagship Mission: Report of the Venus Science and Technology Definition Team," 40 th Lunar and Planetary Science Conference (Lunar and Planetary Science XL), The Woodlands, TX, March 23-27 (2009). E. Glass, J.P. Jones, A. V. Shevade, D. Bhakta, E. Raub, R. Sim, R. V. Bugga, “High temperature primary battery for Venus surface missions”, J. Power Sources . 449, 227492 (2020). doi:10.1016/j.jpowsour.2019.227492.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.216
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicSpacecraft and Cryogenic TechnologiesFrench-language works237,207