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Record W3103242424 · doi:10.1063/5.0032175

Massively-parallel microcell arrays and thermal runaway in unilluminated cells

2020· article· en· W3103242424 on OpenAlexafffund
Richard S. Norman, Étienne Léveillé, Luc G. Fréchette, Vincent Aimez

Bibliographic record

VenueAIP conference proceedings · 2020
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersInstitut National des Sciences Appliquées de LyonFonds de recherche du Québec – Nature et technologiesUniversité Grenoble AlpesCMC MicrosystemsUniversité de SherbrookeCentre National de la Recherche ScientifiqueUniversité de LyonIndian National Science Academy
KeywordsMicrocellThermal runawayMassively parallelComputer scienceThermalFocus (optics)Parallel computingComputer networkPhysicsOptics

Abstract

fetched live from OpenAlex

Prior work has found that under some conditions, multi-millimeter CPV cells in parallel can trigger thermal runaway in an unilluminated cell when fully-illuminated cells drive current through it [1]-[2]. A highly-parallel microcell array has been proposed [3] to accommodate the unevenness of a parabolic trough mirror's focus and to gain the efficiency and cooling advantages of microcells without the cost penalty of handling numerous individual micro- receivers. This paper evaluates the vulnerability of the proposed massively-parallel microcell system to this thermal runaway mode and finds that a simple test shows that, for the type of cell tested, it is not vulnerable.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.184
Teacher spread0.171 · 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 source (direct Gemma or distilled Codex), 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 routes2
Has abstractyes

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