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Record W2891449716 · doi:10.1063/1.5053497

On-sun testing of a 100-shingled-cell dense receiver array at ∼50 W/cm2 using overlapped single-axis foci

2018· article· en· W2891449716 on OpenAlexaff
Richard S. Norman, Boussairi Bouzazi, Étienne Léveillé, Brad Siskavich, Jean-François Dufault, Osvaldo Arenas, Richard Arès, Vincent Aimez, Luc G. Fréchette

Bibliographic record

VenueAIP conference proceedings · 2018
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsFocus (optics)RowOpticsFlash (photography)PhysicsMaterials scienceComputer science

Abstract

fetched live from OpenAlex

Dish CPV can achieve high concentration at low cost by overlapping the foci of single-axis mirrors into an oblong compound focus, which secondary optics can convert to a rectangular focus with even intensity on its major axis. The resulting focus is suitable for a dense receiver array with rows of cells-in-parallel on the minor axis with the rows themselves in series along the major axis. A dense receiver array was previously proposed in which multiple receiver segments are covered with rows of CPV cells that are shingled to minimize gaps in the photoreceptive surface. Segments are built on thermal-expansion-matched micro-channel cold plates for up to 1000× concentration. Segments share a coolant manifold and a steel housing with cooled secondary mirrors, so a receiver with hundreds of cells can be handled in-field as a unit. The first receiver segments have been built, populated with cells and flash-tested, and the first segment has been installed in a receiver and tested on sun at up to 50 W/cm2 at the focus of a multiple-single-axis-mirror dish.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.039
GPT teacher head0.228
Teacher spread0.190 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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