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Record W2033816118 · doi:10.1063/1.4897062

Uniform temperature profile for a dense array CPV receiver under non uniform illumination profile

2014· article· en· W2033816118 on OpenAlexaff
Sara Riera, Jérôme Barrau, Arnaud Perona, Alain Dollet, Joan Rosell, Luc G. Fréchette

Bibliographic record

VenueAIP conference proceedings · 2014
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsUniversité de Sherbrooke
FundersMinisterio de Ciencia e Innovación
KeywordsCoolantMaterials scienceThermalInletJet (fluid)Temperature measurementThermal resistanceHeat transferMechanicsMechanical engineeringAcousticsPhysicsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Previous experimental and numerical studies of hybrid cooling devices for CPV receivers were developed under uniform illumination profile conditions; but literature review shows that this uniformity assumption is difficult to satisfy in real conditions. This investigation presents the design and the validation of a hybrid cooling device able to tailor its local heat extraction capacity to 2D illumination profiles in order to provide a uniform temperature profile of the PV receiver as well as a low global thermal resistance coefficient. The inputs of the design procedure are the solar concentration, the coolant flow rate and its inlet temperature. As the illumination profile is 2D dependent, a matrix of pin fins is implemented and a hybrid Jet Impingement /Matrix of Pin Fins cooling device is experimentally tested and compared to a hybrid Jet Impingement / Microchannels cooling device developed previously. The results demonstrate similar performances for both designs. Furthermore, in contrast to the cooling scheme using longitudinal fins, the distribution of the pin fins can be tailored, in two dimensions, to the local need of heat extraction capacity.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.011
GPT teacher head0.212
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations7
Published2014
Admission routes1
Has abstractyes

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