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Record W2058148621 · doi:10.1115/ht2013-17210

Assisted Turbulence Convective Heat Transfer for Cooling the Photovoltaic Cells

2013· article· en· W2058148621 on OpenAlexaff
Iman Arianmehr, David S.‐K. Ting, Stephen D. Ray

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPhotovoltaic systemRenewable energyPassive coolingTurbulenceEnvironmental scienceHeat transferSolar gainConvective heat transferMeteorologyWork (physics)Passive solar building designActive coolingSolar energyConvectionElectricityWind tunnelMaterials scienceMechanical engineeringWater coolingMechanicsPhysicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Solar PV (photovoltaic) is a rapidly advancing renewable energy technology which converts sunlight directly into electricity. One of the outstanding challenges of the current PV technology is the reduction in its conversion efficiency with increasing PV panel temperature, which is closely associated with the increase in solar intensity and the ambient temperature surrounding the PV panels. To more effectively capture the available energy when the sun is most intense, significant efforts have been invested in active and passive cooling research over the last few years. While integrated cooling systems can lead to the highest total efficiencies, they are usually neither the most feasible nor the most cost effective solution. This work examines some simple passive means of manipulating the prevailing wind turbulence to enhance convective heat transfer over a heated plate in a wind tunnel. Specific turbulence generators are devised based on knowledge gained from orificed perforated plate turbulence study.

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 categoriesInsufficient payload (model declined to judge)
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 score0.121
Threshold uncertainty score0.998

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)0.0020.001

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 head0.012
GPT teacher head0.208
Teacher spread0.195 · 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.

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

Citations4
Published2013
Admission routes1
Has abstractyes

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