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Record W4380991176 · doi:10.1051/e3sconf/202339603018

PV Panel Heat Transfer Rate Enhancement Using a Novel Heat Exchanger with High-Resolution Spikes and Dimples

2023· article· en· W4380991176 on OpenAlexaff
Mohammad Emamjome Kashan, Alan S. Fung, John Swift, Rakesh Kumar

Bibliographic record

VenueE3S Web of Conferences · 2023
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTurbulenceMechanicsMaterials scienceReynolds numberHeat transferDimpleHeat exchangerComputational fluid dynamicsAirflowHeat transfer enhancementThermodynamicsPhysicsComposite material

Abstract

fetched live from OpenAlex

PV cell electrical efficiency has an inverse relationship with its temperature. This study uses a novel and commercialized backsheet to reduce PV panel surface temperature. The novel backsheet consists of spikes and dimples to increase the air turbulence and convection heat transfer rate. Experimentally validated computational fluid dynamic (CFD) models are developed to investigate the influence of GripMetal backsheet on PV cell temperature and efficiency of PV panels and PVT collectors. Under 1000 W/m 2 solar irradiation, it is shown that a PVT collector with a 2.25 mm spikes height has almost 10% more electrical efficiency than a PVT collector with a flat plate channel. A maximum 22.5℃ temperature drop was observed by using the GM-based PVT collector for slow-motion airflow. It is shown that the GM spikes are more efficient in low Reynolds numbers and can enhance air turbulence more effectively. The GM PVT collector has the best performance at low Reynolds numbers which is suitable for air-based PVT applications.

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 score0.125
Threshold uncertainty score0.988

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 head0.075
GPT teacher head0.258
Teacher spread0.182 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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