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Record W2321835522 · doi:10.1016/j.egypro.2015.11.379

Multiple-inlet BIPV/T Modeling: Wind Effects and Fan Induced Suction

2015· article· en· W2321835522 on OpenAlexaff
Efstratios Dimitrios Rounis, Edvinas Bigaila, Peter Luk, Andreas Athienitis, T. Stathopoulos

Bibliographic record

VenueEnergy Procedia · 2015
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsBuilding-integrated photovoltaicsInletThermalBoundary layerEnvironmental scienceStratification (seeds)SuctionAirflowPhotovoltaic systemMechanicsComputational fluid dynamicsMeteorologyMarine engineeringMechanical engineeringEngineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Building Integrated Photovoltaic/Thermal (BIPV/T) collectors take up the role of energy and heat production, while acting as a rain-screen cladding. A multiple inlet BIPV/T system counters the effect of high temperature stratification on the PV layer, by enhancing the convection inside the air channel with the introduction of more than one openings for the intake of fresh air that break up the surface boundary layer. To investigate the uniformity of heat extraction from the PV panels, the fluid mechanics of the system are studied separately from the thermal effects. A numerical flow distribution model, which incorporates wind effects, is introduced for the optimal design of multiple inlet systems so as to have flow rates through each inlet that maximize the heat extracted from the PV panels.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.232
Teacher spread0.198 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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