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Record W4383722855 · doi:10.3390/en16145266

Numerical Study of the Impact of Transverse Ribs on the Energy Potential of Air-Based BIPV/T Envelope Systems

2023· article· en· W4383722855 on OpenAlexaff
Barilelo Nghana, Fitsum Tariku, Girma Bitsuamlak

Bibliographic record

VenueEnergies · 2023
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsBritish Columbia Institute of TechnologyWestern University
Fundersnot available
KeywordsTransverse planeBuilding-integrated photovoltaicsOverheating (electricity)AirflowMaterials scienceBuilding envelopeThermalHeat transferMechanicsEnvironmental scienceMeteorologyStructural engineeringElectrical engineeringPhotovoltaic systemEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Overheating in BIPV/T applications is a concern due to its negative impacts on the electrical conversion efficiency of the solar cells. Forced air cooling can be an effective thermal management strategy. However, its effectiveness is limited by the thermal resistance associated with the boundary layer formation on the walls of the air channel. The heat transfer effectiveness can be improved by appending transverse ribs to the BIPV/T air channel. This study numerically investigates the energy improvements associated with appending transverse ribs to a BIPV/T air channel using CFD. The impact of the varying the transverse rib roughness shape, pitch and height on the thermo-hydraulic performance parameter, electrical efficiency of the BIPV and building heating/cooling load is quantified. With the optimized transverse rib geometry, the heat removal rate is 2.73 times greater than with the smooth channel. This translated to a 30.5% reduction in the PV surface temperature and an increase in the electrical efficiency by 11.3% compared with the smooth channel under peak summer conditions for a mild oceanic climate. The wall heat gain during summer is also reduced by 45.2%.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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 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

Citations5
Published2023
Admission routes1
Has abstractyes

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