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Record W2014798932 · doi:10.1115/es2014-6394

Computational Fluid Dynamics (CFD) Analysis of Building Integrated Photovoltaic Thermal (BIPV/T) Systems

2014· article· en· W2014798932 on OpenAlexaff
Getu Hailu, Tingting Yang, Andreas Athienitis, Alan S. Fung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsConcordia UniversityToronto Metropolitan University
Fundersnot available
KeywordsMultiphysicsComputational fluid dynamicsBuilding-integrated photovoltaicsMechanicsHeat transferCFD in buildingsTurbulenceMaterials sciencePhotovoltaic systemMechanical engineeringFinite element methodPhysicsThermodynamicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents CFD study of a BIPV/T system with forced convection. Air was circulated behind PV arrays and used as a coolant with various air flow rates (air velocities) to recover the thermal energy that could be used for space and/or domestic water heating. Turbulent flows were considered with Reynolds number ranging from 5199 to 9392. COMSOL Multiphysics finite element analysis (FEA) software was used to develop CFD models for the BIPV/T system using: (a) measured temperature profile at different flow rates, and (b) measured solar radiation as boundary condition. Predictions of the air temperature profiles inside the air flow channel and the backside of the PV were obtained and compared to experimentally obtained temperature profiles using both boundary conditions. In general, better agreement with the experimentally measured temperature profiles was obtained when the measured solar radiation was used as a boundary condition. The results of the study can be used to establish relationships between the average/local convective heat transfer coefficients and air flow velocity. The relationships obtained will also be useful for developing correlations and simple mathematical models that facilitate the design and optimization of different parts of the BIPV/T system, such as inlet regions.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.228
Teacher spread0.217 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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