Computational Fluid Dynamics (CFD) Analysis of Building Integrated Photovoltaic Thermal (BIPV/T) Systems
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".