Convective Heat Transfer Coefficients in a Building-Integrated Photovoltaic/Thermal System
Bibliographic record
Abstract
This paper presents an experimental study for the development of convective heat transfer correlations for an open loop air-based building-integrated photovoltaic/thermal (BIPV/T) system. The BIPV/T system absorbs solar energy on the top surface, which includes the photovoltaic panels and generates electricity while also heating air drawn by a variable speed fan through a channel formed by the top roof surface with the photovoltaic modules and an insulated attic layer. The BIPV/T system channel has a length/hydraulic diameter ratio of 38, which is representative of a BIPV/T roof system for 30–45 deg tilt angles. Because of the heating asymmetry in the BIPV/T channel, two average Nusselt number correlations are reported as a function of Reynolds number: one for the top heated surface and the other for the bottom surface. For the top heated surface, the Nusselt number is in the range of 6–48 for Reynolds numbers ranging from 250 to 7500. For the bottom insulated surface, the Nusselt number is in the range of 22–68 for Reynolds numbers ranging from 800 to 7060. This paper presents correlations for the average Nusselt number as a function of Reynolds number; this correlation is considered adequate for the design of BIPV/T systems where forced convection dominates. Local Nusselt number distributions are also presented for laminar and turbulent flow conditions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".