Numerical analysis of convective heat transfer coefficient for building facades
Bibliographic record
Abstract
The latest architectural trends demand an extensive use of glazed curtain walls running from building floor to ceiling. While glazing poorly controls the heat flow, it is important for viewing, daylighting, and solar design features. In order to evaluate building energy consumption accurately, knowledge of convective heat transfer coefficient (CHTC) distribution over the façade of the building is important. In this article, high-resolution numerical simulations that use three-dimensional steady Reynolds-averaged Navier–Stokes and energy equations are performed. Convective heat transfer coefficient values at the windward facade of five buildings, with rectangular floor plans, and heights of 3, 10, 15, 20–30 stories, have been produced. The influence of building height on CHTC distribution is investigated at Reynolds numbers ranging from 0.7 × 10 6 to 33 × 10 6 , and a correlation equation as a function of building height and a reference wind velocity is developed. For example, as the height increases from 10.1 to 101 m in the study cases, the surface-averaged convective heat transfer coefficient on the windward façade increases by 55%. The high-resolution spatial distribution of convective heat transfer coefficient over façade of the tallest building indicates that the top-corner zone convective heat transfer coefficient values are higher by 24% and the base-center zone values are lower by 27% compared to the average CHTC value, implying the necessity for zonal treatment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".