Convective heat transfer in domed skylight cavities
Bibliographic record
Abstract
Domed skylights are important architectural design elements that deliver daylight and solar heat into buildings, and connect the building's occupants to the outdoor environment. Despite the widespread use of domed skylights, there is limited information on the convective heat transfer within cavities of multi-glazed domes. This information is required to evaluate the thermal performance of domed skylights for product rating purposes, or to evaluate the heat loss or gain of installed skylights in buildings. This article presents a numerical study on the laminar natural convection in horizontal concentric domed cavities heated from the inside surface. A commercial CFD package is used to solve for the flow and temperature fields. The results show that for large cavity gap spacing-to-radius ratios, the cavity flow is mono-cellular and steady state. For small gap spacing ratios, however, the cavity flow may be multi-cellular and transient periodic. Practical correlations for the heat transfer coefficient as a function of the cavity shape and gap spacing ratio are developed for both flow regimes. The critical gap spacing ratio that yields the maximum Nusselt number is quantified for each cavity shape.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".