NATURAL CONVECTION HEAT TRANSFER WITHIN VERTICALLY ECCENTRIC DOMED SKYLIGHTS CAVITIES
Bibliographic record
Abstract
Domed skylights are important architectural design elements to deliver daylight and solar heat into buildings and connect building’s occupants to outdoor. However, most of building energy simulation programs do not cover such skylights to quantify their energy performance when installed in buildings. This paper presents a numerical study on natural laminar convection within vertically eccentric domed cavities when heated from the exterior surface. Both interior and exterior surfaces are held at uniform temperatures. A commercial CFD package employing the control volume approach is used to solve the laminar convective heat transfer within the cavity. The obtained results showed that the flow is mono-cellular for small and moderate heating intensities as depicted by the Grashof number. For high Grashof numbers, small vortex cells appear within a larger vortex cell at the lower part of the cavity. The multi-cellular flow increases heat transfer. The critical gap spacing that yields the maximum heat transfer was quantified for several dome profiles. KEYWORDS Natural laminar convection; domed cavity; domed skylight, eccentric domed cavity NOMENCLATURE k Fluid thermal conductivity Lmax Maximum gap thickness Lmin Minimum gap thickness L Local gap thickness P Pressure P * Dimensionless pressure qcond Pure conduction heat transfer qi Convection heat transfer from interior surface qo Convection heat transfer from exterior surface
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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 | 0.000 |
| 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".