Prediction Model of Optical Characteristics for Barrel Vault Skylights
Bibliographic record
Abstract
The topic of this paper is part of a project to develop software to analyze the optical characteristics anddaylighting performance of conventional and tubular skylights. Skylights are found in many building types,such as commercial and institutional buildings, houses, shopping malls, hotels, etc. However, skylightmanufacturers lack design tools to predict the optical performance of skylight products. Prediction ofskylight optical characteristics is a difficult task, due to the complexity of skylight shapes that change withdesign requirements. This paper presents an analytical model to compute the overall optical characteristicsof barrel vault skylights under direct beam light. The model is based on a ray-tracing technique, and canhandle skylights with different glazing types (gables opaque, or glazed with different glazing types from thetop surface of the skylight), different shapes (short/long with high/low-rise profiles) and different orientation(north-south, west-east, or any direction). Applications of the model showed that barrel vault skylightstransmit much more light at high incidence angle on a horizontal surface than similar flat skylights. Theskylight length-to-radius ratio (L/R) has a significant impact on the optical characteristics. Short skylights(L/R = 2) with uniform glazing transmit approximately the same amount of light at any sun position.However, long skylights (L/R = 5) transmit about 64% more light when the sun is perpendicular to theskylight axis than when the sun is parallel to the skylight axis. Short skylights with clear gables and tintedtop transmit substantially more light than flat skylights with similar tinted glazing, particularly when the sunis parallel to the skylight axis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".