Tubular daylighting devices—Development and validation of a thermal model (1415-RP)
Bibliographic record
Abstract
This article presents the development and validation of a simplified model to compute the thermal characteristics (solar heat gain coefficient and thermal conductance (U-factor)) and surface temperatures of tubular daylighting devices. The model takes into account the three modes of heat transfer: conduction, convection, and surface-to-surface radiation. A one-dimensional heat conduction model is applied to tubular daylight device glazing layers. The convective heat transfer from tubular daylight device surfaces to their adjacent air spaces uses existing correlations for natural flows in enclosed air cavities and free stream air spaces. A zonal model, in which the pipe air space is divided into a number of thermally stacked zones, is used to predict the vertical average temperature distribution in the air cavity and wall surface of pipe. Thermal radiation exchange among surfaces uses the formulation of the form factor applied to the aforementioned zonal model. An iterative sequential procedure is proposed to solve the temperature distribution in tubular daylight device glazing layers and air cavities. The U-factor predictions of the simplified model are compared with the National Fenestration Rating Council certified product rating measurement data and detailed computational fluid dynamic simulations. Four tubular daylight device products are simulated under the National Fenestration Rating Council standard rating conditions for the residential (insulation at ceiling level) and commercial (insulation at roof level) settings. The temperatures of the tubular daylight device glazing layers and vertical temperature distribution inside the pipe air space are also compared with the computational fluid dynamic simulations. The results show that the U-factor predictions of the simplified model are in good agreement with the measurement data and computational fluid dynamic simulations, within a maximum deviation of 15% for both the residential and commercial rating 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".