The Effect of Coverings on Heat Transfer from a Window to a Room
Bibliographic record
Abstract
The presence of a blind adjacent to a window affects the natural convective and radiant heat transfer from the window to the room. As a result, the use of a shading device will change the heat transmission and solar heat gain through the window. A number of numerical and experimental studies of the effects of blinds on the heat transfer from a window have therefore been undertaken, with some of the main features of these studies being described here. In these studies, attention has been given to Venetian, vertical, and plane blinds, although the major attention has been given to Venetian blinds. Initial studies examined the effect of all three types of blinds on the natural convective heat transfer at an indoor glazing surface when there is no solar irradiance. Supporting experimental studies using mainly interferometry were then undertaken, particularly for the Venetian blind case. The numerical and experimental work was then extended to include the effects of solar radiation, in particular the effect of heat generation in the blind resulting from absorbed solar radiation. In addition to providing basic information on the effects of blinds on the heat transfer process, the studies described here will assist in expanding available software for predicting window heat transfer to include the effect of window coverings and assist in the selection of energy-efficient window coverings.
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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 | 0.002 |
| 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) | 0.002 | 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".