Are we overestimating radiant effects? Re-examining view factors in the seated posture
Bibliographic record
Abstract
Radiant heat exchange plays a critical role in occupant thermal comfort, and can be leveraged to increase building energy efficiency through radiant heating and/or cooling systems and personal comfort systems, for example. However, the view factors and projected area factors f p of seated occupants, necessary for the adequate design of these systems, remain underexplored and often rely on outdated assumptions. This study investigates the impact of two long-standing simplifications—anterior-posterior (A/P) symmetry and the neglect of seat shading—on radiation data for both adult and child occupants in the seated posture. Using numerical manikins in various seated configurations (floating, on a stool, and on a chair), f p values are calculated and tabulated, and their influence on view factors and mean radiant temperature is assessed. Results indicate that the A/P symmetry assumption holds only where no seat is present (as if the occupant were floating), and seat shading significantly reduces radiant exposure—up to 86 % for an adult in an office chair. Comparisons between adult and child manikins reveal minimal differences (<12 %) in the seated posture, suggesting that age-specific radiation data may be less critical for seated children than previously found for standing postures. Two test cases demonstrate that using standard view factors relying on these assumptions overestimates t ¯ r by up to 2 °C, which could compromise occupant thermal comfort. These findings call for updated design and evaluation practices to account for seat shading effects in indoor spaces where occupants remain in the seated posture for prolonged periods.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".