Numerical assessment of indoor thermal comfort in residential buildings: A comparison of radiant and all-air cooling systems
Bibliographic record
Abstract
Thermal comfort in residential buildings has become increasingly important due to rising summer temperatures and solar radiation exposure. This study addresses the limitations of existing simulation models in capturing dynamic solar effects by developing a validated numerical model using COMSOL Multiphysics ® . The research focuses on analyzing the impact of solar radiation patterns and evaluating the performance of different cooling systems in maintaining thermal comfort. Numerical simulations reveal significant temperature variations, with sun-exposed areas reaching up to 25°C higher than shaded zones during peak summer conditions. While ceiling cooling systems prove sufficient for uniformly shaded spaces, the study demonstrates that Air Handling Units most effectively address localized overheating caused by solar radiation. The energy savings that can be realized between uses of Air Handling Units are relatively marginal. Consequently, achieving a reasonable balance between thermal comfort and energy consumption is crucial for occupants. The developed model provides a reliable alternative to experimental measurements for assessing temperature distribution and heat accumulation in building configurations, offering valuable insights for optimizing cooling system performance in typical residential settings.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".