Heat transfer through wavy clothing layers with varied permeability
Bibliographic record
Abstract
The heat exchange between the human body and the environment is significantly influenced by the microclimate created between the clothing and the skin, which is essential for maintaining thermophysiological comfort. In the present study, a three-dimensional (3D) numerical model was developed to investigate heat transfer between the skin and the environment through wavy microclimate structures with different clothing permeability. The air penetration through the clothing was considered, and the clothing was treated as a porous and air-permeable material in the model. Viscous shear and inertial effects were included in the governing equations to accurately simulate airflow in the fabric domain. The numerical model was first validated against experimental data obtained from a sweating guarded hotplate and showed good agreement. The validated model was then used to study the effect of airflow direction on the heat transfer performance. The results showed that airflow parallel to the fabric folds enhances heat transfer compared to airflow perpendicular to the fabric folds. Additionally, the effects of the wavy fold aspect ratio (amplitude to wavelength, W/H=2.4, 4.8 and infinite) and fabric air permeability ( 10 − 14 m 2 to 10 − 6 m 2 ) were analyzed. The findings revealed that heat dissipation is more effective in wavy shapes than flat configurations. Moreover, heat flux decreased with increasing permeability until a critical minimum was reached, after which heat flux started to increase sharply. This research provides detailed insights into heat transfer in clothing microclimates, which is valuable for advancing clothing design.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".