Heat and moisture transfer through skin-clothing microclimate
Bibliographic record
Abstract
The microclimate between the clothing and human skin has a significant effect on the heat and moisture exchange between the body and the environment, and therefore plays an important role in maintaining thermo-physiological comfort. In the present study, a numerical model of heat and moisture transfer through the microclimate and a single layer fabric under dry and wet conditions was developed. In the model, the fabric was considered as an air-permeable porous medium. The model was validated against experiments with satisfactory agreement. The effects of horizontal microclimate thickness between 3 mm and 40 mm on the total heat flux (𝑞′′), thermal resistance (𝑅𝑐𝑡) and evaporative resistance (𝑅𝑒𝑡) were analyzed. The findings revealed that natural convection plays an important role in heat and moisture transfer in thicker microclimates. The developed numerical model was then used to study the effect of the microclimate tilt angle (0°, 30°, 45°, 60°, and 90°) and airflow direction (parallel and normal to the fabric surface) on heat and moisture transfer. The results indicated that, in microclimates with thicknesses below a critical value, the tilt angle has negligible influence on heat and moisture transfer regardless of the airflow direction. However, once the microclimate thickness exceeds the critical value, the inclination angle becomes an important factor affecting the moisture transfer. It was also found that normal airflow was more effective in facilitating the heat and moisture transfer from the skin to the environment compared to parallel airflow. Further, the microclimate temperature distribution, water vapor concentration distribution and airflow pattern were also studied under both dry and wet conditions. This study provides guidance on designing comfortable clothing for different activities and postures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".