Thermal Insulation of Protective Clothing Materials in Extreme Cold Conditions
Bibliographic record
Abstract
Background: Thermophysiological comfort in a cold environment is mainly ensured by clothing. However, the thermal performance and protective abilities of textile fabrics may be sensitive to extreme environmental conditions. This article evaluated the thermal insulation properties of three technical textile assemblies and determined the influence of environmental parameters (temperature, humidity, and wind speed) on their insulation capacity. Methods: Thermal insulation capacity and air permeability of the assemblies were determined experimentally. A sweating-guarded hotplate apparatus, commonly called the "skin model," based on International Organization for Standardization (ISO) 11092 standard and simulating the heat transfer from the body surface to the environment through clothing material, was adopted for the thermal resistance measurements. Results: It was found that the assemblies lost about 85% of their thermal insulation with increasing wind speed from 0 to 16 km/h. Under certain conditions, values approaching 1 clo have been measured. On the other hand, the results showed that temperature variation in the range (-40°C, 30°C), as well as humidity ratio changes (5 g/kg, 20 g/kg), had a limited influence on the thermal insulation of the studied assemblies. Conclusion: The present study showed that the most important variable impacting the thermal performance and protective abilities of textile fabrics is the wind speed, a parameter not taken into account by ISO 11092.
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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) | 0.000 | 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".