Assessing thermal resistance of a nonwoven textile under wind exposure: Challenging ISO 9920 with experimental insights
Bibliographic record
Abstract
Numerous workers across various industries, from construction and transportation to agriculture and emergency response, face harsh working environments characterized by cold temperatures and intense winds. These conditions present serious health and safety risks, which may result in hypothermia. Although established standards, such as ISO 11092, are crucial in measuring the thermal resistance of textile assemblies, an essential factor is often overlooked: the influence of wind speed and direction. In this context, this article aimed to address this gap in current knowledge by investigating the effects of wind on the thermal resistance of nonwoven textile assemblies, to develop a more effective protective clothing system for harsh environments. This study investigated the effect of horizontal and vertical wind speeds on the thermal resistance of a technical bio-based nonwoven assembly, composed of milkweed, kapok and polylactic acid, aiming to understand how forced convection influences heat transfer in real-world conditions. Three samples (A, B, and C) were tested under wind speeds ranging from 0 to 4 m·s −1 , and their thermal resistance was measured in both horizontal and vertical wind directions. Results showed that increasing wind speed consistently decreased thermal resistance for all samples. Vertical wind demonstrated a more pronounced effect, with reductions in thermal resistance reaching 81% for Sample C compared to 51% under horizontal wind. Comparison of experimental and theoretically predicted thermal resistance values using the models presented in the ISO 9920 standard, indicated significant discrepancies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".