Phase change cooling garment for thermal stress reduction: experimental evaluation and predictive modeling

This study experimentally evaluated a novel phase change cooling garment (PCCG) designed to alleviate heat stress for live-line workers. Tests were conducted in a climate chamber (38°C, 75% RH) with 15 participants performing treadmill walking at 5km/h. The PCCG demonstrated a maximum observed cooling duration of 116 minutes. It significantly reduced mean skin temperature by 2.6 ± 0.8°C and core temperature by 0.7 ± 0.2°C. Furthermore, the garment notably decreased sweat volume from 0.83 ± 0.13 kg to 0.55 ± 0.10 kg, reducing the comprehensive heat stress index from severe to mild. Subjective thermal comfort and thermal sensation votes improved significantly without inducing cold discomfort. A machine learning model (BP-RFE-SVM-PSO) developed to predict thermal comfort achieved an accuracy of 95.6% under these controlled laboratory conditions. The results demonstrate the PCCG's effectiveness in enhancing physiological and perceptual responses for workers in hot-humid environments.

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Publication Details

Journal
Ergonomics
Published
2026-01-05
DOI
https://doi.org/10.1080/00140139.2025.2606780
Primary Topic
Thermoregulation and physiological responses
Type
article
Field-Weighted Citation Impact
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article

Phase change cooling garment for thermal stress reduction: experimental evaluation and predictive modeling

Shengwei Wang, Tian Liu, Xiaohong Gui, Li Ding
Ergonomics
Thermoregulation and physiological responses
article

Phase change cooling garment for thermal stress reduction: experimental evaluation and predictive modeling

Shengwei Wang, Tian Liu, Xiaohong Gui, Li Ding
article en

Abstract

This study experimentally evaluated a novel phase change cooling garment (PCCG) designed to alleviate heat stress for live-line workers. Tests were conducted in a climate chamber (38°C, 75% RH) with 15 participants performing treadmill walking at 5km/h. The PCCG demonstrated a maximum observed cooling duration of 116 minutes. It significantly reduced mean skin temperature by 2.6 ± 0.8°C and core temperature by 0.7 ± 0.2°C. Furthermore, the garment notably decreased sweat volume from 0.83 ± 0.13 kg to 0.55 ± 0.10 kg, reducing the comprehensive heat stress index from severe to mild. Subjective thermal comfort and thermal sensation votes improved significantly without inducing cold discomfort. A machine learning model (BP-RFE-SVM-PSO) developed to predict thermal comfort achieved an accuracy of 95.6% under these controlled laboratory conditions. The results demonstrate the PCCG's effectiveness in enhancing physiological and perceptual responses for workers in hot-humid environments.

Ergonomics
China University of Mining and Technology (CN), Ministry of Industry and Information Technology (CN)
Climate action
Openalex Percentile: Top 97%
Thermoregulation and physiological responses
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