Development of thermal resistance prediction model and measurement of thermal resistance of clothing under fully wet conditions

Abstract

When clothing is immersed in water, the water will rapidly occupy the internal void of the clothing, and the air layer inside the clothing will disappear, which leads to a drastic reduction in the thermal resistance of the clothing. To calculate the thermal resistance of clothing under fully wet conditions, a novel serial method was proposed. The method is a combination of the thermal resistance of water and the thermal resistance of wet fibers. To verify the effectiveness of the new method, the thermal resistances of three typical suits under fully wet conditions were measured. Three typical suits, a summer suit (T-shirt + shorts), a spring suit (sportswear + sweatpants), and a winter suit (cotton coat + wool pants), were chosen as test objects. The thermal resistances of these suits under fully wet conditions were measured in an experimental water tank built in a chamber using a thermal manikin, “Walter.” The thermal resistance measurement in this study can eliminate the experimental error caused by the evaporation of water so as to obtain an accurate thermal resistance measurement. According to the experimental results and the model, the key factors affecting the thermal resistance under fully wet conditions were thickness ( Rpearson = 0.992), porosity ( Rpearson = 0.980), and moisture content ( Rpearson = 0.964). Compared to other thermal resistance prediction models, the prediction results by the novel method were more consistent with the measured results, with all deviations less than 10% and the correlation value R2 = 0.998.

Thermal resistance prediction of aerogel fabrics after repeated washing based on small-sample data

“…Due to their complexity and requirement for larger sample sizes, machine learning methodologies are less suited for small-sample problems related to aerogel fabrics than the gray method. 53,54 Several studies [8][9][10]14,[16][17][18] have identified influencing factors of fabrics following repetitive washing based on previous knowledge, neglecting the variable contributions throughout the washing process. Real-world projects exhibit dynamic and changing correlations between independent and dependent variables, significantly impacting the accuracy of aerogel fabric thermal resistance predictions.…”

“…However, these models are insufficient for accurately predicting the thermal resistance of compounded aerogel fabrics, primarily due to limited data and the involvement of multiple physical and mechanical properties. According to Yang et al., 9 trapped air plays a crucial role in fabric thermal resistance, which may change following repeated washing as water displaces the confined air. Moreover, additional variables, including moisture content, 16,27 abrasion, 28,29 quilted fabrics, 30 wrinkle-free treatment, 31 charcoal treatment, 32 nanosilver treatment, 33 and flock coating, 34 can also affect the thermal comfort properties of fabrics.…”

“…Thermal resistance, a crucial determinant of comfort attributes, is influenced by numerous factors, including yarn structure, air permeability, thickness, thermal absorption, and moisture content. 8,9 The relationships between fabric characteristics (e.g., weaving patterns, fabric mass, thickness, porosity, and hydrophobicity) and thermal resistance have been rigorously investigated over several decades. Prior studies have proposed various prediction models for the thermal properties of fabric products, such as statistical models, [8][9][10][11][12][13][14] artificial neural networks, [15][16][17][18] theoretical models of heat transfer, [19][20][21] and finite element models.…”

“…8,9 The relationships between fabric characteristics (e.g., weaving patterns, fabric mass, thickness, porosity, and hydrophobicity) and thermal resistance have been rigorously investigated over several decades. Prior studies have proposed various prediction models for the thermal properties of fabric products, such as statistical models, [8][9][10][11][12][13][14] artificial neural networks, [15][16][17][18] theoretical models of heat transfer, [19][20][21] and finite element models. [22][23][24] More recent studies 3,25,26 have examined the effects of recurrent washing on the thermal insulation performance of textiles, confirming a strong correlation between thermal resistance and parameters such as thickness, weight, air permeability, and porosity.…”

Analysis and evaluation of the thermal performance of combinations of suits and life jackets in water

“…To calculate the thermal resistance of clothing under fully wet conditions, a thermal resistance prediction model was proposed by the authors. 17 The Transport Canada report TP13822E 8 states that fallen persons have usually fallen into the water accidentally while wearing the suit and the life jacket. Based on the previous paper, this paper analyzes and evaluates the thermal performance of combinations of suits and life jackets in water.…”