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

Abstract

Aerogel fibers utilized in thermal protective apparel exhibit exceptional heat insulation capabilities; however, concerns arise regarding potential degradation due to the detachment of aerogel particles during repeated washing. Accurate prediction of aerogel fiber thermal resistance is critical for assessing hydrothermal aging in aerogel textiles, yet the precision of such predictions is significantly hindered by limited sample data and numerous uncertainties constrained by testing time and expenses. The present study endeavors to ascertain the optimal parameters dictating aerogel fabric thermal resistance post-washing and establish a prediction model based on these variables using small-sample data. Four aerogel fabric candidates were selected and subjected to multiple washing cycles (0, 1, 5, 10, 15, and 20 cycles). Gray relational analysis (GRA) was initially employed to prioritize the primary thermal resistance parameters, thereby identifying the interrelations among various factors and circumventing the unreasonable equal treatment of samples in conventional gray predictions. Subsequently, a discrete gray linear regression (DGLR) algorithm was proposed and validated to estimate thermal resistance using input from four principal fabric parameters: the fabric weight, thickness, air permeability, and surface temperature distribution coefficient. The findings revealed that the GRA-DGLR model achieved relatively high accuracy, closely aligning with the experimental results. Following repeated washing, aerogel fabric thermal resistance diminished, with the air permeability, weight, thickness, and surface temperature distribution coefficient ranked in descending order of significance. This investigation highlights the considerable impact of repeated washing on aerogel fabric thermal resistance and the efficacy of the GRA-DGLR model in estimating this parameter.