显汗状态下运动服面料动态热湿舒适性预测

Prediction of dynamic thermal and wet comfort of sportswear fabric under the sweat state

文章为分析出汗过程对运动服面料热湿阻变化的影响,选取15种常见运动服面料作为研究对象,运用自行研制的动态出汗装置和SGHP-10.5服装热湿阻测试系统,测量织物从吸湿到干燥整个动态过程的热阻和湿阻。以织物的性能参数为影响因素,构建了线性回归模型和RBF神经网络模型,提出了热、湿阻变化率两个指标来分析织物的动态热湿传递性能。结果表明:线性回归模型数据预测误差较大,而RBF神经网络预测的热、湿阻变化率平均绝对百分误差分别为2.2968%和2.0862%,预测精度高。

For analyzing the effect of the sweating process on the changes of thermal and wet resistance of sportswear fabric,15 kinds of common sportswear fabrics were selected as the research objects to measure the thermal and wet resistance of fabrics in the dynamic process from moisture absorption to drying by the self-developed dynamic sweating device and SGHP-10.5 thermal and wet resistance test system for clothing.Based on the performance parameters of the fabric,a linear regression model and a RBF neural network model were constructed.And two indexes of thermal and wet resistance change rate were proposed to analyze the dynamic thermal and wet transfer performance of fabrics.The results show that the data prediction errors of the linear regression model are large.The mean absolute percentage errors of thermal and wet resistance change rate predicted by RBF neural network respectively are 2.2968%and 2.0862%respectively,indicating the high prediction accuracy.