报告主题： Data Modeling in Functional Clothing Design：Forward and Inverse Problems Approaches
报告人：徐定华 教授 （上海财经大学）
报告时间：2018年12月4日（周二）10:00
报告地点：校本部G507
邀请人：王卿文
主办部门：太阳成集团tyc33455数学系
报告摘要：Textile material design is of paramount important in the study of functional clothing design. The experimental data shows that there are great challenges in Intelligent Manufacturing in Clothing Industry, such as Thermal Comfort Clothing (TCC) and Thermal Protective Clothing (TPC). The experimental Data varies from the data on clothing parameters, environmental situation, human body comfort Index and skin Injury. Therefore the data modelling of functional clothing design will based on physical model of heat and moisture transfer. The advantages of the data modelling may reduce the design cost and experimental risk.
We focus on revealing heat and moisture transfer characteristics in the system of human body-clothing-environment, which directly determine thermal comfort/safety level of human body. Based on the parabolic model of dynamic heat and moisture transfer, we present inverse problems of textile parameters determination (IPTPD), including thickness, thermal conductivity and porosity determination. Moreover we mathematically formulate a new space-fractional parabolic model of heat transfer within thermal protective clothing under high environmental temperature- humidity, and the corresponding inverse problems of textile material design are put forward. Some numerical algorithms are presented by the regularization approaches. Theoretical study and numerical simulation results validate the formulation of the IPTPD and demonstrate effectiveness of the proposed numerical algorithms.
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