低温环境下纺织材料类型设计反问题

摘要

为给纺织材料的设计试验提供理论支持与科学依据,基于低温环境下纺织材料热湿传递稳态模型,提出了纺织材料类型设计反问题,即根据人体所处环境温度-湿度值,依据人体服装舒适性指标,已知材料微观结构与厚度,决定材料的类型.根据正则化思想将类型设计反问题的求解归结为一个函数极小化问题.利用非线性常微分方程的正演算法与函数极小化问题的一维Hooke-Jeeves搜索,构造了反问题正则化解的迭代算法.通过分析不同环境下着装的MatLab数值模拟结果,得出结论,正则化算法与Hooke-Jevees模式搜索法能够求解纺织材料类型设计反演问题,同时数值模拟试验验证了算法的有效性和反问题提法的合理性.%To offer theoretical support and scientific basis for textile material design and experiment, an inverse problem of textile material design at low temperature is put forward based on the model of steady-state heat and moisture transfer through textiles, that is, the selection of textile material depends on the ambient temperature and humidity where people live in, the desired comfort index of clothing, the known microstructure and thickness of material. According to the idea of regularization method, the inverse problem of textile material design can be formulated into a function minimization problem. Combining the finite difference algorithm for nonlinear ordinary differential equation with direct search method of one-dimensional minimization problems, an iterative algorithm for the regularized solution of the inverse problem is constructed. By analyzing the results of MatLab numerical simulation of clothing for different climates, some conclusions are obtained; the regularization method and Hooke-Jevees direct search method can be applied to solve the inverse problem of type design for textile materials. Meanwhile, numerical simulation shows the effectiveness of the algorithm and the rationality of the proposed inverse problem.