Abstract
For the clothing design for high-temperature operation, the theory or method such as partial differential, nonlinear programming and finite difference method was first applied to construct the overall heat transfer model of “high temperature environment--clothing--air layer--skin” and draw the temperature distribution map. Secondly, according to the human body burn model, the optimal parameters of fabric thickness are obtained preliminarily. Finally, the weights and thresholds of BP neural network were optimized by genetic algorithm, and these optimized values were assigned to the optimized BP neural network, and the nonlinear thickness function was approximated and optimized with MATLAB.
Highlights
When working in a high temperature environment, people need to wear special clothes to avoid burns
In the "high temperature environment-clothing-air layerskin" system, we assume that the porosity and tortuous coefficient of the fibers and the thermal conductivity of the fabric materials are known
Genetic algorithm is used to solve the weights and thresholds of BP neural networks, Because the results of each operation are different and easy to fall into the local optimal solution, So we use the results of running 50 times as the weights and thresholds of the neural network
Summary
When working in a high temperature environment, people need to wear special clothes to avoid burns. Such clothing can promote heat emission, prevent heat stroke, burns and other hazards. From the point of view of numerical simulation, Miao Tian et al [3] evaluated the performance of thermal protective clothing, reviewed the heat transfer model, skin burn prediction model and so on. She summed up the development process, characteristics and shortcomings of the model, and predicted the development trend of the numerical simulation of thermal protective clothing. The temperature distribution and optimum thickness of each layer of thermal protective clothing are studied from the point of view of mathematics and thermodynamics
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