篇名 | Thickness Optimization of Multi-layer High Temperature Clothing |
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卷期 | 30:4 |
作者 | Jianchi Sun 、 Kang Zhou 、 Wanying Liang 、 Huadi Wu 、 Shiyu Jia 、 Chengyong Huang 、 Jin Gao |
頁次 | 193-205 |
關鍵字 | multi-layer high temperature heat conduction 、 thermal density continuity 、 partial differential equation 、 difference algorithm 、 multi-objective optimization 、 EI 、 MEDLINE 、 Scopus |
出刊日期 | 201908 |
DOI | 10.3966/199115992019083004019 |
The design problem of multi-layer high-temperature clothing is based on the numerical solution of partial differential equation. Based on the solution of the problem, it is obtained that the temperature satisfies the law of heat conduction equation in the same layer transfer, the temperature and thermal density continuity conditions are considered at the critical point of the layer and layer. In this paper, the mathematical model of partial differential equation of multilayer heat conduction is established for simulation experiment. The mathematical model of dual objective optimization is established to solve the optimum thickness of a certain layer, and the difference algorithm of partial differential equation is designed. The boundary condition is obtained by the constraint condition in the solution of the optimal thickness. The optimal solution is obtained by traversing the thickness of the simulation experiment. In addition, the temperature distribution of each layer is simulated in detail. In order to find out whether a certain thickness satisfies the fact law of temperature change, this paper takes the average difference of layer II in data simulation experiment as the criterion to judge whether it conforms to the actual situation. Thus the index of this judgment can be quantified. In the traversal search, the average difference of each thickness is calculated, and the optimum thickness is selected. In the dual objective mathematical model, the control variable method is used to solve the average difference between the thickness of the fourth layer and the thickness of the second layer, from which the Pareto solution set is found and the optimal solution is selected from the Pareto solution set.