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描述了基于遗传算法的双层多孔介质骨架发散冷却的优化方法.冷却剂在固定压差(Be数)与多孔介质特征尺寸的条件下被注入固体骨架,通过改变多孔介质的材料、孔隙率和厚度比例,在满足固体骨架的质量和成本等约束条件下,以最低热端表面温度为优化目标,利用遗传算法找出可行的最优冷却结构设计.计算结果表明,靠近冷端的第一层多孔介质孔隙率应当尽可能的大以提高冷却剂流量,但是其组成材料对热端表面温度影响很小,而第二层靠近热端的多孔介质的组成材料对热端表面温度有很重要的影响,它的孔隙率取决与其对有效导热系数、冷却剂流量和内部热交换系数三方面影响的平衡.
The optimization method based on genetic algorithm for the divergent cooling of bilayer porous media is described. The coolant is injected into the solid framework under the condition of constant pressure difference (Be) and characteristic size of porous media. By changing the material, porosity and Thickness ratio to meet the solid skeleton of the quality and cost constraints, the lowest hot end surface temperature as the optimization objective, using genetic algorithms to find feasible optimal cooling structure design.The results show that the first layer near the cold end of the porous The porosity of the medium should be as large as possible to increase the coolant flow but its compositional material has little effect on the hot end surface temperature whereas the second layer of porous material near the hot end has a significant effect on the hot end surface temperature, Its porosity depends on the balance of its effect on effective thermal conductivity, coolant flow and internal heat exchange coefficient.