Abstract: Current optimization for regenerative cooling structure of scramjet depends on empirical relations， and insufficient attention is given to flow pressure loss. Optimal design of fin height， slot width， fin thickness of single regenerative cooling channel was carried out by the response surface methodology (RSM) combined with multi-objective genetic algorithm (MOGA) ， with the average wall temperature at the hot-gas side and flow pressure loss as optimization objective. The results show that the most sensitive design variable is fin height， and then the slot width， fin thickness， and the influence of design variable to optimization objective is the same at different inlet mass flow. After the Pareto optimal solution set in design condition is found， multiple structures can be obtained which performs better in the flow and heat transfer characteristic than the initial channel. Design program of cooling panel are obtained and the allowed range of inlet mass flow is found to be 1.539～9.604kg/s by rounding of one optimization channel and building response surface with the inlet mass flow as design variable.

Key words: Scramjet; Regenerative cooling channel; Response surface methodology; Genetic algorithm; Multi-objective optimization

摘要： 针对当前超燃冲压发动机再生冷却结构的优化研究存在对经验关联式依赖的问题，且对流动压力损失问题重视不足，采用响应面法结合多目标遗传算法，以燃气侧平均壁温和流动压力损失为优化目标，对单根再生冷却通道的肋高、槽宽和肋厚进行优化设计。结果表明:肋高对优化目标的影响程度最大，其次是槽宽、肋厚，且不同进口质量流量下设计变量对优化目标的影响规律是相似的。计算得到设计工况下的Pareto 最优解集后，从中可选取多组综合流动换热性能优于初始通道的结构。对解集中一组优化通道进行圆整并以进口质量流量为设计变量建立响应面，获得了冷却平板的设计方案及1.539～9.604kg/s的允许进口质量流量范围。

关键词: 超燃冲压发动机；再生冷却通道；响应面法；遗传算法；多目标优化