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喷雾冷却有望用于飞行器地面热环境模拟装置,获得不同工况下的热流密度是对该过程进行控制的基础。实验过程中热流密度需要由被冷却材料的表面温度测量值计算。本文对该问题进行了建模,将粒子群算法用于求解具有幂级数形式表达式的热流密度,给出了一种使用优化算法求解导热反问题的方法。数值验证的结果表明,该方法可以较准确地预测热流密度随时间的变化规律,其计算结果在存在一定的温度测量误差的情况下也具有较高的精度,该方法较为适用于大热流密度条件下的喷雾冷却过程。
Spray cooling is expected to be used in aircraft thermal simulation of the ground environment, access to heat flux under different conditions is the basis for the process control. Heat flux density during the experiment needs to be calculated from the measured surface temperature of the material being cooled. In this paper, the problem is modeled, the particle swarm optimization is used to solve the heat flux density in the form of power series, and a method to solve the inverse heat conduction problem by using the optimization algorithm is given. The numerical results show that this method can predict the variation of heat flux with time more accurately. The results show that the proposed method has higher accuracy when there is a certain temperature measurement error. This method is more suitable for large heat flux density conditions Under the spray cooling process.