预测喷雾冷却热流密度反问题的粒子群算法研究

Investigation of Particle Swarm Optimization Algorithm in Inverse Problems of Estimating Spray Cooling Heat Flux

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 a promising cooling technique for flight vehicle thermal testing facilities and obtaining heat fluxes under different conditions is the basis to control the process. The heat fluxes need to be estimated using surface temperature values of test pieces measured in experiments. A model was built for this problem and particle swarm optimization algorithm was used to estimate the heat fluxes in power series form. Details of solving an inverse heat conduction problem by optimization method are given. Numerical verification shows that the method provides a good prediction of the changing heat flux and a relatively precise results can be obtained with certain measurement errors. A conclusion is draw that the method is suitable for reverse estimation of high heat fluxes in spray cooling conditions.