Investigation of particle swarm optimization algorithm in inverse problems of estimating spray cooling heat flux

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.