Reduced order model based algorithm for inverse convection heat transfer problem

In this study, a reduced order model based algorithm was developed for inverse convection heat transfer. The reduced order models were established for the direct problem, the sensitivity problem and the adjoint problem respectively with the proper orthogonal technique(POD). The performance of the present algorithm was examined by an inverse forced convection problem to determine the unknown space-dependent heat flux at the outer boundary of a circular pipe. The inverse problem was resolved in a function optimization way by the conjugate gradient method. The results show that the present POD based inverse algorithm can significantly reduce the influence of measurement error on the computational results and obtain accurate solution in very short time. The computational speed of the present inverse algorithm is 80 times higher than that of the CFD based inverse algorithm.