Paralell PSO method for estimation Heat Transfer Coefficients

The methodology based on the Particle Swarm Optimization (PSO) method, as a recent stochastic optimization technique to solve complex inverse heat transfer problems is outlined. Temporal and spatial dependent Heat Transfer coefficient obtained on the surfaces of a cylindrical work piece is recovered by solving the inverse heat conduction problem. The fitness function to be minimized by the PSO approach is defined by the deviation of the measurements and the calculated temperatures is minimized. The PSO algorithm has been parallelized and implemented on a GPU architecture. Numerical results are demonstrated that the determination of Heat Transfer Coefficient functions can be performed by using the PSO method, as well as, the GPU implementation; provide a less time consuming and accurate estimation.