Optimization and inverse identification are two procedures usually encountered in many industrial processes reputed gourmand for the computing time view point. In fact, optimization implies to propose a trial solution whose accuracy is then evaluated, and if needed it must be updated in order to minimize a certain cost function. In the case of mold cooling optimization the evaluation of the solution quality needs the solution of a thermal model, in the whole domain and during the thermal history. Thus, the optimization process needs several iterations and then the computational cost can become enormous. In this work we propose the use of model reduction for accomplishing this kind of simulations. Thus, only one thermal model is solved using the standard discretization technique. After that, the most important modes defining the temperature evolution are extracted by invoking the proper orthogonal decomposition, and all the other thermal model solutions are performed by using the reduced order approximation basis just extracted. The CPU time savings can be impressive.
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