Shape optimization of multi-scales and multi-physics problems: application to heat exchangers

Heat exchangers are used in many industrial applications. Optimizing their performances is a key point to improve energy efficiency. Heat exchanger behaviour is a multi-scale issue where local scale enhancement mechanisms coexist with global scale distribution ones. It is also multi-physics such as fluid mechanics, heat transfer and fouling phenomenons appear. The present work deals with multi-objective shape optimization of heat exchanger. The proposed method is sufficiently robust to address multi-scale and multi-physics issues and allows industrial applications. Heat exchanger performances are evaluated using computational fluid dynamics (CFD) simulations and global methods (ε-NUT). The optimization tools are a genetic algorithm coupled with kriging-based meta modelling. Clustering and Self-Organizing Maps (SOM) are used to analyse the optimization results. A meta-model builds an approximation of a simulator response (CFD) whose evaluation cost is reduced to be used with the genetic algorithm. Kriging can address discontinuities or perturbations of the response by introducing a nugget effect. Adaptive sampling is used to built cheap and precise approximation. The present optimization method is applied to different configurations which are representative of the heat exchanger behaviour for both multi-scale and multi-physics (fouling) aspects. Results show that meta-modelling is a key point of the method, ensuring the robustness and the versatility of the optimisation process. Also, it allows to built correlations of the local scale used to determine the global performances of the heat exchanger. Clustering and SOM highlight a finite number of shapes, which represent a compromise between antagonist objective functions, directly usable in an industrial context. (author)