Topology optimization of fluid problems using genetic algorithm assisted by the Kriging model

A non-gradient-based approach for topology optimization using a genetic algorithm is proposed in this paper. The genetic algorithm used in this paper is assisted by the Kriging surrogate model to reduce computational cost required for function evaluation. To validate the non-gradient-based topology optimization method in flow problems, this research focuses on two single-objective optimization problems, where the objective functions are to minimize pressure loss and to maximize heat transfer of flow channels, and one multi-objective optimization problem, which combines earlier two single-objective optimization problems. The shape of flow channels is represented by the level set function. The pressure loss and the heat transfer performance of the channels are evaluated by the Building-Cube Method code, which is a Cartesian-mesh CFD solver. The proposed method resulted in an agreement with previous study in the single-objective problems in its topology and achieved global exploration of non-dominated solutions in the multi-objective problems.