SUMMARYA 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 multiobjective 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.

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This paper proposes a novel approach for fluid topology optimization using genetic algorithm. In this study, the enhancement of mixing in the passive micromixers is considered. The efficient mixing is achieved by the grooves attached on the bottom of the microchannel and the optimal configuration of grooves is investigated. The grooves are represented based on the graph theory. The mixing performance is analyzed by a CFD solver and the exploration by genetic algorithm is assisted by the Kriging model to reduce the computational cost. The characteristics of the convex and the concave grooves are compared. To balance the global exploration and the reasonable computational cost, this paper investigates three cases with the convex grooves subject to constraint that differs in handling of design variables. In each case, genetic algorithm finds several local optima since the objective function is a multi-modal function, and these optima reveal the specific characteristic for efficient mixing. Moreover, this paper optimizes the micromixer with the concave grooves and reveals the different properties of the mixing. Finally, to guarantee the obtained solutions competitive, the sensitivity analysis is performed to the best solution in each case.

This paper proposes a novel approach for fluid topology optimization using genetic algorithm. In this study, the enhancement of mixing in the passive micromixers is considered. The efficient mixing is achieved by the grooves attached on the bottom of the microchannel and the optimal configuration of grooves is investigated. The grooves are represented based on the graph theory. The micromixers are analyzed by a CFD solver and the exploration by genetic algorithm is assisted by the Kriging model in order to reduce the computational cost. Three cases with different constraint and treatment for design variables are considered. In each case, GA found several local optima since the objective function is a multi-modal function and each local optimum revealed the specific characteristic for efficient mixing in micromixers. Moreover, we discuss the validity of the constraint for optimization problems. The results show a novel insight for design of micromixer and fluid topology optimization using genetic algorithm.

Thermo-fluid dynamic design optimization of a concentric tube heat exchangerNomenclature design parameter in Eq. (1) [-] cross-sectional area [mm 2 ] design parameter in Eq. ( 1) [-] specific heat [J/(kg•K)] heat capacity rate [W/K] grid width [mm] thermal conductivity [W/(m•K)] overall heat transfer coefficient [W/(m 2 •K)] design parameter in Eq. (1) [-] ̇ mass flow rate [kg/s] design parameter in Eq. (1) [-] number of transfer units [-] pressure [Pa] radial coordinate for the cross-sectional shape [mm] Reynolds number [-] heat transfer area [m 2 ] temperature [K] x-directional velocity component [m/s] y-directional velocity component [m/s]