Shape optimization of inlet part of a printed circuit heat exchanger using surrogate modeling

Aim of the present work is to optimize the shape of inlet part of a printed circuit heat exchanger to enhance the thermal–hydraulic performance using surrogate modeling. The fluid flow was analyzed using three-dimensional Reynolds-averaged Navier–Stokes analysis with the shear stress transport turbulence model. The non-dimensional parameters related to the angle and radius of curvature of the inlet plenum wall, and diameter of the inlet pipes were selected as design variables for the optimization. The objective function was defined as a weighted-sum of two objectives related to uniformity of the mass flow rate distribution and pressure loss, respectively. Twenty six design points were obtained by Latin hypercube sampling method. The Kriging and the radial basis neural networks were used as surrogate models to approximate the value of the objective function. The results of the optimization with a weighting factor show that the objective function values of the optimum designs obtained by the two surrogate models were similar to each other and improved from that of the reference design.