In this study, the design of a multi-hole orifice (MHO) commonly used in cavitation reactors, is optimized to maximize the vapor production and the turbulence intensity downstream of the MHO. The independent optimization parameters are non-homogenous distribution for peripheral holes (vertical radius, δ_y, and horizontal radius, δ_x), the diameter of the central hole, D_c/D, and the orifice thickness, t/D. A design of experiment, a set of CFD simulations, and radial basis function neural network (RBFNN) are used to study the effect of the mentioned independent parameters on the two-objective functions. The model analysis shows that the orifice thickness is a most influential factor in vapor production, while the diameter of the central hole remarkably influences the intensity of turbulence following the MHO. The, NSGA-II algorithm is used for obtaining Pareto optimal design, which propose that an MHO with a relative orifice thickness around t/D=0.15 produces both maximum vapor output and remarkable turbulence intensity. The turbulence intensity is maximized by a nonhomogeneous distribution of holes, i.e., δ_x=0.4 and δ_y=0.6. Therefore, δ_x and δ_y are responsible for collapsing bubbles due to jets mixing behind MHO. As compared to MHO with D_c/D= 0.05, MHO with D_c/D= 0.2 showed a pressure recovery length that was twice as long.
