Reynolds stress model (RSM) turbulence models are expected to yield more accurate numerical results for flows with strong anisotropy, such as round free jets, because they directly solve Reynolds stresses rather than modeling them. However, when computational fluid dynamics (CFD) analyses were performed at moderate jet Reynolds numbers using the isotropization by production (IP) RSM model, it was observed that the calculated jet half-widths, decay constants, and spreading rates differed from experimental results due to uncertainties inherent in the turbulence model. In this study, the closure coefficients of the IP RSM turbulence model were calibrated using a variant of the Multi-Objective Genetic Algorithm based on jet half-width data obtained experimentally in the near-field region of the jet. With the use of appropriate discretization schemes and computational grids, the calibrated coefficient combination for the IP RSM turbulence model showed improved accuracy in modeling jet half-widths at Reynolds numbers of 10 000 and 20 000, reducing the errors of calculated decay constants and spreading rates approximately from 2% to 1% and from 16% to 5%, respectively. A detailed examination of the turbulence budget along the longitudinal axis in the self-similar region revealed that the new model coefficients enhanced the modeling of diffusion term but compromised the advection term. As a result of the altered advection term, increased error margins were observed in turbulence intensity (TI) and velocity distribution along the jet centerline, although dissipation along the axis was improved. Consequently, the modeling error in jet half-width calculations using the CFD method was decreased, enhancing the computational cost-effectiveness of the RSM turbulence model compared to more complex turbulence models.
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