Three-dimensional high-lift configuration simulation using data-driven turbulence model

Traditional Reynolds-averaged Navier-Stokes equations often struggle to predict separated flows accurately. Recent studies have employed data-driven methods to enhance predictions by modifying baseline equations, such as field inversion and machine learning with symbolic regression. However, data-driven turbulence models exhibit limited adaptability and are rarely applied to complex engineering problems. This study examines the application of data-driven turbulence models to complex three-dimensional high-lift configurations, extending their usability beyond previous applications. First, the generalizability of the shear-stress transport model for conditioned field inversion (SST-CND) is validated, where CND is the abbreviation for conditioned. Then, the spatially varying correction factor obtained through conditioned field inversion is transferred to the three-equation k-v(2<overline>)-omega model. The 30P30N three-element airfoil, the Japan aerospace exploration agency standard model, and the high-lift version of the common research model (CRM-HL) are numerically simulated. The results indicated that the SST-CND model significantly improves the prediction of stall characteristics, demonstrating satisfactory generalizability. The corrected k-v(2<overline>)-omega-CND model accurately predicts the stall characteristics of CRM-HL, with a relative error of less than 5% compared to experimental results. This confirms the strong transferability of the model correction derived from conditioned field inversion across different turbulence models.