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−v2¯−ω 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−v2¯−ω−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.