Classic turbulence models often struggle to accurately predict complex flows. Although data-driven techniques have addressed these shortcomings, most existing research has concentrated on two-dimensional (2D) cases. In this study, we bridge this gap by enhancing a data-driven turbulence model, the shear stress transport-conditioned (SST-CND) model, which was originally trained on 2D separated flows, in three-dimensional (3D) scenarios. An additional correction term ${\beta }_{3\text{D}}$ is introduced to account for 3D effects. The distribution of this term is determined through a 3D field inversion process using high-fidelity data obtained from the flow around a cube. An algebraic expression for ${\beta }_{3\text{D}}$ is then derived through symbolic regression and formulated to degrade to zero in 2D cases. The performance of the resulting SST-CND3D model is evaluated across a range of flows. In 2D flows, the SST-CND3D model performs identically to its 2D-trained predecessor. However, the model exhibits superior performance in 3D flows, such as the flow around the complex JAXA standard model high-lift configuration. These findings indicate that a sequential approach, constructing a 3D correction term that vanishes in 2D on top of a 2D-trained model, constitutes a promising method for developing data-driven turbulence models that perform accurately in 3D while preserving their effectiveness in 2D.

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• Turbulence
• Turbulence modeling