Abstract
This paper presents the application of a data-driven method for turbulence modeling. The main aim is to enhance the prediction capabilities of an existing Reynolds-averaged Navier–Stokes (RANS) model for laminar separation bubbles. The recently developed field inversion method has been chosen to infer corrections to the shear stress transport turbulence model. An innovative approach has been proposed to reduce the computational cost of the modeling procedure. The objective function makes use of the friction coefficient to compare data coming from the RANS model with reference large-eddy simulation data available in the literature. The performed test cases responded well to the optimization procedure which provided a correction field that boosts the turbulent kinetic energy within the bubble. The proposed procedure is the necessary building block to define a new turbulence model able to overcome the present limitations in simulating laminar separation bubbles.
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