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As turbulence modeling has become an indispensable approach to perform flow simulation in a wide range of industrial applications, how to enhance the prediction accuracy has gained increasing attention during the past years. Of all the turbulence models, RANS is the most common choice for many OEMs due to its short turn-around time and strong robustness. However, the default setting of RANS is usually benchmarked through classical and well-studied engineering examples, not always suitable for resolving complex flows in specific circumstances. Many previous researches have suggested a small tuning in turbulence model coefficients could achieve higher accuracy on a variety of flow scenarios. Instead of adjusting parameters by trial and error from experience, this paper introduced a new data-driven method of turbulence model recalibration using adjoint solver, based on Generalized k-ω (GEKO) model, one variant of RANS.

The effect of jet geometry on flow, heat transfer and defrosting characteristics was numerically investigated for elliptic and rectangular impinging jets on an automobile windshield. Initially, various turbulence models within the commercial computational fluid dynamics (CFD) package FLUENT were employed and validated for a single jet, and the results indicated that the impinging jet heat transfer was more accurately predicted by the SST k -ω turbulence model, which was then utilized for this study. The aspect ratios (AR) of elliptic and rectangular jets were respectively 0.5, 1.0, and 2.0, with jet-to-target spacing h/d=2, 4 and jet-to-jet spacing c/d=4, and all those situations were numerically analyzed with the same air mass flow and jet open area. It was observed that the heat transfer coefficient and defrosting performance of the inclined windshield were significantly affected by the shape of the jet, and the best results were obtained with the elliptic jet arrangements.