The demand in solving complex turbulent fluid flows has been growing rapidly in the automotive industry for the last decade as engineers strive to design better vehicles to improve drag coefficients, noise levels and drivability. This paper presents the implementation of an arbitrary hybrid turbulence modeling (AHTM) approach in OpenFOAM for the efficient simulation of common automotive aerodynamics with unsteady turbulent separated flows such as the Kelvin–Helmholtz effect, which can also be used as an efficient part of aerodynamic design optimization (ADO) tools. This AHTM approach is based on the concept of Very Large Eddy Simulation (VLES), which can arbitrarily combine RANS, URANS, LES and DNS turbulence models in a single flow field depending on the local mesh refinement. As a result, the design engineer can take advantage of this unique and highly flexible approach to tailor his grid according to his design and resolution requirements in different areas of the flow field over the car body without sacrificing accuracy and efficiency at the same time. This paper presents the details of the implementation and careful validation of the AHTM method using the standard benchmark case of the Ahmed body, in comparison with some other existing models, such as RANS, URANS, DES and LES, which shows VLES to be the most accurate among the five examined. Furthermore, the results of this study demonstrate that the AHTM approach has the flexibility, efficiency and accuracy to be integrated with ADO tools for engineering design in the automotive industry. The approach can also be used for the detailed study of highly complex turbulent phenomena such as the Kelvin–Helmholtz instability commonly found in automotive aerodynamics. Currently, the AHTM implementation is being integrated with the DAFoam for gradient-based multi-point ADO using an efficient adjoint solver based on a Sparse Nonlinear optimizer (SNOPT).
An Arbitrary Hybrid Turbulence Modeling Approach for Efficient and Accurate Automotive Aerodynamic Analysis and Design Optimization