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Predicting aerodynamic flow physics around automotive vehicles is a complex endeavor, often leaving the engineer with the need to balance cost and accuracy. While steady-state approaches (such as RANS) are attractive for their low computational cost, they usually fail to predict all flow phenomena correctly. More fidelity can be achieved by using unsteady scale-resolving models such as DES or wall-modeled LES, of course at a significantly higher cost.
An ideal model would offer an accurate solution within a small turnaround time, therefore being applicable across the board and allowing for a faster, more consistent, decision making.
In this webinar, we present a comparison between standard RANS models, Stress-Omega RSM implementation, and unsteady scale resolving approaches on several representative test cases including Windsor body and DrivAer.