Predicting Aerodynamic Flow Physics Around Automotive Vehicles – Webinar on-demand
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, especially so in the presence of strong wakes like the ones found in automotive aerodynamics.
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. The need for accuracy during the design cycle (from style to certification) varies, as well as the desired turnaround time and number of simulations, leading to different choices being made at various stages of design.
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.
Our findings show that using a robust RSM approach can provide an attractive compromise, as it combines the inexpensive nature of a robust steady solver with the accuracy of an advanced turbulence model.
Learning benefits:
- Demonstration of the full CFD simulation workflow for car external aerodynamics
- Comparison of speed and accuracy for different turbulence modeling approaches
- Application to multiple reference car models
