Webinar: Predicting Aerodynamic Flow Around Automotive Vehicles

Predicting aerodynamic flow physics around automotive vehicles is a complex endeavor, often requiring engineers 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 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, be applicable across the board, and allow for 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.

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

Registration Details

This webinar is moderated by Digital Engineering’s Kenneth Wong and presented by Cadence’s Vincent Doutrelant.

  • DATE: September 21, 2022
  • TIME: 12:00pm EDT/ 9:00am PDT

Don’t delay; register today.

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