Adaptive mesh refinement (AMR) criteria comparison for the DrivAer model.

Area of Science:

• Vehicle Aerodynamics
• Computational Fluid Dynamics (CFD)
• Computational Science

Background:

• Aerodynamic design is crucial for vehicle development, impacting fuel economy and electric vehicle range.
• Computational Fluid Dynamics (CFD) offers efficient aerodynamic simulations.
• Resource optimization in CFD, particularly spatial discretization, is a key challenge.

Purpose of the Study:

• To investigate the application of Adaptive Mesh Refinement (AMR) in the aerodynamic design of private vehicles.
• To compare the effectiveness of different fluid dynamic criteria for AMR implementation.
• To validate AMR results against experimental and existing computational data.

Main Methods:

• Utilized the DrivAer model for aerodynamic simulations.
• Implemented Adaptive Mesh Refinement (AMR) based on four distinct fluid dynamic criteria.
• Compared simulation results (drag coefficient, pressure coefficient, total pressure wake) with established data.

Main Results:

• AMR significantly optimizes computational resources by refining meshes only where necessary.
• The study demonstrated high accuracy in correlating drag coefficient, pressure coefficient, and total pressure wake.
• Specific AMR criteria proved effective in optimizing the wake region, crucial for drag prediction.

Conclusions:

• Adaptive Mesh Refinement (AMR) is a highly effective technique for optimizing computational resources in vehicle aerodynamic simulations.
• The validated AMR approach enhances the accuracy of predicting aerodynamic performance, particularly drag.
• This method enables more efficient and accurate aerodynamic design for private vehicles.