Vehicle External-Flow Surrogate & Wind-Tunnel Digitalization

Background

The drag coefficient (Cd) is a key parameter for high-speed energy consumption; each 0.01 Cd reduction saves about 0.5 L/100km. Wind-tunnel tests are costly and a single LES run takes hours, so surrogates have become popular among automotive aero engineers.

Approach

Data sources

• DrivAerML (RWTH Aachen, 31 TB, multiple DrivAer variants)
• HiLiftAeroML (NASA / SAI, 66.9 TB, real-vehicle LES)

Surrogate

• Graph Neural Network (GNN): unstructured surface meshes
• FNO: field-data mapping
• CNN + Attention: volumetric field data

Key results

• GNN surrogate: Cd error < 3% on DrivAer
• FNO: 40% lower MSE than POD on pressure reconstruction

Use cases

• Rapid concept-stage evaluation (replacing some wind-tunnel tests)
• Aero-kit (spoiler, underbody) parameter optimization
• High-speed stability analysis (side force, lift prediction)

Resources

• DrivAerML (apply for access; free for academic use)
• HiLiftAeroML (usage agreement with NASA/SAI)