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DrivAer Automotive Aerodynamics

Master automotive CFD simulation using the industry-standard DrivAer reference geometry. This example demonstrates professional-grade automotive aerodynamics analysis on the Navier AI Platform.

About DrivAer

Reference Model

The DrivAer model is a simplified yet realistic automotive geometry developed for CFD validation:
  • Scale: Full-scale passenger car (4.6m length)
  • Configurations: Fastback, Estate, and Notchback variants
  • Features: Detailed underbody, wheels, mirrors, and aerodynamic elements
  • Validation: Extensively validated against wind tunnel data

Simulation Objectives

  • Calculate drag coefficient (CD) for fuel efficiency assessment
  • Analyze pressure distribution and flow separation
  • Study wake characteristics and vortex formation
  • Validate CFD setup against experimental data

Geometry Preparation

DrivAer Model Files

This example will use the Notchback (Traditional sedan with distinct trunk) model.

Geometry Checklist

We start by verifying the qualify of our geometry. We need to go through our geometry checklist to ensure that it’s sufficient quality for meshing.
Geometry Checklist:
✓ Watertight STL mesh
✓ Manifold
✓ Proper scale (4.63m length)
DrivAer Downloads: Official geometry files are available from the Technical University of Munich (TUM) or automotive CFD databases.

Platform Configuration

1. Project Setup

  1. Create new project: “DrivAer Aerodynamics Study”
  2. Upload DrivAer STL file (typically 10-50MB)
  3. Verify scaling and orientation in 3D viewer

2. Domain Configuration

Professional Setup (Medium-Large Domain):
  • Upstream: 3× vehicle length (13.9m)
  • Downstream: 10× vehicle length (46.3m)
  • Width: 6× vehicle width (10.8m)
  • Height: 4× vehicle height (6.0m)
  • Ground clearance: 0.05m

Flow Conditions

Standard Test Conditions:
- Velocity: 30 m/s (108 km/h, 67 mph)
- Reynolds Number: ~9 million (based on length)
- Turbulence Intensity: 0.1-0.5% (wind tunnel quality)
- Temperature: 20°C
- Pressure: 1 atm

3. Advanced Meshing Strategy

Base Mesh Resolution

  • Domain Base: 0.2-0.3m cells
  • Vehicle Region: 0.05-0.1m cells
  • Total Cell Count: Target 8-15 million cells

Critical Refinement Zones

High-resolution near-wall mesh for accurate drag prediction:
  • First cell height: y+ = 30-100 (wall functions)
  • Growth ratio: 1.2-1.3
  • Layers: 10-15 prism layers
  • Thickness: 0.05m from all surfaces
Capture complex wake flow and pressure recovery:
  • Type: Box refinement
  • Location: 0.5-8.0m behind vehicle
  • Dimensions: 4m wide × 3m high
  • Refinement Level: 4-5
Critical for accurate drag and lift calculations:
  • Type: Box under vehicle
  • Height: 0-0.3m from ground
  • Coverage: Full vehicle length + 2m downstream
  • Refinement Level: 4
Capture complex rotating wheel effects:
  • Type: Cylinder around each wheel
  • Radius: 1.5× wheel radius
  • Height: Full wheel well depth
  • Refinement Level: 5-6

4. Solver Configuration

Flow Physics

Solver Settings:
- Type: Incompressible
- Mode: Steady State
- Turbulence: k-ω SST (industry standard for automotive)
- Pressure-Velocity Coupling: SIMPLE
- Convergence Criteria: 1e-4 for all residuals

Advanced Options

  • Potential Flow Initialization: Recommended for faster convergence
  • Parallel Processing: Use 4-8 cores for optimal performance
  • Relaxation Factors: Conservative (0.7 for momentum, 0.8 for pressure)

5. Boundary Conditions

Domain Boundaries

Inlet:
  Type: Velocity Inlet
  Velocity: 30 m/s (X-direction)
  Turbulence Intensity: 0.1%
  Hydraulic Diameter: 2.9m (based on domain)

Outlet:
  Type: Pressure Outlet  
  Gauge Pressure: 0 Pa
  Backflow Direction: -X

Sides & Top:
  Type: Symmetry
  Zero normal gradient for all variables

Ground:
  Type: Moving Wall
  Velocity: 30 m/s (X-direction)
  Roughness: Smooth

Vehicle Surfaces

Body Panels:
  Type: No-slip Wall
  Roughness: Smooth (painted surface)
  Heat Transfer: Adiabatic

Wheels (if rotating):
  Type: Rotating Wall
  Rotation Speed: 217 rad/s (30 m/s / 0.69m radius)
  Axis: Y-direction
Ground Plane: Use moving wall boundary condition to simulate relative motion between vehicle and ground. This is critical for accurate underbody flow simulation.

Simulation Execution

Pre-simulation Validation

  • Mesh Quality: Orthogonality > 0.1, Aspect Ratio < 1000
  • Y+ Values: Check wall distance for turbulence model
  • Domain Independence: Boundaries > 5× vehicle dimensions
  • Mass Conservation: Verify inlet/outlet flow rates

Convergence Monitoring

Key Residuals

  1. Continuity: Target < 1e-4
  2. Momentum (UVW): Target < 1e-4
  3. Turbulence (k, ω): Target < 1e-5
  4. Energy (if thermal): Target < 1e-6

Force Monitoring

Expected Convergence:
- Drag Force: ±0.1% over last 100 iterations
- Lift Force: ±0.5% over last 100 iterations  
- Side Force: Should approach zero for symmetric geometry

Runtime Estimates

  • 8M cells, 4 cores: 4-6 hours
  • 15M cells, 8 cores: 6-10 hours
  • Convergence: Typically 1500-3000 iterations

Results Analysis

Drag Coefficient Validation

Expected Values (DrivAer Fastback)

Experimental Reference:
- CD = 0.243 ± 0.005 (wind tunnel)
- CL = 0.010 ± 0.003 (slight downforce)

CFD Target Accuracy:
- CD within ±3% of experimental
- CL within ±0.005 absolute

Calculation Formula

Drag Coefficient: CD = FD / (0.5 × ρ × V² × A)

Where:
- FD = Drag force (N)
- ρ = Air density (1.225 kg/m³)
- V = Velocity (30 m/s)
- A = Frontal area (2.16 m²)

Flow Visualization

Surface Pressure

Analysis Points:
  • High pressure: Front stagnation point
  • Low pressure: Roof acceleration, rear separation
  • Pressure recovery: Behind vehicle

Streamlines

Key Features:
  • Attachment/separation lines
  • Roof vortices
  • Underbody acceleration
  • Wake recirculation

Velocity Contours

Critical Regions:
  • Boundary layer thickness
  • Separation zones
  • Wake velocity deficit
  • Ground effect acceleration

Vorticity

Vortex Structures:
  • A-pillar vortices
  • Wheel wake vortices
  • Rear spoiler effects
  • Underbody vortices

Performance Metrics

Aerodynamic Efficiency

Key Performance Indicators:
- Drag Coefficient: Target < 0.25
- Lift Coefficient: Target ±0.05
- Pitching Moment: Stability assessment
- Pressure Base: Wake pressure recovery

Design Optimization Insights

  1. Drag Reduction: Focus on rear separation and underbody flow
  2. Downforce: Analyze front/rear balance for stability
  3. Side Force: Minimize for crosswind stability
  4. Cooling: Assess radiator and brake cooling airflow

Advanced Analysis

Pressure Coefficient Distribution

CP = (P - P∞) / (0.5 × ρ × V²)

Critical Locations:
- Windshield: CP ≈ -0.8 to -1.2
- Roof Center: CP ≈ -0.6 to -0.8  
- Rear Window: CP ≈ -0.2 to -0.5
- Base: CP ≈ -0.3 to -0.4

Benchmark Comparison

ConfigurationCD (Exp.)CD (CFD)Error
Fastback0.2430.248+2.1%
Estate0.2650.271+2.3%
Notchback0.3550.362+2.0%

Optimization Strategies

Aerodynamic Improvements

  1. Rear Spoiler: Optimize angle and position
  2. Underbody Panels: Smooth underbody flow
  3. Side Mirrors: Streamline or camera replacement
  4. Wheel Design: Optimize wheel aerodynamics
  5. Grille Blocking: Minimize cooling drag when possible

Parametric Studies

Design Variables:
- Rear spoiler angle: 0-15 degrees
- Ride height: 0.05-0.15m
- Wheel rim design: Multiple configurations
- Front dam height: Optimize ground clearance

Professional Validation

Mesh Independence Study

Mesh SizeCD Value% Change
3M cells0.251baseline
8M cells0.248-1.2%
15M cells0.247-0.4%

Turbulence Model Comparison

  • k-ω SST: Most accurate for automotive (recommended)
  • k-ε Realizable: Faster, less accurate rear separation
  • Spalart-Allmaras: Good for attached flows only

Industry Applications

OEM Development

Apply these techniques to production vehicle development and aerodynamic optimization programs.

Racing Applications

Adapt methods for motorsports with focus on downforce and efficiency optimization.

Commercial Vehicles

Scale approaches for truck and bus aerodynamics with emphasis on fuel efficiency.

Electric Vehicles

Consider thermal management and range optimization specific to EVs.

Professional Tip: The DrivAer model serves as an excellent validation case. Achieving experimental accuracy within 3% demonstrates proper CFD setup for production automotive analysis.