Guidelines for Steady State Aerodynamics of 

Large Vehicles
Introduction

Measuring aerodynamic drag for production Class 8 tractor/trailer 

combination has proven to be a real challenge. Today, OEM’s are using Cost 

down testing, Wind Tunnels, and computational fluid dynamics (CFD). Cost down 

testing has proven difficult due to climate control of the test area. Wind tunnel 

have proven difficult due to the large blockage factor of a Class 8 trailer.

CFD is not without it’s own challenges. Due to the size of the model, to full 

capture the unsteady structure of the flow around the vehicle, billions of 

numerical grid points would be needed. Therefore, compromises need to be 

taken to capture accurate results, yet be able to achieve results within a 

reasonable turn-around period.

One such compromise is to use turbulence model to capture the small flow 

structures. This is common practice in both aerospace, and with production 

vehicles, and is commonly used with commercial vehicles. This allows accurate 

results to be captured with 30-60 million cells.

Even with turbulence models, there are precautions the user most take to 

make sure they achieve reasonable results. This document discuss some of 

those precautions, and is meant to serve as a guideline for users conduction 

CFD studies using a Navier Stokes solver.

Vehicle Aerodynamics 
Outline

• Computational Domain Best Practices

– Grid Size

– Wall Modeling

• Modeling the Environment

– Physics Employed

• Turbulence Model

• Compressibility

• Thermal Environment

– Open Road vs Wind Tunnel

• Post Processing Techniques

– Visualization checks on the solution

– Visualizing flow separation

Vehicle Aerodynamics 
Computation Grid Size

• Cell density studies examining curvature refinement on for external 

aerodynamics. As grid is refined, CPU hours needed to solve increases. For 

Class 8 trailers, a grid size of 14-16 mm based upon curvature is recommended 

for compromise between accuracy and turn-around time.

• Refinement studies were also conducted looking at the underbody of the trailer. 

Coarse grid in the underbody significantly increased drag of the vehicle. By 

keeping volume source around the front and rear wheels, the error in drag was 

minimized.
General Settings

Description Setting

Vehicle Aerodynamics

Curvature refinement on exterior 8-16 mm

Number Points Around Circle 60

A-pillar Refinement 4-8mm

Rear lip of vehicle 2-4 mm

Prism Growth Rate (Dual Level)

Small Cells Fast

Default Size Slow

Cut-Off 50% Base

Exterior Prism Layer

Near Wall Size 0.03-0.06 mm

Number of Layers 8-12

Prism Layer Size 8 mm

Engine Compartment/Underbody

Number of Layers 1

Prism Layer Size 1 - 2 mm

5 
General Settings

Description Setting

Vehicle Thermal Protection

Exterior 16-28mm

Number Points Around Circle 32-60

PowerTrain 5-8mm

Grill 2-4 mm

Heat Exchangers 5-8 mm

Prism Growth Rate (Dual Level)

Small Cells Fast

Default Size Medium

Cut-Off 50% Base

Exterior Prism Layer

Near Wall Size 1 - 2 mm

Number of Layers 2

Prism Layer Size 4 - 6 mm

Engine Compartment/Underbody

Number of Layers 1 - 2

Prism Layer Size 1.5 mm
Wall Modeling

• Studies have shown most 

accurate results are obtained 

using low Reynolds grid at the 

wall. Low Reynolds grid models 

the details of the boundary layer 

growth and aid in the prediction 

of separation.

Vehicle Aerodynamics 

Simulation

Experiment
Wall Modeling

• Best practices for Class 8 vehicles is to use 

Low Reynolds mesh on the complete CAB and 

wheels. Typically, there will not be a large 

difference between Low and High Reynolds 

grid, but as the shapes become more rounded, 

and under Yaw conditions, prediction of flow 

separation becomes critical for getting correct 

drag value.

• Proper Low Reynolds mesh models near wall 

size to a Y+ value of below 1, and contains 

more than 20 layers to model the boundary 

layer profile. Typically, each layer adds nearly 1 

million cells to the grid. Studies have shown 

decent results with just using 8-10 layers, with 

Y+ values below 5.

• Trailer separation is easier to predict due to the 

sharp edges. Low Reynolds mesh on the trailer 

is less critical.

Vehicle Aerodynamics 
Optional Refinements

Vehicle Aerodynamics 

Full Size Trailer can benefit by local 

refinement on edges. Either by 

feature edge, or marked separated 

boundary definition. Using volume source between 

CAB and Trailer can also help, 

depending on the vehicle and 

interest of the analysis.
Example Volume Source Refinements

Vehicle Aerodynamics 

Volume sources are used to refine flow going into the engine 

compartment, gap between CAB and trailer, underbody, 

ground, and rear of the trailer.
Modeling The Environment

Turbulence Modeling

A lot of flavors of different turbulence models have been developed over the years. While a 

lot of different turbulence models are available, there are really only a few that are designed 

for aerodynamics and have proven to provide reliable results. The K-omega SST model is an 

example of a turbulence model developed by the aerospace industry. It also does great at 

predicting accurate drag coefficient for commercial vehicles, but can be unstable. The KEpsilon turbulence model has probably been studied the most, and really the best 

understood. The newer realizable k-epsilon models make higher accuracy with more stable 

solution over the older standard k-e, and RNG variants. Hence, the realizable k-epsilon is the 

recommended turbulence model for vehicle aerodynamics due to speed, robustness and 

accuracy.

Vehicle Aerodynamics 
Turbulence Modeling For External 

Aerodynamics

Turbulence Model Pros Cons

Standard K-Epsilon Initially used with Star-CD. Have lots of 

validation data showing good results. With 2-

Layer grid, separation prediction fairly good.

Over prediction of turbulent

viscosity at the stagnation point

K-Omega SST Improves stagnation point, had cases where 

separation by rear of SUV were improved 

over standard K-Epsilon

Solution is fairly unstable making 

difficult to track small changes in 

drag. F1 polar studies were difficult 

to achieve high accuracy

Realizable K-Epsilon Corrects stagnation region by front nose of 

vehicle. Provides steady flow field similar to 

standard k-epsilon. Shows great trends.

Most part, good results. Few studies

showed early separation on F1 wing. 

Also had some cases miss absolute 

drag value.

K-Epsilon, Durbin 

Limiter

Similar to realizable K-Epsilon. Improves 

predictions of stagnation point over 

realizable k-epsilon. More sensitive to the 

computational environment.

Still testing to determine limitations.
Design Optimization: Realizable K-Epsilon

• Parameterized morphing of a 

vehicle using ANSA. Model 

Frontier used to drive the 

optimization process.

– Optimizing 6 parameters

• Modifications made to hood, roof, 

and rear quarter panels of the 

vehicle.

Realizable k-epsilon model was 

able to accurately capture 

trends from small changes in 

drag.
Large Vehicle Aerodynamic Study

• Both predict trends

• Realizable k-e converges to a 

steady solution within 2000 

iterations.

• K-Omega SST needed to be 

averaged over last 3000 

iterations.
Checking Turbulence Behavior

Turbulent Viscosity Ratio

• Turbulent Viscosity Ratio check

– Scale: 0-1000

– Checks:

• Use to check to see boundary layer growth looks correct

• Checks for proper settings on inlet

• Model above shows normal growth for moving ground, open road 

test.

Vehicle Aerodynamics 
Wind Tunnel vs. Open Road

Open road, with moving ground is the most common analysis configuration for CFD. The 

road is simulated with a moving boundary condition. The tire of the walls are model with a 

rotating wall boundary. The RPM of the wheels are calculated based upon the distance from 

the center of the wheel to the ground.

Typical runs for Class 8 Truck do not consider the motion of the interior of the rim. For 

performance vehicles/passenger cars, the rims typically are solved in a rotating reference 

frame. But due to the style of the rim for Class 8 Trucks, it has not yet been deemed 

important to include the rotating of non-axis symmetric parts of the wheel.

When correlating results to a wind tunnel, the recommended best practice is to include the 

modeling of the wind tunnel in the CFD model. Since there is not a wind tunnel large enough 

for a Class 8 Truck, wind tunnel affects are significant, and should not be neglected.

Vehicle Aerodynamics 
Simulation of Yaw

Over the years, there has been a few techniques used to simplify Yaw simulation. First 

method is to morph the wind tunnel around the vehicle. By moving the wind tunnel around 

the vehicle, post processing remains the same, and does not need to change per Yaw 

degree. Plus drag is normally measured in the vehicle reference frame, not at the Yaw angle. 

In recent years, a more simplistic approach has been used. The vehicle is modeled in a large 

domain. The side walls are connected using periodic boundaries. Therefore, wind angle can 

quickly be changed. This is typically done on the fly, during a single run, so results for 

multiple Yaw angles can be achieved from single run.

When comparing to Yaw angles in a wind tunnel, periodic boundaries will not work. 

Morphing, only works for small changes relative to the size of the tunnel. For cases with high 

blockage factors, the grid should be regenerated for each Yaw angle. This still can be 

automated by setting up a box for the vehicle. Several mesh’s can be produced in advanced 

with the box at different angles. Then during the analysis, the grid can be swapped during 

the run.

Vehicle Aerodynamics 
Virtual Wind Tunnel

Vehicle Aerodynamics 

To impact vehicle design, automation of the process from CAD -> Results is critical to 

minimize turn-around time. This allows different modifications to be quickly analyzed. The 

automation forces similarity between different models that provides consistent results.

CD-adapco has created a virtual wind tunnel, the goal is not to design better vehicles, but 

help provide a tool that can provide accurate results for their clients. This quickly allows for 

new geometry to be brought in, and analyzed, while minimizing effort by the engineer.
Hardware For Testing

Hardware

• 256 Processor Cluster Dedicated 

for Testing and Improving Vehicle 

Aerodynamics

– Head Node

• 64 Gbytes

• 1 Queue for Grid Generation

– Cluster Nodes

• 64 Nodes, with 2 dual core processors

• 12 Gbytes per node

Man power

– Limited resources

Vehicle Aerodynamics 
• To handle random set of data quickly, and turn it into an aerodynamic 

model, automation was needed.

• Enabler: STAR-CCM+ environment provided the tools and flexible to 

handle different styles of imported CAD data.

Automation Was Needed To Conduct Studies 

with Minimum User Interaction

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The CFD Challenge

• Data comes in from different 

types of sources

– Example: STL, CAD, ANSA, etc

• Surface quality:

– Starting surface may have selfintersection, free edges, 0-

thickness and non-manifold 

surfaces

Additional Challenges From Our 

Starting point:

– Have no control over the CAD. 

We really need to work with the 

data that is received

• Naming convention: depends on 

client who send the model

• Can not count on surfaces tagged 

or grouped from in CAD

Vehicle Aerodynamics 
Aerodynamics Process Automation

STAR-CCM+ reading setup information directly from excel

• To adapt to the differences in imported CAD, grouping information, 

boundary conditions, etc are imported from an Excel spreadsheet.

– Starting point of each test case just starts with CAD data, and spreadsheet.

• Results are created as part of the analysis

– PowerPoint generated for each case ran.

Run: Mesh/Solve/Post Output: PowerPoint 

Input: Cad/Excel 

Run: Mesh/Solve/Post 

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Import CAD

Wrap/Resurface 

Mesh

Generate 

Volume Mesh

Vehicle Aerodynamics 
Production Environment: Hardware

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Dual Queue

Job: Meshing

CPU Time: 1.5 –

8 hours

Queue: 32-64 Nodes

Job: Solver

Elapsed: 24-48 hours

Queue: Part Of 

Solution

Job: Posting

Elapsed: 0.5 hours
Applying “Virtual Tunnel” to EPA Wind Tunnel 

Model Study

Vehicle Aerodynamics 

• Grid Generation

• 3.5 Hours – 45 million cells

• Head Node

• Two Simultaneously

13 Cases Prepared Per Day

• Solver

– 80 Processors per case

– Yaw Angle: 0, 3, 6, 9, -3, -6, -9

– 70 Hours Per Case

– 7 Studies per week
Vsim Spreadsheet for EPA Geometry

• First Page

– Describes boundary 

condition, Yaw angle, 

ground condition, and 

solver settings

Vehicle Aerodynamics 
Vsim Spreadsheet for EPA Geometry

• Group Definitions

– Creates groups for post 

processing, or applied 

mesh sizes.

– Filters build link to 

clients naming 

convention to groups 

needed to fit to the Class 

8 Truck Template.

Vehicle Aerodynamics 
Vsim Spreadsheet for EPA Geometry

• Group Settings

– Assigns mesh size per 

group, and/or prism 

layer size. Any field not 

specified uses the 

default settings set on 

the first page.

Vehicle Aerodynamics 
Vsim Spreadsheet for EPA Geometry

• Volume Source Definitions

– Define Volume boxes around vehicle

– Sets wake refinements

– Parameters are not vehicle dependent. They use the surface 

ranges defined from the “Surface Range” tab.

Vehicle Aerodynamics 
Vsim Spreadsheet for EPA Geometry

• Surface Range

– Define group for surface range calculations

– Used to calculate volume sources, and post processing 

groups.

Vehicle Aerodynamics 
Vsim Spreadsheet for EPA Geometry

• Post Processing

– Defines a set of section, contour plots, and isosurfaces to be 

used for posting processing each vehicle and Yaw angle.

– Images go into final PowerPoint

Vehicle Aerodynamics