Comments on EPA-HQ-OAR-2010-0162: 

Greenhouse Gas Emissions Standards and Fuel 

Efficiency Standards for Medium and Heavy-Duty 

Engines and Vehicles

CD-adapco

June 2011

Updated July 13, 2011
CD-adapco has been developing commercial CDF tools since 1987 beginning with STAR-CD and

continuing with the recent next generation CFD tool STAR-CCM+. Fred Ross is a veteran engineer with 

CD-adapco and one of their experts in external aerodynamic usage of STAR-CCM+. Fred was able to 

review some of the online documents available from the EPA. His comment summarize recent studies, 

and also focuses on the 20+ years of CFD experience that CD-adapco has from working with most of the 

major OEMs around the world.

Fred Ross’s Comments: 

Over the past 15 years, CFD has been used for vehicle aerodynamics of road vehicles. This consists all 

the way from motorcycles, to large vehicles, such as the Class 8 trucks. During this time, a lot of 

correlation work has been done. There tend to be two types of correlation:

1) Prediction of absolute drag/lift values

2) Prediction of trends

In 2010, we were involved with correlation runs for Daimler Trucks of North America, PACCAR, and 

Volvo3P for North America commercial vehicles. Daimler Trucks of North America, PACCAR, and Volvo3P 

are all using our tool to some degree on production work (not all for North America). Lawrence 

Livermore Labs, and Argonne National labs both have experienced people using CD-adapco software on 

commercial vehicles as well. Both government labs have presented at past conferences for the 

Aerodynamics of Large Vehicles. 

To achieve highest accuracy in results compared to experiment, it is important to match the test 

environment, as well as have exactly same geometry used in the test. For prediction of trends, 

environment tends to have less of an impact. Still, there are cases where incorrect trends can be 

achieved due to effects of the test environment. Therefore, for production runs, most analyses are 

conducted using the open road configuration since this is really the environment that the vehicle is 

being designed for.

For testing trend predictions, correlations are first made modeling the environment from where the 

experimental data has been obtained. From these tests, best practices are produced. These best 

practices are then applied to the open road geometry. It is most typical that this validation data comes 

from wind tunnel testing since they are the most reproducible source, and small changes in drag can be 

measured. 

I have been reviewing the original document found on the internet that was prepared last fall: First, it 

states solution needs to be time accurate.

There is a reference to the Aerodynamics of Heavy Vehicles II. At that conference, and even the first 

conference, Argonne National Laboratory presented a Navier-Stokes based solution, even before the 

experimental data was available. This correlated well with the pressures on the vehicle, even with the 

steady solver. In that study, the author did look at cell density and K-Epsilon/K-omega SST turbulence 

models. This was based upon a scaled model, not full scale production vehicle.

Since that study, there have been quite a few validation cases made. What we have been looking at is 

always to improve trends so we can measure small changes in drag. Still, some of the best validation 
data comes from clients such as the Formula 1 industry. In that industry, some of our clients have two 

working wind tunnels, running 7 days a week, 24 hours per day. In addition, they have thousands of CPU 

s to work with CFD. They are continuously running both CFD and wind tunnel data. Not all are compared 

directly, but as improvements are made, both get compared in wind tunnel and CFD. Even small changes 

in down force can make the difference in winning a race. Typical CFD models are all running steady 

simulations, and are getting accurate solutions. This includes wake interaction from front wing, tires, 

and flow below the vehicle.

I would also say it is not really important to specify the turbulence model used. While we don t 

recommend RNG wall functions to our users, it is the default wall functions used by some CFD codes, 

even though new turbulence models are better. But the difference that we see between the turbulence 

models, and running with a time accurate solution, is really smaller than the impact of the ambient 

conditions in an open road test. So as we get more accurate test data, and understand the test 

environment, changing turbulence model may improve results.

I have also read the comments from Kevin Horrigan, of Exa Corportation.

He did provide a very nice docket explaining several issues that we are all needing to deal with. 

However, I would not agree with his summary on the RANS approach since he has little experience in 

that field. While some wake devices, such as speakers, need to be fully time accurate, other wake 

devices, such as turbulators that work on decreasing the wake bubble size may be more critical 

modeling without using wall functions, and modeled with a proper grid to predict the correct separation 

point would be more critical than the unsteady wake structure.

The benchmark/validation data we gather from clients tends to be confidential. But we are continuously 

benchmarking and validating our code for all types of vehicles. In 2010 alone, there were at least 6 

internal validation runs made for OEM's around the world who build large commercial vehicles. Most of 

this validation information will not be published to the general public but are used for internal 

verification.

One example was based upon Volvo 3P case study in the NRC wind tunnel using a half scale model. Both 

time-accurate and steady were within 1-2% of the predicted drag using STAR-CCM+ for this geometry. 

Considering the differences between the geometry used for CFD compared to the test vehicle, that is 

really about as close as anyone can achieve. The study was useful in improving the accuracy of the time 

accurate solution, really proving that resolving the small flow structures were important with an LES 

code.

A second example included a detail truck in the ARC wind tunnel done by Daimler Trucks of North 

America. What was good about that experimental data, by using CAD, and STL machine to make the 

truck parts, the geometry of the truck closely matched the vehicle ran in the wind tunnel. Extra data was 

taking in the wake to view the unsteady wake structures. The disadvantage of this test was that you are 

using a 1/8th scaled model. Still, Daimler was able to show decent correlation from the 1/8th scale wind 

tunnel test, to open road configuration. Their studies show that the trends predicted from the 1/8th 

scale model correlated well with the Open Road, full vehicle simulation as long as both studies were 

conducted at the same Reynolds number. Higher/lower flow rates did show slightly different trends.

A third study used STAR-CCM+ to correlate to experimental drag data from an open road test. Overall,

the correlation was fairly consistent between STAR-CCM+ and the road test. The main issue was 
matching the environmental conditions of the test track. This left a little larger margin of error than the 

controlled wind tunnel environments.

Other commercial vehicle tests were done in Europe and Asia. While all did well, there has always been 

a margin of error. By being on site during some of these tests, we are trying to help our clients reduce 

this margin of error, and get closer correlations, and in the end, a better understanding of the open road 

environment.

It seems that the general best practices by OEM s tend to be to first run CFD on a few known 

configurations. Experimental data may be provided either by Road, or Wind tunnel. Some run scaled 

test, other attempt full scale tests. It is important to match geometry of the test vehicle, and understand 

the test environment of where the experimental data comes from for best correlation. So far, there has 

not yet been a best method for correlation by any single OEM. But all the OEMs do seem to come up 

with a way to justify their current best practices, understanding the limitations of both Experiment, and 

CFD. In the end, they are using the tool (CFD) successfully to reduce drag.

Our clients tend to run both RANS and time accurate DES simulations. For base drag values, most of the 

work does concentrate on RANS due to the quick turn-around time and accurate trend predictions. For 

cases where fluid/solid interaction are important, or aero-acoustic noise is an issue, the OEM would 

switch to a DES, time accurate solution.
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

Ve

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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 

Ve

hi
Import CAD

Wrap/Resurface 

Mesh

Generate 

Volume Mesh

Vehicle Aerodynamics 
Production Environment: Hardware

Ve

hi

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