## 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, 2011CD

- 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 fr om 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 OEM s around the world. Fred Ross’s Comments: Over the past 15 years, CFD has been used for vehicl e 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/li ft 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 so me 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 t he 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 l ess 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 intern et that was prepared last fall: First, it states sol ution 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 b ased 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 val idationdata 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 ste ady 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 fun ctions 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 tur bulators 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 crit ical 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 val idation 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 stud y 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 ab out 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 t he wake to view the unsteady wake structur es . 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 sho w 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 stu dy 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 wasmatching the environmental conditions of the test track. This left a lit tle 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 t o 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 predicti ons. 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 Power Train 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 K
Epsilon 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 hi cl e Ae ro dy na mi cs

# The CFD Challenge

• Data comes in from different 

## types of sources

– Example: STL, CAD, ANSA, etc



## • Surface quality:

– Starting surface may have self
intersection, 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 cl e Ae ro dy na mi csImport CAD
Wrap/Resurface Mesh
Generate Volume Mesh



## Vehicle Aerodynamics

# Production Environment: Hardware

Ve hi cl e Ae ro dy na mi cs 

### 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