# 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