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Class 8 Truck External AerodynamicsChoice of Numerical Methods
1Security Classification Line
PVE Vehicle Analysis
Portland, March, 19th 2013
Dinesh Madugundi, Anna Garrison
Product Validation Engineering
Vehicle Analysis
Agenda
� Motivation
� Review of existing literature
� Understanding Truck aerodynamics
� Choice of numerical methods for Truck aerodynamics
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� Numerical methods comparison study
� References
Product Validation Engineering
Vehicle Analysis
MotivationClass-8 Truck with Standard 53‘ Trailer
� Accurately predicting complex flow phenomenon in Truck aerodynamics using
numerical methods can be challenging.
Daimler Trucks North America
Wake interaction between the drive tires and trailer bogies
PVE Vehicle Analysis 3
Source:
Tractor trailer gap
Trailer back face
Product Validation Engineering
Vehicle Analysis
MotivationWhy CFD to Evaluate Class 8 Truck Aerodynamics?
� Standard Class 8 trucks are ~2.8m wide and ~22m long including the trailer.
� Very few full scale wind tunnels can accommodate full tractor trailer
configuration, while simulating real road wind conditions.
� Advantages in using CFD
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— Full tractor trailer configuration
— Simulating real road conditions
— Predicting performance of an aero component before modifying or installing
— Wide range of design validations
— Detailed flow visualization
Product Validation Engineering
Vehicle Analysis
CFD ModellingStandard Numerical Methods
� Reynolds Averaged Navier Stokes (RANS)
� Unsteady RANS
� Large Eddy Simulation (LES)
� Detached Eddy Simulation (DES)
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� Detached Eddy Simulation (DES)
Product Validation Engineering
Vehicle Analysis
� Bruce L. Storms AerospaceComputing Inc. “A Summary of the Experimental Results for a Generic
Tractor-Trailer in the Ames Research Center 7- by 10-Foot and 12-Foot Wind Tunnels”.
� Generic Conventional model (GCM) of class-8 tractor-trailer 1/8th scale results available for
validation study.
Experimental Setup (Reference)NASA/TM – 2006-213489
� Simplified model of standard class-8 tractor-trailer,
no grille opening, no underhood components.
� Experiments were performed at Re* = 1.1e6 –
Daimler Trucks North America 6PVE Vehicle Analysis 09.04.2013
� Experiments were performed at Re* = 1.1e6 –
6.2e6.
� For this study, results from Re = 6.0e6 are
compared, no T-T gap aero treatment, no trailer
aero treatment.
*Re was calculated based on Truck width.
Product Validation Engineering
Vehicle Analysis
CFD SetupGeometry and CFD Mesh Overview� GCM 1:1 scale, closed grille, flat underbody.
� No T-T gap aero treatment, no trailer aero devices
� Computational domain with far field domain, moving ground, and
spinning tires.
� Mesh settings
— Base size = 40mm
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— Base size = 40mm
— Trim mesh, surface size 5mm – 40mm
— Wake refinement
— Low Re prism mesh
— Total number of volume cells ~15M
Product Validation Engineering
Vehicle Analysis
CFD Results of GCMTransient vs Steady� Flow conditions, constant density, Re = 6.0e6, 0yaw
and 6yaw. No side extenders.
� Solvers RANS, URANS and DES with Spalart-Allmaras
turbulence model, are compared.
� The three solvers predicted Cd that matched relatively
close to the measurements.
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� RANS and URANS results matched well with each
other.
� DES results are closer to the measurements at yaw,
compared to RANS. More accurate wake predictions?
80mph @0yaw: DES
80mph @6yaw: DES
80mph @0yaw: URANS
80mph @6yaw: URANS
Plane View
Time Avg Ptotal Plots Time Avg Ptotal Plots
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Vehicle Analysis
CFD ResultsUnderstanding Truck Aerodynamics 1/2� Production Cascadia sleeper, 45” T-T gap, and 53’
standard trailer.
� CFD Methods: Current DTNA best practices.
� About 50% of total drag is from tractor.
� Yaw effects are predominant on trailer bogies and
trailer back face.
∑=
−−
=
l
i
xxx iil
CdCdCumulative
1
1
Tracto
r d
rag
~5
0%
Traile
r d
rag
~5
0%
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� Sections of drag effects
— A-surface
� Stagnation pressure on the grille
� Flow around the bumper and hood
� Stagnation pressure on wind shield
� Flow over the roof cap
� Effectiveness of roof deflector
� Effectiveness of side extenders and chassis fairings
Cumulative Cd[-] plot over the length of the Truck, normalized by
total vehicle Cd0yaw.
Tracto
r d
rag
Product Validation Engineering
Vehicle Analysis
CFD ResultsUnderstanding Truck Aerodynamics 2/2� Sections of drag effects, cntd…
— Underhood pressure: flow below the bumper determines
underhood pressure.
— Underbody flow: chassis components and drive tires are
exposed to high speed flow.
— T-T gap: Pressure in T-T gap influences effectiveness of
side-extenders and roof deflector.
∑=
−−
=
l
i
xxx iil
CdCdCumulative
1
1
T-T gap Pressure
Daimler Trucks North America
Cumulative Cd[-] plot over the length of the Truck, normalized by
total vehicle Cd0yaw.
10PVE Vehicle Analysis 09.04.2013
side-extenders and roof deflector.
— Trailer bottom and trailer back face.
Plan view: Z-section along tire center: 55mph, 0yaw
Plan view: Z-section along tire center: 55mph, 6yaw
55mph, 0yaw 55mph, 6yaw
Time Avg Velocity Magnitudes
Product Validation Engineering
Vehicle Analysis
CFD ModelingChoice of Numerical Methods for Truck Aero� Choice of numerical methods is critical to capture complex flow phenomenon of Truck
aerodynamics.
— Surface bounded flow (Current industry standards, RANS, k-e, or SA).
— Under the cab wake interaction (accurate prediction of vortex shedding).
— Tractor trailer wake interaction.
� RANS methodology with Low-Re mesh can achieve accurate boundary flow.
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� LES methodology to capture vortex shedding.
— Highly mesh dependant in BL.
— Can be computationally expensive.
� For Truck aero applications, hybrid model DES (Detached Eddy Simulation) can deliver best aspects
of RANS and LES methodologies.
— Less sensitive to boundary layer mesh with RANS methodology.
— Low Re mesh to accurately predict flow separation.
— LES methodology to predict wakes; sensitive to mesh wake refinements.
— Computationally less expensive than LES
Product Validation Engineering
Vehicle Analysis
CFD ResultsRANS vs DES 1/6� The CFD models are created using STAR-CCM+ v6.06.017. The following numerical methods are
compared for this study
— RANS
� Turbulence model – Spalart Allmaras
� Time dependency – Steady
� Segregated Flow
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� Segregated Flow
� Wall Treatment – All y+
— DES (As per DTNA’s best practices)
� Turbulence model– Spalart Allmaras Detached Eddy
� Time dependency – Implicit Unsteady
� Segregated Flow
� Wall Treatment – All y+
Product Validation Engineering
Vehicle Analysis
CFD ResultsRANS vs DES 2/6� The difference in total vehicle drag between RANS vs
DES is about 15% - 20% depending on yaw condition.
� Delta Cd on tractor is about 5% - 8%; resultant
difference is on trailer bogies and back face.
� Flow Comparison,
— Flow separation over the hood.
∆C
d ~
15
% -
20
%
∑=
−−
=
l
i
xxx iil
CdCdCumulative
1
1
∆Cd ~5% - 8%
Daimler Trucks North America13PVE Vehicle Analysis 09.04.2013
— More diffusion under the bumper.
� Effects underhood pressure.
� Higher drag on chassis and drive tires.
— Difference in T-T gap pressure (influences roof deflector
and side extenders’ performance).
Cumulative Cd[-] plot over the length of the Truck, normalized by
total vehicle Cd0yaw.
DES RANSTime Avg Velocity Magnitudes
Product Validation Engineering
Vehicle Analysis
CFD ResultsRANS vs DES 3/6� RANS predicted drag on the trailer is ~12% - 15%
lower.
— Difference in drag is higher at 0yaw; can be accounted
to wake interaction.
— At 6yaw, the wake interaction is reduced due to free
stream effects; shift in wake direction.
— Similar differences on trailer back face at 0yaw and
∑=
−−
=
l
i
xxx iil
CdCdCumulative
1
1
∆C
d ~
12
% -
15
%
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— Similar differences on trailer back face at 0yaw and
6yaw.
� Transient phenomenon with controlled wake under the
trailer? For example, trailer skirts.Cumulative Cd[-] plot over the length of the Truck, normalized by
total vehicle Cd0yaw.
DES
RANS
DES
RANS
Time Avg Velocity Magnitudes
Product Validation Engineering
Vehicle Analysis
CFD ResultsRANS vs DES 4/6� Trailer skirts shield high speed flow impinging the
trailer bogies.
� With controlled wake under the trailer, we expect less
transient phenomenon under the trailer.
� The difference in total vehicle drag from RANS vs DES
increased to 25% .
∑=
−−
=
l
i
xxx iil
CdCdCumulative
1
1
∆Cd ~5% - 8%
∆C
d ~
15
% -
25
%
Daimler Trucks North America15PVE Vehicle Analysis 09.04.2013
— Tractor drag difference remained at 5% – 8%; significant
differences on trailer drag.
� With larger wake, transient phenomenon becomes
more prominent.DES (0yaw)
RANS (0yaw)
DES (6yaw)
RANS (6yaw)
Cumulative Cd[-] plot over the length of the Truck, normalized by
total vehicle Cd0yaw.
Time Avg Velocity Magnitudes
Product Validation Engineering
Vehicle Analysis
Degree of AccuracyRANS vs DES 5/6� Drag predictions using DES methods are
compared to WT testing for validation; the
results are within 2% accurate at a given
Re.
� Drag predictions using RANS are off by 15%
- 20%.
� Flow characteristics with DES methods
W/o Trailer Skirts With Trailer Skirts
[DES - RANS] [DES – Exp] [DES - RANS] [DES – Exp]
∆Cd 0yaw 20.84% 1.4% 25.49% 1.5%
Chassis 3.66% 3.89%
Tractor Tires 0.91% 1.36%
Trailer 14.81% 18.87%
∆Cd 6yaw 14.47% Not Avail 16.10% Not Avail
Chassis 2.52% 1.79%
Tractor Tires 0.79% 1.25%
Trailer 10.38% 11.81%
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� Flow characteristics with DES methods
— The amplitude of Cd oscillations are
about10% - 20% of average Cd.
— Requires longer physical time to achieve
converged solution (say, 10ms of TS, 25s
total physical time with 5s/10s running
avg); computationally expensive.
— Not appropriate for design optimization
study when the Cd resolution per design
iteration is ∆Cd<1%.
� RANS applicability in Truck aerodynamics?
Product Validation Engineering
Vehicle Analysis
Degree of AccuracyRANS vs DES 6/6� RANS methods applicability in Truck aerodynamics?
— Qualitative analysis of aero performance of design
variants., Eg., mirrors.
— Possible to obtain general drag trend due to the
variants.
— The drag trends can be misleading depending on the
location of aero device.
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location of aero device.
� For example, validation of aerodynamic performance of
multiple design variants of a roof cap.
— Evaluating all the design variants using DES methods
can be very expensive.
— RANS methods to get preliminary understanding of the
performance of each variant.
— Best design was re-evaluated using DES methods for
final confirmation.
— Drag performance on the trailer showed inverse trend.
� In most cases, evaluating an aero component using DES methods becomes necessary!!!!
Truck image is only for reference. Actual
Truck used for this study is not shown.
Product Validation Engineering
Vehicle Analysis
Conclusions
� To achieve accurate aerodynamic drag evaluation of Class-8 trucks, numerical methods capable of
predicting vortex shedding can be influential in design evolution.
� DES methods are
— Proved to be accurate during validation of CFD methods.
— Accurate evaluation of aero components.
— Computationally expensive.
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— Sensitive to mesh refinement.
— Capturing aero performance of minor changes in design can be questionable.
� RANS methods are
— Good for qualitative analysis of aero performance of design variants.
— Capable of generating general drag trend of a given design modification.
— Trends can be misleading depending on the type of aero application.
Product Validation Engineering
Vehicle Analysis
References
1. Bruce L. Storms Aerospace Computing Inc. “A Summary of the Experimental
Results for a Generic Tractor-Trailer in the Ames Research Center 7- by 10-Foot
and 12-Foot Wind Tunnels”
2. Product Validation Engineering – Analysis, DTNA LLC. “Computational Fluid
Dynamics Certification”
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3. SAE J2966, “Guidelines for Aerodynamic Assessment of Medium and Heavy
Commercial Ground Vehicles Using Computational Fluid Dynamics”.
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