Class 8 Truck External Aerodynamics Choice of Numerical Methods - - PowerPoint PPT Presentation

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Class 8 Truck External Aerodynamics Choice of Numerical Methods - - PowerPoint PPT Presentation

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Class 8 Truck External Aerodynamics Choice of Numerical Methods Portland, March, 19 th 2013 Dinesh Madugundi, Anna Garrison PVE Vehicle Analysis Security Classification Line 1 Product Validation Engineering Vehicle Analysis Agenda

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SLIDE 1

Class 8 Truck External Aerodynamics

Choice of Numerical Methods

1

Security Classification Line

PVE Vehicle Analysis

Portland, March, 19th 2013 Dinesh Madugundi, Anna Garrison

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SLIDE 2

Product Validation Engineering Vehicle Analysis

Agenda

Motivation Review of existing literature Understanding Truck aerodynamics Choice of numerical methods for Truck aerodynamics

Daimler Trucks North America PVE Vehicle Analysis 2 Source:

Numerical methods comparison study References

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SLIDE 3

Product Validation Engineering Vehicle Analysis

Motivation Class8 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

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SLIDE 4

Product Validation Engineering Vehicle Analysis

Motivation Why 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

Daimler Trucks North America PVE Vehicle Analysis 4 Source:

— 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

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SLIDE 5

Product Validation Engineering Vehicle Analysis

CFD Modelling Standard Numerical Methods

Reynolds Averaged Navier Stokes (RANS) Unsteady RANS Large Eddy Simulation (LES) Detached Eddy Simulation (DES)

Daimler Trucks North America PVE Vehicle Analysis 5 Source:

Detached Eddy Simulation (DES)

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SLIDE 6

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 – 2006213489

  • 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 6 PVE 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.

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SLIDE 7

Product Validation Engineering Vehicle Analysis

CFD Setup Geometry 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

Daimler Trucks North America 7 PVE Vehicle Analysis 09.04.2013

— Base size = 40mm — Trim mesh, surface size 5mm – 40mm — Wake refinement — Low Re prism mesh — Total number of volume cells ~15M

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SLIDE 8

Product Validation Engineering Vehicle Analysis

CFD Results of GCM Transient 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.

Daimler Trucks North America 8 PVE Vehicle Analysis 09.04.2013

  • RANS and URANS results matched well with each
  • ther.
  • 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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SLIDE 9

Product Validation Engineering Vehicle Analysis

CFD Results Understanding 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 x x x

i i l

Cd Cd Cumulative

1

1

Tractor drag

~50%

Trailer drag

~50%

Daimler Trucks North America 9 PVE Vehicle Analysis 09.04.2013

  • 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. Tr

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Product Validation Engineering Vehicle Analysis

CFD Results Understanding 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 x x x

i i l

Cd Cd Cumulative

1

1

TT gap Pressure

Daimler Trucks North America

Cumulative Cd[;] plot over the length of the Truck, normalized by total vehicle Cd0yaw.

10 PVE 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

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SLIDE 11

Product Validation Engineering Vehicle Analysis

CFD Modeling Choice 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.

Daimler Trucks North America PVE Vehicle Analysis 11 Source:

  • 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
  • f 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

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Product Validation Engineering Vehicle Analysis

CFD Results RANS vs DES 1/6

  • The CFD models are created using STARCCM+ v6.06.017. The following numerical methods are

compared for this study — RANS Turbulence model – Spalart Allmaras Time dependency – Steady Segregated Flow

Daimler Trucks North America12 PVE Vehicle Analysis 09.04.2013

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+

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Product Validation Engineering Vehicle Analysis

CFD Results RANS 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.

∆Cd ~15% 20%

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= −

−

=

l i x x x

i i l

Cd Cd Cumulative

1

1

∆Cd ~5% 8%

Daimler Trucks North America13 PVE 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 RANS Time Avg Velocity Magnitudes

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Product Validation Engineering Vehicle Analysis

CFD Results RANS 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

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= −

−

=

l i x x x

i i l

Cd Cd Cumulative

1

1

∆Cd ~12% 15%

Daimler Trucks North America14 PVE Vehicle Analysis 09.04.2013

— 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

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Product Validation Engineering Vehicle Analysis

CFD Results RANS 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 x x x

i i l

Cd Cd Cumulative

1

1

∆Cd ~5% 8% ∆Cd ~15% 25%

Daimler Trucks North America15 PVE 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

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Product Validation Engineering Vehicle Analysis

Degree of Accuracy RANS 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%

Daimler Trucks North America PVE Vehicle Analysis 16 Source:

  • 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?
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Product Validation Engineering Vehicle Analysis

Degree of Accuracy RANS 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.

Daimler Trucks North America PVE Vehicle Analysis 17 Source:

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.

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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.

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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”

Daimler Trucks North America PVE Vehicle Analysis 19 Source:

  • 3. SAE J2966, “Guidelines for Aerodynamic Assessment of Medium and Heavy

Commercial Ground Vehicles Using Computational Fluid Dynamics”.