Return to search

Comparative investigation of large eddy simulation and RANS approaches for external automotive flows

This thesis investigates the accuracy and scalability of RANS and LES
approaches applied to external automotive aerodynamics. Due to the availability
of considerable experimental and computational data available on the Ahmed
body, this reference model was chosen for this study. The relative simple
geometry of the Ahmed body model is able reproduce the common flow
features of a hatch back style vehicle. The 25° slant angle configuration was
used as it is a major challenge in terms of flow prediction. The RANS model
used included the Standard K-ε, RNG K-ε, Realizable k-ε and K-ω SST. The
LES simulations were run with the Smagorinsky-Lilly SGS model. Three grids
with different level of refinement were generated. A viscous hybrid mesh
approach was used for all the simulations. This type of mesh is commonly used
by automotive manufactures and motorsport organizations. The commercial
package Fluent 12 was used as a solver.
The K-ω SST and LES models showed good agreement with the experimental
data. LES in particular was the only model to predict flow re-attachment over
the slant angle as seen on the experimental and computational data available in
literature. The richness of the unsteady data available from the LES simulations
and correct interpretation of flow topology balance in part the major
computational requirements compared to the RANS models. Taking into
account the hardware resources available to automotive manufactures, the LES
is suitable to be part of the design process.

Identiferoai:union.ndltd.org:CRANFIELD1/oai:dspace.lib.cranfield.ac.uk:1826/7106
Date07 1900
CreatorsBrondolo, Luca
ContributorsShapiro, Evgeniy
PublisherCranfield University
Source SetsCRANFIELD1
LanguageEnglish
Detected LanguageEnglish
TypeThesis or dissertation, Masters, MSc
Rights© Cranfield University 2011. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright owner.

Page generated in 0.0591 seconds