Abstract
The demand in solving complex turbulent fluid flows has been growing rapidly in the automotive industry for the last decade as engineers strive to design better vehicles to improve drag coefficients, noise levels and drivability. This paper presents the implementation of an arbitrary hybrid turbulence modeling (AHTM) approach in OpenFOAM for the efficient simulation of common automotive aerodynamics with unsteady turbulent separated flows such as the Kelvin–Helmholtz effect, which can also be used as an efficient part of aerodynamic design optimization (ADO) tools. This AHTM approach is based on the concept of Very Large Eddy Simulation (VLES), which can arbitrarily combine RANS, URANS, LES and DNS turbulence models in a single flow field depending on the local mesh refinement. As a result, the design engineer can take advantage of this unique and highly flexible approach to tailor his grid according to his design and resolution requirements in different areas of the flow field over the car body without sacrificing accuracy and efficiency at the same time. This paper presents the details of the implementation and careful validation of the AHTM method using the standard benchmark case of the Ahmed body, in comparison with some other existing models, such as RANS, URANS, DES and LES, which shows VLES to be the most accurate among the five examined. Furthermore, the results of this study demonstrate that the AHTM approach has the flexibility, efficiency and accuracy to be integrated with ADO tools for engineering design in the automotive industry. The approach can also be used for the detailed study of highly complex turbulent phenomena such as the Kelvin–Helmholtz instability commonly found in automotive aerodynamics. Currently, the AHTM implementation is being integrated with the DAFoam for gradient-based multi-point ADO using an efficient adjoint solver based on a Sparse Nonlinear optimizer (SNOPT).
Highlights
The automobile industry is still developing, and the number of cars is increasing day after day [1]
In order to achieve a compromise between accuracy and computational efficiency, the Very Large Eddy Simulation (VLES) was developed by Speziale [14]
The latest VLES model introduces the resolution control function Fr, which modifies the turbulent viscosity from the RANS modeling [18]. This resolution control factor is the ratio of sub-grid turbulent stress to RANS/URANS turbulent stress, which can roughly represent the ratio of modeled turbulent energy to total turbulent energy. It is responsible for smooth transitioning between RANS/URANS/LES/DNS modes depending on local mesh density in comparison with turbulence integral and Kolmogorov length scales
Summary
The automobile industry is still developing, and the number of cars is increasing day after day [1]. The turbulent flow at the rear part of the car has a strong influence on the above due to the wake that occurs because of the flow separation in that region. G., et al [2] define flow separation as a condition due to the lack of energetic flow and inability of flow to move over sharp edges as shown. 2 of 23 2 of 23 the flow separation in that region. G., et al [2] define flow separation as a condition due to the lack of energetic flow and inability of flow to move over sharp edges as showinnFiinguFrigeu1r.eI1n. TThhiissssttuuddyyaaiimmeeddttooiimmpplleemmeenntttthheeAArrbbiittrraarryyHHyybbrriiddTTuurrbbuulleenncceeMMooddeelliinngg((AAHHTTMM)) aapppprrooaacchhbbaasseedd oonn tthhee VVeerryy LLaarrggee EEddddyy SSiimmuullaattiioonn ((VVLLEESS))iinnOOppeennFFOOAAMM//DDAAFFooaamm ttoo eexxpplloorree iittss flfelexxibibiliiltiytyfofrorefefifcfiiecnietnatnaanlyasliyssaisndanddesidgensiwgnithwaiuthtomauottoimveoatipvpeliacaptpiolincsatiinomnsinidn. TThhiissssttuuddyyaaiimmeeddttooiimmpplleemmeenntttthheeAArrbbiittrraarryyHHyybbrriiddTTuurrbbuulleenncceeMMooddeelliinngg((AAHHTTMM)) aapppprrooaacchhbbaasseedd oonn tthhee VVeerryy LLaarrggee EEddddyy SSiimmuullaattiioonn ((VVLLEESS))iinnOOppeennFFOOAAMM//DDAAFFooaamm ttoo eexxpplloorree iittss flfelexxibibiliiltiytyfofrorefefifcfiiecnietnatnaanlyasliyssaisndanddesidgensiwgnithwaiuthtomauottoimveoatipvpeliacaptpiolincsatiinomnsinidn. mThinisdc. oTuhlids choeulpldthheeldpetshigendeensigginneeenrgsintoeetrasyltoortathyeloirrhthiseimr ehsihs mfoersdhifffoerredniftferereqnutirreemqueinrets-, msuecnhtsa,ssudcehsiagsndoepstiigmniozpattiiomnizfaotriomninfoimr muminidmruagm, ldorwagl,elvoewl olfevneolisoef nanodisegoanodd gdoriovdabdirliivty-, ainbilditiyff,eirnendtifpfearretsntopf athrtes flofowthefifellodwtofiealcdhiteovaechthieevreeqthueirreedquaicrceudraaccycuarnadcyraesnodluretisoonluwtiiotnh wsuitfhficsieunfftiecfifiencitenecffyicaitenthceysaatmtehteimsaemfoer rtaimpied ftourrnr-aaproiduntdurtnim-aeroaundndimtpimroeveadndpriomdpucrotivveitdy. productivity
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