Multi-Objective Aerodynamic Optimization of Vehicle Shape Using Adjoint Approach Based on Steady-State and Transient Flow Solutions

Zhou, Hua; Ren, Chao; Wu, Haibo; Chen, Qian; Jadhav, Tushar

doi:10.4271/2021-01-0945

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Optimization by adjoint methods [2,[7][8][9] has been well researched for some time now and proves its efficiency either for finite volume [3] or for least-square Galerkin software. However, this method is still under development for direct coupling with Lattice Boltzmann solvers [10].…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Optimization of a Reduced Scale Model of a Ground Vehicle with a Shape Morphing Technique

Erdem

Eulalie

Gilotte

et al. 2022

Fluids

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Aerodynamic performances of ground vehicle continuously improve and a lot of both wind tunnel measurements and Computational Fluid Dynamics (CFD) investigations contribute in the identification of local zones where shape deformation can lead to drag force reduction. Gradient-based optimization with optimal system involving CFD models is one of the powerful methods for shape optimization, but a genetic algorithm applied on the surrogate model can also explore a large design space in a reasonable period of computation time. In this paper, we present an aerodynamic optimization technique using a Kriging model in order to perform CFD simulations of different front air dam geometries situated below the front bumper of a reduced scale road vehicle. A first design-of-experiment (DoE) is undertaken with Large Eddy Simulations (LES), involving height geometric parameters for radial-basis-function of the front air dam, utilizing a Sobol algorithm. Then, a multi-objective-genetic-algorithm (MOGA) is applied on the constituted surrogate model, depending on the geometric parameters of the front air dam, in order to reach a minimum drag coefficient value by considering pressure constraints. Results show that a front air dam can increase the pressure at the rear of the tailgate, especially by slowing the airflow below the underfloor, but an optimum balance is necessary in order to not increase the stagnation pressure on the air dam, leading to the loss of this benefit. The Sobol technique driven by the Kriging model enables the retrieval of optimum airdam shapes found with wind tunnel tests, even with relatively coarse numerical meshes used for CFD simulations.

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Section: Introductionmentioning

confidence: 99%

Aerodynamic Optimization of a Reduced Scale Model of a Ground Vehicle with a Shape Morphing Technique

Erdem

Eulalie

Gilotte

et al. 2022

Fluids

View full text Add to dashboard Cite

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Adjoint-Based Model Tuning and Machine Learning Strategy for Turbulence Model Improvement

Wu¹,

Zhang²,

Xu³

et al. 2022

SAE Technical Paper Series

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Drag Reduction Study on Vehicle Shape Optimization Using Gradient-based Adjoint Method

Wei,

Hua,

Jiang

2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Given the increasingly stringent environmental regulations, most automotive manufacturers were confronted with tougher exhaust emission and energy consumption standards, thus, improving fuel economy has been the top priority for OEMs during the past few years. In this context, it is quite essential to improve the aerodynamic characteristics, especially drag reduction in vehicle shape development, considering its close correlation with fuel consumption and E-range. Of all the optimization approaches, the gradient-based adjoint method has currently received growing attention for its proven effectiveness in calculating the drag sensitivity with respect to geometry parameters, which is indispensable for subsequent shape modification. In this work, we aim to utilize the adjoint approach to optimize the vehicle shape for a lower drag on the DrivAer models. First, the exterior flow field was solved as the primal solution; then, surface drag sensitivity was calculated by adjoint solver, which helps to confirm the most drag-sensitive regions need to be modified in the next step. After that, the surface mesh was morphed based on the Normal Optimal Displacement results; and then the flow field of the new design was resolved to validate the drag reduction effect. Results showed that it’s around 24 counts of C<sub>D</sub> decrease for the Notchback, which means 9% drag optimization. This paper evaluated the effectiveness of adjoint method for aerodynamic optimization of a generic vehicle, which indicates more extensive and promising application of this approach in the early stage of vehicle development for its high efficiency as well as strong robustness.</div></div>

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Multi-Objective Aerodynamic Optimization of Vehicle Shape Using Adjoint Approach Based on Steady-State and Transient Flow Solutions

Cited by 3 publications

References 18 publications

Aerodynamic Optimization of a Reduced Scale Model of a Ground Vehicle with a Shape Morphing Technique

Aerodynamic Optimization of a Reduced Scale Model of a Ground Vehicle with a Shape Morphing Technique

Adjoint-Based Model Tuning and Machine Learning Strategy for Turbulence Model Improvement

Drag Reduction Study on Vehicle Shape Optimization Using Gradient-based Adjoint Method

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