Vehicle aerodynamic shape optimisation is a typical non-linear and computationally expensive black box problem, which is severely limited by time and cost of the objective function evaluations during the global optimi...
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Vehicle aerodynamic shape optimisation is a typical non-linear and computationally expensive black box problem, which is severely limited by time and cost of the objective function evaluations during the global optimisation process. To solve the shortcomings of low efficiency and high cost of the existing vehicle drag reduction method, an improved efficient global optimisation (EGO) algorithm is used to optimise a four-dimensional aerodynamic drag reduction design of a vehicle combined with computational fluid dynamics numerical simulation technology. Moreover, data mining technologies are used to reveal the influence mechanisms of design variables on aerodynamic drag and to analyse the relationship between the variables. It is demonstrated that the improved EGO algorithm, based on the kriging response surface and expected improvement function, can achieve the global optimum with minimum function evaluations. The aerodynamic drag of the optimal design is 1.56% lower than that of the original vehicle. The data mining results showed that the engine hood inclination and the tail upturn angle play a leading role in the vehicle's aerodynamic drag, and the hood inclination has the greatest impact.
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