%0 Electronic Article
%A Chengping Yan
%A Chenguang Lai
%A Qingyu Wang
%A Bo Hu
%A Liangsheng Deng
%K four-dimensional aerodynamic drag reduction design
%K data mining technologies
%K vehicle drag reduction method
%K original vehicle
%K global optimum
%K design variables
%K global optimisation algorithm
%K computationally expensive black box problem
%K vehicle aerodynamic shape optimisation
%K computational fluid dynamics numerical simulation technology
%K EGO algorithm
%K low efficiency
%K global optimisation process
%K engine hood inclination
%K tail upturn angle
%K minimum function evaluations
%X 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.
%T Aerodynamic drag reduction in a vehicle based on efficient global optimisation
%B The Journal of Engineering
%D January 2019
%V 2019
%N 13
%P 384-391
%I Institution of Engineering and Technology
%U https://digital-library.theiet.org/;jsessionid=ag9nm4ldjjson.x-iet-live-01content/journals/10.1049/joe.2018.8954
%G EN