Research On Automotive Aerodynamic Drag Reduction Based On Traveling Wave Wall

In face of the increasingly severe energy crisis,improving the fuel economy of vehicles has become an inevitable choice for Rearch & Design.The research results shows that reducing the aerodynamic drag coefficient is an important means to reduce fuel consumption and.At present,most automotive designs adopt the overall optimization design idea.But most of the passive drag reduction designs are easily limited by driving conditions.In order to solve this problem,the concept of drag reduction with traveling wave wall waves has been proposed.At present,domestic and foreign researches on drag reduction include both passive passive drag reduction and dynamic active drag reduction,but they are all unitary studies,and the research conditions are special and not universally applicable.Therefore,exploring the traveling wave wall drag reduction measures in a simple model can effectively improve fuel economy and it may be widely applied to various types of vehicles.This article firstly takes Ahmed as the basic research object and determines the simulation strategy based on wind tunnel experiments.At the same time,the modeling and simulation of different static traveling wave models were completed.The effects of different structural parameters on the aerodynamic gain and flow field were analyzed.And the mechanism of drag reduction of static traveling waves was studied from the velocity field,shear stress,and pressure distribution.The results show that the structural parameters such as the placement position,depth,and wavelength of the static traveling wave all have an important impact on the aerodynamic gain.The local optimal drag reduction rate is 3.1%,but all have the best practical working conditions and cannot be used to overcome the problem of narrow scope application,which widely exists in passive drag reduction measures.Secondly,through the UDF program,the influence of dynamic traveling wave and dynamic static wave coupling on the aerodynamic characteristics of Ahmed was explored.Orthogonal experiment shows that the coupling mode of the traveling wave cannot overcome the disadvantages of passive drag reduction,and the range analysis shows that it is not suitable for a control model,and the dynamic traveling wave model can reduce the drag in different working conditions by changing the traveling wave parameters,which the drag reduction effect remained at 3.4%.Range analysis shows that the order of importance of each parameter is vehicle speed,amplitude,frequency,and wavelength.Finally,dynamic traveling wave model was selected,and the vehicle speed,the amplitude and the wavelength of the traveling wave were selected as independent variables,and simulation of 120 groups was completed.The source code of BP and RBF neural network for predicting aerodynamic gain were written,and training were completed respectively.The prediction results show that the BP neural network is more suitable,and the data coincidence rate of the 15 test samples is 96%.The trained BP neural network can perform predictive more accurately.In summary,the paper uses Ahmed model and UDF to establish a dynamic traveling wave control model,and performs full-factor simulation calculations,then builds a neural network prediction model,which has certain practical significance for the application of traveling wave drag reduction.