Multi-objective optimization design of truck fairing considering crosswind condition
The effect of environmental crosswind will increase the side force of the truck,so designing a guide hood device that takes into account the longitudinal air resistance coefficient(Cd)and side force coefficient(Cs)is of great significance to improve the economy and driving stability of the vehicle.Taking the simplified truck model as the research object,the Latin hypercube sampling method is used to construct 300 data samples,and the CFD software is used to calculate the values as the monitoring values of the corresponding samples.The data samples are divided into training set,verification set and test set according to 7:2:1.The distributed training model is used to predict Cd and Cs,and the goodness of fit(R2)of the training model for Cd and Cs on the test set reached 0.87 and 0.83,respectively.Subsequently,the constructed data samples are used to train a deep learning model,and the trained BP neural network model served as the objective function for multi-objective optimization using NSGA-II.In the Pareto frontier solution set obtained via NSGA-II,three validation models,i.e.,A,B,and C,are selected based on the principles of minimizing Cd,achieving moderate Cd and Cs,and minimizing Cs,respectively.Com-pared with CFD,the maximum error of prediction is 1.9317%.Among them,model B has a 12.7167%reduction in Cd compared to the original model.In model C,Cs is reduced by 11.9957%compared with the original model.
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