Research on Optimization of the Suction Parameters of an Automotive Wind Tunnel

The suction system of an automotive wind tunnel is important in guaranteeing the precision of experimental data. Optimization methodology employing computational fluid dynamics and experimental data for the parameter design of a suction system was presented. The optimization process consists of three stages: design of experiments, response surface modelling and optimization algorithm execution. The Latin hypercube sampling method was adopted in the design of experiments to generate test sample points; the response values were obtained from experimental data measures in the wind tunnel and from computational fluid dynamics. An approximate model of the suction system was created by using the Kriging model. A non-dominated sorting genetic algorithm was used in the multi-objective optimization of the parameters of the pump system. The effects of the suction system on the wind tunnel test results of the MIRA ladder model, with and without optimization, were compared and analyzed. Then, the physical mechanisms of the variation in the flow field with different suction parameters were investigated. Experimental and simulation results showed that, after parameter optimization, the flow field quality in the wind tunnel was improved and the test data were more reliable.