Film cooling is a cooling technique in which a protective layer is generated by the coolant along the wall to shield the wall surface against high temperature flow. When the fluids of the coolant and high-temperature gas are of different qualities, it is often accompanied by the mass and energy transfer. During the film cooling process, the lower-temperature gas is transported to the surface through the tangential gap on the wall which requires thermal protection. And then the film is formed along the direction of the high-temperature flow, causing the solid wall isolated from the high temperature gas, thereby it can prevent the wall surface from being touched and damaged by hot stream. Since the film cooling sets high standards for hole shape, the structure of injection, the length of the coolant supply tube, the angle of incidence, the blowing ratio, the thickness of the boundary layer, ribs and the Reynolds number, etc., the analysis and optimization of these factors have been particular significance on improving cooling efficiency. This paper introduces the dominant factors which influence film cooling effectiveness, besides, analyzes the methods to achieve the best cooling results. The impact of the vortex structures on enhancing film cooling performance is explored. Moreover, a new train of thought is proposed to improve the cooling efficiency under the joint action of the vortex generator and the synthetic jet.

Firstly, the MBD (multi-body dynamics) model of the whole vehicle is built according to the measured vehicle parameters. Then, the road spectra acquisition test is carried out. Under the test road conditions, the acceleration, displacement, and force signals of multiple positions of the whole vehicle are collected. Taking the road test signals as the iterative target and the vehicle MBD model as the iterative carrier, the vehicle equivalent excitation is obtained by using the VIM (virtual iteration method). By applying the equivalent excitation obtained by VIM to the vehicle MBD model, the load spectra of key points of the driving rear axle are obtained. The accuracy of the model is verified by comparing the measured signals with the iterative signals, and the reliability of the key points load spectra is enhanced. Secondly, the FEA (finite element analysis) model of the rear axle is built, and the static analysis results of each key point under unit load are obtained with the help of FEA software. Thirdly, the FEA results, key points load spectra, and material fatigue characteristic curve of the rear axle are input into the fatigue software to conduct the fatigue simulation of the rear axle. According to the fatigue analysis results, the position where the fatigue life does not meet the set goal is accurately located. Finally, the structure is optimized based on the above results. The fatigue life evaluation of the optimized rear axle shows that its durability has been significantly improved.