随机风作用下的高速列车气动特性研究OA

To address the effects of the fluctuating and stochastic nature of ambient wind on the opera-tional safety of high-speed trains under strong-wind conditions,this study investigates the aerody-namic characteristics of high-speed trains in a random wind field.First,a random wind model that more closely represents atmospheric turbulence is developed from field-measured wind-speed data and validated.Next,numerical models of a high-speed train in random and mean wind fields are estab-lished.Appropriate physical models,boundary conditions,and solvers are used to capture the develop-ment of the flow field around the train and the characteristics of the aerodynamic loads.Finally,case studies are conducted to reveal the evolution of the train-surface boundary layer,underbody flow field,and wake under the two wind conditions and to clarify how the wind-field characteristics affect the aerodynamic loads.The results show that the wind-speed time history generated using harmonic syn-thesis and a Davenport spectrum corrected through power-spectral fitting effectively represents the fluctuating and stochastic characteristics of random wind.Compared with the mean-wind case,the random-wind case exhibits broadly similar train-surface boundary layers,underbody flow fields,wakes,and large-scale leeward vortices;however,the flow field is substantially less stable,the flow structures fluctuate more strongly,and the small-scale structures around the train exhibit pronounced uncertainty.The surface-pressure distribution varies periodically,and the aerodynamic loads on indi-vidual cars fluctuate markedly,with different degrees of fluctuation among the aerodynamic forces and moments.The mean aerodynamic forces increase by less than 5%,whereas their extreme values in-crease by more than 50%.These findings provide theoretical support for aerodynamic shape optimiza-tion and operational safety control of high-speed trains in complex wind environments and can inform train-safety design for extreme conditions in random wind fields.