Numerical study of the influence of dome shape on the unsteady aerodynamic performance of a high-speed train's pantograph subjected to crosswind

This study aims to investigate the unsteady aerodynamic performance of a high-speed train’s pantograph with respect to two different dome shapes and without dome under a20°yaw angle using a delayed detached eddy simulation method.Further,the influence of the dome shape on the simulation results is determined.The accuracy of the numerical method was validated by comparing a few of the numerical results with the wind tunnel test results,and high consistency was observed.An analysis of aerodynamic forces and flow structures around the pantograph was performed.The dome had significant influence on velocity field distribution surrounding the pantograph,particularly in the wake of flow region.Compared with the case where the dome was absent,vortex intensity around the pantograph increased after installing the dome.The existence of the bathtub-type dome resulted in greater flow field disturbance and vortex strength than the baffle-type dome.Moreover,the dome considerably affected time-averaged aerodynamic coefficients and their fluctuations,especially the bathtub-type dome.Additionally,the power spectral density of the unsteady aerodynamic coefficient of each pantograph component exhibited significant peaks and typical broadband distribution characteristics.

Time-history performance optimization of flapping wing motion using a deep learning based prediction model

Flapping Wing Micro Aerial Vehicles(FWMAVs)have caused great concern in various fields because of their high efficiency and maneuverability.Flapping wing motion is a very important factor that affects the performance of the aircraft,and previous works have always focused on the time-averaged performance optimization.However,the time-history performance is equally important in the design of motion mechanism and flight control system.In this paper,a time-history performance optimization framework based on deep learning and multi-island genetic algorithm is presented,which is designed in order to obtain the optimal two-dimensional flapping wing motion.Firstly,the training dataset for deep learning neural network is constructed based on a validated computational fluid dynamics method.The aerodynamic surrogate model for flapping wing is obtained after the convergence of training.The surrogate model is tested and proved to be able to accurately and quickly predict the time-history curves of lift,thrust and moment.Secondly,the optimization framework is used to optimize the flapping wing motion in two specific cases,in which the optimized propulsive efficiencies have been improved by over 40%compared with the baselines.Thirdly,a dimensionless parameter C_(variation)is proposed to describe the variation of the time-history characteristics,and it is found that C_(variation)of lift varies significantly even under close time-averaged performances.Considering the importance of time-history performance in practical applications,the optimization that integrates the propulsion efficiency as well as C_(variation)is carried out.The final optimal flapping wing motion balances good time-averaged and time-history performance.