Research On The Aerodynamic Characteristics And Shape Optimization Of Smart Bullets

Smart bullet is a very popular research field this year.It has the characteristics of high precision,high integration and the ability of self-searching.Different from traditional bullet,smart bullet can change its flight attitude independently depending on servo mechanism according to operational requirements,which makes it has unique feature in aerodynamic characteristics.Nowadays,the unconventional flow mechanism and complex aerodynamic characteristics around smart bullet are still unclear,and need to be further studied.In this paper,the aerodynamic characteristics of smart bullet are analyzed based on CFD numerical simulation method,and the RBF neural network proxy model and genetic algorithm are used to optimize its shape.The conservative RANS equations based on arbitrary Lagrangian Euler scheme are used as the governing equations,the SST k-? turbulence model is used to close the governing equations,and the uncoupled solution is used to solve the governing equations and the turbulence model respectively.Parallel technique and implicit residual smoothing technique are used to accelerate convergence of numerical computation.On this basis,a CFD numerical simulation method for simulating the flow field of bullet is established,and its effectiveness is verified by an example of 5.56 mm ordinary bullet.The results show that the numerical results are in good agreement with the experimental results.The numerical method established in this paper is suitable for simulating the flow field of bullets.By using the numerical simulation method,the flow field environment of the reference shape of sharp smart bullet under different inflow conditions(Mach number,angle of attack)is described,its aerodynamic characteristics are predicted,and the effects of different shape parameters on the aerodynamic characteristics of the bullet are analyzed.The results show that smart bullet has greater drag and lift,and higher longitudinal static stability compared with traditional bullet.With the increase of tip radius of smart bullet,drag increases gradually,lift and pitching moment do not change significantly.With the increase of arc radius,drag increases gradually,lift decreases gradually,and static stability gradually.With the increase of the radius at the intersection,the drag decreases,the lift decreases and the static stability decreases.Considering the technical requirements of device size and aerodynamic characteristics,a new type of dull smart bullet is designed.The aerodynamic characteristics of smart bullet are studied by means of numerical simulation and wind tunnel experiments,and the influence ofthe size of bullet tail on the aerodynamic characteristics of smart bullet is analyzed.The results show that the numerical simulation results are basically consistent with the wind tunnel test results,which proves the validity of the numerical simulation method.With the increase of the length of the contraction section of smart bullet,the drag decreases first and then increases,and the absolute values of lift and pitch moment basically remain unchanged and then increase.With the increase of the radius at the intersection,the absolute values of drag,lift and pitch moment all decrease.With the increase of the radius of the bottom,the drag increases first and then decreases,and the absolute value of lift and pitch moment decreases first and then increases.A proxy model based on RBF neural network is constructed by using Latin hypercube sampling method to select sample points.The validity of the model is verified by numerical calculation results.The genetic algorithm is used to optimize the model.On this basis,a smart bullet shape optimization method based on RBF neural network and genetic algorithm is established.The results of single-objective and multi-objective optimization for bullet shape are obtained and compared with reference shape.The results show that the model has a high prediction accuracy.The single-objective optimization results are far superior to the reference shape in one aspect,while the multi-objective optimization results are superior to the reference shape in all aspects.