The duck rudder correction mechanism of 2D correction projectiles has important applications in modern high-precision munitions due to its advantages of low cost and high accuracy. In order to study the aerodynamic characteristics of the correction mechanism of two-dimensional correction projectile, the simulation model of two-dimensional correction projectile was established by using the hydrodynamic analysis software Fluent, and the aerodynamic simulation calculation was carried out for the two-dimensional correction projectile at different correction mechanism speed and different projectile body speed, and the drag coefficient and lift coefficient of two-dimensional correction projectile and their fitted curves were obtained. The curves show that the change of the aerodynamic parameters of the 2D correction projectile with the speed of the correction mechanism and the speed of the projectile body, and the analysis results show that as the speed of the projectile body decreases, the change of the deceleration force of the correction mechanism is greater, and the influence of the deceleration of the projectile body on the overall aerodynamic values is smaller than that of the correction fuze on the aerodynamic values. The results show that as the speed of the projectile decreases, the variation of the deconvolution force on the overall aerodynamic value is greater.
In order to solve the identification of projectile surface defect category of which body defect detection system, the classifier of the body defect detection system was designed. The mathematical model of BP neural network and support vector machine (SVM) network classifier were established respectively and realized by using VC + + program and MATLAB, the number of nodes in the middle layer were determined, and the detection performance of the two kinds of classifiers were tested. Test samples were collected from magnetic particle detection images of 3 models which included 20 samples containing cracks and 600 without defects. The results show that the SVM defect classification network classifier has higher recognition rate than the BP neural network, but BP network has stronger stability classification than the SVM.
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