Abstract
The finite element model of projectile penetrating multi-layered reinforced concrete target was established via LS-DYNA solver. The penetration model was validated with the test data in terms of residual velocity and deflection angle. Parametric analyses were carried out through the verified penetration model. Seven influential factors for penetration conditions, including the initial velocity of projectile, initial angle of attack of projectile, initial dip angle of projectile, the first layer thickness of concrete target, the residual layer thickness of concrete target, target distance and the layer number of concrete target, were put emphasis on further analysis. Furthermore, the influence of foregoing factors on residual velocity and deflection angle of projectile were numerically obtained and discussed. Based on genetic algorithm, the BP neural network model was trained by 263 sets of data obtained from the parametric analyses, whereby the prediction models of residual velocity and attitude angle of projectile under different penetration conditions were achieved. The error between the prediction data obtained by this model and the reserved 13 sets of test data is found to be negligible.
Highlights
Since the Kosovo and Iraq wars, precision-guided ground-drilling weapons represented by the US Army's ‘Jedam’ ground-drilling missiles have developed rapidly
As the projectile passes through the fourth layer of the target board, the body deflection angle is enlarged to 1.23°, and the residual velocity of the projectile is 966.52 m/s
When penetrating the same layer of target plate, each time the initial angle of attack increases by 1° leading to 20% increase of projectile deflection angle
Summary
Since the Kosovo and Iraq wars, precision-guided ground-drilling weapons represented by the US Army's ‘Jedam’ ground-drilling missiles have developed rapidly. Ji et al [5] carried out numerical calculations on the projectile penetrating three layers of homogeneous steel plates, and obtained the velocity and acceleration change curves of the projectile, and simulated the residual velocity of the projectile perforating the three-layer target plate. Sun et al simulated the inclination of the projectile penetrating the three-layer concrete target board, obtained the time-history change curve of the projectile velocity and acceleration, and summarized the change law [7]. A finite element model of the projectile penetrating the multilayer reinforced concrete target is established with validation against the test results. Using the genetic algorithm's BP (GA-BP) neural network model for the parametric analysis results to perform machine learning training, the prediction models of the missile's residual velocity and attitude angle under different penetration conditions can be developed. The GA-BP neural network predictions agree well with the foregoing numerical modeling results
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