Data-driven prediction of plate velocities and plate deformation of explosive reactive armor  

作　　者：Marvin Becker Andreas Klavzar Thomas Wolf Melissa Renck 

机构地区：[1]Institute of Saint-Louis,5 Rue du Général Cassagnou,68330,Saint Louis,Cedex,France [2]Ecole Centrale de Lille,CitéScientifique,59651,Villeneuve d'Ascq,Cedex,France

出　　处：《Defence Technology（防务技术）》2022年第12期2141-2149,共9页Defence Technology

摘　　要：Explosive reactive armor(ERA)is currently being actively developed as a protective system for mobile devices against ballistic threats such as kinetic energy penetrators and shaped-charge jets.Considering mobility,the aim is to design a protection system with a minimal amount of required mass.The efficiency of an ERA is sensitive to the impact position and the timing of the detonation.Therefore,different designs have to be tested for several impact scenarios to identify the best design.Since analytical models are not predicting the behavior of the ERA accurately enough and experiments,as well as numerical simulations,are too time-consuming,a data-driven model to estimate the displacements and deformation of plates of an ERA system is proposed here.The ground truth for the artificial neural network(ANN)is numerical simulation results that are validated with experiments.The ANN approximates the plate positions for different materials,plate sizes,and detonation point positions with sufficient accuracy in real-time.In a future investigation,the results from the model can be used to estimate the interaction of the ERA with a given threat.Then,a measure for the effectiveness of an ERA can be calculated.Finally,an optimal ERA can be designed and analyzed for any possible impact scenario in negligible time.

关 键 词：Artificial neural network Explosive reactive armor Finite element simulation Particle simulation Flash X-ray 

分 类 号：TJ810.38[兵器科学与技术—武器系统与运用工程]