Research On Three-dimensional Reconstruction Technology Of Power Field Parameters Based On Laser High-speed Transient Test

The accuracy of the fragmentation force field behind the target can greatly improve the accuracy of the damage assessment process.Accurate and real-time damage assessment of weapons and equipment is the key to the innovative development and efficient utilization of weapons and equipment,which is of great practical significance to fully exerting the combat performance of weapons and equipment and improving their combat capabilities.Therefore,this paper focuses on the three-dimensional reconstruction of the fragment force field behind the target.Using laser high-speed transient test technology to obtain the power field data under specific working conditions,and use this as a benchmark for simulation modeling.By studying the characteristics of the fragment power field behind the target,modeling its quantity,quality and energy distribution through parametric scanning of simulation data under multiple working conditions,and using mathematical models and deep learning models to reconstruct a more reliable three-dimensional power field to support the follow-up damage effects and damage effectiveness evaluation.Firstly,a laser high-speed transient test system is built to complete the power field test,and on this basis,digital simulation is carried out to obtain the dynamic parameter data in the three-dimensional space of the power field.The field test is carried out for the vertical incidence penetration condition.The key data of the quality and quantity of the fragments behind the target are collected at the test site,and the sequence images obtained from the test are processed to obtain the maximum flight speed and the up and down scattering angle of the fragments.Using the finite element method and taking the test results as the standard,the power field dynamics simulation modeling is completed.The working conditions were designed and traversal simulations were carried out,and 480 sets of dynamic parameter simulation data of the power field under non-normal penetration working conditions were obtained.Projectile fragments and target plate fragments were processed separately to generate 960 sets of raw data,and software was written for statistical analysis and data visualization.Secondly,the complex power field data is converted into a two-dimensional grayscale image,and a mathematical model of the radial distribution of the quantity and quality of the fragment power field behind the target is constructed through statistical analysis.Map the physical quantities of the fragments to the position and intensity information in the generalized space,and obtain its "intensity" and "coordinates" on the two-dimensional grayscale image;discretize the entire power field data by dividing the grid,and generate and neural network The energy discrete distribution data of fragments behind the target with consistent layer structure is input,and all "pixels" of the grayscale image are unified.A mapping method based on two-dimensional standard normal distribution from asymmetric force field distribution under oblique penetration condition to centrosymmetric force field distribution under normal penetration condition is proposed.The quantile method is used to estimate the key parameters of the radial distribution of the power field,and the correction is made according to the simulation data to obtain the revised distribution node values,and the analytical correction distribution function is established by fitting,and the fragment power field behind the target is constructed.The mathematical model of quantity and quality distribution has a goodness of fit of 99.94%.Finally,the neural network based on the variational autoencoder is used to train the grayscale image data,and the energy distribution model of the power field is given.Combined with the mathematical model of the quantity and quality distribution of the power field,the three-dimensional reconstruction of the power field is realized.Build a neural network based on a variational autoencoder structure to train grayscale image data and obtain a codec.Under non-simulation or experimental preset working conditions,based on the mathematical model of the quantity and mass distribution of the power field and the neural network model of the power field energy distribution,the dynamic parameters of the power field are reconstructed and visualized in three-dimensional space.