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Inverse problem annular shaped charge optimization method based on machine learning

A technology of concentrated energy charge and optimization method, which is applied in machine learning, neural learning method, design optimization/simulation, etc., and can solve problems such as heavy workload of multi-parameter coupling design and failure to find optimal design

Inactive Publication Date: 2020-06-16
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] The purpose of the present invention is to solve the problem that the multi-parameter coupling design of the existing annular shaped charge powder cover has a large workload and cannot find the optimal design, and provides a machine learning optimization method for the ring shaped charge charge cover

Method used

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  • Inverse problem annular shaped charge optimization method based on machine learning
  • Inverse problem annular shaped charge optimization method based on machine learning
  • Inverse problem annular shaped charge optimization method based on machine learning

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Embodiment 1

[0056] The structural schematic diagram of the annular shaped charge disclosed in this embodiment is as follows: Figure 8 As shown, it is mainly composed of four parts: end cover, shell, Comp B explosive, and drug type cover. End cap material is steel, thickness δ 2 =4.0mm, there is an opening with diameter d'=25.0mm in the center; the shell material is steel thickness δ 1 =2.0mm, height L=100.0mm; charge type is Comp B explosive, diameter D=100.0mm; liner material is copper, liner radius R near the explosive side 1 =24.0mm, the diameter of the middle hole of the medicine-shaped cover d=4.0mm, and other parameters of the medicine-shaped cover can be expressed by the known parameters and three parameters of g, e, and f.

[0057] A machine learning-based optimization method for annular shaped charge, the main process is as follows figure 1 shown, including the following steps:

[0058] Step 1: Build a deep convolutional neural network (CNN) model;

[0059] Step 1.1: The ba...

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Abstract

The invention relates to an inverse problem annular shaped charge optimization method based on machine learning, and belongs to the field of equipment design. According to the invention, key parameters of annular shaped charge are coupled with each other; the problem that an ideal annular shaped charge liner structure cannot be designed through one-by-one optimization of single parameters is solved; a deep convolutional neural network model is constructed; deep convolutional neural network model training and test data are generated through a numerical simulation technology, and an ideal annular penetration body is input into the trained deep convolutional neural network model, so that an optimized annular shaped charge structure is designed, and the problem of annular shaped charge multi-parameter coupling design is solved.

Description

technical field [0001] The invention relates to an inverse-problem ring-shaped energy-gathering charge optimization method based on machine learning, which belongs to the field of equipment design. Background technique [0002] The penetration aperture of the annular penetrator formed by the annular shaped charge on the hard target is much larger than that of the shaped jet, the shaped rod penetrator and the explosively formed projectile, which is of great value in both military and civilian fields . In the military field, ring-shaped shaped charges can be applied to the front-stage charges of tandem warheads to open channels for the latter-stage charges to enter the interior of the target to achieve efficient destruction. In the civilian field, ring-shaped shaped charges can be used for underwater explosion cutting and firefighters to quickly establish temporary fire passages. The liner is the key component of the annular shaped charge, and the optimal balance between the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08G06N20/00G06F111/10
CPCG06N3/08G06N20/00G06N3/045
Inventor 徐文龙王成贾时雨杨同会齐方方刘保华
Owner BEIJING INSTITUTE OF TECHNOLOGYGY