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Research on structural parameter optimization of elliptical bipolar linear shaped charge based on machine learning.

Bo Wu1,2, Shixiang Xu1, Guowang Meng1

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|October 24, 2022
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This study optimizes elliptical bipolar linear shaped charge structures using machine learning and grey correlation theory. Optimized designs significantly improve rock breaking efficiency in practical engineering applications.

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Area of Science:

  • Engineering
  • Materials Science
  • Computational Mechanics

Background:

  • Shaped charges are crucial for rock fragmentation in engineering.
  • Understanding the relationship between shaped charge parameters and jet performance is vital for optimization.
  • Existing methods may not fully capture the complex interactions influencing shaped charge effectiveness.

Purpose of the Study:

  • To analyze the correlation between elliptical bipolar linear shaped charge parameters and jet performance using grey correlation theory.
  • To optimize shaped charge structure using machine learning for enhanced rock breaking.
  • To validate the optimized design through practical engineering applications.

Main Methods:

  • Numerical simulations using the Smoothed Particle Hydrodynamics (SPH) method.
  • Grey correlation theory for parameter analysis.
  • Machine learning, specifically a genetic algorithm (GA)-based support vector machine (SVM) regression model, for structural optimization.

Main Results:

  • Identified the influence of structural parameters on jet head velocity and jet length.
  • Achieved high prediction accuracy with the GA-SVM regression model.
  • Demonstrated significant improvement in rock breaking effect through optimized shaped charge structures in field applications.

Conclusions:

  • Structural parameters have a consistent impact on jet head velocity but varying effects on jet length.
  • The optimized shaped charge design offers substantial improvements in rock fragmentation.
  • The study provides valuable guidance for the practical application of shaped charges in engineering.