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An improved mesostructure construction method for precise modeling of polymer-bonded explosives

Author

Listed:
  • Zihao Yang

    (School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710072, P. R. China)

  • Chongnan Zhao

    (School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710072, P. R. China)

  • Jieqiong Zhang

    (School of Mathematics, Northwest University, Xi’an 710027, P. R. China)

  • Yiqiang Li

    (School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710072, P. R. China)

Abstract

The polymer-bonded explosives (PBXs) contain randomly distributed particles with irregular shapes, high volume fraction (greater than 90%) and gradation feature of size. The precise mesostructure modeling is essential to predict the mechanical properties of PBXs. This study proposes a Voronoi-based mesostructure modeling method of PBXs by considering the real characteristics of PBXs, which can meet both the requirements of high volume fraction and specified size gradation of explosive particles. The key point of the method is to place the particles generated by Monte Carlo method into the representative volume element of PBXs by proposing an improved random walking algorithm (RWA). In the improved RWA, a subregion-independent particle delivery criterion is introduced to enhance the placement efficiency, and an overlapping judgment criterion by tolerating slight overlaps between particles is applied to make the size of placed particles match the grading relationship better. Numerical examples have been provided to verify the correctness and efficiency of the proposed mesostructure modeling method.

Suggested Citation

  • Zihao Yang & Chongnan Zhao & Jieqiong Zhang & Yiqiang Li, 2023. "An improved mesostructure construction method for precise modeling of polymer-bonded explosives," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 34(02), pages 1-15, February.
    • Handle: RePEc:wsi:ijmpcx:v:34:y:2023:i:02:n:s0129183123500237
    DOI: 10.1142/S0129183123500237
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