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

Zihao Yang, Chongnan Zhao, Jieqiong Zhang and Yiqiang Li
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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

International Journal of Modern Physics C (IJMPC), 2023, vol. 34, issue 02, 1-15

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.

Keywords: Polymer-bonded explosives; mesostructure modeling; Monte Carlo method; improved random walking algorithm (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0129183123500237

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