The stress-strain curves of polymer bonded explosives (PBXs) are generally nonlinear and poorly unsymmetric.The construction of constitutive model is a difficult problem in the research of mechanical properties of explosives.In this paper, tak-ing PBX-901 as the research object, the uniaxial tension and compression experiments at different temperatures were carried out firstly.Then, a four-parameter and a two-parameter constitutive model were respectively established and the relative undeter-mined parameters were determined based on the Boltzmann function according to S-shaped stress-strain curves obtained.The parameters of two-parameter constitutive model can be determined only using analytical solution including the compression strength and initial segment elastic modulus, and its description error is less than 5%, while the parameters of the four-parameter model though parameter fitting.Finally the second development module of ANSYS software was adopted to realize the numerical application of the two -parameter constitutive model for the Brazilian disk tests.Through comparison with the experimental re-sults, the relative error of numerical results only is about 5.11%.The description precision of the nonlinear two-parameter consti-tutive model established in the text is satisfactory and it meets the needs of engineering

Quasi-static Tension-compression Nonlinear Constitutive Model of TATB-based PBX and its Application

Due to the violent reaction potential of high explosives, in tests where a reaction is not desired, it can be safe and convenient to use an inert mock that can mimic relevant properties of the associated explosive. Use of mock materials is well-established in explosives work, but most existing mocks are generally meant to mimic only one or two properties of a given explosive, potentially failing to match the explosive in other critical ways. Recently, there has been interest in developing robust thermomechanical mocks for many common explosives. Here, eleven materials are systematically chosen and investigated as potential thermomechanical mocks for TATB, the explosive component of PBX 9502. DSC, nanoindentation, and compression testing are used to narrow down to most appropriate mocks and identify their advantages and limitations. Hardness and elastic modulus are reported for the first time for the molecular crystals iodoanthranilic acid, hexamine, melamine, and trithiolane dioxide, and the crystal structure is reported for the first time for the molecular crystals iodoanthranilic acid and iodosalicylic acid.

/pdf/high-fidelity-mock-development-for-the-insensitive-high-1atd4w8u.pdf

High-Fidelity Mock Development for the Insensitive High Explosive TATB

https://hal.archives-ouvertes.fr/hal-02934332/document

Quasistatic mechanical behavior of HMX- and TATB-based plastic-bonded explosives

The materials science community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges. However, despite their effectiveness in building highly predictive models, e.g., predicting material properties from microstructure imaging, due to their opaque nature fundamental challenges exist in extracting meaningful domain knowledge from the deep neural networks. In this work, we propose a technique for interpreting the behavior of deep learning models by injecting domain-specific attributes as tunable “knobs” in the material optimization analysis pipeline. By incorporating the material concepts in a generative modeling framework, we are able to explain what structure-to-property linkages these black-box models have learned, which provides scientists with a tool to leverage the full potential of deep learning for domain discoveries.

https://pubs.acs.org/doi/pdf/10.1021/acsomega.1c04796

Attribution-Driven Explanation of the Deep Neural Network Model via Conditional Microstructure Image Synthesis

/pdf/pbx-9502-literature-review-an-engineering-perspective-33y40v35ey.pdf

PBX 9502 Literature Review: An Engineering Perspective

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PBX 9502 Literature Review: An Engineering Perspective