Predictive simulations of the shock-to-detonation transition (SDT) in heterogeneous energetic materials (EM) are vital to the design and control of their energy release and sensitivity. Due to the complexity of the thermo-mechanics of EM during the SDT, both macro-scale response and sub-grid mesoscale energy localization must be captured accurately. This work proposes an efficient and accurate multiscale framework for SDT simulations of EM. We introduce a new approach for SDT simulation by using deep learning to model the mesoscale energy localization of shock-initiated EM microstructures. The proposed multiscale modeling framework is divided into two stages. First, a physics-aware recurrent convolutional neural network (PARC) is used to model the mesoscale energy localization of shock-initiated heterogeneous EM microstructures. PARC is trained using direct numerical simulations (DNS) of hotspot ignition and growth within microstructures of pressed HMX material subjected to different input shock strengths. After training, PARC is employed to supply hotspot ignition and growth rates for macroscale SDT simulations. We show that PARC can play the role of a surrogate model in a multiscale simulation framework, while drastically reducing the computation cost and providing improved representations of the sub-grid physics. The proposed multiscale modeling approach will provide a new tool for material scientists in designing high-performance and safer energetic materials.

翻译：异质含能材料中冲击转爆轰过渡过程的预测性模拟对于控制和设计其能量释放与敏感性至关重要。由于含能材料在冲击转爆轰过程中的热力学复杂性，必须同时准确捕捉宏观尺度响应和亚网格介观尺度能量局域化。本文提出了一种高效且精准的多尺度框架，用于含能材料的冲击转爆轰模拟。我们引入一种新方法，通过深度学习对冲击引发的含能材料微结构介观尺度能量局域化进行建模。所提出的多尺度建模框架分为两个阶段：首先，使用物理感知循环卷积神经网络（PARC）对冲击引发的异质含能材料微结构介观尺度能量局域化进行建模。PARC基于直接数值模拟对不同冲击强度下压装HMX材料微结构中热点点火与增长过程的数据进行训练。训练完成后，PARC被用于为宏观尺度冲击转爆轰模拟提供热点点火与增长速率。研究表明，PARC可作为多尺度模拟框架中的替代模型，大幅降低计算成本并提供改进的亚网格物理表征。所提出的多尺度建模方法将为材料科学家设计高性能且更安全的含能材料提供新工具。