A Physics‐Aware Deep Learning Model for Energy Localization in Multiscale Shock‐To‐Detonation Simulations of Heterogeneous Energetic Materials
A Physics‐Aware Deep Learning Model for Energy Localization in Multiscale Shock‐To‐Detonation Simulations of Heterogeneous Energetic Materials
复制标题
用于多尺度冲击中能量定位的物理感知深度学习模型 - 异种含能材料的爆炸模拟
DOI：
10.1002/prep.202200268
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Baek, Stephen
中科院分区：
文献类型：
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作者：
Nguyen, Phong C. H.;Nguyen, Yen‐Thi;Seshadri, Pradeep K.;Choi, Joseph B.;Udaykumar, H. S.;Baek, Stephen
非均质含能材料（EM）中冲击-爆轰转变（SDT）的预测模拟对于设计和控制其能量释放和灵敏度至关重要。由于SDT过程中电磁热力学的复杂性，必须准确捕获宏观尺度响应和子网格中尺度能量局部化。这项工作提出了一个高效，准确的多尺度框架SDT模拟EM。我们介绍了一种新的SDT模拟方法，即使用深度学习来模拟冲击引发的EM微结构的中尺度能量局部化。提出的多尺度建模框架分为两个阶段。首先，使用物理感知的递归卷积神经网络（PARC）来模拟冲击引发的异质EM微结构的中尺度能量局部化。PARC的训练使用直接数值模拟（DNS）的热点点火和增长的微观结构内的压HMX材料受到不同的输入冲击强度。训练后，PARC被用来提供热点点火和宏观SDT模拟的增长率。我们表明，PARC可以在多尺度仿真框架中发挥代理模型的作用，同时大大降低计算成本，并提供改进的亚网格物理表示。提出的多尺度建模方法将为材料科学家设计高性能和更安全的含能材料提供新的工具。
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.