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
employ deep learning to model the mesoscale energy localization of
shock-initiated EM microstructures upon which prediction results are used to
supply reaction progress rate information to the macroscale SDT simulation. 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