A Physics‐Aware Deep Learning Model for Energy Localization in Multiscale Shock‐To‐Detonation Simulations of Heterogeneous Energetic Materials

• Published in: Propellants, explosives, pyrotechnics Vol. 48; no. 4
• Main Authors: Nguyen, Phong C. H, Nguyen, Yen‐Thi, Seshadri, Pradeep K., Choi, Joseph B., Udaykumar, H. S, Baek, Stephen
• Format: Journal Article
• Language: English
• Published: Weinheim Wiley Subscription Services, Inc 01.03.2023
• Subjects: Artificial neural networks; Computer simulation; Deep learning; Detonation; Direct numerical simulation; Energetic materials; heterogeneous energetic materials; HMX; Ignition; Localization; Mathematical models; mesoscale energy localization; Mesoscale phenomena; Microstructure; multiscale simulation; Physics; physics-aware machine learning; shock-to-detonation; Simulation

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 mesosca...