Department of Mechanical Engineering

PRESENTATION: In this work, an atomistically informed continuum framework is developed to model shock-induced pore collapse and energy localization in energetic materials, with emphasis on RDX. Molecular dynamics (MD) simulations are used to extract thermophysical properties and constitutive response, enabling calibration of a modified rate-dependent Johnson–Cook (M–JC) strength model. The formulation is extended with a post-yield relaxation mechanism to capture the effects of nanoscale shear localization.

Direct comparisons between MD and continuum simulations are performed across a range of impact velocities using consistent reverse ballistic configurations. The model captures key features of pore collapse, including the transition from strength-dominated deformation at low loading to hydrodynamic-like behavior at higher shock intensities. Incorporating relaxation improves energy redistribution, reducing excessive shear localization and bulk heating while yielding more accurate hotspot morphology and temperature fields.

Orientation-dependent simulations in RDX and HMX highlight anisotropic response and limitations of isotropic continuum assumptions. Overall, the results demonstrate that MD-informed modifications significantly enhance predictive capability, providing a pathway toward improved mesoscale modeling of hotspot formation and energetic material sensitivity under shock loading.

PRESENTER: Jake Herrin is a Ph.D. candidate in Mechanical Engineering at the University of Iowa. His research focuses on multiscale modeling of energetic materials, with an emphasis on shock-induced deformation, pore collapse, and energy localization. He integrates molecular dynamics simulations with continuum constitutive modeling to develop physics-informed descriptions of material response under extreme loading conditions. His work aims to improve predictive capability for hotspot formation and sensitivity in energetic crystals such as RDX and HMX. Following graduation, he plans to pursue opportunities in computational modeling.