Applying Machine Learning and Data-Driven Methods to High-Velocity Penetration and Dynamic Material Modeling

Dear Colleagues,

In order to predict high-velocity impact processes by simulations, the involved highly dynamic material behavior of projectiles and targets has to be captured appropriately in numerical or analytical models. Regarding the application of hydrocodes, the phenomena of plasticity, damage, and failure are represented by constitutive models that describe the material behavior depending on key quantities such as strain, strain rate, stress triaxiality, or temperature. Similarly, analytical penetration models of terminal ballistics are based on model descriptions for the rate-dependent material strength that underlies the transient projectile and target response.

The traditional approach is to characterize the relevant material behavior in specialized material tests, to derive constitutive parameters therefrom, and to validate and finally apply those model approaches and parameters for the predictive simulation of penetration, perforation, fragmentation, or damage patterns in impact scenarios. A drawback of that approach is that deterministic equations for a complex material response are always based on assumptions, engineering judgment, and simplifications. Furthermore, sophisticated models often rely on material parameters that are only accessible in sophisticated dynamic tests under transient conditions, which sometimes struggle in realizing idealized loading conditions.

In an era of digitalization, it is obvious that new innovative approaches to the modeling of dynamic material behavior in the context of impact mechanics will be possible through data-based methods, machine learning, and other techniques from the field of artificial intelligence. The current scientific literature reflects the coming up of such approaches for ballistics and material dynamics, yet this advancement is still in its infancy. We would therefore like to invite you to contribute to the state of the science by submitting your manuscript to this Special Issue. The focus lies in applications of machine learning and data-driven methods to high-velocity penetration and dynamic material modeling by numerical or engineering methods.

Dr. Andreas Heine
Dr. Robbert Rietkerk
Prof. Dr. Werner Riedel
Guest Editors

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• high-velocity impact
• machine learning
• data-driven methods
• penetration modeling
• dynamic material modeling