Surrogate drag model of non-spherical fragments based on artificial neural networks

The hazard evaluation of the fragments produced from an explosion requires an accurate prediction of the mo-tion of fragments subjected to gravity and aerodynamic forces. The drag coefficient (C-d) is among the most crucial components of various aerodynamic models. Limited experiments cannot reproduce the C-d (Mach) curves of all shapes of fragments. In this study, we develop a surrogate model of the average drag coefficient, C-d, for the randomly tumbling non-spherical fragment using artificial neutral networks. To train and validate the surrogate model, a comprehensive dataset was developed through mesoscale simulations of C-d for a wide variety of fragment shapes combined with icosahedron average method. The surrogate model shows that the dependence of C-d on different Mach numbers varies with fragment shape. The fully validated surrogate model of C-d allows us to derive the statistics of C-d for a host of fragments from a specific explosion, subsequently gaining insight into the terminal ballistics of the fragments. (C) 2022 Published by Elsevier B.V.

Automated summary

This study proposes a surrogate model for the average drag coefficient (C-d) of randomly tumbling non-spherical fragments using artificial neural networks. The model shows that the dependence of C-d on different Mach numbers varies with fragment shape, which can provide insights into the terminal ballistics of fragments.