VULNERABILITY ASSESSMENT OF GROUND VEHICLE SYSTEMS ENABLED WITH ACTIVE PROTECTION SYSTEMS (APS) THROUGH SURROGATE MODELING

Analytical performance assessment of Active Protection Systems (APS) and the vulnerability assessment of ground vehicles using classical physics-based modeling and simulations has many challenges. Also, modeling many of the factors involved in the interaction during Hard-Kill (HK) of the incoming threat with a countermeasure and the resulting outcomes are quite complex and have varied effects on the survivability of the vehicle. Therefore, relying only on deterministic solutions, are time consuming and computationally cost prohibitive.

This effort is focused on changing this paradigm by researching for a suitable machine learning algorithm which takes in simulation data from high fidelity physics-based models as training data. Through decomposition, interpolation and reconstruction techniques, surrogate models can be constructed using the simulation data. These surrogate models can then be used for a quick assessment (fraction of a second compared to a day per simulation) during Analysis of Alternatives (AoA), and Vehicle Protection Systems (VPS) trade studies.

Citation: Kumar B Kulkarni, Venkatesh Babu, Sanjay Kankanalapalli, Aditya Vipradas, P Jayakumar Ph.D. and Ravi Thyagarajan Ph. D., “Vulnerability assessment of ground vehicle systems enabled with Active Protection Systems (APS) through surrogate modeling,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 15-17, 2023.