Designing Jetting with Gradient Based Tools for Dynamic Compression Experiments

Recent advances in scientific programming, particularly with regards to topology optimization and machine learning, have necessitated computational methods that generate gradients for optimization. One method to do so is to utilize a tool called automatic differentiation, a mechanism to algorithmically calculate derivatives of functions and combine them to generate gradients of compositions of functions. Code bases such as Jax and PyTorch (which particularly focused on machine learning applications) have demonstrated the ability to scale automatic differentiation to large problems. This allows for rapid gradient calculations, leading to reduced development time as well as significantly higher complexity in the equations used to study a physical phenomenon. While these methods have been studied in the context of machine learning, these approaches have only been applied to mechanics in a handful of cases. This presents an opportunity to study a large variety of optimization problems, such as topology, material parameters, or initial-value problems using this automatic differentiation infrastructure. We demonstrate these results by designing conditions for a dynamic compression experiment driven by a high-current pulsed power system. By using gradient based design tools, we find optimal conditions for RMI jetting in a bent shock configuration and validate these results with both computational and experimental methods.