A Robust and Efficient Multi-physics Numerical System for Intensive Blast Wave Propagation in Complex Environments

TL;DR

This paper introduces a high-performance multiphysics simulation system for modeling intense blast wave propagation in complex environments, combining advanced numerical methods, neural network-based EOS, and parallel computing for large-scale applications.

Contribution

It presents a novel multiphysics modeling framework with an artificial neural network EOS and an extended reconstruction scheme, enabling accurate and efficient large-scale blast wave simulations.

Findings

Successful simulation of blast waves in urban environments

Enhanced computational accuracy and efficiency

Validation of numerical schemes for practical engineering use

Abstract

We establish a high-resolution, high-performance, and high-confidence compressible multiphysics system in a Cartesian grid with irregular boundary topologies to simulate intensive blast waves propagating in large-scale and extremely complex environments. The multiphysics system is modeled by a multi-component model solved using a generalized Godunov method and a classical material point method in a combination of Lagrangian particles and a rigid material model. An artificial neural network equation of state (EOS) is proposed based on experimental data to simulate the intensive explosion products and real gas under extreme pressure and temperature. To improve computational accuracy and efficiency, a deepMTBVD reconstruction scheme of our previous work is extended to the multiphysics system. With the aid of high-performance parallel computation, several large-scale blast wave applications, such as blast wave propagating in a local and entire urban city, are simulated in a reasonable time period, which can validate numerical schemes and lead to more practical engineering applications.