Multi-scale modeling of shock interaction with a cloud of particles using an artificial neural network for model representation

AbstractThe evolution of a solid-gas mixture under the influence of a shock wave depends on particle-particle and particle-shock interactions; i.e. the macroscopic distribution of particles is determined by physics at the particle (micro)-scale. This work seeks to simulate the macro-scale dynamics of gas-solid mixtures by employing information accumulated from direct numerical simulations (DNS) at the micro- (i.e., particle) scale. Data on the forces experienced by particles in a cloud are collected from DNS using a compressible Eulerian solver and provided to an artificial neural network (ANN); the simulations are performed for a range of control parameters, such as Mach number, particle radii, particle-fluid density ratio, position, and volume fraction. Beginning with a simple single stationary particle case and progressing to moving particle laden clouds, the ANN is trained to evolve and reproduce correlations between the control parameters and particle dynamics. The trained ANN is then used in computing the macro-scale flow behavior in a model of shocked dusty gas advection. The model predicts particle motion and other macro-scale phenomena in agreement with experimental observations