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

C Lu; S Sambasivan; A Kapahi; H.S Udaykumar

doi:10.1016/j.piutam.2012.03.003

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

Journal article

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

C Lu, S Sambasivan, A Kapahi and H.S Udaykumar

Procedia IUTAM, Vol.3, pp.25-52

2012

DOI: 10.1016/j.piutam.2012.03.003

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Abstract

The 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.

Artificial Neural Networks

Cartesian Grid

Inter-scale coupling

Levelsets

Multi-scale modeling

Multimaterial Flows

Sharp Interfaces

Shock Waves

Details

Title: Subtitle: Multi-scale modeling of shock interaction with a cloud of particles using an artificial neural network for model representation
Creators: C Lu - Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA-52242, USA
S Sambasivan - Theoretical Division, Group T-5, MS-B284, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
A Kapahi - Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA-52242, USA
H.S Udaykumar - Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA-52242, USA
Resource Type: Journal article
Publication Details: Procedia IUTAM, Vol.3, pp.25-52
DOI: 10.1016/j.piutam.2012.03.003
ISSN: 2210-9838
eISSN: 2210-9838
Publisher: Elsevier B.V
Language: English
Date published: 2012
Academic Unit: Injury Prevention Research Center; Mechanical Engineering
Record Identifier: 9984121961302771

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