Partitioned Deep Learning of Fluid-Structure Interaction

05/14/2021
by   Amin Totounferoush, et al.
0

We present a partitioned neural network-based framework for learning of fluid-structure interaction (FSI) problems. We decompose the simulation domain into two smaller sub-domains, i.e., fluid and solid domains, and incorporate an independent neural network for each. A library is used to couple the two networks which takes care of boundary data communication, data mapping and equation coupling. Simulation data are used for training of the both neural networks. We use a combination of convolutional and recurrent neural networks (CNN and RNN) to account for both spatial and temporal connectivity. A quasi-Newton method is used to accelerate the FSI coupling convergence. We observe a very good agreement between the results of the presented framework and the classical numerical methods for simulation of 1d fluid flow inside an elastic tube. This work is a preliminary step for using neural networks to speed-up the FSI coupling convergence by providing an accurate initial guess in each time step for classical numerical solvers

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/30/2021

The Performance Impact of Newton Iterations per Solver Call in Partitioned Fluid-Structure Interaction

The cost of a partitioned fluid-structure interaction scheme is typicall...
research
08/26/2020

Adaptive Neural Network-Based Approximation to Accelerate Eulerian Fluid Simulation

The Eulerian fluid simulation is an important HPC application. The neura...
research
01/08/2020

Quasi-Newton Waveform Iteration for Partitioned Fluid-Structure Interaction

We present novel coupling schemes for partitioned multi-physics simulati...
research
03/15/2023

On the number of subproblem iterations per coupling step in partitioned fluid-structure interaction simulations

In literature, the cost of a partitioned fluid-structure interaction sch...
research
01/22/2020

A Multi-Vector Interface Quasi-Newton Method with Linear Complexity for Partitioned Fluid-Structure Interaction

In recent years, interface quasi-Newton methods have gained growing atte...
research
04/01/2022

A Robin-Neumann Scheme with Quasi-Newton Acceleration for Partitioned Fluid-Structure Interaction

The Dirichlet-Neumann scheme is the most common partitioned algorithm fo...
research
09/09/2019

Calibration of a Fluid-Structure Problem with Keras

In this short paper we report on an inverse problem issued from a physic...

Please sign up or login with your details

Forgot password? Click here to reset