Data-driven multi-grid solver for accelerated pressure projection
Data-driven multi-grid solver for accelerated pressure projection
Pressure projection is the single most computationally expensive step in an unsteady incompressible fluid simulation. This work demonstrates the ability of data-driven methods to accelerate the approximate solution of the Poisson equation at the heart of pressure projection. Geometric Multi-Grid methods are identified as linear convolutional encoder-decoder networks and a data-driven smoother is developed using automatic differentiation to optimize the velocity-divergence projection. The new method is found to accelerate classic Multi-Grid methods by a factor of two to three with no loss of accuracy on eleven 2D and 3D flow cases including cases with dynamic immersed solid boundaries. The optimal parameters are found to transfer nearly 100% effectiveness as the resolution is increased, providing a robust approach for accelerated pressure projection of unsteady flows.
Data-driven, Linear algebra, Pressure projection
1-7
Weymouth, Gabriel
b0c85fda-dfed-44da-8cc4-9e0cc88e2ca0
15 October 2022
Weymouth, Gabriel
b0c85fda-dfed-44da-8cc4-9e0cc88e2ca0
Abstract
Pressure projection is the single most computationally expensive step in an unsteady incompressible fluid simulation. This work demonstrates the ability of data-driven methods to accelerate the approximate solution of the Poisson equation at the heart of pressure projection. Geometric Multi-Grid methods are identified as linear convolutional encoder-decoder networks and a data-driven smoother is developed using automatic differentiation to optimize the velocity-divergence projection. The new method is found to accelerate classic Multi-Grid methods by a factor of two to three with no loss of accuracy on eleven 2D and 3D flow cases including cases with dynamic immersed solid boundaries. The optimal parameters are found to transfer nearly 100% effectiveness as the resolution is increased, providing a robust approach for accelerated pressure projection of unsteady flows.
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More information
Accepted/In Press date: 8 August 2022
e-pub ahead of print date: 12 August 2022
Published date: 15 October 2022
Additional Information:
Funding Information:
The author thanks the Lloyds Register Foundation for funding the Data Centric Engineering Program of the Alan Turing Institute.
Publisher Copyright:
© 2022 The Author(s)
Keywords:
Data-driven, Linear algebra, Pressure projection
Identifiers
Local EPrints ID: 469612
URI: http://eprints.soton.ac.uk/id/eprint/469612
ISSN: 0045-7930
PURE UUID: 65dfa07c-a0c9-450a-86f7-4367535eda7b
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Date deposited: 21 Sep 2022 16:38
Last modified: 17 Mar 2024 03:32
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