Graph convolutional multi-mesh autoencoder for steady transonic aircraft aerodynamics
Graph convolutional multi-mesh autoencoder for steady transonic aircraft aerodynamics
Calculating aerodynamic loads around an aircraft using computational fluid dynamics is a user's and computer-intensive task. An attractive alternative is to leverage neural networks bypassing the need of solving the governing fluid equations at all flight conditions of interest. Neural networks have the ability to infer highly nonlinear predictions if a reference dataset is available. This work presents a geometric deep learning based multi-mesh autoencoder framework for steady-state transonic aerodynamics. The framework builds on graph neural networks which are designed for irregular and unstructured spatial discretisations, embedded in a multi-resolution algorithm for dimensionality reduction. The test case is for the NASA Common Research Model wing/body aircraft configuration. Thorough studies are presented discussing the model predictions in terms of vector fields, pressure and shear-stress coefficients, and scalar fields, total force and moment coefficients, for a range of nonlinear conditions involving shock waves and flow separation. We note that the cost of the model prediction is minimal having used an existing database.
Massegur Sampietro, David
d5bc71e8-f1b8-4c9f-9537-7ff63ad19426
Da Ronch, Andrea
a2f36b97-b881-44e9-8a78-dd76fdf82f1a
Massegur Sampietro, David
d5bc71e8-f1b8-4c9f-9537-7ff63ad19426
Da Ronch, Andrea
a2f36b97-b881-44e9-8a78-dd76fdf82f1a
Massegur Sampietro, David and Da Ronch, Andrea
(2024)
Graph convolutional multi-mesh autoencoder for steady transonic aircraft aerodynamics.
Machine Learning: Science and Technology.
(In Press)
Abstract
Calculating aerodynamic loads around an aircraft using computational fluid dynamics is a user's and computer-intensive task. An attractive alternative is to leverage neural networks bypassing the need of solving the governing fluid equations at all flight conditions of interest. Neural networks have the ability to infer highly nonlinear predictions if a reference dataset is available. This work presents a geometric deep learning based multi-mesh autoencoder framework for steady-state transonic aerodynamics. The framework builds on graph neural networks which are designed for irregular and unstructured spatial discretisations, embedded in a multi-resolution algorithm for dimensionality reduction. The test case is for the NASA Common Research Model wing/body aircraft configuration. Thorough studies are presented discussing the model predictions in terms of vector fields, pressure and shear-stress coefficients, and scalar fields, total force and moment coefficients, for a range of nonlinear conditions involving shock waves and flow separation. We note that the cost of the model prediction is minimal having used an existing database.
Text
IOP_MLST_journal_paper__GCN_MM_AE_for_Steady_Aerodynamics
- Accepted Manuscript
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Submitted date: 19 September 2023
Accepted/In Press date: 22 February 2024
Identifiers
Local EPrints ID: 485885
URI: http://eprints.soton.ac.uk/id/eprint/485885
ISSN: 2632-2153
PURE UUID: f0fb88fa-09f5-4e49-b164-b6f2515acee4
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Date deposited: 03 Jan 2024 20:16
Last modified: 18 Mar 2024 03:59
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Author:
David Massegur Sampietro
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