Bulletin of the American Physical Society
76th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 19–21, 2023; Washington, DC
Session A29: Data-Driven Approaches to Fluid Dynamics
8:00 AM–9:57 AM,
Sunday, November 19, 2023
Room: 152B
Chair: C. Ricardo Constante-Amores, University of Wisconsin-Madison
Abstract: A29.00008 : A POD-driven Machine Learning Algorithm to predict 3D Patient-Specific Aortic flows*
9:31 AM–9:44 AM
Presenter:
Chotirawee Chatpattanasiri
(University College London)
Authors:
Chotirawee Chatpattanasiri
(University College London)
Catriona Stokes
(University College London)
Vanessa Diaz-Zuccarini
(University College London)
Stavroula Balabani
(University College London)
The algorithm is driven by a Reduced Order Model (ROM) based on Proper Orthogonal Decomposition (POD). It consists of two neural networks, each trained separately. The first network is trained to predict POD coefficients directly from the flow rate at the aortic inlet, while the second network uses POD coefficient sequences from the past to predict future POD coefficients. Working synergistically, these networks can accurately predict POD coefficients throughout the entire cardiac cycle, even during diastole where traditional single network approaches fail to make reliable predictions because the blood flow rate is typically lower, providing less information. The predicted coefficients can then be used to compute velocity and pressure fields inside the 3D flow domain, allowing for more detailed medical analysis. This approach also has the potential for a wide range of applications beyond medical flow fields, extending to various domains of fluid dynamics.
*UK Research and Innovation (UKRI)UCL Department of Mechanical EngineeringWellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS)
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