Abstract
In this work the development of a machine learning-based Reduced Order Model (ROM)
for the investigation of hemodynamics in a patient-specific configuration of Coronary Artery
Bypass Graft (CABG) is proposed. The computational domain is referred to left branches
of coronary arteries when a stenosis of the Left Main Coronary Artery (LMCA) occurs.
The method extracts a reduced basis space from a collection of high-fidelity solutions via a
Proper Orthogonal Decomposition (POD) algorithm and employs Artificial Neural Networks
(ANNs) for the computation of the modal coefficients. The Full Order Model (FOM) is
represented by the incompressible Navier-Stokes equations discretized using a Finite Volume
(FV) technique. Both physical and geometrical parametrization are taken into account, the
former one related to the inlet flow rate and the latter one related to the stenosis severity.
With respect to the previous works focused on the development of a ROM framework for
the evaluation of coronary artery disease, the novelties of our study include the use of the
FV method in a patient-specific configuration, the use of a data-driven ROM technique and
the mesh deformation strategy based on a Free Form Deformation (FFD) technique. The
performance of our ROM approach is analyzed in terms of the error between full order and
reduced order solutions as well as the speed-up achieved at the online stage.
Lingua originale | English |
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pagine (da-a) | N/A-N/A |
Rivista | Journal of Scientific Computing |
Volume | 94 |
DOI | |
Stato di pubblicazione | Pubblicato - 2023 |
Keywords
- Coronary artery bypass grafts
- Finite volume
- Hemodynamics
- Machine learning
- Neural networks
- Proper orthogonal decomposition
- Reduced order models