Pierfrancesco Siena
A machine learning-based reduced order model for the investigation of the blood flow patterns in presence of a stenosis of the left main coronary artery.
Rel. Claudio Canuto, Gianluigi Rozza. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Matematica, 2021
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Abstract
Coronary artery diseases represent a significant cause of death worldwide. In fact, their occlusion can cause lack of oxygen to the heart tissue, leading to heart failure or heart attack. Coronary artery bypass grafting is a surgical procedure to restore a proper blood supply. In this context, Computational Fluid Dynamics (CFD) can be a significant tool to improve coronary artery bypass grafts and to avoid unfavorable flow conditions in the region of the anastomosis, which can be associated with the failure of the surgical procedure. In this work the development of a Reduced Order Model (ROM) for the investigation of hemodynamics in a patient-specific configuration of coronary artery bypass graft is proposed.
The method deployed extracts a reduced basis space from a collection of highfidelity solutions via a Proper Orthogonal Decomposition (POD) algorithm and employs Artificial Neural Networks (ANN) for the computation of the modal coefficients
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