Data-Driven Reduced Order Modelling for Patient-Specific Hemodynamics of Coronary Artery Bypass Grafts with Physical and Geometrical Parameters

In this work the development of a machine learning-based Reduced Order Model (ROM)\r\nfor the investigation of hemodynamics in a patient-specific configuration of Coronary Artery\r\nBypass Graft (CABG) is proposed. The computational domain is referred to left branches\r\nof coronary arteries when a stenosis of the Left Main Coronary Artery (LMCA) occurs.\r\nThe method extracts a reduced basis space from a collection of high-fidelity solutions via a\r\nProper Orthogonal Decomposition (POD) algorithm and employs Artificial Neural Networks\r\n(ANNs) for the computation of the modal coefficients. The Full Order Model (FOM) is\r\nrepresented by the incompressible Navier-Stokes equations discretized using a Finite Volume\r\n(FV) technique. Both physical and geometrical parametrization are taken into account, the\r\nformer one related to the inlet flow rate and the latter one related to the stenosis severity.\r\nWith respect to the previous works focused on the development of a ROM framework for\r\nthe evaluation of coronary artery disease, the novelties of our study include the use of the\r\nFV method in a patient-specific configuration, the use of a data-driven ROM technique and\r\nthe mesh deformation strategy based on a Free Form Deformation (FFD) technique. The\r\nperformance of our ROM approach is analyzed in terms of the error between full order and\r\nreduced order solutions as well as the speed-up achieved at the online stage.