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arXiv · 2206.15384

A data-driven Reduced Order Method for parametric optimal blood flow control: application to coronary bypass graft

Caterina Balzotti·Pierfrancesco Siena·Michele Girfoglio·Annalisa Quaini·Gianluigi Rozza
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Abstract

We consider an optimal flow control problem in a patient-specific coronary artery bypass graft with the aim of matching the blood flow velocity with given measurements as the Reynolds number varies in a physiological range. Blood flow is modelled with the steady incompressible Navier-Stokes equations. The geometry consists in a stenosed left anterior descending artery where a single bypass is performed with the right internal thoracic artery. The control variable is the unknown value of the normal stress at the outlet boundary, which is need for a correct set-up of the outlet boundary condition. For the numerical solution of the parametric optimal flow control problem, we develop a data-driven reduced order method that combines proper orthogonal decomposition (POD) with neural networks. We present numerical results showing that our data-driven approach leads to a substantial speed-up with respect to a more classical POD-Galerkin strategy proposed in [59], while having comparable accuracy.

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Caterina Balzotti, Pierfrancesco Siena, Michele Girfoglio, Annalisa Quaini, Gianluigi Rozza. 2022-06-30. A data-driven Reduced Order Method for parametric optimal blood flow control: application to coronary bypass graft. https://arxiv.org/abs/2206.15384

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