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DOI: 10.23952/cot.2022.26
Received July 23, 2022; Accepted September 26, 2022; Published November 31, 2022
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 [62], while having comparable accuracy.
How to Cite this Article:
C. Balzotti, P. Siena, M. Girfoglio, A. Quaini, G. Rozza, A data-driven reduced order method for parametric optimal blood flow control: Application to coronary bypass graft, Commun. Optim. Theory 2022 (2022) 26.