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Boundary Control in Computational Haemodynamics

by T. Koltukluoğlu and P. Blanco

(Report number 2017-41)

Abstract
In this work, a data assimilation method is proposed following an optimise-then- discretise approach, and is applied in the context of computational haemodynamics. The methodology aims to make use of phase-contrast magnetic resonance imaging to perform optimal flow control in computational fluid dynamic simulations. Flow matching between observations and model predictions is performed in luminal regions, excluding near-wall areas, improving the near-wall flow reconstruction to enhance the estimation of related quantities such as wall shear stresses. The proposed approach remarkably improves the flow field at the aortic root and reveals a great potential for predicting clinically relevant haemodynamic phenomenology. This work presents model validation against an analytical solution using the standard 3-D Hagen–Poiseuille flow, and validation with real data involving the flow control problem in a glass replica of a human aorta imaged with a 3T magnetic resonance scanner. In vitro experiments consist of both a numerically generated reference flow solution, which is considered as the ground truth, as well as real flow MRI data obtained from phase-contrast flow acquisitions. The validation against the in vitro flow MRI experiments is performed for different flow regimes and model parameters including different mesh refinements.

Keywords: Data Assimilation, Boundary Control, Blood Flow, Interlacing CFD with 4D flow MRI.

@Techreport{KB17_737,
  author = {T.  Koltukluoğlu and P. Blanco},
  title = {Boundary Control in Computational Haemodynamics},
  institution = {Seminar for Applied Mathematics, ETH Z{\"u}rich},
  number = {2017-41},
  address = {Switzerland},
  url = {https://www.sam.math.ethz.ch/sam_reports/reports_final/reports2017/2017-41.pdf },
  year = {2017}
}

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