Immersed smoothed finite element method for fluid---structure interaction simulation of aortic valves

  • Authors:

  • Jianyao Yao;G. R. Liu;Daria A. Narmoneva;Robert B. Hinton;Zhi-Qian Zhang

  • Affiliations:

  • School of Aerospace Systems, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, USA;School of Aerospace Systems, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, USA;Department of Biomedical Engineering, University of Cincinnati, Cincinnati, USA;Division of Cardiology, The Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, USA;Singapore-MIT Alliance (SMA), National University of Singapore, Singapore, Singapore 117576

  • Venue:
  • Computational Mechanics
  • Year:
  • 2012

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Abstract

This paper presents a novel numerical method for simulating the fluid---structure interaction (FSI) problems when blood flows over aortic valves. The method uses the immersed boundary/element method and the smoothed finite element method and hence it is termed as IS-FEM. The IS-FEM is a partitioned approach and does not need a body-fitted mesh for FSI simulations. It consists of three main modules: the fluid solver, the solid solver and the FSI force solver. In this work, the blood is modeled as incompressible viscous flow and solved using the characteristic-based-split scheme with FEM for spacial discretization. The leaflets of the aortic valve are modeled as Mooney-Rivlin hyperelastic materials and solved using smoothed finite element method (or S-FEM). The FSI force is calculated on the Lagrangian fictitious fluid mesh that is identical to the moving solid mesh. The octree search and neighbor-to-neighbor schemes are used to detect efficiently the FSI pairs of fluid and solid cells. As an example, a 3D idealized model of aortic valve is modeled, and the opening process of the valve is simulated using the proposed IS-FEM. Numerical results indicate that the IS-FEM can serve as an efficient tool in the study of aortic valve dynamics to reveal the details of stresses in the aortic valves, the flow velocities in the blood, and the shear forces on the interfaces. This tool can also be applied to animal models studying disease processes and may ultimately translate to a new adaptive methods working with magnetic resonance images, leading to improvements on diagnostic and prognostic paradigms, as well as surgical planning, in the care of patients.