Effective Block Preconditioners for Fluid Dynamics Coupled to Reduced Models of a Non-Local Nature

TL;DR

This paper introduces a novel block preconditioner for coupled fluid-structure interaction models in cardiovascular simulations, significantly improving computational efficiency over existing methods.

Contribution

A new 3x3 block preconditioner based on block factorization and Schur complement approximation for coupled Navier-Stokes and reduced-order models.

Findings

Up to six times shorter computational time.

Half the number of linear iterations compared to existing schemes.

Effective in complex, patient-specific cardiovascular simulations.

Abstract

Modeling cardiovascular blood flow is central to many applications in biomedical engineering. To accommodate the complexity of the cardiovascular system, in terms of boundary conditions and surrounding vascular tissue, computational fluid dynamics (CFD) often are coupled to reduced circuit and/or solid mechanics models. These allow for realistic simulations of hemodynamics in the heart or the aorta, but come at additional computational cost and complexity. In this contribution, we design a novel block preconditioner for the solution of the stabilized Navier-Stokes equations coupled to reduced-order models of a non-local nature. These models encompass lumped-parameter systems that impose flux-dependent boundary tractions, and Galerkin reduced-order models that can be used to account for outlying mechanical structures. Here we propose a 3x3 preconditioner derived from the block factorization and approximation to the Schur complement(s). The solver performance is demonstrated for a series of examples with increasing complexity, culminating in a reduced FSI simulation in a patient-specific contracting left heart model. For all test cases, we show that our proposed approach is superior to other frequently presented 2x2 schemes that merge stiffness contributions from reduced models into the fluid Jacobian or consolidate some variables for the purpose of efficiency - with an up to six times shorter overall computing time and/or only half as many linear iterations.