Model order reduction of flow based on a modular geometrical approximation of blood vessels

Abstract

We are interested in a reduced order method for the efficient simulation of blood flow in arteries. The blood dynamics is modeled by means of the incompressible Navier-Stokes equations. Our algorithm is based on an approximated domain-decomposition of the target geometry into a number of subdomains obtained from the parametrized deformation of geometrical building blocks (e.g. straight tubes and model bifurcations). On each of these building blocks, we build a set of spectral functions by proper orthogonal decomposition of a large number of snapshots of finite element solutions (offline phase). The global solution of the Navier-Stokes equations on a target geometry is then found by coupling linear combinations of these local basis functions by means of spectral Lagrange multipliers (online phase). Being that the number of reduced degrees of freedom is considerably smaller than their finite element counterpart, this approach allows us to significantly decrease the size of the linear system to be solved in each iteration of the Newton-Raphson algorithm. We achieve large speedups with respect to the full order simulation (in our numerical experiments, the gain is at least of one order of magnitude and grows inversely with respect to the reduced basis size), whilst still retaining satisfactory accuracy for most cardiovascular simulations.

References in corpus (4)

• Orthogonal polynomials of several variables
• Parametric model order reduction and its application to inverse analysis of large nonlinear coupled cardiac problems
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Cited by in corpus (4)

• Automated generation of 0D and 1D reduced-order models of patient-specific blood flow
• A one-shot overlapping Schwarz method for component-based model reduction: application to nonlinear elasticity
• Space-time reduced basis methods for parametrized unsteady Stokes equations
• A reduced order model for domain decompositions with non-conforming interfaces