A new stabilized time-spectral finite element solver for fast simulation of blood flow

TL;DR

This paper introduces a stabilized time-spectral finite element method leveraging frequency domain discretization to significantly accelerate blood flow simulations, maintaining accuracy and stability while reducing computational costs.

Contribution

The paper presents a novel stabilized time-spectral finite element method that efficiently simulates time-periodic blood flows with improved speed and parallel scalability.

Findings

Achieves accurate blood flow simulations with as few as 7 modes.

Reduces computational cost to 11% of traditional methods.

Demonstrates improved parallel scalability for real-time applications.

Abstract

The increasing application of cardiorespiratory simulations for diagnosis and surgical planning necessitates the development of computational methods significantly faster than the current technology. To achieve this objective, we leverage the time-periodic nature of these flows by discretizing equations in the frequency domain instead of the time domain. This approach markedly reduces the size of the discrete problem and, consequently, the simulation cost. With this motivation, we introduce a finite element method for simulating time-periodic flows that are physically stable. The proposed time-spectral method is formulated by augmenting the baseline Galerkin's method with a least-squares penalty term that is weighed by a positive-definite stabilization matrix. An error estimate is established for the convective-diffusive system, showing that the proposed method emulates the behavior of existing standard time methods including optimal convergence rate in diffusive regimes and stability in strong convection. This method is tested on a patient-specific Fontan model at nominal Reynolds and Womersley numbers of 500 and 10, respectively, demonstrating its ability to replicate conventional time simulation results using as few as 7 modes at 11% of the computational cost. Owing to its higher local-to-processor computation density, the proposed method also exhibits improved parallel scalability for fast simulation of time-critical applications.