A Meshless Solver for Blood Flow Simulations in Elastic Vessels Using Physics-Informed Neural Network

TL;DR

This paper introduces a meshless, physics-informed neural network method for simulating blood flow in elastic vessels, offering efficiency and flexibility over traditional discretization techniques, especially for complex geometries.

Contribution

The paper presents a novel meshless PINN approach for blood flow simulation in deformable vessels, integrating fluid-structure interaction and leveraging GPU acceleration.

Findings

Achieved accurate blood flow simulations with low relative error.

Demonstrated significant computational efficiency over finite element methods.

Successfully handled complex vessel geometries and plaque presence.

Abstract

Investigating blood flow in the cardiovascular system is crucial for assessing cardiovascular health. Computational approaches offer some non-invasive alternatives to measure blood flow dynamics. Numerical simulations based on traditional methods such as finite-element and other numerical discretizations have been extensively studied and have yielded excellent results. However, adapting these methods to real-life simulations remains a complex task. In this paper, we propose a method that offers flexibility and can efficiently handle real-life simulations. We suggest utilizing the physics-informed neural network (PINN) to solve the Navier-Stokes equation in a deformable domain, specifically addressing the simulation of blood flow in elastic vessels. Our approach models blood flow using an incompressible, viscous Navier-Stokes equation in an Arbitrary Lagrangian-Eulerian form. The mechanical model for the vessel wall structure is formulated by an equation of Newton's second law of momentum and linear elasticity to the force exerted by the fluid flow. Our method is a mesh-free approach that eliminates the need for discretization and meshing of the computational domain. This makes it highly efficient in solving simulations involving complex geometries. Additionally, with the availability of well-developed open-source machine learning framework packages and parallel modules, our method can easily be accelerated through GPU computing and parallel computing. To evaluate our approach, we conducted experiments on regular cylinder vessels as well as vessels with plaque on their walls. We compared our results to a solution calculated by Finite Element Methods using a dense grid and small time steps, which we considered as the ground truth solution. We report the relative error and the time consumed to solve the problem, highlighting the advantages of our method.