A non-intrusive data-driven ROM framework for hemodynamics problems

TL;DR

This paper introduces a non-intrusive, data-driven reduced order modeling framework for simulating hemodynamics in cardiovascular applications, enabling efficient predictions from high-fidelity CFD data without requiring extensive numerical expertise.

Contribution

The paper presents a novel non-intrusive ROM approach using PODI for hemodynamics, along with a user-friendly web application for easy deployment and testing.

Findings

Efficient reconstruction of blood flow patterns for different parameters.

Application to aortic blood flow with LVAD shows promising results.

Framework reduces computational effort compared to traditional methods.

Abstract

Reduced order modeling (ROM) techniques are numerical methods that approximate the solution of parametric partial differential equation (PDE) by properly combining the high-fidelity solutions of the problem obtained for several configurations, i.e. for several properly chosen values of the physical/geometrical parameters characterizing the problem. In this contribution, we propose an efficient non-intrusive data-driven framework involving ROM techniques in computational fluid dynamics (CFD) for hemodynamics applications. By starting from a database of high-fidelity solutions related to a certain values of the parameters, we apply the proper orthogonal decomposition with interpolation (PODI) and then reconstruct the variables of interest for new values of the parameters, i.e. different values from the ones included in the database. Furthermore, we present a preliminary web application through which one can run the ROM with a very user-friendly approach, without the need of having expertise in the numerical analysis and scientific computing field. The case study we have chosen to test the efficiency of our algorithm is represented by the aortic blood flow pattern in presence of a Left Ventricular Assist Device (LVAD) when varying the pump flow rate.