Macraild, M., Sarrami-Foroushani, A., Lassila, T. orcid.org/0000-0001-8947-1447 et al. (1 more author) (2024) Accelerated Simulation Methodologies for Computational Vascular Flow Modelling. Journal of the Royal Society Interface, 21 (211). 20230565. ISSN 1742-5689
Abstract
Vascular flow modelling can improve our understanding of vascular pathologies and aid in developing safe and effective medical devices. Vascular flow models typically involve solving the nonlinear Navier–Stokes equations in complex anatomies and using physiological boundary conditions, often presenting a multi-physics and multi-scale computational problem to be solved. This leads to highly complex and expensive models that require excessive computational time. This review explores accelerated simulation methodologies, specifically focusing on computational vascular flow modelling. We review reduced order modelling (ROM) techniques like zero-/one-dimensional and modal decomposition-based ROMs and machine learning (ML) methods including ML-augmented ROMs, ML-based ROMs and physics-informed ML models. We discuss the applicability of each method to vascular flow acceleration and the effectiveness of the method in addressing domain-specific challenges. When available, we provide statistics on accuracy and speed-up factors for various applications related to vascular flow simulation acceleration. Our findings indicate that each type of model has strengths and limitations depending on the context. To accelerate real-world vascular flow problems, we propose future research on developing multi-scale acceleration methods capable of handling the significant geometric variability inherent to such problems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
Keywords: | simulation acceleration, reduced order modelling, machine learning, vascular flow modelling, haemodynamics |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number Royal Academy of Engineering CiET1819\19 |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Jan 2024 11:23 |
Last Modified: | 26 Feb 2024 17:00 |
Published Version: | https://royalsocietypublishing.org/doi/10.1098/rsi... |
Status: | Published |
Publisher: | The Royal Society |
Identification Number: | 10.1098/rsif.2023.0565 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:207222 |