Literature DB >> 27709800

An implicit solver for 1D arterial network models.

Jason Carson1, Raoul Van Loon1.   

Abstract

In this study, the 1D blood flow equations are solved using a newly proposed enhanced trapezoidal rule method (ETM), which is an extension to the simplified trapezoidal rule method. At vessel junctions, the conservation of mass and conservation of total pressure are held as system constraints using Lagrange multipliers that can be physically interpreted as external flow rates. The ETM scheme is compared with published arterial network benchmark problems and a dam break problem. Strengths of the ETM scheme include being simple to implement, intuitive connection to lumped parameter models, and no restrictive stability criteria such as the Courant-Friedrichs-Lewy (CFL) number. The ETM scheme does not require the use of characteristics at vessel junctions, or for inlet and outlet boundary conditions. The ETM forms an implicit system of equations, which requires only one global solve per time step for pressure, followed by flow rate update on the elemental system of equations; thus, no iterations are required per time step. Consistent results are found for all benchmark cases, and for a 56-vessel arterial network problem, it gives very satisfactory solutions at a spatial and time discretization that results in a maximum CFL of 3, taking 4.44 seconds per cardiac cycle. By increasing the time step and element size to produce a maximum CFL number of 15, the method takes only 0.39 second per cardiac cycle with only a small compromise on accuracy.
Copyright © 2016 John Wiley & Sons, Ltd.

Keywords:  1D arterial network; Lagrange multipliers; finite elements; implicit solvers; lumped models; penalty method

Mesh:

Year:  2016        PMID: 27709800     DOI: 10.1002/cnm.2837

Source DB:  PubMed          Journal:  Int J Numer Method Biomed Eng        ISSN: 2040-7939            Impact factor:   2.747


  10 in total

1.  Determining the impacts of venoarterial extracorporeal membrane oxygenation on cerebral oxygenation using a one-dimensional blood flow simulator.

Authors:  Bradley Feiger; Ajar Kochar; John Gounley; Desiree Bonadonna; Mani Daneshmand; Amanda Randles
Journal:  J Biomech       Date:  2020-03-03       Impact factor: 2.712

2.  A data-driven model to study utero-ovarian blood flow physiology during pregnancy.

Authors:  Jason Carson; Michael Lewis; Dareyoush Rassi; Raoul Van Loon
Journal:  Biomech Model Mechanobiol       Date:  2019-03-05

3.  Computational instantaneous wave-free ratio (IFR) for patient-specific coronary artery stenoses using 1D network models.

Authors:  Jason M Carson; Carl Roobottom; Robin Alcock; Perumal Nithiarasu
Journal:  Int J Numer Method Biomed Eng       Date:  2019-11       Impact factor: 2.648

4.  Non-invasive coronary CT angiography-derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies.

Authors:  Jason Matthew Carson; Sanjay Pant; Carl Roobottom; Robin Alcock; Pablo Javier Blanco; Carlos Alberto Bulant; Yuri Vassilevski; Sergey Simakov; Timur Gamilov; Roman Pryamonosov; Fuyou Liang; Xinyang Ge; Yue Liu; Perumal Nithiarasu
Journal:  Int J Numer Method Biomed Eng       Date:  2019-08-16       Impact factor: 2.747

5.  Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis.

Authors:  Neeraj Kavan Chakshu; Igor Sazonov; Perumal Nithiarasu
Journal:  Biomech Model Mechanobiol       Date:  2020-10-16

6.  A framework for incorporating 3D hyperelastic vascular wall models in 1D blood flow simulations.

Authors:  Alberto Coccarelli; Jason M Carson; Ankush Aggarwal; Sanjay Pant
Journal:  Biomech Model Mechanobiol       Date:  2021-03-08

7.  Personalising cardiovascular network models in pregnancy: A two-tiered parameter estimation approach.

Authors:  Jason Carson; Lynne Warrander; Edward Johnstone; Raoul van Loon
Journal:  Int J Numer Method Biomed Eng       Date:  2020-01-13       Impact factor: 2.648

8.  Influence of ageing on human body blood flow and heat transfer: A detailed computational modelling study.

Authors:  Alberto Coccarelli; Hayder M Hasan; Jason Carson; Dimitris Parthimos; Perumal Nithiarasu
Journal:  Int J Numer Method Biomed Eng       Date:  2018-07-23       Impact factor: 2.747

9.  A semi-active human digital twin model for detecting severity of carotid stenoses from head vibration-A coupled computational mechanics and computer vision method.

Authors:  Neeraj Kavan Chakshu; Jason Carson; Igor Sazonov; Perumal Nithiarasu
Journal:  Int J Numer Method Biomed Eng       Date:  2019-02-20       Impact factor: 2.747

10.  Artificial intelligence approaches to predict coronary stenosis severity using non-invasive fractional flow reserve.

Authors:  Jason M Carson; Neeraj Kavan Chakshu; Igor Sazonov; Perumal Nithiarasu
Journal:  Proc Inst Mech Eng H       Date:  2020-08-03       Impact factor: 1.617

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.