CFD Simulations AC7-04: Difference between revisions
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'''Application Challenge AC7-04''' © copyright ERCOFTAC 2021 | '''Application Challenge AC7-04''' © copyright ERCOFTAC 2021 | ||
=CFD Simulations= | =CFD Simulations= | ||
==Overview of CFD | ==Overview of CFD Simulation== | ||
Large Eddy Simulations were carried out using the in-house, massively parallel and multiphysics YALES2BIO solver based on YALES2 [4] developed at CORIA (Rouen, France). YALES2BIO is dedicated to the simulation of blood flows at the macroscopic and microscopic scales. The base is a solver for the incompressible Navier-Stokes equations. The equations are discretised using a finite-volume fourth-order scheme, adapted to unstructured meshes [5,6]. The divergence-free | Large Eddy Simulations were carried out using the in-house, massively parallel and multiphysics YALES2BIO solver based on YALES2 [4] developed at CORIA (Rouen, France). YALES2BIO is dedicated to the simulation of blood flows at the macroscopic and microscopic scales. The base is a solver for the incompressible Navier-Stokes equations. The equations are discretised using a finite-volume fourth-order scheme, adapted to unstructured meshes [5,6]. The divergence-free | ||
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fourth-order Runge-Kutta scheme [6,8] in a prediction step. This predicted field is then corrected by a pressure gradient, obtained by solving a Poisson equation to calculate pressure. This equation is solved with the Deflated Preconditioned Conjugate Gradient algorithm [9]. YALES2BIO was validated and successfully used in many configurations relevant to cardiovascular biomechanics (see [10] for a list of publications). The boundary conditions applied at the inlet came from the data acquired during the experiment (2D cine PC-MRI). | fourth-order Runge-Kutta scheme [6,8] in a prediction step. This predicted field is then corrected by a pressure gradient, obtained by solving a Poisson equation to calculate pressure. This equation is solved with the Deflated Preconditioned Conjugate Gradient algorithm [9]. YALES2BIO was validated and successfully used in many configurations relevant to cardiovascular biomechanics (see [10] for a list of publications). The boundary conditions applied at the inlet came from the data acquired during the experiment (2D cine PC-MRI). | ||
== | ==Simulation Case== | ||
==Computational Domain== | ===Computational Domain=== | ||
==Boundary Conditions== | ===Solution Strategy=== | ||
== | ===Boundary Conditions=== | ||
==Numerical Accuracy== | ===CFD post-processing=== | ||
===Numerical Accuracy=== | |||
<br/> | <br/> | ||
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Revision as of 12:31, 26 July 2021
A pulsatile 3D flow relevant to thoracic hemodynamics: CFD - 4D MRI comparison
Application Challenge AC7-04 © copyright ERCOFTAC 2021
CFD Simulations
Overview of CFD Simulation
Large Eddy Simulations were carried out using the in-house, massively parallel and multiphysics YALES2BIO solver based on YALES2 [4] developed at CORIA (Rouen, France). YALES2BIO is dedicated to the simulation of blood flows at the macroscopic and microscopic scales. The base is a solver for the incompressible Navier-Stokes equations. The equations are discretised using a finite-volume fourth-order scheme, adapted to unstructured meshes [5,6]. The divergence-free property of the velocity field is ensured thanks to the projection method introduced by Chorin [7]. The velocity field is first advanced in time using a low-storage fourth-order Runge-Kutta scheme [6,8] in a prediction step. This predicted field is then corrected by a pressure gradient, obtained by solving a Poisson equation to calculate pressure. This equation is solved with the Deflated Preconditioned Conjugate Gradient algorithm [9]. YALES2BIO was validated and successfully used in many configurations relevant to cardiovascular biomechanics (see [10] for a list of publications). The boundary conditions applied at the inlet came from the data acquired during the experiment (2D cine PC-MRI).
Simulation Case
Computational Domain
Solution Strategy
Boundary Conditions
CFD post-processing
Numerical Accuracy
Contributed by: Morgane Garreau — University of Montpellier, France
© copyright ERCOFTAC 2021