Research Papers

Variability of Computational Fluid Dynamics Solutions for Pressure and Flow in a Giant Aneurysm: The ASME 2012 Summer Bioengineering Conference CFD Challenge

[+] Author and Article Information
David A. Steinman

e-mail: steinman@mie.utoronto.ca

Yiemeng Hoi, Damiaan F. Habets

University of Toronto,
Toronto, ON, M5S 3G8Canada

Liam Morris

Galway Mayo Institute of Technology,
Galway, Ireland

Michael T. Walsh, Adrian G. Lynch

University of Limerick,
Limerick, Ireland

Andreas S. Anayiotos

Cyprus University of Technology,
Limassol, 3036Cyprus

Yannis Papaharilaou

Foundation for Research and Technology–Hellas,
Heraklion, 71110Greece

Shawn C. Shadden

Illinois Institute of Technology,
Chicago, IL, 60616

Gábor Janiga

Otto von Guericke University of Magdeburg,
Magdeburg, 39106Germany

Patrick Segers

Ghent University,
9000 Ghent, Belgium

Neil W. Bressloff

University of Southampton,
Southampton, Hampshire, SO17 1BJUK

Frank H. Gijsen

Erasmus MC,
Rotterdam, Netherlands

Jordi Pallarés

University Rovira i Virgili,
Tarragona, Catalonia, 43007Spain

Jan Vierendeels

Ghent University,
Ghent, 9000Belgium

Aike Qiao

Beijing University of Technology,
Beijing, 100124China

David F. Kallmes

Mayo Clinic,
Rochester, MN, 55905

Quan Long

Brunel University,
London, UB8 3PHUK

Ender A. Finol

University of Texas at San Antonio,
San Antonio, TX, 78229

Kenichi Kono

Wakayama Rosai Hospital,
Wakayama, 640-8505Japan

Alexandra Lauric

Tufts Medical Center,
Boston, MA 02111

Kerem Pekkan

Carnegie Mellon University,
Pittsburgh, PA 15219

Alison L. Marsden

University of California San Diego,
San Diego, CA 92093

Marie Oshima

The University of Tokyo,
Tokyo, 153-8505Japan

Kengo Katagiri

Shibaura Institute of Technology,
Tokyo, 135-8548Japan

Spencer J. Sherwin

Imperial College London,
London, SW7 2AZUK

Leonid Goubergrits

Charite-Universitatsmedizin Berlin,
Berlin, 14195Germany

Mariana Mendina

Universidad de la Republica,
Montevideo, 11300Uruguay

Hui Meng

State University of New York at Buffalo,
Buffalo, NY 14203

George E. Karniadakis

Brown University,
Providence, RI 02912

Francis Loth

University of Akron,
Akron, OH 44325

1Corresponding author.

Contributed by the Bioengineering Division of ASME for publication in the JOURNAL OF BIOMECHANICAL ENGINEERING. Manuscript received November 7, 2012; final manuscript received December 22, 2012; accepted manuscript posted January 18, 2013; published online February 11, 2013. Editor: Victor H. Barocas.

J Biomech Eng 135(2), 021016 (Feb 11, 2013) (13 pages) Paper No: BIO-12-1542; doi: 10.1115/1.4023382 History: Received November 07, 2012; Revised December 22, 2012; Accepted January 18, 2013

Stimulated by a recent controversy regarding pressure drops predicted in a giant aneurysm with a proximal stenosis, the present study sought to assess variability in the prediction of pressures and flow by a wide variety of research groups. In phase I, lumen geometry, flow rates, and fluid properties were specified, leaving each research group to choose their solver, discretization, and solution strategies. Variability was assessed by having each group interpolate their results onto a standardized mesh and centerline. For phase II, a physical model of the geometry was constructed, from which pressure and flow rates were measured. Groups repeated their simulations using a geometry reconstructed from a micro-computed tomography (CT) scan of the physical model with the measured flow rates and fluid properties. Phase I results from 25 groups demonstrated remarkable consistency in the pressure patterns, with the majority predicting peak systolic pressure drops within 8% of each other. Aneurysm sac flow patterns were more variable with only a few groups reporting peak systolic flow instabilities owing to their use of high temporal resolutions. Variability for phase II was comparable, and the median predicted pressure drops were within a few millimeters of mercury of the measured values but only after accounting for submillimeter errors in the reconstruction of the life-sized flow model from micro-CT. In summary, pressure can be predicted with consistency by CFD across a wide range of solvers and solution strategies, but this may not hold true for specific flow patterns or derived quantities. Future challenges are needed and should focus on hemodynamic quantities thought to be of clinical interest.

Copyright © 2013 by ASME
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Fig. 1

Phase I CFD model geometry. Top left panel shows an isometric overview of the model, with scale bar indicating size and inset indicating density of surface triangulation provided to Challenge participants. The remaining three panels show the geometry viewed along each of the cardinal axes.

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Fig. 2

Phase I pulsatile flow rates. Both flow waveforms have identical shapes, with means derived from the nominal cycle-averaged inlet WSS indicated, as described in the Methods.

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Fig. 3

Physical model construction. (a) Low melting point metal alloy inner core. (b) Final clear polyester resin aneurysm flow model, with a penny (diameter 19.05 mm) indicating scale of model on the right. (c) Surface reconstruction of CT scans of model. (d) Final CFD model surface, after pressure port removal, surface smoothing, and end clipping.

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Fig. 4

Phase II measured flow rates. Shown in light red are the nine consecutive cycles superimposed, which were used to determined the respective phase-averaged flow rates (black lines).

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Fig. 5

Phase II measured pressures for pulsatile2 case. Shown are the measured inlet and outlet pressures, and the pressure drop derived from inlet-outlet pressure drop. Shown in red are the nine consecutive cycles superimposed, which were used to determine the respective phase-averaged inlet and outlet pressures, from which the phase-averaged pressure drop was calculated.

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Fig. 6

Surface maps of peak systolic pressures for phase I, pulsatile2 flow (PK2). Letters identify the different contributed solutions, and in all cases are presented with the inlet pressure set to 120 mm Hg and a color scale ranging from 80–120 mm Hg, as shown in panel A. Note the outlier cases H, M, U, as well as the original panel from Ref. [11], bottom right.

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Fig. 7

Surface maps of cycle-averaged pressures for phase I pulsatile2 flow (AV2). Inlet pressure is assumed to be 90 mm Hg, and the color scale now ranges from 75–90 mm Hg. Note the outlier cases M, N, U.

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Fig. 8

Phase I centerline pressures for pulsatile flows. Shown are peak systolic (PK1, PK2) and cycle-averaged (AV1, AV2) pressures relative to their respective inlet values. Nominal outliers (E, H, M, N, U) are shown as dotted lines. The two Nektar solutions (W, X) are shown as solid black lines. Inset is the model and centerline, indicating the locations of the axial coordinates.

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Fig. 9

Inlet-outlet pressure drops versus flow rate for phase I. Included are both pulsatile (AV1, AV2, PK1, PK2) and steady state (SS1, SS2, SS3, SS4) data. Symbols identify the median values; error bars identify the interquartile ranges. Best-fit second-order polynomial curves demonstrate the quadratic nature of the pressure versus flow relationship (R2 > 0.999 in both cases).

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Fig. 10

Phase I, pulsatile2 peak systolic (PK2) velocities. Shown are isosurfaces of velocity magnitude at 50 cm/s. The original panel from Ref. [11] is shown at bottom right.

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Fig. 11

Phase I, pulsatile2 cycle-average (AV2) velocities. Shown are isosurfaces of velocity magnitude at 30 cm/s.

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Fig. 12

Phase I centerline velocities for pulsatile2 flow. Shown are peak systolic (PK2) and cycle-averaged (AV2) velocity magnitudes. Nominal outliers from the pressure plots (E, H, M, N, U) are shown as dotted lines. The two Nektar solutions (W, X) are shown as solid black lines.

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Fig. 13

Surface maps of peak systolic pressures for phase II, pulsatile2 flow (PK2). Letters identify the different contributed solutions, and in all cases are presented with the inlet pressure set to 120 mm Hg and a color scale ranging from 80–120 mm Hg, as shown in panel A.

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Fig. 14

Surface maps of cycle-averaged pressures for phase II, pulsatile2 flow (AV2). Inlet pressure is assumed to be 90 mm Hg, and the color scale now ranges from 75–90 mm Hg.

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Fig. 15

Phase II centerline pressures, relative to CFD model inlet pressure. As before the two Nektar solutions (W, X) are shown as solid black lines; however, outliers are not highlighted by dotted lines. Inset is the model and centerline, indicating the locations of the axial coordinates. Also shown are the locations where inlet and outlet pressure were measured experimentally.

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Fig. 16

Phase II pressure drop versus flow rate. As in Fig. 9, both pulsatile and steady pressures are included, and phase I and phase II data are shown as the median and interquartile ranges. Experimental data are shown as means ± standard deviation based on three repeat runs for the pulsatile flows and six measurements for the steady flows. As indicated by the asterisk in the legend, phase I pressures have been scaled by a factor of 1.113 to account for the higher fluid density used for the phase II simulations and experiments. Best-fit second-order polynomial curves demonstrate the quadratic nature of the pressure drop versus flow relationship (R2 > 0.999 except for Expt Pulsatile, R2 = 0.98).

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Fig. 17

Cross-sectional areas of the two CFD models versus those from CT and OCT scans of the flow model. Note that the phase II CFD model and CT (London) area essentially overlap since the former was based on (volume-preserving) smoothing of the latter.

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Fig. 18

Phase III pressure drop versus flow rate, compared to phase I and experiments. See caption of Fig. 16 for further explanation of plot elements.



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