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Automated adaptive cardiovascular flow simulations

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Abstract

We present an automatic adaptive procedure to perform blood flow simulations in the cardiovascular system. The procedure allows the user to start with subject-specific data collected through clinical measurements, like magnetic resonance imaging (MRI) data, and evaluate physiological parameters of interest, like flow distribution, pressure variations, wall shear stress, in an automatic and efficient manner. The process involves construction of geometric models of blood vessels, specification of flow conditions and application of an adaptive flow solver. The latter is based on incompressible Navier–Stokes equations using adaptive spatial discretization (meshing) techniques. In this article, we demonstrate the method on a model of a human abdominal aorta of a normal subject with geometry and flow rates assimilated from MRI data. The results obtained show that boundary layer mesh adaptivity offers a better alternative leading to more accurate predictions, especially for key physiological quantities like wall shear stress.

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Acknowledgments

We gratefully acknowledge the support of this work by NSF grants ACI-0205741 and 0749152. We would also like to acknowledge that some of the computations carried in this study were performed on parallel computers obtained through NSF grant 0420703. The results presented in this article made use of the linear algebra library provided by ACUSIM Software Inc. The attribute management system used in this study was provided by Simmetrix Inc.

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Authors and Affiliations

  1. Scientific Computation Research Center, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Onkar Sahni, Kenneth E. Jansen & Mark S. Shephard

  2. E350 Clark Center, Stanford University, 318 Campus Drive, Stanford, CA, 94305, USA

    Charles A. Taylor

Authors
  1. Onkar Sahni
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  2. Kenneth E. Jansen
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  3. Charles A. Taylor
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  4. Mark S. Shephard
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Correspondence to Onkar Sahni.

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Sahni, O., Jansen, K.E., Taylor, C.A. et al. Automated adaptive cardiovascular flow simulations. Engineering with Computers 25, 25–36 (2009). https://doi.org/10.1007/s00366-008-0110-5

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  • DOI: https://doi.org/10.1007/s00366-008-0110-5

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