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Reducing the impact of geometric errors in flow computations using velocity measurements.

David NolteCristóbal Bertoglio
Published in: International journal for numerical methods in biomedical engineering (2019)
Numerical blood flow simulations are typically set up from anatomical medical images and calibrated using velocity measurements. However, the accuracy of the computational geometry itself is limited by the resolution of the anatomical image. We first show that applying standard no-slip boundary conditions on inaccurately extracted boundaries can cause large errors in the results, in particular the pressure gradient. In this work, we therefore propose to augment the flow model calibration by slip/transpiration boundary conditions, whose parameters are then estimated using velocity measurements. Numerical experiments show that this methodology can considerably improve the accuracy of the estimated pressure gradients and 3D velocity fields when the vessel geometry is uncertain.
Keyphrases
  • blood flow
  • deep learning
  • healthcare
  • patient safety
  • adverse drug
  • single molecule
  • emergency department
  • molecular dynamics
  • electronic health record
  • quality improvement
  • monte carlo