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Accurate wall shear stress in immersed flow analysis with application to point cloud-based CFD

Monu Jaiswal, Ming-Chen Hsu

2026enpoint cloudcfdimmersed methodwall shear stressstress recoverynon-symmetric nitsche’s method

Abstract

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Point cloud-based CFD enables flow analysis directly on discrete points obtained from 3D scanning and medical imaging, bypassing surface reconstruction, geometry cleanup, and boundary-fitted mesh generation. Derived from immersogeometric analysis, the method immerses the point cloud in a background mesh and enforces no-slip conditions on discrete points through a Nitsche-based weak boundary condition (BC). The framework delivers accurate velocity fields, pressure distribution, and integrated loads; however, accurate prediction of the local wall shear stress (WSS) has remained a critical challenge. The geometry intersects the background mesh arbitrarily, producing cut elements that lack the regularity required for consistent gradient evaluation. The issue is compounded by the stabilization term of the weak BC, whose parameter estimation in the symmetric Nitsche formulation is dependent on the cut configuration and affects the variationally consistent definition of traction for which the WSS is computed. In this work, we propose a new method to obtain accurate wall shear stress in immersed flow analysis with application to point cloud-based CFD, using a non-symmetric Nitsche’s formulation with near-wall modeling and a patch-based stress recovery approach with traction compatibility. The method is validated on canonical benchmarks and applied to turbulent flow past a sphere and to a patient-specific aorta, showcasing excellent agreement with reference results.

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Cite This Work

@article{2b34908d-6119-4b5b-909a-745982cbcfd3,
  title={Accurate wall shear stress in immersed flow analysis with application to point cloud-based CFD},
  author={Monu Jaiswal and Ming-Chen Hsu},
  year={2026},
  language={en}
}
TY  - JOUR
TI  - Accurate wall shear stress in immersed flow analysis with application to point cloud-based CFD
AU  - Monu Jaiswal
AU  - Ming-Chen Hsu
PY  - 2026
LA  - en
ER  -

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