Grid convergence errors in hemodynamic solution of patient-specific cerebral aneurysms

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Science

Background:

• Computational fluid dynamics (CFD) is a powerful tool for analyzing hemodynamic dysfunctions.
• CFD offers detailed insights into phenomena not easily captured by in vivo imaging.
• Accurate CFD solutions in complex geometries are challenging due to grid distortion.

Purpose of the Study:

• To present a comprehensive solution verification of intra-aneurysmal flow in patient-specific cerebral aneurysms.
• To assess grid convergence errors across diverse aneurysm morphologies.
• To establish the reliability of CFD analysis for cerebral aneurysm hemodynamics.

Main Methods:

• Utilized five patient-specific cerebral aneurysm models with varying dome morphologies.
• Estimated grid convergence errors using the Richardson extrapolation method.
• Analyzed velocity, pressure, and wall shear stress on multiple high-quality grids.

Main Results:

• Four out of five models exhibited monotonic grid convergence for key hemodynamic parameters.
• Maximum uncertainty magnitudes ranged from 12% to 16% on the finest grids for these models.
• The fifth model, due to geometric complexity, showed oscillatory grid convergence errors.

Conclusions:

• Solution verification is crucial for trusting CFD results in cerebral aneurysm analysis.
• Patient-specific grid convergence studies are necessary, especially for complex geometries.
• CFD provides quantifiable hemodynamic data, but rigorous verification ensures accuracy.