Adaptive grid generation in a patient-specific cerebral aneurysm.

Area of Science:

• Computational Fluid Dynamics (CFD)
• Biomedical Engineering
• Medical Imaging

Background:

• Adaptive grid techniques enhance CFD accuracy and reduce computational cost.
• Traditional grid adaptation methods are often time-consuming and neglected.
• Cerebral aneurysm simulations require accurate flow analysis for rupture risk assessment.

Purpose of the Study:

• To develop a novel technique for adaptive grid generation in CFD simulations of cerebral aneurysms.
• To correlate flow characteristics with optimal grid size distribution.
• To improve accuracy and efficiency in patient-specific hemodynamics modeling.

Main Methods:

• Calculating kinematic curvature and torsion from the velocity field in cerebral aneurysm geometry.
• Developing a mathematical equation linking flow kinematics to adaptive grid element size.
• Quantifying flow complexity using velocity, vorticity, and their angular relationship.

Main Results:

• A direct relationship was established between flow complexity (kinematic curvature/torsion) and adaptive grid size.
• Specific element size requirements were identified for different flow regions (e.g., <10% radius in blebs, <1% in boundary layers) for 0.5% error.
• The technique successfully identified regions requiring grid refinement for accurate solutions.

Conclusions:

• Kinematic curvature and torsion are effective metrics for quantifying flow complexity in aneurysms.
• This adaptive remeshing technique enhances CFD accuracy and reduces computational time for hemodynamics simulations.
• The method holds potential for improving patient-specific aneurysm rupture risk assessment.