Endothelial shear stress from large-scale blood flow simulations

Area of Science:

• Biomedical Engineering
• Computational Fluid Dynamics
• Cardiovascular Research

Background:

• Accurate assessment of endothelial shear stress (ESS) is crucial for understanding cardiovascular health.
• Real-life case studies necessitate robust methods for ESS evaluation using anatomical data.
• Geometric complexities in anatomical data can introduce significant artifacts in fluid dynamic simulations.

Purpose of the Study:

• To determine the optimal smoothing parameters for geometric datasets in fluid dynamic simulations for ESS evaluation.
• To investigate the impact of geometric smoothing on flow patterns, particularly near bifurcations.
• To establish a reliable threshold for data smoothing to ensure numerical stability and accuracy in ESS analysis.

Main Methods:

• Anatomical data acquisition for patient-specific modeling.
• Fluid dynamic simulations were performed on geometrically smoothed datasets.
• A systematic series of simulations were conducted varying the level of geometric smoothing.
• Analysis focused on flow patterns and numerical stability in the proximity of vascular bifurcations.

Main Results:

• Geometric dataset smoothing is essential to prevent major artifacts in flow patterns.
• The numerical results of ESS simulations are insensitive to further smoothing below a length of approximately 0.5 mm.
• This smoothing threshold ensures reliable flow pattern analysis, especially at arterial bifurcations.

Conclusions:

• A smoothing length of approximately 0.5 mm represents an optimal parameter for evaluating endothelial shear stress in real-life case studies.
• Proper geometric data preparation is critical for accurate computational fluid dynamics analysis in cardiovascular research.
• The findings provide a guideline for improving the reliability and reproducibility of ESS evaluations.