Lagrangian methods for blood damage estimation in cardiovascular devices--How numerical implementation affects the...

Area of Science:

• Biomedical Engineering
• Computational Fluid Dynamics
• Hemodynamics

Background:

• Cardiovascular devices require accurate prediction of blood damage.
• Lagrangian methods coupled with Eulerian computational fluid dynamics (CFD) are used for blood damage modeling.
• Numerical implementation choices can influence simulation outcomes.

Purpose of the Study:

• To evaluate the impact of various numerical implementation assumptions on blood damage prediction.
• To assess the influence of seeding patterns, stochastic walk models, and trajectory calculations.
• To analyze the effect of post-processing options like stress accumulation and time averaging.

Main Methods:

• Employed Lagrangian methods integrated with Eulerian CFD simulations.
• Investigated different seeding patterns for particle tracking.
• Utilized stochastic walk models and simplified pathline calculations.
• Evaluated single and repeated passage stress accumulation, along with time averaging for post-processing.

Main Results:

• Numerical implementation assumptions significantly alter predicted blood damage.
• Variations in seeding, stochastic models, and trajectory calculations lead to different stress accumulation results.
• Post-processing choices like repeated passages and time averaging also affect the final blood damage predictions.

Conclusions:

• Implementation assumptions critically influence Lagrangian blood damage model predictions.
• Researchers must carefully select assumptions based on the specific physics of cardiovascular devices.
• Sensitivity analyses are essential to validate model predictions and ensure reliability.