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

Summary

This paper evaluated the influence of various numerical implementation assumptions on predicting blood damage in cardiovascular devices using Lagrangian methods with Eulerian computational fluid dynamics. The implementation assumptions that were tested included various seeding patterns, stochastic walk model, and simplified trajectory calculations with pathlines. Post processing implementation options that were evaluated included single passage and repeated passages stress accumulation and time averaging. This study demonstrated that the implementation assumptions can significantly affect the resulting stress accumulation, i.e., the blood damage model predictions. Careful considerations should be taken in the use of Lagrangian models. Ultimately, the appropriate assumptions should be considered based the physics of the specific case and sensitivity analysis, similar to the ones presented here, should be employed.

KEYWORDS:

• Blood damage model
• computational fluid dynamics
• Lagrangian methods
• cardiovascular devices
• numerical modeling

Acknowledgments

The software was provided by an ANSYS Academic Partnership with Stony Brook University.

Key issues

• When comparing a single-passage stress accumulation (SA) in a device to a control case, for example, as in the case of a stented artery compared to an artery without one, the SA may become biased with higher values for the control because the closest particles to the wall are trapped in the much slower boundary layer flow and higher shear stress levels. In the presence of a device, the disturbed flow patterns prevent this bias from occurring.

• Repeated passages SA helps to eliminate SA bias for the case of laminar flow in straight tubes but is less useful for optimizing the device design for reduced thrombogenicity (lower SA).

• Seeding pattern can amplify a straight tube SA bias as it will be augmented in denser seeding regions. In the absence of flow mixing, this will not necessarily be eliminated by repeated passages.

• Time-averaging and nondimensionalizing the SA might help comparing trajectories with varying exposure times with the full range of the platelet activation level locus.

• Trajectories with stochastic walk model, representing turbulence effects, have slightly increased SA levels relative to trajectories that do not account for fluctuations.

• Coupled discrete phase model trajectories and pathlines show similar SA distribution but with different magnitudes.

• Instantaneous pathlines cannot be used for comparison of devices with turbulent flow even if the boundary conditions are constant.

Financial and competing interests disclosure

This study was funded by grants from the National Institutes of Health: NIBIB Quantum Award: Implementation Phase II U01 EB012487-0 (D.B.) received by D. Bluestein. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

ORCID

Gil Marom http://orcid.org/0000-0003-3130-3402

Additional information

Funding

We would like to acknowledge the funding that made this project possible. Funding came in part from The Nature Conservancy and from USDA-OREI grant # 22-1608-4873. We would also like to thank two anonymous reviewers for their valuable suggestions for the manuscript.