Work

Scale-dependent mechanisms of near-wall platelet enrichment in arterial flows

Year: 2026

Type: article

Abstract: Platelet transport is essential for arterial thrombosis, yet the mechanisms governing near-wall platelet enrichment at arterial scales remain poorly understood. While red blood cell (RBC)–driven platelet margination is well established in microvascular flows, its relevance in larger arteries with higher Reynolds numbers and complex three-dimensional structures remains unclear. Here, we systematically examine platelet transport across arterial scales by comparing four platelet transport models, including inertial and non-inertial equations with and without a hematocrit-enhanced drift velocity. In axisymmetric flows, RBC-driven platelet margination induces a pronounced near-wall platelet enrichment at the arteriole scale, but this effect rapidly diminishes with increasing vessel diameter and Reynolds number, resulting in nearly uniform platelet distributions in arteries regardless of the transport equation employed. In contrast, simulations in three-dimensional curved vessels show that curvature-induced secondary flows generate substantial cross-stream platelet transport at arterial scales. The magnitude of this near-wall platelet enrichment correlates with the Dean number, indicating that secondary flows, rather than RBC-driven margination, dominate platelet transport in arteries. Together, these findings demonstrate that platelet transport mechanisms are scale-dependent: RBC-driven margination governs platelet transport in arterioles, whereas geometry-driven secondary flow advection governs arterial-scale transport. This work clarifies the physical relevance of hematocrit-enhanced drift models across vascular scales and informs the selection of platelet transport formulations for multiscale thrombosis simulations.

Source: Physics of Fluids

DOI: 10.1063/5.0324694

Authors: Arnav Garcha, Noelia Grande Gutiérrez

Institution: Carnegie Mellon University

Language: English

Topic: Blood properties and coagulation

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

Open Access status: hybrid

Funders: Natural Sciences and Engineering Research Council of Canada, Center for Machine Learning and Health, School of Computer Science, Carnegie Mellon University

Award: G2345266238