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Updated: Jul 21, 2026

Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation
Published on: June 4, 2015
Unresolved RBCs: An upscaling strategy for the CFD-DEM simulation of blood flow with deformable cells
Carmine Porcaro1, Mahdi Saeedipour1
1Department of Particulate Flow Modelling, Johannes Kepler University, A-4040 Linz, Austria; Linz Institute of Technology (LIT), Johannes Kepler University, A-4040 Linz, Austria.
Abstract:
Numerical simulation of blood flow is a challenging topic due to the multiphase nature of this biological fluid. The choice of a specific method among the ones available in literature is often motivated by the physical scale of interest. Single-phase approximation allows for lower computational time, but does not consider this multiphase nature. Cell-level simulation, on the other hand, requires high computational resources and is limited to small scales. This work proposes a scale-up approach for cell-level simulation of blood flow, in the framework of unresolved CFD-DEM technique. This method offers the possibility to simulate hundreds of thousands of particles with limited computational effort, but requires specific models for fluid-particle interactions. Regarding blood flow, drag and lift force acting on the red blood cells (RBCs) are responsible for several macroscopic blood characteristics. Despite several correlations available for drag and lift force acting on rigid particles, specific force models for the simulation of deformable particles compatible with RBCs physics are missing. This study employs data obtained from cell-level simulations to derive equations then used in unresolved simulation of RBCs. The strategy followed during the modeling phase is presented, together with the model verification and validation. This approach returns satisfying results when used to simulate blood flow in large-scale channels. Up to half a million RBCs are considered, and computational effort is reported to allow a comparison with other existing methods. Future perspectives include further improvement of the model, such as a deeper understanding of particle-particle interactions.
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