Abstract Blood, a complex non-Newtonian suspension, exhibits macroscopic flow behavior emerging from the microscopic dynamics of highly deformable cells. This review analyzes the theoretical foundations and recent advances in simulating cellular blood flow, beginning with biomechanical models for cell deformation, aggregation, and adhesion, alongside numerical frameworks like fluid-structure interactions and embedded boundary methods. Driven by high-performance computing, simulations with billions of cells are now feasible, while artificial intelligence enhances capabilities in cell tracking, behavior prediction, and reduced-order modeling, all further supported by growing open-source tools. These capabilities are being adopted in biomedical fields, facilitating accurate patient-specific simulations. However, significant challenges persist in reconciling physical realism with computational efficiency, involving multiscale integration, physiological boundaries, computational cost, and experimental validation. Looking ahead, emerging paradigms like multiscale modeling, high-performance computing and data-physics integration offer a promising pathway toward building reliable digital twins of blood flow, narrowing the gap between simulation and clinical practice.
Ye et al. (Fri,) studied this question.