  Efficient sorting of target cells is crucial for advancing cellular research
in biology and medical diagnostics. Inertial microfluidics, an emerging
technology, offers a promising approach for label-free particle sorting with
high throughput. This paper presents a comprehensive study employing numerical
computational fluid dynamics (CFD) simulations to investigate particle
migration and sorting within a symmetric serpentine microchannel. By adopting a
Eulerian approach to solve fluid dynamics and a Lagrangian framework to track
particles, the research explores the impact of flow Reynolds number and the
number of loops in the serpentine channel on sorting efficiency. To generate a
robust data-driven model, the authors performed CFD simulations for 200
combinations of randomly generated data points. The study leverages the
collected data to develop a data-centric machine learning model capable of
accurately predicting flow parameters for specific sorting efficiencies.
Remarkably, the developed model achieved a 92% accuracy in predicting the
Channel Reynolds Number during testing. However, it is worth noting that the
model currently faces challenges in accurately predicting the required number
of loops for efficient sorting.
