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Visual inspections for identifying focusing points in inertial microfluidicflows are prone to misinterpreting stable locations and focusing shifts in thecase of non-trivial focusing patterns. We develop and deploy an approach forautomating the calculation of focusing patterns for a general channel geometry,and thereby reduce the dependence on empirical/visual procedures to confirm thepresence of stable locations. We utilize concepts from interpolation theory (torepresent continuous force-fields using discrete points), and stability theoryto identify "basins of attraction" and quantitatively identify stableequilibrium points. Our computational experiments reveal that predictingequilibrium points accurately requires upto $\times$10-20 times more refinedforce-maps that conventionally used, which highlights the spatial resolutionrequired for an accurate representation of cross-sectional forces. Thesefocusing patterns are validated using experimental results for a rectangularchannel, and triangular channel with an apex angle of $90^\circ$. We then applythe approach to predict and explain focusing patterns and shifts for a$90^\circ$-isosceles triangular channel across a range of Reynolds numbers for$\frac{a}{H} = 0.4$ (particle-to-channel size ratio). We observe that thepredicted focusing patterns match experiments well. The force-maps also revealcertain "clouds" of localized stable points, which aid in explaining the onsetof bifurcation observed in experiments. The current algorithm is agnostic tochannel cross-sections and straight/curved channels, which could pave way togenerating a library of focusing patterns as a function of channel geometry,and $Re$, to assist in design of novel devices for tailored particle-streams.

High throughput, automated prediction of focusing patterns for inertial microfluidics