Shape matters: Lagrangian tracking of complex nonspherical microparticles in superellipsoidal approximation

Abstract

Humans are constantly exposed to airborne pollutants such as pollen, exhaust residues, microplastics, fabrics, aerosols, or, as recently, ash particles from volcanic eruptions, which are rarely perfectly spherical. In order to reduce the impact of harmful particles or, on the contrary, to improve the targeted delivery of drugs, understanding the motion of complex shaped particles in fluid flows is of key interest. Common models mainly use shape factors to account for deviations from spherical shape, but these often fail to accurately predict particle motion. We advocate a more accurate modeling of complex particles by a superellipsoidal shape approximation, which allows covering a wide range of particle geometries. Superellipsoidal particle shapes allow for a novel approximation of translational and rotational resistance tensors, derived based on data from dedicated DNS computations. Our surrogate approach for convex bodies (ϵ1,ϵ2∈(0,2)), implemented in OpenFOAM® based on Lagrangian particle tracking, is first validated based on experimental and in-silico results from the literature, followed by a comparison of the effects of non-sphericity for some well known fluid flow cases, such as a lid-driven cavity (validated for Re=470, St=0.0023), pipe flow (validated for Re=137, St=0.01) and a simplified bifurcation (validated for Re=500, St=0.5×10−2−0.5). We show that using shape factor assisted spherical or ellipsoidal approximations of the particles leads to insufficient accuracy of computation of the particle’s trajectory, whereas the newly derived superellipsoidal drag and torque models proved to provide a superior accuracy of the Lagrangian particle tracking over simplified non-spherical particle approximations.