Drag coefficient prediction of complex-shaped snow particles falling in air beyond the Stokes regime
Giorgia Tagliavini, Mark W. McCorquodale, C. D. Westbrook, Pascal Corso, Quirine Krol, Markus Holzner
Abstract
This study considers complex ice particles falling in the atmosphere: predicting the drag of such particles is important for developing of climate models parameterizations. A Delayed-Detached Eddy Simulation model is developed to predict the drag coefficient of snowflakes falling at Reynolds number between 50 and 2200. We first consider the case where the orientation of the particle is known a posteriori, and evaluate our results against laboratory experiments using 3D-printed particles of the same shape, falling at the same Reynolds number. Close agreement is found in cases where the particles fall stably, while a more complex behavior is observed in cases where the flow is unsteady. The second objective of this study is to evaluate methods for estimating the drag coefficient when the orientation of the particles is not known a posteriori. We find that a suitable average of two orientations corresponding to the minimum and maximum eigenvalues of the inertia tensor provides a good estimate of the particle drag coefficient. Meanwhile, existing correlations for the drag on non-spherical particles produce large errors (≈ 50%). A new formula to estimate snow particles settling velocity is also proposed. Our approach provides a framework to investigate the aerodynamics of complex snowflakes and is relevant to other problems that involve the sedimentation of irregular particles in viscous fluids.