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Event-Driven Simulation of Particle-Particle and Particle-Surface Collisions in Ice Crystal Icing
Technical Paper
2019-01-2014
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
This paper describes an event-driven simulation tool for predicting particle-particle and particle-surface interactions in ice crystal icing (ICI). A new accretion model which is much less empirical than existing models for predicting ICI accretion is also described. Unlike previous models, the new “gouge/bounce model” (GBM) differentiates between (erosion) losses resulting from particle bounce and those resulting from particle gouging. A bounce threshold based on the tangential Stokes number is used to calculate most of the bounce loss. The GBM also predicts ejecta velocities and directions, at least approximately, which is important because most of the mixed-phase mass flux impacting a surface actually bounces off or erodes existing material in ICI, thereby increasing the mass flux downstream. The event-driven simulation tool, denoted COLLIDE, has been applied to two test cases in which accretion growth appeared to be affected by TWC in a manner beyond that which would be expected from the accumulation parameters. An existing correlation-based accretion model (CBM), modified to predict erosion dependence on particle diameter, is also implemented and applied to the test cases. COLLIDE predicted the observed accretion dependence on TWC in a least a qualitative fashion for the majority of model/test case permutations, supporting the hypothesis that collisions between backscattered and incident particles reduces erosion and thereby increases sticking efficiency as observed in experiments with larger particles. The predictions suggest scattering of incident particles by impacts with ejecta is the dominant mechanism responsible for the flux interference effect, not particle size reduction due to particle-particle collisions.
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Currie, T., "Event-Driven Simulation of Particle-Particle and Particle-Surface Collisions in Ice Crystal Icing," SAE Technical Paper 2019-01-2014, 2019, https://doi.org/10.4271/2019-01-2014.Data Sets - Support Documents
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