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Computational analysis of particle-laden-airflow erosion and experimental verification

Research output: Contribution to journalJournal articlepeer-review

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  • A. Castorrini
  • P. Venturini
  • A. Corsini
  • F. Rispoli
  • K. Takizawa
  • T.E. Tezduyar
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<mark>Journal publication date</mark>24/03/2020
<mark>Journal</mark>Computational Mechanics
Volume65
Number of pages17
Pages (from-to)1549–1565
Publication StatusPublished
Early online date24/03/20
<mark>Original language</mark>English

Abstract

Computational analysis of particle-laden-airflow erosion can help engineers have a better understanding of the erosion process, maintenance and protection of turbomachinery components. We present an integrated method for this class of computational analysis. The main components of the method are the residual-based Variational Multiscale (VMS) method, a finite element particle-cloud tracking (PCT) method with ellipsoidal clouds, an erosion model based on two time scales, and the Solid-Extension Mesh Moving Technique (SEMMT). The turbulent-flow nature of the analysis is addressed with the VMS, the particle-cloud trajectories are calculated based on the time-averaged computed flow field and closure models defined for the turbulent dispersion of particles, and one-way dependence is assumed between the flow and particle dynamics. Because the target-geometry update due to the erosion has a very long time scale compared to the fluid–particle dynamics, the update takes place in a sequence of “evolution steps” representing the impact of the erosion. A scale-up factor, calculated based on the update threshold criterion, relates the erosions and particle counts in the evolution steps to those in the PCT computation. As the target geometry evolves, the mesh is updated with the SEMMT. We present a computation designed to match the sand-erosion experiment we conducted with an aluminum-alloy target. We show that, despite the problem complexities and model assumptions involved, we have a reasonably good agreement between the computed and experimental data.