QUANTIFYING THE IMPACT OF RANS STRUCTURAL UNCERTAINTY ON PARTICLE TRANSPORT IN COMPRESSORS

Abstract

Reliable prediction of particle transport in turbomachinery is essential for evaluating deposition, erosion, and performance degradation. Most existing particle-laden flow simulations rely on RANS solutions, yet the inherent structural uncertainty (i.e., uncertainty in the shape and orientation of the modelled stress tensor) of RANS models is often overlooked, raising concerns regarding the credibility of particle-behavior predictions. To address the gap between flow-field uncertainty and particle response, this work applies the improved Eigenspace Perturbation Framework to NASA Rotor 67. Using six physically realizable perturbation modes, this study quantifies how RANS structural uncertainty propagates through particle-laden flows. The results demonstrate that structural bias in turbulence modeling introduces non-negligible modulation effects on particle deposition and exit-plane distribution. Specifically, the most influential 1C mode significantly alters the turbulent kinetic energy distribution within the passage. For 0.25 µm particles, whose near-wall deposition is dominated by turbulent diffusion, this leads to a relative uncertainty in capture efficiency of 54.5%. Concurrently, the 1C mode enhances hub-to-casing radial velocity, affecting exit-plane particle migration; the relative deviation in distribution uniformity (coefficient of variation) reaches 27.6%. These findings confirm that RANS structural uncertainty influences particle behavior across scales through distinct physical pathways, and neglecting this effect introduces systematic bias into deposition-risk assessment and performance prediction. By establishing a quantitative framework linking carrier-phase uncertainty to particle response, this work provides the foundation for high-reliability design and life prediction of turbomachinery in particle-laden environments.