Session: 33-04: Particle Transport and Deposition in Gas Turbine Engines I

Submission Number: 175237

Quantifying the Impact of RANS Structural Uncertainty on Particle Transport in Compressors 

Reliable prediction of particle transport in turbomachinery is essential for evaluating deposition, erosion, and long-term performance degradation. Most existing particle-laden flow simulations rely on Reynolds-averaged Navier-Stokes (RANS) solutions for the carrier phase, yet the inherent structural uncertainty of RANS models is often overlooked in such studies, raising fundamental concerns regarding the credibility of particle-behavior predictions. To address the current gap between ``flow-field uncertainty'' and ``particle response'', this work introduces the application of the improved Eigenspace Perturbation Framework (EPF) to the transonic compressor NASA Rotor~67. Using six perturbation modes constructed under physical realizability constraints, this study presents a comprehensive quantification of how RANS structural uncertainty propagates through particle-laden flow systems. The results demonstrate that structural bias in turbulence modeling introduces non-negligible modulation effects on both 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 um particles, whose near-wall deposition is dominated by turbulent diffusion, this leads to a relative uncertainty in capture efficiency as high as 54.5%. Concurrently, the 1C mode enhances the hub-to-casing radial velocity, systematically affecting particle migration at the exit plane; the relative deviation in distribution uniformity, represented by the coefficient of variation, reaches 27.6%. These findings confirm that RANS structural uncertainty influences particle behavior across different scales through distinct physical pathways, and neglecting this effect introduces systematic bias into deposition-risk assessment and performance prediction. By establishing a complete quantitative framework linking ``carrier-phase uncertainty'' to ``key flow structures'' and ultimately to ``particle response'' in compressor particle-laden flow, this work provides essential methodological support for high-reliability design and life prediction of turbomachinery operating in particle-laden environments.

Presenting Author: Zhenfei Wang Xi'an Jiaotong University

Presenting Author Biography: Mr. Zhenfei Wang is a Ph.D. candidate in the Department of Fluid Machinery and Engineering at Xi'an Jiaotong University, China. He earned his Bachelor of Engineering degree from Xi'an Jiaotong University in 2022. His current research focus centers on the uncertainty quantification of turbulence modeling and its applications.

Authors:

Zhenfei Wang Xi'an Jiaotong University
Zhiheng Wang Xi'an Jiaotong University
Min Zeng Xi’an Jiaotong University
Zhu Huang Xi’an Jiaotong University
Guang Xi Xi’an Jiaotong University