Session: 36-02 Hot Section Deposition

Paper Number: 79511

79511 - Predicting and Validating Spatial Distributions of Particulate Deposition in Gas Turbine Components 

Cooling passages and the secondary air system of gas turbine components are prone to blockage from sand, dust and ash. The ability to model deposition in CFD would improve the ability to predict in-service performance degradation and the design of deposition-tolerant hardware. Predictions of deposition requires accurate modelling of particle transport and wall interactions. Bounce stick models predict whether a particle will bounce, stick, or shatter upon impact and calculate rebound trajectories while turbulent transport models allow the interaction of particles with near-wall turbulent structures to be predicted in RANS CFD solutions. This paper implements a Continuous Random Walk (CRW) transport model with a Discrete Elements Methods (DEM) based bounce stick model to predict deposition in CFD. The resulting spatial trends of deposition are compared with experimental data of particle deposition in dual S-bends at elevated temperatures. The case considered is representative of the flow conditions and particle behaviour seen in gas turbine cooling passages. Numerical predictions of deposition show good qualitative agreement with experimental data, hence validating the combined CRW/DEM approach for modelling particulate deposition in gas turbine components. To the author’s knowledge, this is the first example in the open literature of experimentally-validated deposition trends in CFD. Particle tracking simulations without turbophoresis modelling showed significant differences in particle impact velocities and locations, justifying the inclusion of the CRW model. Collision-induced rotation produced a 6% increase in total system deposition and altered its spatial distribution throughout the domain. The performance of the DEM bounce stick model was favourable compared to an energy-based alternative which showed reduced accuracy in inertia-dominated regimes where multiple bounces occurred per particle. Indeed, twenty-two impacts were observed, on average, per particle which reinforces the importance of an accurate bounce stick model.

Presenting Author: Jack Gaskell University of Oxford

Presenting Author Biography: Jack is undertaking his DPhil through the Centre for Doctoral Training (CDT) in Gas Turbine Aerodynamics. He completed his Master of Research degree at the University of Cambridge as part of the same programme following undergraduate studies at the University of Warwick.<br/><br/>Jack’s research focusses on modelling particulate deposition in gas turbines, primarily bounce-stick behaviour. Predicting deposition and erosion is desirable both to quantify the cost of ownership effects that arise from atmospheric contaminants and to allow the optimisation of component design to minimise engine deterioration. He is currently developing high performance numerical models of bounce-stick behaviour in addition to machine learning surrogate models for use with commercial CFD solvers. Modelling particle trajectory, rebound and deposition also has applications in additive manufacturing, pipeline engineering and biomedical sciences – particularly concerning the respiratory system.

Authors:

Jack Gaskell University of Oxford
Nikul Vadgama University of Oxford
Florian Villain University of Oxford
Simon Beal University of Oxford
Matthew Mcgilvray University of Oxford
David Gillespie University of Oxford
Benjamin Littley Rolls-Royce Plc.