Modelling Particulate Deposition in Gas Turbines Part II: Prediction and Validation of Deposition Distributions

Abstract 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 of non-spherical particles accurately using CFD would allow the prediction of in-service performance degradation and the design of deposition-tolerant hardware. 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. These are the first such simulations in the open literature to incorporate particle shape and surface roughness. The resulting spatial trends of deposition are compared with experimental data of particle deposition in an S-bend. The case considered is representative of the flow and metal conditions, particle size and loading distribution, and passage geometry seen in HPT blade cooling passages. Numerical predictions of deposition distribution show good qualitative agreement with experiments. The approach is therefore suitable to assess and compare the susceptibility of components to particulate damage in industry. To the authors' knowledge, this is the first example in the open literature of experimental comparison of deposition trends in CFD. The approach provided reasonable first-order estimations of quantitative deposition heights, which may be used to estimate component-level degradation in-engine. More accurate quantitative predictions of deposition will require a suitable deposit evolution and erosion model and improved material data, which are not yet available in the literature. The performance of the DEM bounce stick model was favourable compared to energy-based alternatives.