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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
https://doi.org/10.1115/gt2017...
Conference object . 2017 . Peer-reviewed
Data sources: Crossref
Journal of Engineering for Gas Turbines and Power
Article . 2017 . Peer-reviewed
Data sources: Crossref
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Development and Applications of a Coupled Particle Deposition Dynamic Mesh Morphing Approach for the Numerical Simulation of Gas Turbine Flows

Authors: Forsyth, P; Gillespie, D; McGilvray, M;

Development and Applications of a Coupled Particle Deposition Dynamic Mesh Morphing Approach for the Numerical Simulation of Gas Turbine Flows

Abstract

The presence and accretion of airborne particulates, including ash, sand, dust, and other compounds, in gas turbine engines can adversely affect performance and life of components. Engine experience and experimental work has shown that the thickness of accreted layers of these particulates can become large relative to the engine components on which they form. Numerical simulation to date, using a variety of flow coupling models, has largely ignored the effects of resultant changes in the passage geometry due to the build-up of deposited particles. This paper will focus on updating the boundaries of the flow volume geometry by integrating the deposited volume of particulates on the solid surface. Numerical models of small particulate turbulent motion and stick/bounce models are developed and integrated within commercial software to perform 3D fluid simulations to capture the deposition behaviour. The technique is implemented using a novel, coupled deposition-dynamic mesh morphing approach to the simulation of particulate-laden flows using RANS modelling of the bulk fluid, and Lagrangian-based particulate tracking. On an iterative basis the calculated particle deposition distributions are used to modify the surface topology by altering the locations of surface nodes. The mesh, continuous phase solution, and particle tracking are then recalculated, from which the mesh is again modified. The sensitivity to the modelling time steps employed is explored. This mesh morphing technique is further refined through the application of the particle stick-bounce model of Bons et al. [1] and the Continuous Random Walk model. An impingement geometry case is used to assess the validity of the technique, and a passage with film cooling holes is interrogated. The paper illustrates that for engine realistic levels of internal deposition this can lead to a significant disparity in the local aerodynamic flow field. Modelling of several internal flow fields have been investigated to illustrate the use of the technique. Differences are seen for all of the sticking and solid phase motion models employed. Notably, there are real discrepancies in using commercial and bespoke models. At small solid particle sizes considerable disparity is observed between the discrete and continuous random walk modelling approaches, while the position and level of accretion is altered through the use of a non-isotropic stick and bounce model.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
27
Top 10%
Top 10%
Top 10%
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Energy Research