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Research Papers

A Simple Physics-Based Model for Particle Rebound and Deposition in Turbomachinery

[+] Author and Article Information
J. P. Bons

Aerospace Research Center,
Department of Mechanical and
Aerospace Engineering,
The Ohio State University,
Columbus, OH 43235
e-mail: bons.2@osu.edu

R. Prenter, S. Whitaker

Aerospace Research Center,
Department of Mechanical and
Aerospace Engineering,
The Ohio State University,
Columbus, OH 43235

1Corresponding author.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received January 10, 2017; final manuscript received January 25, 2017; published online March 28, 2017. Editor: Kenneth Hall.

J. Turbomach 139(8), 081009 (Mar 28, 2017) (12 pages) Paper No: TURBO-17-1007; doi: 10.1115/1.4035921 History: Received January 10, 2017; Revised January 25, 2017

A new model is proposed for predicting particle rebound and deposition in environments relevant to gas turbine engines. The model includes the following physical phenomena: elastic deformation, plastic deformation, adhesion, and shear removal. It also incorporates material property sensitivity to temperature and tangential-normal rebound velocity cross-dependencies observed in experiments. The model is well-suited for incorporation in computational fluid dynamics (CFD) simulations of complex gas turbine flows due to its algebraic (explicit) formulation. Model predictions are compared to coefficient of restitution data available in the open literature as well as deposition results from two different high-temperature turbine deposition facilities. While the model comparisons with experiments are in many cases promising, several key aspects of particle deposition remain elusive. The simple phenomenological nature of the model allows for parametric dependencies to be evaluated in a straightforward manner. It is hoped that this feature of the model will aid in identifying and resolving the remaining stubborn holdouts that prevent a universal model for particle deposition.

Copyright © 2017 by ASME
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References

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Figures

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Fig. 1

Microscope images of (a) ARD particles and (b) Icelandic volcanic ash particles

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Fig. 2

Particle representation as circular cylinder

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Fig. 3

Stress–strain plot indicating impact phases

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Fig. 4

Ideal normal CoR (CoRni) versus normal impact velocity (Vn1) for different σy (SY) values of ash particulate

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Fig. 5

Normalized contact area radius (acont/d) versus normalized deformation (w/d) for 10 μm ARD particle impacting Inco625 at Vn1 = 50 m/s. Comparison of current model with Singh and Tafti [15] elastic and elastic–plastic models.

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Fig. 6

Normal CoR and CoRni versus normal velocity (Vn1) for 1 < d < 50 μm. Ash particles with Fig. 4 properties.

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Fig. 7

CoRn versus Vn1 for a range of shear velocities from 2 to 23 m/s and CoRni versus Vn1. Data for 50 μm ash particle with low yield strength (σy = 50 MPa).

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Fig. 8

CoRn and CoRt versus α1 for quartz particle impact on aluminum. Model for 150 μm particles compared to data from Bons et al. [25] and Grant and Tabakoff [26].

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Fig. 9

CoRn versus Vn1 for three ash particle types impacting on 410SS. Model for 60 μm particles compared to data from Whitaker and Bons [23]. Singh and Tafti [15] plastic model predictions included. (a) JBPS subbituminous, (b) bituminous, and (c) lignite.

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Fig. 10

CoRn and CoRt versus α1 for ARD particle impact on 304SS. Model for 30 μm particles compared to data from Reagle et al. [17]. Model predictions using Singh and Tafti [15] for CoRn included.

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Fig. 11

Size distribution for JPBS subbituminous ash used by Ai and Fletcher [10] with piecewise linear distribution used in model

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Fig. 12

Two-dimensional fluent model of Ai and Fletcher [10] experimental apparatus used in case 4 comparison

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Fig. 13

Variation of particle normal and tangential impact velocity as well as impact efficiency versus diameter for the flow modeled in Fig. 12

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Fig. 14

Variation of CoRn versus diameter as a function of temperature for JBPS subbituminous ash

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Fig. 15

Predicted capture efficiency versus temperature including comparison to experimental data from Ref. [10]. Prediction from Ai and Fletcher [10] also included.

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Fig. 16

Two-dimensional fluent model of cooled nozzle guide vane from Prenter et al. [14]

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Fig. 17

Two-dimensional fluent model prediction of vane surface temperature

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Fig. 18

Capture efficiency for three vane cooling levels. Model predictions versus experimental data from Ref. [14].

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Fig. 19

Predicted deposit thickness versus percent wetted distance on the pressure surface. Thickness values are normalized by the peak value in the corresponding no cooling case for both experiment and the model.

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Fig. 20

Predicted deposit thickness versus percent wetted distance on the pressure surface for the uncooled case only: all impacts, first impacts, and repeat impacts. Thickness values are normalized by the peak value.

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