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Research Papers

An Energy-Based Fouling Model for Gas Turbines: EBFOG

[+] Author and Article Information
Nicola Casari

Engineering Department,
University of Ferrara,
Via Saragat, 1,
Ferrara 44122, Italy;
Department of Mechanical Engineering,
Imperial College London,
London SW7 2AZ, UK
e-mails: nicola.casari@unife.it; nicola.casari15@imperial.ac.uk

Michele Pinelli, Alessio Suman

Engineering Department,
University of Ferrara,
Via Saragat, 1,
Ferrara 44122, Italy

Luca di Mare

Department of Mechanical Engineering,
Imperial College London,
London SW7 2AZ, UK

Francesco Montomoli

Department of Aeronautics,
Imperial College London,
London SW7 2AZ, UK

1Corresponding author.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received July 19, 2016; final manuscript received August 5, 2016; published online September 27, 2016. Editor: Kenneth Hall.

J. Turbomach 139(2), 021002 (Sep 27, 2016) (8 pages) Paper No: TURBO-16-1162; doi: 10.1115/1.4034554 History: Received July 19, 2016; Revised August 05, 2016

Fouling is a major problem in gas turbines for aeropropulsion because the formation of aggregates on the wet surfaces of the machine affects aerodynamic and heat loads. The representation of fouling in computational fluid dynamics (CFD) is based on the evaluation of the sticking probability, i.e., the probability a particle touching a solid surface has to stick to that surface. Two main models are currently available in literature for the evaluation of the sticking coefficient: one is based on a critical threshold for the viscosity, and the other is based on the normal velocity to the surface. However, both models are application specific and lack generality. This work presents an innovative model for the estimation of the sticking probability. This quantity is evaluated by comparing the kinetic energy of the particle with an activation energy which describes the state of the particle. The sticking criterion takes the form of an Arrhenius-type equation. A general formulation for the sticking coefficient is obtained. The method, named energy-based fouling (EBFOG), is the first “energy”-based model presented in the open literature able to account any common deposition effect in gas turbines. The EBFOG model is implemented into a Lagrangian tracking procedure, coupled to a fully three-dimensional CFD solver. Particles are tracked inside the domain, and equations for the momentum and temperature of each particle are solved. The local geometry of the blade is modified accordingly to the deposition rate. The mesh is modified, and the CFD solver updates the flow field. The application of this model to particle deposition in high-pressure turbine vanes is investigated, showing the flexibility of the proposed methodology. The model is particularly important in aircraft engines where the effect of fouling for the turbine, in particular the reduction of the high pressure (HP) nozzle throat area, influences heavily the performance by reducing the core capacity. The energy-based approach is used to quantify the throat area reduction rate and estimate the variation in the compressor operating condition. The compressor operating point as a function of the time spent operating in a harsh environment can be in this way predicted to estimate, for example, the time that an engine can fly in a cloud of volcanic ashes. The impact of fouling on the throat area of the nozzle is quantified for different conditions.

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References

Figures

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Fig. 1

Computational procedure

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Fig. 2

Sticking evaluation procedure

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Fig. 3

Comparison between different cases. References: 1 [24], 2 [16], 3 [15], 4 [17], 5 [18], and 6 [30].

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Fig. 4

Comparison between different cases. References: 1 [17], 2 [15], 3 [16], 4 [24], 5 [18], and 6 [30].

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Fig. 5

Universal law for the activation energy variation with the temperature. References: 1 [24], 2 [16], 3 [15], 4 [17], 5 [18], 6 [31], 7 [31], and 8 [30].

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Fig. 6

Thickness distribution on the LS89 VKI blade—particle size 1 μm at T = 1800 K

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Fig. 7

Thickness distribution on the LS89 VKI blade—particle size 25 μm at T = 1800 K

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Fig. 8

Area variation as function of the concentration and the reduced temperature

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