Mechanism and modelisation of fouling in gas turbine ... Fer… · Aula Magna ex Chiesa di...

Mechanism and modelisation of fouling

in gas turbine compressors

M. Pinelli, P.R. Spina

Engineering DepartimentUniversity of Ferrara

Giornata di studio sulle Turbomacchine, Bergamo 15 Luglio 2016, Università degli Studi di Bergamo

Aula Magna ex Chiesa di Sant’Agostino

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Gruppo di Macchine a Fluido al Dipartimento di Ingegneria di Ferrara

Name Rank Research topic

Pier Ruggero Spina Full Professor Turbomachinery and Energy Systems

Michele Pinelli Associate Professor Measurement and CFD

Mauro Venturini Associate Professor Prognostic and Energy Systems

Alessio Suman Research Associate Measurement and CFD

Nicola Aldi Ph.D. student CFD and Design methods

Nicola Casari Ph.D. student Open Source CFD

Carlo Buratto Ph.D. student Open Source CFD

Enrico Munari Ph.D. student Dynamic modelling and Measurement

Hilal Bahalawan Ph.D. student Energy Systems

Devid Dainese Post-graduate internship CFD and Design methods

Matteo Occari Post-graduate internship Non-Newtonian pumps

Visiting and external collaborator

Mirko Morini Assistant professor (UniPr) Dynamic modelling

Alessandro Carandina Consultant (Fluid-A) Measurement and control

Claudio Pavan Consultant (Solid Energy) Rapid prototyping and reverse engineering

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Attività sperimentale (Ricerca)

Banco prova di piccola taglia per compressori� Banco prova con motore elettrico da 87 kW e velocità massima 36000 rpm� Utilizzato prevalentemente per prove su un compressore Allison 250-C18 per

� validazione di modelli dinamici� studio della wet compression� analisi vibro-acustiche a scopo diagnostico� studio del degrado per ingestione di aria con acqua e/o polvere

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Attività sperimentale (Ricerca)

� Ricostruzione di geometrie tridimensionali reali attraverso braccio antropomorfo con scanner laser 3D e generazione del file CAD 3D

� Dominio CFD altamente rappresentativo della geometria reale

Real components

Laser scanner RE procedure

3D CAD models

Laboratorio di Reverse Engineering

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0.6

0.7

0.8

0.9

1.0

1.1

0.8 0.9 1.0 1.1φ*

ψp*

Scaling

CFD - base-S

CFD - thick-R1

Simulazione numerica (Ricerca)

� Analisi CFD finalizzate alla validazione, messa a punto di modelli per la simulazione delle macchine e ottimizzazione delle prestazioni di pompe, ventilatori compressori

� Utilizzo di codici commerciali e Open Source

Analisi numerica CFD di macchine operatrici

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Modelli dinamici (Ricerca)

� Sviluppo di modelli dinamici per l’analisi del comportamento in condizioni di stallo e di pompaggio

Modellizzazione dinamica di macchine operatrici

� Confronto e messa a punto attraverso risultati sperimentali ottenuti con il banco prova compressori

Mechanism and quantification of fouling in gas turbine compressors, M. Pinelli

SP

EC

IAL

DU

TY

CE

NT

RIF

UG

AL

PU

MP

SO

PE

RAT

ING

WIT

H

NO

N-NE

WTO

NIA

NF

LUID

S

REVERSE ENGINEERING

CF

D S

IMU

LAT

ION

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USING SHAPE MEMORY ALLOYS FOR IMPROVING AUTOMOTIVE FAN BLADE

PERFORMANCES: EXPERIMENTAL AND CFD ANALYSIS

Polymeric mixture of Nylon PA 6.6, glass fibers and elastomer

+SMA stripsNi50.2Ti49.8

Morphing blade activation test: SMA strips tend to recover the memorized bent shape and the blade structure is forced to bend

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FLUID DYNAMIC DESIGN AND OPTIMIZATION OF TWO STAGE HIGH

PERFORMANCE CENTRIFUGAL FAN FOR INDUSTRIAL BURNERS

Two-stage centrifugal fan for large size burners (up to 8-10 MW) with aerodynamically stabilized flames .


Mechanism

and modelisation

of fouling

in gas turbine compressors

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Introduction

� Blade deterioration (fouling, erosion, mechanical damage) is one of the most common causes of GT performance losses.

� Fouling is responsible of about 80 % of all gas turbine performance losses accumulated during the operation

� Output losses between 2 % (under favorable conditions) and 15 % to 20 % (under adverse conditions) have been experienced

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Outline

� Starting from the early ’90, our group is working on diagnostic of gas turbine operating state by using Gas Path Analysis methods

� Gas turbine operating state determination consists of the assessment of the modification, due to deterioration and fault, of performance and geometric data characterizing machine components.

� In particular, fouling mechanism, modeling and effect on axial compressor performance have been studied by using different approaches

o Map modification by means of stage stacking techniques

o Numerical simulations by adding thickness and roughness to blade surfaces

o Set-up of numerical models to identify the regions where deposit accumulate (impact and adhesion)


Outline

Map m

odification by m

eans of stage stackingtechniques

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generalized stageperformance

stage outlet conditionevaluation

stage matching

� A common approach for modelisation was multiplying point by point the performance maps in new and clean condition by scaling factors .

� A different approach consists in investigating the effects of blade deterioration by means of stage-by-stage models.

Stage stacking

0.4

0.6

0.8

1.0

1.2

1.4

0.70 0.80 0.90 1.00 1.10µ ∗

β ∗

healthyfouling

0.88

0.90

0.92

0.94

0.96

0.98

1.00

1.02

0.70 0.80 0.90 1.00 1.10µ ∗

η ∗

healthy

fouling

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1. ψp* = ψp

*(φ *, SF) curve by Muir at al.

( ) ( )( )

2* * * *p,max p,max p,max* *

p p,max 2* *

p,max p,max

1 1

1 1

SF

SF

ψ ψ

ψ ψ

ψ ϕ ϕ ϕψ ψ

ϕ ϕ

− ⋅ + ⋅ − − = −

+ ⋅ − −

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6φ∗

ψ∗

SF = -0.5

SF = 0.0

SF = 1.0

Muir et al. (1989)

Stage stacking

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6ψ∗/φ∗

η∗

Howell and Bonham (1950)

2. η* = η*(ψp*/φ*) curve by Howell and Bonham

( )*

*/p p* min

**p*

min

11 1

1

A

A

ψ ϕη ψ

ηϕψ

ϕ

− = − −

−

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Subsonic case – Stage stacking vs CFD

• Comparison of the performances of each compressor stage with the generalized performance curves present in literature• Reconstruction of the multistage compressor performance maps by applying a zero-dimensional stage-stacking procedure


Num

erical simulations by

adding thickness and roughness to blade

surfaces

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� Fouling on compressor stages is simulated by imposingo increased roughness

Values to be imposed to added thickness coming from experience and from empirical knowledge

o added thicknessTreatment of roughness rely on specific models, either phenomenological or detailed

to the blade surface.

� Roughness and thickness can be added to stator and rotor in order to predict the modifications in the performance of the entire stage rather than the rotor alone.

� Modifications can imposed on all the relevant blade surfaces(suction side, pressure side, leading edge, trailing edge), from inlet to outlet and from hub to shroud (100 % coverage), both as uniform and non-uniform patterns

Added thickness and roughness

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� Thickness simulate the presence of dust and particles which alter thegeometry of the airfoil. Thickness magnitude and distribution could bedrawn from experience and experimental observations

� Surface roughness, since the effect of deposition of contaminants oncompressor surface is also the alteration of its surface finish.Measured roughness is typically quoted in terms of the Centre LineAveraged (CLA) roughness, Ra

� Different patterns could be imposed

o uniform on all the blade

o localized on most susceptible regionsFor instance, the leading edge has demonstrated to be acritical region

Added thickness and roughness

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� CFD models, and in particular RANS models, have demonstrated to be a reliable tool also to study roughness effects on performance

� Roughness effects can be accounted for by modifying the logarithmic profile as follows

Added thickness and roughness – RANS models

� As an index of the wall roughness, instead of the Ra, the equivalent sand grain roughness ks have been widely used by many researchers. The two quantities relates to each other empirically.

)1(a

s >== CconstR

k

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Subsonic reference case

o Tip clearance = 0.45 % of the blade span

o Nominal rotational speed = 6,054 rpm

o Peripheral blade tip velocity = 206 m/s

o Tip Mach number = 0.62

� First stage of a multistage heavy-duty compressor

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Transonic case - Performance maps

1.50

1.70

1.90

2.10

18 19 20 21m [kg /s ]

p03/p01

C F D - base-S

C F D - base-R

C F D - thick-S

C F D - thick-

R 1

� Main effect of fouling is the decrease in mass flow rate (from 0.7 % decrease for base-R to 3.6 % decrease for thick-R3)

� If a plausible compressor working line is followed, it is possible to notice a decrease in the stage compressor ratio.

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� Degradation is almost constant in the inner half of the blade span and then increases over the outer half

� Reduction is mostly concentrated from leading edge to mid-chord (where the effect of roughness has shown to be more intense [5])

-60

-30

0

30

60

0 0.5 1

SmRot-SmStat

RouRot-RouStat

(p s)rel

[kPa]

S [-]

0%

25%

50%

75%

100%

1.80 2.05 2.30

SmRot-SmStat

RouRot-RouStat

β

Span

Pitchwise mass-averaged total pressure ratio

Streamwise static pressure distributions at 50 % span

[5] Gbadebo, S. A., Hynes, T. P., Cumpsty, N. A., 2004, “Influence of Surface Roughness on Three-Dimensional Separation in Axial Compressors”, ASME J. Turbomach., 126(4), pp. 455-463.


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� Blockage for the different fouling conditions was calculated on a blade-to-blade plane approximately normal to streamlines at outlet

� Blade roughness leads to an increase in blockage

� Variation of thickness can account for some of the blockage not seen by increased roughness

added thickness (∆tb = + 0.3 mm) corresponds to a geometric area decrease of 1.6 %, while the variation of the effective flow area ∆Aflow is 3.5 %, which corresponds to a blockage increase of 27.8 %.

geo

flow1A

AB −=

( ) ( )∫ ⋅⋅=

⋅geo EEmax

flow maxA

dAAnu

nu

nuρ

ρ

B [-] ∆B ∆Aflow

Datum 0.108 - -

Rough 40 0.123 13.9 % −1.7 %

Thick and smooth 0.138 27.8 % −3.5 %

Thick and rough 5 0.145 34.3 % −4.3 %

Thick and rough 40 0.166 53.7 % −7.0 %

Thick and rough 75 0.174 61.1 % −8.0 %

Transonic case – Blockage

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RoughSmooth

� At the blade-tip: the alteration of the tip gap boundary layer induces lesstip-leakage vortex for the roughness case

Transonic case – Fluid dynamics

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� Non-uniform roughness distribution can simulate different situation which can be found in practice (first or subsequent stages, not-fully washed stages, difference between etc.)


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Outline

Set-up of numerical models to identify the regions where

deposit accumulate

(impact and adhesion)

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Particle injection

BSp

pD

p )(FF

gF

dt

du++

−+=

ρρρ

inertial term

drag term gravity and buoyancy contributes

Saffman and Brownian terms

� Particles are injected into the numerical domain and tracked by force balance

Time-averaged flow field

Mean pathTurbulentdispersion Particle path

Instantaneous flow field

Actual representation of trajectory

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Particle impact velocity and angle

� The impact velocity is obtained by a vector sum of the three velocity components ux, uy and uz along the axes x, y and z respectively

� The impact velocity is decomposed with respect to the normal (vn) and tangential (vt) direction

� The impact angle α is the angle between the surface normal vector nand the impact velocity vector vi


0.15 µm

0.25 µm

Particle im

pact velocity maps –

results for transonic

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� Larger particles impact at the top of the blade at SS dragged into that area by the tip leakage vortex

450

0

300

150

[m/s]

Transonic Rotor Subsonic Rotor

Fottner, L., 1989, Review of Turbomachinery Blading Design Problems, Report No. AGARD-LS-167

Impact on airfoil – contamination – 3D effects

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450

0

300

150

[m/s]

[m/s] 10 %

blade span [m/s] 10 %

blade span

Gbadebo, S. A., Cumpsty, N. A., Hynes, T. P., 2005, Three-Dimensional Separations in Axial Compressors, J. Turbomach., 127, pp. 331-339

� Three-dimensional vortex drags the contaminants into the vicinity of the hub (low particle velocity zone)

Transonic Rotor Subsonic Rotor

Impact on airfoil – contamination – 3D effects

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* Hertz, H., 1896, MACMILLAN AND CO., LTD., London, UK** Johnson et al., 1971, Proc. of the Royal Society of London. Series A, Mathematical and Physical, 324(1558)

o Empirical/analytical approacho Energetic approach

� Particle adhesion is due to a combination of impact mechanics and particle-surface interaction

� The relative impact kinematic characteristics between blade and particles determine the sticking probability SP ( likelihood of a particle to stick to a surface)

Sticking and adhesion

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Sticking probability – Empirical/analytical

* Poppe, T., Blum, J., Henning, T., 2000, “Analogous experiments on the stickiness of micron-sized preplanetary dust” The Astrophysical Journal, 533, pp. 454-471

� The challenge is to find the sticking probability values for the problem under investigation

� As an example, SP reported in the work* of Poppe et al. was calculated for each normal impact velocity vn

� The smaller particles have a wider velocity range for which the impact with the blade surface becomes (with a high probability) a permanent adhesion

dp = 0.37 µm dp = 0.64 µm

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� The sticking probability of a particle depends on its energy content . Temperature and kinetic energy are kept into account

Sticking probability – Energetic approach

� Rather than relying on empirical/analytical formula on velocity of the impact, a more general approach can start from the assessment of the energy involved in the impact

Three unknowns:A – Pre-exponential FactorC1- Activation energy – ConstantC2- Constant of reduced temperature

Material dependent

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� Deposits pattern: suction side appears more uniformly contaminated, while the peak is in pressure side

mSS,peak

1.84e-12 kg/s

mPS,peak

1.39e-11 kg/s

1.39e-11

1.11e-11

8.34e-12

5.56e-12

2.78e-12

0 [kg/s]

hubhuble

adin

ged

ge

lead

ing

edge

Suction side Pressure side

Sticking probability – Transonic

o SS peak is due to the separation @ hub

o PS peak is due to the largest particles

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Hayashi, R., Yamamoto, M., 2014, Numerical Simulation on Ice Shedding Phenomena in Turbomachinery, ASME Paper GT2014-25839

� Physical (and thus computational) domain continouslymodifies due to fouling

� Surface shape changes occur due to deposition of contaminants

� Accretion models takes into account these modifications by regenerating the domain (and the grid consequently) or by reclassifying the cells (switching from fluid to solid)

Accretion models

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• Uniform growth of the boundary element• Smoothing of the internal mesh for quality reasons

The deposited layer grows uniformly in the

computational cell

Accretion models

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References

• Nicola Aldi, Mirko Morini, Michele Pinelli, Pier Ruggero Spina, Alessio Suman, Mauro Venturini, 2014, “Performance Evaluation of Non-Uniformly Fouled Axial Compressor Stages by Means of Computational Fluid Dynamic Analyses”, Journal of Turbomachinery, 136, p. 021016

• Alessio Suman, Rainer Kurz, Nicola Aldi, Mirko Morini, Klaus Brun, Michele Pinelli, Pier Ruggero Spina, “Quantitative CFD Analyses of Particle Deposition on an Axial Compressor Blade, Part I: Particle Zones Impact”, Journal of Turbomachinery, 2015, 137, p. 021009

• Alessio Suman, Mirko Morini, Rainer Kurz, Nicola Aldi, Klaus Brun, Michele Pinelli, Pier Ruggero Spina, “Quantitative CFD Analyses of Particle Deposition on an Axial Compressor Blade, Part II: Impact Kinematics and Particle Sticking Analysis”, Journal of Turbomachinery, 2015, 137, p. 021010

• Alessio Suman, Mirko Morini, Rainer Kurz, Nicola Aldi, Klaus Brun, Michele Pinelli, Pier Ruggero Spina, 2016, “Estimation of the Particle Deposition on a Transonic Axial Compressor Blade”, J. Eng. Gas Turbine and Power, 138(1), p. 012604

• Alessio Suman, Rainer Kurz, Nicola Aldi, Mirko Morini, Klaus Brun, Michele Pinelli, Pier Ruggero Spina, 2016, “Quantitative CFD Analyses of Particle Deposition on a Subsonic Axial Compressor Blade”, J. Eng. Gas Turbine and Power, 138(1), p. 012603

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References

• Alessio Suman, Mirko Morini, Rainer Kurz, Nicola Aldi, Klaus Brun, Michele Pinelli, Pier Ruggero Spina, 2016, “Estimation of the Particle Deposition on a Subsonic AxialCompressor Blade”, J. Eng. Gas Turbine and Power (in press)

• Nicola Aldi, Mirko Morini, Michele Pinelli, Pier Ruggero Spina, Alessio Suman, 2016, “An Innovative Method for the Evaluation of Particle Deposition Accounting for the Rotor/Stator Interaction”, ASME Turbo Expo 2016

• Melino, F., Morini, M., Peretto, A., Pinelli, M., Ruggero Spina, P. Compressor foulingmodeling: Relationship between computational roughness and gas turbine operationtime (2012) Journal of Engineering for Gas Turbines and Power, 134 (5), art. no.052401

• Morini, M., Pinelli, M., Spina, P.R., Venturini, M. Numerical analysis of the effects ofnonuniform surface roughness on compressor stage performance (2011) Journal ofEngineering for Gas Turbines and Power, 133 (7), art. no. 072402

• Morini, M., Pinelli, M., Spina, P.R., Venturini, M. Computational fluid dynamicssimulation of fouling on axial compressor stages (2010) Journal of Engineering forGas Turbines and Power, 132 (7), art. no. 072401

• Nicola Casari , Michele Pinelli, Alessio Suman Luca di Mare, Francesco Montomoli,2016, AN ENERGY BASED FOULING MODEL FOR GAS TURBINES: EBFOG,ASME Paper GT2016-58044

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References

• Aldi, N., Morini, M., Pinelli, M., Spina, P.R., Suman, A. Cross-validation of multistagecompressor map generation by means of computational fluid dynamics and stage-stacking techniques (2014) Proceedings of the ASME Turbo Expo, 3B,

• Aldi, N., Morini, M., Pinelli, M., Spina, P.R., Suman, A., Venturini, M. Numericalanalysis of the effects of surface roughness localization on the performance of an axial compressor stage (2014) Energy Procedia, 45, pp. 1057-1066.

• Morini, M., Pinelli, M., Spina, P.R., Venturini, M. Influence of blade deterioration on compressor and turbine performance (2010) Journal of Engineering for Gas Turbinesand Power, 132 (3), art. no. 032401

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