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©2018 Rolls-Royce Group

The information in this document is the property of Rolls-Royce Group and may not be copied or communicated to a third party, or used for any

purpose other than that for which it is supplied without the express written consent of Rolls-Royce Group.

This information is given in good faith based upon the latest information available to Rolls-Royce Group, no warranty or representation is given

concerning such information, which must not be taken as establishing any contractual or other commitment binding upon Rolls-Royce Group or any

of its subsidiary or associated companies.

Tuesday, 18 December 2018, Sydney

Combustion Modelling

Dr. Ir. Ruud Eggels

Rolls-Royce Deutschland Ltd & co KG

Eschenweg 11

Dahlewitz-Berlin

15827 Blankenfelde-Mahlow

Germany

Introduction

About me..

Studied Physics at Technical University in Eindhoven, NL

Ph.D. in mechanical engineering at the Technical University

Eindhoven

AMSL, Veldhoven (NL) manufacturing and service engineer

Rolls-Royce marine and power engineering, Ansty, UK

Rolls-Royce Deutschland, Dahlewitz, Germany, since 2001

I am working in close cooperation with Universities and research

centres on combustion methods, mainly onCFD development

Objectives of this presentation

First part of 1st Lecture: Provide basic aspects regarding

understanding and modelling of turbulent combustion

Theory, flow, mixing, turbulence, kinetic, reduction..

Combustion modelling aspects

Emissions modelling

Second part of 1st lecture on thermo-acoustics

Theory, modelling aspects

Thermo-acoustics in aero-space combustors

Second Lecture (Thursday)

Gas turbine combustors performance

Emissions of aero gas turbines

CFD in aero combustion design

Alternative fuels in aero-space

Overview

Combustion CFD requirements

Modelling of combustion processes

Reaction chemistry

Combustion modelling

Flamelets / Detailed and reduced reaction mechanisms

Chemistry turbulence interaction

Emissions modelling

Spray modelling

Combustor CFD Modelling

Requirements for using CFD in industry

Short turn around times, easy to use

prediction of combustor exit temperature profiles

prediction combustor wall temperatures and heat transfer

prediction of NOx, CO, UHC and soot emissions

prediction of combustion efficiency

optimisation process of combustor and fuel injector configurations


Challenges of modelling aero engine combustors:

Complex geometries

Complex physics

Flow field (high swirl, recirculation, jet mixing)

Liquid fuel spray (2-phase flow, atomisation)

Combustion (chemistry)

Emissions (NOx, CO and soot)

Unsteady effects: instabilities, rumble…


Rolls-Royce proprietary information - private

Ref: prof. Johannes Janicka,TUD

Turbulence /

Mixing

Chemistry-

Turbulence

Interaction

Chemistry

Two phase

flow

Heat

transfer /

Radiation

Equations describing combustion processes



Challenges of modelling aero engine combustors:

Complex geometries

Complex physics

Flow field (high swirl, recirculation, jet mixing)

Liquid fuel spray (2-phase flow, atomisation)

Combustion (chemistry)

Emissions (NOx, CO and soot)

Unsteady effects: instabilities, rumble…




Turbulence /

Mixing

Chemistry-

Turbulence

Interaction

Chemistry

Two phase

flow

Heat

transfer /

Radiation

Reaction equation

CH4 + 2 O2 = CO2 + 2 H2O

(this is a global reaction.....)

Conservation of elements:

C-atoms: 𝑌𝐶𝐻4𝑀𝐶

𝑀𝐶𝐻4

+ 𝑌𝐶𝑂2𝑀𝐶

𝑀𝐶𝑂2

= 𝐸𝐶

O-atoms: 2𝑌𝑂2𝑀𝑂

𝑀𝑂2

+ 2𝑌𝐶𝑂2𝑀𝑂

𝑀𝐶𝑂2

+ 𝑌𝐻2𝑂𝑀𝑂

𝑀𝐻2𝑂= 𝐸𝐶,

Which becomes simpler in terms of specific mole numbers: 𝑌𝑖 =ϕ𝑖/𝑀𝑖:

C-atoms: ϕ𝐶𝐻4 + ϕ𝐶𝑂2 = χ𝐶

O-atoms: 2ϕ𝑂2 + 2ϕ𝐶𝑂2 + ϕ𝐻2𝑂 = χ𝐶

Therefore often specific mole numbers are used for combustion

simultations

Chemical Reactions

Detailed reaction mechanism

ELEMENTS

O H N

END

SPECIES

H2 O2 N2 H2O NO

O H OH HO2 H2O2

N NO2 HNO

END

REACTIONS

H2 + O2 <=> OH + OH

H + O2 <=> OH + O

O + H2 <=> OH + H

OH + H2 <=> H2O + H

OH + OH <=> H2O + O

H + OH + M<=> H2O + M

H + H + M <=> H2 + M

H + O + M <=> OH + M

H + O2 + M<=> HO2 + M

H2 /2.00/ H2O/16.00/

HO2 + H <=> H2 + O2

HO2 + H <=> OH + OH

HO2 + H <=> H2O + O

HO2 + O <=> O2 + OH

HO2 + OH <=> H2O + O2

HO2 + HO2 <=> H2O2+ O2

H + H2O2 <=> H2 + HO2

O + H2O2 <=> OH + HO2

OH + H2O2 <=> H2O + HO2

H2O2+ M <=> OH + OH + M

O + O + M <=> O2 + M

H2 /2.50/ H2O/16.00/

N + N + M <=> N2 + M

N + O2 <=> NO + O

N + NO <=> N2 + O

N + OH <=> NO + H

H + NO + M<=> HNO + M

HNO + H <=> NO + H2

HNO + O <=> NO + OH

HNO + OH <=> NO + H2O

HNO + HO2 <=> NO + H2O2

HO2 + NO <=> NO2 + OH

NO2 + H <=> NO + OH

NO2 + O <=> NO + O2

NO2 + M <=> NO + O + M

This a very small reaction mechanism for hydrogen !

Computation of reaction rates

Chemical reaction

O2 + H = OH + O

Reaction rate constant:

kf = Tβ Exp(Ea/RT), with

β a constant and Ea the activation energy

Forward reaction rate:

rf = kf [O2] [H]

Backward reaction rate:

rb = [OH] [O]

Equilibrium constant:

K = kf/kb

Chemistry

The net reaction rate of a species is the sum of all

reactions:

𝜔𝑖 = 𝜇𝑖𝑗 𝑟𝑗𝑁𝑟𝑗=1

With μij the stoichiometric reaction constants

For example for the reaction:

O2 + H = OH + O

The stoichiometric constants for this reaction are:

-1 -1 1 1

Simulation of combustion processes

.. thus the system of equations is essentially known

However, generally unsolvable due to complexity

Large systems of chemical components (hundreds to thousands)

and elementary equations (from thousands to tens of thousands),

but the reaction rates are not known accurately

The stiff system of equations require extremely small time steps

and / or coupled (expensive) solvers.

Technical systems have high geometric complexity

In technical systems the flow is turbulent!

Consequently required computational resource very large

Simplification and Modelling required

Look at flame regimes


Combustion regimes

Diffusion flames

Partially premixed flames

Premixed flames

Laminar Turbulent

http://en.wikipedia.org/wiki/File:Fire_breathing_2_Luc_Viatour.jpg

Simple Flames

1D premixed adiabatic flame

Counter-flow diffusion flame

There flames are relative easily to solve, while they are

1D,thus limited number of grid points required

How can these flames numerically be solved?


1D Flames

Convection Diffusion equation for species

𝜕𝜌𝑢𝑌𝑘𝜕𝑥

=𝜕

𝜕𝑥𝜌𝐷𝑘

𝜕𝑌𝑘𝜕𝑥

+ ω𝑘

Convection diffusion chemistry source term

Before solving them, the equation have to be discretised

First integrate over ∆x to remove one gradient

𝜌𝑢𝑌𝑘 = 𝜌𝐷𝑘𝜕𝑌𝑘𝜕𝑥

+ ω𝑘∆𝑥

Discretisation of gradient 𝜕𝑌𝑘𝜕𝑥

≈∆𝑌𝑘∆𝑥

Definition of residu (solution: r=0)

𝑟𝑘 = 𝜌𝑢𝑌𝑘 − 𝜌𝐷𝑘∆𝑌𝑘∆𝑥

− ω𝑘∆𝑥

Linearization: 𝑟𝑘 ≈ 𝑟𝑘,0 + 𝜕𝑟𝑘

𝜕𝑌𝑗∆𝑌𝑗


Solving equations 1D flames

Full implicit solution procedure: one matrix

Can be solved completely implicitly


With J the Jacobian of the system:

Jj at position j

1D laminar premixed flame

Laminar one dimensional premixed laminar adiabatic

flame

0

500

1000

1500

2000

-0.10 0.00 0.10 0.20 0.30

X in cm

Te

mp

era

ture

in

K

0.00

0.01

0.02

0.03

0.04

0.05

0.06

Specie

s m

ass fra

ctions

TempCH4OHCO

Flame is freely

propagating with burning

velocity

Can be easily computed

using detailed chemistry

Burning velocity is

eigenvalue of system of

equations

Laminar burning velocity

Ref: Ph.D. Roy Hermans, EUT

Combustion modelling

3D CFD basically not different, but much more

complex and large (sparse) Matrix.

Solving this matrix fully implicitly requires a large

amount of memory.

Reduce complexity by reducing variables

Reduce chemistry




Turbulence /

Mixing

Chemistry-

Turbulence

Interaction

Chemistry

Two phase

flow

Heat

transfer /

Radiation





Turbulence /

Mixing

Chemistry-

Turbulence

Interaction

Chemistry

Two phase

flow

Heat

transfer /

Radiation

Chemistry

Reduction

Chemistry Reduction

Equilibrium

Global reaction mechanism

Flamelets: FGM: Flamelet Generated Manifold model

(premixed) or diffusion

Systematic reduction of detailed chemistry


Equilibrium chemistry

Assume chemistry is infinitely fast

All reactions will be in equilibrium:

The forward reaction rate is equal to the backward reaction rate:

rf = kf [O2] [H] = rb = [OH] [O]

Equilibrium constant:

K = kf/kb = [OH] [O] / ( [O2] [H] )

Species concentrations can be computed from thermo-dynamical

properties and depend on composition, temperature and pressure


Flamelet model

Based on the assumption that the chemistry is fast

Flame structure described by laminar diffusion flamelets

Chemical state described by mixture fraction

Can be extended by solving range of flames, similar as for FGM

mode


300

800

1300

1800

2300

2800

0.00 0.20 0.40 0.60 0.80 1.00

Mixture fraction

Tem

pera

ture

Temp (equilibrium)

Temp (flamelet IC)

Global reaction mechanisms Describe reaction process by only one or two global reactions

CH4+ 1.5 O2= CO + 2 H2O

CO + 0.5 O2 = CO2

Reaction rates Arrhenius type:

r1 = A1 [CH4]α[O2]

β e(-E1/RT)

Constants Ai , Ei , αi and βi are fitted to get correct burning velocity

Only 6 species (including N2)

By solving an equation for fuel/air mixing and using the 4

conservation equations for elements

=>Only solve 3 equations

Limited range of validity (temperature, pressure, equivalence

ratio)… but can be improved by temperature and pressure

dependent coefficients

Pollution products, like CO, NOx and soot cannot be modelled.


Adiabatic 1-D flame Based on the assumption that the flame front is small compared to

the flow field / turbulent length scales


0

500

1000

1500

2000

-0.10 0.00 0.10 0.20 0.30

X in cm

Te

mp

era

ture

in

K

0.00

0.01

0.02

0.03

0.04

0.05

0.06

Specie

s m

ass fra

ctions

TempCH4OHCO

Pre-heat zone reaction-zone oxidation-zone

• Propagating flame:

flame moves by flame

speed

• Flame speed is

eigenvalue of system

• Structure determined

by detailed chemistry

Flamelet Generated Manifold Model

Based on the assumption that the flame front is small compared to

the flow field / turbulent length scales

Assumed that flame structure is determined by chemistry and 2D

effect negligible

Compute adiabatic laminar one dimensional flames at different

equivalence ratios

Describe / tabulate chemistry as function of progress variable and

mixture fraction

Progress variable should be monotone

=> Solve 2 differential equations


Intermediate species are captured

NOx and Soot source terms are derived from FGM table

Only solve one equation for progress variable

Source term NO

OH C2H6 Temperature

Flamelet Generated Manifolds.

Mixture fraction

Pro

gre

ss v

ariable

Validation of FGM vs. detailed chemistry in 2D DNS

Mass fraction of OH in

lean (φ = 0.6) methane/air

flame.

2D FGM only slightly

better due to small

preferential diffusion

effects.

Mass fraction of H in lean

methane/hydrogen/air flame.

Hydrogen addition

introduces significant

preferential diffusion effects:

2D FGM much more

accurate!

Ref: Rameakers, Technical University Eindhoven

Systematic reduction of mechanism


Reduce reaction mechanism systematically

Systematically reduced reaction mechanisms

Original detailed mechanism: JetSurF v1.0-1

120 transported species, 977 reactions

Developed in 2009 by Sirjean et al. (2009)

Generated using the YARC- Tool from Pepiot (Pepiot,2008)

Skeletal mechanism

Removal of 68 species and corresponding reactions

Remaining 52 species

Quasi-Steady-State Assumptions (QSSA)

„Level of Importance“ criterion (LOI)

27 QSS Species

Objective: prediction of (CO) und Acetylene (C2H2)

Reduction performed by Anne van Felden et al. (2018)


Reduced reaction mechanism


Fuel/ Oxidiser n-Dodecane (C12H26)/Air

Application Partially premixed flames

CO- and soot (focus on C2H2)

Original mechanism JetSurF v1.0-1

(120 species/ 977 reactions)

Number of species/ reactions/QSS

species 25/373/27

Transported Species

H, H2, O, O2, OH, H2O,HO2, H2O2,

CH3, CH4, C2H2, C2H4, C2H6, C3H6, C4H6,

C4H8-1, C5H10, C6H6, C6H12, n-C12H26,

CO, CO2, CH2O, CH2CO, N2

QSS Species

CH, CH2, CH2*, C2H, H2CC, C2H3, C2H5, C3H3,

p-C3H4, a-C3H5, CH3CHCH, n-C3H7, C4H2, C4H4,

i-C4H5, C4H5-2, C4H7, o-C6H4, C6H5, C6H5CH3, C2O,

HCO, CH3O, HCCO, CH2CHO, H2C4O, C6H5CO

Considered operation range during

reduction

p= 1-10 bar

T= 300 – 700K

Performance of reduced reaction mechanism

• 1-D premixed adiabatic flames

• Comparison with 3 other detailed reaction mechanism:

JetSurF v1.0-1, Dodecane (Warnatz 1992) and

ModelFuel (Warnatz, EU project CFD4C)

• Validation by comparing burning velocities, mass

fraction profiles, and chemical source terms and

computation time.

• Comparison with literature (Felden et al., 2018)

• Focus: p=20bar, T=1000K


Reduced reaction mechanism: burning velocity


Very good agreement in burning velocity in the testing range of conditions

400 < T < 1000 K

5 < P < 10 bar

Reduced reaction mechanism: species conc.


Good agreement in radical species concentration

Reduced reaction mechanism: species conc.


Good agreement for C2H2, but large difference for C6H6

Comparison of mechanism


Mechanism Dodecane ModelFuel ARC_JetSurF

Kind of mechanism detailed detailed reduced

Number of species 57 198 25

Number of reactions 273 1086 373

Fuel species C12H26

Two components, main

component: C10H22

(𝑌𝐶10𝐻22=91,42%)

C12H26/

Number of QSS

species: 27

Comparison of reaction mechanism

Temperature



Burning velocity


Considerable differences in burning velocities of reaction mechanisms


Comparison of species profiles

Good agreement with results of “model fuel” mechanism


Comparison of species profiles

Good agreement with model fuel mechanism!



Comparison of mechanism


Conditions tM [s] tD [s] tARC [s] 𝑡𝑀𝑡𝐴𝑅𝐶

[−] 𝑡𝐷𝑡𝐴𝑅𝐶

[−]

p=5bar,

T=500K 5553 342,4 267,43 20,8 1,28

p=5bar,

T=800K 6097,6 310,05 213,21 28,6 1,45

p=10bar,

T=700K 5300,26 381,08 258,5 20,5 1,47

p=20bar,

T=800K 9007,5 356,71 284 31,7 1,25

p=20bar,

T=1000K 7252,7 475,01 337,1 21,5 1,41

Ø=24,62 Ø=1,37

Computation times:

Systematic reduced mechanism: summary

• Results of ARC reduced mechanism compare well with

detailed reaction mechanism JetSurF v1.0-1 s

• Good agreement with Modelfuel mechanism even

outside of operating rate considered during reduction

(1bar≤p≥10bar) und temperature (300K≤T≥700K)

• Considerable difference in burning velocity between the

different (detailed) mechanisms





Turbulence /

Mixing

Chemistry-

Turbulence

Interaction

Chemistry

Two phase

flow

Heat

transfer /

Radiation

Turbulent combustion

Flame front

is wrinkled

by

turbulence

Boukhalfa & Renou, Vervisch, INSA de Rouen, CORIA

Turbulent combustion modelling

When modelling turbulent flows the variables are

described by a mean and a variance:

u = u + u’

As a consequence the computation of the chemical

source term is not straightforward:

w(y1,y2…yn) ≠ w(y1,y2…yn)

RANS equations for the species

Mixture fraction

Mixture fraction variance

With scalar dissipation rate

Turbulent flows


Problem Turbulent combustion modelling

Large range of time and length scales

Flow length scales determined by turbulence

Chemistry time scales determined by reaction rates

Chemical time scales << Turbulent length scales

(10-9 .. 10-7 sec) (10-6..10-3 sec)

To fully resolve the equations the grid has to be smaller than the

smallest length scale, and time step smaller than the smallest

chemical / turbulence length scale

Complicated set of equations

To be solved numerically

=>Simplification / modelling required

Grid resolution problem

Ref: Guido Kuenne, TU Darmstadt

Artificially Thickened Flame model

Reduce reaction rate and increase turbulent diffusion

So that correct burning

velocity is obtained

Ref: Guido Kuenne, TU Darmstadt

Combustion chemistry interaction

Presumed PDF closure

Flame surface density model

Transported PDF models

Artificially Thickened Flame Model


Probability density functions

Averaged chemical reaction rate defined as:

To compute the PDF is very expensive, assuming

statistic independence makes it easier:

Subsequently presumed PDF are applied

The PDF is prescribed with a known function


Presumed Probability Density Function

Beta PDF, Gauss, delta…

P x x xa b( ) ( ) / 1 11

a x x 2 21 1( ) /

b x x ( ) /1 12 2

P x dx( )

Beta PDF:

Solving the PDF

Simillarly equations for scalars can be derived

Unclosed terms III and IV, but reaction term is closed


Solving PDF

PDF equation can be solved using

An Langrangian method (Pope)

Large number of partices required

Difficulties to conserve mass

Difficult to model diffusion term

Field PDF method (Valino, Jones…)

Solve different fields for each species

Add stochastic term (relative to turbulent viscosity)


Stochastic term

Stochastic Fields with detailed chemistry allows for correct

prediction of minor species formation like e.g. CO

Detailed Chemistry Stochastic Fields – FLAME D Temperature Yco (MEAN)

4Step 25Step 4Step 25Step

DATA

4-STEP GLOBAL

25-STEP DETAILED

TEMPERATURE YCO (MEAN)

Stochastic Fields with detailed chemistry shows overall

manageable computational costs, which makes simulations

using detailed chemistry feasible

Detailed Chemistry Stochastic Fields

Temperature CO OH

Application to aero- engine combustor

• Large Eddy Simulation

• Dodecane mech. with 39 Species

• 8 Stochastic Fields

• Only 3.5 Times more computational

expensive as than 4 global step

Flamelet surface density model


NOx: Detailed knowledge of OH, O, and CH radical concentration required.

CO, Unburned Hydrocarbons: reaction progress required, mixing important

NOx, CO and UHC Emissions are in principle included in reaction mechanisms.

However, these are not known and/or accurate

Soot, however, is more complicated

Emission Modelling

Nox: key reactions

O + N2 = NO + N

N + O2 = NO + O

N + OH = NO +H

NCO + NO = N2O + CO

NH + NO = N2O + H

N2O + O = 2 NO

Dimer

(C20Hx—C36Hx)

Soot; why is it difficult

Kerosene

C12H26 / C10H24 …..

…….. OH, O, H

C2Hx/C6Hx

……..

CO

CO2

PAH‘s

(C10Hx--C18Hx)

SOOT

NOx

+N2

Chemistry plays an essential rule in soot

formation

Reaction mechanisms (for soot) include

• Hundreds of species

• Thousands of reactions

Limited validation for kerosene

Soot formation is strongly coupled with combustion chemistry,

which is very complex. Soot oxidation is especially challenging

Liquid => droplets

Gaseous

Solid particles

From kerosene to soot: 2 phase shifts:

• Liquid to gaseous

• Gaseous to solid

Exit soot = soot produced – soot oxidized (difference of two large numbers)

C8Hx

+C2H4

Soot Modelling: a Complex Multi Physics & Multi Scale Problem

Required component to model soot in gas turbine combustors

6

4

A. Wick, RWTH

Soot particle size distribution 6

5 Soot particle size distribution (PSD) is essential for soot oxidation, and for future

emission nvPM regulation

Two approaches are being followed:

• Statistical approach for PDF (IC,

RWTH); Methods of Moments

• Bin approach (IC, DLR-VT), for

PAH’s and Soot

It is not clear which approach is

sufficiently accurate and

computationally affordable

Chemistry 6

6

• Chemistry not understood yet

• Detailed chemistry too expensive for application to CFD

• Reduced mechanism

• Tabulated chemistry:

Source terms for PAH’s

Solve eq. for PAH’s and soot

Temperature

Mixture fraction P

rog

ress v

aria

ble

Practical soot modelling will need to employ reduced or

tabulated chemistry




Turbulence /

Mixing

Chemistry-

Turbulence

Interaction

Chemistry

Two phase

flow

Heat

transfer /

Radiation

Spray modelling: Euler-Lagrange


Gas phase:

Eulerian approach

Balance equations (Mass,

Momentum….)

Liquid phase:

Lagrangian approach

Equation of motion

up,1

up,2

Uin,pin,

ρin

Uout,pout,

ρout

U,p,ρ

Source

terms

Spray modelling: fuel break-up


DNS of primary breakup

Design optimization

Data base for injection model development

Modelling of primary breakup

Initial conditions for Euler-Lagrange simulations

Fuel

Air

Modelling

Ref: Simon Holz, KIT

Two-phase modelling

Particle injection

Primary break-up modelling requires very high resolution: about 10 µm

Can be done on very small domain

Computation approaches:

Volume of Fluid (VOF: Eulerean)

Stochastic Particle Method (SPH: particles)

Level Set

Conditions for aircraft fuel injectors


1 2 3 4 5 6

ug [m/s] 20 50 100 20 50 100

[kg/s²] 0.025 0.0085

Reg 5,300 13,300 26,700 17,000 42,500 85,100

Wer,film 1.9 11.9 47.5 18.4 114.7 458.8

Take-off Idle Climb Cruise Approach Alt.

Relight

ug [m/s] 98 74 97 93 90 60

[kg/s²] 0.0023 0.0188 0.0045 0.0105 0.0088 0.025

Reg 364,200 95,470 320,000 169,700 231,600 9,700

Wer,film 18,320 305 7,990 1,680 2,510 11.2

Source: Lufthansa

Source: Behrendt, 2004 & Mosbach et al., 2010

Altitude Relight

Idle

Primary break-up modelling

ug = 20 m/s

rg = 1.2 kg/m³,

= 0.025 N/m

ug = 50 m/s ug = 100 m/s

rg = 3.9 kg/m³,

= 0.0085 N/m

So

urc

e: B

. S

au

er.

20

14

Primary breakup mechanism

1,9 11,9 18,4 47,5 114,7 458,8

OP 1 OP 2 OP 3 OP 4 OP 5 OP 6 Wer,film

Stretched

Ligament

Breakup

Torn-Sheet

Breakup

Thin Ligament

Breakup

Membrane

Breakup

So

urc

e: B

. S

au

er.

20

14

Parameter of primary breakup

Source Experiment:

Gepperth et al., 2012

Parameter

Breakup length lBU

Breakup time tBU

Breakup frequency fBU

Intensity of breakup grows with We

Influence of grid size is small

Exp. Gepperth

20 µm

10 µm

Modelling of primary breakup

2D Simulations, here SPH

ugas Axial

Radial

pair: 5 bar

ubulk: 70 m/s

ha: 230 µm

ubulk

ubulk Use numerical and experimental results to

extract correlations for droplet position,

velocity and particle size distribution Ref: Simon Holz, KIT

Sector model with 5 degrees, resolution 4 microns, 3.6 · 106 cells

Spray boundary conditions have a large impact especially on emissions

VOF enables modelling of fuel break-up, but is computational expensive

Low air swirl: liquid fuel placed in shear layer good atomisation

High air swirl: Liquid fuel does not interact with shear layer bad atomisation

Engineering example

Modelling primary break-up using VOF (Volume of Fluid)

V1_35_VEL.avi

V1_45_VEL.avi

Empirical break-up model for CFD

Correlations for

Droplet size distribution

- Typically Rosin Rammler distribution:

P=P0exp(-d/dm)q

Distribution of position

Distribution of initial droplets velocity


© 2012 Rolls-Royce Deutschland Ltd & Co KG

The information in this document is the property of Rolls-Royce Deutschland Ltd & Co KG and may not be copied or communicated to a third

party, or used for any purpose other than that for which it is supplied without the express written consent of Rolls-Royce Deutschland Ltd &

Co KG.

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representation is given concerning such information, which must not be taken as establishing any contractual or other commitment binding

upon Rolls-Royce Deutschland Ltd & Co KG or any of its subsidiary or associated companies.

©2018 Rolls-Royce Group

The information in this document is the property of Rolls-Royce Group and may not be copied or communicated to a third party, or used for any

purpose other than that for which it is supplied without the express written consent of Rolls-Royce Group.

This information is given in good faith based upon the latest information available to Rolls-Royce Group, no warranty or representation is given

concerning such information, which must not be taken as establishing any contractual or other commitment binding upon Rolls-Royce Group or any

of its subsidiary or associated companies.

Tuesday, 18 December 2018, Sydney

Thermo-acoustics

Dr. Ir. Ruud Eggels

A.Fischer, C.Lahiri

Rolls-Royce Deutschland Ltd & co KG

Eschenweg 11

Dahlewitz-Berlin

15827 Blankenfelde-Mahlow

Germany

RR Private | © 2018 Rolls-Royce RRD proprietary information Export Control classification NLR

Agenda

79

Combustion instabilities – Rolls-Royce Classification

SCARLET – thermo-acoustic test rig and 1st results

01

02

03 CFD methods


80

Combustion instabilities

01


81

Combustion Technology for Civil Aircrafts

- Fulfil emission requirements

- High efficiency with lowest fuel consumption

- Lowest pressure drop as possible

- Stable combustion during all flight cycles and transient manoeuvres

- Re-light / weak-extinction

Phase 5 technology (RQL - rich quench lean)

Lean burn (pilot flame is rich and mains are lean)

Aim: Further emission reduction with lean burn systems and extension of operability without thermo-acoustic instabilities


82

Screech-Töne High Frequency Rumble (HFR)

Low Frequency Rumble (LFR)

Combustion Instabilities

RR Classification

• axial convective mode

• 50-200 Hz

• customer annoyance

• start-up/ground idle

• circumferential mode

• 400-1200 Hz

• damaging

• mid/high power

• circ. & radial mode

• >1000 Hz

• damaging

• afterburners

For civil aviation only LFR and HFR are of interest


83

Thermoacoustic Feedback Cycle

Heat Release Fluctuations

Flow and Mixture

Perturbations

Acoustic Oscillations

Temperature Spot

Acceleration

Flame

NGV

Injector and Mixing ports

HFR

LFR

Interaction of complex physics phenomena

• coupling between aerodynamics, acoustics and combustion (thermodynamics and Chemistry)

• unsteady, compressible, two-phase and reacting flow

• high temperature and high pressure conditions

convected temperature fluctuations

Combustion chamber


84

Thermosacoustic instabilities

Video (top)

Approx. 20 years old

Simulation (bottom)

Current state of the art – approx. 2 months computational time for 0.1 sec resolution

Temperature (color) and kerosene spray (black) fluctuations

• With CFD a better physical understanding of TA-

instabilities can be generated

• Experiments are indispensable for product development


85

Injector Design

Emissions

Operability

Cooling

FANN/EDP test Acoustics

Cost loop

Injector design FTF/FTM

Stability Prediction

TA Network Model (LOTAN)

Acoustics

CFD/CAA

TA Rig (SCARLET)

validatio

n

iteration at TRL5/TRL6 level

iteration at TRL4 level

small changes, no effect on other parameters assumed

larger changes, other parameters need to be checked again

FANN/EDP testing stable

instable

validation

• TA-Instabilities need to be considered in the beginning of a development process • Design rules for stable injectors and prediction methods are necessary


86

Thermo-acoustic modelling

FTF – flame transfer function

1D - LOTAN Modell

• Computation of 1D-flow simulation overlaid with acoustic solver for all engine conditions are necessary FTF needs to be measured for all conditions

FTF (flame response on p’ and u’ fluctuations) not predictable so far

Measurements of FTF: - Acoustic measurements via FTF/FTM - Optical via indicator of heat release e.g. OH*

𝐹𝑇𝐹(𝜔) = 𝑄′ 𝑄

𝑢′ 𝑢 = 𝐺 𝜔 𝑒𝑖𝜑(𝜔)


87

SCARLET – test rig

02


88

Acquisition of Flame transfer functions

Theory pu’+

p‘u-

p‘d+

p‘d-

• Representative flame as „black box“ • Variation of inlet temperature, pressure, fuel flow pressure drop

• Forced excitation of the flame with acsoustic aquisition (upstream and downstream)

• Damper are needed to suppress Eigen modes of the rig

• Optical access for investigations of the primary zone

• Realized in SCARLET-rig


89

SCARLET (SCaled Acoustic Rig for Low Emission Technology)

Setup

• SCARLET-Rig (top)

• Probe adapter at CC (bottom)

• Pictures of setup, laser light sheet, field of view

Acoustic excitation

Damper

Variable nozzle

Combustion chamber

Acoustic measurement section

Laser probe

Observation probe

Combustion chamber


90

Flame Transfer Matrix

• Post-processing software delivers complex amplitudes (Riemann invariants) of forward “f” and backward “g” travelling wave via MMM

• pressure and velocity fluctuation in reference plane

• transfer-matrix computation

• Flame transfer matrix computation


91

Example LOTAN output

Frequency ( Hz)

Gro

th r

ate

(1/s

) instable

damped

damped

Eigen mode Frequency ( Hz)

Wac

hst

um

srat

e (

1/s

) W

ach

stu

msr

ate

(1

/s)

Wac

hst

um

srat

e (1

/s)

Wac

hst

um

srat

e (

1/s

) W

ach

stu

msr

ate

(1

/s)

Gro

th r

ate

(1/s

)

instable

damped

m=2 (2nd azimuthal mode) -

instable m=1 (1st azimuthal mode) – stable

Instable

Eigen mode


92

Variation of fuel split

P30 ~ 14 bar

T30 ~ 800K

Frequency ( Hz)

Gro

wth

ra

te (

1/s

)

Frequency( Hz) Frequency( Hz)

1

20% 30% 40%

Test results

~7psi ~6psi ~0.5psi

Pilot Split 20% 30% 40%

Test results

Frequency [Hz]

455 465 475

Amplitude [psi]

7.2 6.4 0.6

LOTAN model using SCARLET-FTF

Frequency [Hz]

434 (instable)

439 (instable)

430 (stable)

Amplitude [psi]

7.1 6.8 na

Prediction of stability is possible with strong dependency on the 1-D flow simulation


93

Laser setup

• Laser system (top)

• Laser stabilization (middle)

• Probe and camera adaption (bottom right)

• Probe access at CC (bottom left)

Probe access

Laser guide

Laser setup

Laser stabilization

Probe/Camera adaption


94

FTF –Computation from OH*-images

Comparison between acoustical and optical FTF

94

FTF =𝑞′/𝑞

𝑢′/𝑢

• Spectral data of HS-OH*-images • Mean values of HS-OH*-images

• Continuity equation (𝑢 = 𝑚

𝜌𝐴)

• From acoustic data

0 200 400 600 800 1000Frequency [Hz]

Gain

[-]

Flame transfer function

0 200 400 600 800 1000Frequency [Hz]

phase [ra

d]

akustisch

optisch

optisch (CB)

0 200 400 600 800 1000Frequency [Hz]

Am

plitu

de [a.u

.]

260Hz 560Hz Excited modes Post processing is Work in progress


95

Achieved objectives Summary

• Thermo-acoustic high pressure test rig (SCARLET) has been designed, build and first measurements have been obtained

• Accuracy of measurement amplitude A < 10% and φ < 5°

• First demonstration of simulation measurement of optical and acoustic flame transfer function for kerosene flames

Vorreiter in der Forschung durch weltweit einmaligen Datensatz


96

CFD methods for TA

03


97

Options of using CFD to model thermo-acoustics

compressible LES

50 million cells

0.25s simulated

Direct full compressible system modelling

Solve flow (CFD) and acoustics (CAA) separate

One sector with appropriate acoustic cyclic boundary conditions

Modelling one sector to determine flame transfer functions and model acoustics with low order models

... all are expensive, some more expensive than others


98

CFD – CAA Coupling


99

CFD – CAA Coupling


100

FTF Extraction from LES


101

FTF Extraction from LES

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