John C. DeLaat, Kevin J. Breisacher, Joseph R. Saus, and Daniel E. Paxson

Glenn Research Center, Cleveland, Ohio

Active Combustion Control for

Aircraft Gas Turbine Engines

NASA/TM—2000-210346

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Glenn Research Center

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John C. DeLaat, Kevin J. Breisacher, Joseph R. Saus, and Daniel E. Paxson

Glenn Research Center, Cleveland, Ohio

Active Combustion Control for

Aircraft Gas Turbine Engines

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1

American Institute of Aeronautics and Astronautics

ACTIVE COMBUSTION CONTROL FOR AIRCRAFT GAS TURBINE ENGINES

John C. DeLaat, Kevin J. Breisacher, Joseph R. Saus, Daniel E. Paxson

NASA Glenn Research Center at Lewis Field

21000 Brookpark Road

Cleveland, Ohio 44135

Abstract

Lean-burning combustors are susceptible to

combustion instabilities. Additionally, due to nonuniformities in the fuel-air mixing and in the

combustion process, there typically exist hot areas in

the combustor exit plane. These hot areas limit the

operating temperature at the turbine inlet and thus

constrain performance and efficiency. Finally, it is

necessary to optimize the fuel-air ratio and flame

temperature throughout the combustor to minimize the

production of pollutants. In recent years, there has

been considerable activity addressing Active

Combustion Control. NASA Glenn Research Center’s

Active Combustion Control Technology effort aims to

demonstrate active control in a realistic environment

relevant to aircraft engines. Analysis and experiments

are tied to aircraft gas turbine combustors.

Considerable progress has been shown in demonstrating

technologies for Combustion Instability Control,

Pattern Factor Control, and Emissions Minimizing

Control. Future plans are to advance the maturity of

active combustion control technology to eventual

demonstration in an engine environment.

Introduction

Future aircraft engines must provide ultra-low

emissions and high efficiency at low cost while

maintaining the reliability and operability of present

day engines. The demands for increased performance

and decreased emissions have resulted in advanced

combustor designs that are critically dependent on

effective fuel-air mixing and lean operation. However,

 

Copyright ©2000 by the American Institute of Aeronautics and

Astronautics, Inc. No copyright is asserted in the United States under

Title 17, U.S. Code. The U.S. Government has a royalty-free license

to exercise all rights under the copyright claimed herein for

Goverernmental Purposes. All other rights are reserved by the

copyright owner.

lean-burning low-emissions combustors are particularly

susceptible to combustion instabilities. These

instabilities are typically caused by the interaction of

the fluctuating heat release of the combustion process

with naturally occurring acoustic resonances.1

 These

interactions can produce large pressure oscillations

within the combustor and can reduce component life

and potentially lead to premature mechanical failures.

Additionally, due to non-uniformities in the fuelair mixing and in the combustion process, there

typically exist hot areas in the combustor exit plane

entering the turbine. These hot streaks limit the

operating temperature at the turbine inlet and thus

constrain performance and efficiency. In addition,

these hot streaks can be zones of increased formation of

oxides of nitrogen (NOx). The non-uniformities in

combustor exit temperature are described by a

parameter called pattern factor. Elimination of the hot

streaks, that is, a reduction in pattern factor, can provide

greater turbine life, can effectively increase the

maximum combustor operating temperature and thus

increase engine efficiency and performance, and can

also contribute to emissions reduction.

Finally, the combustor flame temperature is

largely a function of the combustion zone fuel-air

mixture ratio. In order to minimize the formation of

carbon monoxide (CO) and unburned hydrocarbons

(UHC’s), it is desirable to maintain a mixture ratio near

stoichiometric. Unfortunately, mixture ratios near

stoichiometric give high flame temperatures that lead to

increased NOx formation. In order to minimize CO,

UHC, and NOx production, tight control over the fuelair ratio is required.

Advanced design and analysis tools such as the

National Combustion Code2,3 and physics-based

combustion dynamics models4,5,6 can guide the design

and development process for modern low-emissions,

high-performance gas turbine combustors. However,

NASA/TM—2000-210346
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American Institute of Aeronautics and Astronautics

dynamic modeling is not as mature as steady-state CFD

modeling. This limits the use of dynamic models

during the design phase. As a result, passive

approaches to dealing with combustor dynamic

problems are often applied late in the combustor

development process. This tends to be expensive.

Active Combustion Control (ACC), which provides

feedback-based control of the fuel injection, the fuel-air

mixing process, and the staging of fuel sources, can

provide an alternative approach to achieving acceptable

combustor dynamic behavior, and thus can provide

flexibility (additional margin) during the combustor

design process.

In recent years, there has been considerable

activity addressing ACC. Government, academia, and

industry research efforts, through analysis and the use

of laboratory combustors, have shown the considerable

potential for active control.7,8,9,10,11,12,13 The NASA

Glenn ACC Technology effort aims to demonstrate

active control in a realistic environment relevant to

aircraft engines by providing experiments tied to

aircraft gas turbine combustors. The intent is to allow

the maturity of active combustion control technology to

advance to eventual demonstration in an engine

environment.

NASA Glenn’s effort in ACC includes three

related efforts: Combustion Instability Control, Burner

Pattern Factor Control, and Emission Minimizing

Control. Each of these efforts is described in this

paper. Significant results are given as well as

references to where more detailed results can be found.

Finally, the remaining challenges to ACC are given

along with recommendations for future work.

Combustion Instability Dynamics and Control

Combustion instabilities are usually the result of

interactions between the combustion process and the

acoustic fields within the combustor. In-phase coupling

of the heat addition and combustor acoustics can result

in self-excited thermo-acoustic instabilities (Figure 1).

The control measures taken in order to damp or

eliminate these instabilities include passive and active

techniques.1,14 Passive control techniques include

hardware and design modifications that do not have a

dynamic component. Examples of passive techniques

include modifications to the fuel delivery system and/or

the combustor hardware in order to reduce the variation

in the heat release process or increase acoustic

damping. Active control techniques depend on a

dynamic or time-varying hardware component

(actuator). Typically, the intent of an active

combustion instability control approach is to perturb the

combustion process in a fashion that disrupts the

coupling between the combustion process and the

combustor acoustics (see Fig. 1). The approach most

often considered for aircraft gas turbine engines is

dynamic modulation of the fuel flow since it has the

most direct influence.

Combustion Dynamic Modeling

Stability Analysis Models It is not currently

possible to predict a priori the stability of a gas turbine

combustor. This inability is due largely to

computational and physical modeling limitations. The

computational requirements to compute a sufficiently

long time history (hundreds of acoustic wave cycles)

with a 3D (for tangential modes) multi-nozzle engine

geometry over the operational envelope of the engine

remains beyond the capabilities of most institutions.

The physical modeling limitations are due to the wide

variety of physical mechanisms that can lead to

instabilities, and to the intractability of two phenomena

of particular importance: 1) the evolution of vortical

structures in a combusting, acoustically active

environment; and 2) the dynamics of the two-phase fuel

flow.

For the foreseeable future, stability analysis will

have to rely on codes of varying fidelity and

computational requirements. One and two dimensional

time domain and three dimensional acoustic codes

(particularly when energy release models are anchored

with flametube data) can be used to identify regions of

low stability margin in the operating envelope. AYT

Corp. and NASA Glenn are using a 2D “thin-annular”

code4

 anchored with flametube data to make predictions

of the stability characteristics of upcoming full scale

low emissions combustor sector tests.

Combustor 

Acoustics

Combustion

Process

Actuator Controller Sensor

+

+

Closed-Loop Self-Excited System

Figure 1 – Combustion Thermo-Acoustic

Instability with Active control

NASA/TM—2000-210346
3

American Institute of Aeronautics and Astronautics

 Multi-dimensional CFD codes can be used to

model problem areas with a fidelity that the lower

dimensional models lack. NASA Glenn has a grant

with Pennsylvania State University to use Large Eddy

Simulation (LES) techniques to develop a fuel nozzle

response model to calculate nozzle resonances.6

 The

resulting nozzle response information can be utilized

along with less detailed calculations of the frequency

response of other combustor sub-components to ensure

that there are no matches in sub-component resonances.

The fuel injector that Penn State is analyzing in this

effort is the same engine injector being used for the

Empirical Combustion Instability Control effort

described later in this paper.

Control Development Models A significant

amount of combustor dynamic modeling will most

likely be required to implement active control schemes

on practical systems. The one- and two- dimensional

time domain codes will play an important role in

assessing the feasibility of various control schemes.

Simplified, 1-D, Quasi-1-D, and Sectored-1-D

combustor dynamic models15 are being developed by

NASA Glenn to aid control system development and

testing. These approaches utilize the one-dimensional

(or quasi-one-dimensional), Navier-Stokes equations as

their basis. In addition to mass, momentum, and energy

equations, there are also one or more species transport

equations. Associated with these are relatively simple

reaction and heat release equations. The governing

equations of motion are solved numerically using a very

simple, second-order MacCormack scheme. Artificial

viscosity is added in order to damp non-physical

oscillations in vicinities of strong spatial gradients such

as those brought about by the combustion process. The

numerical scheme and associated artificial viscosity

were chosen because of the computational speed which

they afford.

If the combustor geometry is such that there are

abrupt changes in cross section, a sectored approach is

used. Here, the combustor is approximated by dividing

it into a finite number of one-dimensional (constant

area) sectors as shown in Figure 2. Within each sector,

the equations of motion and species transport are

integrated numerically. Across the sectors, mass and

energy are conserved, and momentum loss is prescribed

using appropriately compatible boundary conditions

that account for the area change.

The resulting simulation and associated boundary

conditions essentially represent a one-dimensional,

multi-block technique. This approach has been used to

successfully reproduce self-excited instabilities that

have been observed in several experimental rigs.

Figure 3 shows the geometrical layout of one

such experiment that was simulated using the sectoredone-dimensional approach. This is a lean, premixprevaporize (LPP) combustor design that exhibited

instabilities at certain operating points. When the

simulation was run under the same conditions, selfexcited, unstable operation commenced, and eventually

reached limit cycle behavior. The computed peak-topeak amplitude of the pressure fluctuations at the

location of a pressure transducer was approximately

7.7% of the mean. This is close to the 8% value

measured experimentally. Contour plots of nondimensional pressure and velocity fluctuations

(deviations from the time-mean values) are presented in

Figure 4 for approximately 2 oscillatory cycles. These

are presented in order to show the mode shape of the

instability. It would appear that this limit cycle is

primarily the quarter wave mode of the combustor

section proper.

Sector 1

Sector 2

Sector 3

Injector Region

Combustor Region

Figure 2 - Lean Premix Combustor Schematic

0 20 40 60 80 100

0

20

40

60

80

100

120

x (inches)

Area (sq. in.)

fuel injection

cooling

tee-section exhaust

air

combustion 

zone

Figure 3 - Combustion Rig Geometry

NASA/TM—2000-210346
4

American Institute of Aeronautics and Astronautics

Measured and computed Power Spectral Density

distributions (PSD) of pressure at the experimental

transducer location are presented in Figure 5. Critical

similarities are the frequency and power density

associated with the instability. For reference, the

measured dominant frequency was 275 Hz. The

computed value was 291 Hz. Although differing

significantly in magnitude, the presence of higher

frequency modes can also be seen at approximately 570

and 850 Hz. in both the computed and measured

spectra. This suggests that the subtle acoustic

properties of the rig are reasonably reproduced in the

simulation.

The sectored-one-dimensional, numerical,

reacting flow solver technique is relatively simple and

fast. The combustor rig simulation just described took

188 seconds of CPU time to simulate 0.116 seconds of

real time running on a Sun Ultra 2 Workstation. Yet

the technique is also reasonably accurate. These

features make it useful for characterization and testing

of control strategies. Furthermore, modeling and

implementation of control actuation, such as fuel flow

perturbations, are straightforward in the 1-D CFD

framework. Further details on the approach are given

in Ref. 15.

The 2-D “thin annular” codes mentioned earlier4

may play an important role in analyzing nozzle-tonozzle interaction for both passive and active

approaches to controlling tangential mode instabilities.

AYT Corporation, NASA Glenn and the Department of

Energy have begun a collaboration to address these

issues.

Combustion Instability Control

The lack of detailed combustor dynamic models

makes a rigorous control development methodology (an

exact reasoning approach) difficult to pursue. On the

other hand, control development methods that primarily

treat the combustor as an input/output relationship (an

approximate reasoning approach) may be feasible, but

may be unable to take into account some of the

important dynamic relationships in the combustor

physics.

To address this spectrum of potential control

approaches, the NASA Combustion Instability Control

element has efforts covering three different control

development methods. First, research into neuralnetwork-based control is being conducted using a

relatively complex lean premix-prevaporize (LPP)

combustor rig in order to demonstrate a near-term

approximate reasoning approach. Second, as a

medium-term, more exact reasoning approach, an

empirical control development method using non-linear

system identification and control techniques is being

pursued for a single-nozzle combustor rig with many

real combustor features. And third, research is being

conducted for physics-based control approaches using a

simple, tube-type laboratory combustor in order to

allow the eventual demonstration of an exact reasoning

approach on a more real combustor.

Neural Combustion Instability Control The

neural-network-based control approach is seeking to

develop a neural controller capable of adequately

attenuating thermo-acoustic instabilities observed in an

LPP combustor flame-tube. The control action is to

occur through the modulation of the primary fuel

supply to the combustor. Figure 6 shows the block

diagram for the neural controller approach. The “plant”

actually consists of three parts: the combustor, the fuel

actuation dynamics, and the sensor dynamics. The

Figure 4 - Contours of Non-dimensional Pressure

and Velocity Fluctuations for 2 Cycles of the Limit

Cycle

0 500 1000 1500 2000 10−10

10−8

10−6

10−4

10−2

100

Power Density (psi**2/hz.)

Frequency (hz.)

Computed

Measured

Figure 5 - Power Spectral Density Distribution of

Pressure for x=42.1 Inches During Limit Cycle

NASA/TM—2000-210346
5

American Institute of Aeronautics and Astronautics

neural identifier learns the dynamics of the plant and

provides the desired output value for the neural

controller. The neural identifier and neural controller

networks are both of a Bama Optimized Recurrent

Neural Network (BORN) type.16

Initially, the neural control approach is being

developed through software simulation. The quasi-1-D

model of an LPP combustor discussed earlier is being

used to train the networks and will serve as a way to

measure the neural controller’s performance. The

model will be run at five different operating points

between low and full power, with an input signal, u,

that spans prescribed ranges of frequency and

amplitude. This input and the pressure output from the

plant, P, will be recorded in a data file. Half of this data

will be used to train the neural identifier off-line; the

other half will be used for its testing.

 Once trained, the neural identifier replaces the

plant during training of the neural controller. The

neural identifier pressure output is compared to a priori

background pressure measurements (i.e., measurements

taken when no combustion is taking place). The

difference is backpropagated through the input layer of

the neural identifier to determine what the input should

have been in order to minimize the error. This ideal

input is the desired value for the output of the neural

controller. The error between the desired and the

controller’s actual output is then used in a conventional

backpropagation sense to train the neural controller.

Once proof of concept has been demonstrated in a

simulation environment, NASA plans to implement the

trained controller in real time and demonstrate the

neural control with a NASA-owned LPP flame-tube

combustor rig.

Empirical Combustion Instability Control The

goal of the empirical combustion instability control

effort is to develop a control for a realistic combustion

instability. The approach consists of developing a

combustor rig which replicates an engine combustion

instability, conducting system identification in order to

develop reduced-order models of the rig, and then using

the reduced-order models for control development. The

sample problem selected for this effort is a combustion

instability that was observed during the development of

a high-performance aircraft gas-turbine engine. The

frequency of the observed instability at a mid-power

operating condition was 525Hz, and the magnitude of

the pressure oscillations was sufficient to cause

unacceptable vibratory stresses in the turbine.

The empirical combustion instability control

effort has designed, fabricated, and tested an enginescale, liquid-fueled single-nozzle research combustor

rig. The rig successfully replicates the engine

instability and operates at engine pressure and

temperature conditions. The single-nozzle combustor

rig has many of the complexities of a real engine

combustor including an actual engine fuel nozzle and

swirler, dilution cooling, and an effusion-cooled liner

(Figure 7). The research combustor rig was developed

in partnership with Pratt & Whitney and United

Technologies Research Center (UTRC). Experimental

testing with the combustor rig is taking place at UTRC.

Delay

Neural

Controller Plant u(t) P(t)

Neural

Identifier

Delay

Delay -

+ Ei

(t)

Et

(t)

Pi

(t)

P(t-n)

u(t-n)

u(t-n)

+

-

P0 E (t) c(t)

Figure 6 – Neural Combustion Instability

Control Approach

Figure 7 - Combustor Rig for Empirical

Combustion Instability Control Research

(Photograph by United Technologies Research Center)

NASA/TM—2000-210346
6

American Institute of Aeronautics and Astronautics

The single-nozzle combustor rig was operated in

the baseline configuration shown in Figure 8 at

pressures, temperatures, and fuel-air ratios

corresponding to three different engine operating

conditions. For the conditions corresponding to the

mid-power condition chosen for evaluation (T3=770oF,

P3=200psia, fuel-air ratio=0.03), test results established

the existence of a combustion instability at

approximately 566 Hz. The measured pressure

spectrum inside the combustor at a location

approximately 2 inches downstream of the fuel injector

is shown in Figure 9. Pressure measurements at

different axial locations and at the equivalent axial

station but different circumferential locations showed

this to be a longitudinal acoustic mode. This mode was

also observed at the other two operating conditions,

although at smaller amplitude and slightly lower

frequencies.

A comparison between the pressure spectrum in

the engine and in the single-nozzle combustor rig at

comparable operating conditions is shown in Figure 10.

The combustor rig approximates the frequency and

amplitude of the engine instability. However, the

engine provides a narrower, more coherent frequency

peak, and the rig exhibited a higher overall level of

noise.17

Overall, the single-nozzle rig provides a suitable

test environment for combustion instability control

research. A second phase of the empirical control effort

has been initiated to develop a controller to suppress the

observed combustion instability. Non-linear system

identification and analytical models are guiding the

development of reduced-order models of the research

combustor. The reduced-order models will be used to

develop control laws for experimental demonstration on

the combustor rig.

PRE-DIFFUSER

AIRFLOW

!

" "

Figure 8 – Empirical Combustion Instability Control Rig Baseline Configuration

0 500 1000 1500 2000 2500

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Frequency (Hz)

Unsteady Combustor Pressure Amplitude (psi)

Figure 9 – Measured pressure spectrum of unsteady

combustion pressure for the single nozzle combustor rig

in the baseline configuration.

0 100 200 300 400 500 600 700 800 900 1000 0

0.1

0.2

0.3

0.4

N ASA SNR R004p38 and Engine TX3081.2B

Frequency (Hz)

Amplitude (psi)

Rig Engine

Figure 10 – Comparison of engine and baseline

combustor rig pressure spectra

NASA/TM—2000-210346
7

American Institute of Aeronautics and Astronautics

In addition to the baseline rig configuration shown

in Figure 8, the combustor rig configuration was also

changed to an extended configuration that placed the

two 9.72 inch spool pieces between the pre-diffuser and

the fuel injector. This extended configuration, when

operated at the same mid-power evaluation condition as

the baseline configuration, showed a dramatically

different instability frequency (273 Hz) and magnitude

(Figure 11). Higher order harmonics are evident, and

the peaks here are narrow and coherent as compared

with the results for the baseline configuration.

18

The extended configuration also showed a certain

“tune-ability”, that is, the amplitude and the frequency

of the instability varied considerably with operating

condition and fuel-air ratio (Figure 12). This tendency

to produce an instability of varying amplitude and

frequency over a wide range of conditions seems to

indicate that the extended configuration has the

potential for producing instabilities for other

nozzle/combustor configurations.

Physics-Based Combustion Instability Control

The final activity in Combustion Instability Control

utilizes an integrated systems approach toward physicsbased combustion control. Under a grant with Virginia

Tech, methods are being investigated for increasing the

physical understanding of combustion instabilities as

they pertain to theoretical and test-based system models

amenable to control. The end-goal of the effort is to

develop more effective control strategies for

combustion instability suppression.19,20 CFD and

experimental studies have been conducted to support

detailed analyses of the various physical mechanisms

(fluid mechanics, chemical kinetics, acoustics, and heat

transfer) that underlie the inter-related phenomena

observed during the occurrence of combustion

instabilities. The roles of these various mechanisms

have been studied for a simple laboratory tube

combustor, which has been nonetheless a challenge to

accurate theoretical, physics-based modeling efforts.

This research has resulted in detailed characterization

of: the laminar flame's dynamic response to acoustic

perturbations, the interaction of thermo-diffusive and

thermo-acoustic instabilities, and first-generation

nonlinear models that can be used to estimate the

frequency and amplitude of limit cycling behavior

observed in the tube combustor.

The Virginia Tech research is also leading to

better insight about techniques that produce reliable

control of combustion instabilities.21,22 For example,

this part of the effort recently provided analytical and

experimental results that illustrate specific requirements

for robust use of the least mean squares (LMS) adaptive

signal processing algorithm in suppressing

instabilities.23 The precise role of the system

identification task required for compensation of the

feedback in the reference signal has now been clearly

shown and a robust implementation of the LMS

controller has been demonstrated in simulation and

experiments conducted on the tube combustor (Figure

13).

23 Future work will address the use of adaptive

control techniques on a dump combustor.

0 500 1000 1500 2000 2500

0

1

2

3

4

5

6

Frequency (Hz)

Unsteady Combustor Pressure Amplitude (psi)

Figure 11 – Measured pressure spectrum of unsteady

combustion pressure for the single-nozzle rig in the

extended configuration

0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045

0

2

4

6

8

10

12

Peak Amplitude, psi

Fue l/Air Ratio

Pe a k Am plitude vs. FAR, Ex te nde d Configura tion

P3=70psia 

P3=110psia

P3=170psia

0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045

200

220

240

260

280

300

320

340

Peak Frequency, Hz

Fue l/Air Ratio

Pe a k Fre que ncy vs. FAR, Ex te nde d Configura tion

P3=70psia 

P3=110psia

P3=170psia

Figure 12 – Peak amplitude and peak frequency vs.

fuel-air ratio for the extended configuration at three

operating conditions

NASA/TM—2000-210346
8

American Institute of Aeronautics and Astronautics

Burner Pattern Factor Control

The Burner Pattern Factor Control element seeks

to develop an active engine fuel distribution system

capable of producing a more uniform combustor exit

temperature. The Active Pattern Factor Control (APFC)

system architecture is shown in Figure 14. The APFC

works in series with the primary fuel supply system.

The fuel control system delivers the required total

combustor fuel flow. Based on temperature feedback

from circumferentially arranged temperature sensors at

the combustor exit plane, the APFC issues commands

to fuel flow modulators (valves) circumferentially

arranged inside the combustor. The command signals

dictate how these valves are to distribute the total fuel

inside the combustor so as to achieve as uniform a

temperature distribution at the exit plane as possible.

The pattern factor control work was performed by

Honeywell Engines and Systems (formerly

AlliedSignal Engines) under the NASA Advanced

Subsonic Technology (AST) Program.

The highest risk element in this work was the

development of thin film temperature sensors. A major

effort was expended to develop thin-film type-S

thermocouples that could be affixed to the backside

surface of the turbine inlet stator vanes to provide the

temperature feedback information needed by the APFC.

A number of fabrication methods were investigated for

the thin-film thermocouples and durability testing of the

resulting thermocouples was conducted on base

material prior to fabrication onto the turbine stators.

Research was also conducted into determining a

suitable method for affixing the sensors to the stators.24

The stator ring to be used in the rig tests was cut into 19

sectors (two vanes per sector) to fit into the

thermocouple fabrication facility. A thin film sensor

was then affixed to each of the 38 vanes. Figure 15

shows a photograph of an instrumented stator.

Specifications for the fuel flow modulators were

determined and several candidates were examined. A

downselect was made to use a Sturman Industries

magnetically-latched solenoid valve typically used for

small automotive diesel engines. The valve consists of a

high-speed, pressure balanced spool and a hermetically

sealed magnetic circuit. Residual magnetism provides a

0 50 100 150 200 250 300 350 400 -100

-80

-60

-40

-20

0

20

dB

Freq.(Hz)

... Uncontrolled

--- Controlled

Figure 13 - Experimental data for a Rijke tube

combustor operating at an equivalence ratio of

about 0.51 and a total flow rate of 120 cc/sec.

.....

......

Turbine

Engine

Inertia Speed Sensor

Electronic

Control

Fuel

Control

Electronic

PF Control

......

Combustor & Atomizers

Fuel In

Speed Control Loop (Fast)

Fuel Flow

Modulators

Multiple

Control Signals

Pattern Factor

Control Loop (Slow)

Multiple Thin Film

Temperature Sensors

Figure 14 – Active Pattern Factor Control

System Architecture

Figure 15 - Thin-film thermocouple attached to

a turbine stator vane

(Photograph by Honeywell Engines and Systems)

NASA/TM—2000-210346
9

American Institute of Aeronautics and Astronautics

small holding or latching force that requires a brief,

high current to switch but low current to hold in

position. Simulation studies and hardware tests of the

valves confirmed that the fuel distribution could be

varied using pulse-width-modulation in response to

sensed changes in exit temperature distribution. Bench

testing revealed that a single valve influenced

approximately a 20o sector of the exit plane temperature

profile, requiring a minimum of 18 circumferentially

distributed valves. To provide a slight overlap, 19

valves were used.

Several control algorithms were evaluated for

performing the APFC. From simulation evaluations it

was decided to further investigate the performance of

both a Proportional-Integral-Derivative (PID) and a

fuzzy logic controller.

Two series of rig tests were performed. In the

first series a significant number of the thin film sensors

were damaged and the data collected were deemed

questionable. Inspection of the sensors after the testing

revealed lead wire splice failure as the primary failure

mechanism. There was also some delamination of the

thermocouple material. Valuable experience (in the

form of lessons learned) was gained for furthering the

development of this technology area.

In the second series of tests, the thin film sensors

were replaced with conventional platinum-rhodium

thermocouples. Two rig operating conditions (idle and

medium power) were examined. Baseline

measurements were taken without APFC for both of

these conditions. For this effort, the pattern factor, PF,

is defined as:

−= 1

Avg

Peak

T

T

PF

where TPeak is the measured peak temperature at the

combustor exit plane and TAvg is the average measured

temperature.

Preliminary testing showed the potential for

pattern factor reduction with both the PID and fuzzy

logic control techniques. For the idle condition,

(T3=344oF, P3=48psia) the PID controller was

successful in achieving a 47% reduction in pattern

factor and a 7.3% reduction in NOx. It did, however,

cause a 1.1% increase in CO and a 1.8% increase in

UHC. The Fuzzy Logic controller achieved a 51.9%

reduction in pattern factor and a 4.5% reduction in

NOx; it caused an increase in CO of 1.0% and an

increase in UHC of 1.3%. For the medium power

condition, (T3=707oF, P3=145psia) the PID controller

achieved a 30.8% reduction in pattern factor with a

1.4% reduction in NOx, a 0.04% reduction in CO, and a

0.2% reduction in UHC. The Fuzzy Logic controller

achieved a 42.5% reduction in pattern factor, a 1.3%

reduction in NOx, a 0.08% increase in CO, and a 0.62%

increase in UHC. APFC data for the PID and Fuzzy

controllers is shown in Figure 16.

0

0.5

1

1.5

1 3 5 7 9 11 13 15 17 19

% Open / 100

0

0.5

1

1 3 5 7 9 11 13 15 17 19

600

800

1000

1200

1400

1600

147

10131619222528313437

1.5

Fuel Injector # Fuel Injector #

No Control PID Control

0

0.5

1

1.5

1 3 5 7 9 11 13 15 17 19

Fuel Injector #

Fuzzy Control

600

800

1000

1200

1400

1600

600

800

1000

1200

1400

1600

147

10131619222528313437

147

10131619222528313437

Thermocouple # Thermocouple # Thermocouple #

Temperature °F

Figure 16 - Sample Active Pattern Factor Control results for the medium power condition. Steady-state

performance of the PID and Fuzzy Controllers relative to the baseline uncontrolled case is shown.

NASA/TM—2000-210346
10

American Institute of Aeronautics and Astronautics

Emission Minimizing Control

The Emission Minimizing Control effort seeks to

minimize emissions through active feedback control of

fuel-air ratio. Combustors often incorporate fuel

staging, that is, sequencing of the fuel injection through

one or more local injection zones, in order to tailor the

fuel-air mixture ratio over a large engine operating

(airflow) range. Increased levels of staging allow

increased resolution in the fuel-air mixture ratio.

However, increased levels of staging also require

increased valving to accomplish the staging.

Under the NASA AST Program, General Electric

developed an intelligent fuel staging concept which

includes a Control Pressure Fuel Nozzle (CPFN) along

with model-based staging control. The CPFN provides

a single pressure-driven linear actuator with multiple

ports at each fuel injection station to selectively provide

fuel to one or more fuel injection points. The modelbased staging control uses sensed combustor inlet

pressure and temperature along with a calculated

compressor airflow to estimate the emissions formation

in order to determine when to sequence the injection

points. The CPFN and model-based staging control

have been successfully demonstrated on the GE control

system test rig. This rig incorporates actual control

system hardware with a simulated engine. Also under

the NASA AST Program, GE developed a fuel

multiplexer (FMUX) for more advanced engines

incorporating even more levels of staging than would

be practically supportable by the CPFN. The FMUX

incorporates a rotating slotted cylinder to control the

fuel to multiple injection locations. The FMUX has

been fuel flow bench tested but has not been tested on

the control system test rig.

Concluding Remarks

Early results from the NASA Glenn Research

Center’s Active Combustion Control Technology effort

demonstrate the promise of active combustion control

for aircraft gas turbine engines. The NASA Glenn

effort is being conducted in partnership with academia

and industry. Emphasis has been placed on engine

relevance through experiments and analysis tied to

aircraft gas turbine engine combustors. NASA Glenn’s

effort includes Combustion Instability Control, Burner

Pattern Factor Control, and Emission Minimizing

Control.

There are a number of technical challenges that

must continue to be addressed in order for Active

Combustion Control to become a reality for aircraft gas

turbine engines. Temperature, emissions, and pressure

sensors that can exist in the harsh environment in or

near the combustor are needed as feedback devices for

the controller. Highly-distributed fuel actuation,

potentially employing micro-valving, is needed for

precisely controlling fuel distribution. Fast, full

authority fuel valves are needed for instability

suppression. Accurate physics-based models of

combustor dynamics continue to be required to guide

the development of passive and active instability

suppression methods. Simplified dynamic models are

needed for controls development. And control methods

are needed which provide or enhance the capabilities of

combustors to achieve high performance and low

emissions while maintaining full-envelope stability and

operability margins.

The challenges to Combustion Instability Control,

Burner Pattern Factor Control, and Emission

Minimizing Control are being addressed in a somewhat

synergistic fashion in the current NASA Glenn effort.

However, in the future it is likely that a more closely

coupled effort would result in a more efficient solution.

NASA Glenn’s long range vision is for the systems to

implement instability control, pattern factor control, and

emissions control be combined into a smart, fast,

highly-distributed fuel-air management system. This

would provide maximum flexibility to the designers of

future combustors as they strive to meet the ever

increasing performance and emissions demands placed

on future engine systems.

 

References

1

 Lefebvre, A.H.: Gas Turbine Combustion, 2nd edition,

Taylor & Francis, 1999.

2

 Stubbs, R.M.; Liu, N.-S.: “Preview of the National

Combustion Code,” presented at the 33rd Joint Propulsion

Conference and Exhibit, Seattle, WA, July 1997, AIAA

97-3114.

3

 Liu, N.-S.; Quealy, A.: “NCC – A Multi-Disciplinary

Design/Analysis tool for Combustion Systems,” in

proceedings of NASA Ames Research Center HPCP/CAS

Workshop 98, NASA CP-1999-208757, January 1999.

4

 Yang, Z.; Breisacher, K.; Radhakrishnan, K.; Oyediran,

O.: “Quick Stability Calculations for Longitudinal and

Tangential Modes in Pre-Mixed Gas Turbine

Combustors,” Penn State Symposium on Propulsion, 1997.

5

 Peracchio, A.A.; Proscia, W.M.: “Nonlinear HeatRelease / Acoustic Model for Thermoacoustic Instability

in Lean Premixed Combustors,” presented at the

International Gas Turbine & Aeroengine Congress &

Exhibition, Stockholm, Sweden, June 1998, ASME Paper

98-GT-269.

NASA/TM—2000-210346
11

American Institute of Aeronautics and Astronautics

 

6

 Wang, S.; Hsiey, S.Y.; and Yang, V.: “Vortical Flow

Dynamics and Acoustic Response of Swirl-Stabilized

Injector,” presented at 38th Aerospace Sciences Meeting

and Exhibit, January 2000, AIAA 2000-0434.

7

 Schadow, K.; Yang, V.; Culick, F.; Rosfjord, T.;

Sturgess, G.; Zinn, B.: “Active Combustion Control for

Propulsion Systems,” AGARD Report 820, September

1997.

8

 Yu, K.; Wilson, K.J.; Schadow, K.C.: “Active

Combustion Control in a Liquid-Fueled Dump

Combustor,” AIAA Paper 97-0462, January 1997.

9

 Zinn, B.T.; Neumeier, Y.: “An Overview of Active

Control of Combustion Instabilities,” AIAA Paper 97-

0461, January 1997.

10 McManus, K.R.; Magill, J.C.; Miller, M.F.; Allen,

M.G.: “Closed-Loop System for Stability Control In Gas

Turbine Combustors,” AIAA Paper 97-0463, January

1997.

11 Annaswamy, A.M.; El Rifai, O.M.; Fleifil, M.; Hathout,

J.P.; Ghoniem, A.F.: “A Model-based Self-tuning

Controller for Thermoacoustic Instability,” in Combustion

Science and Tech., Vol 135, pp. 213-240, 1998.

12 Whitelaw, J.H.: “Combustion Oscillations, Extinction,

and Control,” final technical report, U.S. Army contract

N68171-97-C-9035, May 1998.

13 Hibshman, J.R.; Cohen, J.M.; Banaszuk, A.; Anderson,

T.J.; Alholm, H.A.: “Active Control of Combustion

Instability in a Liquid-Fueled Sector Combustor,”

presented at the International Gas Turbine & Aeroengine

Congress and Exhibition, June 1999, ASME Paper 99-GT215.

14 McManus, K.R.; Poinsot, T.; Candel, S.M.: “A Review

of Active Control of Combustion Instabilities,” in

Progress in Energy and Combustion Science, Volume 19,

Issue 1, p. 1-29, February 1993.

15 Paxson, D.E.: “A Sectored-One-Dimensional Model For

Simulating Combustion Instabilities in Premix

Combustors,” presented at 38th AIAA Aerospace Sciences

Meeting & Exhibit, Reno, Nevada, January 2000, NASA

TM 1999-209771, AIAA-2000-0313.

16 KrishnaKumar, K.; Nishita, K.: “BORN – Bama

Optimized Recurrent Neural Networks,” Proceedings of

the World Congress on Neural Networks, San Diego, CA,

September 1996, p. 305-310.

 

17 Cohen, J.M. et al: “Experimental Replication of an

Aeroengine Combustion Instability,” presented at ASME

TurboExpo 2000, Munich, Germany, May 2000, ASME

Paper 2000-GT-0093.

18 Cohen, J.M. et al: “Longitudinal-Mode Combustion

Instabilities: Modeling and Experiments,” presented at the

RTO Symposium on Active Control Technology,

Braunschweig, Germany, May 2000, NASA TM-2000-

210067.

19 Saunders, W.R.; Vaudrey, M.A.; Eisenhower, B.A.;

Vandsburger, U.; Fannin, C.A.: “Perspectives on Linear

Compensator Designs for Active Combustion Control,”

presented at 37th AIAA Aerospace Sciences Meeting and

Exhibit, Reno Nevada, January 1999, AIAA 99-0717.

20 Fannin, C.A.; Saunders, W.R.; Vaudrey, M.A.;

Eisenhower, B.A.; Vandsburger, U.: “Analytical and

Practical Considerations for Control of Thermoacoustic

Instabilities,” presented at 37th AIAA Aerospace Sciences

Meeting and Exhibit, Reno, Nevada, January 1999, AIAA

99-0718.

21 Vaudrey, M.A.; Saunders, W.R.; Eisenhower, B.: “A

Test-Based Methodology For A Priori Selection of

Gain/Phase Relationships in Proportional, Phase-Shifting

Control of Combustion Instabilities,” presented at the

ASME TurboExpo 2000, Munich, Germany, May 2000,

ASME Paper 2000-GT-530.

22 Vaudrey, M.A.; Saunders, W.R.; Baumann, W.T.:

“Control of Combustor Instabilities Using An Artificial

Neural Network,” presented at the ASME TurboExpo

2000, Munich, Germany, May 2000, ASME Paper 2000-

GT-529.

23 Vaudrey, M.A.; Saunders, W.R.; Baumann, W.T.: “An

Investigation of Adaptive Signal Processing Methods for

Active Control of Combustion,” presented at the RTO

Symposium on Active Control Technology,

Braunschweig, Germany, May 2000.

24 Niska, R.H.; Constant, A.P.; Witt, T.; Gregory, O.J.:

“Chemical Vapor Deposition of Alpha Aluminum Oxide

for High-Temperature Aerospace Sensors,” presented at

the American Vacuum Society 46th International

Symposium on Vacuum, Thin Films, Surfaces/Interfaces

and Processing, Seattle, WA, October 1999, Paper AVS

TF-TuA9.

NASA/TM—2000-210346
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Aircraft engines; Engine control; Combustion control; Combustion stability; Control

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Active Combustion Control for Aircraft Gas Turbine Engines

Prepared for the 36th Joint Propulsion Conference and Exhibition sponsored by the American Institute of Aeronautics

and Astronautics, Huntsville, Alabama, July 17–19, 2000. Responsible person, John C. DeLaat, organization code 5530,

(216) 433–3744.

John C. DeLaat, Kevin J. Breisacher, Joseph R. Saus, and Daniel E. Paxson

Lean-burning combustors are susceptible to combustion instabilities. Additionally, due to non-uniformities in the fuel-air

mixing and in the combustion process, there typically exist hot areas in the combustor exit plane. These hot areas limit the

operating temperature at the turbine inlet and thus constrain performance and efficiency. Finally, it is necessary to optimize

the fuel-air ratio and flame temperature throughout the combustor to minimize the production of pollutants. In recent years,

there has been considerable activity addressing Active Combustion Control. NASA Glenn Research Center’s Active

Combustion Control Technology effort aims to demonstrate active control in a realistic environment relevant to aircraft

engines. Analysis and experiments are tied to aircraft gas turbine combustors. Considerable progress has been shown in

demonstrating technologies for Combustion Instability Control, Pattern Factor Control, and Emissions Minimizing Control.

Future plans are to advance the maturity of active combustion control technology to eventual demonstration in an engine

environment.