Transcript Numerical Simulation of Centrifugal Compressor
School of Aerospace Engineering
RECENT PROGRESS IN COMPRESSOR STALL AND SURGE CONTROL
L. N. Sankar, J. V. R. Prasad, Y. Neumeier, W. M. Haddad N. Markopoulos, A. Stein, S. Niazi, A. Leonessa School of Aerospace Engineering Georgia Institute of Technology Supported by the U.S. Army Research Office Under the Multidisciplinary University Research Initiative (MURI) on Intelligent Turbine Engines
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Background
• Modern turbine engines are highly developed, complex systems.
• There is a continuing trend towards fewer stages, and high pressure ratios per compression stage.
• Compressor instabilities (rotating stall and surge) develop, that must be controlled at high pressure ratios, especially at low mass flow rates.
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Compressor Performance Map
Desired Extension of Operating Range Lines of Constant Efficiency Lines of Constant Rotational Speed Volumetric Flow Rate
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Surge
Mean Pressure Operating Rise Point Limit Cycle Oscillations Pressure Rise Peak Performance Mild Surge Flow Rate Deep Surge Flow Rate An “axisymmetric” phenomenon that causes periodic variations in mass flow rate and pressure rise. Deep surge can create a reversed flow in the entire compression system.
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ROTATING STALL
1
Rotating Stall is a local separation pattern that rotates at a fraction of the spool RPM
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Bleed Valves
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Different Strategies for Compressor Control
Guide Vanes Controller Unit Bleed Air Pressure Sensors Air Injection Steady Blowing
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Movable Plenum Walls
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Prior Work
• An excellent survey by Bram de Jager summarizes worldwide activities on rotating stall and surge control.
• A number of researchers in U. S. are exploring compressor stall and surge control, using theoretical, computational, and experimental techniques.
– MIT, Purdue, Penn State, Cal Tech, Wright Labs, and all major U. S. Industries • This presentation will focus on Georgia Tech Activities.
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Georgia Tech Center for Intelligent Turbine Engines
– Start Date : November 1, 1995 – Research Team: Eleven faculty members with expertise in controls, compressors, combustion, propulsion, fluid mechanics, diagnostics, MEMS and neural net.
– Facilities: Combustion, compressor, micro electronics and fluid mechanics laboratories – Research Areas: Control of combustor processes, Nonlinear control theory, Control of compressor stall and surge, MEMS
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MITE Program Objectives
Develop general • Control approaches • • Sensors/actuators Computational approaches that will permit engine manufacturers to improve the design process, performance, operability and safety of future gas turbines.
Demonstrate developed technologies on small-scale experiments Transfer developed technologies to industry and government
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MITE Research Team Name
Dr. Mark Allen Dr. Martin Brooke Dr. Ari Glezer Dr. Wassim Haddad Dr. Jeff Jagoda Dr. Suresh Menon Dr. Y. Neumeier Dr. J.V. R. Prasad Dr. L.N. Sankar Dr. Jerry Seitzman Dr. Ben Zinn
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ECE ECE ME AE AE AE AE AE AE AE AE/ME
Research Area
MEMS Hardware Neural Networks Flow control/actuators Nonlinear control theory Combustion and spray diagnostics LES of reacting flows Control of combustor and compressor processes Control of compressor instabilities CFD of compressor flow Combustion mixing control and sensors Control of instabilities and combustion processes
Supporting Staff:
and electronic shops Research engineers, post doctoral fellows, graduate students, machine personnel, computer group, library and administrative support personnel.
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Research Activities
Control of combustor mixing processes (e.g., fuel-air, combustor pattern factor) via synthetic jets Control of axial and centrifugal compressor stall by passive and active (e.g., flow throttling, fuel flow rate control) means Wireless MEMS pressure sensor for high temperature applications Neural net control of combustion processes Nonlinear control framework for engine compression systems CFD of compression systems LES of two-phase reacting flows “Smart” fuel injection systems
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Compressor Control- Modeling Efforts
• Two and three-dimensional compressible flow solvers for modeling compressor stall and surge control • Multi-mode models for rotating stall and surge in axial flow compressors • Centrifugal compressor model for surge control involving pressure, mass flow rate, and impeller RPM dynamics • Model extensions for compressor stall control via fuel modulations
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Compressor Control- Theory
• Reduced order models based on CFD for modeling compression system transients • Optimal nonlinear control framework to address disturbance rejection, control saturation and robustness • Adaptive control framework for elimination of rotating stall and surge • Nonlinear stabilization framework for interaction between higher order system modes • Combined model and fuzzy rule based methodology to address actuator rate and amplitude limits • Corrections to rotating stall control theories.
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A Simplified Compressor Model with Heat Addition
d
d
dA d
K K
1
n n N
1
p odd
2
n n N
0
p even
(
c n
) (
c n
)
n
!
n
!
s n
( )
r n
( )
A n A n
d ( d ) K 3 K 4 K 5 K T ~ out ΔΨ K 6
d
~
d τ G
K 7
K 4 Φ
K 5 K T ΔΨ
~
T out
~
d T out d τ
K 8
1
~
G
K 9 ΔΨ
1
~
T out
1(t
t c )
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Experimental Studies
• Experimental Demonstrations – Rotating stall control through • Throttling • Recirculation of air from plenum to inlet • Combustion process modulations • Passive means • New facility development – A centrifugal compressor facility for the study of flow dynamics, and for the development of active and passive control methods
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Sample Results
• Experimental Studies • Control Theory • CFD Modeling
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Schematic of the Axial Compressor Facility (Bleed Control)
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Schematic of the Axial Compressor Facility (Fuel Control)
Diffusion flame simulates heat release in a real engine combustor Operating point around 300 0 F
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Fuzzy Logic Control of Rotating Stall
• Fuzzy Rules were developed using numerical simulations.
• The numerical simulations utilized the Moore Greitzer Model, a system of ODEs.
• Control variable was the amount of opening of a bleed valve placed in the plenum chamber.
• Following simulations, these rules were implemented in hardware, at our axial compressor facility.
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Fuzzy Logic Controller
Throttle Opening Output Compression System Measured/Computed Pressure Fluctuations at compressor casing Defuzzifier Inference Engine Fuzzifier
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Fuzzy Logic Control of Rotating Stall
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800000 700000 600000 500000 400000 100% 300000 200000 100000 0 0 10 20 30 Closed-Loop Fuzzy Logic Control 50% Bleed 50% bleed No bleed 40 50 60 70
Main Throttle(%)
80 90 100
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Rotating Stall Control by Flow Separators
0.02
0.018
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0 35.0
No Separator No Separator with active feedback control 8 Separators 8 Separators with active feedback control 40.0
45.0
50.0
Main Throttle Openning (%) 55.0
60.0
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CFD Modeling
•
Detailed study and simulation of NASA Low Speed Centrifugal Compressor
•
Simulation and Validation of Air Bleeding & Blowing/Injection as a Means to Control and Stabilize Compressors Near Surge Line
•
Useful Operating Range of Compressor was Extended to 60% Below Design Conditions
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Perspective View of the NASA Low Speed Centrifugal Compressor
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Simulation Setup
NASA Low Speed Centrifugal Compressor • 20 Full Blades with 55° Backsweep • • • Inlet Diameter 0.87 m Exit Diameter 1.52 m Tip Clearance 2.54 mm (1.8% of Blade Height ) •
Design Conditions:
– Mass Flow Rate 30 kg/sec – Rotational Speed 1862 RPM – Total Pressure Ratio 1.14
– Adiabatic Efficiency 0.992
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Uncontrolled Operation
1.25
1.23
1.21
1.19
1.17
1.15
1.13
1.11
1.09
1.07
1.05
15 C Stable Operation Stall, Unstable 20 Design Point 25 30 35 Corrected Mass Flow (kg/s) Experiment CFD 40 45 Uncontrolled, Stall Operation
Large, Unbounded Fluctuations
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-25
C
2 1 -15 0 -5 -1 -2 % of Mass Flow Rate Fluctuations 5
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Off-Design Results (Uncontrolled)
Velocity Vectors at Midpassage TE Growing Reversed Flow Unstable Condition
Blades Stall
After 3 Cycles (t*) LE
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Compressor Control Setup
Casing 0.04R
Inlet
a 5
° R Inlet Impeller
Injection Angle, a =5º Yaw Angle, =0º 5% or 10% Injected Mass Flow Rate
Rotation Axis
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Controlled Operation
1.25
1.23
1.21
1.19
1.17
Stall, Unstable Experiment CFD 10% Injection 5% Injection
D
2 1 D 0 1.15
1.13
1.11
Controlled Air Injection Design Point -25 -5 -1 1.09
1.07
-2 % of Mass Flow Rate Fluctuations 1.05
5 15 25 35 45 Corrected Mass Flow (kg/s) Controlled Operation with 10% Air Injection
( .
m=17.5 kg/sec ) Fluctuations are Decreased to 2~3% Extension of Useful Operating Range (60% Below Design)
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Injected Air (10%)
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Air Injection
Controlled, Stable Operation
Injection Suppresses Stalled Reverse Flow
Regions Near LE
0 25 50 No Injection (t*) 5% Injection 10% Injection -0.3
75 100 -0.1
0.1
0.3
Normalized Axial Velocity, V n /U t 0.5
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DLR Centrifugal Compressor
Control simulations are currently in progress 3 2.5
2 1.5
1 0.5
0 0 Experiment (time mean) CFD 0.2
0.4
0.6
Meridional Chord, S/S max 0.8
1
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NASA ROTOR-67Axial Compressor
Relative Mach No. at 30% Pitch
1.6
1.4
CFD EXP.l
125 1.2
1 0.8
-50 0.6
25 100 % Chord 175 250
Results for Rotating Stall Simulation considering six flow passages are in progress 51.4 cm
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Concluding Remarks
• A concerted effort involving control theory, simulations and experimental studies is underway at Georgia Tech to understand and control compressor instabilities.
• Encouraging results have been obtained in all these areas.
• A combined CFD-Feedback Control simulation is currently in progress.
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