Computational Analysis of Stall and Separation Control in Compressors

Transcription

1 Computational Analysis of Stall and Separation Control in Compressors Alexander Stein, Ph.D. (General Electric Power Systems) Saeid Niazi, Ph.D. (Sukra Helitek Inc.) Lakshmi Sankar, Ph.D., Regents Professor (Georgia Tech) (work was carried out at Georgia Institute of Technology) Supported by the U.S. Army Research Office Under the Multidisciplinary University Research Initiative (MURI) on Intelligent Turbine Engines

2 Outline of Presentation Research objectives and motivation Background of compressor control Centrifugal compressor modeling Off-design results without control (surge analysis) Off-design results with air injection control (steady and pulsed jets) Axial compressor modeling Off-design results without control (rotating stall analysis) Off-design results with bleed valve control Conclusions and recommendations 2

3 Motivation and Objectives Use CFD to explore and understand compressor stall and surge Desired Extension of Operating Range Lines of Constant Efficiency Develop and test control strategies (air injection, bleed) for compressors Apply CFD to compare lowspeed and high-speed configurations Total Pressure Rise Surge Limit Flow Rate Lines of Constant Rotational Speed Choke Limit 3

4 Motivation and Objectives Compressor instabilities can cause fatigue and damage to entire engine 4

5 DLR High-Speed Centrifugal Compressor 24 Main blades 30 Backsweep Grid 141 x 49 x 33 (230,000 nodes) A grid sensitivity study was done with up to 1.8 Million nodes. Design Conditions: 22,360 RPM Mass flow = 4.0 kg/s Total pressure ratio = 4.7 Adiab. efficiency = 83% Exit tip speed = 468 m/s Inlet M rel =

6 Off-Design Results (Centrifugal) Performance Characteristic Map D C B Computational and experimental data are within 5% Total Pressure Ratio Experiment A Fluctuations at 3.2 kg/sec are 23 times larger than at 4.6 kg/sec CFD Mass Flow (kg/sec) 6

7 Pressure Rise Fluctuations (%) Off-Design Results (Centrifugal) A: 4.6 kg/sec Mass Flow Fluctuations (%) Performance Characteristic Map Pressure Rise Fluctuations (%) B: 3.8 kg/sec Mass Flow Fluctuations (%) Large limit cycle oscillations develop Pressure Rise Fluctuations (%) C: 3.4 kg/sec Mass Flow Fluctuations (%) Pressure Rise Fluctuations (%) D: 3.2 kg/sec Mass Flow Fluctuations (%) Oscillations remain bound => mild surge 7

8 Off-Design Results (Centrifugal) Velocity vectors colored by M rel at mid-passage Flowfield vectors show a large separation zone near the leading edge 8

9 Off-Design Results (Centrifugal) Stagnation Pressure Contours View Vortex shedding causes reversed flow Origin of separation occurs at leading edge pressure side 9

10 0.04R Inlet Casing Air Injection Setup 5 Rotation Axis R Inlet Impeller Systematic study: injection rate and yaw angle were identified as the most sensitive parameters. Injected Fluid Sheet Compressor Face Yaw Angle β Compressor Casing Related work: Rolls Royce, Cal Tech, NASA Glenn /MIT, Main Flow 10

11 Air Injection Results (Centrifugal) Velocity vectors colored by M rel at mid-passage Leading edge separation is suppressed by injection 11

12 Pulsed Jets Results (Centrifugal) Nondim. Surge Fluctuations (%) With Phase Angle Adjustments Without Phase Angle Adjustments Amplitude of pulsed jets has a stronger impact than mean injection rate => reduction in external air requirements by 50% Rotor Revolutions, ωt/2π m inj m ( t) flow = sin( ω t +φ) surge 12

13 Pulsed Jets Results (Centrifugal) A short boost from the injected air is sufficient to suppress surge onset m inj m ( t) flow = sin( ω t +φ) surge 13

14 Pulsed Jets Results (Centrifugal) No separation occurs m inj m ( t) flow = sin( ω t +φ) surge 14

15 Pulsed Jets Results (Centrifugal) Nondim. Surge Fluctuations (%) Rotor Revolutions, ωt/2π m m inj ( t) flow = sin(4ω t +φ) surge Jets pulsed at higher frequencies are more effective than low-frequency jets (increased mixing, higher turbulent intensity) 1.5% injected mass is sufficient to suppress surge 15

16 NASA Rotor 67 Axial Compressor Meridional Plane LE TE 4 Blocks 73X32X21 Total of cells Hub Plane Normal to Streamwise 16

17 Off-Design Results (Axial) Rotating Stall I II III IV Numerical pressure probes are located at 0.3c upstream of the rotor and 90% span. They are fixed in space. TE LE IV III II I 17

18 Off-Design Results (Axial) Without Inlet Distortion 1.7 I IV III II I Pressure II III IV Rotor Revolution, ωt/2π Probes show identical fluctuations. Flow while unsteady, is still symmetric from blade to blade. 18

19 Off-Design Results (Axial) With Inlet Distortion in one Flow Passage Pressure Time (Rotor Revolution) Inlet distortion simulated by dropping the stagnation pressure in one flow passage by 20% Flow is no longer periodic from blade to blade. Frequency of rotating stall is NΩ/3.6, where ΝΩ : blade passing frequency 19

20 Off-Design Results (Axial) Rotating Stall 20

21 Off-Design Results (Axial) Rotating Stall 21

22 Bleed Valve Results (Axial) Without Control % Total Pressure Fluctuations % bleed air reduces the total pressure fluctuations by 75% With Diffuser Bleed Valve % Mass Flow Rate Fluctuations 22

23 Conclusions A Viscous flow solver has been developed to obtain a detailed understanding of surge and rotating stall in compressors. determine fluid dynamic factors that lead to stall onset. High-pressure jets are effective in alleviating surge (Centrifugal): Alter local incidence angles and suppress boundary layer separation. Pulsed jets yield additional performance enhancements: Jets pulsed at higher frequencies perform better than low-frequency jets. A small amount of inlet distortion can trigger rotating stall (Axial) Stall and surge can be eliminated by the use of small amounts of diffuser bleeding. 23

24 Recommendations Perform studies that link actuation system to stall amplitude via a feedback control law. Employ aeroelasticity methods to improve aeromechanical airfoil design. Use CFD to analyze and optimize other control strategies, e.g. inlet guide vanes, synthetic jets, casing treatments. Improve turbulence modeling of current generation turbomachinery solvers. Analyze the feasibility of LES methods. 24