Computational Analysis of Stall and Separation Control in Centrifugal Compressors

1. Computational Analysis of Stall and Separation Control in Centrifugal Compressors Presented By Alexander Stein 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

2. Outline of Presentation • Research objectives and motivation • Background of compressor control • Introduction of numerical tools • Configurations and validation results • DLR high-speed centrifugal compressor (DLRCC) • NASA Glenn low-speed centrifugal compressor (LSCC) • Off-design results without control • Surge analysis • Off-design results with air injection control • Steady jets • Pulsed jets • Conclusions and recommendations

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

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

5. Throttle Length Lc Plenum Vp Ac Compressor Greitzer’s Phenomenological Model • Assumptions: • Compressor modeled as actuator disk • Fluid inertia contained in pipes • Spring-like properties confined to plenum Helmholtz-Resonator Model Non-dimensional B-Parameter (Greitzer): B < Bcritical => Rotating Stall B > Bcritical => Surge

6. Mean Operating Point Pressure Rise Peak Performance Pressure Rise Limit Cycle Oscillations Flow Rate Flow Rate Period of Mild Surge Cycle Period of Deep Surge Cycle Flow Rate Flow Rate Flow Reversal Time Time What is Surge? Mild Surge Deep Surge

7. Bleed Valves Movable Plenum Walls Guide Vanes Air-Injection How to Alleviate Surge • Diffuser Bleed Valves • Pinsley, Greitzer, Epstein (MIT) • Prasad, Neumeier, Haddad (GT) • Movable Plenum Wall • Gysling, Greitzer, Epstein (MIT) • Guide Vanes • Dussourd (Ingersoll-Rand Research Inc.) • Air Injection • Murray (CalTech) • Weigl, Paduano, Bright (NASA Glenn) • Fleeter, Lawless (Purdue)

8.        ˆ ˆ ˆ ˆ ˆ ˆ    q dV  E i  F j  G k  n dS  R i  S j  T k  n dS  t  Numerical Formulation (Flow Solver) Reynolds-averaged Navier-Stokes equations in finite volume representation: • q is the state vector. • E, F, and G are the inviscid fluxes (3rd order accurate). R, S, and T are the viscous fluxes (2nd order accurate). • A one-equation Spalart-Allmaras model is used. • Code can handle multiple computational blocks and rotor-stator-interaction.

9. Boundary Conditions (Flow Solver) Periodic boundary at clearance gap Solid wall boundary at impeller blades Solid wall boundary at compressor casing Inflow boundary Periodic boundary at diffuser Solid wall boundary at compressor hub Outflow boundary (coupling with plenum) Periodic boundary at compressor inlet

10. Plenum chamber • up(x,y,z) = 0 • pp(x,y,z) = const. • isentropy . mt ap, Vp Outflow boundary . mc Outflow Boundary Condition (Flow Solver) Conservation of mass and isentropic expression for speed of sound:

11. NASA Low-Speed Centrifugal Compressor • Designed and tested at NASA Glenn • Mild pressure ratio • Ideal CFD test case

12. NASA Low-Speed Centrifugal Compressor • 20 Main blades • 55 Backsweep • Grid 129 x61 x 49 (400,000 nodes) • A grid sensitivity study was done with up to 3.2 Million nodes. • Design Conditions: • 1,862 RPM • Mass flow = 30 kg/s • Total pressure ratio = 1.19 • Adiab. efficiency = 92.2% • Tip speed = 492 m/s • Inlet Mrel = 0.31

13. Mass flow = 30 kg/sec (design) ¾ CFD pressure side ¾ CFD suction side Exp pressure side Exp suction side Validation Results (Low-Speed)Blade Pressure Computations vs. Measurements 5% Span 49% Span 79% Span p/p¥ Meridional Chord

14. Mass flow = 23.5 kg/sec (75% of design mass flow) ¾ CFD pressure side ¾ CFD suction side Exp pressure side Exp suction side Validation Results (Low-Speed)Blade Pressure Computations vs. Measurements 5% Span 49% Span 79% Span p/p¥ Meridional Chord

15. 40cm DLR High-Speed Centrifugal Compressor • Designed and tested by DLR • High pressure ratio • AGARD test case

16. DLR High-Speed Centrifugal Compressor • 24 Main blades • 30 Backsweep • Grid 141 x49 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 Mrel = 0.92

17. Validation Results (High-Speed) Static Pressure Along Shroud Excellent agreement between CFD and experiment Local Static Pressure, p/pstd

18. Validation Results (High-Speed) Same momentum deficit was observed experimentally in other configurations. Near suction side Mid-passage Near pressure side

19. D C B A Off-Design Results (High-Speed) Performance Characteristic Map Computational and experimental data are within 5% Fluctuations at 3.2 kg/sec are 23 times larger than at 4.6 kg/sec

20. B: 3.8 kg/sec A: 4.6 kg/sec 20 20 10 10 Pressure Rise Fluctuations (%) Pressure Rise Fluctuations (%) 0 0 -10 -10 -20 -20 -30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30 Mass Flow Fluctuations (%) Mass Flow Fluctuations (%) C: 3.4 kg/sec D: 3.2 kg/sec 20 20 10 10 Pressure Rise Fluctuations (%) Pressure Rise Fluctuations (%) 0 0 -10 -10 -20 -20 -30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30 Mass Flow Fluctuations (%) Mass Flow Fluctuations (%) Off-Design Results (High-Speed) Performance Characteristic Map Large limit cycle oscillations develop Oscillations remain bound => mild surge

21. Off-Design Results (High-Speed) Mass Flow Fluctuations Mild surge cycles develop Surge amplitude grows to 60% of mean flow rate Surge frequency = 90 Hz (1/100 of blade passing frequency)

22. Off-Design Results (High-Speed) Velocity vectors colored by Mrel at mid-passage Flowfield vectors show a large separation zone near the leading edge

23. View Off-Design Results (High-Speed) Stagnation Pressure Contours • Vortex shedding causes reversed flow • Origin of separation occurs at leading edge pressure side

24. Off-Design Results (Low-Speed)Velocity Vectors at Design Speed Flowfield stalls but no surge occurs This is in accordance with Greitzer’s B-Criterion:

25. Off-Design Results (Low-Speed) Velocity vectors at 200% design speed at mid-passage

26. 200% Design Speed Design Speed Off-Design Results (Low-Speed)Performance Characteristic Map Unsteady fluctuations are denoted by size of circles Surge fluctuations at 200% design speed are 7 times larger than at 100% design speed

27. Off-Design Results (Low-Speed)Comparison of Different Shaft Speed • Conclusions: • Compressibility effects are fundamental for surge • For surge to occur B > Bcritical

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

29. Air Injection Results (High-Speed)Different Yaw Angles, 3% Injected Mass Flow Rate Yaw angle directly affects incidence angle => Maximum control for designer

30. Air Injection Results (High-Speed)Different Yaw Angles, 3% Injected Mass Flow Rate Optimum yaw angle of 7.5deg. yields best result Mass Flow (kg/sec) Rotor Revolutions, wt/2p Reduction in Surge Amplitude (%) Positive yaw angle is measured in opposite direction of impeller rotation Yaw Angle (Degree)

31. Air Injection Results (High-Speed) Velocity vectors colored by Mrel at mid-passage Leading edge separation is suppressed by injection

32. Air Injection Results (High-Speed) Leading edge reversed flow regions has vanished

33. Air Injection Results (Parametric Studies) High-Speed Compressor Low-Speed Compressor Nondim. Surge Amplitude (%) Nondim. Surge Amplitude (%) Yaw Angle (Deg.) Yaw Angle (Deg.) Injection Rate (%) Injection Rate (%) • An optimum yaw angle exists for both compressors. • A reasonable amount (3% to 5%) of injected air is sufficient in both configurations to suppress surge.

34. Input Hidden Layer Hidden Layer Output Layer W W W Surge Amplitude + + + b b b Yaw Angle Injection Rate Air Injection Results (Neural Network Model) • A Neural Network can be trained to model the injection maps: • Include more parameters (shaft speed, throttle settings, etc.) • Use NN-model as a controller in a real engine • Training of such a controller by CFD is much cheaper than by experiments

35. Air Injection Results (Neural Network Model) High-Speed Compressor Low-Speed Compressor Nondim. Surge Amplitude (%) Nondim. Surge Amplitude (%) Yaw Angle (Deg.) Yaw Angle (Deg.) Injection Rate (%) Injection Rate (%) Reasonable agreement between CFD injection performance maps and NN models is observed.

36. With Phase Angle Adjustments Without Phase Angle Adjustments Air Injection Results (Pulsed Jets) Surge fluctuations decrease as long as the injection phase was lagged 180 deg. relative to the flow => suggests feedback control Nondim. Surge Fluctuations (%) Rotor Revolutions, wt/2p

37. With Phase Angle Adjustments Without Phase Angle Adjustments Air Injection Results (Pulsed Jets) Amplitude of pulsed jets has a stronger impact than mean injection rate => reduction in external air requirements by 50% Nondim. Surge Fluctuations (%) Rotor Revolutions, wt/2p

38. Air Injection Results (Pulsed Jets) A short boost from the injected air is sufficient to suppress surge onset

39. Air Injection Results (Pulsed Jets) No separation occurs

40. Air Injection Results (Pulsed Jets) • Jets pulsed at higher frequencies are more effective than low-frequency jets (increased mixing, higher turbulent intensity) • There is a practical limitation on the highest possible frequency Nondim. Surge Fluctuations (%) Rotor Revolutions, wt/2p

41. Air Injection Results (Pulsed Jets) 1.5% injected mass is sufficient to suppress surge Nondim. Surge Fluctuations (%) Rotor Revolutions, wt/2p

42. Conclusions • A Viscous flow solver has been developed to • obtain a detailed understanding of surge in centrifugal compressors. • determine fluid dynamic factors that lead to stall onset. • The non-dimensional B-Parameter is a useful way to determine a priori which configuration will surge. • Steady jets are effective means of controlling surge: • Alter local incidence angles and suppress boundary layer separation. • Yawed jets are more effective than parallel jets. • An optimum yaw angle exists for each configuration. • Air injection can be modeled by a multi-parameter neural network. • Pulsed jets yield additional performance enhancements: • Lead to a reduction in external air requirements. • Jets pulsed at higher frequencies perform better than low-frequency jets.

43. Recommendations • Perform studies that link air injection rates to surge amplitude via a feedback control law. • Use flow solver to analyze and optimize other control strategies, e.g. inlet guide vanes, synthetic jets, casing treatments. • Employ multi-passage flow simulations to study rotating stall and appropriate control strategies. • Study inflow distortion and its effects on stall inception. • Improve turbulence modeling of current generation turbomachinery solvers. Analyze the feasibility of LES methods.

44. Bleed Air Controller Unit Pressure Sensors Air Injection How to Control Surge (Active Control)

45. Literature Survey on Air Injection • Rolls Royce (Day et al., 1997): • Injection into Tip Region is More Effective than Injection into the Core Flow • Cal Tech (Murray et al., 1997): • Steady Air Injection Reduces Bandwidth Requirements for Bleed Valves • NASA/MIT (Bright et al., 2000): • Effectiveness of Air Injectors is Independent of • 1.) Azimuthal Jet Arrangement • 2.) Number of Jets

46. Stencil for qright Stencil for qleft Right Left * * * * i-1 i i+1 i+2 Cell Face i+1/2 Numerical Formulation (Flow Solver) A Four Point Stencil is Used to Compute the Inviscid Flux Terms at the Cell Faces According to Roe’s Flux Splitting Scheme:: Third-Order Accurate in Space • Turbulence is Modeled by One-Equation Spalart-Allmaras Model • Code Can Handle Multiple Computational Blocks and Rotor-Stator-Interaction

47. Overview of Configurations

48. The Present Approach The Tools The Results

49. Validation Results (Low-Speed) Velocity Vectors in Meridional Planes Clearance Gap Flow Produces Velocity Deficit Trailing Edge Leading Edge Same Phenomenon was Observed Experimentally Wake-like momentum deficit 97% away from Pressure Side 4% away from Pressure Side 50% away from Pressure Side

50. Validation Results (High-Speed)