1. School of Aerospace Engineering 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. School of Aerospace Engineering 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. School of Aerospace Engineering 3 Motivation and Objectives 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 Lines of Constant Rotational Speed Lines of Constant Efficiency Choke Limit Surge Limit Flow Rate Total Pressure Rise Desired Extension of Operating Range

4. School of Aerospace Engineering 4 Motivation and Objectives Compressor instabilities can cause fatigue and damage to entire engine

5. School of Aerospace Engineering 5 Greitzer’s Phenomenological Model Plenum V p Throttle AcAc Length L c Compressor Helmholtz-Resonator Model Non-dimensional B-Parameter (Greitzer): Assumptions: Compressor modeled as actuator disk Fluid inertia contained in pipes Spring-like properties confined to plenum B Rotating Stall B > B critical => Surge

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

7. School of Aerospace Engineering 7 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) How to Alleviate Surge Bleed Valves Movable Plenum Walls Guide Vanes Air-Injection

8. School of Aerospace Engineering 8 Numerical Formulation (Flow Solver)    t q  dV  E ˆ i  F ˆ j  G ˆ k     ndS  R ˆ i  S ˆ j  T ˆ k     ndS 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. School of Aerospace Engineering 9 Boundary Conditions (Flow Solver) Outflow boundary (coupling with plenum) Periodic boundary at compressor inlet Solid wall boundary at compressor casing Periodic boundary at diffuser Solid wall boundary at impeller blades Periodic boundary at clearance gap Solid wall boundary at compressor hub Inflow boundary

10. School of Aerospace Engineering 10 Outflow Boundary Condition (Flow Solver) Plenum chamber u p (x,y,z) = 0 p p (x,y,z) = const. isentropy a p, V p mcmc. mtmt. Outflow boundary Conservation of mass and isentropic expression for speed of sound:

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

12. School of Aerospace Engineering 12 NASA Low-Speed Centrifugal Compressor 20 Main blades 55  Backsweep Grid 129 x 61 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 M rel = 0.31

13. School of Aerospace Engineering 13 Validation Results (Low-Speed) Blade Pressure Computations vs. Measurements p/p  Meridional Chord 5% Span49% Span 79% Span Mass flow = 30 kg/sec (design)  CFD pressure side  CFD suction side Exp pressure side Exp suction side

14. School of Aerospace Engineering 14 Validation Results (Low-Speed) Blade Pressure Computations vs. Measurements p/p  Meridional Chord 5% Span49% Span 79% Span Mass flow = 23.5 kg/sec (75% of design mass flow)  CFD pressure side  CFD suction side Exp pressure side Exp suction side

15. School of Aerospace Engineering 15 DLR High-Speed Centrifugal Compressor Designed and tested by DLR High pressure ratio AGARD test case 40cm

16. School of Aerospace Engineering 16 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 = 0.92

17. School of Aerospace Engineering 17 Validation Results (High-Speed) Static Pressure Along Shroud Excellent agreement between CFD and experiment Local Static Pressure, p/p std

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

19. School of Aerospace Engineering 19 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 A B CD

20. School of Aerospace Engineering 20 Off-Design Results (High-Speed) Performance Characteristic Map Large limit cycle oscillations develop Oscillations remain bound => mild surge -30-20-100102030 -20 -10 0 10 20 Mass Flow Fluctuations (%) A: 4.6 kg/sec Pressure Rise Fluctuations (%) -30-20-100102030 -20 -10 0 10 20 Mass Flow Fluctuations (%) B: 3.8 kg/sec Pressure Rise Fluctuations (%) -30-20-100102030 -20 -10 0 10 20 Mass Flow Fluctuations (%) D: 3.2 kg/sec Pressure Rise Fluctuations (%) -30-20-100102030 -20 -10 0 10 20 Mass Flow Fluctuations (%) C: 3.4 kg/sec Pressure Rise Fluctuations (%)

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

22. School of Aerospace Engineering 22 Off-Design Results (High-Speed) Flowfield vectors show a large separation zone near the leading edge Velocity vectors colored by M rel at mid-passage

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

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

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

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

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

28. School of Aerospace Engineering 28 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, 0.04R Inlet Casing 5° Rotation Axis Impeller R Inlet Yaw Angle  Main Flow Injected Fluid Sheet Compressor Face Compressor Casing

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

31. School of Aerospace Engineering 31 Air Injection Results (High-Speed) Leading edge separation is suppressed by injection Velocity vectors colored by M rel at mid-passage

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

33. School of Aerospace Engineering 33 Air Injection Results (Parametric Studies) High-Speed CompressorLow-Speed Compressor 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. Injection Rate (%) Nondim. Surge Amplitude (%) Injection Rate (%) Nondim. Surge Amplitude (%) Yaw Angle (Deg.) Yaw Angle (Deg.)

34. School of Aerospace Engineering 34 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 InputHidden Layer Output Layer Yaw Angle Injection Rate W b W b Surge Amplitude W b + ++

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

36. School of Aerospace Engineering 36 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 With Phase Angle Adjustments Without Phase Angle Adjustments Nondim. Surge Fluctuations (%) Rotor Revolutions,  t 

37. School of Aerospace Engineering 37 Amplitude of pulsed jets has a stronger impact than mean injection rate =>reduction in external air requirements by 50% Air Injection Results (Pulsed Jets) With Phase Angle Adjustments Without Phase Angle Adjustments Nondim. Surge Fluctuations (%) Rotor Revolutions,  t 

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

39. School of Aerospace Engineering 39 Air Injection Results (Pulsed Jets) No separation occurs

40. School of Aerospace Engineering 40 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 Air Injection Results (Pulsed Jets) Nondim. Surge Fluctuations (%) Rotor Revolutions,  t 

41. School of Aerospace Engineering 41 1.5% injected mass is sufficient to suppress surge Nondim. Surge Fluctuations (%) Rotor Revolutions,  t  Air Injection Results (Pulsed Jets)

42. School of Aerospace Engineering 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. School of Aerospace Engineering 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. School of Aerospace Engineering 44 How to Control Surge (Active Control) Controller Unit Bleed Air Pressure Sensors Air Injection

45. School of Aerospace Engineering 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. School of Aerospace Engineering 46 Numerical Formulation (Flow Solver) * * i-1 i i+1 i+2 Cell Face i+1/2 Stencil for q left Stencil for q right Left Right 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. School of Aerospace Engineering 47 Overview of Configurations

48. School of Aerospace Engineering 48 The Present Approach The Tools The Results

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

50. School of Aerospace Engineering 50 Validation Results (High-Speed)

51. School of Aerospace Engineering 51 Validation Results (High-Speed)

52. School of Aerospace Engineering 52 Eigenmode Analysis (GTSYS3D) Calculates eigenvalues/-vectors of the compression system matrix Based on small perturbation Euler model: q = q 0 +  q The resulting form is: d/dt(  q) = A  q where:-  q is the state vector of small perturbations - A is the system matrix of size 5N 1 N 2 N 3 x 5N 1 N 2 N 3

53. School of Aerospace Engineering 53 Off-Design Results (High-Speed) System eigenvalues at stable condition (4.6 kg/sec) Mostly acoustic modes with Re < 0 (damping, stable) Complex conjugate pairs are oscillatory Simple poles (Im = 0) near origin are unstable

54. School of Aerospace Engineering 54 Off-Design Results (High-Speed) System eigenvalues during surge cycle At beginning of surge cycle After 25% of surge cycle After 75% of surge cycle After 50% of surge cycle Most acoustic (damping) modes have vanished Simple pole at origin destabilizes system