Aero Days 2011, Madrid. FUTURE Flutter-Free Turbomachinery Blades Torsten Fransson, KTH Damian Vogt, KTH 2011-03-31 1
A Typical Turbomachine RR Trent 1000 Picture courtesy of RR 2
What is it flutter? 3
Turbomachinery Flutter Flutter denotes a self-excited and self-sustained aeroelastic instability Very harmful unless properly damped Blades oscillate in traveling wave mode Neighbor blades usually lead to instability An isolated blade would not flutter 4
Why do turbomachinery blades flutter? 5
Underlying Mechanisms Flutter involves the interaction of fluid and structure Upon the motion of a component, the surrounding fluid will respond with an aerodynamic force The direction and phase of this force will lead to having the motion damped or augmented In case of augmentation, flutter will establish The character of the fluid response depends on many factors such as Geometrical aspects (i.e. profile shape, blade size, blade count) Operating point (idle, take-off, cruise) Ambient conditions (air temperature, etc) Dynamics (engine acceleration, deceleration) Flutter might establish only at very few of the above conditions. Due to its harmful character it must however be avoided at any cost 6
How can we ensure flutter-free turbomachinery blades? 7
Flutter-Free Turbomachinery Blades A good design does not flutter How to ensure a good design? Design for stability performing accurate predictions of the unsteady behavior of the structural dynamics (FEM) and aerodynamics (CFD) in a turbomachine Ensure large-enough stability limits (i.e. moderate changes in operating conditions, profile shape, etc will not directly lead to a flutter instability) A good design must also be economically viable Engine development costs and time Fulfilling other objectives such as performance, weight, manufacturing cost, maintainability etc During component design, industry nowadays largely relies on numerical simulations at affordable analysis costs (model size and run time) 8
How well are we to date doing on aeroelastic predictions? 9
Prediction Accuracy Test case: transonic compressor Each industry partner is using their own (trusted) aeroelastic analysis tool to analyze the aeroelastic behavior Variation of minimum aerodynamic damping with operating point π mass flow 10
Background Despite the high level of sophistication in today s numerical prediction tools, it is not uncommon that we have to deal with an accuracy of +-40% of predicted minimum aerodynamic damping In the present test case: 2 out of 5 predict flutter, 3 do not Test cases exist but these do not fully cover the spectrum needed for modern turbomachine designs Component types (blisks, bladed disks) Flow conditions (transonic flow, high loading, separations) Combinations of unsteady pressure and vibration data This empty spot shall be filled-in by the FUTURE project Establishing of new experimental test cases Extensive validation of state-of-the-art prediction tools 11
Presentation of FUTURE Project Flutter-Free Turbomachinery Blades www.future-project.eu 12
EU FP7 Project FUTURE Project aiming at the acquiring new sets of relevant validation data on turbomachinery aeroelasticity (compressor, turbine) and validating numerical tools Project coordinator: KTH, Prof Torsten Fransson Partners: 25 partners from industry, research institutes, academia Budget: 10.6M Duration: July 2008 June 2012 13
FUTURE Project Partners Industry Research Institutes Academia 14
Project Concept Picture courtesy of RR Turbine Fan Compressor Aeroelastic experiments Aeroelastic computations Synthesis of experiments and computations x x x x x x 15
Project Structure Two main streaks of validation test cases as follows Transonic compressor High subsonic Low-Pressure Turbine (LPT) These test cases have been conceived within FUTURE Interconnected experiments Non-rotating cascade tests, controlled blade oscillation Rotating tests, multi-blade row, free and forced oscillation Mechanical characterizations of components (blisk, bladed disks) Application of novel measurement techniques such as PSP Interconnected computations Performed by virtually all partners in the project Pre-test predictions Post-test predictions 16
Work Package Structure WP1: Turbine and compressor cascade flutter Paolo Calza, Avio WP2: LPT Rotating rig flutter Roque Corral, ITP WP3: Multi-row compressor flutter Jan Östlund, Volvo Aero WP4: Synthesis of experiments and computations Detlef Korte, MTU WP5: Project management Damian Vogt, KTH Shortcut to Benefits 17
Presentation of FUTURE Test Cases 18
Transonic Compressor Design intent Aeroelastic stable operation at design point N 18 000rpm, φ ~ 0.6 Reduction of positive aerodynamic damping as stall line is approached 19
Compressor Flow Field 50% span 90% span ADP, Π 1.412 20
Compressor - Overview of Tests Non-rotating tests (isolated blade row, EPFL) Detailed steady aerodynamics Aerodynamic damping (controlled oscillation, free oscillation) Data: inlet/outlet flow parameters, blade loading, time-resolved blade surface pressure Rotating tests (1 ½ stage compressor, TUD) Detailed steady aerodynamics (blade loading, probe traverses) Mechanical characterization of rotor blisk (ECL) Damping measurements at various operating points Data: inlet/outlet flow parameters, blade loading, time-resolved blade surface pressure, blade vibration (tip-timing) 21
Non-Rotating Compressor Test Facility (EPFL) Annular cascade module 22
Rotating Compressor Test Facility (TUD) Rotor blisk 23
High Subsonic LPT Rotor Design intent Controlled aeroelastic instability at design point Limit Cycle Oscillations (LCO) N 2 416rpm, M 2 ~ 0.75 Goal: measurable LCO amplitudes displacement 24
LPT Rotor Flow Field SS PS Mach number 50% span Outlet ptot Surface oil flow 25
LPT - Overview of Tests Non-rotating tests (isolated blade row sector, KTH) Detailed steady aerodynamics Aerodynamic damping (controlled oscillation influence coefficients) Data: inlet/outlet flow parameters, blade loading, time-resolved blade surface pressure Rotating tests (1 stage LPT, CTA) Detailed steady aerodynamics (probe traverses) Two test objects: 1) cantilever 2) interlock Mechanical characterization of rotor bladed disks (AVIO) Damping measurements at various operating points Data: inlet/outlet flow parameters, blade vibration (tip-timing) 26
Non-Rotating LPT Test Facility (KTH) Annular sector cascade module 27
Cascade Flow Field Annular sector cascade 5 blades, 6 passages 70% span loading of rotating rig matched Fig with midspan loading Outlet Mach number distribution 28
Rotating LPT Test Facility (CTA) Cantilever configuration Interlock configuration Assembled rotor blades 29
What are the expected benefits of the FUTURE project? 30
Expected Benefits The FUTURE project shall contribute to making turbomachinery aeroelastic predictions more reliable Numerical tools validated on new, relevant and unique aeroelastic test cases that shall lead to best practice guidelines Achieving this will help making turbomachinery blades flutter-free make new aircraft engines more efficient cut development costs and time frames The FUTURE project will provide key enabling technologies towards a green, safe, reliable and affordable air transport of the future 31
Dissemination Great attention is given to the dissemination of project findings Feeding-back findings to education and life-long learning Examples Sharing of audiovisual instruction material from industry partners with universities Development of e-learning tools THRUST TurbomacHinery AeRomechanical UniverSity Training The world s first Masters programme in turbomachinery aeromechanics www.explorethrust.eu Upcoming THRUST+ Joint PhD programme on aeromechanics EXPLORE Aero World Virtual University 32
What do we envision after FUTURE? 33
Within the FUTURE project many questions will be answered but there might be unresolved topics at the end Having a strong project consortium and unique hardware in place, we envision research in the following directions Control of flutter (active, mistuning, novel damping concepts) Influence of flow distortion and impedance Flutter in the presence of other unsteady aerodynamic phenomena Development of new improved numerical models 34
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