Reynolds number influence on delta wing vortex flows
Transcrição
Reynolds number influence on delta wing vortex flows
Technische Universität München Reynolds number influence on delta wing vortex flows TUM-AER project Outline Background and expertise Objectives and exploitation ETW experiments – model & instrumentation Partners – consortium PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar 1 Technische Universität München Background and expertise Objectives and exploitation ETW experiments – model & instrumentation Partners – consortium PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Outline 2 Technische Universität München Flow physics – basics Evolution of large scale vortices … determine lift characteristics, maneuver capabilities and stability α Main parameters w Incident and surface flow U∞ Angle of attack α Boundary layer (laminar / turbulent) φ Geometry Wing sweep φ (planform) rN Leading-edge radius rN (airfoil) PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Background 3 Technische Universität München Flow physics – basics Vortex development depend on leading-edge sweep φ and angle of attack α α [°] 40 35 30 Thin, Thin, planar planar wings; wings; sharp sharp leading–edge leading–edge αmax 25 20 15 4: Vortex bursting over the wing 3: Span–wise fixed vortex φW 3 α 4 2: Fully developed vortex, moving inboard αBursting (trailing–edge) 2 α turbulent laminar 10 1: Vortex formation ∆α 5 turbulent α 1 laminar 0 50 PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar 55 60 65 70 75 Background 80 85 φ [°] 4 Technische Universität München Flow physics – Re influence (secondary separation) Separation line of secondary vortex Re x = U∞ x ν x y Laminar region –CP y Transition Upper- / lower side: Turbulent region –CP Re > Re crit ,upper Laminar / laminar : Rex < 0.9 x 106 = Recrit,upper y –CP Turbulent / laminar : 0.9 x 106 < Rex < 1.9 x 106 Re > Re crit ,lower Turbulent / turbulent : Rex > 1.9 x 106 = Recrit,lower PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar y Background 5 Technische Universität München VFE-2 (RTO-AVT-113, RTO-AVT-183) PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Expertise 6 Technische Universität München b = 0.933 cr VFE-2 configuration – Geometry φ = 65° Rounded LE Sharp LE cr t = 0.034 cr 0.15 cr PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar 0.10 cr r/lµ = 0.15 % Expertise 7 Technische Universität München VFE-2 config. – TUM–AER wind tunnel model Sharp leading-edge Rounded leading-edge r/lµ = 0.0015 PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Expertise 8 Technische Universität München VFE-2 config. – TUM–AER wind tunnel model PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar root chord cr 0.980 m wing span b = 2s 0.914 m wing area F 0.448 m2 mean aerodynamic chord lµ 2/3 cr aspect ratio Λ 1.865 leading edge sweep φ 65° Expertise 9 Technische Universität München φ = 65° 0.2 0.4 0.6 0.8 0.95 177 pressure pos.: diam. 0.3 mm cr 5 chord stations t = 0.034 cr 0.15 cr PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar b = 0.933 cr VFE-2 config. – TUM–AER model instrumentation 0.10 cr 133 steady sensors (PSI) 44 unsteady sensors (Kulites) 10 Technische Universität München Laser light sheet flow visualization Burst leading–edge vortex; α = 30°: x/cr = 1.10 0.20 0.40 0.60 0.80 0.95 PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Expertise 11 Technische Universität München Laser light sheet flow visualization Fully developed leading–edge vortex; α = 18°: Sharp leading edge PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Rounded leading edge Expertise 12 Technische Universität München Flow field – mean velocity Partly developed leading–edge vortex; α = 13°: x/cr = 0.2, 0.4, 0.6, 0.8, and 0.95: PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Expertise 13 Technische Universität München Flow field – turbulence intensity Partly developed leading–edge vortex; α = 13°: x/c x/crr == 0.4 0.4 x/c x/crr == 0.6 0.6 x/c x/crr == 0.8 0.8 uurms /U ∞ rms/U∞ PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Expertise 14 Technische Universität München Flow field – turbulence intensity Burst leading–edge vortex; α = 23°: x/c x/crr == 0.4 0.4 x/c x/crr == 0.6 0.6 x/c x/crr == 0.8 0.8 uurms /U ∞ rms/U∞ PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Expertise 15 Technische Universität München Surface pressure – turbulence intensity Re Re == 2.0 2.0 xx 10 1066 αα == 23° 23° PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar 16 Technische Universität München Complex flow topology – Re influence / multiple vortices Ma = 0.4 (const.) Re = 1 x 106 Re = 2 x 106 Re = 3 x 106 URANS simulations (Courtesy W. Fritz, AIAA Paper 2008-393) PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Expertise 17 Technische Universität München Flow physics – Re influence U∞ M = 0.14 Topology of Re = 2.0 x 106 vortex system α = 13° Laminar separation Inboard vortex Separation Attachment Turbulent separation Primary vortex Separation Attachment Secondary vortex Separation Attachment Oil flow PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Expertise 18 Technische Universität München Flow physics – Re influence VFE-2 delta wing KKK tests (T: 240 K – 150 K) Ma = 0.05 – 0.16 M = 0.14 Re = 2.0 x 106 α = 13° DLR – TSP TUM – Oil flow (Courtesy R. Konrath) Re = 1 x 106 – 6 x 106 α = 5° – 28° PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Expertise 19 Technische Universität München Background and expertise Objectives and exploitation ETW experiments – model & instrumentation Partners – consortium PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Outline 20 Technische Universität München Flow physics – Re influence ¾ Separating shear layer φ ¾ Vortex core (fully developed / bursting) ¾ Boundary layer – secondary separation α = 25.0° u ′2 U ∞ 0.28 U∞ 0.20 α = 30.0° 0.10 0.02 Associated characteristic instabilities PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar φ = 76° Objectives and exploitation 21 Technische Universität München Flow physics – Re influence Multiple vortex system Re = 2.0 x 106 α = 18° Re = const. α Laminar separation Turbulent separation PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Objectives and exploitation 22 Technische Universität München Flow physics – Re influence Rounded leading edge PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar α = 18° Re = 1·106 Objectives and exploitation 23 Technische Universität München Objectives Analysis of aerodynamic characteristics and corresponding flow topologies – selected test cases Improving flow physics knowledge and modeling Vortex flow data base associated with significant Reynolds number variation Extending the VFE-2 data base for high-fidelity CFD applications (hybrid RANS/LES methods) The test case is currently addressed within the research activities GARTEUR AG49 and ATAAC. PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Objectives and exploitation 24 Technische Universität München Flow physics – CFD challenges (Re impact) GARTEUR AG49: Scrutinizing Hybrid RANS/LES methods For Aerodynamic Applications Test case 2.2: VFE-2 delta wing ATAAC – Advanced Turbulence Simulation for Aerodynamic Application Challenges (Implicit LES TUM-AER) Test case: ST08 Delta wing with sharp leading edge (VFE-2) Test case: AC06 Full aircraft with small aspect ratio wing (FA5) PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Objectives and exploitation 25 Technische Universität München Background and expertise Objectives and exploitation ETW experiments – model & instrumentation Partners – consortium PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Outline 26 Technische Universität München VFE-2 Model – cryogenic testing Model designed for cryogenic testing PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar ETW experiments 27 Technische Universität München VFE-2 Model – balance and sting ¾ Balance: Wxxx suitable for ETW ¾ ETW tail sting PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar ETW experiments 28 Technische Universität München Test conditions Flow parameter • Ma ≈ 0.1 – 0.5 (load limit) • Re ≈ 1 x 106 – 30 x 106 •α ≈ 0° – 35° • V = const; Ma & Re variable • q = const; T variable • β = 0° PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar ETW experiments 29 Technische Universität München Measured data and analysis Aerodynamic characteristics … Forces and moments Development stages of dominant vortices … Flowfield (PIV) VFE-2 KKK (Courtesy R. Konrath) PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar ETW experiments 30 Technische Universität München Background and expertise Objectives and exploitation ETW experiments – model & instrumentation Partners – consortium PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Outline 31 Technische Universität München Partner nations PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Consortium 32 Technische Universität München Partner institutes National Technical University of Athens, NTUA Warsaw University of Technology Czech Aeronautical Research and Test Institute Swedish Defence Research Agency PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Consortium 33 Technische Universität München Consortium – links National Technical University of Athens, NTUA Warsaw University of Technology Czech Aeronautical Research and Test Institute GARTEUR AG49: CIRA, Cassidian, DLR, FOI, NLR, ONERA, TUM PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Consortium 34 Technische Universität München Partners – TUM-AER (Institute of Aerodynamics and Fluid Mechanics) Proposal initiative / preparation Data analysis and exploitation – nucleus for future projects Importance of proposed work (commitment of partners) Knowledge improvement of vortex physics Experimental database for high-fidelity CFD verification Contribution to improved transition/turbulence modeling Fostering activities in vortex flow analysis and testing Participation Definition and support of test program and data reduction Contribution to vortex flow measuring techniques PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar 35 Technische Universität München Partners – City University London Background and expertise ¾ Analysis of aerodynamic performance, flow control, aerodynamic optimization; in particular vortex flows and high-angle of attack aerodynamics ¾ Transition physics and turbulence modeling ¾ Subsonic wind tunnel facility; measurement techniques Exploitation of data and results ¾ Re effects - enhancement of vortex flow analysis and modeling ¾ Analysis of laminar-turbulence transition, shear-layer instabilities, vortex evolution ¾ Improved understanding w.r.t vortex manipulation Key personnel (School of Engineering) Dr. S. Prince, Dr. D. Greenwell, Prof. C. Atkins PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Consortium 36 Technische Universität München Partners – FOI, KTH (Swedish Defence Research Agency Background and expertise Royal Institute of Technology) ¾ Advanced modeling of flow physics (turbulence and transition) ¾ Development of CFD methods and in-house CFD solver (EDGE) ¾ CFD analysis of air-vehicle aerodynamic performance, flow control, aero-acoustic noise, as well as for other multi-disciplinary aerodynamic applications ¾ Hybrid RANS-LES simulations of vortex flows in conceptual studies of delta wing and fighter models Exploitation of data and results ¾ Validation for development of advanced URANS and hybrid RANS-LES methods ¾ Validation of turbulence-resolving simulations in modeling local laminar-turbulence transition, shear-layer instabilities, vortex formation, bursting and shedding ¾ In-depth understanding towards vortex flow control in relation to flight stability ¾ Extrapolation to higher Re-number flow conditions Key personnel Dr. S.-H. Peng (FOI), Prof. A. Rizzi (KTH), Prof. C. Hirschel PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Consortium 37 Technische Universität München Partners – NTUA (National Technical University of Athens) Background and expertise ¾ Testing of UAVs, airfoil sections, scaled wind turbine rotors, flow control concepts ¾ Flow predictions of vortical flows using various CFD models associated with fixed and rotary aircraft configurations ¾ Subsonic wind tunnel facility (M = 0.15); Force, PIV measurement techniques, … Participation in EU projects Exploitation of data and results ¾ CFD based validation ¾ Support of PhD theses and post-doctoral research using data which will become available in this project Key personnel (School of Mech. Eng., Fluids. Dept., Aero Lab.) Prof. K. Giannakoglou, Ass. Prof. S. Voutsinas, Ass. Prof. D. Mathoulakis PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Consortium 38 Technische Universität München Partners – VZLU (Aeronautical Research and Test Institute, CZ) Background and expertise ¾ distinguished research and test center; center of excellence ¾ substantial computational capacities and skills ¾ operation of several wind tunnel facilities (Mach 0.2 ÷ 3.5) Exploitation of data and results ¾ verification of URANS CFD code EDGE ¾ improvement of CFD application for high-agility A/C, high-α-regime ¾ possible extension of in-house flight dynamics analysis Key personnel Dr. Z. Patek, Dr. J. Fiala, Dr. P. Vrchota PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar 39 Technische Universität München Partners – Warsaw University of Technology Background and expertise ¾ Faculty of Power and Aero. Eng. – center of excellence for CFD ¾ Development of CFD methods ¾ CFD analysis of aircraft aerodynamic performance Exploitation of data and results ¾ Widening of experience to be used in preparation of 2 Ph.D. theses ¾ Improvement of research methodology and education for aerospace students Key personnel Prof. Z. Goraj PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar 40 Technische Universität München Concluding remarks Research topic of high relevance for improving flow physics knowledge and high-fidelity numerical modeling European research consortium established W/T model and instrumentation available for cryogenic testing Creating a sounded data base PD Dr.-Ing. C. Breitsamter, Dipl.-Ing. J.-U. Klar Summary 41