Hostname: page-component-5cf477f64f-zrtmk Total loading time: 0 Render date: 2025-04-08T12:31:32.938Z Has data issue: false hasContentIssue false
  • English
  • Français

Estimation of three-dimensional aerodynamic damping using CFD

Published online by Cambridge University Press:  12 November 2019

R.J. Higgins Open the ORCID record for R.J. Higgins [Opens in a new window] ,
G.N. Barakos Open the ORCID record for G.N. Barakos [Opens in a new window]  and
E. Jinks
R.J. Higgins
Affiliation:
University of Glasgow, Glasgow, U.K.
G.N. Barakos*
Affiliation:
University of Glasgow, Glasgow, U.K.
E. Jinks
Affiliation:
Dowty Propellers, Anson Business Park, Gloucester, U.K.
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Aeroelastic phenomena of stall flutter are the result of the negative aerodynamic damping associated with separated flow. From this basis, an investigation has been conducted to estimate the aerodynamic damping from a time-marching aeroelastic computation. An initial investigation is conducted on the NACA 0012 aerofoil section, before transition to 3D propellers and full aeroelastic calculations. Estimates of aerodynamic damping are presented, with a comparison made between URANS and SAS. Use of a suitable turbulence closure to allow for shedding of flow structures during stall is seen as critical in predicting negative damping estimations. From this investigation, it has been found that the SAS method is able to capture this for both the aerofoil and 3D test cases.

Keywords

Flutterdampingmoment loopaeroelasticity
Type
Research Article
Information
The Aeronautical Journal , Volume 124 , Issue 1271 , January 2020 , pp. 24 - 43
Copyright
© Royal Aeronautical Society 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

McAlister, K.W., Pucci, S.L., McCroskey, W.J., and Carr, L.W. An experimental study of dynamic stall on advanced airfoil sections volume 2: Pressure and orce data, National Aeronautics and Space Administration: Technical Report, 1982, TM-84245.Google Scholar
Carta, F.O. and Niebanck, C.F. Prediction of rotor instability at high forward speeds: Volume III stall flutter, USAAVLABS: Technical Report, 1969, 68-18C.Google Scholar
McCroskey, W.J. The Phenomenon of Dynamic Stall, National Aeronautics and Space Administration: Technical Memorandum, 1981, 81264.Google Scholar
Sterne, L.H.G. Spinning tests on fluttering propellers, Aeronautical Research Council: Reports and Memoranda 1945, 2022.Google Scholar
Burton, P.E. Straingauge test report on a spin test carried out on a type (c) R.305/3-82-F/6 propeller on the spinning tower at the R.A.E. Farnborough, Hants., DOWTY ROTOL, Technical Report, 1979, 093.1.592.Google Scholar
Smith, A.F. Analysis and test evaluation of the dynamic stability of three advanced turboprop models at zero forward speeds, National Aeronautics and Space Administration: Contractor Report, 1985, 175025.Google Scholar
Baker, J.E. The effects of various parameters, including Mach number, on propeller-blade flutter with emphasis on stall flutter, National Advisory Commitee for Aeronautics: Technical Report, 1955, 3357.Google Scholar
Hubbard, H.H., Burges, M.F. and Sylvester, M.A. Flutter of thin propeller blades, including effects of Mach number, structural damping, and vibratory-stress measurements near the flutter boundaries, National Advisory Commitee for Aeronautics: Technical Report, 1956, 3707.Google Scholar
Reddy, T.S.R. and Kaza, K.R.V. Analysis of an unswept propfan blade with a semiempirical dynamic stall model, National Aeronautics and Space Administration: Technical Memorandum, 1989, 4083.Google Scholar
Delamore-Sutcliffe, D. Modelling of Unsteady Stall Aerodynamics and Prediction of Stall Flutter Boundaries for Wings and Propellers, PhD Thesis, University of Bristol, 2007.Google Scholar
Ognev, V. and Rosen, A. Influence of using various unsteady aerodynamic models on propeller flutter prediction, Journal of Aircraft, 2011, 48 (5).CrossRefGoogle Scholar
Dehaeze, F. and Barakos, G.N. Mesh deformation method for rotor flows, Journal of Aircraft, 2012, 49 (1).CrossRefGoogle Scholar
Chirico, G., Barakos, G.N. and Bown, N. Numerical aeroacoustic analysis of propeller designs, The Aeronautical Journal, 2018, 122 (1248).Google Scholar
Barakos, G.N. and Johnson, C. Acoustic comparison of propellers, International Journal of Aeroacoustics, 2016, 15 (6–7).CrossRefGoogle Scholar
Higgins, R.J., Jimenez-Garcia, A., Barakos, G.N. and Bown, N. A time-marching aeroelastic method applied to propeller flutter, Proceedings of AIAA SciTech Forum, San Diego, California, USA, January 2019.CrossRefGoogle Scholar
Higgins, R.J., Jimenez-Garcia, A., Barakos, G.N. and Bown, N. High-fidelity computational fluid dynamics methods for the simulation of propeller stall flutter, AIAA Journal, 2019, doi: 10.2514/1.J058463 CrossRefGoogle Scholar
Scrase, N. and Maina, M. The evaluation of propeller aero-acoustic design methods by means of scaled-model testing employing pressure tapped blades and spinner, 19th ICAS Congress, 1994.Google Scholar
Gomariz-Sancha, A., Maina, M. and Peace, A.J. Analysis of propeller-airframe interaction effects through a combined numerical simulation and wind-tunnel testing approach, AIAA SciTech Forum, 53rd AIAA Aerospace Sciences Meeting, Kissimmee, Florida, 2015.CrossRefGoogle Scholar
Knepper, A. and Bown, N. IMPACTA Wind-tunnel instrumentation specification, Dowty Propellers (GE Aviation Systems), Technical Report, 2014, ITS 01777 (3).Google Scholar
Barakos, G., Steijl, R., Badcock, K. and Brocklehurst, A. Development of CFD capability for full helicopter engineering analysis, 31st European Rotorcraft Forum, Florence, Italy, 2005.Google Scholar
Steijl, R., Barakos, G. and Badcock, K. A framework for CFD analysis of helicopter rotors in hover and forward flight, International Journal of Numerical Methods in Fluids, 2005, 51, 8.Google Scholar
Lawson, S., Woodgate, W., Steijl, R. and Barakos, G. High performance computing for challenging problems in computational fluid dynamics, Progress in Aerospace Sciences, 2012, 52.CrossRefGoogle Scholar
Crozon, C., Steijl, R. and Barakos, G.N. Coupled flight dynamics and CFD – demonstration for helicopters in shipborne environment The Aeronautical Journal, 2018, 122, 1247.CrossRefGoogle Scholar
Babu, S.V., Loupy, G.J.M., Dehaeze, F., Barakos, G.N. and Taylor, N.J. Aeroelastic simulations of stores in weapon bays using Detached-Eddy simulation, Journal of Fluids and Structures, 2016, 66.CrossRefGoogle Scholar
Babu, S.V., Zografakis, G., Barakos, G.N. and Kusyumov, A. Evaluation of scale-adaptive simulation for transonic cavity flows, International Journal of Engineering Systems Modelling and Simulation, 2016, 8 (2).CrossRefGoogle Scholar
Menter, F.R. and Egorov, Y. The scale-adaptive simulation method for unsteady turbulent flow predictions. Part 1: Theory and model Description, Flow, Turbulence and Combustion, 2010, 85 (1).CrossRefGoogle Scholar
Menter, F. R. and Egorov, Y. A scale adaptive simulation model using two-equation models, Proceedings of 43rd AIAA Aerospace Sciences Meeting and Exhibit, 2005.CrossRefGoogle Scholar
Loupy, G.J.M., Barakos, G.N. and Taylor, N.J. Multi-disciplinary simulations of stores in weapon bays using scale adaptive simulation. Journal of Fluids and Structures, 2018, 81.CrossRefGoogle Scholar
Jarkowski, M., Woodgate, M.A., Barakos, G.N. and Rokicki, J. Towards consistent hybrid overset mesh methods for rotorcraft CFD, International Journal for Numerical Methods in Fluids, 2014, 74 (8).CrossRefGoogle Scholar
Corke, T.C. and Thomas, F.O. Dynamic stall in pitching airfoils: Aerodynamic damping and compressibility effects, Annual Review of Fluid Mechanics, 2015, 47.CrossRefGoogle Scholar
Mellen, C.P., Fröhlich, J. and Rodi, W. Lessons from LESFOIL Project on large-eddy-simulation of flow around an airfoil, AIAA Journal, 2003, 41 (4).CrossRefGoogle Scholar
Ramasamy, M., Sanayei, A., Wilson, J.S., Martin, P.B., Harms, T., Nikoueeyan, P. and Naughton, J. Data-driven optimal basis clustering to characterize cycle-to-cycle variations in dynamic stall measurements, Proceedings of the Vertical Flight Society 75th Annual Forum and Technology Display, 2019.Google Scholar