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Experiments in Fluids

, 60:9 | Cite as

Characterization of a canonical helicopter hub wake

  • Christopher E. Petrin
  • Balaji Jayaraman
  • Brian R. Elbing
Research Article

Abstract

The current study investigates the long-age wake behind rotating helicopter hub models composed of geometrically simple, canonical bluff-body shapes. The models consisted of a 4-arm rotor mounted on a shaft above a 2-arm (scissor) rotor with all the rotor arms having a rectangular cross section. The relative phase between the 2- and 4-arm rotors was either 0° (in-phase) or 45° (out-of-phase). The rotors were oriented at zero angle-of-attack and rotated at 30 Hz. Their wakes were measured with particle-image-velocimetry within a water tunnel at a hub diameter based Reynolds number of 820,000 and an advance ratio of 0.2. Mean profiles, fluctuating profiles, and spectral analysis using time-series analysis as well as dynamic mode decomposition were used to characterize the wake and identify coherent structures associated with specific frequency content. The canonical geometry produced coherent structures that were consistent with previous results using more complex geometries. It was shown that the dominant structures (2 and 4 times per hub revolution) decay slowly and were not sensitive to the relative phase between the rotors. Conversely, the next strongest structure (6 times per hub revolution) was sensitive to the relative phase with almost no coherence observed for the in-phase model. This is strong evidence that the 6 per revolution content is a nonlinear interaction between the 2 and 4 revolution structures. This study demonstrates that the far wake region is dominated by the main rotor arms wake, the scissor rotor wake, and interactions between these two features.

Graphical abstract

Notes

Acknowledgements

The authors would like to thank Mr. Jacob Niles for 3D printing the fairing, the DML staff that assisted with fabrication of the hub models (Mr. John Gage, Mr. Carson Depew, and Mr. Joe Preston), Dr. Andrew Arena for use of equipment, and the MS thesis committee of C.E. Petrin (Drs. Jamey Jacob and Kurt Rouser) for reviewing material. This work was supported in part by B.R. Elbing’s Halliburton Faculty Fellowship endowed professorship.

References

  1. Berry JD (1997) Unsteady velocity measurements taken behind a model helicopter rotor hub in forward flight. In: NASA Technical Memorandum, TM-4738, Hampton, VAGoogle Scholar
  2. Bridgeman JO, Lancaster GT (2010) Physics-based analysis methodology for hub drag prediction. In: Proceedings of the 66th Annual American Helicopter Society Forum, 66–2010–000214, Phoenix, AZ (May 11–13)Google Scholar
  3. Coder JG, Foster NF (2017) Structured, overset simulations for the 1st rotor hub flow workshop. In: Proceedings of the 73rd Annual American Helicopter Society Forum, 73–2017–0098, Fort Worth, TX (May 9–11)Google Scholar
  4. Coder JG, Cross PA, Smith MJ (2017) Turbulence modeling strategies for rotor hub flows. In: Proceedings of the 73rd Annual American Helicopter Society Forum, 73–2017–0023, Fort Worth, TX (May 9–11)Google Scholar
  5. Daniel L (2014) Design and installation of a high Reynolds number recirculating water tunnel. Thesis MS, Oklahoma State University, USAGoogle Scholar
  6. Delany NK, Sorensen NE (1953) Low-speed drag of cylinders of various shapes. National Advisory Committee for Aeronautics (NACA), Technical Note 3038, Washington, DCGoogle Scholar
  7. Dombroski M, Egolf TA (2012) Drag prediction of two production rotor hub geometries. In: Proceedings of the 68th Annual American Helicopter Society Forum, 68–2012–000269, Fort Worth, TX (May 1–3)Google Scholar
  8. Elbing BR, Daniel L, Farsiani Y, Petrin CE (2018) Design and validation of a recirculating, high-Reynolds number water tunnel. J Fluids Eng 140:081102–081106CrossRefGoogle Scholar
  9. Farsiani Y, Elbing BR (2016) Characterization of a custom-designed, high-Reynolds number water tunnel. In: ASME fluids engineering division summer meeting, FEDSM2016-7866, Washington, DC (July 10–14)Google Scholar
  10. Hoerner SF (1965) Fluid dynamic drag: practical information on aerodynamic drag and hydrodynamic resistance, 2nd edn. Hoerner Fluid Dynamics, Bakersfield, CA. http://dl.kashti.ir/ENBOOKS/NEW/FDD.pdf Google Scholar
  11. Holmes P, Lumley JL, Berkooz G, Rowley CW (2012) Turbulence, coherent structures, dynamical systems and symmetry, 2nd edn. Cambridge University Press, New YorkCrossRefGoogle Scholar
  12. Jacobson KE, Smith MJ (2018) Carefree hybrid methodology for rotor hover performance analysis. J Aircr 55(1):52–65CrossRefGoogle Scholar
  13. Keys CN, Wiesner R (1975) Guidelines for reducing helicopter parasite drag. J Am Helicopter Soc 20(1):31–40CrossRefGoogle Scholar
  14. Knisely CW (1990) Strouhal numbers of rectangular cylinders at incidence: a review and new data. J Fluids Struct 4:371–393CrossRefGoogle Scholar
  15. Koopman BO (1931) Hamiltonian systems and transformation in Hilbert space. Proc Natl Acad Sci 17(5):315–318CrossRefGoogle Scholar
  16. Koukpaizan NK, Wilks AL, Grubb AL, Smith MJ (2018a) Reduced-order modeling of complex aerodynamic geometries using canonical shapes. In: Proceedings of the AIAA modeling and simulation technologies conference, 209959, Kissimmee, FL (Jan 8–12)Google Scholar
  17. Koukpaizan NK, Afman JP, Wilks AL, Smith MJ (2018b) Rapid vehicle aerodynamic modeling for use in early design. In: Proceedings of the American Helicopter Society International Technical Meeting, sm-aeromech_2018_58, San Francisco, CA (Jan 16–19)Google Scholar
  18. Lumley JL (2007) Stochastic Tools in Turbulence. Dover Publications, New YorkzbMATHGoogle Scholar
  19. Maskell EC (1963) A theory of the blockage effects on bluff bodes and stalled wings in a closed wind tunnel. Technical Report: ARC Reports and Memoranda R&M 3400, Aeronautical Research Council, LondonGoogle Scholar
  20. Metkowski L, Reich D, Sinding K, Jaffa N, Schmitz S (2018) Full-scale Reynolds number experiment on interactional aerodynamics between two model rotor hubs and a horizontal stabilizer. In: Proceedings of the 74th Annual American Helicopter Society Forum, 74–2018–0144, Phoenix, AZ (May 14–17)Google Scholar
  21. Mezić I (2005) Spectral properties of dynamical systems, model reduction and decompositions. Nonlinear Dyn 41(1):309–325MathSciNetCrossRefGoogle Scholar
  22. Mezić I (2013) Analysis of fluid flows via spectral properties of the Koopman operator. Annu Rev Fluid Mech 45:357–378MathSciNetCrossRefGoogle Scholar
  23. Monniaux D (2016) File:Sikorsky S-92 rotor P1230176.jpg. Wikimedia Commons. the free media repository, photo from the 2007 Paris Air Show; retrieved February 19, 2018 from https://commons.wikimedia.org/w/index.php?title=File:Sikorsky_S-92_rotor_P1230176.jpg&oldid=192883546. Accessed 28 Feb 2018
  24. Norberg C (1993) Flow around rectangular cylinders: Pressure forces and wake frequencies. J Wind Eng Ind Aerodyn 49:187–196CrossRefGoogle Scholar
  25. Okajima A (1982) Strouhal number of rectangular cylinders. J Fluid Mech 123:379–398CrossRefGoogle Scholar
  26. Petrin CE (2017) Frequency content in the wakes of rotating bluff body helicopter hub models. Thesis MS, Oklahoma State University, USAGoogle Scholar
  27. Phelps AE, Berry JD (1987) Description of the U.S. Army small-scale 2-meter rotor test system. NASA Technical Memorandum, TM-87762, HamptonGoogle Scholar
  28. Pope SB (2000) Turbulent flows. Cambridge University Press, New YorkCrossRefGoogle Scholar
  29. Potsdam M, Cross P, Jacobson K, Hill M, Singh R, Smith MJ (2017) Assessment of CREATE-AV Helios for complex rotating hub wakes. In: Proceedings of the 73rd Annual American Helicopter Society Forum, 73–2017–0225, Fort Worth, TX (May 9–11)Google Scholar
  30. Prosser DT, Smith MJ (2015) A physics-based, reduced-order aerodynamics model for bluff bodies in unsteady, arbitrary motion. J Am Helicopter Soc 60(3):032012CrossRefGoogle Scholar
  31. Quon EW, Smith MJ, Whitehous GR, Wachspress D (2012) Unsteady Reynolds-averaged Navier-Stokes-based hybrid methodologies for rotor-fuselage interaction. J Aircr 49(3):961–965CrossRefGoogle Scholar
  32. Raffel M, Richard H, Ehrenfried K, Van der Wall B, Burley C, Beaumier P, McAlister K, Pengel K (2004) Recording and evaluation methods of PIV investigations on a helicopter rotor model. Exp Fluids 36:146–156CrossRefGoogle Scholar
  33. Raghav V, Komerath N (2015) Advance ratio effects on the flow structure and unsteadiness of the dynamic-stall vortex of a rotating blade in steady forward flight. Phys Fluids 27:027101CrossRefGoogle Scholar
  34. Reich DB, Elbing BR, Berezin CR, Schmitz S (2014a) Water tunnel flow diagnostics of wake structures downstream of a model helicopter rotor hub. J Am Helicopter Soc 59:032001CrossRefGoogle Scholar
  35. Reich DB, Shenoy R, Schmitz S, Smith MJ (2014b) An assessment of the long-age unsteady rotor hub wake physics for empennage analysis. In: Proceedings of the 70th Annual American Helicopter Society Forum, 70–2014–0001, Montréal, Québec (May 20–22)Google Scholar
  36. Reich D, Willits S, Schmitz S (2015) Effects of Reynolds number and advance ratio on the drag of a model helicopter rotor hub. In: Proceedings of the 71st Annual American Helicopter Soceity Forum, 71–2015–063, Virginia Beach, VA (May 5–7)Google Scholar
  37. Reich D, Shenoy R, Smith M, Schmitz S (2016) A review of 60 years of rotor hub drag and wake physics: 1954–2014. J Am Helicopter Soc 61(2):1–17CrossRefGoogle Scholar
  38. Reich D, Willits S, Schmitz S (2017) Scaling and configuration effects on helicopter rotor hub interactional aerodynamics. J Airc 54(5):1692–1704CrossRefGoogle Scholar
  39. Reich D, Sinding K, Schmitz S (2018) Visualization of a helicopter rotor hub wake. Exp Fluids 59(7):116CrossRefGoogle Scholar
  40. Roesch P, Dequin A-M (1985) Experimental research on helicopter fuselage and rotor hub wake turbulence. J Am Helicopter Soc 30(1):43–51Google Scholar
  41. Rowley CW, Dawson STM (2017) Model reduction for flow analysis and control. Annu Rev Fluid Mech 49:387–417MathSciNetCrossRefGoogle Scholar
  42. Rowley CW, Mezić I, Bagheri S, Schlatter P, Henningson DS (2009) Spectral analysis of nonlinear flows. J Fluid Mech 641:115–127MathSciNetCrossRefGoogle Scholar
  43. Schiacchitano A, Wieneke B (2016) PIV uncertainty propagation. Meas Sci Technol 27:084006CrossRefGoogle Scholar
  44. Schmid PJ (2010) Dynamic mode decomposition of numerical and experimental data. J Fluid Mech 656:5–28MathSciNetCrossRefGoogle Scholar
  45. Schmitz S, Reich D, Smith MJ, Centolanza LR (2017) First rotor hub flow prediction workshop experimental data campaigns and computational analyses. In: Proceedings of the 73rd annual American Helicopter Society Forum, 73–2017–0128, Fort Worth, TX (May 9–11)Google Scholar
  46. Sheehy TW (1977) A general review of helicopter rotor hub drag data. J Am Helicopter Soc 22(2):2–10Google Scholar
  47. Shenoy R, Holmes M, Smith MJ, Komerath NM (2013) Scaling evaluations on the drag of a hub system. J Am Helicopter Soc 58(3):1–13CrossRefGoogle Scholar
  48. Smith MJ, Shenoy R (2013) Deconstructing hub drag. Office of Naval Research Final Technical Report, N0001409-1-1019Google Scholar
  49. Taira K, Brunton SL, Dawson STM, Rowley CW, Colonius T, McKeon BJ, Schmidt OT, Gordeyev S, Theofilis V, Ukeiley LS (2017) Modal analysis of fluid flows: an overview. AIAA J 55(12):4013–4041CrossRefGoogle Scholar
  50. Tanaka H, Nagano S (1973) Study of flow around a rotating circular cylinder. Bull Jpn Soc Mech Eng 16(92):234–243CrossRefGoogle Scholar
  51. Towne A, Schmidt OT, Colonius T (2018) Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis. J Fluid Mech 847:821–867MathSciNetCrossRefGoogle Scholar
  52. Wieneke B (2015) PIV uncertainty quantification from correlation statistics. Meas Sci Technol 26:074002CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical and Aerospace EngineeringOklahoma State UniversityStillwaterUSA

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