Skip to main content

Advertisement

Log in

Displacement thickness evaluation for semi-empirical airfoil trailing-edge noise prediction model

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

This study proposes a criterion for evaluating the turbulent boundary layer displacement thickness when predicting airfoil trailing-edge noise with semi-empirical methods. The boundary layer integral parameter is usually employed as the typical turbulence length-scale in the classic NASA-BPM semi-empirical airfoil self-noise prediction model and its variations. Although the semi-empirical noise prediction methods have been, in theory, superseded by more complex and demanding simplified-theoretical methods, they arguably remain the most suitable methods for noise investigation during the preliminary design phase of airfoils and wind turbine blades. The purpose of the criterion discussed is to limit the adverse impact of the uncertainty associated with the scaling parameter into the overall intrinsic quality of the semi-empirical noise prediction method. The criterion may be then employed, along with computational efficiency, to sort out methods for the task of feeding the popular BPM noise prediction model and its variations. As an illustration of the application of the proposed criterion, the performance of CFD-RANS and XFoil codes are examined and compared with experimental data from turbulent, incompressible flow available from the literature in the range \(5.0 \times 10^{5} < \text{Re}_{\text{C}} < 1.5\, \times 10^{6}\).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. δ* is also affected by the freestream turbulence level, which determines transition, but the concern here is with airfoil self-noise only.

  2. Average of 36 min per point in a four processor 3 GHz machine.

Abbreviations

A :

Empirical spectral shape based on the Strouhal number (dB)

C :

Airfoil chord (m)

\(\bar{D}_{\text{h}}\) :

Directivity function, high-frequency noise ()

f :

Frequency (Hz)

K 1 :

SPL level experimental correction factor (dB)

L :

Span of the airfoil (m)

M :

Mach number ()

OASPL:

Overall sound pressure level (dB)

ReC :

Reynolds number, based on airfoil chord ()

r e :

Effective observer distance (m)

SPL1/3 :

Sound pressure level for a 1/3 octave band (dB)

SPLp, 1/3 :

Sound pressure level for a 1/3 octave band, at pressure side (dB)

St:

Strouhal number, fδ*/U ()

Stp, St1 :

Strouhal number, peak frequency ()

TU:

Turbulence intensity (% of U)

U :

Local mean velocity (m/s)

U :

Uniform flow velocity (m/s)

Y + :

Wall coordinate, dimensionless distance to wall ()

α :

Angle of attack (°)

δ * :

Boundary layer displacement thickness (m)

\(\delta^{*}_{p}\) :

Boundary layer displacement thickness, pressure side (m)

k - ω :

Turbulence model based on the turbulent kinetic energy and energy dissipation rate transport equations for mathematical closure

γ - Reθ :

Transition model based on the intermittency factor and momentum thickness Reynolds number

AOA:

Angle of attack

BEM:

Blade-element momentum theory

BL:

Boundary layer

BPM:

Brooks, Pope, Marcolini, NASA semi-empirical noise prediction model

CAA:

Computational aero-acoustic (noise prediction models)

CFD:

Computational fluid mechanics

HAWT:

Horizontal axis wind turbine

IAG:

Institut für Aerodynamik und Gasdynamik, Stuttgart

LE:

Leading-edge

NREL:

National Renewable Energy Laboratory, USA

POLI-USP:

Polytechnic School of the University of Sao Paulo

RANS:

Reynolds-averaged Navier–Stokes equations

R&D:

Research and development

SE:

Semi-empirical (noise prediction models)

SST:

Shear stress transport

ST:

Simplified-theoretical (noise prediction models)

TBL:

Turbulent boundary layer

TBL-FP:

Turbulent boundary layer over a flat plate model

TE:

Trailing-edge

TU-Berlin:

Technische Universität Berlin

WT:

Wind turbine

WTN:

Wind turbine noise

References

  1. Abbott I, Von-Doenhoff A (1959) Theory of wing sections, 2nd edn. Dover, New York

    Google Scholar 

  2. Bareiss R, Guidati G, Wagner S (1994) An approach towards refined noise prediction of wind turbines. Thessaloniki. In: Proceedings of the European wind energy association conference and exhibition, pp. 785–790

  3. Bertanoglio F, Madsen HA, Bak C (2009) Experimental validation of the TNO trailing edge noise model and application to airfoil optimization, Roskilde, s.n

  4. Bies DA, Hansen CH (2009) Engineering noise control, 4th edn. Spon Press, Abingdon

    Google Scholar 

  5. Bistafa SR (2011) Acústica Aplicada ao Controle de Ruído. Segunda ed. São Paulo: Edgar Blücher

  6. Blake WK (1986) Mechanics of flow-induced sound and vibration. vol I ed. Academic Press, Orlando

  7. Brooks T, Hodgson TH (1981) Trailing edge noise prediction from measured surface pressures. J Sound Vib 78(1):69–117

    Article  Google Scholar 

  8. Brooks T, Marcolini M (1985) Scaling of airfoil self-noise using measured flow parameters. AIAA J 23(2):207–213

    Article  Google Scholar 

  9. Brooks T, Marcolini M (1986) Airfoil trailing-edge flow measurements. AIAA J 24(8):1245–1251

    Article  Google Scholar 

  10. Brooks T, Pope S, Marcolini M (1989) Airfoil self-noise and prediction, Langley: NASA Reference Publication, p. 1218

  11. Celik I et al (2008) Procedure for estimation of uncertainty due to discretization in CFD applications. J Fluids Eng 130:1–4

    Google Scholar 

  12. Doolan C, Moreau D (2013) Review of NACA 0012 turbulence trailing edge noise data at zeero angle of attack. Denver Co, INCE Europe, pp 1–10

    Google Scholar 

  13. Drela M, Giles MB (1987) Viscous-inviscid analysis of transonic and low reynolds number airfoil. AIAA J 25(10):1347–1355

    Article  MATH  Google Scholar 

  14. Eisele O, Pechlivanoglou G, Nayeri CN, Paschereit CO (2013) Experimental & numerical investigation of inflow turbulence on the performance of wind turbine airfoils. Proceeding of the ASME Turbo Expo 2013: Turbine Technical Conference and Exposition, GT2013, 3–7 June 2013, San Antonio, Texas, USA, pp 1–10

  15. Ferziger J, Peric M (2002) Computational methods for fluid dynamics, 3rd edn. Springer-Verlag, Berlin

    Book  MATH  Google Scholar 

  16. Ffowks Williams J, Hall L (1970) Aerodynamic sound generation by turbulent flow in the vicinity of a scattering half-plane. J Fluid Mech 40(4):657–670

    Article  Google Scholar 

  17. Fuglsang P, Antoniou I, Sorensen N, Madsen A (1998) Validation of a wind tunnel testing facilitiy for blade surface pressure measurements. RISO, Denmark

    Google Scholar 

  18. Fuglsang P, Bak C (2004) Development of the Risø wind turbine airfoils. RISO, Roskilde

    Google Scholar 

  19. Fuglsang P, Madsen H (1996) Implementation and verification of an aeroacoustic noise prediction model for wind turbines. RISO, Roskilde R_867

    Google Scholar 

  20. Glegg S (1987) Significance of unsteady thickness noise sources. AIAA J 25(6):839–844

    Article  Google Scholar 

  21. Glegg S, Morin B, Atassi ORR (2010) Using Reynolds-Averaged Navier-Stokes calculations to predict trailing edge noise. AIAA J 48(7):1290–1301

    Article  Google Scholar 

  22. Göçmen T, Özerdem B (2012) Airfoil optimization for noise emission problem and aerodynamic performance criterion on small scale wind turbine. J Energy 04:36

    Google Scholar 

  23. Guidati G, Wagner S (2000) Design of reduced noise airfoils for wind turbines, Barcelona, s.n

  24. Kamruzzaman M et al. (2014) Rnoise: A RANS based airfoil trailing-edge noise prediction model. Atlanta, GA, 20th AIAA/CEAS Aeroacoustics Conference

  25. Kamruzzaman M, Lutz T, Herrig A, Krämer E (2012) Semi-Empirical modeling of turbulent anisotropy for airfoil self-noise predictions. AIAA J 50(1):46–60

    Article  Google Scholar 

  26. Kamruzzaman M, Lutz T, Nübler K, Krämer E (2011) Implementation and verification of an aeroacoustic wind turbine blade analysis tool. INCE Europe, Rome, pp 1–16

    Google Scholar 

  27. Kamruzzaman M et al (2012) Validations and improvements of airfoil trailing-edge noise prediction models using detailed experimental data. Wind Energy 15:45–61

    Article  Google Scholar 

  28. Kamruzzaman M et al (2010) Wind turbine aerodynamics and aeroacoustics at University of Stuttgart–an overview of research and development. IAG, Stuttgart

    Google Scholar 

  29. Lockhard DP (1999) An overview of computational aeroacoustic modeling at NASA Langley. NASA, Hampton, pp 1–14

    Google Scholar 

  30. Lowson M (1993) Assessment and prediction of wind turbine noise, Bristol: ETSU W/13/00284/REP USDOE

  31. Lutz T, Herrig A, Kamruzzaman M, Krämer E (2007) Design and wind-tunnel verification of low-noise airfoils for wind turbines. AIAA J 45(4):779–785

    Article  Google Scholar 

  32. Lutz T et al (2004) Numerical optimization of silent airfoil sections. Wilhemshaven, Deutsches Windenergie

    Google Scholar 

  33. Marten D (2010) Extension of an aerodynamic simulator for wind turbine blade design and performance analysis. TU Berlin, Berlin

    Google Scholar 

  34. Moriarty P (2005) NAFNoise user’s guide. NREL, Golden

    Google Scholar 

  35. Moriarty P, Guidati G, Migliore P (2005) Prediction of turbulent inflow and trailing-edge noise for wind turbines. AIAA, Monterrey, pp 1–16

    Google Scholar 

  36. Moriarty P, Migliore P (2003) Semi-empirical aeroacoustic noise prediction code for wind turbines. NREL, Golden

    Book  Google Scholar 

  37. Oerlemans S (2011) Wind turbine noise: primary noise sources. Nationaal Lucht-en Ruimtevaartilaboratorium–NLR, Amsterdam

    Google Scholar 

  38. Oerlemans S, Fisher M, Maeder T, Kögler K (2009) Reduction of wind turbine noise using optimized airfoils and trailing-edge serrations. AIAA J 47(6):1470–1481

    Article  Google Scholar 

  39. Pechlivanoglou G et al. (2009) QBlade, Berlin, s.n

  40. Saab JY Jr, Pimenta M (2014) Airfoil self-noise–development of a trailing edge noise prediction tool suitable for the preliminary aeroacoustic design of quieter wind turbine blades. EPUSP, Sao Paulo

    Google Scholar 

  41. Somers D, Tangler J (2005a) The S822 and S823 airfoils, Golden, NREL/SR-500-36342

  42. Somers D, Tangler J (2005b) The airfoils S830, S831 and S832, Golden:NREL/SR-500_36339

  43. Vargas LFC (2008) Wind turbine noise prediction. Instituto Superior Técnico, Lisboa

    Google Scholar 

  44. Wagner S, Bareiß R, Guidati G (1996) Wind turbine noise, 1st edn. Springer, Berlin

    Book  Google Scholar 

  45. Wolf A et al. (2011) Trailing edge noise reduction of wind turbine airfoils by active flow control. Rome, s.n., pp. 1–12

  46. Zhu W (2004) Modelling of noise from wind turbines. DTU, Lyngby

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Joseph Y. Saab Jr..

Additional information

Technical Editor: Fernando Alves Rochinha.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saab, J.Y., de Mattos Pimenta, M. Displacement thickness evaluation for semi-empirical airfoil trailing-edge noise prediction model. J Braz. Soc. Mech. Sci. Eng. 38, 385–394 (2016). https://doi.org/10.1007/s40430-015-0341-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40430-015-0341-5

Keywords

Navigation