Reduction of Turbulent Boundary Layer Noise with Actively Controlled Carbon Fiber Reinforced Plastic Panels

  • Stephan Algermissen
  • Malte Misol
  • Oliver Unruh
Part of the Research Topics in Aerospace book series (RTA)


The turbulent boundary layer (TBL) is one of the dominant external noise sources in high subsonic aircrafts. Especially in modern aircrafts where common materials for fuselages are currently substituted by carbon-fiber-reinforced-plastics (CFRP), it is essential to avoid a decrease of passenger comfort as a result of an inferior transmission loss of the new materials. To increase the transmission loss of CFRP panels they are equipped with active noise reduction systems. In this paper the results of an experimental study in the aeroacoustic wind tunnel of the German Aerospace Center (DLR) are presented. An active panel excited by a TBL is tested at flow speeds up to Mach 0.16. The CFRP panel (500 × 800 × 2.7 mm3) is equipped with five piezo-ceramic patch actuators and ten accelerometers. Active structural acoustic control (ASAC) and active vibration control (AVC) are used to reduce the broadband TBL noise transmission in the bandwidth from 1 to 500 Hz. Feedforward (FF) and feedback (FB) control algorithms are applied in the experiments and show high performance even in presence of plant uncertainties. To improve control results the generalized plant framework of robust control is utilized for global feedback control. Finally, an overview of the achieved results is given.


Turbulent Boundary Layer Carbon Fiber Reinforce Plastic Sound Power Sound Power Level Normal Surface Velocity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Wilby, J.F.: Aircraft interior noise. J. Sound Vib. 190(3), 545–564 (1996)CrossRefGoogle Scholar
  2. 2.
    Corcos, G.M.: The structure of the turbulent pressure field in boundary-layer flows. J. Fluid Mech. 18, 353–378 (1964)zbMATHCrossRefGoogle Scholar
  3. 3.
    Gibbs, G.P., Cabell, R.H.: Active control of turbulent boundary layer induced sound radiation from multiple aircraft panels. In: Proceedings of 8th AIAA/CEAS Aeroacoustics Conference, Breckenridge, CO (2002)Google Scholar
  4. 4.
    Schiller, N.H., Fuller, C.R.: A high-authority/low-authority control strategy for coupled aircraft-style bays. In: Proceedings of ACTIVE 2006, Adelaide, Australia (2006)Google Scholar
  5. 5.
    Rohlfing, J., Gardonio, P.: Homogeneous and sandwich active panels under deterministic and stochastic excitation. J. Acoust. Soc. Am. 125(6), 3696–3706 (2009)CrossRefGoogle Scholar
  6. 6.
    Thomas, D.R., Nelson, P.A.: Feedback control of sound radiation from a plate excited by a turbulent boundary layer. J. Acoust. Soc. Am. 98(5), 2651–2662 (1995)CrossRefGoogle Scholar
  7. 7.
    Maury, C., Gardonio, P., Elliott, S.J.: Model for active control of flow-induced noise transmitted through double partitions. AIAA J. 40, 1113–1121 (2002)CrossRefGoogle Scholar
  8. 8.
    Heintze, O., Rose, M., Algermissen, S., Misol, M.: Development and experimental application of a pre-design tool for active noise and vibration reduction systems. In: Proceedings of ACTIVE 2009, Ottawa, Canada (2009)Google Scholar
  9. 9.
    Ewert, R.: Rpm––the fast random particle-mesh method to realize unsteady turbulent sound sources and velocity fields for caa applications. In: Proceedings of 13th AIAA/CEAS Aeroacoustics Conference. (2007)
  10. 10.
    Siefert, M., Ewert, R. (2009), Anisotropic synthetic turbulence with sweeping generated by random particle-mesh method. In: Peinke, J., Oberlack, M., Talamelli, A. (eds.) Progress in Turbulence III: Proceedings of the iTi Conference in Turbulence, p. 143Google Scholar
  11. 11.
    Katayama, T.: Subspace Methods for System Identification. Springer, London (2005)zbMATHGoogle Scholar
  12. 12.
    Algermissen, S., Rose, M., Keimer, R., Monner, H.P., Breitbach, E.: Automated synthesis of robust controllers for smart-structure applications in parallel robots. In: Proceedings of AIAA/ASME/AHS Adaptive Structures Conference, Honolulu, USA (2007)Google Scholar
  13. 13.
    Algermissen, S., Rose, M., Keimer, R., Sinapius, M.: Robust gain-scheduling for smart-structures in parallel robots. In: 16th Annual International Symposium on Smart Structures and Materials, SPIE, San Diego (2009)Google Scholar
  14. 14.
    Elliott, S.: Signal Processing for Active Control. Academic Press, London (2001)Google Scholar
  15. 15.
    Kuo, S.M., Morgan, D.R.: Active Noise Control Systems. John Wiley & Sons, New York (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stephan Algermissen
    • 1
  • Malte Misol
    • 1
  • Oliver Unruh
    • 1
  1. 1.Institute of Composite Structures and Adaptive SystemsGerman Aerospace Center DLRBraunschweigGermany

Personalised recommendations