Trailing Edge Noise Control Using Active Flow Control Methods

  • Matthew Szoke

Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)

Abstract

Active flow control methods are investigated experimentally with the aim of reducing the trailing edge noise on a zero pressure gradient flat plate test rig. Flush-mounted microphones are embedded in the wall of the plate between the flow control section and the trailing edge to study the surface pressure fluctuations exposed on the wall by the turbulent boundary layer. Three different types of active flow control techniques are considered, namely uniform inclined flow suction, uniform inclined flow injection and inclined transverse jets.

The velocity statistics reveal that inclined flow suction can relaminarize the boundary layer flow. As the boundary layer reaches a laminar state, flow suction significantly reduces the trailing edge noise at mid-frequencies, while some penalties observed at low and high frequencies. Once laminarisation is achieved, the noise reduction capabilities of flow suction reach its maximum, and further increasing the suction rate does not provide any additional benefit of noise reduction.

Flow injection triggers the boundary layer separation and the development of a shear layer. As a result, the trailing edge noise increases in a broadband manner. Increasing the blowing rate to moderate levels can reduce the trailing edge noise at mid and high frequencies, while noise increase is observed at low frequencies. A more significant noise reduction can be achieved at high injection angles and blowing rates when the boundary layer entirely separates from the wall.

Finally, a single line of jet nozzles is installed parallel to the trailing edge of the plate. The individual jets merge downstream of the jet nozzles and they form a layer of jet fluid characterized by low energy content. The estimates of the trailing edge noise show that jets injection can reduce the trailing edge noise over the whole range of frequencies under analysis.
Date of Award23 Jan 2019
Original languageEnglish
Awarding Institution
  • The University of Bristol
SupervisorDaniele Fiscaletti (Supervisor) & Mahdi Azarpeyvand (Supervisor)

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