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The low-pressure turbine (LPT) has become a potential noise source for future ultra-high by-pass ratio engines. In this paper, the feasibility and mechanism of wavy leading edge (WLE) noise control in the LPT cascade model are analyzed. The flow field and acoustic data are obtained with the large eddy simulation and Ffowcs Williams–Hawkings methods, which are validated using experimental data. The acoustic results are compared for different models; the maximum noise reduction can achieve 8.6 and 3.7 dBA in the frequency bands of FR#2 (315–4000 Hz) and FR#4 (6300–16 000 Hz), respectively; the noise reduction does not vary proportionally to the WLE parameter. The noise source is identified in the baseline model, and then the effect of WLE amplitude and wavelength on the noise source and its control on pressure fluctuations are evaluated. The pressure statistics demonstrate that WLE with a smaller wavelength and a larger amplitude can reduce the impingement of stator wakes on the leading edge of the rotor and stabilize the pressure fluctuation. To analyze the mechanism of WLEs on noise control, the pressure spectrum in terms of amplitude and coherence coefficient is utilized to explain the excellent noise performance of the WLE model in FR#2. The proposed similarity coefficient of coherence can quantify the destructive interference level and thus the coherence characteristics of the sound source. Generally, the noise reduction level can be predicted by the combination of the similarity coefficient and the amplitude spectrum of the pressure fluctuations for the WLE models.
The low-pressure turbine (LPT) has become a potential noise source for future ultra-high by-pass ratio engines. In this paper, the feasibility and mechanism of wavy leading edge (WLE) noise control in the LPT cascade model are analyzed. The flow field and acoustic data are obtained with the large eddy simulation and Ffowcs Williams–Hawkings methods, which are validated using experimental data. The acoustic results are compared for different models; the maximum noise reduction can achieve 8.6 and 3.7 dBA in the frequency bands of FR#2 (315–4000 Hz) and FR#4 (6300–16 000 Hz), respectively; the noise reduction does not vary proportionally to the WLE parameter. The noise source is identified in the baseline model, and then the effect of WLE amplitude and wavelength on the noise source and its control on pressure fluctuations are evaluated. The pressure statistics demonstrate that WLE with a smaller wavelength and a larger amplitude can reduce the impingement of stator wakes on the leading edge of the rotor and stabilize the pressure fluctuation. To analyze the mechanism of WLEs on noise control, the pressure spectrum in terms of amplitude and coherence coefficient is utilized to explain the excellent noise performance of the WLE model in FR#2. The proposed similarity coefficient of coherence can quantify the destructive interference level and thus the coherence characteristics of the sound source. Generally, the noise reduction level can be predicted by the combination of the similarity coefficient and the amplitude spectrum of the pressure fluctuations for the WLE models.
This paper presents numerical studies on airfoil leading-edge turbulence interaction noise reduction using poro-wavy leading edges. Three different bionic treatments including wavy leading edges, porous leading edges, and a novel combined poro-wavy leading edges are modeled. The turbulent flow field is solved using the improved delayed detached eddy simulation method. The aerodynamic noise is predicted using the Ffowcs Williams and Hawkings acoustic analogy theory. The inflow Mach number is approximately 0.12 with an angle of attack of 0°, and the chord-based Reynolds number is 400 000. The present numerical method is first validated against experimental data and previous studies. Then the effects of the three bionic treatments on the aerodynamic performance and the aeroacoustic performance are analyzed. The results show that all the three bionic treatments will increase the mean drag of the airfoil, especially for the airfoils with porous treatment, while the lift and drag fluctuations are significantly reduced by the three bionic treatments. The wavy leading edges are found to be more effective for the reduction of broadband noise, while the porous leading edges are more effective for the reduction of the tonal noise. For the poro-wavy leading edges, both the tonal noise and broadband noise are significantly reduced, which means that the combined poro-wavy leading edges possess both the advantages of the wavy and porous treatments. The underlying flow mechanisms responsible for the noise reduction are finally analyzed in detail.
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