Nedic J.,Imperial College London |
Ganapathisubramani B.,Imperial College London |
Vassilicos J.C.,Imperial College London |
Boree J.,University of Poitiers |
And 5 more authors.
Oneofthe major environmental problems facing the aviation industry isthatofaircraft noise. The work presented in this paper, doneaspartof the European Union's Optimisation for Low Environmental Noise Impact Project, looks at reducing spoiler noise while maintaining aerodynamic performance, through means of large-scale fractal porosity. It ishypothesized that the highly turbulent flow generated by fractal grids from the way the multiple-length scales are organized in space, would reduce the impact of the recirculation region and, with it, the low-frequency noise it generates. In its place, a higher frequency noise is introduced, which is more susceptible to atmospheric attenuation and is less offensive to the human ear. A total of nine laboratory-scaled spoilers were looked at, seven of which hadafractal design, one with aregular grid design, and one solid for reference. The spoilers were inclinedatan angle of 30 deg. Force, acoustic, and flow visualization experiments on a flat plate were carried out and it was found that the present fractal spoilers reduce the low-frequency noise by 2.5 dB. Results show that it is possible to improve the acoustic performance by modifying a number of parameters defining the fractal spoiler, some of them very sensitively. From these experiments, two fractal spoilers were chosen for a detailed aeroacoustic study on a threeelement wing system, where it was found that the fractal spoilers had a reduction of up to 4 dB in the sound pressure level, while maintaining similar aerodynamic performances as conventional solid spoilers on the measured wing system. Copyright © 2012 bythe American Institute of Aeronautics and Astronautics, Inc. Source
Plantier G.,ESEO |
Moreau S.,Institute Prime |
Moreau S.,CNRS Roberval Laboratory (Mechanical Research Unit) |
Simon L.,LAUM |
And 3 more authors.
Digital Signal Processing: A Review Journal
Spectral estimation of data sequences with randomly missing samples is considered in this paper. A nonparametric missing-data method is proposed based on interpolation followed by a deconvolution procedure. Sample-and-hold interpolation is considered here. The method is based on the analytic expression of the autocorrelation function of the interpolated data as a linear function of the autocorrelation function of the data to be estimated. Bias and standard deviation of both autocorrelation function and power spectral density are detailed for simulated data based on Monte Carlo analysis. The method is also compared with a fuzzy slotting technique with local normalization and weighting algorithm. Based on the results of these simulations, it is concluded that the performance of the proposed method is better than those of the slotting technique. © 2012 Elsevier Inc. All rights reserved. Source