The absorption of sound by helmholtz resonators with and without flow

Helmholtz resonators are commonly used as absorbers of incident acoustic power. Theoretical and experimental investigations have been performed in the four cases of no mean flow, grazing mean flow, bias mean flow and a combination of grazing and bias mean flows. In the absence of a mean flow, the absorption coefficient (deflned as the proportion of incident energy absorbed) is a non-linear function of the acoustic pressure and high incident acoustic pressures are required before the absorption becomes signiflcant. In contrast, when there is a mean flow present, either grazing or bias, the absorption is linear and thus absorption coefficient is independent of the magnitude of the acoustic pressure, and absorption is obtained over a wider range of frequencies. Non-linear effects are only discernible very close to resonance and at very-high amplitude. With grazing mean flow, there is the undesirable effect that sound can be generated over a range of frequencies due to the interaction between the unsteadily shed vorticity waves and the downstream edge of the aperture. This production is not observed when there is a bias flow because here the vorticity is shed all around the rim of the aperture and swept away by the mean flow. When there is both a grazing mean flow and a mean bias flow, we flnd that only a small amount of bias mean flow, compared with grazing mean flow, is required to destroy the production of acoustic energy. © 2002 by the author(s). Published by the American Institute of Aeronautics and Astronautics, Inc.

When will a flame describing function approach to thermoacoustics work well?

In any thermoacoustic analysis, it is important not only to predict linear frequencies and growth rates, but also the amplitude and frequencies of any limit cycles. The Flame Describing Function (FDF) approach is a quasi-linear analysis which allows the prediction of both the linear and nonlinear behaviour of a thermoacoustic system. This means that one can predict linear growth rates and frequencies, and also the amplitudes and frequencies of any limit cycles. The FDF achieves this by assuming that the acoustics are linear and that the flame, which is the only nonlinear element in the thermoacoustic system, can be adequately described by considering only its response at the frequency at which it is forced. Therefore any harmonics generated by the flame's nonlinear response are not considered. This implies that these nonlinear harmonics are small or that they are sufficiently filtered out by the linear dynamics of the system (the low-pass filter assumption). In this paper, a flame model with a simple saturation nonlinearity is coupled to simple duct acoustics, and the success of the FDF in predicting limit cycles is studied over a range of flame positions and acoustic damping parameters. Although these two parameters affect only the linear acoustics and not the nonlinear flame dynamics, they determine the validity of the low-pass filter assumption made in applying the flame describing function approach. Their importance is highlighted by studying the level of success of an FDF-based analysis as they are varied. This is achieved by comparing the FDF's prediction of limit-cycle amplitudes to the amplitudes seen in time domain simulations.

Multilinear function factorisation for time series feature extraction

This work applies a variety of multilinear function factorisation techniques to extract appropriate features or attributes from high dimensional multivariate time series for classification. Recently, a great deal of work has centred around designing time series classifiers using more and more complex feature extraction and machine learning schemes. This paper argues that complex learners and domain specific feature extraction schemes of this type are not necessarily needed for time series classification, as excellent classification results can be obtained by simply applying a number of existing matrix factorisation or linear projection techniques, which are simple and computationally inexpensive. We highlight this using a geometric separability measure and classification accuracies obtained though experiments on four different high dimensional multivariate time series datasets. © 2013 IEEE.