Low-frequency sound wave parameter measurement in gravels

Swenson, George W.; White, Michael J.; Oelze, Michael L.

doi:10.1016/j.apacoust.2009.07.003

Cited by 3 publications

(2 citation statements)

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“…This hypothesis is based on results from acoustic investigations of other granular materials. For instance, Swenson et al [ 39 ] found a similar behaviour in granular materials with similar and larger particle sizes, i.e ., gravels (2–25 mm). Nevertheless, researches on granular materials with smaller particle sizes, i.e ., sands (0.25–1.60 mm) [ 40 ] or synthetic rubbers and minerals (0.40–3.50 mm) [ 34 ], found the opposite phenomenon, which was an increase of the sound absorption when increasing the particle size.…”

Section: Resultsmentioning

confidence: 88%

A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption Spectra

Gasso-Tortajada

Ward

Mansur

et al. 2010

Sensors

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A non-destructive and novel in situ acoustic sensor approach based on the sound absorption spectra was developed for identifying and classifying different seed types. The absorption coefficient spectra were determined by using the impedance tube measurement method. Subsequently, a multivariate statistical analysis, i.e., principal component analysis (PCA), was performed as a way to generate a classification of the seeds based on the soft independent modelling of class analogy (SIMCA) method. The results show that the sound absorption coefficient spectra of different seed types present characteristic patterns which are highly dependent on seed size and shape. In general, seed particle size and sphericity were inversely related with the absorption coefficient. PCA presented reliable grouping capabilities within the diverse seed types, since the 95% of the total spectral variance was described by the first two principal components. Furthermore, the SIMCA classification model based on the absorption spectra achieved optimal results as 100% of the evaluation samples were correctly classified. This study contains the initial structuring of an innovative method that will present new possibilities in agriculture and industry for classifying and determining physical properties of seeds and other materials.

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Section: Resultsmentioning

confidence: 88%

A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption Spectra

Gasso-Tortajada

Ward

Mansur

et al. 2010

Sensors

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“…The experimental method was essentially the same as reported earlier (1,2,3) for two other kinds of gravel:…”

Section: Introductionmentioning

confidence: 99%

Low-frequency sound propagation in porous media: Glass spheres and pea gravel

et al. 2012

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The sound propagation properties of two air-filled granular materials: large sifted pea gravel and 10 mm diameter glass spheres have been measured in an impedance tube. The experimental method was essentially the same as reported earlier [Swenson et al., "Low-frequency sound wave parameter measurement in gravels," Appl. Acoust. 71, 45-51 (2010)] for two other kinds of gravel: crushed limestone and undifferentiated pea gravel. Additional sampling and processing steps were applied to the microphone signals such that instead of tones, band-limited random noise was used as the input signal, and spectral domain complex pressures are now offered as input to the estimation algorithm. The estimation process extracts the best-fit attenuation coefficient, phase velocity, and characteristic impedance for the material over the signal frequencies, all with better precision than we previously obtained. Quadratic approximations for the acoustical parameters are given over the frequency range 25-160 Hz. The media are both slightly attenuating and dispersive, having attenuation coefficients within (0.13-0.34 Np/m), phase velocities smaller than those in air (180-240 m/s), and characteristic impedance approximately 3-5 times that for air. Pea gravel was more attenuating, and had slightly higher characteristic impedance, but lower phase velocities than the glass spheres.

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