Particle characterisation in highly concentrated dispersions using ultrasonic backscattering method

Weser, Robert; Wöckel, Sebastian; Wessely, Benno; Hempel, U.

doi:10.1016/j.ultras.2012.10.013

Cited by 34 publications

(25 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The reflection coefficient R is a simple way to compare the different methods in Section 6. Many scattering experiments aim to estimate R [67,66]. The accuracy of estimating R is also directly related to the accuracy of calculating the transmitted waves.…”

Section: Comparing Reflection Coefficientsmentioning

confidence: 99%

Multiple Waves Propagate in Random Particulate Materials

Gower¹,

Parnell²,

Abrahams³

2019

SIAM J. Appl. Math.

View full text Add to dashboard Cite

For over 70 years it has been assumed that scalar wave propagation in (ensembleaveraged) random particulate materials can be characterised by a single effective wavenumber. Here, however, we show that there exist many effective wavenumbers, each contributing to the effective transmitted wave field. Most of these contributions rapidly attenuate away from boundaries, but they make a significant contribution to the reflected and total transmitted field beyond the low-frequency regime. In some cases at least two effective wavenumbers have the same order of attenuation. In these cases a single effective wavenumber does not accurately describe wave propagation even far away from boundaries. We develop an efficient method to calculate all of the contributions to the wave field for the scalar wave equation in two spatial dimensions, and then compare results with numerical finite-difference calculations. This new method is, to the authors' knowledge, the first of its kind to give such accurate predictions across a broad frequency range and for general particle volume fractions.

show abstract

Section: Comparing Reflection Coefficientsmentioning

confidence: 99%

Multiple Waves Propagate in Random Particulate Materials

Gower¹,

Parnell²,

Abrahams³

2019

SIAM J. Appl. Math.

View full text Add to dashboard Cite

show abstract

“…Similar findings were obtained by R. Weser et. al [14,15] for experiments on glass microparticles. In these publications, it was pointed out, that the observed back scattering spectra could be approximated by multiplying the analytical solution described by Faran [12] and Hickling [13], by a concentration dependent constant.…”

Section: Resultsmentioning

confidence: 99%

“…The acoustic scattering of microparticle in fluids has been studied for past few decades by analytical solutions [12][13][14][15][16][17] as well as with finite element methods (FEM) [18] to understand their behavior. Resonance scattering theory has also been developed in gain more information regarding scattering responses from elastic objects in water [19].…”

Section: Introductionmentioning

confidence: 99%

“…Resonance scattering theory has also been developed in gain more information regarding scattering responses from elastic objects in water [19]. Experimentally, high frequency ultrasound has been used to evaluate particle sizes and concentrations in liquid suspensions/emulsions [14][15][16]18], and to detect changes in cells or tissues suspended in solutions [16,17] For many cases, the acoustic attenuation spectrum and back scattering amplitude and frequency is used as important evaluation parameters [14][15][16][17]. Most of these studies were carried to evaluate the elastic or rigid spherical particle suspended or floating in the fluid [12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

PVDF copolymer transducers used to evaluate microparticle suspensions

Wagle

Decharat

Melandsø

et al. 2014

2014 IEEE International Ultrasonics Symposium

View full text Add to dashboard Cite

The ability of using high frequency ultrasonic transducers made from piezoelectric polymers, to characterize microparticle suspensions, has been investigated both in an experimental setup and in a numerical model. Both investigations, which consider microparticles settled on a glass substrate, show that the backscattered frequency spectra contain a number of minima values arising from wave resonances in the microparticles. The locations of these resonances are in the experiments, found to be independent of the particle concentration, but strongly dependent on the particle size. A comparison to previous results for floating microspheres, show that the material properties of the glass substrate, also has to be included for obtaining the correct resonance frequencies for settled particles

show abstract

“…25.Dd,43.20.Fn,05. 10.Ln Under close inspection, many materials are composed of small randomly distributed particles or inclusions. So it is no surprise that the need to measure particle properties, such as their average size and concentration, spans many physical disciplines.…”

mentioning

confidence: 99%

Characterising particulate random media from near-surface backscattering: A machine learning approach to predict particle size and concentration

Gower

Deakin

et al. 2018

EPL

View full text Add to dashboard Cite

To what extent can particulate random media be characterised using direct wave backscattering from a single receiver/source? Here, in a two dimensional setting, we show using a machine learning approach that both the particle radius and concentration can be accurately measured when the boundary condition on the particles is of Dirichlet type. Although the methods we introduce could be applied to any particle type. In general backscattering is challenging to interpret for a wide range of particle concentrations, because multiple scattering cannot be ignored, except in the very dilute range. Across the concentration range from 1% to 20% we find that the mean backscattered wave field is sufficient to accurately determine the concentration of particles. However, to accurately determine the particle radius, the second moment, or average intensity, of the backscattering is necessary. We are also able to determine what is the ideal frequency range to measure a broad range of particles sizes. To get rigorous results with supervised machine learning requires a large, highly precise, dataset of backscattered waves from an infinite half-space filled with particles. We are able to create this dataset by introducing a numerical approach which accurately approximates the backscattering from an infinite half-space.PACS numbers: 42.25.Dd,43.20.Fn,05.10.Ln Under close inspection, many materials are composed of small randomly distributed particles or inclusions. So it is no surprise that the need to measure particle properties, such as their average size and concentration, spans many physical disciplines. For quick non-invasive measurements, waves, either mechanical, electromagnetic or quantum, are the preferred choice. However, measuring a broad range of particle concentrations and sizes is still an open challenge. For high concentrations the wave undergoes multiple scattering, which requires specialised methods to compute and interpret. And further, measuring a wide range of particle sizes means a wide range of frequencies needs to considered.The type of wave used depends on the type of particle: acoustic waves are used to measure liquid emulsions 1 , sediment on the ocean floor 2 and polycrystalline materials 3 . Microwaves are vital in remote sensing of ice 4 ; optics for aerosols 5 and cellular components, both micrometer 6 and nanoscale 7 structures, among many other applications. In all these applications, there are cases when transmission experiments are impractical, because either the material is too opaque or, for example, has an unknown depth. The next natural choice is to use reflected, or backscattered, waves.Here we ask can one source/receiver measure the properties of a random particulate medium? And is it possible a) arturgower@gmail.com; https://arturgower.github.io/ b) gowerrobert@gmail.com; https://perso.telecomparistech.fr/rgower/ to do so without measuring the backscattering for a range of scattering angles, and without knowing the depth of the medium? Figure 1 illustrates a backscattered wave in time measured...

show abstract

Particle characterisation in highly concentrated dispersions using ultrasonic backscattering method

Cited by 34 publications

References 19 publications

Multiple Waves Propagate in Random Particulate Materials

Multiple Waves Propagate in Random Particulate Materials

PVDF copolymer transducers used to evaluate microparticle suspensions

Characterising particulate random media from near-surface backscattering: A machine learning approach to predict particle size and concentration

Contact Info

Product

Resources

About