Particle Size Characterization in Liquid-Solid Dispersion With Aggregates by Broadband Ultrasound Attenuation

Ultrasound attenuation methods for particle size characterization generally assume that the dispersed phases are uniformly dispersed in the continuous phase. However, the aggregation of particles in dispersions affects ultrasound attenuation, which limits the estimation accuracy of the attenuation model. A broadband ultrasound method is proposed, which utilizes a fractal approach to address the acoustic attenuation influence of particle aggregation. First, a chirp-based swept-frequency mode is designed to demodulate the ultrasonic attenuation at a low time cost to ensure the consistency of multifrequency attenuation. Next, considering the effect of particle aggregation on attenuation, the ultrasound attenuation model based on Faran scattering theory is modified by the fractal approach. Then, the inversion of particle size characterization is formulated as an optimization problem of nonlinear and nonconvex functions. An iterative optimization method is proposed to solve the ill-posed inverse problem, in which covariance matrix adaptive evolutionary strategy (CMA-ES) is developed as its inverse solver, and the fractal modified attenuation model is developed as its forward solver. Finally, the experiments were carried out with a mixture of micron-sized polystyrene particles and water. Experimental results indicate that the attenuation predicted by the proposed attenuation model is in good agreement with that measured in liquid-solid dispersion with particle aggregation, and the effectiveness of CMA-ES based on multiple distribution functions is verified for the inversion of particle size characterization.

Automated summary

The study introduces a new broadband ultrasound method that utilizes fractal approach to address the influence of particle aggregation on acoustic attenuation, enhancing the accuracy of the attenuation model. By modifying the ultrasound attenuation model and formulating the inversion of particle size characterization as an optimization problem, the iterative optimization method successfully solves the inverse problem.