Volume 95
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Leng, C., Sun, C., Liao, Z., & Xu, J. (2024). Quantitative characterization of granular size segregation: A critical review. Particuology, 95, 166-177. https://doi.org/10.1016/j.partic.2024.09.013
Quantitative characterization of granular size segregation: A critical review
Cong Leng, Chengfeng Sun, Zhehan Liao, Jian Xu *
College of Materials Science and Engineering, Chongqing University, Chongqing, 400044, China
10.1016/j.partic.2024.09.013
Volume 95, December 2024, Pages 166-177
Received 11 August 2024, Revised 14 September 2024, Accepted 14 September 2024, Available online 26 September 2024, Version of Record 16 October 2024.
E-mail: jxu@cqu.edu.cn

Highlights

• State-of-the-art methodologies of evaluating size segregation are emphasized.

• Features of quantitative methodologies are organized across three distinct scales.

• Four questions regarding various segregation indices are discussed.

• Insights into future development of advanced methodologies are provided.


Abstract

Granular size segregation is an inevitable phenomenon in both natural and industrial processes. To understand the underlying mechanisms and develop effective optimization strategies, it is essential to employ robust methodologies that can quantitatively characterize and evaluate size segregation behaviors in granular systems. This review critically examines a wide variety of state-of-the-art methodologies from recent studies to quantify granular size segregation. The features of these methodologies are extracted and organized into a comprehensive framework. Four key questions are thoroughly discussed: evaluation criteria for identical segregation states, sensitivity to sample size, the influence of sampling division pattern, and the capability of handling multiple-component system. Finally, we provide an outlook on the future development of advanced and effective methodologies for granular size segregation characterization.

Graphical abstract
Keywords
Granular system; Size segregation; Quantitative characterization; Multi-scale framework