Kahlil F. E. Cui, Gordon G. D. Zhou, Teng Man, Yu Huang, Yongshuang Zhang, Xueqiang Lu, Tao Zhao
{"title":"Modeling the Dense Granular Flow Rheology of Particles With Different Surface Friction: Implications for Geophysical Mass Flows","authors":"Kahlil F. E. Cui, Gordon G. D. Zhou, Teng Man, Yu Huang, Yongshuang Zhang, Xueqiang Lu, Tao Zhao","doi":"10.1029/2024JF008048","DOIUrl":null,"url":null,"abstract":"<p>Geophysical mass flows often consist of various types of materials with different surface roughnesses. Predicting the dynamics of flows such as rock-ice avalanches, where particles have highly distinct surface friction, remains challenging due to the limited knowledge on how friction differences impact the rheology and microstructure. To study the flow of surface friction-different granular mixtures, we conducted discrete element method simulations of dense granular flows with varying concentrations of a highly frictional and a less frictional particle type. Each mixture is characterized by three interparticle friction coefficients defined for contacts between similar and dissimilar particle species. We show that the mixture rheology can be modeled by combining the rheologies of single-phase flows having interparticle frictions equal to those that exist in the mixture, weighted according to particle contact probabilities. Furthermore, by applying the contact probabilities on a recently developed friction-dependent constitutive model, it is possible to predict the rheology and micro-structural parameters of a wide range of mixture scenarios and flow geometries requiring only the interparticle friction coefficients as inputs. Results here improve the determination of the flow resistance due to friction differences in geophysical flows, allowing for more reliable predictions of their dynamics, which in turn are necessary for hazard risk reduction and mitigation.</p>","PeriodicalId":15887,"journal":{"name":"Journal of Geophysical Research: Earth Surface","volume":"130 3","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Geophysical Research: Earth Surface","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024JF008048","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Geophysical mass flows often consist of various types of materials with different surface roughnesses. Predicting the dynamics of flows such as rock-ice avalanches, where particles have highly distinct surface friction, remains challenging due to the limited knowledge on how friction differences impact the rheology and microstructure. To study the flow of surface friction-different granular mixtures, we conducted discrete element method simulations of dense granular flows with varying concentrations of a highly frictional and a less frictional particle type. Each mixture is characterized by three interparticle friction coefficients defined for contacts between similar and dissimilar particle species. We show that the mixture rheology can be modeled by combining the rheologies of single-phase flows having interparticle frictions equal to those that exist in the mixture, weighted according to particle contact probabilities. Furthermore, by applying the contact probabilities on a recently developed friction-dependent constitutive model, it is possible to predict the rheology and micro-structural parameters of a wide range of mixture scenarios and flow geometries requiring only the interparticle friction coefficients as inputs. Results here improve the determination of the flow resistance due to friction differences in geophysical flows, allowing for more reliable predictions of their dynamics, which in turn are necessary for hazard risk reduction and mitigation.