Rheology of concentrated fiber suspensions with a load-dependent friction coefficient

Khan, Monsurul; More, Rishabh V.; Banaei, Arash Alizad; Brandt, Luca; Ardekani, Arezoo M.

doi:10.1103/physrevfluids.8.044301

Cited by 7 publications

(11 citation statements)

References 60 publications

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“…Resolving these issues may finally give the ability to predict and tune the rheology of non-Brownian suspensions in industrial settings. Beyond such developments, the extension of frictional rheology to suspensions of highly anisotropic particles such as flexible rods [309] will considerably increase its range of applicability, e.g. to cellulose suspensions, a sustainable alternative additive to synthetic polymers in certain products [310].…”

Section: Discussionmentioning

confidence: 99%

Soft matter roadmap^*

Barrat,

Del Gado,

Egelhaaf

et al. 2023

J. Phys. Mater.

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Soft materials are usually defined as materials made of mesoscopic entities, often self-organized, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterize them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g., tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and numerical methods, and coarse-grained models, have become central to predict physical properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations.This roadmap paper intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterization, instrumental, simulation and theoretical methods as well as general concepts.

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Section: Discussionmentioning

confidence: 99%

Soft matter roadmap^*

Barrat,

Del Gado,

Egelhaaf

et al. 2023

J. Phys. Mater.

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“…In this section, we discuss the governing equations and numerical methods used to simulate the shear flow of the bi-disperse fiber suspension. We consider a suspension of total N fibers of two different aspect ratios (A s and A l ) in a shear flow with top and bottom walls moving in opposite directions with an imposing shear rate _ g. We use the method and algorithm of Khan et al 13 which we briefly discuss here.…”

Section: Simulation Methodologymentioning

confidence: 99%

“…11 Moreover, the rheological properties of these suspensions are complex and heavily dependent on several variables, including suspending fluid properties, fiber size distribution, shape, flexibility, roughness, and volume fractions. [12][13][14][15][16][17][18][19][20][21] Even though fiber size distributions are more commonly encountered in the applications and natural environments stated above than mono-disperse suspensions, the role of bi-or poly-dispersity on the rheology of fiber suspensions is limited in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…6 The dependence of jamming on the fiber surface roughness and aspect ratio (A = L/D, where L is the length and D is the diameter of the fiber) has been quantified in our previous publications on mono-disperse fiber suspensions. 13,14 The decrease in relative viscosity is a widely recognized phenomenon when the length of the longer fiber grows, hence highlighting the need for studying the jamming volume fraction in bi-disperse solutions. Nevertheless, the impact of bidispersity on the jamming volume percentage has solely been examined in the context of suspensions consisting of spherical particles with two distinct sizes.…”

Section: Introductionmentioning

confidence: 99%

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Rheology of bi-disperse dense fiber suspensions

Khan,

Corder,

Erk

et al. 2024

Soft Matter

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“…Khan et al 13 present a constitutive model for frictional fiber suspensions in a steady shear flow. Khan et al 14 also investigate the effects of fiber aspect ratio, roughness, flexibility, and volume fraction on the rheology of concentrated suspensions in a steady shear flow using direct numerical simulations. Through analysis of the hydrodynamic mechanism of the fibers, it was found that with an increase in suspension concentration, the short-range interference between fibers is enhanced, resulting in increased shear stress of the fluid, thereby achieving the desired viscosity effect.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Proppant Settling Velocity in Fiber-Containing Fracturing Fluids

Bai,

2023

ACS Omega

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Hydraulic fracturing is the main stimulation method in the development of oil and gas fields. It is helpful to predict the fracture support effect during fracturing by calculating the settling velocity of particles in the fracturing fluid. Experimental research shows that fibers mixed into the fracturing fluid can improve the performance of suspended sand. In this study, fiber was considered a solvent in the fracturing fluid, and the constitutive model of the fiber-containing fracturing fluid was modified according to the fluid rheology. From the analysis of the mechanical behavior in the fibercontaining fluid, the settling velocity of particles slowed down because of some reasons. First, the viscosity of the fiber-containing fracturing fluid increased significantly. The other mechanism is the resistance mechanism of the fiber acting on the particle. The apparent viscosity was fitted based on the rheological model and the measurements. Then, the drag coefficient model of the settling particles was built according to the rheological data of the fibrous fluid. A semi-empirical model was developed to predict the terminal settling velocity through research on dynamics. By characteristic analysis of the results, we found that the fiber on the settling velocity is closely related to the concentration of the base fluid. This prediction model is suitable for the base fluid and fiber-containing fracturing fluid, and the average relative difference between the prediction model and measurements was acceptable.

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Rheology of concentrated fiber suspensions with a load-dependent friction coefficient

Cited by 7 publications

References 60 publications

Soft matter roadmap^*

Soft matter roadmap^*

Rheology of bi-disperse dense fiber suspensions

Prediction of Proppant Settling Velocity in Fiber-Containing Fracturing Fluids

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Rheology of concentrated fiber suspensions with a load-dependent friction coefficient

Cited by 7 publications

References 60 publications

Soft matter roadmap*

Soft matter roadmap*

Rheology of bi-disperse dense fiber suspensions

Prediction of Proppant Settling Velocity in Fiber-Containing Fracturing Fluids

Contact Info

Product

Resources

About

Soft matter roadmap^*

Soft matter roadmap^*