Particle-particle Interactions Research Articles

Modeling two-phase gas-solid flow in axisymmetric diffusers using cut cell technique: an Eulerian-Eulerian approach

AbstractAxisymmetric diffusers find wide applications in various industrial processes, including combustion systems, pneumatic conveying, and fluidized bed reactors. Understanding and accurately modeling the behavior of two-phase flows in these devices are critical for optimizing their performance and efficiency. Various aspects of the two-phase gas-solid flow, including particle-particle and particle-wall interactions, interphase momentum transfer, and phase segregation, are investigated. Our currently developed technique, which uses a cut-cell technique, is an established new method to handle the inclined wall of an axisymmetric diffuser. The near-wall cells are treated as five faces for the new grid; one is the inclined wall. This helps treat the boundary condition at the wall in an accurate physical way. The code FORTRAN, developed in-house, was used to solve the axisymmetric diffuser using a cut-cell technique. The results of this study will provide valuable insights into the behavior of gas-solid two-phase flows in axisymmetric diffuser. A parametric study of the impact of particles diameters ($100,\ 200,\ \text{and} \ 300\mu m$ 100 , 200 , and 300 μ m ), the solid volume loading ratios $(0.005,\ 0.008,\ 0.01)$ ( 0.005 , 0.008 , 0.01 ) , and cant angle $(4.5^{\circ},\ 7^{\circ},\ 9.5^{\circ}) $ ( 4.5 ∘ , 7 ∘ , 9.5 ∘ ) effect of axisymmetric diffuser on the local skin friction, pressure, velocity, turbulent kinetic energy, and separation zone.

Distinct Contributions of Particle Adsorption and Interfacial Compression to the Surface Pressure of a Fluid Interface.

Particle-laden interfaces stabilize emulsions and foams and can serve as a platform for multiscale materials. Favorable wetting of a particle to a fluid interface reduces the apparent interfacial tension through area replacement with a linear relationship between the apparent surface pressure and the particle area fraction. The area replacement model is widely employed, often up to particle area fraction reaching the maximum hexagonal packing. However, data directly supporting the area replacement model are limited, and the description ignores contributions from particle-particle interactions and does not describe the surface pressure during the compression of a particle-laden interface. This work reports on the direct validation of the area replacement model through the direct measurement of the adsorption energy, surface pressure, and area fraction of adsorbed particles. Experiments combining tensiometry and confocal imaging during the adsorption of colloidal particles to the oil-water interface confirm the area replacement model within the observed range of area fraction, but only when the drop area is kept constant. Results highlight the importance of keeping the droplet area constant during particle adsorption to extract the adsorbed amount from tensiometry experiments. As particles adsorb to the interface, the droplet area tends to change and compresses or expands the interface. This change in area is associated with an increase in area fraction at nearly constant surface pressure, which deviates from the area replacement model. In contrast to particle adsorption, slow compression of the fluid interface leads to a negligible change in surface pressure up to an area fraction of η ∼ 0.26 for the materials systems investigated. Increase in surface pressure during compression is due to particle-particle interactions, while compression at higher strain rates introduces additional contributions from interfacial rheology.

Effect of Liquid Properties on the Non-Newtonian Rheology of Concentrated Silica Suspensions: Discontinuous Shear Thickening, Shear Jamming, and Shock Absorbance.

Concentrated particle suspensions exhibit rheological behavior, such as discontinuous shear thickening (DST) and dynamic shear jamming (SJ), which affect applications such as soft armors. Although the origin of this behavior in shear-activated particle-particle interactions has been identified, the effect of chemical factors, especially the role of liquids, on this behavior remains unexplored. Hydrogen bonding in suspensions has been proposed to be essential for frictional contacts between particles, and therefore, most studies on DST and SJ have focused on aqueous and protic organic media with a definite hydrogen bonding ability. To identify an alternative molecular mechanism, this study explored the effects of liquid polarity and an aprotic nature on the rheological behavior of concentrated suspensions of silica microparticles. Owing to their excellent particle dispersion, the DST behavior of polar liquids was observed, independent of protic and aprotic liquids. In contrast, nonpolar liquids formed particle agglomerates because of the particle-particle attraction and became a paste at a high particle fraction. The SJ behavior was confirmed for three aprotic organic liquids (propylene carbonate, 1,3-dimethyl-2-imidazolidinone, and 1,3-dimethylpropyleneurea), suggesting the hydrogen bonding ability of these aprotic liquids. The diverse mechanisms of shear-activated interactions between particles present material design possibilities for the non-Newtonian rheology of concentrated particle suspensions.

Adaptive AI-based surrogate modelling via transfer learning for DEM simulation of multi-component segregation

Segregation of granular materials is a critical challenge in many industries, often aimed at being controlled or minimised. The discrete element method (DEM) offers valuable insights into this phenomenon. However, calibrating DEM models is a crucial, albeit time-consuming, step. Recently, using machine learning (ML)-based surrogate models (SMs) in the calibration process has emerged as a promising solution. Nevertheless, developing such SMs is challenging due to the high number of DEM simulations required for training. Additionally, choosing a suitable ML model is not trivial. This study aims to develop SMs that effectively link particle-particle and particle-wall DEM interaction parameters to segregation of a multi-component mixture. We evaluate several ML models, ranging from artificial neural networks to ensemble learning, that are trained on a very cost-effective dataset, employing Bayesian optimisation with cross-validation to tune their hyperparameters. Next, we introduce a novel transfer learning (TL)-based approach that leverages knowledge from a few scenarios to handle new “unseen” ones. This method enables the construction of adaptive SMs for unseen scenarios, such as a new initial configuration (IC) of granular mixtures, without the need for a full-sized dataset. Our findings indicate that Gaussian process regression (GPR) efficiently builds accurate SMs on a very small dataset. We also demonstrate that only a few samples are required to build an accurate SM for the unseen IC, which significantly reduces the data preparation burden. By incorporating one and five samples from unseen scenarios to update the TL-GPR-based surrogate model, the SM’s performance (based on \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{R}^{2}$$\\end{document}) on unseen scenarios improves by 17 and 47%, respectively. The insights and methodology presented in this study will facilitate and accelerate the development of accurate SMs for DEM calibration, assisting in developing reliable DEM models in a shorter timeframe.

Machine Learning Aided Modeling of Granular Materials: A Review

AbstractArtificial intelligence (AI) has become a buzzy word since Google’s AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community of granular materials. This work offers a detailed review of the recent advances in machine learning-aided studies of granular materials from the particle-particle interaction at the grain level to the macroscopic simulations of granular flow. This work will start with the application of machine learning in the microscopic particle-particle interaction and associated contact models. Then, different neural networks for learning the constitutive behaviour of granular materials will be reviewed and compared. Finally, the macroscopic simulations of practical engineering or boundary value problems based on the combination of neural networks and numerical methods are discussed. We hope readers will have a clear idea of the development of machine learning-aided modelling of granular materials via this comprehensive review work.

Rheological investigation of heavy oil-in-water pickering emulsions for potential applications of fracturing fluids

Pickering emulsions as a kind of emulsified fracturing fluids can enhance the efficacy of shale fracturing. Investigation of the rheological properties of Pickering emulsions yields valuable insights for their in-situ fracturing applications. In this work, nano-clay particles and heavy crude oil were employed to form oil-in-water (O/W) Pickering emulsions. The impact of salinity, pH, and temperature on the rheological properties, including viscosity property and viscoelastic property, of Pickering emulsions were tested and analyzed. The results showed that the viscosity of heavy O/W Pickering emulsions were significantly affected by the change of salinity and pH values, with higher salinity or lower pH resulting in increased viscosity. Under ultra-high salinities (>5 wt% NaCl), the coalescence stability of Pickering emulsion strengthens and creaming stability declines. The massive formation of clay clusters leads to a reduction in the number of nano-clay particles used to generate the network, causing instability in the emulsion creaming. The heavy O/W Pickering emulsions exhibited high stability and high viscosity characteristics, which were mainly caused by the change of surface charge of nano-clay particles and the corresponding change of particle adsorption on droplet surface and particle-particle interactions. At a salinity of 2.5% NaCl and a pH of 4.6, the emulsion system remained stable at a temperature up to 120 °C, holding a viscosity reaching 74.1 mPa s at a shear rate of 6.81 s−1. Besides, heavy O/W Pickering emulsions were viscoelastic fluids, with both noticeable viscous property and elastic property at the same time. Elastic modulus (G′) and viscous modulus (G″) of the emulsion increased with increasing salinity and decreasing pH, enhancing great deformation capacity. Remarkably, the Pickering emulsions could maintain its viscoelastic behavior at varying temperature conditions. For the first time, rheological study of Pickering emulsions formed by nano-clay nanoparticles as a single emulsifier to heavy oil. Our study underscores the emulsifying ability of nano-clay particles to heavy crude oils, under a wide range of salinity and pH, yielding Pickering emulsions with exceptional stability and rheological properties. This novel emulsion system, possessing high resistance to temperature and salinity, holds promise for a high-performance fracturing fluid design.

The method of fundamental solutions for multi-particle Stokes flows: Application to a ring-like array of spheres

A method is presented for calculating Stokes flow around multiple particles of arbitrary shape. It uses the Method of Fundamental Solutions (MFS) applied to single particles, combined with an iterative scheme to resolve the many particle-particle hydrodynamic interactions; an approach that is reminiscent of the Method of Reflections. The attractive features of the proposed method are inherited from the MFS — simplicity and accuracy — while providing orders of magnitude computational speed-up for large particle systems. The method is verified through a series of test cases, including those involving strong lubrication forces and non-spherical particles. Unlike applications of the Method of Reflections reported in the literature, the iterative scheme we propose (a block Gauss-Seidel approach to solving a particle-particle interaction matrix) converges for all the cases we consider, for both resistance and mobility problems. The scheme is applied to the study of Stokes flow around ring-like arrays of spheres. We show that the relationship between globally applied velocity or force to the response of individual spheres can be described by just 5 coefficients (or 9 in total) for any given configuration. The results indicate that for 7–10 spheres in sedimentation, there exists a certain spacing that produces steady-state translation of the ring, independent of its orientation. For large numbers of spheres, slender-body theory can be applied to the problem, providing remarkably close agreement to the numerical results over a wide range of parameters.

Aligned colloidal clusters in an alternating rotating magnetic field elucidated by magnetic relaxation

Precise control at the colloidal scale is one of the most promising bottom-up approaches to fabricating new materials and devices with tunable and precisely engineered properties. Magnetically driven colloidal assembly offers great versatility because of the ability to externally tune particle-particle interactions and to construct a host of particle arrangements. However, despite previous efforts to probe the parameter space, global orientational control in conjunction with two-dimensional microstructural control has remained out of reach. Furthermore, the magnetic relaxation time of superparamagnetic beads has been largely overlooked despite being a key feature of the magnetic response. Here, we take advantage of the magnetic relaxation time of superparamagnetic beads in an alternating rotating magnetic field and show how harnessing this feature facilitates the formation of oriented clusters. The orientation of these clusters can be controlled by field parameters. Using experiments, simulations, and theory, we probe a two-particle system (dimer) under this alternating rotating magnetic field and use its dynamics to provide insights into the collective response that forms clusters. We find that the type of field has significant implications for the dipolar interactions between the colloids because of the nonnegligible magnetic relaxation. Moreover, we find that the competing time scales of the magnetic relaxation and the alternating field generate an anisotropic interaction potential that drives cluster alignment. By exploiting the magnetic relaxation time of magnetic systems, we can tailor new types of interparticle interactions, thereby expanding the capabilities of colloidal assembly in engineering unique materials and devices.

Characterization of Surface Patterning on Polymer-Grafted Nanocubes Using Atomic Force Microscopy and Force Volume Mapping.

Atomic force microscopy (AFM), in particular force spectroscopy, is a powerful tool for understanding the supramolecular structures associated with polymers grafted to surfaces, especially in regimes of low polymer density where different morphological structures are expected. In this study, we utilize force volume mapping to characterize the nanoscale surfaces of Ag nanocubes (AgNCs) grafted with a monolayer of polyethylene glycol (PEG) chains. Spatially resolved force-distance curves taken for a single AgNC were used to map surface properties, such as adhesion energy and deformation. We confirm the presence of surface octopus micelles that are localized on the corners of the AgNC, using force curves to resolve structural differences between the micelle "bodies" and "legs". Furthermore, we observe unique features of this system including a polymer corona stemming from AgNC-substrate interactions and polymer bridging stemming from particle-particle interactions.

Pharmaceutical aerosol transport in airways: A combined machine learning (ML) and discrete element model (DEM) approach

The continuum and discrete phase interaction could significantly affect the inhaled particle's transport behaviour. The accurate analysis of continuum and discrete phase interaction needs computational fluid dynamics (CFD) and Discrete Element Method (DEM) simulation, which is computationally expensive. Therefore, this study aims to develop a novel machine learning (ML) prediction model from CFD-DEM data to predict pharmaceutical aerosol transport in airways accurately. This study uses the CFD model for the continuum and DEM for the discrete phases. A soft sphere approach was used to calculate the overlap of the colliding particles. Proper validation was performed to ensure the accuracy of the present model. The CFD-DEM model analysed the particle transport in an idealised and realistic airway model, and different methods were used to analyse the transport behaviour. As the flow rate increased from 15 to 60 lpm, the deposition efficiency (DE) significantly improved due to particle interaction, rising from 20 % to 42 % without interaction and from 50 % to a remarkable 76 % with interaction, demonstrating the critical role of particle interaction in enhancing DE across varying flow rates. During the particle-particle interaction, a stagnation point and a high-pressure zone were observed at the airway model's carinal angle. Finally, a ML prediction model is developed from CFD-DEM data, which accurately predicts the pharmaceutical aerosol deposition in airways. The ML prediction model predicts the deposition pattern for different flow rates and particle sizes without CFD-DEM simulations. The present findings and more case-specific investigation would advance the knowledge of aerosol transport in airways and benefit more efficient targeted drug delivery devices.

Electrophoretic mobility of nanoparticle aggregates: Independence from aggregate size

The zeta potential is a widely used parameter to categorize particle-particle interaction and aggregation. This parameter is often determined by the measurement of electrophoretic mobility. For single particles, various approximations allow a direct correlation between the mobility and the zeta potential. However, for aggregates, which are often found in the environment and in industrial applications, such approximations are less utilized. The relation between the size of aggregates and the resulting electrophoretic mobility has not yet been extensively explored and is often not considered. Here, we control the aggregate size additive-free of two fumed silica types with different primary particle diameters for comprehensive electrophoretic and dynamic light scattering measurements. We evaluate the influence of primary particle diameter and aggregate size on electrophoretic mobility. Our results reveal that the primary particle diameter is the defining factor for the electrophoretic mobility of the aggregates. This finding provides new insights into the electrokinetic behavior of aggregates and emphasizes the importance of primary particle properties in predicting colloidal interactions.

Seeded granulation process in twin-screw granulators: A DEM approach

Twin-screw granulation is a promising continuous manufacturing technique for producing granules with tailored properties. This study investigates the impact of screw configuration and speed on seeded granule formation using discrete element method (DEM) simulations. The aim is to identify optimal screw designs and operating conditions that yield desirable granule characteristics, such as high coordination, fine particle coverage, and throughput. DEM simulations were performed using Altair's EDEM software, employing Hertz-Mindlin and JKR contact models to capture particle-particle interactions and binder liquid effects. Five screw configurations (C1, C2, C3, C4, and CI) with varying conveying zone lengths and kneading elements were investigated, and screw speed was varied from 300 to 800 RPM. The Results show that screw configuration plays a crucial role in determining seeded granule characteristics. Increasing screw speed had a detrimental effect on granule properties, with lower speeds of 300 RPM yielding the granules with the greatest coordination number with the highest fine coverage. The study highlights the complex interplay between screw configuration, conveying zone length, kneading elements, and residence time in determining granule properties. Recommendations are provided for selecting suitable screw configurations and operating conditions for specific applications. Further research is suggested to investigate granule size distribution and morphology at different liquid properties screw speeds, and liquid-to-solid ratios.

Interaction of charged magnetic nanoparticles with surfaces

The behavior of magnetic nanoparticles covering the surface with positive charges (MN), suspended in a continuous medium, was simulated by Brownian dynamics. Then, we studied the behavior of the MN in an aqueous-like suspension and their adsorption on a negatively charged surface, mimicking a mica surface. After several microseconds, particles are deposited onto the charged surface. Experimental results of ordered magnetic nanoparticles in surfaces are compared with the present simulation results of MN in two dimensions. We also demonstrate the effect of charged MN on the hexagonal structure when electrical repulsions dominate against magnetic dipole-dipole and van der Waals attractions. On one hand, the adsorption of MN on the surface depends on the electrostatic attraction force with the surface, while the surface organization of MN results from balancing electrostatic repulsion forces and magnetic attraction forces among particles. In magnetic nanoparticles simulated with a non-charged surface or weakly charged surface, dipole-dipole interactions dominate the particle-particle interactions, and the interactions between particles and a mica-like surface are conducted by van der Waals forces.

CFD-DEM investigation of centrifugal slurry pump with polydisperse particle feeds

Polydisperse particle feed into centrifugal slurry complicates hydraulic performance and wall erosion but is poorly understood. This paper presents a numerical study of a centrifugal slurry pump, focusing on the effect of particle size distribution (PSD) using the combined computational fluid dynamics and discrete element method (CFD-DEM). It incorporates rigid impeller rotation and wall erosion. On this basis, the hydraulic and erosion model validity is verified by clear water performance and jet erosion test, respectively. Additionally, this CFD-DEM model is compared with the dense discrete phase model (DDPM), demonstrating more precise erosion prediction as its fully resolved particle-particle interactions. Generally, total pump erosion severity intensifies when the PSD is broadened. Compartment-wise, it indicates the necessity of tailoring flow structure to enlarge drag force, therefore, mitigate particle aggregation in the impeller. This in-house CFD-DEM model is promising for addressing particle-fluid flow problems in centrifugal pumps or coupling with data driven method.

Disorder and demixing in bidisperse particle systems assembling bcc crystals.

Colloidal and nanoparticle self-assembly enables the creation of ordered structures with a variety of electronic and photonic functionalities. The outcomes of the self-assembly processes used to synthesize such structures, however, strongly depend on the uniformity of the individual nanoparticles. Here, we explore the simplest form of particle size dispersity-bidispersity-and its impact on the self-assembly process. We investigate the robustness of self-assembling bcc-type crystals via isotropic interaction potentials in binary systems with increasingly disparate particle sizes by determining their terminal size ratio-the most extreme size ratio at which a mixed binary bcc crystal forms. Our findings show that two-well pair potentials produce bcc crystals that are more robust with respect to particle size ratio than one-well pair potentials. This suggests that an improved self-assembly process is accomplished with a second attractive length scale encoded in the particle-particle interaction, which stabilizes the second-nearest neighbor shell. In addition, we document qualitative differences in the process of ordering and disordering: in bidisperse systems of particles interacting via one-well potentials, we observe a breakdown of order prior to demixing, while in systems interacting via two-well potentials, demixing occurs first and bcc continues to form in parts of the droplet down to low size ratios.

Sensitivity analysis of a dense discrete phase model for 3D simulations of a tapered fluidized bed

This study aims to conduct a sensitivity analysis of closure models and modeling parameters for the Dense Discrete Phase Modeling (DDPM) approach in order to investigate the hydrodynamics of a 3D lab-scale Tapered Fluidized Bed (TFB). The closure models and model parameters under investigation include the gas-solid drag force, viscous models, particle-particle interaction models, restitution coefficient, specularity coefficient, and rebound coefficient. The primary objective of this sensitivity analysis is to optimize the numerical model's performance. The numerical results, in terms of axial and lateral Solid Volume Fraction (SVF) profiles obtained from the sensitivity analysis, indicate that the drag force and restitution coefficient significantly influence the hydrodynamics of the TFB. Properly selecting these parameters could result in the improved performance of the numerical model. However, the sensitivity of turbulence models, particle-particle interaction models, specularity coefficient, and rebound coefficient has a lesser impact on the hydrodynamics results. This work concludes with the recommendation of a set of closure models and modeling parameters that offer the most accurate prediction of the hydrodynamics of the TFB.

Investigation of the surface charge behaviour of ettringite: Influence of pH, calcium, and sulphate ions

Ettringite is an important mineral that contributes to the overall performance of cementitious materials. Knowledge of the surface charge behaviour of a solid is necessary for a mechanistic description of surface processes such as adsorption or particle-particle interactions. The objective of this study was to develop a model capable of reproducing ettringite surface charge as a function of calcium, sulphate, and pH.Ettringite was synthesised and characterised using different analytical, microscopic, and spectroscopic techniques with the help of density functional theory. Electrophoretic mobility was measured using laser Doppler electrophoresis in alkaline waters representative of the cementitious environment.The behaviour of the ettringite surface charge was shown to be quite complex as sulphate and calcium acted in a competitive manner on the overall charge. The ζ-potential increases when the calcium content increases, whereas it decreases when sulphate increases. This is due to the possible adsorption of these ions at the surface, and the extent of the effect depends on the relative concentrations of Ca and SO42−.An electrostatic double layer model (DLM) was used to calculate the surface potential, considering the adsorption of both calcium and sulphate, as possible ions determining the potential (IDP), and formation of different complexes with ettringite surface functional groups (SOH). The variations of the ζ-potential could be satisfactorily predicted under the different chemical conditions of interest in a cementitious environment.

Transport coefficient approach for characterizing nonequilibrium dynamics in soft matter

Nonequilibrium states in soft condensed matter require a systematic approach to characterize and model materials, enhancing predictability and applications. Among the tools, X-ray photon correlation spectroscopy (XPCS) provides exceptional temporal and spatial resolution to extract dynamic insight into the properties of the material. However, existing models might overlook intricate details. We introduce an approach for extracting the transport coefficient, denoted as [Formula: see text], from the XPCS studies. This coefficient is a fundamental parameter in nonequilibrium statistical mechanics and is crucial for characterizing transport processes within a system. Our method unifies the Green-Kubo formulas associated with various transport coefficients, including gradient flows, particle-particle interactions, friction matrices, and continuous noise. We achieve this by integrating the collective influence of random and systematic forces acting on the particles within the framework of a Markov chain. We initially validated this method using molecular dynamics simulations of a system subjected to changes in temperatures over time. Subsequently, we conducted further verification using experimental systems reported in the literature and known for their complex nonequilibrium characteristics. The results, including the derived [Formula: see text] and other relevant physical parameters, align with the previous observations and reveal detailed dynamical information in nonequilibrium states. This approach represents an advancement in XPCS analysis, addressing the growing demand to extract intricate nonequilibrium dynamics. Further, the methods presented are agnostic to the nature of the material system and can be potentially expanded to hard condensed matter systems.

Positional microstates and probability fields in real systems

Abstract A previously published paper provided six examples of spontaneous processes for ideal systems that cannot be explained using classical thermodynamics. These six examples include free expansion of an ideal gas, mixing of ideal gases, diffusion of an ideal solute, mixing of ideal solutes, osmosis with ideal solutions, and free discharge of a concentration battery with ideal solutions. The previous paper demonstrated how energy was not a driving force in any of these examples and then proceeded to develop a positional entropy model, S D, that explains why these spontaneous processes occur. This new paper provides a method for calculating positional entropy, S D, for the same six systems, but for real particles that include nonzero volumes, particles with different volumes, and particles with different particle-particle interactions. The important outcome of this work shows that spontaneous discharge of these six example systems, either for ideal or real particles, is the result of a probability field created by the non-equilibrium distribution of the microstates that exists after the constraints on the system are changed, e.g., by removal of a separating partition or the shorting of a concentration cell. The probability field biases the movement of the particles toward the equilibrium distribution, where the bias is a consequence of an increased probability and not because of a decrease in energy. An additional conclusion of this work shows that the discharge of the probability field to the final equilibrium distribution of positional microstates removes the potential energy in the system but does not violate the law of conservation of energy.

Flow and rheology of suspensions of two-dimensional cylindrical or anisotropic particles with Navier slip

Through boundary integral simulations, we investigate, in the creeping flow limit and in the absence of Brownian noise, the effects of Navier slip on the orientational dynamics and effective shear viscosity of a semidilute suspension of two-dimensional particles with either circular or elongated (platelike) shape, interacting only via hydrodynamic and contact forces. We have recently shown that it is theoretically possible for a dilute system of slip platelike particles to display an effective shear viscosity smaller than the viscosity of the suspending fluid. This large viscosity reduction is primarily due to the suppression of the tumbling motion predicted for a no-slip particle and the attainment of a stable orientation. In this paper, we show that the effect of particle-particle interaction at semidilute concentrations is to cause the particles to fluctuate about the stable orientation and, above a threshold solid fraction ccrt, to tumble. As a consequence, a sharp increase in the effective shear viscosity with solid fraction c occurs for c>ccrt. Our results suggest that, for a given particle aspect ratio, there is a value of c that maximizes the reduction in the effective shear viscosity of the suspension. Published by the American Physical Society 2024