Rheologica Acta最新文献

The blending of homopolymers is commonly used to create polymeric materials with synergistic properties. Most polymer blends are immiscible, consequently they have a multiphase structure, conferring them highly tunable properties. The twin-screw extruder is the most common device used for polymer compounding. In this work, the evolution of a disperse polymer blend morphology in a twin-screw extruder is studied by numerical simulations. The fluid dynamics of the blend, treated as a pseudo-homogeneous Newtonian fluid, is solved by the finite element method under quasi-steady and isothermal conditions. The velocity gradient obtained along several trajectories of the flow field is used as input in a previously developed model able to predict the blend morphology. The history of deformation of the droplets and their topological changes in terms of evolution of the stretch ratio and the unstretched droplet radius are investigated. The average blend morphology is computed for a population of droplets, highlighting the effect of the screw rotation speed and the blend viscosity ratio.

In this paper, the temperature and frequency sweep experiments for ethylene propylene diene monomer (EPDM) samples are performed, and the master curves of the dynamic mechanical properties of EPDM rubber are built by using time–temperature superposition principle (TTSP). It shows that the EPDM rubber exhibits temperature- and frequency-dependent properties, and its temperature T_α at which the dissipation is maximum for a given frequency increases with the frequency. The constructed master curve can characterize the dynamic mechanical properties of EPDM rubber covering 12 decades on the angular frequency scale. Then, the fractional derivative Kelvin (FDK) model and the fractional derivative Zener (FDZ) model are introduced, and a fractional derivative five-element (FDFE) model is proposed. The master curves of EPDM rubber are fitted by using these models. The results indicate that compared with the FDK and FDZ models, the FDFE model has more discrete relaxation times due to its multiple parallel branches, which enables it to accurately describe the dynamic mechanical behavior of EPDM rubber and well characterize the asymmetric characteristics of the loss factor curve. Finally, the influences of parameters in the FDFE model on the dynamic mechanical performance curves of polymers are investigated. It suggests that both fractional parameters and relaxation times control the dynamic response mechanisms, and the proposed FDFE model has a potential broad applicability in characterizing the dynamic mechanical properties of polymers. 

Graphical Abstract

We numerically evaluate the performance of two pendant drop techniques — Capillary Pressure Tensiometry (CPT) and Stress-Fitting Elastometry (SFE) — based on their ability to calculate the interfacial stress and dilatational rheological properties of complex interfaces. Although both methods incorporate simultaneous shape and pressure measurements, CPT assumes a spherical cap shape with isotropic deformations, allowing the interface to be fully characterized by a single scalar value for the surface stress. On the contrary, SFE accounts for mechanically resistant interfaces that exhibit non-uniform tensorial strain and stress fields. To compare these methods, we numerically generate drops with perfectly elastic (non-dissipative) interfaces and subject them to step-strain compressions of varying magnitudes. The calculations span a range of dimensionless parameters representing realistic drop volumes, geometries, and physical properties. We show that the local strain and/or stress vary along the surface, depending on the relative magnitude of the shear versus dilatational moduli. We analyze the strained interfaces using CPT and SFE, quantitatively evaluating their ability to predict the interfacial strains, stresses, and dilatational moduli. We then identify the configurations and analysis methods that yield the most accurate results. Finally, we assess the robustness of these methods by introducing random Gaussian noise to the interface profiles, with a magnitude comparable to experimental errors from image acquisition and processing. The performance of both methods is compared under both idealized and experimentally realistic (noisy) conditions.

The interaction between the slow and fast eigenmodes of a viscoelastic material gives rise to “dampened elasticity,” playing a crucial role in both technical applications and natural processes. This phenomenon occurs during the flow of a viscoelastic liquid or the deformation of a viscoelastic solid, as slow eigenmodes work to elastically restore the material’s previous shapes, while fast relaxation modes resist any such elastic recovery by attempting to preserve the material’s current shape. In this way, fast modes create a viscous background that dampens elastic recovery. Elastic-dominated and viscous-dominated stress components act across a sequence of process times, 0 < s < ∞, when probing eigenmodes with a spectrum of relaxation times spanning 0 < τ < τ_max. Classification of an eigenmode as fast or slow depends on the respective value of the Deborah function, D(τ;s) = τ/s, a key parameter introduced here. The critical value D = 1 separates the eigenmodes into two groups, those dominated by elasticity (D > 1) and those dominated by viscosity (D < 1). The resulting ratio of elastic to viscous stress, referred to as elastic-to-viscous ratio EVR, defines the viscoelastic state of soft matter under specific flow or strain conditions. The EVR(x,t) can vary with position (x) and/or evolve over time. Additionally, the damping potential, P_d, defines the ratio of transient to permanent elasticity in solids. Illustrating examples involve gels and the process of gelation with its characteristic evolution of relaxation times. The distinction between elasticity-dominated and viscosity-dominated stress is equally applicable to linear and non-linear viscoelasticity.

Graphical Abstract

Accurately resolving spatially inhomogeneous flows is one of the essential roles of computational rheology. Compared to conventional flow predictions using constitutive models (CMs), multiscale simulations (MSSs), where mesoscopic models are embedded in macroscopic computational domains, offer accurate predictions but are accompanied by high computational costs. To avoid the computational issue in these MSSs (which we refer to as “full”-MSSs), we employed machine learning (ML) techniques, which we denote as “ML”-MSS, to predict spatially inhomogeneous flows. We obtained approximate CMs using a sparse identification algorithm for training data numerically generated by the dumbbell-based mesoscopic model with the Hookean or finite extensible nonlinear elastic (FENE) spring. Our sparse identification algorithm accurately identifies the CM for the dumbbell model with the Hookean spring and provides an approximate CM that reproduces the shear rheology of the dumbbell model with the FENE spring. The ML-MSSs with these CMs were compared to the full-MSSs for a flow between parallel plates driven by an external force. We confirmed that the relative error in the primary velocity along the centerline between ML-MSS and full-MSS is within approximately 20%, indicating the fundamental validity of our data-driven approach, with a computational time equivalent to that of a conventional approach employing CMs.

Recent advancements in machine learning (ML) and artificial intelligence (AI) have profoundly influenced various scientific and engineering fields. In rheology, data-driven approaches offer innovative solutions to challenges that conventional methods struggle to address. We demonstrate an application of data-driven approaches to psychorheology, a field that significantly benefits from such methodologies, by analyzing yogurt texture through the integration of rheological analysis and machine learning techniques. A total of 105 yogurt samples were prepared by varying whey separation time and milk powder content. Their rheological behavior was analyzed using various measurements, including large-amplitude oscillatory shearing (LAOS), reflecting flow conditions during consumption. Sensory attributes—thickness, stickiness, swallowing, and preference—were evaluated via panel tests. A predictive machine learning model was developed using the rheology-sensory texture dataset, achieving root mean square error values below 6 on a 100-point scale. Feature importance and permutation importance analyses identified key rheological parameters influencing each sensory texture. These results were interpreted in relation to flow conditions during eating, categorized into scooping, first bite, repeated shear, and swallowing. This study enhances our understanding of sensory perception during food intake from a rheological perspective and offers insights into yogurt texture design and control.

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This paper provides a rapid overview of the mechanics of cohesive granular materials and powders, with a particular focus on the development of constitutive equations for the steady flowing regime. We begin by reviewing the various sources of adhesion between particles, before exploring the onset of flow in cohesive materials. While yield conditions are central to many characterization methods, they provide limited insight into flow behavior. We then discuss recent studies on the flow of cohesive granular materials, emphasizing the development of constitutive equations. A direct comparison of results from DEM simulations across different studies highlights the importance of the interaction model details but reveals key insights into the relevance of different dimensionless numbers, paving the way for a more comprehensive rheological description of powders.

This paper reports the development of innovative magnetic-sensitive biopolymer composites and the subsequent investigation of their rheological properties in relation to in situ optical studies of microstructures. Positively charged iron oxide magnetic nanoparticles (IONP) that had been chemically functionalised by grafting 3-Aminopropylphosphonic acid molecules onto their surfaces were mixed in an entangled aqueous solution of sodium alginate chains. Steady shear flow and viscoelastic measurements were then performed on the resulting nanocomposites using a home-made magneto-opto-rheological device. The increase of low shear viscosity and the linear viscoelastic moduli as the magnitude of the magnetic field increased was clearly demonstrated. This is explained by electrostatic interactions between -NH₃⁺ and -COO⁻ groups at the surface of IONP and polymer chains, respectively. The resulting microstructure, which depends on both the shear rate and the magnetic field amplitude, was observed for the first time using in situ optical microscopy and deeply analysed.

Graphical Abstract

While they were still marginal around 30 years ago and even the very existence of the yield stress was still under debate, research work involving yield stress fluids has exploded over the last 20 years in rheology and physics, even to the point of sometimes appearing hackneyed. Yield stress fluids are now fully recognized as a specific state of matter by physicists and widely studied for this reason. They are also used for their remarkable mechanical behavior in a rapidly growing range of applications, notably in additive manufacturing or 3D printing in bioengineering, civil engineering, food processing, etc. This review first discusses the areas in which a sufficient knowledge might be considered acquired to be used in yield stress fluid engineering. This in particular includes the characterization of materials, through practical tests or sophisticated approaches, the use of simplistic constitutive equations or more complex models including various subtleties of behavior in view of flow simulations, a basic rheophysical framework for predicting the behavior of yielding dispersions or aggregated systems, but also for the widespread practical case of suspensions in yield stress fluids. However, there also appear large areas of major impact for which a comprehensive knowledge seems still lacking. This is in particular the case of: a relevant 3D formulation of the constitutive equation to describe the complex flows encountered in numerous applications, the physical and mechanical characteristics of the solid–liquid transition, the characterization and description of thixotropy, the transition to pasty materials, at the very frontier of “pure” solids.

Magnetorheological fluid converts into semisolid under the action of the magnetic field. Yield stress is the minimum external stress required to initiate the flow in the presence of a magnetic field. Predicting yield stress is vital for analyzing the performance of any MR application. Various yield stress models are available in the literature. However, they are valid for a particular composition of MRF or magnetic field strength range. Here, the yield stress model is derived using magnetostatics principles. Further, the characteristic magnetic field strength is used to introduce the non-linear magnetization of MRF. The Herschel-Bulkley model is used to present the post-yield nature of MRF flow. The constants of the proposed model are determined using the gradual reduced gradient method. They can predict shear stress with ± 10% accuracy irrespective of magnetic field strength zone.