Rheologica Acta最新文献

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.

Unlike the continuous relaxation spectrum (CRS), discrete relaxation spectra (DRS) are nonunique. This means that the linear viscoelastic response of a material can be described by two or more distinct DRS. Constraints like parsimony and consistency help us to infer meaningful DRS, but are not sufficient to induce uniqueness as it is an inherent property of discretization. Using parsimonious DRS from two different programs (DISCRETE and pyReSpect) on data drawn from experiments, simulations, and theory, we demonstrate that nonuniqueness does not hinder the two most common applications of relaxation spectra, viz. characterization and interconversion. Furthermore, information for reconstructing the CRS underlying the data is embedded in the DRS. Therefore, for most practical considerations, we find that the nonuniqueness of the DRS does not matter.

This work presents a physics-based two-dimensional model for simulating displacement flows of power-law fluids in Hele-Shaw cells. The model is derived by approximating fully developed velocity profiles across the gap-wise direction and averaging the mass and momentum conservation equations, resulting in a two-dimensional formulation that efficiently captures complex fluid dynamics. Implemented in OpenFOAM, this approach achieves computational speeds over 200 times faster than comparable 3D simulations, while preserving the accuracy of displacement dynamics. Validated against 3D DNS results and experimental data, this 2D model accurately replicates observed flow phenomena. Simulations of over 70 cases examined the effect of the ratio of friction pressure gradients (RFG) between fluid pairs on interface stability. Results show that RFGs below unity maintain a flat interface, while higher values induce viscous fingering. In cases with RFG closer to unity, a longer duct or extended displacement time is required for significant finger growth.

It has been reported that the length (molecular weight) of oligomeric solvents has a significant influence on nonlinear rheology of the corresponding polymer solutions. While different explanations, such as flow-induced reduction of monomeric friction or flow-induced phase separation, have been proposed, we show that the influence of oligomeric solvents on nonlinear shear and extensional rheology can be ignored when the length of the oligomers is long enough. We compared three polystyrene (PS) solutions, 600 k-4 k-50%, 600 k-10 k-50%, and 600 k-8a4k-50%, all containing the same weight fraction of the same long PS chains but different styrene oligomeric solvents. The first two contain linear oligomers with different length, while the last two contain oligomers with a similar span length but different molecular architectures (linear and star, respectively). All the solutions show identical nonlinear rheological behavior in startup shear and extensional flows until steady state, and also the same stress relaxation behavior after step shear strain.

The linear and nonlinear rheological behavior of filled polymers has been a research focus for decades. In this study, multi-walled carbon nanotubes (MWCNTs) were incorporated into a linear polypropylene (PPC) and co-extruded with a long-chain branched polypropylene (PPH) to form a multilayer system with a layered distribution of MWCNTs. The nonlinear shear and extensional rheological behaviors of the product films were then characterized in both machine direction (MD) and transverse direction (TD). Interestingly, the number of layers and layer thickness had a significant impact on rheological behavior. When fewer layers with thicker dimensions were present, strain hardening during extension was decreased in the filled system compared with the neat polymer multilayer system. Conversely, when the number of layers increased and the layer thickness decreased, strain hardening in the filled system was notably enhanced, particularly in the transverse direction (TD) during extensional rheology tests. This behavior is attributed to the PPC/MWCNTs layers were confined by the PPH layers effectively as the number of layers increased and layer thickness decreased close to or below the average length of the MWCNTs. In the multipliers, this confinement synergized with the extrusion flow, enhancing the orientation of MWCNTs in the machine direction (MD). In comparison with the multilayer systems composed of only LLDPE and MWCNTs, the neat LLDPE layer showed less impacts to the LLDPE/MWCNTs layer and the MWCNTs orientation. In addition, MWCNTs orientation effects to the elongational viscosities were more significant at lower Hencky strain rates. The enhancement of the MWCNTs orientation was further confirmed and studied by morphology analysis.

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We derive an operating limit line for the non-ideal artifacts caused by machine stiffness (instrument compliance) which causes measured apparent viscoelastic moduli to be systematically lower than the true values. The limit is represented as a maximum measurable apparent shear modulus (G_{max }), or tensile modulus (E_{max }), which can be shown explicitly on plots of viscoelastic moduli independent of the applied displacement, load, or frequency. Uncorrected data should be much lower than these limits. Corrected data can be above these limits and credible. These interpretations are supported by studying how correction equations can be re-written in terms of (G_{max }) or (E_{max }) and how error propagates in the corrections. We also show how the dynamic compliance representation leads to simpler corrections and how machine stiffness can be calibrated from apparent dynamic compliance measurements of a single sample at two different geometry conditions. Equations are provided for rotational rheometers as well as linear displacement dynamic mechanical analyzers. Used as an operational limit line, (G_{max }) or (E_{max }), the method can assess the credibility of data from others—even without access to their primary data of displacement, force, torque, or amount of correction, which are rarely reported. The method can also anticipate future issues before data are taken, e.g., to understand operational limits when selecting instruments and test geometries.