Rheologica Acta最新文献

We consider the corotational Maxwell model which is perhaps the simplest constitutive model with a nontrivial oscillatory shear response that can be solved analytically. The exact solution takes the form of an infinite series. Due to exponential convergence, accurate analytical approximations to the exact solution can be obtained by truncating the series after a modest number ((varvec{approx }) 10–20) of terms. We compare the speed and accuracy of this truncated analytical solution (AS) with a fast numerical method called harmonic balance (HB). HB represents the periodic steady-state solution using a Fourier series ansatz. Due to the linearity of the constitutive model, HB leads to a tridiagonal linear system of equations in the Fourier coefficients that can be solved very efficiently. Surprisingly, we find that the convergence properties of HB are superior to AS. In terms of computational cost, HB is about 200 times cheaper than AS. Thus, the answer to the question posed in the title is affirmative.

Identifying the different concentration regimes in polyelectrolytes is helpful for tuning the viscosity in personal care products, as well as in creating other polymer materials, including anion exchange membranes. Viscosity scaling distinguishes various concentration regimes in polyelectrolyte solutions, which change in the presence of salt. Here, the first objective was to measure the viscosity scaling for two cationic polyelectrolytes in water, acid (0.1 M HCl), and salt (0.1 M NaCl) solutions. Two polymers containing the same cationic group were compared, namely, a copolymer poly(acrylamide-co-diallyldimethylammonium chloride) (PAAcDMAC) and a homopolymer poly(diallyldimethylammonium chloride) (PDADMAC). Polyelectrolyte concentrations from 0.25 to 18 wt% spanned from dilute to entangled concentration regimes depending on the polyelectrolyte. Acid and salt had comparable effects on the polyelectrolytes’ viscosity. Specifically, the viscosity of the PAAcDMAC in 0.1 M NaCl in the dilute region decreased by 57% compared to DI water. Since salt ions screen the electrostatic interactions, polymer chains assume a more compact conformation. Little difference in zero-shear viscosity existed in the semi-dilute regimes for DI water and 0.1 M NaCl solution of PAAcDMAC. However, zero-shear rate viscosity increased by up to 18% with salt addition in the entangled regime. Since the rheology of entangled polyelectrolytes has not been extensively studied, small and large amplitude oscillatory experiments were completed to elucidate differences in viscoelasticity upon the addition of salt. Subtle differences in viscoelastic properties of 18 wt% PAAcDMAC solution were found upon salt addition in entangled regime. For example, large amplitude oscillatory experiments measured changes in maximum and minimum storage moduli upon NaCl addition. Thus, a disproportional change to the elastic behavior was captured upon salt addition.

Graphical abstract

Squeeze flow has been proven as an interesting technique for the rheological evaluation of many classes of materials, being relatable to common compressive phenomena from various processing and application procedures. Despite the simplicity of the experimental setup needed to run it, the results from the test are rather complex, involving multiple variables and factors that are not fully clarified by the bulk stress response. One additional piece of information that can be valuable is the pressure distribution over the sample area, since it is related to key aspects of the flow. The addition of a pressure mapping system to the traditional setup of the test has been recently proposed as a way to enrich the information obtained, in a method deemed pressure mapped squeeze flow (PMSF). This paper presents the evolution and state of the art of this technique, and analyzes a plastic clay with two different water contents in three displacement rates to demonstrate the potential and possibilities that PMSF offers. The experimental setup is presented in detail, along with the calibration procedure and data treatment suggested, as well as multiple types of analyses including bulk stress curves, raw pressure distribution plots, measured contact area, evolution of the mean profile, comparison to theoretical models supported by error analysis, and investigation of variation over the area. With the procedure established and presented in this work, it should be possible to apply PMSF as a valuable technique throughout the materials science and engineering community.

Recent experimental observation in fast extensional flow of polymer melts and solutions displayed as presence and absence of viscosity thinning, respectively, has necessitated and also initiated nonlinear modification of the Rouse model, the fundamental molecular model for unentangled polymeric liquids. On that account, concept of reduction of bead friction is introduced in the form of variable friction coefficient (zeta (t)) sometimes with corresponding variation of the Brownian force. This work presents mathematical constraint based on reasonable assumptions for volume conservation of deforming chains and accordingly formulates the rheological constitutive equation. The equation of constraint for volume conservation possibly relieves in part the complication introduced by the friction reduction and intrinsic flow-induced anisotropy in nonlinear modification of the Rouse model. The suggested constitutive equation expresses description in simple rheometric flows quite similar to that of the previous model with B-variation given by Sato et al. (2021) when both are formulated with effects of finite extensibility (FENE) and friction reduction. In addition, the molecular dynamics simulation demonstrates possible validity of the current hypothesis, the constraint of chain volume conservation.

Graphical abstract

Numerical simulations are used to study the dynamics of a developing suspension Poiseuille flow with monodispersed and bidispersed neutrally buoyant particles in a planar channel, and machine learning is applied to learn the evolving stresses of the developing suspension. The particle stresses and pressure develop on a slower time scale than the volume fraction, indicating that once the particles reach a steady volume fraction profile, they rearrange to minimize the contact pressure on each particle. We consider the timescale for stress development and how the stress development connects to particle migration. For developing monodisperse suspensions, we present a new physics-informed Galerkin neural network that allows for learning the particle stresses when direct measurements are not possible. We show that when a training set of stress measurements is available, the MOR-physics operator learning method can also capture the particle stresses accurately.

This study presents the development of an artificial neural network (ANN) model to predict the viscosity of ethylene–glycol based nanofluids with different types of nanoparticles using four input parameters: nanoparticle type, size, concentration, and temperature of measurement. The model was trained and validated using 470 experimental measurements. The ANN model demonstrated high accuracy in predicting the viscosity of nanofluids. The obtained statistical error metrics between the measured and predicted values of viscosity were found to be very low. MAPE values were equal to 1.19% and 2.33% for training and testing respectively. The developed model can help researchers to better understand EG-based nanofluids viscosity behavior, and this could be considered as a good step forward to help researchers design new nanofluids with enhanced properties. To make the model more accessible for engineers and researchers, a user-friendly web application was developed using Angular and Django, allowing users to input parameters and obtain viscosity predictions without dealing with complex code. The web application offers multiple output options, including figures, tables, and Excel files. This multidisciplinary research study combines web technology, data science, and fluid mechanics to provide a valuable tool to predict nanofluids’ viscosity for different input parameters.

Graphical abstract

A multi-step, iterative technique for the local non-parametric identification of the standard linear solid (SLS) material model employing fractional order time differential operators is presented. Test input data consists of a set of identified material complex modulus values estimated at different frequency values, obtained from input–output experimental measurements made on a material specimen by means of forced harmonic excitation and from experimental measurements made on the same specimen in quasi-static relaxation conditions. The proposed technique is mainly based on an algebraic procedure leading to the solution of an overdetermined system of linear equations, in order to get the optimal value of the model unknown parameters. The procedure is non-parametric, since the SLS model order is initially unknown. The optimal model size can be found by evaluating the stability properties of the solution associated to any model size and by automatically discarding computational, non-physical contributions. The identification procedure is first validated by means of numerically simulated test data from within known model examples, and then it is applied to some experimentally obtained test data associated to different materials.