Viscosity Model Research Articles

Underlying mechanisms of the influence of fine aggregates' content and properties on mortar's plastic viscosity

It is well known that the fine aggregate's content and properties have a significant influence on the plastic viscosity of mortar made with them. However, the underlying mechanisms of this influence remain unclear. The aim of this paper is to clarify these mechanisms by analysing the basic mechanical interactions working during mortar's flow. Twelve types of fine aggregates with different grading and morphology were employed in the analysis. The experimental work confirmed previously documented results reporting that the different content, grading, and morphology of the fine aggregates cause, indeed, significant differences in the plastic viscosity of mortar made with them. Based on the basic mechanical behaviour of mortar flow, the authors deduced that the plastic viscosity is determined by the viscous stress generated during the process as a result of the hydrodynamic interaction between fine aggregate particles and cement paste, as well as the contact interaction between the various fine aggregate particles. Then, a semi-quantitative model of the relative plastic viscosity of mortar was developed, combining the contribution of hydrodynamic and contact interactions. This model can explain the experimental results and provide a good prediction of the plastic viscosity of mortar.

Fractional Derivative Viscosity of ANCF Cable Element

Typical engineering cable structures, such as high-voltage wire and wire rope, usually bring a damping effect which cannot be ignored due to the technological problems of manufacturing. For such problems, especially the damping of cable structures undergoing large displacement and severe deformation, few studies have been reported in the past. In this work, the fractional derivative viscosity model is introduced into the cables described by the absolute nodal coordinate formulation. The computer implementation algorithm of the proposed cable damping model is given based on the three-parameter fractional derivative model. Two numerical examples demonstrate the effectiveness and convergence property of the proposed cable damping model. An experiment is proposed in which a wire is tensioned and released. Configurations are captured by the high-speed camera and compared with the results obtained from the numerical simulation. The agreement of the simulation and experimental results validates the proposed cable damping in application.

Learning time-averaged turbulent flow field of jet in crossflow from limited observations using physics-informed neural networks

Physics-informed neural networks (PINNs) are becoming popular in solving fluid mechanics problems forwardly and inversely. However, under limited observations, the application of PINNs was found to be difficult in solving the inverse problems of three-dimensional Reynolds-averaged Navier–Stokes (RANS) equations. In this study, the classical turbulent case of jet in crossflow was representatively adopted into the investigation. The dataset was obtained from a high-fidelity large-eddy simulation. The tensor-basis eddy viscosity (t-EV) model was imported first into the structure of PINNs as prior knowledge. Observations of five measured planes were preliminarily used to reconstruct the time-averaged turbulent flow field. After embedding the t-EV model, the highest absolute error and the relative L2 error of streamwise velocity were reduced by 11.1% and 31.4%, respectively. To cut down the volume of limited observations, a more effective training dataset containing only two planes and two pairs of lines was determined based on the flow characteristics (e.g., shear layer and counter-rotating vortex pair). Compared with those of five planes, the highest absolute error and the relative L2 error of streamwise velocity were further reduced by 30.0% and 6.4%, respectively. The investigation in this study provided an alternative to resolve the inverse problems of three-dimensional RANS equations with limited observations, which extended the deep learning application in fluid mechanics.

Physics-Based and Data-Driven Polymer Rheology Model

Summary Polymer flooding is a common enhanced oil recovery (EOR) method used to increase aqueous phase sweep efficiency by increasing viscosity. Estimating polymer viscosity for given reservoir conditions (i.e., oil viscosity, temperature, and brine composition) requires intensive laboratory work. There are existing empirical models to estimate polymer bulk rheology without prior laboratory work; however, they have many coefficients, simple brine composition, and lack physics-based regression boundaries. This study benchmarks the existing polymer empirical and machine learning (ML) models against a new data-driven model with some physics basis for common synthetic polymers. We cover a broad range of polymer concentrations, temperature, salinity, and hardness with an upper limit of 5,000 ppm, 120℃, 290,000 ppm, and 33,000 ppm, respectively. The data were preprocessed through data analytics techniques, and a model was developed with some physics basis by fitting Martin’s equation for Carreau model coefficients. Our regression boundaries obey flexible polymers’ physical and laboratory behavior. We benchmarked the bulk rheological model with existing models in the literature. We used the published models’ coefficients and then tuned their coefficients for our data set for a fair comparison. We then investigated ML as a predictive tool without compromising overfitting the data using the simplest ML model (linear regression) all the way to artificial neural network (ANN) and hybrid ML models. This is the first study that comprehensively benchmarks polymer rheology models and proposes a simple, least number of coefficients, and tunable polymer-rheology model. We provide a predictive bulk rheology model that enables the user to accurately predict polymer viscosity without laboratory measurements and for a wide range of temperatures and brine compositions. Moreover, our study includes the recently common polymer SAV-10 that was not previously studied. We present a simple water viscosity model for a broad brine salinity and temperature range. Our study shows that ML techniques might provide deceptively high accuracy for small data sets, unless due diligence is done to avoid a high-variance model.

Discovering explicit Reynolds-averaged turbulence closures for turbulent separated flows through deep learning-based symbolic regression with non-linear corrections

This work introduces a novel data-driven framework to formulate explicit algebraic Reynolds-averaged Navier–Stokes (RANS) turbulence closures. Recent years have witnessed a blossom in applying machine learning (ML) methods to revolutionize the paradigm of turbulence modeling. However, due to the black-box essence of most ML methods, it is currently hard to extract interpretable information and knowledge from data-driven models. To address this critical limitation, this work leverages deep learning with symbolic regression methods to discover hidden governing equations of Reynolds stress models. Specifically, the Reynolds stress tensor is decomposed into linear and non-linear parts. While the linear part is taken as the regular linear eddy viscosity model, a long short-term memory neural network is employed to generate symbolic terms on which tractable mathematical expressions for the non-linear counterpart are built. A novel reinforcement learning algorithm is employed to train the neural network to produce best-fitted symbolic expressions. Within the proposed framework, the Reynolds stress closure is explicitly expressed in algebraic forms, thus allowing for direct functional inference. On the other hand, the Galilean and rotational invariance are craftily respected by constructing the training feature space with independent invariants and tensor basis functions. The performance of the present methodology is validated through numerical simulations of three different canonical flows that deviate in geometrical configurations. The results demonstrate promising accuracy improvements over traditional RANS models, showing the generalization ability of the proposed method. Moreover, with the given explicit model equations, it can be easier to interpret the influence of input features on generated models.

An analytical model for transverse velocity distributions of overbank flows with submerged and emergent vegetated floodplains

AbstractThe emergence of floodplain vegetation enhances the local drag and affects the velocity distribution of the overbank flow in a two‐stage channel. Numerous analytical models (e.g., the modified SKM models) have been established to simulate the transverse velocity distributions of overbank flows with vegetation. However, the dimensionless eddy viscosity (λ) was simplified to be a constant in these models when quantifying transverse momentum exchange. In this paper, we develop a novel analytical model to simulate the depth average velocity profiles of overbank flows with floodplain vegetation. Considering a micro‐hexahedral element on a vegetated‐bed area for a two‐stage rectangular channel, the governing equation of the vegetated overbank flow is derived. An improved eddy viscosity model that considers the transverse variation of the λ is employed to quantify the transverse momentum transfer, and a linear empirical function simplifies the secondary current. The simulated depth average velocities agreed well with the published measurements. In comparison with existing models, the proposed model shows better prediction accuracy, particularly in the shearing layer domain. More importantly, for the optimized model parameters, we found that the dimensionless eddy viscosity plays the most influential role when simulating depth‐averaged velocities in the shearing layer domain, whereas the drag force dominates for vegetated floodplains. This study provides a convenient method for predicting the flow velocity in overbank floods with vegetation.

A unified method of data assimilation and turbulence modeling for separated flows at high Reynolds numbers

In recent years, machine learning methods represented by deep neural networks (DNNs) have been a new paradigm of turbulence modeling. However, in the scenario of high Reynolds numbers, there are still some bottlenecks, including the lack of high-fidelity data and the stability problem in the coupling process of turbulence models and the Reynolds-averaged Navier–Stokes (RANS) solvers. In this paper, we propose an improved ensemble Kalman inversion method as a unified approach of data assimilation and turbulence modeling for separated flows at high Reynolds numbers. A novel ensemble design method based on transfer learning and a regularizing strategy are proposed to improve the method. The trainable parameters of DNN are optimized according to the given experimental surface pressure coefficients in the framework of mutual coupling between the RANS solvers and DNN eddy viscosity models. In this way, data assimilation and model training are integrated into one step to get the high-fidelity turbulence models agree well with experiments directly. The effectiveness of the method is verified by cases of flows around S809 airfoil at high Reynolds numbers. Through assimilation of few experimental states, we can get turbulence models generalizing well to both attached and separated flows at different angles of attack, which also perform well in stability and robustness. The errors of lift coefficients at high angles of attack are significantly reduced by more than three times compared with the traditional Spalart–Allmaras model.

Continuum Models for Bulk Viscosity and Relaxation in Polyatomic Gases

Bulk viscosity and acoustic wave propagation in polyatomic gases and their mixtures are studied in the frame of one-temperature and multi-temperature continuum models developed using the generalized Chapman–Enskog method. Governing equations and constitutive relations for both models are written, and the dispersion equations are derived. In the vibrationally nonequilibrium multi-component gas mixture, wave attenuation mechanisms include viscosity, thermal conductivity, bulk viscosity, diffusion, thermal diffusion, and vibrational relaxation; in the proposed approach these mechanisms are fully coupled contrarily to commonly used models based on the separation of classical Stokes–Kirchhoff attenuation and relaxation. Contributions of rotational and vibrational modes to the bulk viscosity coefficient are evaluated. In the one-temperature approach, artificial separation of rotational and vibrational modes causes great overestimation of bulk viscosity whereas using the effective internal energy relaxation time yields good agreement with experimental data and molecular-dynamic simulations. In the multi-temperature approach, the bulk viscosity is specified only by rotational modes. The developed two-temperature model provides excellent agreement of theoretical and experimental attenuation coefficients in polyatomic gases; both the location and the value of its maximum are predicted correctly. One-temperature dispersion relations do not reproduce the non-monotonic behavior of the attenuation coefficient; large bulk viscosity improves its accuracy only in the very limited frequency range. It is emphasized that implementing large bulk viscosity in the one-temperature Navier–Stokes–Fourier equations may lead to unphysical results.

Characterizing Experimental Monoclonal Antibody Interactions and Clustering Using a Coarse-Grained Simulation Library and a Viscosity Model.

Attractive protein-protein interactions in concentrated monoclonal antibody (mAb) solutions may lead to the formation of clusters that increase viscosity. Here, we propose an analytical model that relates mAb solution viscosity to clustering by accounting for the contributions of suboptimal mAb packing within a cluster and cluster fractal dimension. The influence of short-range, anisotropic attractions and long-range Coulombic repulsion on cluster properties is investigated by analyzing the cluster-size distributions, cluster fractal dimensions, radial distribution functions, and static structure factors from a library of coarse-grained molecular dynamics simulations. The library spans a vast range of mAb charges and attractive interactions in solutions of varying ionic strength. We present a framework for combining the viscosity model and simulation library to successfully characterize the attraction, repulsion, and clustering of an experimental mAb in three different pH and cosolute conditions by fitting the measured viscosity or structure factor from small-angle X-ray scattering. At low ionic strength, the cluster-size distribution is impacted by strong charges, and both the viscosity and net charge or structure factor and net charge must be considered to deconvolute the effects of short-range attraction and long-range repulsion.

Entropy Generation Optimization in Couple Stress Fluid Flow with Variable Viscosity and Aligned Magnetic Field

This study explores the influence of an inclined magnetic field and variable viscosity on the entropy generation in steady flow of a couple stress fluid in an inclined channel. The walls of the channel are stationary and non-isothermal. The fluid flow is driven due to pressure gradient and gravitational force. Reynold’s model for temperature-dependent viscosity was used. The dimensionless, non-linear coupled equations of momentum and energy was solved, and we obtained an analytical solution for the velocity and temperature fields. The entropy generation and Bejan number were evaluated. The variation of pertinent parameters on flow quantities was discussed graphically. The rate of volume flow, skin friction coefficient, and Nusselt number at the surfaces of the channel were calculated and their variations were discussed through surface graphs. From the results, it is noticed that the entropy generation rate can be minimized by increasing the magnetic field and the temperature difference parameters. The findings of the current study in some special cases are in precise agreement with the previous investigation.

Entropy analysis in single phase nanofluid in square enclosure under effectiveness of inclined magnetic field by executing finite element simulations

One of the important phenomena concerning with dynamical and thermal attributes of heat transferring liquids is the measure of randomness generated at molecular level. This randomness in the system is generated due to presence of multiple factors which includes extensive heat transfer, strong magnetic field and viscous diffusion. To control and minimize disorder ness the concept of entropy is taken into account. Minimization in entropy is valuable in optimization of design objectivity in engineering structures. In addition, it measures and controls exergy loss to generate highly efficient systems likes heat exchangers, heat pumps, power plants, nuclear reactors, air conditioners and so many. So, the prime objective of this work is to analyze entropy in square enclosure filled with water and containing Copper (Cu) particles. Convection in the considered physical domain is generated by providing uniform temperature at upper wall while keeping rest of boundaries cold. In view of velocity field distribution is concerned all the extremities of cavity are at no slip condition. Inclined magnetic field is also employed to measure change in entropy due to magnetization. Formulation of problem expressing thermophysical relation of inducted nanoparticles in manifested in the form dimensionless representation. Patel and Brinkman models for effective thermal conductivity and viscosity are opted to visualize their effectiveness in entropy change. Finite element computations are performed to simulate results showing the impact of flow controlling parameters on associated distributions. Three different types of entropy namely, magnetic, thermal and viscous are measured against different parameters. Quantity of interest like average heat flux coefficient is also measured. It is deduced from the analysis that by increasing nanoparticle volume fraction thermal entropy increases whereas viscous and magnetic entropy uplifts. In addition, it is noticed that by increasing Hartmann number entropy and velocity of fluid decrements. Uplift in temperature magnitude is observed against elevating values of Rayleigh number. It is concluded that employment of magnetic field will assist in controlling flow, thermal and irreversibility phenomenon.

Comparative analysis of the hydrothermal features of TiO2 water and ethylene glycol-based nanofluid transportation over a radially stretchable disk

This study focuses on the comprehensive analysis of the thermal characterization of an electrically conducting nanofluid flow containing TiO2 nanoparticles dissolved in two different common liquids (H2O and C2H6O2) and contained within a continuously extending disk. The heat and concentration formulation are modified by incorporating the standard interpretations of the Joule heating, energy source, dissipation, and modified activation energy with binary chemical reactions. The Darcy–Forchheimer addition is used to highlight the significance of porous media. The Brownian dispersion’s effects are clearly discernible in the current investigation. Furthermore, as a novelty, special thermophysical models of viscosity and thermal conductivity are included. The obtained differential formulations are tackled through the analytical procedure HAM. The veracity of the finding is verified through a numerical scheme (ND-solve technique). Using several graphs and charts, the perception trends of model factors are evaluated. The influence of and are estimated based on flow characteristics. The curve pattern for both nanofluids is declined by the increasing magnitude of and while thermal profile. The drag force and heat transmission rate are shown to optimize with as well as in the pictorial simulations produced from the existing model. The can also be strengthened by the presence of a high degree The outcomes are validated by previous studies and a good degree of consistency is revealed.

Evaluation of spraying technique for heat-reflective coating on asphalt pavements

ABSTRACT Heat-reflective coating (HRC) for pavement can effectively mitigate urban heat island (UHI) and reduce pavement distresses, whose cooling effect, service performance and prospective applications have been widely reported, but construction technology investigation is still unavailable. Following this situation, the spraying technology was evaluated with a typical epoxy resin-based HRC, the Dual-Arrhenius viscosity model for coating curing was established, and the factors affecting the curing time were examined by applying typical correlation analysis. Besides, adhesion and abrasion tests were taken to discuss the effect of spraying conditions on mechanical properties. Results show that the available spraying duration of HRC is exponentially negatively correlated with temperature and duration is insufficient for spraying above 323 K. Raising the diluent content could delay the optimal spraying time with a litter effect on the available spraying duration. Pavement temperature is the main factor affecting the curing time and a 20 K increase could decrease the curing time by up to 34%. Repeated spraying after setting the former layer is the best spraying method for HRC, which can effectively improve adhesion and anti-abrasion.

Empirical model for fitting the viscosity of lithium bromide solution with CuO nanoparticles and E414

To research viscosity fitting model of stable nano-lithium bromide solution (nano-LiBr), the stability of the nano-LiBr and the dynamic viscosity of LiBr were measued by Ultraviolet-visible spectroscopy (UV-vis) and rotational viscometer respectively. Two LiBr with different additives were measured, i.e., LiBr with dispersant (E414) and LiBr with dispersant + copper oxide nanoparticles (CuO). The ranges of measuring temperature were from 25°C–60°C, the concentrations of LiBr were from 50%–59%, the volume fractions of the dispersants were from 0%–4%, and the fractions of nanoparticle volume were from 0%–0.05%. Results indicated that the nano-LiBr with E414 had good stability. The viscosity of the LiBr decreased when temperature increased, and increased when LiBr concentration and dispersant amount were increased. It is also found that the viscosity was directly proportional to the volume fraction of the nanoparticles. This study also showed that the higher the concentration of the base fluid was, the more significant increase of the viscosity was. An empirical viscosity model of stable nano-LiBr with a maximum error of 13% was developed.

Flow Characteristics on Carotid Artery Bifurcation of Different Aneurysmal Morphology

Aneurysm is a vascular disorder characterized by abnormal focal dilation of an artery which is considered as a serious and potentially life-threatening condition. An estimated 2%–5% of the general population is affected by intracranial aneurysms. Through computational fluid dynamic (CFD) investigation, this study aims to learn the flow characteristic on aneurysm afflicted common carotid artery (CCA). This study focused on the velocity, wall shear stress (WSS) and sensitivity of blood viscosity of the CCA flow. CFD method was done to 3 simplified model of CCA which were normal, saccular aneurysm, fusiform aneurysm CCA model. The simulation was done with different blood viscosity model which were Newtonian and non-Newtonian. The high velocity area of blood flow has corresponding effect to the increase of the WSS distribution to the wall of the geometry. The results also showed at certain value of low velocity area, WSS distribution for different blood viscosity model was deviated significantly.

Dynamics features of biological rheology in mass species and thermal energy utilizing two viscosity models over heated disc

In this research, heat energy in the Synovial liquid model is discussed over a heated disk involving external heating source and thermal radiation incorporating two viscosity models with magneto-hydrodynamic inspiration. The concept of (BL) boundary layer is imposed to develop the conservation laws for Synovial liquid with thermal transportation in terms of coupled PDEs. The developed PDEs (partial differential equations) are highly nonlinear, complex and coupled, which are reduced into ODEs (ordinary differential equations) with the help of suitable transformation. The converted ODEs (ordinary equations) are solved numerically via finite element scheme (FES) and grid independent examination is established for three hundred elements. The involvement of numerous emerging parameters has been sketched for velocity and temperature fields. Moreover, heat and mass transfer coefficients are computed numerically and the impact of several involved parameters is discussed. The grid independent survey is established against three hundred elements, which is necessary for a convergent solution. Moreover, augmenting values of Weissenberg number retards the fluid velocity. Temperature field depreciates against escalating values of Prandtl number. Influences of parameters on friction among joints are studied which is utilized to investigate lubricant nature in fluids.

Influence of blood viscosity models and boundary conditions on the computation of hemodynamic parameters in cerebral aneurysms using computational fluid dynamics.

Computational fluid dynamics (CFD) is widely used to calculate hemodynamic parameters that are known to influence cerebral aneurysms. However, the boundary conditions for CFD are chosen without any specific criteria. Our objective is to establish the recommendations for setting the analysis conditions for CFD analysis of the cerebral aneurysm. The plug and the Womersley flow were the inlet boundary conditions, and zero and pulsatile pressures were the outlet boundary conditions. In addition, the difference in the assumption of viscosity was analyzed with respect to the flow rate. The CFD process used in our research was validated using particle image velocimetry experiment data from Tupin et al.'s work to ensure the accuracy of the simulations. It was confirmed that if the entrance length was sufficiently secured, the inlet and outlet boundary conditions did not affect the CFD results. In addition, it was observed that the difference in the hemodynamic parameter between Newtonian and non-Newtonian fluid decreased as the flow rate increased. Furthermore, it was confirmed that similar tendencies were evaluated when these recommendations were utilized in the patient-specific cerebral aneurysm models. These results may help conduct standardized CFD analyses regardless of the research group.

Thermodynamic study of triacetin or ethyl levulinate and alcohol binary mixtures

The subject of presented research is the study of liquid binary mixtures consisting of ester and alcohol combination; triacetin or ethyl levulinate, as one component, and ethanol or isobutanol, as second component. Thermodynamic and transport data were measured in the broad temperature range, T=(288.15/298.15–323.15/333.15) K, and at atmospheric pressure, p = 0.1 MPa. All corresponding excess and deviation properties were calculated including the excess molar volume, viscosity deviation, refractive index deviation, isentropic compressibility deviation and excess molar Gibbs energy of activation of viscous flow, and correlated with the basic Redlich-Kister equation. The analysis of possible chemical interactions and structural effects taking place in the mixtures was led in the terms of ester structure, complexity of ester molecule and chain length and branching of the alcohol molecule. The thermodynamic analyses showed change of the sign of the excess molar volume depending on the alcohol present in the mixture. FT-IR spectra were recorded for all four systems for the compositions where maximum excess molar volumes are obtained to support the conclusions derived from the mixtures’ nonideal behaviour. In addition, modelling of viscosity is performed with the two- and three-parameter correlative models, as well as excess molar volume correlation with the Peng-Robinson equation of state and three-parameter van der Waals mixing rule.

Impact of viscosity modeling on the simulation of aortic blood flow

Modeling issues for the simulation of blood flow in an aortic coarctation are studied in this paper. From the physical point of view, several viscosity models for non-Newtonian fluids as well as a Newtonian fluid model will be considered. From the numerical point of view, two different turbulence models are utilized in the simulations. The impact of both, the physical and the numerical modeling, on clinically relevant biomarkers is investigated and compared.

Processing properties and pyrolysis behavior of novolak-hexamethylenetetramine mixtures

This work investigates the influence of the hexamethylenetetramine (hexa) content on the rheological features, pyrolysis reactions, mass-loss kinetics and char yield for two phenolic resins, each with different novolak-hexa mixtures from 0 wt.-% to 15 wt.-%. Curing and viscosity of resins was studied using a rheometer. Pyrolysis, evolved gases and char yield were investigated with TG-FTIR-GC/MS. The obtained results show that the curing of resins shifts to lower temperatures whereas the viscosity and the residual carbon amount both increase with increasing hexa-contents. The presented data suggests a hexa-induced microgel formation for phenolic resins. Models for viscosity and char yield were developed and present a valuable tool to tailor the resin-hardener mixture for the desired application, enable a more efficient processing technology and contribute to a deeper understanding of the pyrolysis of novolak resins.