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Liquid densities and dynamic viscosities of 2-methyl-2-butanol + 1-alkanol (hexanol to nonanol) were measured at temperatures in the range of 293.15 K to 323.15 K and at pressure of 1 bar. PC-SAFT EoS was used to model the liquid density for pure fluids and binary mixtures composed of 1-alkanol. The advantage of using PC-SAFT EoS is the ability to consider hydrogen bonds between 1-alkanol molecules and between 1-alkanol molecules and 2-methyl-2-butanol molecules. On the other hand, Kendall–Munroe correlation (KMC) and the Nguyenhuynh et al. correlation (NC) were successfully applied to represent the experimental data of dynamic viscosity. Finally, the Redlich–Kister equation correctly correlated the properties of excess molar volume and deviation in dynamic viscosity.

Purpose The purpose of this study is to theoretically analyze the laminar free convection heat transfer of nanofluids in a square cavity. The sidewalls of the cavity are subject to temperature difference, whereas the bottom and top are insulated. Based on the available experimental results in the literature, two new non-dimensional parameters, namely, the thermal conductivity parameter (Nc) and dynamic viscosity parameter (Nv) are introduced. These parameters indicate the augmentation of the thermal conductivity and dynamic viscosity of the nanofluid by dispersing nanoparticles. Design/methodology/approach The governing equations are transformed into non-dimensional form using the thermo-physical properties of the base fluid. The obtained governing equations are solved numerically using the finite element method. The results are reported for the general non-dimensional form of the problem as well as case studies in the form of isotherms, streamlines and the graphs of the average Nusselt number. Using the concept of Nc and Nv, some criteria for convective enhancement of nanofluids are proposed. As practical cases, the effect of the size of nanoparticles, the shape of nanoparticles, the type of nanoparticles, the type of base fluids and working temperature on the enhancement of heat transfer are analyzed. Findings The results show that the increase of the magnitude of the Rayleigh number increases of the efficiency of using nanofluids. The type of nanoparticles and the type of the base fluid significantly affects the enhancement of using nanofluids. Some practical cases are found, in which utilizing nanoparticles in the base fluid results in deterioration of the heat transfer. The working temperature of the nanofluid is very crucial issue. The increase of the working temperature of the nanofluid decreases the convective heat transfer, which limits the capability of nanofluids in decreasing the size of the thermal systems. Originality/value In the present study, a separation line based on two non-dimensional parameters (i.e. Nc and Nv) are introduced. The separation line demonstrates a boundary between augmentation and deterioration of heat transfer by using nanoparticles. Indeed, by utilizing the separation lines, the convective enhancement of using nanofluid with a specified Nc and Nv can be simply estimated.

Deep rock engineering often operates in water-rich environments, resulting in the coupled effects of high water pressure, high stress, and cyclic dynamic loads on rock masses. Under these conditions, deep rock masses exhibit pronounced viscous behavior. The dynamic viscosity factor (DVF) is a significant parameter for characterizing the viscous behavior of rocks. Initially, cyclic impact tests are performed on rocks using a self-created test equipment. Subsequently, an analysis is conducted for the evolution of DVFs in relation to the cyclic impact time and dynamic deformation modulus. Test results show that DVFs decreases exponentially with increasingly cyclic impact times, which is abbreviated to as DVF deterioration. Axial static stresses are shown to promote the occurrence of static damage within the rock. This process is found to accelerate the deterioration rate of DVFs. water pressure has been shown to boost the rock’s resistance to impacts. The effect on DVFs is dominated by enhancement effects, and thus the deterioration rate of DVFs is slowed down. As cyclic impact times rise, the deformation properties of rocks are gradually changed from elastic to viscous behavior. The findings of this research provide valuable insights for addressing the deep rock engineering disasters, such as water inrush, in the water-rich environment. Highlights Cyclic dynamic loads promote the damage accumulation within rocks and enhance their viscous behavior in various water pressure conditions. As cyclic impact times increase, the deformation properties of rocks gradually shift from elastic to viscous behavior in various water pressure conditions. A rock sliding internal friction effect is proposed for elucidating the impact of water pressure on rock viscosity.

In this monograph, we review the theory and establish new and general results regarding spreading properties for heterogeneous reaction-diffusion equations: The characterizations of these sets involve two new notions of generalized principal eigenvalues for linear parabolic operators in unbounded domains. In particular, it allows us to show that

In recent times the development of nanotechnology has taken place at an unprecedented rate. Nano-fluids are one of the remarkable outcomes of the development of new technologies that can be used to increase the efficiency of thermal systems. Nanofluids, which consist of particles in nanometre size and a base fluid, have been hailed as a superior alternative compared to a common heat transfer fluid like water due to their better thermal properties and having many potential applications in many fields, especially in HVAC, electronic cooling, solar heating and cooling etc., The MWCNT-based nanofluid with water-ethylene glycol as base fluid is prepared by two-step method, the water and ethylene glycol are mixed in the ratio 80:20 and four different concentrations of nanofluids: 0% wt, 0.015% wt, 0.15% wt, 1.5% wt are prepared. Rheology analysis are made by using rheometer with temperature ranging from from 10° C to 50° C with steps of 10° C and shear rate was controlled with shear stress varying from 0-10 N/m 2 . The base fluid shows the Newtonian behaviour being shifted to Non-Newtonian Behaviour, specifically shear thinning behaviour. Rate of change of shear also changes with change in temperature and change in shear stress results change in viscosity with higher concentration of nanoparticles showing higher viscosity.

The addition of nanoparticles to a base fluid is one of the significant issues to enhance heat transfer. In this study, different nanofluids were developed by mixing a water base fluid with magnetic nanoparticles. Thermophysical properties such as thermal conductivity and viscosity of the obtained nanofluid were investigated. The effect of different nominal diameters of nanoparticles and concentrations of nanoparticles on the thermal conductivity and viscosity of nanofluids have been examined. Three different diameters of magnetic nanoparticles (about 37 nm, 71 nm, and 98 nm) have been tested in this experimental investigation. Experimental results indicate that thermal conductivity increases as volume fraction increases, and thermal conductivity of the nanofluid increases with a decrease of nanoparticle’s size. Moreover, the nanofluid dynamics viscosity ratio increases with an increase in particle concentration and nanoparticle’s diameter. This paper identifies several important issues that should be considered in future work.

Here we provide a theoretical framework revealing that the radius$R_{d}$of the top droplet ejected from a bursting bubble of radius$R_{b}$and$Bo\\leqslant 0.05$can be expressed as$R_{d}/R_{b}=K_{b}(1-(Oh/Oh_{c}^{\\prime })^{1/2})$for$Oh\\lesssim Oh_{c}^{\\prime }$or as$R_{d}\\approx 18\\,\\unicode[STIX]{x1D707}_{l}^{2}/(\\unicode[STIX]{x1D70C}_{l}\\unicode[STIX]{x1D70E})$for$Oh\\gtrsim Oh_{c}^{\\prime }$, with the numerically fitted constants$K_{b}\\approx 0.2$,$Oh_{c}^{\\prime }\\approx 0.03$,$Oh=\\unicode[STIX]{x1D707}_{l}/\\sqrt{\\unicode[STIX]{x1D70C}_{l}\\,R_{b}\\,\\unicode[STIX]{x1D70E}}\\ll 1$the Ohnesorge number,$Bo=\\unicode[STIX]{x1D70C}_{l}\\,g\\,R_{b}^{2}/\\unicode[STIX]{x1D70E}$the Bond number, and$\\unicode[STIX]{x1D70C}_{l}$,$\\unicode[STIX]{x1D707}_{l}$and$\\unicode[STIX]{x1D70E}$indicating the liquid density, dynamic viscosity and interfacial tension coefficient, respectively. These predictions, which do not only have solid theoretical roots but are also much more accurate than the usual 10 % rule used in the context of marine spray generation via whitecaps for$R_{b}\\lesssim 1$mm, agree very well with both experimental data and numerical simulations for the values of$Oh$and$Bo$investigated. Moreover, making use of a criterion which reveals the mechanism that controls the growth rate of capillary instabilities, we also explain here why no droplets are ejected from the tip of the fast Worthington jet for$Oh\\gtrsim 0.04$. In addition, our results predict the generation of submicron-sized aerosol particles with diameters below 100 nm and velocities${\\sim}\\unicode[STIX]{x1D70E}/\\unicode[STIX]{x1D707}_{l}$for bubble radii$10~\\unicode[STIX]{x03BC}\\text{m}\\lesssim R_{b}\\lesssim 20~\\unicode[STIX]{x03BC}\\text{m}$, within the range found in natural conditions and in good agreement with experiments – a fact suggesting that our study could be applied in the modelling of sea spray aerosol production.

The main purpose of the present paper is to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in predicting the thermophysical properties of Al2O3-MWCNT/thermal oil hybrid nanofluid through mixing using metaheuristic optimization techniques. A literature survey showed that the use of an artificial neural network (ANN) is the most widely used method, although there are other methods that showed better performance. Moreover, it was found in the literature that artificial intelligence methods have been widely used for predicting the thermal conductivity of nanofluids. Thus, in the present study, genetic algorithms (GAs) and particle swarm optimization (PSO) have been utilized to search and determine the antecedent and consequent parameters of the ANFIS model. Solid concentration and temperature were considered as input variables, and thermal conductivity, dynamic viscosity, heat transfer performance, and pumping power in both the internal laminar and turbulent flow regimes were the outputs. In order to evaluate and compare the performance of the models, two statistical indices of root mean square error (RMSE) and determination coefficient (R) were utilized. Based on the results, both of the models are able to predict the thermophysical properties appropriately. However, the ANFIS-PSO model had a better performance than the ANFIS-GA model. Finally, the studied thermophysical properties were developed by the trained ANFIS-PSO model.

The viscosity of primary and secondary organic aerosol (SOA) has important implications for the processing of aqueous organic aerosol phases in the atmosphere, their involvement in climate forcing, and transboundary pollution. Here we introduce a new thermodynamics-based group-contribution model, which is capable of accurately predicting the dynamic viscosity of a mixture over several orders of magnitude (∼10-3 to >1012 Pa s) as a function of temperature and mixture composition, accounting for the effect of relative humidity on aerosol water content. The mixture viscosity modelling framework builds on the thermodynamic activity coefficient model AIOMFAC (Aerosol Inorganic–Organic Mixtures Functional groups Activity Coefficients) for predictions of liquid mixture non-ideality, including liquid–liquid phase separation, and the calorimetric glass transition temperature model by DeRieux et al. (2018) for pure-component viscosity values of organic components. Comparing this new model with simplified modelling approaches reveals that the group-contribution method is the most accurate in predicting mixture viscosity, although accurate pure-component viscosity predictions (and associated experimental data) are key and one of the main sources of uncertainties in current models, including the model presented here. Nonetheless, we find excellent agreement between the viscosity predictions and measurements for systems in which mixture constituents have a molar mass below 350 g mol−1. As such, we demonstrate the validity of the model in quantifying mixture viscosity for aqueous binary mixtures (glycerol, citric acid, sucrose, and trehalose), aqueous multicomponent mixtures (citric acid plus sucrose and a mixture of nine dicarboxylic acids), and aqueous SOA surrogate mixtures derived from the oxidation of α-pinene, toluene, or isoprene. We also use the model to assess the expected change in SOA particle viscosity during idealized adiabatic air parcel transport from the surface to higher altitudes within the troposphere. This work demonstrates the capability and flexibility of our model in predicting the viscosity for organic mixtures of varying degrees of complexity and its applicability for modelling SOA viscosity over a wide range of temperatures and relative humidities.