Properties Of Pure Fluids Research Articles

Chemically Radiative and Slip Effects on MHD Heat and Mass Transfer Flow of Maxwell Nanofluid across an Inclined Surface

In both the laboratory and the manufacturing setting, nanofluids play a crucial role in improving the thermal properties of pure fluids. The heat and mass transmission of an incompressible upper-convected Maxwell fluid along a stretched sheet in the midst of heat radiation, chemical changes, and suction is investigated. The nonlinear slip condition for the Maxwell fluid is put into account herein. The diffusion model for mass and heat transfer introduced by Cattaneo and Christov is incorporated in the modelling process. The partial differential equations that regulate the system are reduced into a more fundamental form via similarity transformations. Mathematica's NDSolve technique is implemented to do a numerical treatment of the dimensionless equations once they have been translated. The upsides of this strategy lie in its ability to automatically track errors and select the best algorithm. Assessments are taken of velocity, temperature, concentration, skin friction, Nusselt number, and Sherwood number, among others. Graphs are used to quickly illustrate and clarify how various material qualities affect the flow of mass and heat transmission. Thermal relaxation and chemical reaction parameters minimize temperature and concentration fields. It is observed that greater Maxwell parameters implies a rise in the intensity of skin friction, whereas a spike in the slip parameter generates a decrease. Nusselt and Sherwood numbers diminish with Maxwell parameter.

Thermodynamic and Thermophysical Properties of Air as a Mixture by Using Schreiber-Pitzer EoS

Air is one of the most important substances used in industrial and technological applications. Applications of air require a consistent set of reliable data on its thermodynamic and thermophysical properties. Over the last few decades, researchers have developed a number of empirical and theoretical models for the correlation and prediction of the thermodynamic and thermophysical properties of pure fluids and mixtures. The ideal gas Equation of State (EoS) is used in gas thermodynamic property calculations extensively, but in applications with higher pressure zones, the error levels are increasing. For most applications, an equation of state with better accuracy of thermodynamic properties will be required for extreme cases. In this study, Schreiber-Pitzer EoS is considered for better accuracy of the thermodynamic properties for air mixture. A set of computer programs were developed in java language to calculate the thermodynamic and thermophysical properties of air as a mixture of Nitrogen, Oxygen and Argon. The Schreiber-Pitzer EoS results are compared with Peng-Robinson EoS, Redlich-Kwong EoS, Van der Waals EoS, and ideal gas EoS utilizing cubic spline curve fitting for cp values. The thermodynamic and thermophysical property results and percentages of differences are calculated. The percentages of differences are increasing with increasing pressure and decreasing temperature.

Further Results on a Generalized van der Waals Model

A generalized van der waals model is considered to study the thermodynamic properties of pure fluids. Analytical solution of the equivalent cubic equation of state is presented and the critical properties in the general form are derived. The fluctuations of number of particles are calculated in the grand canonical ensemble by using three quantities (scaled variance omega (N), skewness Ssigma, and kurtosis ksigma ^2). The critical behavior of these quantities is investigated in terms of the dimensionless particle number density and temperature for different models. It is found that the fluctuations have a singular behavior close to the critical point.

Predictions of thermodynamic properties of pure fluids, refrigerants, and binary mixtures using modified Peng-Robinson equation of state

Predictions of thermodynamic properties of pure fluids, refrigerants, and binary mixtures using modified Peng-Robinson equation of state

A versatile interferometric technique for probing the thermophysical properties of complex fluids

Laser-induced thermocapillary deformation of liquid surfaces has emerged as a promising tool to precisely characterize the thermophysical properties of pure fluids. However, challenges arise for nanofluid (NF) and soft bio-fluid systems where the direct interaction of the laser generates an intriguing interplay between heating, momentum, and scattering forces which can even damage soft biofluids. Here, we report a versatile, pump-probe-based, rapid, and non-contact interferometric technique that resolves interface dynamics of complex fluids with the precision of ~1 nm in thick-film and 150 pm in thin-film regimes below the thermal limit without the use of lock-in or modulated beams. We characterize the thermophysical properties of complex NF in three exclusively different types of configurations. First, when the NF is heated from the bottom through an opaque substrate, we demonstrate that our methodology permits the measurement of thermophysical properties (viscosity, surface tension, and diffusivity) of complex NF and biofluids. Second, in a top illumination configuration, we show a precise characterization of NF by quantitively isolating the competing forces, taking advantage of the different time scales of these forces. Third, we show the measurement of NF confined in a metal cavity, in which the transient thermoelastic deformation of the metal surface provides the properties of the NF as well as thermo-mechanical properties of the metal. Our results reveal how the dissipative nature of the heatwave allows us to investigate thick-film dynamics in the thin-film regime, thereby suggesting a general approach for precision measurements of complex NFs, biofluids, and optofluidic devices.

Modeling of Darcy–Forchheimer bioconvective Powell Eyring nanofluid with artificial neural network

Nano-engineering has recently grown to include the usages of nanoparticles in combination with base fluids to improve the thermal properties of pure fluids. Today’s industry focuses primarily on thermal machine efficiency, and nanomaterials are the key to achieving this goal. The slip and Darcy–Forchheimer phenomena are studied for bioconvective implementations in a Powell–Eyring nanofluid model confined by a stretching surface via artificial neural network in current study. During the research, the activation energy, convective boundary condition and thermal radiation phenomena are considered as novel impacts. The governing expressions are formulated according to fundamental rules. Numerical simulations using a Runge–Kutta fourth order technique via shooting procedure are used to obtain the solution and then applies artificial neural network. A data set has been created for various flow scenarios, and developed an artificial neural network model to predict skin friction coefficient, local Sherwood number, local motile density of microorganisms and local Nusselt number values . The results of the study showed that the developed artificial neural network models can make predictions with very low error, not exceeding 0.53% on average.

A Comparative Study between Two Methods for Calculating the Thermodynamic Properties of Pure Fluids in an Energy System

A Comparative Study between Two Methods for Calculating the Thermodynamic Properties of Pure Fluids in an Energy System

Extension of SAFT equation of state for fluids confined in nano-pores of sedimentary rocks using molecular dynamic simulation

Confined fluids in nano-mineral pores show notably different thermodynamic behavior compared with those of the same fluid in bulk. We have extended an equation of state (EoS) inspired by statistical association fluid theory (SAFT) to incorporate the fluid-mineral effect into the pure fluid properties confined in the nanopore. The established EoS is coupled with a Helmholtz free energy contribution which is a function of confinement characteristics: the potential of fluid-mineral interaction, bulk-like density and CF. The novel fitting parameter CF relies on the local properties of confined fluid and measures the density fraction within the layering and bulk-like zones, i.e., CF=ρlayeringzone/ρbulk-likezone. The first-principle molecular dynamics (MD) simulation was utilized to estimate the confinement parameters. At T = 300 K, 350 K, 400 K, and 450 K, the NVT ensemble was applied to simulate slit-pore calcite with a continuous and homogeneous surface in contact with water, with pore widths ranging from 1.5 nm to 5 nm. After running each NVT ensemble for 6 ns, the energy and density of confined water were analyzed, and a deviation was discovered with different behavior from what had previously been reported. Coupling CF parameter with proposed EoS improves the absolute relative errors (ARE) at low temperatures up about 50%, leading to a good agreement between modified SAFT EoS and MD results. The methodology described here may be extended to include a mixture of associating fluids such as those found in oil composition and implemented in all SAFT versions.

On the thermodynamic consistency of non-random hydrogen bonding lattice-fluid model for multicomponent mixtures

Compressible lattice-fluid Equation of State (EoS) models have been widely used to interpret phase equilibria and related properties of pure fluids and their mixtures, including polymer solutions. One of the first models of this family has been proposed by Sanchez and Lacombe (SL), providing useful expressions for EoS and chemical potentials for both pure components and mixtures. The assessment of the thermodynamic consistency of the expressions provided by these models for the chemical potential of each component in a mixture is a relevant issue, to which has seldom been devoted the attention it deserves. In fact, it has been demonstrated in the literature that the equations provided by the SL theory for the equilibrium chemical potential of a component in a mixture exhibits a thermodynamic inconsistency emerging from the adopted mixing rules for the close packed volume. Consequently, these expressions do not converge to the proper form in the limit of ideal gas mixtures. Later, the Non-Random Hydrogen Bonding (NRHB) lattice-fluid model was introduced to overcome some limitations of the SL model, by accounting for possible presence of strong specific intermolecular interactions, such as hydrogen bonding, as well as for non-random distribution of intermolecular contacts. In the present work, using a thermodynamic framework endowed with internal state variables, it is shown that the expressions of the chemical potential provided by the NRHB model for a multicomponent mixture at equilibrium, converge consistently to the correct form in the limit of ideal gas mixtures, thus overcoming the inconsistency implicit in the most common corresponding formulations of the SL model. We demonstrate that this feature is essentially related to the assumption of a constant value of the segmental volume, v*, that takes the same ‘universal’ value (9.75/NA cm3/molecular segment, where NA is the Avogadro number) for all pure fluids as well as for their mixtures. In addition, we have re-examined also the inconsistency issue of the SL model proving that this model recovers the thermodynamic consistency if it is assumed again a constant value of v* or if a particular type of mixing rule is assumed for v*.

Modeling the thermal conductivity of hydrofluorocarbons, hydrofluoroolefins and their binary mixtures using residual entropy scaling and cubic-plus-association equation of state

Thermal conductivity strongly impacts heat transfer, and thus is an important thermophysical property for refrigeration and medium-low-temperature heat utilization systems. In this work, the residual entropy scaling incorporating cubic-plus-association equation of state, as a convenient and robust modeling approach for the transport properties of pure and mixture fluids of which the experimental data are scarce or unavailable, is extended to the thermal conductivity of hydrofluorocarbons, hydrofluoroolefins, and their binary mixtures. For all the investigated pure and mixture fluids, the dependence of the thermal conductivity on the thermodynamic state is reduced to a ‘universal’ univariate function of the rescaled residual entropy with one adjustable parameter for each pure fluid and no further adjustable parameter for mixtures. A new formulation of the reference thermal conductivity is proposed to improve the accuracy for the binary mixtures. The model reproduces the thermal conductivity of the investigated pure and mixture fluids with the root mean square deviation of 2.9% in gas, liquid, and supercritical regions. The lack or uneven distribution of the data is overcome based on the residual entropy scaling with the extensive data of R134a as a reference.

New Molecular-Mechanics Model for Simulations of Hydrogen Fluoride in Chemistry and Biology.

Hydrogen fluoride (HF) is the most polar diatomic molecule and one of the simplest molecules capable of hydrogen-bonding. HF deviates from ideality both in the gas phase and in solution and is thus of great interest from a fundamental standpoint. Pure and aqueous HF solutions are broadly used in chemical and industrial processes, despite their high toxicity. HF is a stable species also in some biological conditions, because it does not readily dissociate in water unlike other hydrogen halides; yet, little is known about how HF interacts with biomolecules. Here, we set out to develop a molecular-mechanics model to enable computer simulations of HF in chemical and biological applications. This model is based on a comprehensive high-level ab initio quantum chemical investigation of the structure and energetics of the HF monomer and dimer; (HF)n clusters, for n = 3-7; various clusters of HF and H2O; and complexes of HF with analogs of all 20 amino acids and of several commonly occurring lipids, both neutral and ionized. This systematic analysis explains the unique properties of this molecule: for example, that interacting HF molecules favor nonlinear geometries despite being diatomic and that HF is a strong H-bond donor but a poor acceptor. The ab initio data also enables us to calibrate a three-site molecular-mechanics model, with which we investigate the structure and thermodynamic properties of gaseous, liquid, and supercritical HF in a wide range of temperatures and pressures; the solvation structure of HF in water and of H2O in liquid HF; and the free diffusion of HF across a lipid bilayer, a key process underlying the high cytotoxicity of HF. Despite its inherent simplifications, the model presented significantly improves upon previous efforts to capture the properties of pure and aqueous HF fluids by molecular-mechanics methods and to our knowledge constitutes the first parameter set calibrated for biomolecular simulations.

Modeling the viscosity of hydrofluorocarbons, hydrofluoroolefins and their binary mixtures using residual entropy scaling and cubic-plus-association equation of state

Abstract Thermal conductivity strongly impacts heat transfer, and thus is an important thermophysical property for refrigeration and medium-low-temperature heat utilization systems. In this work, the residual entropy scaling incorporating cubic-plus-association equation of state, as a convenient and robust modeling approach for the transport properties of pure and mixture fluids of which the experimental data are scarce or unavailable, is extended to the thermal conductivity of hydrofluorocarbons, hydrofluoroolefins, and their binary mixtures. For all the investigated pure and mixture fluids, the dependence of the thermal conductivity on the thermodynamic state is reduced to a ‘universal’ univariate function of the rescaled residual entropy with one adjustable parameter for each pure fluid and no further adjustable parameter for mixtures. A new formulation of the reference thermal conductivity is proposed to improve the accuracy for the binary mixtures. The model reproduces the thermal conductivity of the investigated pure and mixture fluids with the root mean square deviation of 2.9% in gas, liquid, and supercritical regions. The lack or uneven distribution of the data is overcome based on the residual entropy scaling with the extensive data of R134a as a reference.

A Guide to Computing Interfacial Properties of Fluids from Molecular Simulations [Article v1.0

Molecular simulation is ideally suited to explore and describe the behavior of inhomogeneous fluid mixtures as it allows a unique perspective into the physics at the scale relevant to interfacial properties, filling the gaps between experimental determinations and theoretical predictions. Although rather common, Molecular dynamics and Monte Carlo schemes are employed, the technical implementation and the post-processing of the results are more challenging than for homogeneous fluids due to the spatial dependence of the interfacial properties. This work is devoted to describing and recommending methods for molecular-simulation computation of the most important interfacial properties of pure fluids and fluid mixtures, i.e., interfacial concentration of species, the interfacial thickness, the surface activity or adsorption of species, superficial enthalpy and entropy, wetting between phases and the interfacial or surface tension on planar interfaces. A detailed description is given of the steps required to perform classical molecular simulations including setting up of the initial configuration, choice of cell dimensions, thermodynamic conditions and ensemble, selection of the force field, simulation length, etc. and a discussion of the required post-processing steps in order to obtain interfacial properties. Additionally, general background and description of the expected results of interfacial fluid properties are provided, and step-by-step examples are included for the case of interfacial properties of pure fluids (carbon dioxide, decane) and mixtures (carbon dioxide + decane).

Modeling the Critical and Phase Equilibrium Properties of Pure Fluids and Mixtures with the Crossover Cubic-Plus-Association Equation of State

The cubic-plus-association (CPA) equation of state (EoS) is unable to correctly describe the vapor–liquid critical behavior of pure fluids and mixtures because of its classical behavior. In fact, the traditional parametrization procedure (i.e., matching the saturated pressure and liquid density curves far from the critical point) causes an overprediction of the critical pressures and temperatures of pure components. Besides, the deviations with respect to experimental data are even larger for systems containing hydrogen-bonding species, in comparison to systems composed of nonassociating molecules. To improve the representation of the thermodynamic properties of fluids in near-critical regions with CPA, we have applied White’s recursive procedure to introduce density fluctuations in the classical model, allowing the correct description of the nonanalytical behavior of real fluids close to the critical point. The resulting model (i.e., the crossover CPA (CCPA) EoS) is capable of accurately representing the phase behavior of fluids, specifically normal alkanes and alcohols, carbon dioxide, and water as well as some of their binary mixtures, far away from and close to the critical region. This is shown by the comparison of the results obtained from CCPA and classical EoS with the experimental data.

Predicting the Coefficients of Antoine Equation Using the Artificial Neural Network (TECHNICAL NOTE)

Neural network is one of the new soft computing methods commonly used for prediction of the thermodynamic properties of pure fluids and mixtures. In this study, we have used this soft computing method to predict the coefficients of the Antoine vapor pressure equation. Three transfer functions of tan-sigmoid (tansig), log-sigmoid (logsig), and linear were used to evaluate the performance of different transfer functions to redict the coefficients of the Antoine vapor pressure equation. The critical pressure, critical temperature, critical volume, molecular weight, and acentric factor were considered as the input variables and the Antoine equation coefficients showed by the symbols A, B, and C were considered as the output variables. The results of this study indicated that the linear transfer function had a better performance than other transfer functions and the topology of 5-6-3 with Levenberg–Marquardt learning algorithm and linear transfer function had the best performance for prediction of these coefficients.

Machine learning for predicting thermodynamic properties of pure fluids and their mixtures

Establishing a reliable equation of state for largely non-ideal or multi-component liquid systems is challenging because the complex effects of molecular configurations and/or interactions on the thermodynamic properties must generally be taken into account. In this regard, machine learning holds great potential for directly learning the thermodynamic mappings from existing data, thereby bypassing the use of equations of state. The present study outlines a general machine learning framework based on high-efficiency support vector regression for predicting the thermodynamic properties of pure fluids and their mixtures. The proposed framework is adopted in conjunction with training data obtained from a high-fidelity database to successfully predict the thermodynamic properties of three common pure fluids. The predictions demonstrate extremely low mean square errors. Moreover, little loss in the prediction accuracy is obtained for ternary mixtures of the pure fluids at the cost of a modest increase in the volume of training data provided by state-of-the-art molecular dynamics simulations. Our results demonstrate the promising potential of machine learning for building accurate thermodynamic mappings of pure fluids and their mixtures. The proposed methodology may pave the way in the future for the rapid exploration of novel or complex systems with potentially exceptional thermodynamic properties.

A five-parameter cubic equation of state for pure fluids and mixtures

A five parameter cubic equation of state, named 5PGDN is presented for the prediction of thermodynamic properties of pure fluids and mixtures. A new alpha function is proposed with two compound specific parameters which are estimated by matching experimental saturation vapor pressure data at two fixed temperatures Tr=0.5,0.7. We have cast three of the five parameters in terms of the remaining two parameters. The 5PGDN uses the critical temperature, the critical pressure, the critical compressibility factor, and two parameters of the alpha function to calculate thermodynamic properties such as saturated vapor pressure, liquid and vapor density, enthalpy of vaporization, sound velocity, heat capacity at constant pressure and volume. The 5PGDN is further used for the prediction of bubble point pressure and vapor mole fraction of several binary mixtures. The accuracy of 5PGDN is demonstrated by comparing results with the Patel-Teja (PT), Haghtalab-Kamali-Mazloumi-Mahmoodi (HKM1) and Ghoderao-Dalvi-Narayan (4PGDN1) equations of state.

Classical harmonic model for the behavior of pure fluids at the critical point.

We present a simple model to explain the limiting behavior of many thermodynamic properties of pure classical fluids as one approaches the critical point. The model consists of four main aspects: (i) a focus on the thermodynamic properties as described by Fluctuation Solution Theory (FST); (ii) the removal of all intramolecular energy contributions from the FST expressions; (iii) particle fluctuations (and not the energy fluctuations) dominate the thermodynamics close to the critical point; and (iv) the equipartition theorem applies to the resulting low frequency collective modes of the fluid at the critical point. Using this approach, we predict and compare the thermodynamic results for 121 pure fluids as provided by accurate equations of state. The model explains why some ratios of diverging thermodynamic properties remain finite at the critical point, and reliably predicts the value of these ratios for equations of state that mimic both mean field and Ising system types, suggesting universal behavior.

A four-parameter cubic equation of state for pure compounds and mixtures

A four parameter cubic equation of state, GDN-CEOS, is presented to describe thermodynamic properties of pure fluids and mixtures. We have cast three of the four parameters in terms of the remaining parameter and all the parameters are temperature independent. A new alpha function is proposed in the attractive term of the CEOS; which requires two compound-specific parameters determined from saturation vapor pressure data at two reduced temperature points Tr=0.5 and 0.7. Hence, the GDN CEOS has five inputs per substance: the critical temperature (Tc), the critical pressure (Pc), the critical compressibility factor (Zc) and two compound specific parameters (m, n) of the alpha function. The saturated vapor pressure and liquid density of 334 pure compounds, representing a large variety of functional groups, are predicted successfully. Other thermodynamic properties such as isobaric and isochoric heat capacities, sound velocity, compressed liquid density and enthalpy of vaporization have been calculated using GDN CEOS with remarkably good accuracy. The GDN CEOS is further applied to the prediction of bubble pressure and vapor mole fraction of several binary mixtures using the van der Waals one fluid mixing rules. The accuracy of GDN CEOS is demonstrated by comparing results with three well-known equations of state: Haghtalab-Kamali-Mazloumi-Mahmoodi (HKM1), Modified Peng Robinson (MPR) and Patel-Teja (PT) cubic equations of state.

Accurate non-asymptotic thermodynamic properties of near-critical N2 and O2 computed from molecular dynamics simulations

Four thermodynamic properties of pure N2 and O2 fluids were calculated over a continuous temperature range in the near-critical region employing molecular dynamics combined with the multistate Bennett acceptance ratio (MBAR) technique. The non-asymptotic critical anomalies of the isothermal compressibility, the thermal expansion coefficient and the heat capacities at constant pressure and constant volume were determined in the 0.80Tc–1.20Tc temperature range along the 1.00Pc and 1.15Pc isobars. A single-site united-atom model and a two-site rigid rotor force fields were used in the simulations. The results show that effective force fields developed to describe accurately the behavior of liquids and dense gases in general can be used to obtain near-critical anomalies of the thermodynamic properties. Furthermore, the computed values are in very good agreement with NIST data when available. The results fill existing gaps of the NIST database in the near-critical vicinity of the critical temperature along the critical isobar, especially for the heat capacities. The MBAR technique was crucial to describe the continuous and sharp peak of each property as functions of the temperature. The results indicated that even with a simple force field such as the united-atom one-site approach, very good results could be obtained for the non-asymptotic critical anomalies of thermodynamic properties without using renormalization group techniques. The computation of accurate results was possible due to the availability of precise critical constants especially for the simpler one-site force field. The inclusion of the anisotropy in the force field does not necessarily improve the computed thermodynamic properties in the near critical region.