A new equation for the thermal conductivity of organic compounds

This work presents a wide literature survey of the available data of the experimental thermal conductivity data of organic liquids. The experimental data were collected for 136 compounds belonging to the following families: refrigerants, alkanes, alkenes, aromatics, cycloalkanes, cycloalkenes, ethers, esters, ketones, carboxylic acids, and alcohols. The experimental data were regressed with the most reliable semi-empirical correlating methods existing in the literature and a reliable set of 4,584 experimental data was finally selected. The influence of several physical parameters on the thermal conductivity calculation is discussed and a new equation to represent the thermal conductivity of organic liquids at atmospheric pressure for temperatures below normal boiling point and at saturation for temperatures above the normal boiling point is presented. To minimize the deviation between the predictions and the experimental data and to find the optimal coefficients for the proposed equation, a statistical analysis was performed. The resulting equation is simple and is able to predict the thermal conductivities with low deviations for the major part of the collected data for the studied families.     

Estimation of the thermal conductivity λ(T,P) of ionic liquids using a neural network optimized with genetic algorithms

In this study, an artificial neural network was optimized using a genetic algorithm in order to estimate the thermal conductivity of ionic liquids at different temperatures and pressures. Experimental thermal conductivity data of 41 ionic liquids (400 experimental data points) in the range from 0.10 to 0.22 W m⁻¹ K⁻¹ were used to obtain the proposed method for the temperature range of 273–390 K and the pressure range of 100–20,000 kPa. In addition, the molecular mass M and structure of molecules, represented by the number of well-defined groups forming the molecule, were provided as input parameters in order to characterize the different molecules of ionic liquids. A heterogeneous set of ionic liquids includes cations such as imidazolium, ammonium, phosphonium, pyrrolidinium, and pyridinium. It also includes anions such as halides, sulfonates, tosylates, imides, borates, phosphates, acetates, and amino acids. The whole dataset was divided into a training set with 300 experimental data points and a prediction set with 100 experimental data points. Several architectures were studied, and the optimum weights for the network were determined. The results showed that the proposed method to estimate the thermal conductivity of ionic liquids at different temperatures and pressures presented a good accuracy with lower deviations such as AARD less than 0.91% and R² of 0.9969 for the training set, and AARD less than 0.84% with R² of 0.9963 for the prediction set.     

Validation of intermolecular pair potential model of SiH4: molecular-dynamics simulation for saturated liquid density and thermal transport properties

We demonstrate a validation of the intermolecular pair potential model of SiH(4), which is constructed from ab initio molecular-orbital calculations and expressed as the sum of the exponential and the London dispersion terms. The saturated liquid densities of SiH(4) are calculated for temperatures from 100 to 225 K by molecular-dynamics (MD) simulation. The average deviation between the experiment and the MD simulation using the present potential model is 3.9%, while the deviations exceed 10% for other well-known potential models such as the five-center Lennard-Jones (LJ) model. Subsequently, the shear viscosity, the thermal conductivity, and the self-diffusion coefficient of liquid SiH(4) are calculated by an equilibrium MD simulation with the Green-Kubo formula from 100 to 225 K. The average deviations from experiment are 11.8% and 13.7% for the shear viscosity and the thermal conductivity, respectively. Comparing the present model with an empirical one-center LJ model, it turns out that the rotational energy transfer through the intermolecular potential energy, which comes from the anisotropic potential energy, plays an important role in the thermal conductivity of liquid SiH(4). These results indicate that the present intermolecular potential model has an ability to give realistic pictures for liquid SiH(4) through molecular simulations.     

Thermal Conductivity of Aqueous K2CO3 Solutions at High Temperatures

Thermal conductivities of five aqueous K₂CO₃ solutions of (5, 10, 15, 20, and 25) mass-% have been measured with a concentric-cylinder (steady state) technique. Measurements were made at pressures slightly above the vapor saturation curve and at temperatures from (293.15 to 573.15) K. The total uncertainties of the thermal conductivity, temperature, and concentration measurements were estimated to be less than 2%, 30 mK, and 0.02%, respectively. A maximum in the thermal conductivity was found around 413 K. The measured values of thermal conductivity were compared with data reported in the literature and with values calculated from various prediction techniques. New correlation and prediction equations for the thermal conductivity of solutions studied here were obtained from the experimental data as a function of temperature and composition. The average absolute deviation (AAD) between the measured and predicted values of the thermal conductivity is 0.17%.     

Neural network-based correlations for the thermal conductivity of propane

An alternative approach, exploiting neural networks, is proposed to develop thermal conductivity correlation of propane for the first time. In order to test the accuracy of the proposed technique and demonstrate its utility in fitting the thermal conductivity surface of propane, we have established a thermal conductivity correlation in terms of temperature and density, and then compared its predictions with those obtained by the conventional method. The results obtained are so impressive that the neural network correlation has lower overall average absolute deviations (AADs) in each data set.     

Molecular dynamics simulations of self-diffusion coefficient and thermal conductivity of methane at low and moderate densities

This article demonstrates a highly accurate molecular dynamics (MD) simulation of thermal conductivity of methane using an ab initio intermolecular potential. The quantum effects of the vibrational contribution to thermal conductivity are more efficiently accounted for in the present MD model by an analytical correction term as compared to by the Monte Carlo method. The average deviations between the calculated thermal conductivity and the experimental data are 0.92% for dilute methane and 1.29% for methane at moderate densities, as compared to approximately 20% or more in existing MD calculations. The results demonstrate the importance of considering vibrational contribution to the thermal conductivity which is mainly through the self-diffusion process.     

Calculation of the viscosity of binary liquids at various temperatures using Jouyban-Acree model

Applicability of the Jouyban-Acree model for calculating absolute viscosity of binary liquid mixtures with respect to temperature and mixture composition is proposed. The correlation ability of the model is evaluated by employing viscosity data of 143 various aqueous and non-aqueous liquid mixtures at various temperatures collected from the literature. The results show that the model is able to correlate the data with an overall percentage deviation (PD) of 1.9+/-2.5%. In order to test the prediction capability of the model, three experimental viscosities from the highest and lowest temperatures along with the viscosities of neat liquids at all temperatures have been employed to train the model, then the viscosity values at other mixture compositions and temperatures were predicted and the overall PD obtained is 2.6+/-4.0%.     

Determination of nanolayer thickness and effective thermal conductivity of nanofluids

Nanofluids having high thermal conductivity enhancement relative to conventional pure fluids are fluids engineered by suspending solid nanoparticles into base fluids. In the present study, calculating the Van der Waals interaction energy between a nanoparticle and an ordered liquid nanolayer around it, the nanolayer thickness was determined, the average velocity of the Brownian motion of nanoparticles in a fluid was estimated, and by taking into account both the aggregation of nanoparticles and the presence of a nanolayer a new thermal conductivity model for nanofluids was proposed. It has been shown that the nanolayer thickness in nanofluids is independent on the radius of nanoparticles when the radius of the nanoparticles is much greater than the nanolayer thickness and determines by the specific interaction of the given liquid and solid nanoparticle through the Hamaker constant, the surface tension and the wetting angle. It was proved that the frequency of heat exchange by fluid molecules is two orders of magnitude higher than the frequency of heat transfer by nanoparticles, so that the contribution due to the Brownian motion of nanoparticles in the thermal conductivity of nanofluids can be neglected. The predictions of the proposed model of thermal conductivity were compared with the experimental data and a good correlation was achieved.     

Computer-controlled measurement of thermal conductivities of aqueous salt solutions

A computer-controlled method to measure liquid thermal conductivities is described, and data are presented for aqueous electrolyte solutions. The relative thermal conductivities of sodium chloride and sodium iodide solutions agree well with previously published results. The effect of temperature on the thermal conductivity was investigated, and it was found that in the range 23–67°C the relative thermal conductivity was invariant with temperature within the experimental error (less than 1%). For a given concentration of 1-1 electrolyte, the relative thermal conductivity was found to vary linearly with the molecular weight of the solute.     

色谱法同时测定羰化反应尾气中的O2、CO、CO2

提出用特制活性炭填充柱分离,热导检测器检测,以外标法定量,同时测定O2、CO、CO2.探讨了该方法的校正因子、各组分的线性相关性及微量氧气的分离和定量等.在选定的色谱条件下,O2与CO的分离度达1.7,各组分的相对标准偏差＜0.18%,绝对误差＜0.04%.     

A comparative study in the prediction of thermal conductivity enhancement of nanofluids using ANN-MLP,ANN-RBF,ANFIS, and GMDH methods

In this work, four types of data mining methods, namely adaptive neuro-fuzzy inference system, artificial neural network—multilayer perceptron algorithm (ANN-MLP), artificial neural network—radial basis function algorithm (ANN-RBF), and group method of data handling (GMDH) have been used to predict the enhancement of the relative thermal conductivity of a wide range of nanofluids with different base fluids and nanoparticles. The total number of experimental data used in this work is 483 from 18 different nanofluids. The input parameters are thermal conductivity of base fluid and nanoparticles, volume fraction percent, the average size of nanoparticles, and temperature. Although the results showed that all four models are in relatively good agreement with experimental data, the ANFIS method is the best. The average absolute relative deviations (AARD%) between the experimental data and those of obtained using ANFIS, ANN-MLP, ANN-RBF, and GMDH methods were calculated as 2.7, 2.8, 4.2, and 4.3, respectively, for the test sets and as 1.1, 2.4, 3.9, and 4.5, respectively, for the training sets. Comparison between the predictions of the proposed ANN-MLP, ANN-RBF, ANFIS, and GMDH models and those predicted by traditional models, namely Maxwell and Bruggeman models showed that much better agreements can be obtained using the four models especially ANFIS model. Accordingly, the ANFIS method can able us to predict the relative thermal conductivity of new nanofluids in different conditions with good accuracy.

     

Molecular dynamics simulations of the thermal conductivity of methane hydrate

Nonequilibrium molecular dynamics simulations with the nonpolarizable SPC/E (Berendsen et al., J. Phys. Chem. 1987, 91, 6269) and the polarizable COS/G2 (Yu and van Gunsteren, J. Chem. Phys. 2004, 121, 9549) force fields have been employed to calculate the thermal conductivity and other associated properties of methane hydrate over a temperature range from 30 to 260 K. The calculated results are compared to experimental data over this same range. The values of the thermal conductivity calculated with the COS/G2 model are closer to the experimental values than are those calculated with the nonpolarizable SPC/E model. The calculations match the temperature trend in the experimental data at temperatures below 50 K; however, they exhibit a slight decrease in thermal conductivity at higher temperatures in comparison to an opposite trend in the experimental data. The calculated thermal conductivity values are found to be relatively insensitive to the occupancy of the cages except at low (T相似文献   

Modelling investigation on the thermal conductivity of pure liquid,vapour, and supercritical refrigerants and their mixtures by using Heyen EOS

This work presents a literature survey of the available data regarding the thermal conductivity of refrigerants. About 31 pure refrigerants that contain 7127 data points are selected for the temperature range of 91.35–580.00 K, a pressure range of (0.000111-500) bar, and thermal conductivity range of (0.007–0.27) W m^?1 K^?1 containing liquid, vapour, and supercritical phases. Seven binary and three ternary mixtures are also collected both in liquid and vapour phases with an overall of 803 data points. Based on the similarity between the pressure-volume-temperature and Tλ (thermal conductivity) P diagrams, the thermal conductivity model based on Heyen equation of state has been developed for pure refrigerants and their mixtures. The genetic algorithm is used to determine the adjustable parameters of the model. The calculation results prove that this proposed model can reproduce and predict thermal conductivity of refrigerants with good accuracy (overall AAD = 6.85% for pure compounds, AAD = 6.14% for binary mixtures and AAD = 9.32% for ternary mixtures).     

Influence of Temperature on Liquid–Liquid Equilibrium of Methanol + Toluene + Hexane Ternary Systems at Atmospheric Pressure

Phase equilibria of methanol?+?toluene?+?hexane ternary systems at (278.15, 283.15, 288.15 and 293.15) K at atmospheric pressure were investigated. The influence of temperature on the liquid–liquid equilibrium is discussed. All chemicals were quantified using gas chromatograph with a thermal conductivity detector coupled to a ChemStation and nitrogen as gas carrier, their mass fractions were higher than 0.999. From literature are found two articles from the same system at different temperatures studied here. Experimental data are compared with literature values. Values calculated using the NRTL and UNIQUAC equations are compared with the experimental data and it is found that the UNIQUAC equation fitted the experimental data better than the NRTL model for this ternary system.     

Prediction of the transport properties of a polyatomic gas

An ab initio molecular potential model is employed in this paper to show its excellent predictability for the transport properties of a polyatomic gas from molecular dynamics simulations. A quantum mechanical treatment of molecular vibrational energies is included in the Green and Kubo integral formulas for the calculation of the thermal conductivity by the Metropolis Monte Carlo method. Using CO₂ gas as an example, the fluid transport properties in the temperature range of 300–1000 K are calculated without using any experimental data. The accuracy of the calculated transport properties is significantly improved by the present model, especially for the thermal conductivity. The average deviations of the calculated results from the experimental data for self-diffusion coefficient, shear viscosity, thermal conductivity are, respectively, 2.32%, 0.71% and 2.30%.     

Volumetric and viscosity properties of {propyl propanoate (1) + heptane (or octane) (2)} mixtures at several temperatures and correlation with the Jouyban–Acree model

Densities and viscosities of binary liquid mixtures of propyl propanoate + heptane and propyl propanoate + octane at temperatures of 278.15, 283.15, 288.15, 293.15, 298.15, 303.15, 308.15, 313.15, 318.15 and 323.15 K have been measured at atmospheric pressure over the entire range of composition. Using these experimental data, the excess molar volumes and the viscosity deviation have been calculated. The experimental data of density and viscosity at different investigated temperatures were mathematically represented by the Jouyban–Acree model. The mean relative deviation (MRD) was used as an error criterion, and the MRD values for data correlation of density and viscosity at different investigated temperatures are less than 0.03% and 0.50%, respectively. Excess molar volumes and viscosity deviations were correlated with Redlich–Kister equation. The calculated data point out the absence of speci?c interactions between the molecules of different components, which would be slightly weaker compared to the interactions in the pure components.     

Hard-wall potential function for transport properties of alkali metal vapors

This study demonstrates that the transport properties of alkali metals are determined principally by the repulsive wall of the pair interaction potential function. The (hard-wall) Lennard-Jones (LJ) (15-6) effective pair potential function is used to calculate the transport collision integrals. Accordingly, reduced collision integrals of K, Rb, and Cs metal vapors are obtained from the Chapman-Enskog solution of the Boltzmann equation. The law of corresponding states based on the experimental transport reduced collision integral is used to verify the validity of a LJ(15-6) hybrid potential in describing the transport properties. LJ(8.5-4) potential function and a simple thermodynamic argument with the input PVT data of liquid metals provide the required molecular potential parameters. Values of the predicted viscosity of monatomic alkali metal vapor are in agreement with typical experimental data with average absolute deviations of 2.97% for K in the range of 700-1500 K, 1.69% for Rb, and 1.75% for Cs in the range of 700-2000 K. In the same way, the values of predicted thermal conductivity are in agreement with experiment within 2.78%, 3.25%, and 3.63% for K, Rb, and Cs, respectively. The LJ(15-6) hybrid potential with a hard-wall repulsion character conclusively predicts the best transport properties of the three alkali metal vapors.     

A model of nanofluids effective thermal conductivity based on dimensionless groups

Thermal conductivity is an important parameter in the field of nanofluid heat transfer. This article presents a novel model for the prediction of the effective thermal conductivity of nanofluids based on dimensionless groups. The model expresses the thermal conductivity of a nanofluid as a function of the thermal conductivity of the solid and liquid, their volume fractions, particle size and interfacial shell properties. According to this model, thermal conductivity changes nonlinearly with nanoparticle loading. The results are in good agreement with the experimental data of alumina-water and alumina-ethylene glycol based nanofluids.