Fluid radiation effects in the transient hot-wire technique

The transient hot-wire technique is widely used for absolute measurements of the thermal conductivity of fluids. Refinement of this method has resulted in a capability for accurate and simultaneous measurement of both thermal conductivity and thermal diffusivity together with a determination of the specific heat. However, these measurements, especially those for the thermal diffusivity, may be significantly influenced by fluid radiation. The present work investigates the effect of fluid radiation on the measurements of the thermal conductivity of propane. Recently developed corrections have been used to examine this assumption and rectify the influence of even weak fluid radiation. Measurements at 372 K with a hot-wire instrument demonstrate the presence of radiation effects in both the liquid and vapor phase. The influence is much more pronounced in liquid propane at 15.5 MPa than in the vapor phase at 881.5 kPa. The technique employed to obtain radiation-free thermal conductivity measurements is described.     

Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al<Subscript>2</Subscript>O<Subscript>3</Subscript> nanoparticles by correlation and ANN methods using experimental data

In the present paper, the effects of temperature and volume fraction on thermal conductivity of SWCNT–Al₂O₃/EG hybrid nanofluid are investigated. Single-walled carbon nanotube with outer diameter of 1–2 nm and aluminum oxide nanoparticles with mean diameter of 20 nm with the ratio of 30 and 70%, respectively, were dispersed in the base fluid. The measurements were conducted on samples with volume fractions of 0.04, 0.08, 0.15, 0.3, 0.5, 0.8, 1.5 and 2.5. In order to investigate the effects of temperature on thermal conductivity of the nanofluid, this characteristic was measured in five different temperatures of 30, 35, 40, 45 and 50 °C. The results indicate that enhancement of nanoparticles’ thickness in low volume fractions and at any temperature causes a considerable increment in thermal conductivity of the nanofluid. In this study, the highest enhancement of thermal conductivity was 41.2% which was achieved at the temperature of 50 °C and volume fraction of 2.5%. Based on the experimental data, an experimental correlation and a neural network are presented and for thermal conductivity of the nanofluid in terms of volume fraction and temperature. Comparing outputs of the experimental correlation and the designed artificial neural network with experimental data, the maximum error values for the experimental correlation and the artificial neural network were, respectively, 2.6 and 1.94% which indicate the excellent accuracy of both methods in prediction of thermal conductivity.     

Thermal conductivity equations for pure fluids in a heuristic extended corresponding states framework: I. Modeling techniques

The application of the extended corresponding states modeling technique to thermodynamic and transport properties has demonstrated that different conformality behaviors are followed by a same group of fluids inside each of these two categories of properties. The traditional extended corresponding states technique for transport properties requires the conventional thermodynamic shape factors, derived from accurate equations of state for both the target and the reference fluid, and an additional shape factor, derived from transport property data of the target fluid, in order to fit the transport properties themselves.A new extended corresponding states model is here proposed for thermal conductivity; the technique uses two shape factors, one for each independent variable, which are generated just from the available thermal conductivity data in the range of interest. As a consequence there is no need to import shape factors from thermodynamics.The shape factors are obtained as continuous functions through a neural network, because of its flexibility and high capability to fit the data. The accuracy of thermal conductivity data representation on the basis of this model is better than that achieved through conventional approach by summation of dilute gas, excess and critical enhancement contributions.     

A hybrid sol–gel synthesis of mesostructured SiC with tunable porosity and its application as a support for propane oxidative dehydrogenation

Porous silicon carbide (SiC) is of great potential as catalyst support in several industrially important reactions because of its unique thermophysical characteristics. Previously porous SiC was mostly obtained by a simple sol–gel or reactive replica technique which can only produce a material with low or medium surface area (< 50 m2 g(?1)). Here we report a new hybrid sol–gel approach to synthesize mesostructured SiC with high surface area (151–345 m2 g(?1)) and tunable porosity. The synthesis route involves a facile co-condensation of TEOS and alkyloxysilane with different alkyl-chain lengths followed by carbothermal reduction of the as-prepared alkyloxysilane precursors at 1350 °C. The resulting materials were investigated by X-ray diffraction, N2 adsorption-desorption, transmission electron microscopy, scanning electron microscopy, and X-ray photoelectron spectroscopy. A mechanism for the tailored synthesis of mesostructured SiC was tentatively proposed. To demonstrate the catalytic application of these materials, vanadia were loaded on the mesostructured SiC supports, and their catalytic performance in oxidative dehydrogenation of propane was evaluated. Vanadia supported on the mesostructured silicon carbide exhibits higher selectivity to propylene than those on conventional supports such as Al2O3 and SiO2 at the same propane conversion levels, mainly owing to its outstanding thermal conductivity which makes contributions to dissipate the heat generated from reaction thus alleviating the hot spots effect and over-oxidation of propylene.     

Statistical mechanical theory for steady-state systems. III. Heat flow in a Lennard-Jones fluid

A statistical mechanical theory for heat flow is developed based upon the second entropy for dynamical transitions between energy moment macrostates. The thermal conductivity, as obtained from a Green-Kubo integral of a time correlation function, is derived as an approximation from these more fundamental theories, and its short-time dependence is explored. A new expression for the thermal conductivity is derived and shown to converge to its asymptotic value faster than the traditional Green-Kubo expression. An ansatz for the steady-state probability distribution for heat flow down an imposed thermal gradient is tested with simulations of a Lennard-Jones fluid. It is found to be accurate in the high-density regime at not too short times, but not more generally. The probability distribution is implemented in Monte Carlo simulations, and a method for extracting the thermal conductivity is given.     

FeVO4催化剂的原位电导研究

采用盐类固体研磨法制备了FeVO4催化剂,用原位电导方法测定了FeVO4催化剂在氧气 丙烷→氧气→丙烷连续变化气氛下的电导变化,确定其导电类型.以BET、XRD、H2-TPR等技术对催化剂进行表征,研究了其对丙烷氧化脱氢制丙烯反应的催化性能.     

Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network

The application of nanofluids in energy systems is developing day by day. Before using a nanofluid in an energy system, it is necessary to measure the properties of nanofluids. In this paper, first the results of experiments on the thermal conductivity of MgO/ethylene glycol (EG) nanofluids in a temperature range of 25–55 °C and volume concentrations up to 5 % are presented. Different sizes of MgO nanoparticles are selected to disperse in EG, including 20, 40, 50, and 60 nm. Based on the results, an empirical correlation is presented as a function of temperature, volume fraction, and nanoparticle size. Next, the model of thermal conductivity enhancement in terms of volume fraction, particle size, and temperature was developed via neural network based on the measured data. It is observed that neural network can be used as a powerful tool to predict the thermal conductivity of nanofluids.     

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.

     

Forecasting of water thermal conductivity enhancement by adding nano-sized alumina particles

Operating fluids play an important role in heat transfer equipment. Water is inexpensive popular operating fluid with extensive applications, but its thermophysical properties are not good enough, especially for high temperature processes. Therefore, modification of its inherent characteristics by adding nano-sized solid particles found high popularities. Thermal conductivity is one of the most important thermophysical properties of an operating fluid in relatively all energy-based processes. Variation of thermal conductivity of nanofluids with different operating conditions is required to be understood in such processes. Therefore, the focus of this study is concentrated on modeling of thermal conductivity of water-alumina nanofluids using four different smart paradigms. Multilayer perceptron, radial basis function, cascade feedforward, and generalized regression neural networks are employed for the considered task. The best structure of these paradigms is determined, and then, their accuracies are compared using different statistical indices. Accuracy analyses confirmed that the generalized regression neural network outperforms other considered smart methodologies. It predicted more than 280 experimental datasets with excellent absolute average relative deviation?=?0.71%, mean square error?=?0.0006, root mean square error?=?0.023 and regression coefficient (R²)?=?0.9675. In the final stage, the proposed paradigm is used for investigation of the effect of influential parameters on the thermal conductivity of water-alumina nanofluids. This type of accurate and straightforward paradigm can broaden our insight about thermal behavior of homogeneous suspension of nano-size alumina particles in water.

     

Estimation of thermal conductivity of Na–K liquid metal alloy

For the first time the thermal conductivity of Na–K alloy has been computed using eight theoretical equations, already developed for binary liquid mixtures. The thermal conductivity values of Na–K alloy were obtained at three different temperatures (308,348,423) K, and at four different compositions of alloy. We have employed density and sound speed data for calculating λ alloy with the help of recently developed correlation. Density-Sound speed-thermal conductivity correlation gave excellent results.     

Thermal conductivity of binary liquid mixtures

A semi-empirical method is developed for the prediction of the thermal conductivity of binary liquid mixtures. The proposed method is tested by calculating the thermal conductivity of twelve binary liquid mixtures and an excellent agreement between the observed and calculated values is obtained.     

Cross-property interaction between stiffness,damage and thermal conductivity in particulate nanocomposite

A nanocomposite made from epoxy and nano silica particles was subjected to compressive fatigue loading and the resulting interaction between stiffness, damage and thermal conductivity investigated. First, the thermal conductivity (K) and the elastic modulus (E) of the as-fabricated materials were measured prior to any fatigue loading. Then, the samples were subjected to cyclic loading, and the thermal conductivity and the modulus of elasticity of the specimens were measured after every 5 to 10 thousand cycle intervals until a significant change in the response of the material was observed. In addition, a semi-analytical model is proposed to quantify damage in the material by taking the modulus of elasticity and thermal conductivity data obtained from the experiment. Finally, the cross-property relation between the modulus of elasticity, the thermal conductivity and the damage density in the material at any state of the fatigue cycle is investigated.     

Formation of M2+(O2)(C3H8) species in alkaline-earth-exchanged Y zeolite during propane selective oxidation

The adsorption of oxygen and d2-propane (CH3CD2CH3) on a series of alkaline-earth-exchanged Y zeolite at room temperature was studied with in situ infrared spectroscopy. Surprisingly at room temperature, oxygen adsorption led to the formation of supercage M2+(O2) species. Further, at low propane coverage, propane was found to adsorb linearly on Mg2+ cations, but a ring-adsorption structure was observed for propane adsorbing on Ca2+, Sr2+, and Ba2+ cations. It is demonstrated that O2 and propane can simultaneously attach to one active center (M2+) to form a M2+(O2)(C3H8) species, which is proposed to be the precursor in thermal propane selective oxidation. Selectivity to acetone in the propane oxidation reaction decreases with increasing temperature and cation size due to the formation of 2-propanol and carboxylate ions. An extended reaction scheme for the selective oxidation of propane over alkaline earth exchanged Y zeolites is proposed.     

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%.     

A novel radial basis Bayesian regularization deep neural network for the Maxwell nanofluid applied on the Buongiorno model

The aim of this work is to provide the numerical solutions of the fluid model by using the stochastic computing paradigms. The linear/exponential stretching sheets on magneto-rotating flow based on the Maxwell nanofluid have been provided using the Buongiorno model with the impacts of uneven heat source/sink, varying thermal conductivity and reactive species. The solutions of this transformed ordinary differential exponential stretching sheet model have been presented using a novel ‘radial basis’ (RB) activation function together with the Bayesian regularization deep neural network (BRDNN), i.e., RB-BRDNN. The deep neural network is presented into two hidden layers, while thirteen and twenty-five numbers of neurons have been used in the first and second layer. A reference dataset is proposed using the Runge-Kutta scheme for the model. The correctness of the stochastic RB-BRDNN procedure is examined through the comparison of proposed and database results, whereas minimal absolute error values provide the accuracy of the scheme. The reliability and competence of the computing RB-BRDNN solver is authenticated using the state transitions, correlation, regression, and error histograms.     

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.     

Experimental investigation and modeling of thermal conductivity of CuO–water/EG nanofluid by FFBP-ANN and multiple regressions

The purpose of this study is to predict the thermal conductivity of copper oxide (CuO) nanofluid by using feed forward backpropagation artificial neural network (FFBP-ANN). Thermal conductivity of CuO nanofluid is measured experimentally using transient hot-wire technique in temperature range of 20–60 °C and in volume fractions of 0.00125, 0.0025, 0.005 and 0.01% for neural network training and modeling. In addition, in order to evaluate accuracy of modeling in predicting the coefficient of nanofluid thermal conductivity, indices of root-mean-square error, coefficient of determination (R ²) and mean absolute percentage error have been used. FFBP-ANN with two input parameters (volume fraction and nanofluid temperature) and one output parameter (nanofluid thermal conductivity) in addition to two hidden layers and one outer layer which purelin, logsig and tansig functions are used was considered as the most optimum structure for modeling with neuron number of 4–10–1. In this study, among common methods of theoretical modeling of nanofluid thermal conductivity, theoretical method of Maxwell and also multivariate linear regression model was used for explaining the importance of modeling and predicting the results using neural network. According to this research, the results of indices and predictions show high accuracy and certainty of ANN modeling in comparison with empirical results and theoretical models.     

New poly(ether-imide)/MWCNT nanocomposite

New polyimide (PI) nanocomposites containing two different amounts of MWCNT (PI/MWCNT) were prepared via in situ polymerization technique. Transmission electron microscopy showed that MWCNT was exfoliated in the polymer matrix, resulting in well-dispersed morphologies at 1 and 3 mass% MWCNT contents. The effects of multiwalled carbon nanotubes (MWCNT) on the thermal and flammability properties of new PI derived from 1,3-bis[4,4′-aminophenoxy]propane and biphenyl dianhydride were investigated by thermogravimetric analysis (TG) in nitrogen and air atmosphere, differential scanning calorimetry, and microscale combustion calorimeter (MCC). The PI/MWCNT nanocomposites were electrically conductive with maximum conductivity obtained at 3 mass% MWCNT, which is favorable for many potential applications. TG results showed that the addition of MWCNT resulted in a substantial increase of the thermal stability and char yields of the nanocomposites compared to those of the neat PI. Flame retardancy of the nanocomposites was significantly improved in the presence of MWCNT.     

BiMo基复合氧化物催化剂丙烷直接氧化至丙烯醛研究

The effects of the available zoon above the catalyst bed on the performance of the catalyst were investigated. It has been suggested that propylene is an intermediate species in the reaction of propane to acrolein， and a two-step reaction scheme is proposed， the first step is oxidative dehydrogenation of propane to propylene in the gas phase then followed by the second step， the selective oxidation of propylene to acrolein on the surface of the catalyst. The performance of the catalyst depends on both the oxidative dehydrogenation of propane to propylene in the gas phase and the selective oxidation of propylene to acrolein on the catalyst surface. The thermal cracking， homogeneous oxidative dehydrogenation and heterogeneous catalytic dehydrogenation of propane as well as the selective catalytic oxidation of propane to acrolein over BiMoO based mixed oxides catalysts were studied. Under the optimum reaction conditions of propane dehydrogenation and selective oxidation of propylene， the selectivity and the yield of acrolein approached to 45mol% and 14mol%， respectively under about 30mol% propane conversion.