Theoretical study on the effective thermal conductivity of silica aerogels based on a cross-aligned and cubic pore model

Aerogel nanoporous materials possess high porosity, high specific surface area, and extremely low density due to their unique nanoscale network structure. Moreover, their effective thermal conductivity is very low, making them a new type of lightweight and highly efficient nanoscale super-insulating material. However, prediction of their effective thermal conductivity is challenging due to their uneven pore size distribution. To investigate the internal heat transfer mechanism of aerogel nanoporous materials, this study constructed a cross-aligned and cubic pore model(CACPM) based on the actual pore arrangement of SiO_(2) aerogel. Based on the established CACPM, the effective thermal conductivity expression for the aerogel was derived by simultaneously considering gas-phase heat conduction, solid-phase heat conduction, and radiative heat transfer. The derived expression was then compared with available experimental data and the Wei structure model. The results indicate that, according to the model established in this study for the derived thermal conductivity formula of silica aerogel, for powdery silica aerogel under the conditions of T = 298 K, a_(2)= 0.85, D_(1)= 90 μm, ρ = 128 kg/m^(3), within the pressure range of 0–10^(5)Pa, the average deviation between the calculated values and experimental values is 10.51%. In the pressure range of 10^(3)–10^(4)Pa, the deviation between calculated values and experimental values is within 4%. Under these conditions, the model has certain reference value in engineering verification. This study also makes a certain contribution to the research of aerogel thermal conductivity heat transfer models and calculation formulae.

Investigation of radial heat conduction with 1D self-consistent model in helicon plasmas

A 1D radially self-consistent model in helicon plasmas has been established to investigate the influence of radial heat conduction on plasma transport and wave propagation.Two kinds of 1D radial fluid models,with and without considering heat conduction,have been developed to couple the 1D plasma-wave interaction model,and self-consistent solutions have been obtained.It is concluded that in the low magnetic field range the radial heat conduction plays a moderate role in the transport of helicon plasmas and the importance depends on the application of the helicon source.It influences the local energy balance leading to enhancement of the electron temperature in the bulk region and a decrease in plasma density.The power deposition in the plasma is mainly balanced by collisional processes and axial diffusion,whereas it is compensated by heat conduction in the bulk region and consumed near the boundary.The role of radial heat conduction in the large magnetic field regime becomes negligible and the two fluid models show consistency.The local power balance,especially near the wall,is improved when conductive heat is taken into account.

Parameters of new three-water model based on nuclear magnetic experiment and optimization algorithm

Clastic rock reservoir is the main reservoir type in the oil and gas field.Archie formula or various conductive models developed on the basis of Archie’s formula are usually used to interpret this kind of reservoir,and the three-water model is widely used as well.However,there are many parameters in the threewater model,and some of them are difficult to determine.Most of the determination methods are based on the statistics of large amount of experimental data.In this study,the authors determine the value of the parameters of the new three-water model based on the nuclear magnetic data and the genetic optimization algorithm.The relative error between the resistivity calculated based on these parameters and the resistivity measured experimentally at 100%water content is 0.9024.The method studied in this paper can be easily applied without much experimental data.It can provide reference for other regions to determine the parameters of the new three-water model.

A Prediction Model of Effective Thermal Conductivity for Metal Powder Bed in Additive Manufacturing

In current research,many researchers propose analytical expressions for calculating the packing structure of spherical particles such as DN Model,Compact Model and NLS criterion et al.However,there is still a question that has not been well explained yet.That is:What is the core factors affecting the thermal conductivity of particles?In this paper,based on the coupled discrete element-finite difference(DE-FD)method and spherical aluminum powder,the relationship between the parameters and the thermal conductivity of the powder(ETC_(p))is studied.It is found that the key factor that can described the change trend of ETC_(p) more accurately is not the materials of the powder but the average contact area between particles(a_(ave))which also have a close nonlinear relationship with the average particle size d_(50).Based on this results,the expression for calculating the ETC_(p) of the sphere metal powder is successfully reduced to only one main parameter d_(50)and an efficient calculation model is proposed which can applicate both in room and high temperature and the corresponding error is less than 20.9%in room temperature.Therefore,in this study,based on the core factors analyzation,a fast calculation model of ETC_(p) is proposed,which has a certain guiding significance in the field of thermal field simulation.

Hybrid graded element model for transient heat conduction in functionally graded materials

This paper presents a hybrid graded element model for the transient heat conduction problem in functionally graded materials （FGMs）. First, a Laplace transform approach is used to handle the time variable. Then, a fundamental solution in Laplace space for FGMs is constructed. Next, a hybrid graded element is formulated based on the obtained fundamental solution and a frame field. As a result, the graded properties of FGMs are naturally reflected by using the fundamental solution to interpolate the intra-element field. Further, Stefest＇s algorithm is employed to convert the results in Laplace space back into the time-space domain. Finally, the performance of the proposed method is assessed by several benchmark examples. The results demonstrate well the efficiency and accuracy of the proposed method.

Modeling and Dynamic Analysis of Fractional-Order Buck Converter in Continuous Conduction Mode

According to the fact that the actual inductor and actual capacitor are fractional, the mathematical and state-space averaging models of fractional order Buck converters in continuous conduction mode(CCM) are constructed by using fractional calculus theory. Firstly, the parameter conditions that ensure that the converter working in CCM is given and transfer functions are derived. Also, the inductor current and the output voltage are analyzed. Then the difference between the mathematical model and the circuit model are analyzed, and the effect of fractional order is studied by comparing the integer order with fractional order model. Finally, the dynamic behavior of the current-controlled Buck converter is investigated. Simulation experiments are achieved via the use of Matlab/Simulink. The experimental results verify the correctness of theoretical analysis, the order should be taken as a significant parameter. When the order is taken as a bifurcation parameter, the dynamic behavior of the converter will be affected and bifurcation points will be changed as order varies.

Information Conductivity:Universal Performance Measure for Semantic Communications

As a novel paradigm,semantic communication provides an effective solution for breaking through the future development dilemma of classical communication systems.However,it remains an unsolved problem of how to measure the information transmission capability for a given semantic communication method and subsequently compare it with the classical communication method.In this paper,we first present a review of the semantic communication system,including its system model and the two typical coding and transmission methods for its implementations.To address the unsolved issue of the information transmission capability measure for semantic communication methods,we propose a new universal performance measure called Information Conductivity.We provide the definition and the physical significance to state its effectiveness in representing the information transmission capabilities of the semantic communication systems and present elaborations including its measure methods,degrees of freedom,and progressive analysis.Experimental results in image transmission scenarios validate its practical applicability.

Fractal Prediction Model of Thermal Contact Conductance of Rough Surfaces

The thermal contact conductance problem is an important issue in studying the heat transfer of engineering surfaces, which has been widely studied since last few decades, and for predicting which many theoretical models have been established. However, the models which have been existed are lack of objectivity due to that they are mostly studied based on the statistical methodology characterization for rough surfaces and simple partition for the deformation formats of contact asperity. In this paper, a fractal prediction model is developed for the thermal contact conductance between two rough surfaces based on the rough surface being described by three-dimensional Weierstrass and Mandelbrot fractal function and assuming that there are three kinds of asperity deformation modes： elastic, elastoplastic and fully plastic. Influences of contact load and contact area as well as fractal parameters and material properties on the thermal contact conductance are investigated by using the presented model. The investigation results show that the thermal contact conductance increases with the increasing of the contact load and contact area. The larger the fractal dimension, or the smaller the fractal roughness, the larger the thermal contact conductance is. The thermal contact conductance increases with decreasing the ratio of Young＇s elastic modulus to the microhardness. The results obtained indicate that the proposed model can effectively predict the thermal contact conductance at the interface, which provide certain reference to the further study on the issue of heat transfer between contact surfaces.

Thermal conductivity model of filled polymer composites

Theoretical and empirical models for predicting the thermal conductivity of polymer composites were summarized since the 1920s.The effects of particle shape,filler amount,dispersion state of fillers,and interfacial thermal barrier on the thermal conductivity of filled polymer composites were investigated,and the agreement of experimental data with theoretical models in literatures was discussed.Silica with high thermal conductivity was chosen to mix with polyvinyl-acetate （EVA） copolymer to prepare SiO2/EVA co-films.Experimental data of the co-films＇ thermal conductivity were compared with some classical theoretical and empirical models.The results show that Agari＇s model,the mixed model,and the percolation model can predict well the thermal conductivity of SiO2/EVA co-films.

INTERACTION MODELS FOR EFFECTIVE THERMAL AND ELECTRIC CONDUCTIVITIES OF CARBON NANOTUBE COMPOSITES

The present article provides supplementary information of previous works of analytic models for predicting conductivity enhancements of carbon nanotube composites. The models, though fairly simple, are able to take account of the effects of conductivity anisotropy, nonstraightness, and aspect ratio of the CNT additives on the conductivity enhancement of the composite and to give predictions agreeing well with existing experimental data. The omitted detailed derivation of this model is demonstrated in the present article with a more systematical analysis, which may help with further development in this direction. Furthermore, the effects of various orientation distributions of CNTs are reported here for the first time. The information may be useful in design or fabrication technology of CNT composites for better or specified conductivities.

Temperature waves in chemical reaction-diffusion-heat conduction systems with two ends respectively subject to Dirichlet and no-flux conditions

Taking the Lindemann model as a sample system in which there exist chemical reactions, diffusion and heat conduction, we found the theoretical framework of linear stability analysis for a unidimensional nonhomogeneous two-variable system with one end subject to Dirichlet conditions, while the other end no-flux conditions. Furthermore, the conditions for the emergence of temperature waves are found out by the linear stability analysis and verified by a diagram for successive steps of evolution of spatial profile of temperature during a period that is plotted by numerical simulations on a computer. Without doubt, these results are in favor of the heat balance in chemical reactor designs.

A prediction model for the effective thermal conductivity of nanofluids considering agglomeration and the radial distribution function of nanoparticles

Conventional heat transfer fluids usually have low thermal conductivity, limiting their efficiency in many applications. Many experiments have shown that adding nanosize solid particles to conventional fluids can greatly enhance their thermal conductivity. To explain this anomalous phenomenon, many theoretical investigations have been conducted in recent years. Some of this research has indicated that the particle agglomeration effect that commonly occurs in nanofluids should play an important role in such enhancement of the thermal conductivity, while some have shown that the enhancement of the effective thermal conductivity might be accounted for by the structure of nanofluids, which can be described using the radial distribution function of particles. However, theoretical predictions from these studies are not in very good agreement with experimental results. This paper proposes a prediction model for the effective thermal conductivity of nanofluids, considering both the agglomeration effect and the radial distribution function of nanoparticles. The resulting theoretical predictions for several sets of nanofluids are highly consistent with experimental data.

A composite sphere assemblage model for porous oolitic rocks:Application to thermal conductivity

The present work is devoted to the determination of linear effective thermal conductivity of porous rocks characterized by an assemblage of grains(oolites) coated by a matrix. Two distinct classes of pores, i.e.micropores or intra oolitic pores(oolite porosity) and mesopores or inter oolitic pores(inter oolite porosity), are taken into account. The overall porosity is supposed to be connected and decomposed into oolite porosity and matrix porosity. Within the framework of Hashin composite sphere assemblage(CSA)models, a two-step homogenization method is developed. At the first homogenization step, pores are assembled into two layers by using self-consistent scheme(SCS). At the second step, the two porous layers constituting the oolites and the matrix are assembled by using generalized self-consistent scheme(GSCS) and referred to as three-phase model. Numerical results are presented for data representative of a porous oolitic limestone. It is shown that the influence of porosity on the overall thermal conductivity of such materials may be significant.

Forecasting conductivities of LiBOB-EC/DEC electrolytes by the mass triangle model

Conductivities of lithium bis（oxalato）borate （LiBOB）-ethyl carbonate （EC）/diethyl carbonaten （DEC） electrolytes at 25℃ and 50℃ were studied. The electrolyte component with the highest conductivity at each temperature was obtained through changing the concentration of LiBOB and the ratio of EC/DEC. The mass triangle model was applied to calculate the conductivity of Li- BOB-EC/DEC ternary system at 25℃ and 50℃. The results show that the calculated and experimental results have reached a good agreement. Therefore, it is expected that the experimental work can be vastly reduced by introducing the mass triangle model.

Quantitative models for microstructure and thermal conductivity of vermicular graphite cast iron cylinder block based on cooling rate

The relationships of cooling rate with microstructure and thermal conductivity of vermicular graphite cast iron(VGI) cylinder block were studied, which are important for design and optimization of the casting process of VGI cylinder blocks. Cooling rates at different positions in the cylinder block were calculated based on the cooling curves recorded with a solidification simulation software. The metallographic structure and thermal conductivity were observed and measured using optical microscopy(OM), scanning electrical microscopy(SEM) and laser flash diffusivity apparatus, respectively. The effects of the cooling rate on the vermicularity, total and average areas of all graphite particles, and the pearlite fraction in the VGI cylinder block were investigated. It is found that the vermicularity changes in parabola trend with the increase of cooling rate. The total area of graphite particles and the cooling rate at eutectoid stage can be used to predict pearlite fraction well. Moreover, it is found that the thermal conductivity at room temperature is determined by the average area of graphite particles and pearlite fraction when the range of vermicularity is from 80% to 93%. Finally, the quantitative models are established to calculate the vermicularity, pearlite fraction, and thermal conductivity of the VGI cylinder block.

Finite Element Modeling Of Low Heat Conducting Building Bricks

Heat conduction through conventional and interlocking building bricks with cavities was studied in this work. Heat transfer analysis was carried out using MATLAB? partial differential equation toolbox. Regular and staggered hole arrangements were studied. Results showed that four staggered holed interlocking bricks were effective in thermal resistance into the bricks and increasing the holes beyond four did not give any thermal resistance advantage. For the conventional bricks staggered holes did not give any thermal resistance advantage but the four-holed bricks were also adjudged to be effective in thermal resistance into the brick surface. Increasing the number of holes beyond four in conventional bricks did give some thermal resistivity advantage but very minimal. Structural strengths of holed bricks were not considered in this study.

Evaluation of ocular and cervical vestibular evoked myogenic potentials in a conductive hearing loss model

Objective: To investigate the effects of conductive hearing loss(CHL) on vestibular evoked myogenic potentials(VEMPs) using a simulated CHL model, and to provide the basis for future studies.Methods: Twenty-one healthy subjects were recruited in this study. We measured ocular VEMPs(o VEMPs) and cervical VEMPs(c VEMPs) in these subjects by air-conduction sound(ACS) stimulation. CHL was simulated later by blocking the right external auditory canal with a soundproof earplug to evaluate its impacts on VEMPs. Subjects' responses before simulated CHL served as the control, and were compared to their responses following simulated CHL.Results: o VEMPs following simulated CHL showed decreased response rate, elevated thresholds, attenuated amplitudes and prolonged N1 latencies compared with those before simulated CHL, and the differences were statistically significant. Similarly, c VEMPs following simulated CHL also showed decreased response rate, elevated thresholds and attenuated amplitudes, with prolonged P1 latencies compared with those before simulated CHL, although only differences in response rate, threshold and amplitude were significant.Conclusions: Conductive hearing loss affects the response rate and other response parameters in o VEMPs and c VEMPs.

Time fractional dual-phase-lag heat conduction equation

We build a fractional dual-phase-lag model and the corresponding bioheat transfer equation, which we use to interpret the experiment results for processed meat that have been explained by applying the hyperbolic conduction. Analytical solutions expressed by H-functions are obtained by using the Laplace and Fourier transforms method. The inverse fractional dual-phase-lag heat conduction problem for the simultaneous estimation of two relaxation times and orders of fractionality is solved by applying the nonlinear least-square method. The estimated model parameters are given. Finally, the measured and the calculated temperatures versus time are compared and discussed. Some numerical examples are also given and discussed.

Thermal conductivity modeling of water containing metal oxide nanoparticles

The nano particles have demonstrated great potential to improve the heat transfer characteristics of heat transfer fluids.Possible parameters responsible for this increase were studied. The heat transfer profile in the nanolayer region was combined with other parameters such as volume fraction, particle radius thermal conductivity of the fluid, particle and nanolayer, to formulate a thermal conductivity model. Results predicting the thermal conductivity of nanofluids using the model were compared with experimental results as well as studies by other researchers. The comparison of the results obtained for the Cu O/water and Ti O2/water nanofluids studied shows that the correlation proposed is in closest proximity in predicting the experimental results for the thermal conductivity of a nanofluid. Also, a parametric study was performed to understand how a number of factors affect the thermal conductivity of nanofluids using the developed correlation.

Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks

In this research work, the thermal conductivity and density of alumina/silica(Al_2O_3/SiO_2) in water hybrid nanofluids at different temperatures and volume concentrations have been modeled using the artificial neural networks(ANN). The nanocolloid involved in the study was synthesized by the two-step method and characterized by XRD, TEM, SEM–EDX and zeta potential analysis. The properties of the synthesized nanofluid were measured at various volume concentrations(0.05%, 0.1% and 0.2%) and temperatures(20 to 60 °C). Established on the observational data and ANN, the optimum neural structure was suggested for predicting the thermal conductivity and density of the hybrid nanofluid as a function of temperature and solid volume concentrations. The results indicate that a neural network with 2 hidden layers and 10 neurons have the lowest error and a highest fitting coefficient o thermal conductivity, whereas in the case of density, the structure with 1 hidden layer consisting of 4 neurons proved to be the optimal structure.