Estimation Of Thermal Conductivity Research Articles

Equilibrium Molecular Dynamics (MD) Simulation Study of Thermal Conductivity of Graphene Nanoribbon: A Comparative Study on MD Potentials

The thermal conductivity of graphene nanoribbons (GNRs) has been investigated using equilibrium molecular dynamics (EMD) simulation based on Green-Kubo (GK) method to compare two interatomic potentials namely optimized Tersoff and 2nd generation Reactive Empirical Bond Order (REBO). Our comparative study includes the estimation of thermal conductivity as a function of temperature, length and width of GNR for both the potentials. The thermal conductivity of graphene nanoribbon decreases with the increase of temperature. Quantum correction has been introduced for thermal conductivity as a function of temperature to include quantum effect below Debye temperature. Our results show that for temperatures up to Debye temperature, thermal conductivity increases, attains its peak and then falls off monotonically. Thermal conductivity is found to decrease with the increasing length for optimized Tersoff potential. However, thermal conductivity has been reported to increase with length using 2nd generation REBO potential for the GNRs of same size. Thermal conductivity, for the specified range of width, demonstrates an increasing trend with the increase of width for both the concerned potentials. In comparison with 2nd generation REBO potential, optimized Tersoff potential demonstrates a better modeling of thermal conductivity as well as provides a more appropriate description of phonon thermal transport in graphene nanoribbon. Such comparative study would provide a good insight for the optimization of the thermal conductivity of graphene nanoribbons under diverse conditions.

Sasol Performance Chemicals expands catalyst carrier production capacity

Aluminum foam based structured catalysts were prepared and tested for the CO water gas shift reaction, and the influence of the textural properties of the carriers, over the performances, were evaluated. An estimation of the kinetic parameters, by applying the Langmuir–Hinshelwood model, was also provided. Three aluminum foams with different porosity were used in the preparation of the catalysts; the carriers were previously washcoated with an alumina-based slurry, subsequently, the support and the active component, were loaded by impregnation with an aqueous solution of the precursor salts. The good stability of the structured catalysts to the mechanical stress, was demonstrated with the results of the ultrasonic adhesion test. The results of the activity tests showed no direct correlation between the porosity of the carriers and the performance of the catalysts, suggesting the occurrence of other phenomena. The estimation of the effective thermal conductivity of the three aluminum foams indicated a clear correlation between CO conversion and thermal conductivity of the carriers, highlighting the effect of the redistribution of the generated heat of reaction along the catalytic bed, on the performance of the catalyst. However, the direct dependence of the thermal conductivity of the foams from the thickness of the intersection between the fibers, was recently demonstrated, confirming the dependence of the performance of these catalysts from the textural properties of the carriers.

Simulation of Effective Thermal Conductivity of Metal Hydride Packed Beds

Several mathematical models are available for estimation of effective thermal conductivity of nonreactive packed beds. Keeping in view the salient differences between metal hydride beds in which chemisorption of hydrogen takes place and conventional nonreactive packed beds, modified models are proposed here to predict the effective thermal conductivity. Variation in properties such as solid thermal conductivity and porosity during hydrogen absorption and desorption processes are incorporated. These extended models have been applied to simulate the effective thermal conductivity of the MmNi4.5Al0.5 hydride bed and are compared with the experimental results. Applicability of the extended models for estimation of the effective thermal conductivity at different operating conditions such as pressure, temperature, and hydrogen concentration is discussed.

A Study on Uncertainties in Estimations of Thermal Conductivity of Alumina Nanofluids

An innovative way of improving the thermal conductivities of fluids is to suspend small solid particles in the fluids. Various types of powders such as metallic, non-metallic and polymeric particles can be added into fluids to form slurries. The thermal conductivities of fluids with suspended particles are expected to be higher than that of common fluids. Application of nanoparticles provides an effective way of improving heat transfer characteristics of fluids. By suspending nanophase particles in heating or cooling fluids, the heat transfer performance of the fluid can be significantly improved. Moreover, the thermal conductivity of nanofluid is strongly dependent on the nanoparticle volume fraction. So far it has been an unsolved problem to develop a sophisticated theory to predict thermal conductivity of nanofluids, although there are some semi empirical correlations to calculate the apparent conductivity of two-phase mixture. In this article few correlations were considered and differences were noted between different theories. In conclusion, a lot of uncertainties in determining thermal conductivity were noticed.

Structure and Thermal Conductivity of Air Flow in a Ventilated HPP Building Facade

Thermogravitational air flow in the gaps of a ventilated facade is divided into four parts. In the case of tall buildings, the most elongated section-consequently called the main section-in which longitudinal motion is uniform is selected. For this section, comparative estimates of thermal conductivity are obtained crosswise to the direction of air flow under both laminar and turbulent conditions. It is noted that for stable laminar flow, thermotechnical calculations may consider the thermally insulating properties of the air flow in the vents of the ventilated facade. The possibility that turbulence may occur intermittently (over time and in space) in air flow through the vent is noted.

Estimation of thermal conductivity, heat transfer coefficient, and heat flux using a three dimensional inverse analysis

This paper presents a numerical inverse analysis to estimate the thermal conductivity, the heat transfer coefficient, and the heat flux in three dimensional irregular bodies in steady state heat conduction problems. In this study, a 3-D elliptic grid generation technique is used to mesh the irregular body. The 3-D Laplace equation is solved in the computational domain to compute the temperature at any grid point in the meshed body. A novel and very efficient sensitivity analysis scheme is introduced to compute the sensitivity coefficients in gradient based optimization method. Using this sensitivity analysis scheme, one can solve the inverse problem without need to the solution of adjoint equation. The main advantages of the sensitivity analysis scheme are its simplicity, accuracy, and independency of the number of the direct problem solution from the number of the unknown variables which makes the numerical inverse analysis presented here very accurate and efficient. The conjugate gradient method (CGM) is used to minimize the objective function which is the difference between the computed temperature on part of the boundary and the measured temperature. The obtained results confirm that the proposed algorithm is very accurate, robust, and efficient.

Thermal conductivity correlations of open-cell foams: Extension of Hashin–Shtrikman model and introduction of effective solid phase tortuosity

Open-cell foams have emerged as promising materials for use in heat sink and heat exchanger applications. The thermal behavior of open-cell foams depends on their microscopic structure. The effective thermal conductivity of open-cell porous foams can be measured using experimental techniques, predicted from the 3-D direct numerical simulations on reconstructed foam structures obtained from micro-computed tomography images or derived from idealized structure thermal analysis. Based on the tetrakaidecahedron unit cell and different strut morphologies, three dependent and interdependent empirical correlations for effective thermal conductivity were derived. They encompass all morphological parameters and ratios of constituent phases of foams of different materials.In this process, the Hashin–Shtrikman (HS) bounds model was first extended and applied to the resistor model. A correlation term, Ω was introduced to take into account the thermal conductivities of constituent phases and the morphological parameters of the foam structure. Secondly, a more complex effective model that is a combination of series and parallel models was derived by introducing effective solid phase tortuosity. Lastly, a simple model (KT-model) was derived that can be used to predict either effective thermal conductivity or intrinsic solid phase conductivity depending upon which one of these quantities is known. The present study clearly demonstrates that the proposed empirical correlations yield extremely accurate estimates of the effective thermal conductivity for all the experimental and numerical data of different foam materials reported in the literature.

Modeling and estimation of thermal conductivity of MgO–water/EG (60:40) by artificial neural network and correlation

In this article, artificial neural network (ANN) model has been used to study the thermal conductivity of MgO–water/EG (60:40) nanofluids based on experimental data. MgO nanoparticles in a binary mixture of water/EG (60:40) were scattered to make the above-mentioned nanofluid in two stages. The properties of the nanofluid were measured in different concentrations (0.1, 0.2, 0.5, 0.75, 1, 2, and 3%) and temperatures of 20 to 50°C. Afterwards, two correlations were suggested for predicting the thermal conductivity of the nanofluids. The results of this study show that the ANN model can predict thermal conductivity to a great degree and is in agreement with the experimental results.

Thermal conductivity of granular porous media: A pore scale modeling approach

Pore scale modeling method has been widely used in the petrophysical studies to estimate macroscopic properties (e.g. porosity, permeability, and electrical resistivity) of porous media with respect to their micro structures. Although there is a sumptuous literature about the application of the method to study flow in porous media, there are fewer studies regarding its application to thermal conduction characterization, and the estimation of effective thermal conductivity, which is a salient parameter in many engineering surveys (e.g. geothermal resources and heavy oil recovery). By considering thermal contact resistance, we demonstrate the robustness of the method for predicting the effective thermal conductivity. According to our results obtained from Utah oil sand samples simulations, the simulation of thermal contact resistance is pivotal to grant reliable estimates of effective thermal conductivity. Our estimated effective thermal conductivities exhibit a better compatibility with the experimental data in companion with some famous experimental and analytical equations for the calculation of the effective thermal conductivity. In addition, we reconstruct a porous medium for an Alberta oil sand sample. By increasing roughness, we observe the effect of thermal contact resistance in the decrease of the effective thermal conductivity. However, the roughness effect becomes more noticeable when there is a higher thermal conductivity of solid to fluid ratio. Moreover, by considering the thermal resistance in porous media with different grains sizes, we find that the effective thermal conductivity augments with increased grain size. Our observation is in a reasonable accordance with experimental results. This demonstrates the usefulness of our modeling approach for further computational studies of heat transfer in porous media.

Estimation of thermal conductivity of ionic liquids using quantitative structure–property relationship calculations

In this study, the thermal conductivity at different temperatures of ionic liquids has been estimated using a quantitative structure–property relationship (QSPR) model that involved 11 molecular descriptors based on semi-empirical calculations. Thermal conductivity data of ionic liquids (195 experimental data points) were used to obtain the QSPR model for a wide range of temperatures: 273–390K. The correlative and extrapolative capability of the proposed model was examined using a testing data set not used during the correlation step. The results show that the QSPR model to estimate the thermal conductivity is accurate enough and gives lower deviations than any other QSPR models yet available in the literature with AARD=1.97 and R2=0.9919.

Correction of NPL-2013 estimate of the Boltzmann constant for argon isotopic composition and thermal conductivity

In 2013, a team from NPL, Cranfield University and SUERC published an estimate of the Boltzmann constant based on precision measurements of the speed of sound in argon. A key component of our results was an estimate of the molar mass of the argon gas used in our measurements. To achieve this we made precision comparison measurements of the isotope ratios found in our experimental argon against the ratios of argon isotopes found in atmospheric air. We then used a previous measurement of the atmospheric argon isotope ratios to calibrate the relative sensitivity of the mass spectrometer to different argon isotopes. The previous measurement of the atmospheric argon isotope ratios was carried out at KRISS using a mass spectrometer calibrated using argon samples of known isotopic composition, which had been prepared gravimetrically.We report here a new measurement made at KRISS in October 2014, which directly compared a sample of our experimental gas against the same gravimetrically-prepared argon samples. We consider that this direct comparison has to take precedence over our previous more indirect comparison. This measurement implies a molar mass which is 2.73(60) parts in 106 lighter than our 2013 estimate, a shift which is seven times our 2013 estimate of the uncertainty in the molar mass.In this paper we review the procedures used in our 2013 estimate of molar mass; describe the 2014 measurement; highlight some questions raised by the large change in our estimate of molar mass; and describe how we intend to address the inconsistencies between them. We also consider the effect of a new estimate of the low pressure thermal conductivity of argon at 273.16 K. Finally we report our new best estimate of the Boltzmann constant with revised uncertainty, taking account of the new estimates for the molar mass and the thermal conductivity of the argon.

A Novel Portable Absolute Transient Hot-Wire Instrument for the Measurement of the Thermal Conductivity of Solids

A new portable absolute Transient Hot-Wire instrument for measuring the thermal conductivity of solids over a range of 0.2 $$\,\hbox { W}{\cdot }\mathrm{m}^{-1}{\cdot }\hbox {K}^{-1}$$ to $$4\,\hbox { W}{\cdot }\mathrm{m}^{-1}{\cdot }\hbox {K}^{-1}$$ is presented. The new instrument is characterized by three novelties: (a) an innovative two-wires sensor which provides robustness and portability, while at the same time employs a soft silicone layer to eliminate the effect of the contact resistance between the wires and the sample, (b) a newly designed compact portable printed electronic board employing an FPGA architecture CPU to the control output voltage and data processing—the new board replaces the traditional, large in size Wheatstone-type bridge system required to perform the experimental measurements, and (c) a cutting-edge software suite, developed for the mesh describing the structure of the sensor, and utilizing the Finite Elements Method to model the heat flow. The estimation of thermal conductivity is modeled as a minimization problem and is solved using Bayesian Optimization. Our revolutionizing proposed methodology exhibits radical speedups of up to $$\times $$ 120, compared to previous approaches, and considerably reduces the number of simulations performed, achieving convergence only in a few minutes. The new instrument was successfully employed to measure, at room temperature, the thermal conductivity of two thermal conductivity reference materials, Pyroceram 9606 and Pyrex 7740, and two possible candidate glassy solids, PMMA and BK7, with an absolute low uncertainty of 2 %.

Detailed territorial estimation of design thermal conductivity for façade materials in North-Eastern Spain

The hygrothermal performance of building envelopes varies based on the temperature and moisture content of their materials. However, multiple building codes adopt constant conductivity values for construction materials based on standardised conditions that do not represent real operating conditions. Currently, a new procedure allows for correcting these normative conductivity values with a limited calculation effort, taking into account the characteristic environmental conditions of each location. This article combines climate records compiled at 313 weather stations distributed in North-Eastern Spain and uses this method to determine the applicable correction factors for materials of masonry façades. The exhaustive characterisation of this geographic area allows for creating correction isopleth maps that may be functionally integrated into building codes to improve the thermal design of buildings at any site of the territory. The results indicate a lower need to correct in mountainous areas, while the Mediterranean coast and the Ebro river valley exhibit larger deviations from normative values with respect to the actual conductivity of the materials. These corrections have been validated in two important cities in this area, Barcelona and Zaragoza.

Interpretation of ongoing thermal response tests of vertical (BHE) borehole heat exchangers with predictive uncertainty based stopping criterion

This paper presents a method for analyzing and establishing a stopping criterion for ongoing (TRT) thermal response tests of vertical (BHE) borehole heat exchangers. The predictive uncertainty of the late-time BHE fluid temperature (50 h) forms the basis for determining the time after which further temperature measurements do not significantly improve the calibration of the numerical borehole model. The method relies solely on measured fluid temperatures and can, in principle, be generalized to different BHE geometries and configurations. The method is applied to synthetic and actual TRTs of single and double U BHEs. Predictive uncertainty of the late-time fluid temperature is comparable to measurement uncertainty after 12–28 h of testing at which reliable estimates of soil thermal conductivity and borehole thermal resistance are obtained. Minimum testing times are found to scale with borehole thermal resistance in the synthetic tests. It is further demonstrated that for actual tests, ambient thermal disturbances bias estimates of soil thermal conductivity and inflate predictive uncertainty which increases minimum testing times. The method serves as a diagnostic tool for ongoing TRTs as well as a means to minimize testing times which has potential implications for the associated cost and logistics of field operations.

Applying artificial neural networks (ANN) to the estimation of thermal contact conductance in the exhaust valve of internal combustion engine

Exhaust valve temperature increases significantly due exhaust hot gases obtained from combustion fuel-air mixture in combustion chamber of internal combustion engine. In order to avoid the damage of the combustion chamber and the engine itself, heat must be taken away from the valve. This can be done when the valve in contact with the seat and the periodic contact heat transfer takes place. Therefore study of heat transfer contact between the valve and its seat is important and necessary. In this study back propagation neural (BPN) network has been used to estimate two parameters to determine the heat transfer rate through the valve and its seat due the complexity of thermal contact problem between the valve and its seat. This thermal contact problem is solved to obtain the required information for design the neural network using inverse heat transfer method (conjugate gradient method using a two search step sizes). The results show that, between the different algorithms, Levenberg Marquardt algorithm is produced the best model for estimating the unknown parameters.

Predictive Model for the Thermal Conductivity of Rough and Smooth Silicon Nanowires

The thermal conductivity of semiconductor nanowires (NWs) is significantly reduced with respect to their bulk counterparts. It decreases when the NW diameter is reduced and, in addition, it is dependent on the NW surface morphology, resulting in very low thermal conductivities for rough NWs. The thermal conductivity is a crucial physical magnitude for the thermal management of the nanodevices based on NWs; therefore, a great research effort has been focused in constructing theories that account for the thermal conductivity of semiconductor NWs. However, among all of these approaches, the possibility of predicting the value of the thermal conductivity of the NWs on the basis of the sole knowledge of the nature and morphology of the NWs is still lacking. We present herein a predictive approach for determining the thermal conductivity of both smooth and rough silicon NWs, based on a modified Callaway-Holland formalism. It correlates well with the existing experimental data, and greatly simplifies the complex evaluation of thermal conductivity at the nanoscale by means of simple mathematical expressions. These expressions allow for the estimation of the Si NW thermal conductivity for any combination of diameter and surface morphology parameters.

Limits of Li-Ion Batteries for Public Transportation

Accelerating a 50-ton rather than a 1-ton electric vehicle presents new scientific and engineering challenges. In view of this, our work discusses the power limits of Li-ion batteries as on-board energy storage devices in traction systems used in public transportation. This is based on a thermal analysis of a high power Li-ion battery and an aging/thermal model of it. Included within this process are the following details: 1) General analysis of required heat transfer coefficients. 2) Estimation of internal thermal conductivity along and parallel to electrodes. 3) Use of a thermal network model to calculate internal temperature. Based on this approach, routes to develop higher power Li-ion batteries are discussed based on our current understanding of the electrochemical and heat transfer limitations. A proposal on future electrochemical devices, designed especially for public transportation, is also discussed in detail. Figure 1

Influence of snow cover stratigraphy on its thermal resistance

Effect of the snow thermal resistance on soil freezing is comparable to the influence of mean temperature in the cold period. Available values of the effective thermal conductivity of snow have been analyzed and compared with experimental data on snow cover with different structure collected in Moscow region. Effect of ice crusts and depth hoar layer on the thermal resistance of snow cove have been illustrated with West Spitsbergen and Moscow region examples. Neglect of snow cover stratigraphy in estimations of thermal conductivity was demonstrated to overrate is more than by half. As a result, the estimated rate of soil freezing might be higher than actual one, and therefore transition to the dangerous soil weakening and the beginning of permafrost degradation might past unnoticed.

Cooling history of Earth’s core with high thermal conductivity

Thermal evolution models of Earth’s core constrain the power available to the geodynamo process that generates the geomagnetic field, the evolution of the solid inner core and the thermal history of the overlying mantle. Recent upward revision of the thermal conductivity of liquid iron mixtures by a factor of 2–3 has drastically reduced the estimated power available to generate the present-day geomagnetic field. Moreover, this high conductivity increases the amount of heat that is conducted out of the core down the adiabatic gradient, bringing it into line with the highest estimates of present-day core–mantle boundary heat flow. These issues raise problems with the standard scenario of core cooling in which the core has remained completely well-mixed and relatively cool for the past 3.5Ga. This paper presents cooling histories for Earth’s core spanning the last 3.5Ga to constrain the thermodynamic conditions corresponding to marginal dynamo evolution, i.e. where the ohmic dissipation remains just positive over time. The radial variation of core properties is represented by polynomials, which gives good agreement with radial profiles derived from seismological and mineralogical data and allows the governing energy and entropy equations to be solved analytically. Time-dependent evolution of liquid and solid light element concentrations, the melting curve, and gravitational energy are calculated for an Fe–O–S–Si model of core chemistry. A suite of cooling histories are presented by varying the inner core boundary density jump, thermal conductivity and amount of radiogenic heat production in the core. All models where the core remains superadiabatic predict an inner core age of ≲600Myr, about two times younger than estimates based on old (lower) thermal conductivity estimates, and core temperatures that exceed present estimates of the lower mantle solidus prior to the last 0.5–1.5Ga. Allowing the top of the core to become strongly subadiabatic in recent times pushes the onset of inner core nucleation back to ∼1.5Gyr, but the ancient core temperature still implies a partially molten mantle prior to ∼2Ga. Based on these results, the scenario of a long-lived basal magma ocean and subadiabatic present-day core seems hard to avoid.

Counterbalancing ambient interference on thermal conductivity tests for energy piles

Thermal conductivity tests on Energy Piles suffer from ambient interference on the recorded fluid temperatures. Such interference is observed as sudden fluid temperature increases or stabilizations which require advanced data processing techniques for reliable estimates of the effective ground thermal conductivity. This paper presents a modified testing procedure to eliminate the ambient interference observed in thermal conductivity tests for short ground heat exchangers including Energy Piles. The proposed testing technique relies on counterbalancing the ambient interference when heating the circulating fluid. Supported with a full-scale field experiment on an Energy Pile, the proposed testing technique showed a successful elimination of the ambient interference and an accurate estimate of the ground thermal conductivity using the simple infinite line source (ILS) model. Further, the estimated ground thermal conductivity is independent of the number of hours included in the data processing which offers the potential to reduce the testing duration and consequently the associated testing costs.