Determination Of Thermal Conductivity Research Articles

Dual-channel phonon transport leads to low thermal conductivity in pyrochlore La2Hf2O7

The pyrochlore oxide La2Hf2O7 (LHO) has attracted extensive attention in thermal management applications. Accurate determination and comprehensive understanding of thermal conductivity (κ) stand as imperative prerequisites for the effective utilization and manipulation of LHO in various applications. In this study, with the assistance of machine learning interatomic potential, we investigate the microscopic mechanisms of phonon thermal transport in LHO crystals. Our findings show that the thermal transport in LHO defies a precise explication through the conventional framework of the Boltzmann transport equation (BTE) due to the emergence of diffusive phonon modes, engendered by strong anharmonicity. Using the state-of-the-art dual-phonon model incorporating both propagating and diffusive modes, we successfully reproduce the experimentally measured results in both magnitude and temperature dependence. It is also shown that the thermal conduction in LHO crystals is dominated by the propagating modes at low temperatures while by the diffusive modes at high temperatures, which synergistically lead to a low κ for LHO crystals. The novel insights into phonon thermal transport provide a stepping stone for regulating the thermal transport properties of LHO crystals.

Determination of electrical and thermal conductivities of n- and p-type thermoelectric materials by prediction iteration machine learning method

Synthesising a novel high-performance thermoelectric (TE) material is time-consuming because different compositions of the chemical elements are usually varied using a trial-and-error approach. Moreover, the characterisation of TE materials requires both complex and expensive equipment; these measuring devices often fail during operation. Machine learning (ML) models can be used to accurately predict the properties of a novel composition, saving time as well as the cost of the material and equipment. In this study, two different prediction scenarios have been demonstrated, one for n-type with the general formula BixBayBzYbtTe3, and another for p-type with the general formula SbxBiyBazBtYbwTe3. From the experimental data of the above-mentioned n- and p-type compounds, transport properties of n-type Bi2-xTe3 and p-type Sb1.5Bi0.5-xTe3, where x ranges from 0 to 0.5, involving content variations of Ba, B, and Yb, are predicted. Case 1 deals with the prediction of resistivity and Seebeck values, while case 2 predicts the heat capacity (Cp) and thermal diffusivity values of the n- and p-type TE materials. Herein, different compositions of n-type BixBayBzYbtTe3 and p-type SbxBiyBazBtYbwTe3 are synthesised, and the experimental data are fed to 26 ML models. After training all the ML models, an Artificial Neural Networks (ANN) ML model with the highest R2 values of 0.9943 and 0.9995 in cases 1 and 2, respectively, is found to outperform the other models. The prediction iteration method is applied to the ANN to predict the transport properties of the p-type Sb1.5Bi0.2Ba0.3Te3 and n-type Bi1.9Ba0.1Te3. The accuracy of the prediction iteration method increases with the number of iterations. At the end of the 100th iteration, the prediction error of the ANN model in case 1 is as low as 7%, while it is 9% in case 2.

Predictive radiation heat transfer modeling in fibrous insulation at high temperature

The modeling of heat transfer in fibrous materials is important for designing and improving thermal insulation systems. At high temperatures and low sample density, thermal radiation is expected to be the primary mode of heat transfer in fibrous insulation. Currently, the most common and successful models for modelling heat transfer in insulation at high temperatures require the use of semi-empirical methods. The main limitation of this approach is that the model parameters need to be determined from thermal measurements for each possible material and re-determined if the morphological structure is modified, even for the same material. This paper presents a predictive model for radiation heat transfer based solely on physical and morphological properties. The model was validated using experimental determinations of the effective thermal conductivity in a low-density alumina-based insulation. The model demonstrates good agreement with experimental measurements, and its predictions fall within the experimental uncertainty limits for temperatures between 800 K and 1700 K.

Evaluating an enhanced thermal response test (ETRT) with high groundwater flow

Enhanced thermal response tests (ETRT) enable the evaluation of depth-specific effective thermal conductivities. Groundwater flow can significantly influence the interpretation of ETRT results. Hence, this study aims to critically evaluate an ETRT with high groundwater flow (> 0.2 m d−1). Different approaches in determining the specific heat load of an ETRT are compared. The results show that assuming constant electrical resistance of the heating cable with time can account for an inaccuracy of 12% in the determination of effective thermal conductivities. Adjusting the specific heat loads along the borehole heat exchanger (BHE) depth, the specific heat loads vary within 3%. Applying the infinite line source model (ILS) and Péclet number analysis, a depth–average hydraulic conductivity is estimated to be 3.1 × 10–3 m s−1, thereby, confirming the results of a pumping test of a previous study. For high Darcy velocities (> 0.6 m d−1), the uncertainty is higher due to experimental limitations in ensuring a sufficient temperature increase for the evaluation (ΔT > 0.6 K). In these depths, the convergence criterion of Δλeff/λeff < 0.05/20 h for the ILS sequential forward evaluation cannot be achieved. Thus, it can be concluded that time-averaging of the heat load by monitoring voltage and current during ETRT is essential. Therefore, the specific heat load adjustment along the heating cable is recommended. To improve the estimation of depth-specific effective conductivities with high groundwater flow and to reduce the sensitivity towards temperature fluctuations (ΔT ~ 0.1 K), measures for applying higher specific heat loads during the ETRT are essential, such as actions against overheating of the cable outside the BHE.

Mineralogical Characterization From Geophysical Well Logs Using a Machine Learning Approach: Case Study for the Horn River Basin, Canada

AbstractAccurate estimation of mineral composition is essential for refined reservoir characterization, thermal conductivity and mechanical determinations of sedimentary rocks, but is extremely challenging in shale units due to the mineralogical complexity, low porosity and ultra‐low permeability. Direct mineral measurements derived from laboratory X‐ray diffraction (XRD) analysis on core samples and borehole geochemical logging tool (GLT), and conventional geophysical logs from vertical wells penetrating sediments are widely available in some basins, which enables detailed mineralogical characterization of a well. A hybrid machine learning (ML) architecture that improves model training and validation by combining convolutional neural network (CNN) with XGBoost allows accurate description of the mineralogical compositions across a basin. We applied this ML approach to predict the mineral compositions using conventional well logs from the Horn River Basin, northeast British Columbia, Canada, where extensive drilling for shale‐gas and conventional hydrocarbon resources, complemented by high temperature geothermal energy potential is ideal for case testing. The predicted mineral compositions from the ML approach are consistent with the mineralogical readings from the GLT and are confirmed by the XRD mineral measurements. This allows basin‐wide mineral compositions mapping that reveals spatial trends of major mineral compositions and their relationship with the previously recognized geomechanical and geological features, which have important implications for thermal conductivity modeling, reservoir evaluation and extensive geological studies.

Extended hygrothermal characterization of unstabilized rammed earth for modern construction

Although hygrothermal properties of rammed earth represent great potential for improving thermal comfort during hot summers, still little knowledge is available about this topic. The literature lacks complete sets of hygrothermal characteristics of rammed earth, although they are fundamental for heat and moisture transfer modeling necessary to predict earthen building performances. The present work reports the results of a large collaboration between four academic laboratories in order to obtain an extended hygrothermal characterization of rammed earth, focusing on the material used in a modern construction in Lyon, France. The aim of the article is to provide for the same kind of soil an extend characterization on a large span of relative humidity to provide complete set of material properties for heat and moisture transfer simulation. The hygrothermal measurements cover adsorption and desorption isotherm, intermediate isotherm loop, specific heat capacity, dry thermal conductivity, moisture-dependent thermal conductivity and water vapor resistance factor for a large span of relative humidity. In addition, the capillary rise test was performed to estimate the liquid transport coefficient for suction and redistribution. The results show that variations in the density of the samples have a low impact on sorption isotherm, while they have a non-negligible impact on thermal conductivity and water vapor permeability. Surprisingly, variations in the grain size distribution do not produce a significant modification in the sorption isotherm, while hysteresis slightly reduces when bigger grains are added to the soil composition. Approximation of empirical model coefficients for the determination of moisture-dependent thermal conductivity is proposed for rammed earth materials.

Automatic generation of high-fidelity representative volume elements and computational homogenization for the determination of thermal conductivity in foamed concretes

Automatic generation of high-fidelity representative volume elements and computational homogenization for the determination of thermal conductivity in foamed concretes

Technique and results of determination of vertical variations in rock thermal properties, temperature gradient and heat flow

The paper describes the results of experimental geothermal studies of the formation surrounding Savitskaya-300 well (depth 3555 m) drilled in the Volga-Ural oil and gas basin (Orenburg region, Russia). The work was aimed, firstly, at obtaining geothermal data for basin modeling and, secondly, at improving the instrumental and methodological foundations of experimental studies. Continuous core profiling of the rock thermal conductivity, volumetric heat capacity, and thermal anisotropy was performed on all 2886 recovered core samples with the total length of 329.1 m. Additional measurements were conducted at fluid saturation and elevated temperatures. For non-cored intervals, the rock thermal properties were determined from well-logging data and via measurements on rock cuttings. The studied formation is characterized by high thermal heterogeneity at both the level of strata (horizons) and individual samples. Significant thermal anisotropy up to 1.50–1.66 in the lower part of the well has been observed. To obtain data on vertical variations of equilibrium temperature and its gradient, temperature logging was performed 7 times within the 2 to 186 days of well shut-in. New data on the recovery of temperature and its gradient after drilling are important for the practice of applied and fundamental geothermal surveys. A significant increase in heat flow with depth was established based on the results of temperature gradient and rock thermal conductivity determination for 34 depth intervals. The observed general vertical variation in the whole depth range of the well up to ∼3.3 km depth can be attributed to paleoclimatic effect in the Late Pleistocene-Holocene, while the sharp increase in heat flow at ∼3.3 km within Fransian sediments is possibly due to advective heat transfer by fluid filtration. The apparent heat flow was established to be 84.3 mW⋅m−2 in the lower section of the well, below 3.3 km. The value may be close to the steady-state heat flow value, which is 84% higher than the previous published average data for the studied area.

Influence of method for determining thermal conductivity of coal combustion products on calculation accuracy

The determination of thermal conductivity of the combustion products of coal of various compositions by a simplified method and in accordance with the molecular-kinetic theory according to the Masson – Saxena equation is considered. Calculations were made for hard coal of the Volodarsky mine of grade A, Donetsk basin of grade OS, Kuznetsk basin of grade K, Tallinn section of grade G, Kizelovsky basin of grade G, and Tulganskoye deposit of grade 1B with the content of the combustible part by weight from 70.8% to 25.1%. The temperature of the combustion products varied from 200°C to 400°C in increments of 50°C, and the excess air coefficient varied from α = 1.15 to α = 1.5 in increments of 0.05. The discrepancy between the results of the simplified calculation and the results obtained by the Masson – Saxena equation was estimated using the relative error. Analysis of the calculation results showed that the thermal conductivity coefficient calculated according to the additivity rule using volume and mass fractions of the components of the combustion products is higher than that calculated using the Masson – Saxena equation at any excess air coefficients and temperature of the combustion products. With an increase in the temperature of the combustion products, in both cases, the error in determining the thermal conductivity decreases due to an increase in the value of the thermal conductivity. The use of simplified calculation methods leads to an increase in the thermal conductivity of combustion products by an average of 7.5% – 2% for coal of various compositions, depending on the temperature of the combustion products, compared with the values calculated using the Masson – Saxena equation.

Comparative Studies of Thermal Conductivity Determination in Synthetic Wood with Recyclable Waste Content Using an Experimental Design Approach

Waste has become an everyday subject, especially its efficient recycling due to the increasing decline in the planet’s primary resources. Therefore, their recovery is intended to be total and with minimal energy consumption. Biomass waste is fully recoverable in raw or processed states and in combination with other compatible waste types (including wastewater from construction, polymers, and sunflower seeds). It represents the basic elements for obtaining synthetic wood to replace natural wood, which is very expensive and difficult to obtain (lasts for several years). This paper proposes three methods to determine the thermal conductivity of these new materials (synthetic wood) to guarantee and optimise their thermal characteristics. The determination of thermal characteristics in insulating materials is usually performed experimentally in a double climate chamber or more simply using a special instrument of the ISOMET type, but under these conditions, the sample must meet certain conditions imposed by the manufacturer to be tested. Thus, two experimental investigation methods are used to which a numerical method is added, which consists of modelling by the finite element method with an adequate programme of heat transfer through these materials. Four samples with variable content of recyclable waste obtained through combinations resulting from six different experimental design plans with two controlled factors were analysed to optimise synthetic wood recipes for the efficiency of their hygro-thermal characteristics. The content of the tested samples varied relative to the quantity and number of recyclable wastes included in the final recipe. Thus, the thermal conductivity obtained was different for each sample but close to that of similar synthetic wood-type materials and natural wood.

Experimental determination of the high-temperature thermal conductivity of steel powders

The PBF-LB/M process is highly suitable for the additive manufacturing of complex parts with precise geometrical accuracy using metallic powders. However, certain unknown variables exist within the process. Particularly, the thermal conductivity introduces a significant level of uncertainty due to the substantial impact of heat transfer from the part solid to the bulk material. Insufficient experimental data on the thermophysical properties of powder and a limited understanding of the influencing factors further exacerbate this issue. This study presents the thermophysical properties of steel powders commonly employed in the PBF-LB/M process, utilizing a newly-developed powder container for laser flash analysis. Through a quantitative comparative analysis with the corresponding solid materials, it has been demonstrated that the chemical composition and microstructure play a secondary role in determining the heat conductivity of the powder bed. Instead, it is the powder size distribution that serves as the primary parameter governing the observed behavior.

Prediction of Optimum Veneer Drying Parameters with Artifi cial Neural Networks for Production of Plywood with High Mechanical Properties

Veneer drying is the manufacturing process in the plywood industry that most affects energy consumption and panel properties such as bonding and bending. Therefore, the veneer drying temperature and moisture content should be accurately adjusted. Moreover, the determination of veneer thermal conductivity is as important as these two parameters and the thermal conductivity values should also be specifi ed when forming the drying programs. This study aimed to predict the optimum values of the veneer drying temperatures, moisture content and thermal conductivity, which gave the best mechanical properties, by artifi cial neural network (ANN) analysis. Poplar (Populus deltoidesI-77/51) and spruce (Picea orientalis L.) veneers and urea formaldehyde (UF) resin were used in the production of plywood. The thermal conductivity of veneer and the bonding, bending strength and elasticity modulus of the panels were tested by the relevant standards. The most accurate and reliable prediction models were obtained by analyzing the experimental data with ANN. The optimum veneer drying temperature, moisture content and thermal conductivity values that gave the best values for all three mechanical properties were 149 &amp;deg;C, 6.2 % and 0.02668 W/mK for poplar and 116 &amp;deg;C, 4.4 % and 0.02534 W/mK for spruce.

Simultaneous Determination of Thermal Conductivity and Heat Capacity in Thin Films with Picosecond Transient Thermoreflectance and Picosecond Laser Flash

ABSTRACT Combining the picosecond transient thermoreflectance (ps-TTR) and picosecond laser flash (ps-LF) techniques, we have developed a novel method to simultaneously measure the thermal effusivity and the thermal diffusivity of metal thin films and determine the thermal conductivity () and the heat capacity () altogether. In order to validate our approach and evaluate the uncertainties, we analyzed five different metal films (Al, Cr, Ni, Pt, and Ti) with thicknesses ranging from 297 nm to 1.2 µm. Our results on thermal transport properties and heat capacity are consistent with reference values, with the uncertainties for the thermal conductivity and the heat capacity measurements below 25% and 15%, respectively. Compared with the ps-TTR technique alone, the combined approach substantially lowers the uncertainty of the thermal conductivity measurement. Uncertainty analyses on various materials show that this combined approach is capable of measuring most of the materials with a wide range of thicknesses, including those with low thermal conductivity (e.g., mica) down to thicknesses as small as 60 nm and ultrahigh thermal conductivity materials (such as cubic BAs) down to 1400 nm. Simultaneous measurement of thermal conductivity and heat capacity enables exploration of the thermal physical behavior of materials under various thermodynamic and mechanical perturbations, with potential applications in thermal management materials, solid-state phase transitions, and beyond.

Lattice Boltzmann modeling of the effective thermal conductivity in plant fiber porous media generated by Quartet Structure Generation Set

This study proposes a novel approach that combines a Quartet Structure Generation Set (QSGS) algorithm with the Lattice Boltzmann Method (LBM) to investigate effective thermal conductivity in plant fiber materials. The QSGS algorithm is utilized to construct a two-dimensional digital structure of the porous media, enabling the determination of effective thermal conductivity through LBM simulations. The simulation results demonstrate good agreement with experimental data, exhibiting a maximum relative error of only 1.7%. The study examines the influence of key factors on effective thermal conductivity, including temperature, porosity, fiber distribution, and fractal dimension. The effective thermal conductivity increased by approximately 6.1% for each 10 °C increase in temperature between 10 °C and 50 °C. Moreover, the effective thermal conductivity showed an average decrease of 16.3% with each 10% increment in porosity. The increase in thermal conductivity becomes more significant with increasing temperature and decreasing porosity. Conversely, an increased fractal dimension correlates with a decline in effective thermal conductivity. The effective thermal conductivity is influenced synergistically by the directional growth probability of the solid phase and the direction of heat flow. The findings offer valuable insights into thermal conductivity behavior and heat transfer mechanisms in plant fiber porous media.

Application of the Regular Mode Method to the Experimental Determination of Thermal Conductivity of Fluid

Application of the Regular Mode Method to the Experimental Determination of Thermal Conductivity of Fluid

On Simultaneous Determination of Thermal Conductivity and Volume Heat Capacity of Substance

The study of nonlinear problems associated with heat transfer in substance is important for practice. Earlier, the authors proposed an efficient algorithm for determining the thermal conductivity from experimental observations of the dynamics of the temperature field in an object. In this work, we explore the possibility of extending the algorithm to the numerical solution of the problem of simultaneous identification of the temperature-dependent volume heat capacity and the thermal conductivity of the substance under study. The consideration is based on the Dirichlet boundary value problem for the one-dimensional nonstationary heat equation. The coefficient inverse problem in question is reduced to a variational problem, which is solved by applying gradient methods based on the fast automatic differentiation technique. The uniqueness of the solution to the inverse problem is analyzed.

On Simultaneous Determination of Thermal Conductivity and Volume Heat Capacity of Substance

On Simultaneous Determination of Thermal Conductivity and Volume Heat Capacity of Substance

Engineering-Physical Method for Determining the Thermal Conductivity of Objects with Micrometric Thickness and a Complex Structure

Introduction. The application of functional coatings on products, the performance properties of which are localized in the surface layer is a trend in modern mechanical engineering and materials science. The issues considered in this regard are relevant, in particular, for thermal-barrier coatings of turbine blades of steam and gas turbine engines. It is worth mentioning the materials that experience significant thermal loads during operation. In this case, the lack of reliable methods for predicting the thermophysical properties of the coating seems to be a problem. The work objective is to create a computational and analytical methodology for determining the thermal conductivity of coatings. This approach is based on experimental data and takes into account structural parameters of the material.Materials and Methods. The experiments were carried out with the blades of a high-speed gas turbine of a locomotive engine made of heat-resistant chromium-nickel alloy Inconel 713LC. An experimental multiphase coating of the Nb-Ti-Al intermetallic system with a thickness of about 80 microns was applied using vacuum ion-plasma technology. The two-beam scanning electron microscope Zeiss CrossBeam 340 was used in the work. The thermal conductivity of the coatings was determined by an experimental technique based on the measurement of the contact potential difference (CPD). Numerical values of this difference were obtained using a mirror galvanometer with high voltage sensitivity. A special signal amplifier and a USB oscilloscope were used to record the readings.Results. The calculation apparatus of the thermal conductivity determination technique is based on the experimental values of ∆φ CPD:– for the base metal (Inconel 713LC) +846 mkV;– for the coating Nb-Ti-Al – 90 mkV.The solution to the problem of the distribution of particles in a force field with a potential difference ∆φ is described by the Boltzmann distribution. Starting from the obtained result, we get:– CPD at the boundary of the contacting metals;– energy and thermal conductivity of the Fermi level;– electron relaxation time.The multidirectional influence that the dimensional differences of the particles of the second phase have on the effective thermal conductivity is considered. For this case, a dimensionless value of the effective thermal conductivity in the direction of each axis and the effective thermal conductivity of the composite are found. Porosity is taken into account according to the Maxwell – Aiken dependence and introduced into the general calculation system. The thermal conductivity of Nb-Ti-Al is established: λNbTiAl = 4,76 W/m.K. Thus, the thermal barrier coating Nb-Ti-Al fully meets its functional purpose.Discussion and Conclusion. The method of determining thermal conductivity described in the article is applicable only to conductive consolidated materials or composites with a continuous conductive matrix. The presented work completes the initial stage of creating a computational and analytical model for predicting the thermal conductivity of materials and coatings. The results of testing the model for materials with a complex structure showed its satisfactory accuracy. This indicates the expediency of using the two considered elements of the model. The first one is the instrumental measurement of the CPD. The second one is taking into account the features of the structural and phase state of the material. With the development of the model, it is expected to overcome its weaknesses:– the impossibility of using non-conductive objects to determine the thermal conductivity;– a significant decrease in the accuracy of determining thermal conductivity for materials and coatings with a gradient structure.

Implications of phonon anisotropy on thermal conductivity of fluorite oxides

Fluorite oxides are attractive ionic compounds for a range of applications with critical thermal management requirements. In view of recent reports alluding to anisotropic thermal conductivity in this face-centered cubic crystalline systems, we perform a detailed analysis of the impact of direction-dependent phonon group velocities and lifetimes on the thermal transport of fluorite oxides. We demonstrate that the bulk thermal conductivity of this class of materials remains isotropic despite notable anisotropy in phonon lifetime and group velocity. However, breaking the symmetry of the phonon lifetime under external stimuli including boundary scattering present in nonequilibrium molecular dynamics simulations of finite size simulation cell gives rise to apparent thermal conductivity anisotropy. We observe that for accurate determination of thermal conductivity, it is important to consider phonon properties not only along high symmetry directions commonly measured in inelastic neutron or x-ray scattering experiments but also of those along lower symmetry. Our results suggests that certain low symmetry directions have a larger contribution to thermal conductivity compared to high symmetry ones.

Guarded Parallel-Plate Instrument for the Determination of the Thermal Conductivity of Gases, Liquids, Solids, and Heterogeneous Systems

In the present study, a new version of a guarded parallel-plate instrument (GPPI) for the absolute determination of the thermal conductivity under steady-state conditions is presented. It is based on an existing concept of an automated instrument and includes several modifications with the aim of further improving the achievable accuracy and experimental uncertainty while reducing the measurement time. These modifications mainly include improvements to the guard system in the upper hot part, which lead to a further suppression of parasitic heat fluxes, and the integration of flow circulation systems for the active temperature control in the periphery of the instrument. By using relatively large values for the temperature difference between the heating and cooling plates and for the sample thickness, expanded (k = 2) measurement uncertainties between (2 and 3)% could typically be achieved for the thermal conductivity. For mean temperatures from (283 to 358) K and atmospheric pressure, the measurement results agree well with reference data for liquid water, toluene, and ethylene glycol as well as air, polytetrafluorethylene (PTFE), and stainless steel 316. In addition, the applicability of the new GPPI for the determination of the effective thermal conductivity of heterogeneous systems was demonstrated using water-based nanofluids containing dispersed silicon dioxide, titanium dioxide, or copper oxide nanoparticles.