Prediction Of Thermal Conductivity Research Articles

Estimating the Thermal Conductivity of Unsaturated Sand

A modified parallel model for estimating the thermal conductivity of unsaturated sand was proposed in this study. The heat conduction in the solid phase of sand depends mainly on the form of contacts between solid particles, while water bridges at the particle contacts increase the contact areas and remarkably enlarge the transfer paths of heat conduction in sandy soils. However, the thermal conductivity of the solid particle itself (λs) cannot describe the influence of the form of contacts and water bridges on heat conduction through the solid phase. In this study, the equivalent thermal conductivity of the solid particle (λes) was presented which reflected the influence of the form of contacts and water bridges between particles under dry conditions or a low degree of saturation, respectively. The relationship between λes and degree of saturation was described by hyperbolic expression. The modified model was calibrated using measured values of the thermal conductivity from published datasets, including those for 41 types of sand from 15 studies. Numerical analyses of the temperature field of the energy pile were performed and validated against laboratory measurements. The results illustrated that the modified model was more applicable than the original model for predictions of sand thermal conductivity.

AI-assisted inverse design of sequence-ordered high intrinsic thermal conductivity polymers

Artificial intelligence (AI) promotes the polymer design paradigm from a traditional trial-and-error approach to a data-driven style. Achieving high thermal conductivity (TC) for intrinsic polymers is urgent because of their importance in the thermal management of many industrial applications such as microelectronic devices and integrated circuits. In this work, we have proposed a robust AI-assisted workflow for the inverse design of high TC polymers. By using 1144 polymers with known computational TCs, we construct a surrogate deep neural network model for TC prediction and extract a polymer-unit library with 32 sequences. Two state-of-the-art multi-objective optimization algorithms of unified non-dominated sorting genetic algorithm III (U-NSGA-III) and q-noisy expected hypervolume improvement (qNEHVI) are employed for sequence-ordered polymer design with both high TC and synthetic possibility. For triblock polymer design, the result indicates that qNHEVI is capable of exploring a diversity of optimal polymers at the Pareto front, but the uncertainty in Quasi-Monte Carlo sampling makes the trials costly. The performance of U-NSGA-III is affected by the initial random structures and usually falls into a locally optimal solution, but it takes fewer attempts with lower costs. 20 parallel U-NSGA-III runs are conducted to design the pentablock polymers with high TC, and half of the candidates among 1921 generated polymers achieve the targets (TC > 0.4 W m−1K−1 and SA < 3.0). Ultimately, we check the TC of 50 promising polymers through molecular dynamics simulations and reveal the intrinsic connections between microstructures and TCs. Our developed AI-assisted inverse design approach for polymers is flexible and universal, and can be extended to the design of polymers with other target properties.

First-principles prediction of thermal conductivity of bulk hexagonal boron nitride

First-principles prediction of thermal conductivity of bulk hexagonal boron nitride

Prediction of soil thermal conductivity using individual and ensemble machine learning models

Prediction of soil thermal conductivity using individual and ensemble machine learning models

Interpretable Machine Learning Strategies for Accurate Prediction of Thermal Conductivity in Polymeric Systems

Interpretable Machine Learning Strategies for Accurate Prediction of Thermal Conductivity in Polymeric Systems

Prediction of Thermal Conductivity of Elastic Single Jersey Knitted Fabric

ABSTRACT This work aims to derive a mathematical model that describes the thermal conductivity of the elastic single jersey knitted fabric (SJKF) based on the loop geometry and the yarn and fibers inclination on the direction of heat flow, and to assist the manufactures of elastic SJKF to predict the thermal conductivity from the fabric construction parameters. The main fabric construction parameters in this model are yarn count, loop length, and spandex weight percent. To validate the proposed model, elastic SJKF samples were produced at two different counts and five levels of loop length and spandex weight percent. The thermal conductivity of these samples was measured and compared to the predicted values from the new model rather than the previous three models (Maxwell – Eucken 2, Schuhmeister, Militky models). From the results, it could be concluded that the values of thermal conductivity from the model are very closed to the experimental values; therefore, the new model could be used to predict the thermal conductivity of elastic SJKF.

Thermal conductivity prediction of Al[formula omitted]O[formula omitted]-doped tetragonal YSZ coatings using deep learning

Thermal barrier coatings, including Al2O3-doped tetragonal yttria-stabilized zirconia (t-YSZ) coatings are vital in diverse applications. Thermal conductivity is a key property, but prediction often requires some complex experiments. In this study, a novel dual-structure feature extraction coupled with a multi-scale attention fusion network (RCFNet) is introduced for accurate thermal conductivity prediction using electron backscattered diffraction (EBSD) microstructure images. The method requires minimal data, and the result shows high accuracy with a Mean Square Error (MSE) of 0.003 W(m⋅k)−1 and an R-squared (R2) value of 0.81. Furthermore, the model reveals specific correlations between microstructural characteristics and thermal conductivity, facilitating rapid structure selection for specific thermal conductivity.

Comparative analysis of the application of the Lichtenecker and Lichtenecker – Asaad models for the theoretical assessment of the rocks effective thermal conductivity

The analysis of the conditions and limitations of using the Lichtenecker and Lichtenecker – Asaad models for predictive assessment of the effective thermal conductivity of rocks have been carried out. For a comparative assessment, experimental data on the thermal conductivity of carbonate rocks (limestone, dolomite) from the South-West German Molasse basin, the Chicxulub impact structure (Yucatan Peninsula, Mexico) and the Bazhenov Formation (Western Siberia, Russia) have been considered. Comparative assessments were carried out based on the use of correlation relationships obtained by the authors of experimental works as a result of regression analysis of the petrophysical properties of carbonate rocks. The results of the computational and analytical assessment have shown that the highest accuracy of thermal conductivity prediction using the Lichtenecker – Asaad model can be achieved if experimental data are available that provide a highly accurate determination of the structure factor. The features of the influence of various parameters of the Lichtenecker – Asaad model on the error in estimating the effective thermal conductivity of the rock have been studied. It has been established that at the stage of pre-design research, the Lichtenecker model can be used for a fairly correct theoretical assessment of the thermal conductivity of water-saturated, slightly porous rocks.

Effective thermal conductivity of granular soils: a review of influencing factors and prediction models towards an investigation framework through multiscale characters

The effective thermal conductivity of soil is important to geo-engineering applications, and it is controlled by factors across different length scales. Through a comprehensive review of these factors, we found that while other more traditional factors have been well studied, there is still a lack of characterisation of soil microscale and mesoscale structures and their influence on effective thermal conductivity. In addition, after reviewing the models available in the literature for soil effective thermal conductivity prediction, it was found that compared with empirical and theoretical models, machine learning models can account for the influence of multi-scale factors, however, research into them is scarce. To overcome the limitations of previous research, we proposed a framework that can investigate the factors influencing soil effective thermal conductivity at multiple scale. It includes the impact of soil structural factors at micro to mesoscale, and this impact is integrated with the influence from other factors for accurate thermal conductivity prediction.

Phonon Thermal Transport in UO_{2} via Self-Consistent Perturbation Theory.

Computing thermal transport from first-principles in UO_{2} is complicated due to the challenges associated with Mott physics. Here, we use irreducible derivative approaches to compute the cubic and quartic phonon interactions in UO_{2} from first principles, and we perform enhanced thermal transport computations by evaluating the phonon Green's function via self-consistent diagrammatic perturbation theory. Our predicted phonon lifetimes at T=600  K agree well with our inelastic neutron scattering measurements across the entire Brillouin zone, and our thermal conductivity predictions agree well with previous measurements. Both the changes due to thermal expansion and self-consistent contributions are nontrivial at high temperatures, though the effects tend to cancel, and interband transitions yield a substantial contribution.

Numerical prediction of thermal conductivity and thermal expansion coefficient of glass fiber-reinforced polymer hybrid composites filled with hollow spheres

The optimal performance of composites enriched with hollow spheres has been reported in contemporary literature, whereas their thermal properties have received less attention. In this regard, a finite element method (FEM)-based micromechanical model has been developed systematically to investigate the role of intra-matrix embedding of hollow spheres on the thermal conductivity and coefficient of thermal expansion (CTE) of unidirectional fiber-reinforced hybrid composites. In so doing, the concept of representative volume element (RVE) considers microstructures comprising an epoxy matrix, E-glass fiber, and E-glass hollow spheres, assuming perfect bonding (ideal interface) between the components and modified approximate periodic boundary conditions. By computing the longitudinal and transverse temperature gradients generated due to the application of uniform heat flux as well as the geometrical variation in RVE owing to temperature enhancement, thermal conductivity and CTE have been respectively determined. Comprehensive evaluations have been conducted to examine the effects of microstructural-level features, including fiber volume content and orientation, plus volume content and thickness of hollow spheres, on the effective thermal conductivity and CTE of pseudo-porous ternary E-glass/epoxy composites.

Experimental Investigation and Artificial Neural Network-Based Prediction of Thermal Conductivity of Metal Oxide-Enhanced Organic Phase-Change Materials

This research article presents a comprehensive study on the prediction of thermal conductivity (TC) as a primary outcome for an artificial neural network (ANN) model in the context of nanoenhanced phase change materials (NEPCMs). To improve predictive accuracy and to reduce variation within the NEPCM dataset, a targeted dataset was employed, consisting exclusively of NEPCMs synthesized using paraffin wax (PW) and metal oxide nanoparticles. Unlike existing empirical models that predict TC of NEPCM without simultaneously considering multiple factors influencing it, this study integrates multiple factors, providing a more accurate prediction of NEPCM thermal conductivity. Additionally, the study explores the impact of synthesis parameters on the thermal performance of NEPCMs, focusing on the examination of factors such as the melting temperature of pure phase change material (PCM), nanoparticle size, and NEPCM composition. The thermal characterizations demonstrate outstanding thermophysical properties in NEPCMs, particularly in terms of thermal conductivity, phase change enthalpy, and thermal stability compared to their respective base PCM. An ANN TC prediction model demonstrates exceptional correlation (&gt;99%) with reported NEPCMs, providing a reliable tool for TC forecasting in similar NEPCM categories. The backpropagation ANN model predicts NEPCM TC with a mean squared error (MSE) of 0.031124 within eight epochs. The dataset used exhibits high fit values, with R-values of 0.99825, 0.99208, and 0.9824 for training, validation, and testing, respectively. These values closely match experimentally determined TC, with less than 4% error.

Sampling-accelerated prediction of phonon scattering rates for converged thermal conductivity and radiative properties

The prediction of thermal conductivity and radiative properties is crucial. However, computing phonon scattering, especially for four-phonon scattering, could be prohibitively expensive, and the thermal conductivity for silicon after considering four-phonon scattering is significantly under-predicted and not converged in the literature. Here we propose a method to estimate scattering rates from a small sample of scattering processes using maximum likelihood estimation. The calculation of scattering rates and associated thermal conductivity and radiative properties are dramatically accelerated by three to four orders of magnitude. This allows us to use an unprecedented q-mesh (discretized grid in the reciprocal space) of 32 × 32 × 32 for calculating four-phonon scattering of silicon and achieve a converged thermal conductivity value that agrees much better with experiments. The accuracy and efficiency of our approach make it ideal for the high-throughput screening of materials for thermal and optical applications.

Accelerating thermal conductivity prediction through machine-learning: Two orders of magnitude reduction in phonon-phonon scattering rates calculation

The calculation of phonon-phonon scattering rates is a computational bottleneck in first-principles based phonon thermal conductivity prediction of materials. Here, we report a machine learning approach for phonon scattering rates prediction which is capable of predicting thermal conductivity by employing only 5 % of the total computational cost. We test this approach on more than 200 diverse materials and found that when this approach is combined with that of Guo et al. [npj Computational Materials 9, 95 (2023)] for phonon-phonon linewidth prediction, the cumulative speed-up is more than two orders of magnitude while the accuracy of thermal conductivity prediction is preserved to within 10 %. This drastic speedup is translated into computational time reduction for phonon scattering rates calculation from more than 60,000 cpu-hours to less than 500 cpu-hours for considered 230 materials.

Thermal Conductivity Prediction of SiC/Silicone Rubber Composites Using Combined Separate Axis Theorem-Finite Element Model

Thermal Conductivity Prediction of SiC/Silicone Rubber Composites Using Combined Separate Axis Theorem-Finite Element Model

Calculation model and influence factors of thermal conductivity of composite cement-based materials for geothermal well

The use of cement-based composites (CBC) with high thermal conductivity for geothermal well cementing is extremely important for the efficient development and use of geothermal energy. Accurate prediction of thermal conductivity can save a lot of experimental costs and time. At present, there is no specific calculation model for the thermal conductivity of CBC. In this study, the microstructure, thermal conductivity model and influencing factors of CBC were investigated by experimental tests, theoretical analysis and numerical simulation. The results showed that the cement-based material could be simplified into a two-layer structure of hydrated and unhydrated layers. Mathematical and numerical models based on the coupled Series model and the Maxwell–Eucken model were established to calculate the thermal conductivity for CBC. The mathematical and numerical models were found to be more accurate by comparison with the conventional models and experimental test results. The cubic packing was more favorable than the spherical packing to improve the thermal conductivity of CBC. The plate material had significant anisotropy. The thermal conductivity of CBC showed a rapid decrease followed by a slow decrease, a decrease followed by a slow increase and finally a rapid decrease, a rapid increase followed by an up and down fluctuation and finally a plateau, respectively, with the increase of filler particle diameter, spacing and curing temperature. Based on these results, the effective methods and future research directions were proposed to maximize the thermal conductivity of geothermal well cementing materials in actual engineering applications. The research findings can provide some technical references for the efficient development of geothermal energy and research on CBC with high thermal conductivity.

Construction of the Aerogel Thermal Conductivity Model based on k×k Sierpinski Carpet Fractal Units

With the characteristics of low density, high specific surface area, and high porosity, aerogel boasts prominent advantages in the field of thermal protection. The thermal insulation performance of aerogel has a significant relationship with its internal microstructure. In this study, the thermal conduction model of Sierpinski aerogel filled with solid in ga&#x0D; With the characteristics of low density, high specific surface area, and high porosity, aerogel boasts prominent advantages in the field of thermal protection. The thermal insulation performance of aerogel has a significant relationship with its internal microstructure. In this study, the thermal conduction model of Sierpinski aerogel filled with solid in gas is established based on the equivalent circuit method. We calculated the optimal fractal unit structure of the aerogel via its porosity, applied it to the thermal conductivity calculation of four types of aerogels, and revealed the average relative error of less than 11.58%, which is lower than the calculation results of the thermal conductivity model of the aerogel with the fractal unit structure of , indicating the effectiveness and reliability of the proposed thermal conductivity prediction model.&#x0D; s is established based on the equivalent circuit method. We calculated the optimal fractal unit structure of the aerogel via its porosity, applied it to the thermal conductivity calculation of four types of aerogels, and revealed the average relative error of less than 11.58%, which is lower than the calculation results of the thermal conductivity model of the aerogel with the fractal unit structure of , indicating the effectiveness and reliability of the proposed thermal conductivity prediction model.

Study on the effect of semi-transparency on thermal insulation performance of silica aerogel composites

This paper centralizes its focus on elucidating the distinctive thermal insulation performance of silica aerogel composites, emphasizing their semitransparent attributes under diverse heating methodologies. Firstly, the theoretical prediction of conductive thermal conductivity is undertaken utilizing the spherical hollow cube model and Davies model. Subsequently, the radiative thermal conductivity is numerically derived employing the two-flux approximation model, integrating experimental measurements of optical radiation characteristic parameters. The prediction results under high-temperature are validated by the measured data via a Hot Disk instrument, utilizing the transient plane source method. Finally, the influence of semitransparent properties of the aerogel composites on thermal insulation performance is exhaustively investigated. This is conducted through a meticulous examination of temperature responses resulting from distinct heating modalities, namely contact heating, quartz lamp heating, and arc heated wind tunnel heating. The findings discernibly illustrate that these diverse heating mechanisms significantly impact the thermal insulation performance of aerogel composites endowed with semitransparent characteristics.

Modification of the Acoustic Mismatch Model and Diffuse Mismatch Model for Accurate Prediction of Interface Thermal Conductance At Low Temperatures

Abstract Houston&amp;amp;amp;amp;#39;s method for summing phonon modes in the Brillouin zone is applied to exclude specular transmission of phonon modes of specific symmetries, thus, modifying the Acoustic Mismatch Model when phonon heat flux is incident from a heavier to a lighter medium. The Houston method is also used to impose conservation of the number of phonons in each direction of high symmetry, thus modifying the detailed balance theorem and the Diffuse Mismatch Model. Based on the assumption that phonons are in equilibrium at the interface and are transmitted specularly or diffusely by two-phonon elastic processes, interpolation between the modified Acoustic Mismatch Model and the modified Diffuse Mismatch Model has led to a general analytical formalism for low-temperature interface thermal conductance. The Debye temperature, the only parameter in the derived formalism, is expressed as a function of temperature by assimilating numerically obtained specific heat values to the Debye expression for specific heat. Previous measurements of the thermal conductance of smooth and rough interfaces between dissimilar materials could be reproduced numerically without adjustment of model parameters, demonstrating the importance of modifications to the Acoustic Mismatch Model and the Diffuse Mismatch Model, and supporting the hypothesis that anharmonic processes play a minimal role in heat transport across interfaces below ambient temperature. The formalism developed is used to study the thermal conductance of the interface between silicon and germanium because of the potential of silicon-germanium nanocomposites for thermoelectric applications.

Multi-scale collaborative design method for macroscopic thermal optimization and mesoscopic woven structure of hypersonic vehicle’s TOCMC leading edge

A new thermal protection design method for hypersonic vehicle’s leading edge is proposed, which can effectively reduce temperature of the leading edge without additional cooling measures. This method reduces the leading-edge’s temperature by the multi-scale collaborative design of the macroscopic thermal optimization and the mesoscopic woven structures of Three-dimensional Orthogonal Woven Ceramic Matrix Composites (TOCMC). The macroscopic thermal optimization is achieved by designing different mesoscopic woven structures in different regions to create combined heat transfer channels to dredge the heat. The combined heat transfer channel is macroscopically represented by the anisotropic thermal conductivity of TOCMC. The thermal optimization multiple linear regression model is established to optimize the heat transport channel, which predicts Theoretical Optimal Thermal Conductivity Configuration (TOTCC) in different regions to achieve the lowest leading-edge temperature. The function-oriented mesostructure design method is invented to design the corresponding mesostructure of TOCMC according to the TOTCC, which consists of universal thermal conductivity prediction formulas for TOCMC. These universal formulas are firstly derived based on the thermal resistance network method, which is verified by experiments with an error of 6.25%. The results show that the collaborative design method can effectively reduce the leading edge temperature by about 12.8% without adding cooling measures.