Thermal Conductivity Model Research Articles

A novel mesoscale transitional approach for capturing localized effects in laser powder bed fusion simulations

This paper introduces a new mesoscale approach for considering local fluctuations of powder bed characteristics in laser powder bed fusion (PBF-LB/M) simulation, bridging the gap between computational fluid dynamics (CFD) and homogenized mesoscale models. Gusarov and Sih's models for laser heat input and powder bed thermal conductivity were applied to local powder bed elements (PBE) to capture characteristics in the powder bed. Discrete element method (DEM) simulations of the recoating process generated a representative powder bed and determined local porosity values. These values informed local optical thicknesses and thermal conductivities, providing insights into heat transfer in PBF-LB/M. Single-track test simulations showed melt pool sizes matched best with homogenized parameters. The PBE-based approach resulted in smaller melt pools and revealed asymmetries in heat distribution due to varying powder bed characteristics, highlighting the need for accurate powder bed modeling. This mesoscale approach offers an efficient, time-saving alternative to CFD simulations.

Analysis of Entropy Generation via Non-Similar Numerical Approach for Magnetohydrodynamics Casson Fluid Flow with Joule Heating.

This analysis emphasizes the significance of radiation and chemical reaction effects on the boundary layer flow (BLF) of Casson liquid over a linearly elongating surface, as well as the properties of momentum, entropy production, species, and thermal dispersion. The mass diffusion coefficient and temperature-dependent models of thermal conductivity and species are used to provide thermal transportation. Nonlinear partial differential equations (NPDEs) that go against the conservation laws of mass, momentum, heat, and species transportation are the form arising problems take on. A set of coupled dimensionless partial differential equations (PDEs) are obtained from a set of convective differential equations by applying the proper non-similar transformations. Local non-similarity approaches provide an analytical approximation of the dimensionless non-similar system up to two degrees of truncations. The built-in Matlab (Version: 7.10.0.499 (R2010a)) solver bvp4c is used to perform numerical simulations of the local non-similar (LNS) truncations.

Study on the regulation factors and mechanism of self-heating effects in non-rectangular 14 nm bulk FinFET*

Abstract This work investigates the innovative design of a 14 nm bulk 3D non-rectangular structure fin field-effect transistor (FinFET). By incorporating a cylindrical trapezoidal structure into the upper portion of the FinFET, it transcend the limitations posed by the self-heating (SH) effect observed in traditional rectangular fins.Through the density gradient model and thermal conduction model, the changes in the electron carrier temperature and lattice temperature of the channel are studied, and the relationship between electrical properties and thermal resistance was further analyzed, revealing the effect of SH on the threshold voltage and switching speed of the device. In addition, the SH effect of the doping of source and drain extension regions was also explored, and the effects of electron mobility changes at different ambient temperatures were also studied to clarify their impact on the electrical properties. Ultimately, this work offers novel insights into the design, optimization, and reliability studies of device structures affected by SH effects.

Evaluation of 17 thermal conductivity models for frozen soil

Accurate prediction of thermal conductivity (λ) in frozen soils is essential for understanding their thermal behavior and improving thermal response analyses. Currently, there are numerous models for calculating λ, but unified laboratory data to compare and evaluate these models are lacking. This study focuses on evaluating the performance of 17 models for applied to frozen soils using a unified laboratorial dataset. The heat-pulse-probe (HPP) method was employed to measure the thermal conductivity of silty clay, sand, and sand loam. Nuclear magnetic resonance experiments were conducted to measure the unfrozen water content (θw) and ice content(θi). This provides unified laboratorial dataset of λ-θi-θw. The predictive performance of 17 models demonstrated that the model of Zhang et al. (2018), Sass et al. (1971) and Tian et al. (2016) are the three optimal models: 1). The model of Zhang et al. (2018) is the provided the best predictive performance, with mean absolute error (MAE) values of 0.17 Wm-1K-1, 0.07 Wm-1K-1, and 0.06 Wm-1K-1 for silty clay, sand and sand loam, respectively. This model is advantageous due to its simplicity and the absence of undetermined parameters. 2). The model of Sass et al. (1971) is suitable for soils with low sensitivity to frozen volume deformation, such as sand and sand loam, achieving an MAE of 0.06 Wm-1K-1. 3). The model of Tian et al. (2016) exhibited strong performance for silty clay with an MAE of 0.11 Wm-1K-1, contingent on the precise determination of soil-specific parameters. This study provides a unified laboratory data for the establishment of thermal conductivity models. The evaluation of existing models can serve as a reference for future related research.

A comparative assessment of thermal conductivity of functionally graded and equivalent non-graded ZrB2–B4C–SiC–LaB6 ultra-high-temperature ceramic composites

In this study, functionally graded ZrB2–B4C–SiC–LaB6 composite materials (FGMs) with potential applications in hypersonic aircraft thermal protection systems were fabricated using spark plasma sintering. A systematic study of the thermal conductivity of the FGM, the conductivity of respective FGM layers, and the equivalent non-graded composites, was performed from room temperature up to 450 °C. The results suggest that the thermal conductivity of the FGMs (in the through-thickness direction) and their equivalent non-graded composites ranged between 25 and 34.9 W/mK, which was ∼60 % less than ZrB2. While the overall thermal conductivity of the FGM and equivalent non-graded composites were similar, in the FGM, the topmost layer with high ZrB2-content displayed up to 245 % higher thermal conductivity than the bottom layer with high B4C content. A systematic comparison between experimentally determined conductivity and relevant thermal conductivity models was conducted.

Artificial neural network hyperparameters optimization for predicting the thermal conductivity of MXene/graphene nanofluids

BackgroundThe critical role of thermal conductivity (TC) as a significant thermo-physical property in MXene/graphene-based nanofluids for photovoltaic/thermal systems has motivated recent research into developing precision predictive models. The multilayer perceptron neural network (MLPNN) has emerged as an eminent AI algorithm for this task. MethodsThis study employs Bayesian optimization, random search (RS), and grid search (GS) to fine-tune MLPNN hyperparameters—hidden layers, neurons, activation functions, standardization, and regularization—to elevate TC modeling efficiency. The proposed methodology unfolds in sequential phases: data analysis, data pre-processing, and introduction of MLPNN, GS, RS, Bayesian approach, and their integration algorithm. The next phase entails developing predictive models and presenting optimal cases. Lastly, the final models undergo statistical evaluation and graphical comparison for a thorough analysis. FindingsResults manifest that the GS-MLPNN model excels, achieving the lowest testing data error (MAPE = 0.5261%) and high conformity with empirical data (R = 0.99941). Meanwhile, the RS method adjusts hyperparameters with negligible precision loss (MAPE = 0.6046%, R = 0.99887). Contrarily, Bayesian optimization lags, increasing errors (MAPE = 3.1981%) and lower correlation (R = 0.98099), suggesting its relative inefficacy for this specific application. The optimized models provide efficient predictions, significantly reducing the financial/computing costs associated with experimental/numerical analysis.

Electromagnetic mixed convective flow of dusty hyperbolic tangent hybrid nanofluid over a stretching surface: A quadratic regression analysis using RSM

This article investigates the flow dynamics of electrically conducting dusty hyperbolic tangent fluid over a stretching sheet with thermal radiation and heat source effects. Moreover, the suspension of Aluminium oxide (Al2O3) and Tantalum nanoparticles in engine oil as base fluid at 300 K is considered. The flow and thermal dynamics are analysed using Xue and Hamilton Crosser's thermal conductivity models. Moreover, using various ranges, the effect of the Richardson number on forced, mixed and natural convection is examined. The governing partial differential equations of the flow model are converted into ordinary differential equations and then solved numerically using the boundary value problem solver BVP4C along with the shooting technique in MATLAB. The Xue model enhances the heat transfer rate by 3.55% compared to the Hamilton Crosser model ϕ1 = 0.03, ϕ2 = 0.01, R = 0.1, Q = 0.6, βt = 0.2, βv = 0.07. A moderate adjustment to the Richardson number can reduce skin friction by 0.889% in the case of forced convection. The absence of dust particles enhances the thermal and momentum boundary layer thickness. Tantalum nanoparticles outperform Al2O3 in terms of temperature and velocity. It is possible to regulate the temperature and velocity of the backflow by changing the heat source parameter and the nanoparticle volume percentage. The maximum achievable heat transfer rate in the fourth experimental run is 0.2609 when R = 2.5 and Q = 1.5 as determined by the quadratic regression analysis.

A thermal conductivity model for alpine meadow soils on the Tibetan Plateau and validation analysis

Under the background of global warming, natural disasters such as landslides and mudslides are becoming more frequent on the Tibetan Plateau. It is important to study the thermal behavior of alpine meadow soils, which are widely distributed on the Tibetan Plateau due to the high altitude and extreme climate. In this paper, a quantitative mathematical model of thermal conductivity for alpine meadow soils is presented, which successfully describes the influence of plant roots and organic matter on the thermal behavior of alpine meadow soils. A series of thermal conductivity experiments are carried out on thirty alpine meadow soils collected from Nangqian district, located east of Tibetan Plateau, China. The results of the proposed model are compared with the experimental measured data to validate the model efficiency and satisfactory agreement is achieved. Furthermore, Root mean square error (RMSE) and bias are also used to evaluate the performance of the proposed model and other available models. The results of the validation analysis show that the proposed model, with an RMSE of 0.041 W m−1 K−1 and a bias of 0.005 W m−1 K−1, provides reliable and accurate estimated values of thermal conductivity for alpine meadow soils. This study is beneficial for understanding the thermal behaviors of soil-root system on the Tibetan Plateau.

A model for the lattice thermal conductivity of nanocrystalline materials with arbitrary grain size distribution

Nanocrystalline materials are a popular metamaterial used to control heat transport in semiconductor industrial applications. An accurate and reliable theoretical model is urgently needed but challenging for elucidating detailed microscopic heat transfer phenomena. In this study, we develop a mathematical model to predict the lattice thermal conductivity of nanocrystalline materials with arbitrary grain size distribution. The model first obtains the effective scattering rate of grain boundaries in single-grain-size materials, and then integrates the Landauer’s system transmission concept and series transmission concept to become applicable to multiple grain sizes. It takes into account not only intrinsic and grain boundary scattering effects, but also the effects of finite system size and grain size distribution. Numerical samples of silicon nanocrystals with single grain size, dual size, constant grain size distribution, and logarithmic normal grain size distribution are constructed and full-spectrum Monte Carlo simulations are performed for comparison. The excellent agreement observed between model predictions and simulation results validates the accuracy of the proposed theoretical model. This model will be very helpful in designing micron-scale thermal manipulation systems using nanostructures.

Simulation of Frost-Heave Failure of Air-Entrained Concrete Based on Thermal-Hydraulic-Mechanical Coupling Model.

The internal pore structural characteristics and microbubble distribution features of concrete have a significant impact on its frost resistance, but their size is relatively small compared to aggregates, making them difficult to visually represent in the mesoscopic numerical model of concrete. Therefore, based on the ice-crystal phase transition mechanism of pore water and the theory of fine-scale inclusions, this paper establishes an estimation model for effective thermal conductivity and permeability coefficients that can reflect the distribution characteristics of the internal pore size and the content of microbubbles in porous media and explores the evolution mechanism of effective thermal conductivity and permeability coefficients during the freezing process. The segmented Gaussian integration method is adopted for the calculation of integrals involving pore size distribution curves. In addition, based on the concept that the fracture phase represents continuous damage, a switching model for the permeability coefficient is proposed to address the fundamental impact of frost cracking on permeability. Finally, the proposed estimation models for thermal conductivity and permeability are applied to the cement mortar and the interface transition zone (ITZ), and a thermal-hydraulic-mechanical coupling finite element model of concrete specimens at the mesoscale based on the fracture phase-field method is established. After that, the frost-cracking mechanism in ordinary concrete samples during the freezing process is explored, as well as the mechanism of microbubbles in relieving pore pressure and the adverse effect of accelerated cooling on frost cracking. The results show that the cracks first occurred near the aggregate on the concrete sample surface and then extended inward along the interface transition zone, which is consistent with the frost-cracking scenario of concrete structures in cold regions.

Investigation on thermal conductivities of plain-woven carbon/phenolic and silica/phenolic composites at high temperature: Theoretical prediction and experiment

Phenolic resin-based ablation materials have found widespread applications in the aerospace industry. The prediction of their thermal conductivity is of paramount importance for the optimization and evaluation for thermal protection systems. However, there is rarely reported thermal conductivity performance of phenolic composites during ablation processes. Therefore, this investigation theoretically predicts the dynamic response of thermal conductivity at high temperatures for plain-woven carbon/phenolic and high silica/phenolic composites. By combining the progressive cubic ablation model with existing composite material property formulas, a multiscale prediction model for effective thermal conductivity is developed. The thermal performance of the resin matrix, reinforcing fibers, yarns, and overall fabric composites is calculated. Additionally, mesoscale representative volume elements are implemented to investigate the overall heat transfer characteristics of carbon/phenolic and high-silica/phenolic composites, including temperature and heat flux distributions. Moreover, both composites thermal conductivities are measured in the range from 298 K to 473 K. The proposed prediction model demonstrates good reliability, with average deviations of 3.76 % and 7.36 % compared to finite element analysis results and experimental data, respectively.

Improvement of normalized prediction model of soil thermal conductivity

Soil thermal conductivity (k) is an important parameter for geotechnical engineering temperature field analysis and energy pile design. It is affected by various factors such as degree of saturation, dry density, and mineral composition. Due to the nonlinear correlation between k and these influencing factors, accurate prediction of k is of great significance to guide practical engineering. In this paper 337 soil thermal conductivity data were collected and the intrinsic correlations between k and the influencing factors were systematically analyzed. Based on the Johansen model, a new predictive model was developed by the multivariate probability distribution (MPD) method. The proposed model was also verified by making a comparative analysis with the empirical ones. The results show that the Johansen model has a superior performance in predicting k after the improvement of MPD analysis, in which the correlation coefficient R2 increases from 0.641 to 0.961. The prediction accuracy of the improved model is significantly better than the traditional empirical relationship models. It is recommended to use the Ke - {n, γd, Sr, Cq, Cc, Csi, Csa} model to predict the k when the soil parameters are clear.

Thermal analysis on Ferro Casson nanofluid flow over a Riga plate with thermal radiation and non-uniform heat source/sink

Ferro hybrid nanofluids can be used in electronics and microelectronics cooling applications to reduce heat accumulation and efficiently remove surplus heat. These nanofluids aid to maintain optimum operating temperatures and reduce device overheating by enhancing the heat transfer rate. With this motivation, the aim of the present numerical analysis is to study the three-dimensional incompressible hybrid nanofluid flow over a slippery Riga surface by combining the Casson fluid model. Mathematical modeling is constructed with nanoparticles as Fe3O4 and CoFe2O4 with base fluid as water. The non-uniform heat source/sink and thermal linear radiation effects are taken into account with the Hamilton–Crosser thermal conductivity model. A system of nonlinear PDEs is produced by the proposed problem and then the relevant similarity variables are implemented to transform the set of partial differential equations and their accompanying boundary conditions into the coupled nonlinear differential equations with one independent variable. These modified ordinary differential equations (ODEs) were then successfully solved with the Runge–Kutta fourth-order method by combining via the shooting technique. With the aid of graphical representations, the effects of various influencing parameters were presented and analyzed comprehensively. Furthermore, the impacts of relevant parameters on heat transfer rates and shear stress are concisely discussed and illustrated in tabular forms. The significant findings include the enhancement of the radiation parameter increases the thermal boundary layer thickness and the thickness increases whenever surface experiences slippery conditions. The axial and transverse momentum of the fluid are controlled with the Casson parameter. An effective connection is noticed once the current numerical solutions are verified under particular conditions that were previously described.

An improved analytical model of effective thermal conductivity for hydrate-bearing sediments during elastic-plastic deformation and local thermal stimulation

As one of the crucial thermal properties controlling the dissociation of natural gas hydrate (NGH), effective thermal conductivity (ETC) should be precisely determined to improve the efficiency of NGH exploitation. But so far, most existing analytical ETC models of hydrate-bearing sediments could not accurately characterize the evolution of ETC. In this paper, an improved analytical model is derived for quantitatively revealing physical relations between ETC and various influencing factors during elastic-plastic deformation and local thermal stimulation processes, including hydrate saturation change, complex pore structures evolution, and hydrate occurrence patterns transition. The effectiveness of the proposed model is validated against available experimental data and compared with other existing models, then effects of various critical parameters on ETC are investigated. Results show that the porosity of hydrate-bearing sediments is accurately predicted during both loading and unloading processes, and the calculated ETC values under various conditions (effective stress, hydrate saturation, and temperature) are satisfactory with the test data. Contributions of elastic deformation and plastic deformation to ETC increments are quantitatively obtained. It is noteworthy that this proposed model is significant to reveal the evolution mechanisms of ETC, helping to accurately predict gas production and offer better plans for efficient NGH exploitations.

Effect of boron on the microstructure and performance of HIP-prepared high boron steels

High boron steels containing 2 wt% and 3.3 wt% boron were fabricated using hot isostatic pressing (HIP). The enhanced shielding performance of the steel with higher boron content was quantitatively evaluated through Monte Carlo simulations using software Fluka. The steels were studied with XRD and the phase ratio were calculated using Rietveld refinement method. The microstructure of the materials was investigated using electron backscatter diffraction (EBSD). Comprehensive thermal property measurements, complemented by Hamilton thermal conductivity model analysis, were conducted on high boron steels. The results revealed that the decreased overall thermal conductivity in these materials is primarily attributed to two factors: the increased volume fraction of the boride phase and the enhanced interconnectivity among boride particles. These findings provide crucial insights into the thermal behavior of high boron steels. Room temperature tensile tests indicate that high boron content can cause the fracture mode of the material to transition from ductile to brittle. This study provides a detailed analysis of the effects of boron content on various properties of high boron steels, which can serve as shielding materials in fusion reactors. Advances were proposed for optimizing the thermal performance of high boron steels in this study.

Multi-scale Modeling and Finite Element Analyses of Thermal Conductivity of 3D C/SiC Composites Fabricating by Flexible-Oriented Woven Process

Thermal conductivity is one of the most significant criterion of three-dimensional carbon fiber-reinforced SiC matrix composites (3D C/SiC). Represent volume element (RVE) models of microscale, void/matrix and mesoscale proposed in this work are used to simulate the thermal conductivity behaviors of the 3D C/SiC composites. An entirely new process is introduced to weave the preform with three-dimensional orthogonal architecture. The 3D steady-state analysis step is created for assessing the thermal conductivity behaviors of the composites by applying periodic temperature boundary conditions. Three RVE models of cuboid, hexagonal and fiber random distribution are respectively developed to comparatively study the influence of fiber package pattern on the thermal conductivities at the microscale. Besides, the effect of void morphology on the thermal conductivity of the matrix is analyzed by the void/matrix models. The prediction results at the mesoscale correspond closely to the experimental values. The effect of the porosities and fiber volume fractions on the thermal conductivities is also taken into consideration. The multi-scale models mentioned in this paper can be used to predict the thermal conductivity behaviors of other composites with complex structures.

Thermal conductivity test and model modification of SiO2 aerogel composites based on heat flow meter method

In the composition of thermal protection system, lightweight and efficient super thermal insulation materials-aerogel and its composites have been paid more and more attention and applied in recent years. The heat transfer of aerogel composites is more complicated due to the influence of micro-scale additives, fiber distribution and coupling heat transfer of various heat transfer modes, so it is urgent to calculate the effective thermal conductivity (ETC) accurately. According to the variation law of gas, solid, gas–solid coupling and radiation thermal conductivity with temperature and pressure, the thermal conductivity test is conducted based on the heat flow meter method, so as to the mathematical models of thermal conductivity are modified. The modified models take into account the influence of various micro-scale additives, which can accurately and quickly calculate the ETC of aerogel composites under different working conditions. The questions that different aerogel composites need to reconstruct structural models and the existing theoretical formulas are inaccurate in calculating ETC are solved. This paper is of great theoretical value to accurately predict the thermal insulation performance of aerogel composites.

Fractional numerical analysis of γ-Al2O3 nanofluid flows with effective Prandtl number for enhanced heat transfer

Abstract Nanoparticles have gained recognition for significantly improving convective heat transfer efficiency near boundary layer flows. The characteristics of both momentum and thermal boundary layers are significantly influenced by the Prandtl number, which holds a crucial role. In this vein, the current study conducted a detailed computational analysis of the mixed convection flow of $\gamma$Al$_2$O$_3$-H$_2$O and $\gamma$Al$_2$O$_3$-C$_2$H$_6$O$_2$ nanofluids over a stretching surface. This research integrates an effective Prandtl number, utilizing viscosity and thermal conductivity models based on empirical findings. Additionally, a unique double-fractional constitutive model is debuted to accurately evaluate the effective Prandtl number’s function in the boundary layer. The equations were solved using a numerical technique that combined the finite-difference method with the L$_1$ algorithm. This investigation presents numerical findings related to the velocity, temperature distributions, wall shear stress coefficient, and heat transfer coefficient, contrasting scenarios with and without the effective Prandtl number. The research shows that integrating nanoparticles into the base fluids reduces the temperature of the nanofluid with an effective Prandtl number while enhancing the heat transfer rate irrespective of its presence. Nonetheless, the introduction of a fractional parameter reduced the heat transfer efficiency within the system. Notably, the $\gamma$Al$_2$O$_3$-C$_2$H$_6$O$_2$ nanofluid demonstrates superior heat transfer enhancement capabilities compared to its $\gamma$Al$_2$O$_3$-H$_2$O counterpart but also exacerbates the drag coefficient more significantly. Many practical applications of this study include electronics cooling, industrial process heat exchangers, and rotating and stationary gas turbines in power plants, and efficient heat exchangers in aircraft.

Constructing mathematical models of thermal conductivity in individual elements and units of electronic devices at local heating considering thermosensitivity

This paper considers a heat conduction process for an isotropic medium with local external and internal thermal heating. It was necessary to construct linear and non-linear mathematical models for determining the temperature field, and consequently, for the analysis of temperature regimes in these heat-active environments. To solve the linear boundary value problems and the resulting linearized boundary value problems with respect to the Kirchhoff transformation, the Henkel integral transformation method was used, as a result of which the analytical solutions to these problems were obtained. For a heat-sensitive environment, as an example, a linear dependence of the coefficient of thermal conductivity of the structural material of the structure on temperature, which is often used in many practical problems, was chosen. As a result, analytical relations for determining the temperature distribution in this environment were established. To determine the numerical values of the temperature and analyze the heat exchange processes in the given structure, caused by the external heat load, a geometric image of the temperature distribution was constructed depending on spatial coordinates. The resulting linear and non-linear mathematical models testify to their adequacy to the real physical process. They make it possible to analyze heat-active media regarding their thermal resistance. As a result, it becomes possible to increase it and protect it from overheating, which can cause the destruction of not only individual nodes and their elements but the entire structure as well

Evaluation of thermal conductivity models and dielectric properties in metal oxide-filled poly(butylene succinate-co-adipate) composites

This study examines how various nanofillers impact thermal conductivity, dielectric characteristics, and electromagnetic interference (EMI) shielding potential of bio-based and biodegradable poly(butylene succinate-co-adipate) (PBSA). TiO2, NiFe2O4, Fe2O3, and Fe3O4 were selected as fillers for nanocomposites at 4–50 vol.% (12–81 wt.%). The nanocomposites were analyzed in three domains: structural (scanning electron microscopy, energy dispersive X-ray spectroscopy mapping, density, tensile testing), thermal (light flash analysis, literature models), and dielectric (AC conductivity, permittivity, EM shielding effectiveness (SE)). The investigated fillers showed good dispersion and compatibility with the PBSA matrix. LFA was analyzed according to literature models, where Bruggeman and Agari models showed the best fit at high concentrations. The dielectric analysis revealed that most of the nanocomposites did not reach percolation; thus, producing thermally conductive plastics that are electrically insulating. EMI shielding was limited to frequencies below 10 Hz, with the notable exception of Fe3O4 (100 nm and loading of > 25 vol.%), which showed shielding at frequencies up to 105 Hz. The investigated composites based on a biodegradable polyester and abundant metal oxide nanofillers are suitable for the production of cheap, ecological, and electrically insulating heat dissipation solutions required for modern and lightweight applications.