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Microwave heating has been applied to improve cement-based materials (CBMs) structures deicing efficiency, which reduce the effect of icing on their service life. Moreover, the enhanced thermal properties of CBMs can further improve their deicing efficiency. In this study, silicon carbide (SiC) ceramic particles with favorable thermal properties were used to prepare the SiC cement-based materials (SCBMs). The effect of SiC ceramic on the mechanical strength of CBMs was investigated. Next, the heat transfer and storage ability of CBMs were investigated to analyze the changes in their thermal-physical parameters. Then, the surface thermal responses of the CBMs during microwave heating and off were recorded to characterize their temperature evolution in a low-temperature condition. Furthermore, the deicing efficiency of CBMs was determined using the ice layer shedding time (ILST) as the index. The results show that the filling effect of SiC ceramic on CBMs improved their mechanical strength within a certain content range. However, a large content of SiC ceramic caused an increase in the weak area and porosity within the CBMs, further reducing their mechanical properties. The thermal-physical parameters of CBMs were improved after adding SiC ceramic. The thermal conductivity and thermal effusivity of SCBMs-100 were 13.17 W/(m·K) and 1.44 W/(mm2·K), respectively, 7.90 and 2.35 times higher than the control group. Due to the favorable thermal properties of SCBMs, their heating rate and surface temperature field distribution characteristics were further improved. Furthermore, with the increase of SiC ceramic content, the ILST of SCBMs was further reduced during microwave heating. Therefore, the SiC ceramic was a favorable material, improving the thermal properties and deicing efficiency of CBMs to a certain extent. However, considering its effect on the deterioration of the mechanical properties of CBMs, the optimal content of SiC ceramic might be 75%. [ABSTRACT FROM AUTHOR]

In the realm of high-tech materials and energy applications, accurately measuring the transient heat flow at media boundaries and the internal thermal conductivity of materials in harsh heat exchange environments poses a significant challenge when using conventional direct measurement methods. Consequently, the study of photothermal parameter reconstruction in translucent media, which relies on indirect measurement techniques, has crucial practical value. Current research on reconstructing photothermal properties within participating media typically focuses on single-objective or time-invariant properties. There is a pressing need to develop effective methods for the simultaneous reconstruction of time-varying thermal flow fields and internal thermal conductivity at the boundaries of participating media. This paper introduces a computational model based on the numerical simulation theory of internal heat transfer systems in participating media, stochastic particle swarm optimization algorithms, and Kalman filter technology. The model aims to enable the simultaneous reconstruction of various thermal parameters within the target medium. Our results demonstrate that under varying levels of measurement noise, the inversion results for different target parameters exhibit slight oscillations around the true values, leading to a reduction in reconstruction accuracy. However, overall, the model demonstrates robustness and accuracy in ideal conditions, validating its effectiveness. [ABSTRACT FROM AUTHOR]

Thermal energy storage (TES) using phase change materials (PCMs) is one of the potential solutions for stockpiling thermal energy and utilizing it for different applications, which results in effective energy usage. The main drawback of organic PCMs in practical applications is poor heat transfer due to low thermal conductivity (TC). Therefore, investigations into nano-enhanced PCMs are being explored to improve their thermophysical properties. In this work, the various thermophysical characteristics of nano-enhanced lauryl alcohol as a PCM were investigated using carbon-based and metallic nanoparticles. The results indicated that the addition of nanoparticles improved its thermal properties and affected other physical properties, such as viscosity. The latent heat was degraded with the addition of nanoparticles. The results revealed that by adding MWCNTs and CuO nanoparticles, a maximum of 82.6% and 49.6% improvement in TC was achieved, respectively. The maximum drop in latent heat during melting and freezing for the PCM with MWCNTs was about 10.1% and 9.3%, respectively, whereas for the PCM with CuO, they were about 11% and 10.3%, respectively. The lowest supercooling for the PCM with MWCNTs and CuO nanoparticles was 8.6 and 8.3 °C, respectively. The present work confirms that nano-enhanced PCMs can be a potential material for storing thermal energy for various applications. [ABSTRACT FROM AUTHOR]

Modelling effective thermal properties is crucial for optimizing the thermal performance of materials such as new green insulating fibrous media. In this study, a numerical model is proposed to calculate the effective thermal conductivity of these materials. The fibers are considered to be non-overlapping and randomly oriented in space. The numerical model is based on the finite element method. Particular attention is paid to the accuracy of the results and the influence of the choice of the representative elementary volume (REV) for calculation (cubic or rectangular parallelepiped slice). The calculated effective thermal conductivity of fibrous media under different boundary conditions is also investigated. A set of usual mixed boundary conditions is considered, alongside the uniform temperature gradient conditions. The REV rectangular slice and uniform temperature gradient boundary conditions provide a more accurate estimate of the effective thermal conductivity and are therefore recommended for use in place of the usual cubic representative elementary volume and the usual mixed boundary conditions. This robust model represents a principal novelty of the work. The results are compared with experimental and analytical data previously obtained in the literature for juncus maritimus fibrous media, for different fiber volume fractions, with small relative deviations of 7%. Analytical laws are generally based on simplified assumptions such as infinitely long fibers, and may neglect heat transfer between different phases. Both short and long fiber cases are considered in numerical calculations. [ABSTRACT FROM AUTHOR]

To design polydimethylsiloxane (PDMS)-based nanocomposites with high thermal conductivity, multiscale simulations were adopted to study the effect of the modified hexagonal boron nitride (h-BN) and the layer number of h-BN on the interface improvement of nanocomposites. The results indicate that 6-aminocaproic acid (ACA) effectively improves the interfacial thermal conductivity of the composites. The interaction energy between h-BN modified by ACA (ACA@h-BN) and PDMS is enhanced compared to that of an unmodified h-BN/PDMS composite system. It's interesting that the change rate of thermal conductivity (δ) decreases first and then increases with the increase in the layer number of ACA@h-BN, and gradually increases to a value more equal to that of the single-layer system when the layer number of ACA@h-BN reaches 5. The interfacial thickness increases from 1.93 Å to 2.60 Å as the layer number of ACA@h-BN increases from 1 to 6, which is beneficial to improve the thermal conductivity. However, the matching coefficient (M) of the phonon vibration power spectrum between PDMS and ACA@h-BN decreases from 0.024 to 0.011 as the layer number of ACA@h-BN increases from 1 to 6, which impairs the thermal conductivity. The decrease in M dominates the δ in the composites filled by h-BN with 1–3 layers. The thermal conductivity is more affected by the specific gravity of ACA@h-BN than M in the composites filled by h-BN with 4–6 layers. It is proposed that the ideal single-layer ACA@h-BN can be replaced to some extent by the ACA@h-BN with 5 layers to improve the thermal properties of the composites. This work is expected to provide theoretical support for interface modification of nanocomposites, and preparation of high-thermal conductivity composites. [Display omitted] • Molecular dynamics simulation of the effect of h-BN layers on polymer heat transfer • Change rate of thermal conductivity decreases first and then increases • 6-aminocaproic acid improves interfacial heat transfer • The heat transfer mechanism of 2D fillers in polymer matrix is revealed [ABSTRACT FROM AUTHOR]

• Thermal conductivity of a MOF (HKUST-1) was investigated by EMD. • A new method to truncate heat current autocorrelation function of the Green-Kubo method was devised. • The influence of Ar molecules on decomposed thermal conductivity of the MOF was investigated. • The contribution of the interaction of the atoms was dominant to the thermal conductivity. Metal-organic frameworks (MOFs) are promising porous materials for various applications, which have attracted considerable attention recently. Optimizing their thermal properties, such as their heat accumulation characteristics during the adsorption of fluid molecules, is crucial for enhancing their performance. Molecular dynamics studies are vital for meticulously understanding the thermal transport of MOFs. To determine the thermal conductivity of MOFs using the Green–Kubo formula through equilibrium molecular dynamics (EMD), the heat current autocorrelation function (HCACF) must be truncated at a specific time. However, few studies have been conducted on establishing a method for truncating HCACFs for MOFs, rendering it challenging to assess the reliability of MOF thermal conductivities calculated through EMD. In addition, previous studies focused on the overall thermal conductivity, and the contribution of the components have not been identified. In this study, we devised a method for determining the truncation time of an HCACF to calculate the thermal conductivity of MOFs. After confirming the validity of the method, we decomposed the instantaneous heat flux into individual components and obtained the decomposed thermal conductivities. The results revealed that the contribution of the interaction of atoms was more pronounced than that of the microscopic convection of atoms. In addition, the method introduces an ambiguity while calculating the thermal conductivity of MOFs through EMD. The proposed method is useful for truncating of HCACFs and determining the thermal conductivity of complex materials such as MOFs. [ABSTRACT FROM AUTHOR]

• Analyze the variation of heat transfer as a function of diameter and temperature. • Investigate heat transfer mechanism by crucial direct parameters. • Explore internal mechanism by phonon density of state and phonon overlap energy. Carbyne is a sp1 linear carbon chain (LCC) with characteristics of strong chemical reactivity and extreme instability under environmental conditions, which has become an obstacle to the application of LCC with high thermal and electrical conductivity. Utilizing CNT as a container to store LCC is one of the most feasible ways to realize LCC applications. However, current research of LCC encapsulated in CNT (LCC@CNT) focuses on its preparation and lacks of in-depth exploration of the intrinsic heat transfer mechanism. This paper reports the high axis thermal conductivity of LCC@CNT, explore the thermal transport enhancement degree of LCC@CNT under a wide temperature range and multiple diameters, and analyzed the underling mechanism through crucial thermal parameters and phonon theory. When LCC is loaded in CNT, the axial thermal conductivity of CNT is further increased by 15 %. Moreover, LCC@CNT shows the lower sensitivity diameter than pristine CNT. This work is expected to provide data support for the application of LCC, the lower production standard of CNT, and also attempt the higher efficient one-dimensional thermal energy transfer micro devices. [Display omitted] [ABSTRACT FROM AUTHOR]

Efficiently thermal management in electronic devices calls for creating polymer composites that have superior thermal transport abilities. Compared with single-filler composites, hybrid-filler composites have gained significant attention as they not only utilize the advantages of each filler individually but also allow for the potential cooperation in constructing filler networks between them. Differing from the previous research on hybrid-filler size, geometry, composition, and content and their impact on thermal conductivity (TC) in composites, this study aims to examine the spatial distribution effects of hybrid-fillers. A binary-filler strategy comprising of anisotropic 2D h- BN and 0D isotropic S–Al 2 O 3 has been proposed, with the primary h -BN filler aligned to filly utilize its anisotropy and intrinsic TC properties. Furthermore, positioning a small quantity of S–Al 2 O 3 between adjacent parallel h -BN considerably improves TC in the non-oriented direction of silicon rubber (SiR) composites. A special focus is given to the influence of the interstitial S–Al 2 O 3 filler on the individual through-plane TC (λ ⊥) and in-plane TC (λ ⫽) variation of the binary-filler hybrid SiR composites, which are perpendicular and parallel to the oriented h -BN. When incorporating 40 vol% h -BN and 5 vol% S–Al 2 O 3 , the binary SiR composites exhibit the highest concurrent λ ⫽ and λ ⊥ values of 14.581 and 2.132 W/m·K, respectively. The corresponding effects on the ductility, hardness, and dielectric properties of the SiR composites were explained. Moreover, the use of commercially available TC fillers, along with the effortless preparation method, enables the potential to produce h -BN based TIMs materials on a large scale for thermal management usage. [Display omitted] • A binary filler system consisting in-plane oriented 2D h -BN and 0D isotropic S–Al 2 O 3 is designed. • The addition of S–Al 2 O 3 filler between adjacent parallel h -BN flakes facilitates vertical heat transfer. • The binary structure of h -BN/S–Al 2 O 3 exhibits an anisotropic feature in the filler contact thermal resistances. • The binary filler strategy mitigates the trade-off between in-plane and through-plane thermal conductivity. [ABSTRACT FROM AUTHOR]

The development of lightweight shape memory materials with exceptional thermal insulation properties is highly important in aerospace applications. Herein, shape memory polyimide (PI) aerogels was fabricated by molecularly designed PI molecular chains through freeze gelation and subsequent thermal imidization. The freezing conditions were controlled to regulate the porous microstructures of the aerogels. The interactions between PI chains formed physically crosslinked netpoints, which could overcome the expansive force and capillary force, thereby creating a stable porous structure. Because shape memory was thermally activated, the shape memory mechanisms associated with the thermal conductivity of the aerogels were comprehensively explored. Upon decreasing the pore size, dense frameworks were formed, contributing to the improved thermo-mechanical and compressive properties. Moreover, the heat transfer efficiency in the aerogel skeleton was enhanced, resulting in a rapid shape recovery response. Benefiting from the physically crosslinked structures, including entangled chains and π-π interactions, the fabricated PI aerogels demonstrated exceptional shape fixation and recovery capabilities. These findings may provide guidelines for designing smart aerogels for aerospace applications. [Display omitted] • Shape memory polyimide aerogels with tunable porous microstructures were fabricated. • The size of the micropores was regulated by controlling the freezing conditions. • The reduction of pore size accelerated the shape recovery process. • The comprehensive exploration of the shape memory mechanisms was conducted. [ABSTRACT FROM AUTHOR]

This study explores the use of copper foam (CF) as a thermally conductive scaffold for phase change materials (PCMs), enhancing shape stability. Employing the ice template method (ITM), we fabricate a CF with a low-density, honeycomb structure and anisotropy from flake copper powders. The resulting phase change composites (PCCs), infused with paraffin wax (PW), showcase high thermal conductivity and shape retention compared to pure PW, with anisotropic heat flow due to directional channels in the CF. With a 34.4 wt% filling ratio, the PCCs achieve axial and lateral thermal conductivities of 2.85 W m−1 K−1 and 1.51 W m−1 K−1, respectively, and a thermal storage enthalpy of 158.18 J g−1. This work presents a novel method for advanced thermal energy storage and management, indicating the promise of ITM-derived CF in high-performance PCCs for diverse applications. • 3D porous copper with lighter weight, honeycomb-like and directional structure was prepared. • The composite has good form-stability and anti-leakage property. • The composite indicates high thermal conductivity of 2.85 W m−1k−1. • The composite indicates heat storage capability of 158.18 J g−1. • The composite has good anisotropic thermal conductivity. [ABSTRACT FROM AUTHOR]

• Measured through-thickness thermal conductivity of 2 × 2 twill carbon fabric using C-Therm Trident system with MTPS sensor. • Thermal conductivity is reduced by 38% under vacuum, attributed to presence of air in the porous structure of the fabric. • Layup thickness/configuration have negligible impact on thermal conductivity due to limited heat penetration. • Higher fabric compaction increases thermal conductivity owing to improved fibre nesting and contact. For high-fidelity simulation of heat transfer responses during various composite manufacturing processes involving heat, thermal conductivity (TC) of dry carbon weaves is essential. In this paper, the through-thickness TC properties of a 2 × 2 carbon twill weave are presented, employing a state-of-the-art C-Therm Trident module equipped with a Modified Transient Plane Source (MTPS) sensor. The effects of air presence, layup thickness, layup configuration, and fabric compaction on TC are investigated. Results reveal negligible impacts of layer number and configuration on TC. However, TC is significantly influenced by air presence and stack compaction. These findings provide improved comprehension of heat transfer in textile composites for more accurate process modelling and simulation using homogenized models. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) nanosheets, such as graphene and hexagonal boron nitride, are considered as the most promising fillers for enhancing thermal conductivity of polymers and phase-change materials. Nevertheless, the effect of various 2D nanosheets on the effective thermal conductivity of composites is not fully understood, and the corresponding prediction model is still lacking, since numerous influence factors and complex thermal transfer networks are involved. This paper aims to study the macroscopically effective thermal conductivity of the nanosheets-reinforced composites in a systematical way, and develop a robust machine learning based prediction model. To this end, a series of representative volume elements are reconstructed based on the SEM observations of experimental samples, and high-throughput simulations are performed via the updated lattice Boltzmann scheme proposed in our recent work. The effects of shape, size, orientation, intrinsic thermal conductivity, interface thermal resistance, surface coating, and hybrid filling of the 2D nanosheets are clarified. This work could provide a deep insight into the effective thermal conductivity of the nanosheets-reinforced composites, and may offer important guidelines for the custom-design of polymer and phase-change composites with targeted thermal performances. [Display omitted] [ABSTRACT FROM AUTHOR]

The electronics in logging tools are susceptible to thermal failure due to the extreme thermal environment. Passive thermal management system (PTMS) has been proven to protect the electronics for several hours. In this paper, a numerical model based on the equivalent thermal conductivity method was developed to determine the transient temperature of downhole electronics. Compared with the traditional analytical model or numerical model considering only one single heat transfer process, the complex heat transfer in the logging tool were comprehensively considered through the proposed model. Briefly, the heat transfer processes in the vacuum bottle were theoretically calculated rather than empirically acquired, and the convective and radiative heat transfer inside the vacuum bottle was equated to the temperature-dependent thermal conductivity of air. In addition, the proposed numerical model was compared with experiments and previous numerical models to verify the accuracy of the model, and the temperature deviation of the electronics was within 5 °C. Moreover, the computational time was greatly reduced compared with the traditional three-dimensional numerical simulation process while ensuring the accuracy of calculation. Therefore, the proposed numerical model achieved both computational accuracy and efficiency, which was expected to be widely used in the development of PTMS for logging tools. • A numerical model based on equivalent thermal conductivity method was proposed. • The complex heat transfer processes in PTMS were comprehensively considered. • The proposed model possessed high computational accuracy and efficiency. [ABSTRACT FROM AUTHOR]

U-bend deep borehole heat exchanger (DBHE) is a new promising kind of ground heat exchanger, but nowadays there are lack of accurate efficient models to simulate U-bend DBHE. This paper has developed a semi-analytical model of U-bend DBHE, which considers the unsteady heat transfer of borehole, heterogenous soil thermal properties and geothermal gradient. A 2D numerical model and an analytical model based on infinite line-source theory are also developed, and the three models are compared with experimental data for different boundary conditions. Based on the soil thermal properties estimated by logging data and rock identification, the three models have large errors when compared with experimental data. Based on a soil thermal conductivity λ s estimated by matching experimental data, the three models match better with experimental data, and it is inferred that λ s should be measured by thermal response test instead of rock identification. The semi-analytical model and 2D numerical model match very well for all the time, but the analytical model has large differences with 2D numerical model, which is because the analytical model ignores borehole heat capacity and time-varying heat flows at the borehole wall. This paper could provide a cost-effective semi-analytical model and some advices for U-bend DBHE. [ABSTRACT FROM AUTHOR]

We introduce the concept of physics-guided transfer learning to predict the thermal conductivity of an additively manufactured short-fiber reinforced polymer (SFRP) using micro-structural characteristics extracted from tensile tests. Developing composite manufacturing digital twins for SFRP composite processes like extrusion deposition additive manufacturing (EDAM) require extensive experimental material characterization. Even the same material system printed on different EDAM systems can result in significant changes to the printed micro-structure, affecting the mechanical and transport properties. This, in turn, makes characterization efforts expensive and time-consuming. Therefore, the objective of the paper is to address this experimental bottleneck and use prior information about the material manufactured in one extrusion system to predict its properties when manufactured in another system. To enable this framework, we assume that changes in properties of the same material when manufactured in different systems arise solely due to microstructural changes. To that end, we present a Bayesian framework that can transfer thermal conductivity properties across extrusion deposition additive manufacturing systems. While we discuss the transfer of thermal conductivity properties, the development is such that the framework can be used for the transfer of other properties that depend on microstructural characteristics defined in the manufacturing process. These include the coefficient of thermal expansion, viscoelastic properties, etc. The framework begins by using thermal conductivity data of the composite printed in one extrusion system to probabilistically infer the constituent thermal properties of the fiber and the polymer. Next, by conducting limited tensile tests of the same material printed in another extrusion system, we infer its orientation tensor. Finally, the inferred constituent thermal conductivity properties and the inferred orientation tensor are coupled using a micromechanics model to predict the thermal conductivity properties of the composite printed in the second extrusion system. We experimentally verify the predictions and show that our method provides a reliable framework for transferring material properties while accounting for epistemic and aleatory uncertainties. • A probabilistic framework to transfer material information across manufacturing systems. • A framework to account for surrogate uncertainty in parameter inference. • Demonstrate transfer of thermal conductivity across additive manufacturing systems. • Applications to transfer other properties including coefficient of thermal expansion. [ABSTRACT FROM AUTHOR]

Nanofluid has emerged as a promising heat transfer fluid (HTF) due to their significant thermophysical, and optical characteristics enhancement over base fluids. Hybrid nanofluids with multiple nanomaterials have the advantage of synergistic properties in comparison to monocomponent nanofluids. The present study proposes an energy-efficient and cleaner synthesis method for developing carbon quantum dot (C-dot), MXene, and a hybrid MXene/C-dot hybrid nanofluids, for heat transfer application. In-situ microwave pyrolysis technique and two-step method were adopted for nanomaterial and nanofluid synthesis. The morphological, phase structural, chemical, and elemental compositional analysis of the nanomaterials was performed. The material characterization confirms the hybridization of C-dot on MXene nanosheets. The thermal conductivity and volumetric heat capacity of the nanofluids were measured using the transient plane source (TPS) method. Thermal conductivity was observed to increase with nanofluid concentration and temperature. Results indicate that MXene has the highest thermal conductivity enhancement (50 %) over water, followed by hybrid (42.2 %) and C-dot nanofluid (33.2 %). The volumetric heat capacity of nanofluids decreased with concentration and temperature. A semi-empirical correlation, as a function of nanofluid concentration and temperature, was coined for predicting thermal conductivity and volumetric heat capacity. Optical property characterization study shows that C-dot nanofluid exhibited considerable absorption along the UV range, while MXene nanofluid showed absorption in the visible and near-infrared (NIR) region. Hybrid nanofluids demonstrated complementary absorption properties of C-dot and MXene nanofluids. [Display omitted] • Novel MXene/C-dot hybrid nanofluid was synthesized for heat transfer applications. • Study reveals the material and thermal-optical properties of C-dot, MXene and hybrid nanofluids. • Semi-empirical correlation were coined to predict thermal properties of nanofluids. • Highest thermal conductivity enhancement was exhibited by 0.2 wt% MXene nanofluid. • Hybrid nanofluid showed complementary absorption property of component nanofluids. [ABSTRACT FROM AUTHOR]