With the continuous development of the electronics industry,the energy density of modern electronic devices increases constantly,thus releasing a lot of heat during operation.Modern electronic devices take higher and ...With the continuous development of the electronics industry,the energy density of modern electronic devices increases constantly,thus releasing a lot of heat during operation.Modern electronic devices take higher and higher request to the thermal interface materials.Achieving high thermal conductivity needs to establish an interconnecting thermal conductivity network in the matrix.For this purpose,the suspension of Al203 and curdlan was first foamed to construct a bubble-templated continuous ceramic framework.Owing to the rapid gelation property of curdlan,we can easily remove moisture by hot air drying.Finally,the high thermally conductive composites are prepared by vacuum impregnation of silicone rubber.The result showed that composites prepared by our method have higher thermal conductivity than the samples obtained by traditional method.The thermal conductivity of the prepared composite material reached 1.253 W·m^(-1)·K·^-(1)when the alumina content was 69.6 wt%.This facile method is expected to be applied to the preparation of high-performance thermal interface materials.展开更多
As a thermosetting resin with excellent properties,epoxy resin is used in many areas such as electronics,transportation,aerospace,and other fields.However,its relatively low thermal conductivity limits its wide applic...As a thermosetting resin with excellent properties,epoxy resin is used in many areas such as electronics,transportation,aerospace,and other fields.However,its relatively low thermal conductivity limits its wide application in more demanding fields.Here,a three-dimensional carbon(3DC)network was prepared through NaCl template-assisted in situ chemical vapor deposition(CVD)and used to reinforce epoxy resin for enhancing its thermal conductivity.The 3DC was prepared with a molar ratio of sodium atom to carbon atom of 100:20,and argon atmosphere in CVD led to an optimal improvement in the thermal conductivity of epoxy resin.The thermal conductivity of epoxy resin increased by 18%when the filling content was 3 wt.%of 3DC network because of the high contact area,uniform dispersion,and enhanced formation of conductive paths with epoxy resin.As the amount of 3DC addition increases,the thermal conductivity of composites also increases.As an innovative exploration,the work presented in this paper is of great significance for the thermal conductivity application of epoxy resin in the future.展开更多
Heat dissipation performance is critical to the design of high-end equipment,such as integrated chips and high-precision machine tools.Owing to the advantages of artificial intelligence in solving complex tasks involv...Heat dissipation performance is critical to the design of high-end equipment,such as integrated chips and high-precision machine tools.Owing to the advantages of artificial intelligence in solving complex tasks involving a large number of variables,researchers have exploited deep learning to expedite the optimization of material properties,such as the heat dissipation of solid isotropic materials with penalization(SIMP).However,because the approach is limited by discrete datasets and labeled training forms,ensuring the continuous adaptation of the condition domain and maintaining the stability of the design structure remain major challenges in the current intelligent design methodology for thermally conductive structures.In this study,we propose an innovative intelligent design fram-ework integrating Conditional Deep Convolutional Generative Adversarial Networks(CDCGAN)with SIMP,capable of creating topology structures that meet prescribed thermal conduction performance.This proposed design strategy significantly reduces the computational time required to solve symmetric and random heat sink problems compared with existing design approaches and is approximately 98%faster than standard SIMP methods and 55.5%faster than conventional deep-learning-based methods.In addition,we benchmarked the design performance of the proposed framework against theoretical structural designs via experimental measurements.We observed a 50.1%reduction in the average temperature and a 28.2%reduction in the highest temperature in our designed topology compared with those theoretical structure designs.展开更多
Realizing effective enhancement in the thermally conductive performance of polymer bonded explosives(PBXs) is vital for improving the resultant environmental adaptabilities of the PBXs composites. Herein, a kind of pr...Realizing effective enhancement in the thermally conductive performance of polymer bonded explosives(PBXs) is vital for improving the resultant environmental adaptabilities of the PBXs composites. Herein, a kind of primary-secondary thermally conductive network was designed by water-suspension granulation, surface coating, and hot-pressing procedures in the graphene-based PBXs composites to greatly increase the thermal conductive performance of the composites. The primary network with a threedimensional structure provided the heat-conducting skeleton, while the secondary network in the polymer matrix bridged the primary network to increase the network density. The enhancement efficiency in the thermally conductive performance of the composites reached the highest value of 59.70% at a primary-secondary network ratio of 3:1. Finite element analysis confirmed the synergistic enhancement effect of the primary and secondary thermally conductive networks. This study introduces an innovative approach to designing network structures for PBX composites, significantly enhancing their thermal conductivity.展开更多
The internal hotspot temperature rise prediction in nanocrystalline high-frequency transformers(nanoHFTs) is essential to ensure reliable operation. This paper presents a three-dimensional thermal network(3DTN) model ...The internal hotspot temperature rise prediction in nanocrystalline high-frequency transformers(nanoHFTs) is essential to ensure reliable operation. This paper presents a three-dimensional thermal network(3DTN) model for epoxy resin encapsulated nano HFTs, which aims to precisely predict the temperature distribution inside the transformer in combination with the finite element method(FEM). A magnetothermal bidirectional coupling 3DTN model is established by analyzing the thermal conduction between the core, windings, and epoxy resin, while also considering the convection and radiation heat transfer mechanisms on the surface of the epoxy resin. The model considers the impact of loss distribution in the core and windings on the temperature field and adopts a simplified 1/2 thermal network model to reduce computational complexity. Furthermore, the results of FEM are compared with experimental results to verify the accuracy of the 3DTN model in predicting the temperature rise of nano HFT. The results show that the 3DTN model reduces errors by an average of 5.25% over the traditional two-dimensional thermal network(2DTN) model, particularly for temperature distributions in the windings and core. This paper provides a temperature rise prediction method for the thermal design and offers a theoretical basis and engineering guidance for the optimization of their thermal management systems.展开更多
This article addresses the three-dimensional stretched flow of the Jeffrey fluid with thermal radiation. The thermal conductivity of the fluid varies linearly with respect to temperature. Computations are performed fo...This article addresses the three-dimensional stretched flow of the Jeffrey fluid with thermal radiation. The thermal conductivity of the fluid varies linearly with respect to temperature. Computations are performed for the velocity and temperature fields. Graphs for the velocity and temperature are plotted to examine the behaviors with different parameters. Numerical values of the local Nusselt number are presented and discussed. The present results are compared with the existing limiting solutions, showing good agreement with each other.展开更多
Polyvinyl alcohol hydrogels have been used in wearable devices due to their good flexibility and biocompatibility.However,due to the low thermal conductivity(κ)of pure hydrogel,its further application in high power d...Polyvinyl alcohol hydrogels have been used in wearable devices due to their good flexibility and biocompatibility.However,due to the low thermal conductivity(κ)of pure hydrogel,its further application in high power devices is limited.To solve this problem,melamine sponge(MS)was used as the skeleton to wrap boron nitride nanosheets(BNNS)through repeated layering assembly,successfully preparing a three-dimensional(3D)boron nitride network(BNNS@MS),and PVA hydrogels were formed in the pores of the network.Due to the existence of the continuous phonon conduction network,the BNNS@MS/PVA exhibited an improvedκ.When the content of BNNS is about 6 wt.%,κof the hydrogel was increased to 1.12 W m^(-1)K^(-1),about two times higher than that of pure hydrogel.The solid heat conduction network and liquid convection network cooperate to achieve good thermal management ability.Combined with its high specific heat capacity,the composites have an important application prospect in the field of wearable flexible electronic thermal management.展开更多
As one of the core components of a magnetic refrigerator,magnetic refrigeration materials are expected to have not only a considerable magnetocaloric effect but also excellent thermal conductivity.The poor thermal con...As one of the core components of a magnetic refrigerator,magnetic refrigeration materials are expected to have not only a considerable magnetocaloric effect but also excellent thermal conductivity.The poor thermal conductivity of many competitive oxide-based magnetic refrigerants,exemplified by EuTiO3-based compounds,acts as a major limitation to their practical application.Therefore,improving the thermal conductivity of magnetic refrigeration materials has become a research emphasis of magnetic refrigeration in recent years.In this work,a series of EuTiO_(3)(ETO)/Cu composites with different copper additives was prepared using a solid-phase reaction method by introducing appropriate amounts of copper powder.The influence of the introduction of copper on the phase composition,microstructure,thermal conductivity,and magnetocaloric effect of the composites was systematically investigated.Unexpectedly,the thermal conductivity of the composites is enhanced by up to 260%due to copper addition,accompanied by only a 5%decrease in magnetic entropy change and refrigerating capacity.Copper additive forms localized thermal conductive networks and promotes the densification process,resulting in significantly enhanced thermal conductivity of the composites.This work demonstrates the feasibility of improving the thermal conductivity of oxide-base d magnetic refrigeration materials by introducing highly thermally conductive substances.展开更多
To develop a high performance gray cast iron with high tensile strength and thermal conductivity, multivariable analysis of microstructural effects on properties of gray cast iron was performed. The concerned paramete...To develop a high performance gray cast iron with high tensile strength and thermal conductivity, multivariable analysis of microstructural effects on properties of gray cast iron was performed. The concerned parameters consisted of graphite content, maximum graphite length, primary dendrite percentage and microhardness of the matrix. Under the superposed influence of various parameters, the relationships between thermal conductivity and structural characteristics become irregular, as well as the effects of graphite length on the strength. An adaptive neuro-fuzzy inference system was built to link the parameters and properties. A sensitivity test was then performed to rank the relative impact of parameters. It was found that the dominant parameter for tensile strength is graphite content, while the most relative parameter for thermal conductivity is maximum graphite length. The most effective method to simultaneously improve the tensile and thermal conductivity of gray cast iron is to reduce the carbon equivalent and increase the length of graphite flakes.展开更多
With the increasing power density and integration of electronic devices,polymeric composites with high thermal conductivity(TC)are in urgent demand for solving heat accumulation issues.However,the direct introduction ...With the increasing power density and integration of electronic devices,polymeric composites with high thermal conductivity(TC)are in urgent demand for solving heat accumulation issues.However,the direct introduction of inorganic fillers into a polymer matrix at low filler content usually leads to low TC enhancement.In this work,an interconnected three-dimensional(3D)polysulfone/hexagonal boron nitride-carbon nanofiber(PSF/BN-CNF)skeleton was prepared via the salt templated method to address this issue.After embedding into the epoxy(EP),the EP/PSF/BN-CNF composite presents a high TC of 2.18 W m^(−1) K^(−1) at a low filler loading of 28.61 wt%,corresponding to a TC enhancement of 990%compared to the neat epoxy.The enhanced TC is mainly attributed to the fabricated 3D interconnected structure and the efficient synergistic effect of BN and CNF.In addition,the TC of the epoxy composites can be further increased to 2.85 W m^(−1) K^(−1) at the same filler loading through a post-heat treatment of the PSF/BN-CNF skeletons.After carbonization at 1500°C,the adhesive PSF was converted into carbonaceous layers,which could serve as a thermally conductive glue to connect the filler network,further decreasing the interfacial thermal resistance and promoting phonon transport.Besides,the good heat dissipation performance of the EP/C/BN-CNF composites was directly confirmed by thermal infrared imaging,indicating a bright and broad application in the thermal management of modern electronics and energy fields.展开更多
Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neur...Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.展开更多
Asymmetric tree-like branched networks are explored by geometric algorithms. Based on the network, an analysis of the thermal conductivity is presented. The relationship between effective thermal conductivity and geom...Asymmetric tree-like branched networks are explored by geometric algorithms. Based on the network, an analysis of the thermal conductivity is presented. The relationship between effective thermal conductivity and geometric structures is obtained by using the thermal-electrical analogy technique. In all studied cases, a clear behaviour is observed, where angle （δ,θ） among parent branching extended lines, branches and parameter of the geometric structures have stronger effects on the effective thermal conductivity. When the angle δ is fixed, the optical diameter ratio β＋ is dependent on angle θ. Moreover, γand m are not related to β＊. The longer the branch is, the smaller the effective thermal conductivity will be. It is also found that when the angle θ〈δ2, the higher the iteration m is, the lower the thermal conductivity will be and it tends to zero, otherwise, it is bigger than zero. When the diameter ratio β1 〈 0.707 and angle δ is bigger, the optimal k of the perfect ratio increases with the increase of the angle δ; when β1 〉 0.707, the optimal k decreases. In addition, the effective thermal conductivity is always less than that of single channel material. The present results also show that the effective thermal conductivity of the asymmetric tree-like branched networks does not obey Murray＇s law.展开更多
Several theoretical models have been developed so far to predict the thermal conductivities of carbon nanotube(CNT)networks.However,these models overestimated the thermal conductivity significantly.In this paper,we cl...Several theoretical models have been developed so far to predict the thermal conductivities of carbon nanotube(CNT)networks.However,these models overestimated the thermal conductivity significantly.In this paper,we claimed that a CNT network can be considered as a contact thermal resistance network.In the contact thermal resistance network,the temperature of an individual CNT is nonuniform and the intrinsic thermal resistance of CNTs can be ignored.Compared with the previous models,the model we proposed agrees well with the experimental results of single-walled CNT networks.展开更多
A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature range.In most radiators that are used to cool an engine,water serves as a cooling fluid.The performance ...A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature range.In most radiators that are used to cool an engine,water serves as a cooling fluid.The performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling water.Concentration and uniformity of nanoparticle distribution are the two major factors for the practical use of nanofluids.The shape and size of nanoparticles also have a great impact on the performance of heat transfer.Many researchers are investigating the impact of nanoparticles on heat transfer.This study aims to develop an artificial neural network(ANN)model for predicting the thermal conductivity of an ethylene glycol(EG)/waterbased crystalline nanocellulose(CNC)nanofluid for cooling internal combustion engine.The implementation of an artificial neural network considering different activation functions in the hidden layer is made to find the bestmodel for the cooling of an engine using the nanofluid.Accuracies of the model with different activation functions in artificial neural networks are analyzed for different nanofluid concentrations and temperatures.In artificial neural networks,Levenberg–Marquardt is an optimization approach used with activation functions,including Tansig and Logsig functions in the training phase.The findings of each training,testing,and validation phase are presented to demonstrate the network that provides the highest level of accuracy.The best result was obtained with Tansig,which has a correlation of 0.99903 and an error of 3.7959×10^(–8).It has also been noticed that the Logsig function can also be a good model due to its correlation of 0.99890 and an error of 4.9218×10^(–8).Thus ourANNwith Tansig and Logsig functions demonstrates a high correlation between the actual output and the predicted output.展开更多
Electronic devices generate heat during operation and require efficient thermal management to extend the lifetime and prevent performance degradation.Featured by its exceptional thermal conductivity,graphene is an ide...Electronic devices generate heat during operation and require efficient thermal management to extend the lifetime and prevent performance degradation.Featured by its exceptional thermal conductivity,graphene is an ideal functional filler for fabricating thermally conductive polymer composites to provide efficient thermal management.Extensive studies have been focusing on constructing graphene networks in polymer composites to achieve high thermal conductivities.Compared with conventional composite fabrications by directly mixing graphene with polymers,preconstruction of three-dimensional graphene networks followed by backfilling polymers represents a promising way to produce composites with higher performances,enabling high manufacturing flexibility and controllability.In this review,we first summarize the factors that affect thermal conductivity of graphene composites and strategies for fabricating highly thermally conductive graphene/polymer composites.Subsequently,we give the reasoning behind using preconstructed three-dimensional graphene networks for fabricating thermally conductive polymer composites and highlight their potential applications.Finally,our insight into the existing bottlenecks and opportunities is provided for developing preconstructed porous architectures of graphene and their thermally conductive composites.展开更多
Constructing controllable thermal conduction networks is the key to efficiently improve thermal conductivities of polymer composites.In this work,graphite oxide(GO)and functionalized carbon nanotubes(f-CNTs)are combin...Constructing controllable thermal conduction networks is the key to efficiently improve thermal conductivities of polymer composites.In this work,graphite oxide(GO)and functionalized carbon nanotubes(f-CNTs)are combined to prepare“Line-Plane”-like hetero-structured thermally conductive GO@f-CNTs fillers,which are then performed to construct controllable 3D GO@f-CNTs thermal conduction networks via selfsacrificing template method based on oxalic acid.Subsequently,thermally conductive GO@f-CNTs/polydimethylsiloxane(PDMS)composites are fabricated via casting method.When the size of oxalic acid is 0.24 mm and the volume fraction of GO@f-CNTs is 60 vol%,GO@f-CNTs/PDMS composites present the optimal thermal conductivity coefficient(λ,4.00 W·m^(-1)·K^(-1)),about 20 times that of theλof neat PDMS(0.20 W·m^(-1)·K^(-1)),also much higher than theλ(2.44 W·m^(-1)·K^(-1))of GO/f-CNTs/PDMS composites with the same amount of randomly dispersed fillers.Meanwhile,the obtained GO@f-CNTs/PDMS composites have excellent thermal stability,whoseλdeviation is only about 3%after 500 thermal cycles(20-200℃).展开更多
The advent of the 5G era has stimulated the rapid development of high power electronics with dense integration.Three-dimensional(3D)thermally conductive networks,possessing high thermal and electrical conductivities a...The advent of the 5G era has stimulated the rapid development of high power electronics with dense integration.Three-dimensional(3D)thermally conductive networks,possessing high thermal and electrical conductivities and many different structures,are regarded as key materials to improve the performance of electronic devices.We provide a critical overview of carbonbased 3D thermally conductive networks,emphasizing their preparation-structure-property relationships and their applications in different scenarios.A detailed discussion of the microscopic principles of thermal conductivity is provided,which is crucial for increasing it.This is followed by an in-depth account of the construction of 3D networks using different carbon materials,such as graphene,carbon foam,and carbon nanotubes.Techniques for the assembly of two-dimensional graphene into 3D networks and their effects on thermal conductivity are emphasized.Finally,the existing challenges and future prospects for 3D carbon-based thermally conductive networks are discussed.展开更多
Phase change materials(PCMs)are widely considered as promising energy storage materials for solar/electro-thermal energy storage.Nevertheless,the inherent low thermal/electrical conductivities of most PCMs limit their...Phase change materials(PCMs)are widely considered as promising energy storage materials for solar/electro-thermal energy storage.Nevertheless,the inherent low thermal/electrical conductivities of most PCMs limit their energy conversion efficiencies,hindering their practical applications.Herein,we fabricate a highly thermally/electrically conductive solid-solid phase change composite(PCC)enabled by forming aligned graphite networks through pressing the mixture of the trimethylolethane and porous expanded graphite(EG).Experiments indicate that both the thermal and electrical conductivities of the PCC increase with increasing mass proportion of the EG because the aligned graphite networks establish highly conductive pathways.Meanwhile,the PCC4 sample with the EG proportion of 20wt%can achieve a high thermal conductivity of 12.82±0.38W·m^(-1)·K^(-1)and a high electrical conductivity of 4.11±0.02S·cm^(-1)in the lengthwise direction.Furthermore,a solar-thermal energy storage device incorporating the PCC4,a solar selective absorber,and a highly transparent glass is developed,which reaches a high solar-thermal efficiency of 77.30±2.71%under 3.0 suns.Moreover,the PCC4 can also reach a high electro-thermal efficiency of 91.62±3.52%at a low voltage of 3.6V,demonstrating its superior electro-thermal storage performance.Finally,stability experiments indicate that PCCs exhibit stabilized performance in prolonged TES operations.Overall,this work offers highly conductive and cost-effective PCCs,which are suitable for large-scale and efficient solar/electro-thermal energy storage.展开更多
The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.展开更多
Achieving thermal management composite material with isotropic thermal dissipation property by using an environmentally friendly and efficient method is one of the most challenging techniques as a traditional approach...Achieving thermal management composite material with isotropic thermal dissipation property by using an environmentally friendly and efficient method is one of the most challenging techniques as a traditional approach tending to form a horizontally arranged network within the polymer matrix or the preparation steps which are unduly cumbersome.What presented here is a closestack thermally conductive three-dimensional(3D)hybrid network structure prepared by a simple and green strategy that intercalating the modified aluminum oxide(m-Al_(2)O_(3))spheres of different sizes into the modified two-dimensional(2D)boron nitride(m-h-BN)flakes.An effective 3D network is created by the multi-dimensional fillers through volume exclusion and synergistic effects.The m-h-BN flakes facilitate in-plane heat transfer,while the variously sized m-Al_(2)O_(3)spheres insert into the gaps between adjacent m-h-BN flakes,which is conducive to the heat transfer in the out-of-plane direction.Additionally,strong interactions between the m-Al_(2)O_(3)and m-h-BN promote the effective heat flux inside the 3D hybrid network structure.The 3D hybrid composite displays favorable quasi-isotropic heat dissipation property(through-plane thermal conductivity of 2.2 W·m^(-1)·K^(-1)and in-plane thermal conductivity of 11.6 W·m^(-1)·K^(-1))in comparison with the single-filler composites.Furthermore,the hybrid-filler composite has excellent mechanical properties and thermal stability.The efficient heat dissipation capacity of the hybrid composite is further confirmed by a finite element simulation,which indicates that the sphere-flake hybrid structure possesses a higher thermal conductivity and faster thermal response performance than the single-filler system.The composite material has great potential in meeting the needs of emerging and advancing power systems.展开更多
基金the financial support from the Joint Foundation of Ministry of Education for equipment pre-research(No.6141A020222XX)Post-doctoral Science Fund(No.2020M680405).
文摘With the continuous development of the electronics industry,the energy density of modern electronic devices increases constantly,thus releasing a lot of heat during operation.Modern electronic devices take higher and higher request to the thermal interface materials.Achieving high thermal conductivity needs to establish an interconnecting thermal conductivity network in the matrix.For this purpose,the suspension of Al203 and curdlan was first foamed to construct a bubble-templated continuous ceramic framework.Owing to the rapid gelation property of curdlan,we can easily remove moisture by hot air drying.Finally,the high thermally conductive composites are prepared by vacuum impregnation of silicone rubber.The result showed that composites prepared by our method have higher thermal conductivity than the samples obtained by traditional method.The thermal conductivity of the prepared composite material reached 1.253 W·m^(-1)·K·^-(1)when the alumina content was 69.6 wt%.This facile method is expected to be applied to the preparation of high-performance thermal interface materials.
基金the Key Projects of Tianjin Natural Science Foundation(No.16ZXCLGX00130).
文摘As a thermosetting resin with excellent properties,epoxy resin is used in many areas such as electronics,transportation,aerospace,and other fields.However,its relatively low thermal conductivity limits its wide application in more demanding fields.Here,a three-dimensional carbon(3DC)network was prepared through NaCl template-assisted in situ chemical vapor deposition(CVD)and used to reinforce epoxy resin for enhancing its thermal conductivity.The 3DC was prepared with a molar ratio of sodium atom to carbon atom of 100:20,and argon atmosphere in CVD led to an optimal improvement in the thermal conductivity of epoxy resin.The thermal conductivity of epoxy resin increased by 18%when the filling content was 3 wt.%of 3DC network because of the high contact area,uniform dispersion,and enhanced formation of conductive paths with epoxy resin.As the amount of 3DC addition increases,the thermal conductivity of composites also increases.As an innovative exploration,the work presented in this paper is of great significance for the thermal conductivity application of epoxy resin in the future.
基金Supported by National Natural Science Foundation of China(Grant Nos.52222508 and 52335011)。
文摘Heat dissipation performance is critical to the design of high-end equipment,such as integrated chips and high-precision machine tools.Owing to the advantages of artificial intelligence in solving complex tasks involving a large number of variables,researchers have exploited deep learning to expedite the optimization of material properties,such as the heat dissipation of solid isotropic materials with penalization(SIMP).However,because the approach is limited by discrete datasets and labeled training forms,ensuring the continuous adaptation of the condition domain and maintaining the stability of the design structure remain major challenges in the current intelligent design methodology for thermally conductive structures.In this study,we propose an innovative intelligent design fram-ework integrating Conditional Deep Convolutional Generative Adversarial Networks(CDCGAN)with SIMP,capable of creating topology structures that meet prescribed thermal conduction performance.This proposed design strategy significantly reduces the computational time required to solve symmetric and random heat sink problems compared with existing design approaches and is approximately 98%faster than standard SIMP methods and 55.5%faster than conventional deep-learning-based methods.In addition,we benchmarked the design performance of the proposed framework against theoretical structural designs via experimental measurements.We observed a 50.1%reduction in the average temperature and a 28.2%reduction in the highest temperature in our designed topology compared with those theoretical structure designs.
基金supported by the National Natural Science Foundation of China (Grant Nos. 22475179 and 22275173)。
文摘Realizing effective enhancement in the thermally conductive performance of polymer bonded explosives(PBXs) is vital for improving the resultant environmental adaptabilities of the PBXs composites. Herein, a kind of primary-secondary thermally conductive network was designed by water-suspension granulation, surface coating, and hot-pressing procedures in the graphene-based PBXs composites to greatly increase the thermal conductive performance of the composites. The primary network with a threedimensional structure provided the heat-conducting skeleton, while the secondary network in the polymer matrix bridged the primary network to increase the network density. The enhancement efficiency in the thermally conductive performance of the composites reached the highest value of 59.70% at a primary-secondary network ratio of 3:1. Finite element analysis confirmed the synergistic enhancement effect of the primary and secondary thermally conductive networks. This study introduces an innovative approach to designing network structures for PBX composites, significantly enhancing their thermal conductivity.
基金supported by the Project of the National Key Research and Development Program of China under Grant 2022YFB2404100。
文摘The internal hotspot temperature rise prediction in nanocrystalline high-frequency transformers(nanoHFTs) is essential to ensure reliable operation. This paper presents a three-dimensional thermal network(3DTN) model for epoxy resin encapsulated nano HFTs, which aims to precisely predict the temperature distribution inside the transformer in combination with the finite element method(FEM). A magnetothermal bidirectional coupling 3DTN model is established by analyzing the thermal conduction between the core, windings, and epoxy resin, while also considering the convection and radiation heat transfer mechanisms on the surface of the epoxy resin. The model considers the impact of loss distribution in the core and windings on the temperature field and adopts a simplified 1/2 thermal network model to reduce computational complexity. Furthermore, the results of FEM are compared with experimental results to verify the accuracy of the 3DTN model in predicting the temperature rise of nano HFT. The results show that the 3DTN model reduces errors by an average of 5.25% over the traditional two-dimensional thermal network(2DTN) model, particularly for temperature distributions in the windings and core. This paper provides a temperature rise prediction method for the thermal design and offers a theoretical basis and engineering guidance for the optimization of their thermal management systems.
基金supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah,Saudi Arabia (No. 2-135/HiCi)
文摘This article addresses the three-dimensional stretched flow of the Jeffrey fluid with thermal radiation. The thermal conductivity of the fluid varies linearly with respect to temperature. Computations are performed for the velocity and temperature fields. Graphs for the velocity and temperature are plotted to examine the behaviors with different parameters. Numerical values of the local Nusselt number are presented and discussed. The present results are compared with the existing limiting solutions, showing good agreement with each other.
基金the National Natural Science Foundation of China(Nos.52173078,52130303,and 51803151)the Young Elite Scientists Sponsorship Program by CAST(No.2019QNRC001)。
文摘Polyvinyl alcohol hydrogels have been used in wearable devices due to their good flexibility and biocompatibility.However,due to the low thermal conductivity(κ)of pure hydrogel,its further application in high power devices is limited.To solve this problem,melamine sponge(MS)was used as the skeleton to wrap boron nitride nanosheets(BNNS)through repeated layering assembly,successfully preparing a three-dimensional(3D)boron nitride network(BNNS@MS),and PVA hydrogels were formed in the pores of the network.Due to the existence of the continuous phonon conduction network,the BNNS@MS/PVA exhibited an improvedκ.When the content of BNNS is about 6 wt.%,κof the hydrogel was increased to 1.12 W m^(-1)K^(-1),about two times higher than that of pure hydrogel.The solid heat conduction network and liquid convection network cooperate to achieve good thermal management ability.Combined with its high specific heat capacity,the composites have an important application prospect in the field of wearable flexible electronic thermal management.
基金Project supported by the National Key R&D Program of China(2021YFB3501204)the National Science Fund for Distinguished Young Scholars(51925605)+1 种基金the National Science Foundation for Excellent Young Scholars(52222107)the National Natural Science Foundation of China(52171195,52201036)。
文摘As one of the core components of a magnetic refrigerator,magnetic refrigeration materials are expected to have not only a considerable magnetocaloric effect but also excellent thermal conductivity.The poor thermal conductivity of many competitive oxide-based magnetic refrigerants,exemplified by EuTiO3-based compounds,acts as a major limitation to their practical application.Therefore,improving the thermal conductivity of magnetic refrigeration materials has become a research emphasis of magnetic refrigeration in recent years.In this work,a series of EuTiO_(3)(ETO)/Cu composites with different copper additives was prepared using a solid-phase reaction method by introducing appropriate amounts of copper powder.The influence of the introduction of copper on the phase composition,microstructure,thermal conductivity,and magnetocaloric effect of the composites was systematically investigated.Unexpectedly,the thermal conductivity of the composites is enhanced by up to 260%due to copper addition,accompanied by only a 5%decrease in magnetic entropy change and refrigerating capacity.Copper additive forms localized thermal conductive networks and promotes the densification process,resulting in significantly enhanced thermal conductivity of the composites.This work demonstrates the feasibility of improving the thermal conductivity of oxide-base d magnetic refrigeration materials by introducing highly thermally conductive substances.
文摘To develop a high performance gray cast iron with high tensile strength and thermal conductivity, multivariable analysis of microstructural effects on properties of gray cast iron was performed. The concerned parameters consisted of graphite content, maximum graphite length, primary dendrite percentage and microhardness of the matrix. Under the superposed influence of various parameters, the relationships between thermal conductivity and structural characteristics become irregular, as well as the effects of graphite length on the strength. An adaptive neuro-fuzzy inference system was built to link the parameters and properties. A sensitivity test was then performed to rank the relative impact of parameters. It was found that the dominant parameter for tensile strength is graphite content, while the most relative parameter for thermal conductivity is maximum graphite length. The most effective method to simultaneously improve the tensile and thermal conductivity of gray cast iron is to reduce the carbon equivalent and increase the length of graphite flakes.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(No.51925403)the Major Research Plan of the National Natural Science Foundation of China(No.91934302)+1 种基金the National Natural Science Foundation of China(Nos.21676052,21606042)the Funding for Exploratory Projects of the National Key Laboratory of Chemical Engineering(SKL-ChE-20T07).
文摘With the increasing power density and integration of electronic devices,polymeric composites with high thermal conductivity(TC)are in urgent demand for solving heat accumulation issues.However,the direct introduction of inorganic fillers into a polymer matrix at low filler content usually leads to low TC enhancement.In this work,an interconnected three-dimensional(3D)polysulfone/hexagonal boron nitride-carbon nanofiber(PSF/BN-CNF)skeleton was prepared via the salt templated method to address this issue.After embedding into the epoxy(EP),the EP/PSF/BN-CNF composite presents a high TC of 2.18 W m^(−1) K^(−1) at a low filler loading of 28.61 wt%,corresponding to a TC enhancement of 990%compared to the neat epoxy.The enhanced TC is mainly attributed to the fabricated 3D interconnected structure and the efficient synergistic effect of BN and CNF.In addition,the TC of the epoxy composites can be further increased to 2.85 W m^(−1) K^(−1) at the same filler loading through a post-heat treatment of the PSF/BN-CNF skeletons.After carbonization at 1500°C,the adhesive PSF was converted into carbonaceous layers,which could serve as a thermally conductive glue to connect the filler network,further decreasing the interfacial thermal resistance and promoting phonon transport.Besides,the good heat dissipation performance of the EP/C/BN-CNF composites was directly confirmed by thermal infrared imaging,indicating a bright and broad application in the thermal management of modern electronics and energy fields.
文摘Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.
基金Project supported by the State Key Development Program for Basic Research of China (Grant No 2006CB708612)the National Natural Science Foundation of China (Grant No 10572130)the Natural Science Foundation of Zhejiang Province, China (Grant No Y607425)
文摘Asymmetric tree-like branched networks are explored by geometric algorithms. Based on the network, an analysis of the thermal conductivity is presented. The relationship between effective thermal conductivity and geometric structures is obtained by using the thermal-electrical analogy technique. In all studied cases, a clear behaviour is observed, where angle （δ,θ） among parent branching extended lines, branches and parameter of the geometric structures have stronger effects on the effective thermal conductivity. When the angle δ is fixed, the optical diameter ratio β＋ is dependent on angle θ. Moreover, γand m are not related to β＊. The longer the branch is, the smaller the effective thermal conductivity will be. It is also found that when the angle θ〈δ2, the higher the iteration m is, the lower the thermal conductivity will be and it tends to zero, otherwise, it is bigger than zero. When the diameter ratio β1 〈 0.707 and angle δ is bigger, the optimal k of the perfect ratio increases with the increase of the angle δ; when β1 〉 0.707, the optimal k decreases. In addition, the effective thermal conductivity is always less than that of single channel material. The present results also show that the effective thermal conductivity of the asymmetric tree-like branched networks does not obey Murray＇s law.
基金Project support by the National Natural Science Foundation of China(Grant No.52127811)Department of Science and Technology of Jiangsu Province,China(Grant No.BK20220032)。
文摘Several theoretical models have been developed so far to predict the thermal conductivities of carbon nanotube(CNT)networks.However,these models overestimated the thermal conductivity significantly.In this paper,we claimed that a CNT network can be considered as a contact thermal resistance network.In the contact thermal resistance network,the temperature of an individual CNT is nonuniform and the intrinsic thermal resistance of CNTs can be ignored.Compared with the previous models,the model we proposed agrees well with the experimental results of single-walled CNT networks.
基金supported by the International Publication Research Grant No.RDU223301.
文摘A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature range.In most radiators that are used to cool an engine,water serves as a cooling fluid.The performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling water.Concentration and uniformity of nanoparticle distribution are the two major factors for the practical use of nanofluids.The shape and size of nanoparticles also have a great impact on the performance of heat transfer.Many researchers are investigating the impact of nanoparticles on heat transfer.This study aims to develop an artificial neural network(ANN)model for predicting the thermal conductivity of an ethylene glycol(EG)/waterbased crystalline nanocellulose(CNC)nanofluid for cooling internal combustion engine.The implementation of an artificial neural network considering different activation functions in the hidden layer is made to find the bestmodel for the cooling of an engine using the nanofluid.Accuracies of the model with different activation functions in artificial neural networks are analyzed for different nanofluid concentrations and temperatures.In artificial neural networks,Levenberg–Marquardt is an optimization approach used with activation functions,including Tansig and Logsig functions in the training phase.The findings of each training,testing,and validation phase are presented to demonstrate the network that provides the highest level of accuracy.The best result was obtained with Tansig,which has a correlation of 0.99903 and an error of 3.7959×10^(–8).It has also been noticed that the Logsig function can also be a good model due to its correlation of 0.99890 and an error of 4.9218×10^(–8).Thus ourANNwith Tansig and Logsig functions demonstrates a high correlation between the actual output and the predicted output.
文摘Electronic devices generate heat during operation and require efficient thermal management to extend the lifetime and prevent performance degradation.Featured by its exceptional thermal conductivity,graphene is an ideal functional filler for fabricating thermally conductive polymer composites to provide efficient thermal management.Extensive studies have been focusing on constructing graphene networks in polymer composites to achieve high thermal conductivities.Compared with conventional composite fabrications by directly mixing graphene with polymers,preconstruction of three-dimensional graphene networks followed by backfilling polymers represents a promising way to produce composites with higher performances,enabling high manufacturing flexibility and controllability.In this review,we first summarize the factors that affect thermal conductivity of graphene composites and strategies for fabricating highly thermally conductive graphene/polymer composites.Subsequently,we give the reasoning behind using preconstructed three-dimensional graphene networks for fabricating thermally conductive polymer composites and highlight their potential applications.Finally,our insight into the existing bottlenecks and opportunities is provided for developing preconstructed porous architectures of graphene and their thermally conductive composites.
基金financially supported by the National Natural Science Foundation of China(No.51973173)Technological Base Scientific Research Projects(Highly Thermally Conductive Nonmetal Materials)+3 种基金Natural Science Foundation of Chongqing,China(No.2023NSCQ-MSX2547)Shaanxi Province Key Research and Development Plan Project(No.2023-YBGY-461)Fundamental Research Funds for the Central Universities,the Innovation Capability Support Program of Shaanxi(No.2024RS-CXTD-57)financially supported by Polymer Electromagnetic Functional Materials Innovation Team of Shaanxi Sanqin Scholars。
文摘Constructing controllable thermal conduction networks is the key to efficiently improve thermal conductivities of polymer composites.In this work,graphite oxide(GO)and functionalized carbon nanotubes(f-CNTs)are combined to prepare“Line-Plane”-like hetero-structured thermally conductive GO@f-CNTs fillers,which are then performed to construct controllable 3D GO@f-CNTs thermal conduction networks via selfsacrificing template method based on oxalic acid.Subsequently,thermally conductive GO@f-CNTs/polydimethylsiloxane(PDMS)composites are fabricated via casting method.When the size of oxalic acid is 0.24 mm and the volume fraction of GO@f-CNTs is 60 vol%,GO@f-CNTs/PDMS composites present the optimal thermal conductivity coefficient(λ,4.00 W·m^(-1)·K^(-1)),about 20 times that of theλof neat PDMS(0.20 W·m^(-1)·K^(-1)),also much higher than theλ(2.44 W·m^(-1)·K^(-1))of GO/f-CNTs/PDMS composites with the same amount of randomly dispersed fillers.Meanwhile,the obtained GO@f-CNTs/PDMS composites have excellent thermal stability,whoseλdeviation is only about 3%after 500 thermal cycles(20-200℃).
文摘The advent of the 5G era has stimulated the rapid development of high power electronics with dense integration.Three-dimensional(3D)thermally conductive networks,possessing high thermal and electrical conductivities and many different structures,are regarded as key materials to improve the performance of electronic devices.We provide a critical overview of carbonbased 3D thermally conductive networks,emphasizing their preparation-structure-property relationships and their applications in different scenarios.A detailed discussion of the microscopic principles of thermal conductivity is provided,which is crucial for increasing it.This is followed by an in-depth account of the construction of 3D networks using different carbon materials,such as graphene,carbon foam,and carbon nanotubes.Techniques for the assembly of two-dimensional graphene into 3D networks and their effects on thermal conductivity are emphasized.Finally,the existing challenges and future prospects for 3D carbon-based thermally conductive networks are discussed.
基金supported by the Natural Science Foundation of Hunan Province(No.2024JJ4059)Changsha Outstanding Innovative Youth Training Program(No.kq2306010)+1 种基金National Natural Science Foundation of China(No.52176093)the Central South University Innovation-Driven Research Programme(No.2023CXQD055).
文摘Phase change materials(PCMs)are widely considered as promising energy storage materials for solar/electro-thermal energy storage.Nevertheless,the inherent low thermal/electrical conductivities of most PCMs limit their energy conversion efficiencies,hindering their practical applications.Herein,we fabricate a highly thermally/electrically conductive solid-solid phase change composite(PCC)enabled by forming aligned graphite networks through pressing the mixture of the trimethylolethane and porous expanded graphite(EG).Experiments indicate that both the thermal and electrical conductivities of the PCC increase with increasing mass proportion of the EG because the aligned graphite networks establish highly conductive pathways.Meanwhile,the PCC4 sample with the EG proportion of 20wt%can achieve a high thermal conductivity of 12.82±0.38W·m^(-1)·K^(-1)and a high electrical conductivity of 4.11±0.02S·cm^(-1)in the lengthwise direction.Furthermore,a solar-thermal energy storage device incorporating the PCC4,a solar selective absorber,and a highly transparent glass is developed,which reaches a high solar-thermal efficiency of 77.30±2.71%under 3.0 suns.Moreover,the PCC4 can also reach a high electro-thermal efficiency of 91.62±3.52%at a low voltage of 3.6V,demonstrating its superior electro-thermal storage performance.Finally,stability experiments indicate that PCCs exhibit stabilized performance in prolonged TES operations.Overall,this work offers highly conductive and cost-effective PCCs,which are suitable for large-scale and efficient solar/electro-thermal energy storage.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
基金financially supported by the National Natural Science Foundation of China(No.51972162)。
文摘Achieving thermal management composite material with isotropic thermal dissipation property by using an environmentally friendly and efficient method is one of the most challenging techniques as a traditional approach tending to form a horizontally arranged network within the polymer matrix or the preparation steps which are unduly cumbersome.What presented here is a closestack thermally conductive three-dimensional(3D)hybrid network structure prepared by a simple and green strategy that intercalating the modified aluminum oxide(m-Al_(2)O_(3))spheres of different sizes into the modified two-dimensional(2D)boron nitride(m-h-BN)flakes.An effective 3D network is created by the multi-dimensional fillers through volume exclusion and synergistic effects.The m-h-BN flakes facilitate in-plane heat transfer,while the variously sized m-Al_(2)O_(3)spheres insert into the gaps between adjacent m-h-BN flakes,which is conducive to the heat transfer in the out-of-plane direction.Additionally,strong interactions between the m-Al_(2)O_(3)and m-h-BN promote the effective heat flux inside the 3D hybrid network structure.The 3D hybrid composite displays favorable quasi-isotropic heat dissipation property(through-plane thermal conductivity of 2.2 W·m^(-1)·K^(-1)and in-plane thermal conductivity of 11.6 W·m^(-1)·K^(-1))in comparison with the single-filler composites.Furthermore,the hybrid-filler composite has excellent mechanical properties and thermal stability.The efficient heat dissipation capacity of the hybrid composite is further confirmed by a finite element simulation,which indicates that the sphere-flake hybrid structure possesses a higher thermal conductivity and faster thermal response performance than the single-filler system.The composite material has great potential in meeting the needs of emerging and advancing power systems.
