The Influence of Reinforced Fibers and Opacifiers on the Effective Thermal Conductivity of Silica Aerogels

Fiber-particle-reinforced silica aerogels are widely applied in thermal insulation. Knowing their effective thermal conductivity (ETC) and radiative characteristics under high temperatures is necessary to improve their performance. This article first analyzes the radiation characteristics of silica aerogels doped with opacifier particles and reinforced fibers, and then a universal model is established to predict the ETC. Furthermore, the impacts of different parameters of opacifier particles and reinforced fibers on the thermal insulation performance of silica aerogels are investigated. The results indicate that SiC exhibits comparatively strong absorption characteristics, making it a good alternative for opacifiers to improve thermal insulation performance under high temperatures. For the given type and volume fraction of opacifier particles, there exists an optimal diameter and volume fraction to achieve the best insulation performance of silica aerogel under a certain temperature. Considering that SiO2 fibers exhibit a limited extinction capability and higher conductive thermal conductivity under high temperatures, for fiber-particle-reinforced silica aerogels, it is beneficial for their insulation performance to reduce the fiber volume fraction when the required mechanical properties are satisfied.

Computational Requirements for Modeling Thermal Conduction in Polymeric Phase-Change Materials: Periodic Hard Spheres Case

This research focuses on modeling heat transfer in heterogeneous media composed of stacked spheres of paraffin as a perspective polymeric phase-change material. The main goal is to study the requirements of the numerical scheme to correctly predict the thermal conductivity in a periodic system composed of an indefinitely repeated configuration of spherical particles subjected to a temperature gradient. Based on OpenFOAM, a simulation platform is created with which the resolution requirements for accurate heat transfer predictions were inferred systematically. The approach is illustrated for unit cells containing either a single sphere or a configuration of two spheres. Asymptotic convergence rates confirming the second-order accuracy of the method are established in case the grid is fine enough to have eight or more grid cells covering the distance of the diameter of a sphere. Configurations with two spheres can be created in which small gaps remain between these spheres. It was found that even the under-resolution of these small gaps does not yield inaccurate numerical solutions for the temperature field in the domain, as long as one adheres to using eight or more grid cells per sphere diameter. Overlapping and (barely) touching spheres in a configuration can be simulated with high fidelity and realistic computing costs. This study further extends to examine the effective thermal conductivity of the unit cell, particularly focusing on the volume fraction of paraffin in cases with unit cells containing a single sphere. Finally, we explore the dependence of the effective thermal conductivity for unit cells containing two spheres at different distances between them.

Thermal Conduction in Hybrid Nanofluids and Aggregates

Hybrid nanofluids contain more than one type of nanoparticle and have shown improved thermofluidic properties compared to more conventional ones that contain a single nanocomponent. Such hybrid systems have been introduced to improve further the thermal and mass transport properties of nanoparticulate systems that affect a multitude of applications. The impact of a second particle type on the effective thermal conductivity of nanofluids is investigated here using the reconstruction of particle configurations and prediction of thermal efficiency with meshless methods, placing emphasis on the role of particle aggregation. An algorithm to obtain particle clusters of the core-shell type is presented as an alternative to random mixing. The method offers rapid, controlled reconstruction of clustered systems with tailored properties, such as the fractal dimension, the average number of particles per aggregate, and the distribution of distinct particle types within the aggregates. The nanoparticle dispersion conditions are found to have a major impact on the thermal properties of hybrid nanofluids. Specifically, the spatial distribution of the two particle types within the aggregates and the shape of the aggregates, as described by their fractal dimension, are shown to affect strongly the conductivity of the nanofluid even at low volume fractions. Cluster configurations made up of a high-conducting core and a low-conducting shell were found to be advantageous for conduction. Low fractal dimension aggregates favored the creation of long continuous pathways across the nanofluid and increased conductivity.

Computational Model of Effective Thermal Conductivity of Green Insulating Fibrous Media

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.

Experimental and Theoretical Studies of the Thermal Contact Conductance for Bundles of Round Steel Bars

The paper presents investigations devoted to the analysis of the thermal contact conduction in a bundle of round steel bars. The phenomenon can be expressed quantitatively with the use of thermal contact conductance (hct). The starting points for the presented analysis were the results of the experimental measurements of the effective thermal conductivity. The measurements were performed for samples of a medium in the form of flat packed beds of bars with three different arrangements: staggered, in-line, and crossed and four bar diameters: 10, 20, 30, and 40 mm. Next, a mathematical model was developed, thanks to which the values of the hct coefficient were calculated for the analyzed cases. This approach consists in analyzing thermal resistances in the medium model, which is defined with an elementary cell. It was established that the value of the hct coefficient in the temperature range of 50-600 °C changes within the range of 50-175 W/(m2·K), and it decreases with an increase in the bar diameter. The final effect of the present study was to develop generalized approximation equations describing changes in thermal contact conductance in the heated bar bundle simultaneously in the temperature and bar diameter function.

Neural Network Aided Homogenization Approach for Predicting Effective Thermal Conductivity of Composite Construction Materials

Thermal conductivity is a fundamental material parameter involved in various infrastructure design guides around the world. This paper developed an innovative neural network (NN) aided homogenization approach for predicting the effective thermal conductivity of various composite construction materials. The 2-D meso-structures of dense graded asphalt mixture, porous asphalt mixture, and cement concrete were generated and divided into 2ⁿ × 2ⁿ square elements with specific thermal conductivity values. A two-layer feed-forward neural network with sigmoid hidden neurons and linear output neurons was built to predict the effective thermal conductivity of the 2 × 2 block. The Levenberg-Marquardt backpropagation algorithm was used to train the network. By repeatedly using the neural network, the effective thermal conductivities of 2-D meso-structures were calculated. The accuracy of the above NN aided homogenization approach was validated with experiment, and various factors affecting the effective thermal conductivity were analyzed. The analysis results show that the accuracy of the NN aided approach is acceptable with relative errors of 1.92~4.34% for the dense graded asphalt mixture, 1.10~6.85% for the porous asphalt mixture, and 1.13~3.14% for the cement concrete. The relative errors for all the materials are lower than 5% when the heterogeneous structures are divided into 512 × 512 elements. Ignoring the actual material meso-structures may lead to significant errors (134.01%) in predicting the effective thermal conductivity of materials with high heterogeneity such as porous asphalt mixture. While proper simplification is acceptable for dense construction composite materials. The effective thermal conductivity of composite cement-asphalt mixtures increases with higher saturation of grouted material. However, the improvement effect of the high-conductive cement paste on the composite cement-asphalt mixtures could be significantly reduced when the cement paste concentrates at the bottom of the mixture. Cracked aggregates and segregation of material components tend to decrease the effective thermal conductivity of construction materials. The NN aided homogenization approach presented in this paper is useful for selecting the effective thermal conductivity of construction materials.

A New Theoretical Method for Studying Effects of Microstructure on Effective Thermal Conductivity of Vermicular Graphite Cast Iron

To provide the basis for thermal conductivity regulation of vermicular graphite cast iron (VGI), a new theoretical method consisting of shape interpolation, unit cell model and numerical calculation was proposed. Considering the influence of the graphite anisotropy and interfacial contact thermal conductivity (ICTC), the effective thermal conductivity of a series of unit cell models was calculated by numerical calculation based on finite difference. The effects of microstructure on effective thermal conductivity of VGI were studied by shape interpolation. The experimental results were in good agreement with the calculated ones. The effective thermal conductivity of VGI increases in power function with the decrease in graphite shape parameter, and increases linearly with the increase in graphite volume fraction and thermal conductivity of matrix. When the graphite volume fraction increases by 1%, the thermal conductivity of nodular cast iron increases by about 0.18 W/(m·K), while that of gray cast iron increases by about 3 W/(m·K). The thermal conductivity of cast iron has the same sensitivity to the thermal conductivity of matrix regardless of the graphite shape parameter. The thermal conductivity of matrix increased by 15 W/(m·K) and the thermal conductivity of cast iron increased by about 12 W/(m·K). Moreover, the more the graphite shape deviates from the sphere, the greater the enhancement effect of graphite anisotropy on thermal conductivity than the hindrance effect of interface between graphite and matrix. This work can provide guidance for the development of high thermal conductivity VGI and the study of thermal conductivity of composites containing anisotropic dispersed phase particles with complex shapes.

Prediction and Numerical Study of Thermal Performance of Gradient Porous Structures Based on Voronoi Tessellation Design

Porous materials are a new type of engineering material with both functional and structural properties. Compared with regular porous structures and random porous structures, a gradient porous structure is a porous structure with a spatial variation mechanism, which can adjust the layout of the structure by changing its own load and boundary conditions according to different situations, thus obtaining better performance. In this paper, three spatial Voronoi structures with different spatial gradients are designed using the spatial Voronoi tessellation method. The differences in thermal protection performances between the Voronoi spatial gradient structure and the regular structure and the effects of porosity, gradient direction and heat flow density on the three-dimensional Voronoi stochastic gradient structure were investigated via data simulation. The results show that the effective thermal conductivity of the Voronoi spatial gradient structure is lower than that of the regular structure. The effective thermal conductivity of the structure gradually decreases with increasing porosity. Taking the gradient Voronoi structure consisting of 3 × 3 × 3 units as an example, when the porosity increases from 83% to 94.98%, its effective thermal conductivity decreases from 0.586 to 0.149 Wm-1K-1. The anisotropy of the random structure leads to effective thermal conductivity errors of more than 5% in all three gradient directions. In addition, according to the principle of thermal resistance superposition, we designed a battery pack set for calculating the effective thermal conductivities of pillar-based porous materials, including three-dimensional Voronoi gradient random porous materials on the Grasshopper platform. In this way, the effective thermal conductivity of a pillar-based porous material can be predicted more accurately. The predicted calculation results and the simulation results basically agree with each other, and the relative errors of both are within 10%.

Third-Order Effective Properties for Random-Packing Systems Using Statistical Micromechanics Based on a GPU Parallel Algorithm in Fast Computing n-Point Correlation Functions

Estimating the effective properties of a particulate system is the most direct way to understand its macroscopic performance. In this work, we accurately evaluate the third-order approximations involving the three-point microstructural parameter ζ, which can be calculated from a triple integral involving 1-, 2-, and 3-point correlation functions. A GPU-based parallel algorithm was developed for quickly computing the n-point correlation functions, and the results agree well with analytical solutions. The effective thermal conductivity and diffusion coefficient are calculated by the third-order approximates for the random-packing systems of a super-ellipsoid. By changing the parameters of the super-ellipsoid, the particle-shape effect can be predicted for both the thermal conductivity and diffusion coefficient.

Modelling Thermal Conduction in Polydispersed and Sintered Nanoparticle Aggregates

Nanoparticle aggregation has been found to be crucial for the thermal properties of nanofluids and their performance as heating or cooling agents. Most relevant studies in the literature consider particles of uniform size with point contact only. A number of forces and mechanisms are expected to lead to deviation from this ideal description. In fact, size uniformity is difficult to achieve in practice; also, overlapping of particles within aggregates may occur. In the present study, the effects of polydispersity and sintering on the effective thermal conductivity of particle aggregates are investigated. A simulation method has been developed that is capable of producing aggregates made up of polydispersed particles with tailored morphological properties. Modelling of the sintering process is implemented in a fashion that is dictated by mass conservation and the desired degree of overlapping. A noticeable decrease in the thermal conductivity is observed for elevated polydispersity levels compared to that of aggregates of monodisperse particles with the same morphological properties. Sintered nanoaggregates offer wider conduction paths through the coalescence of neighbouring particles. It was found that there exists a certain sintering degree of monomers that offers the largest improvement in heat performance.

Analysing the Contact Conduction Influence on the Heat Transfer Intensity in the Rectangular Steel Bars Bundle

Bundles of steel bars, besides metal foams, are an example of cellular solids. Such bundles constitute a charge during the heat treatment of bars. The paper presents a mathematical model of transient heat transfer in a bundle of rectangular steel bars based on the energy balance method. The key element of this model is the procedure of determining the effective thermal conductivity using the electrical analogy. Different mechanisms of heat transfer occurring within the analysed medium (conduction in steel and contact conduction) are assigned corresponding thermal resistances. The discussed procedure involves expressing these resistances with the use of arithmetic relationships describing their changes in the temperature function. Thermal contact resistance has been described with the use of the relationships determined experimentally. As a result of the performed calculations, the influence of contact conduction between the adjacent bars and bundle arrangement on its heating time was established. The results of the calculations show that the heating time of bundles can be lowered by 5–40% as a result of a decrease in the thermal contact resistance. This effect depends on the bar size and bundle arrangement. From the practical point of view, the analysed problem is connected with the optimization of the heat treatment processes of steel bars.

Optimization of Effective Thermal Conductivity of Thermal Interface Materials Based on the Genetic Algorithm-Driven Random Thermal Network Model

Polymer-based thermal interface materials (TIMs) are indispensable for reducing the thermal contact resistance of high-power electronic devices. Owing to the low thermal conductivity of polymers, adding multiscale dispersed particles with high thermal conductivity is a common approach to enhance the effective thermal conductivity. However, optimizing multiscale particle matching, including particle size distribution and volume fraction, for improving the effective thermal conductivity has not been achieved. In this study, three kinds of filler-loaded samples were prepared, and the effective thermal conductivity and average particle size of the samples were tested. The finite element model (FEM) and the random thermal network model (RTNM) were applied to predict the effective thermal conductivity of TIMs. Compared with the FEM, the RTNM achieves higher accuracy with an error less than 5% and higher computational efficiency in predicting the effective thermal conductivity of TIMs. Combining the abovementioned advantages, we designed a set of procedures for an RTNM driven by the genetic algorithm (GA). The procedure can find multiscale particle-matching ways to achieve the maximum effective thermal conductivity under a given filler load. The results show that the samples with 40 vol %, 50 vol %, and 60 vol % filler loading have similar particle size distribution and volume fractions when the effective thermal conductivity reaches the highest. It should be emphasized that the optimized effective thermal conductivity can be improved obviously with the increase in the volume fraction of the filler loading. The high efficiency and accuracy of the procedure show great potential for the future design of high-efficiency TIMs.

On Determination of the Effective Thermal Conductivity of a Bundle of Steel Bars Using the Krischer Model and Considering Thermal Radiation

Cellular solid materials are commonly found in industrial applications. By definition, cellular solids are porous materials that are built of distinct cells. One of the groups of such materials contains metal foams. Another group of cellular metals contains bundles of steel bars, which create charges during the heat treatment of the bars. A granular structure connected by the lack of continuity of the solid phase is the main feature that distinguishes bundles from metal foams. The boundaries of the bundle cells are made of adjacent bars, with the internal region taking the form of an air cavity. In this paper, we discuss the possibility of using the Krischer model to determine the effective thermal conductivity of heat-treated bundles of steel bars based on the results of experimental tests and calculations. The model allows the k_ef coefficient to be precisely determined, although it requires the weighting parameter f to be carefully matched. It is shown that the value of f depends on the bar diameter, while its changes within the examined temperature range (25-800 °C) can be described using a third-degree polynomial. Determining the coefficients of such a polynomial is possible only when the effective thermal conductivity of the considered charge is known. Moreover, we analyze a simplified solution, whereby a constant value of the f coefficient is used for a given bar diameter; however, the k_ef values obtained thanks to this approach are encumbered with inaccuracy amounting to several dozen percentage points. The obtained results lead to the conclusion that the Krischer model cannot be used for the discussed case.

Numerical Investigation of Effective Thermal Conductivity of Strut-Based Cellular Structures Designed by Spatial Voronoi Tessellation

Porous materials possess light weight and excellent thermal insulation performance. For disordered porous structures, the number of seed points is an important design parameter which is closely related to the morphology and mean pore size of the structure. Based on the arrangement of points in three-dimensional space, seven kinds of structures were designed by spatial Voronoi tessellation in this paper. The effect of the number of seed points on effective thermal conductivity for Voronoi was studied. Numerical simulation was conducted to research the effects of structural porosity, filling material and structural orientation on the effective thermal conductivity and heat transfer characteristics. The results showed that the effective thermal conductivity is closely related to the porosity and the matrix material. Different number and arrangement of seed points make the structure have different anisotropic performance due to different thermal paths. In addition, required the least number of seed points was obtained for the designation of isotropic random Voronoi.

Modelling of Effective Thermal Conductivity of Composites Filled with Core-Shell Fillers

An effective model to calculate thermal conductivity of polymer composites using core-shell fillers is presented, wherein a core material of filler grains is covered by a layer of a high-thermal-conductivity (HTC) material. Such fillers can provide a significant increase of the composite thermal conductivity by an addition of a small amount of the HTC material. The model employs the Lewis-Nielsen formula describing filled systems. The effective thermal conductivity of the core-shell filler grains is calculated using the Russel model for porous materials. Modelling results are compared with recent measurements made on composites filled with cellulose microbeads coated with hexagonal boron nitride (h-BN) platelets and good agreement is demonstrated. Comparison with measurements made on epoxy composites, using silver-coated glass spheres as a filler, is also provided. It is demonstrated how the modelling procedure can improve understanding of properties of materials and structures used and mechanisms of thermal conduction within the composite.

Modelling Thermal Conduction in Nanoparticle Aggregates in the Presence of Surfactants

Many theoretical and experimental studies have shown that the addition of nanoparticles into conventional fluids may generate nanofluids with significantly improved heat transfer properties. In the present work, the effect of nanoparticle aggregation on the thermal conductivity of nanofluids is studied, considering also the effect of surfactants that are typically added to stabilise the nanofluid. A method for simulating aggregate formation is developed here that allows tailoring of the fractal dimension and the number density of the nanoparticles to desired values. The method is shown to be computationally simple and fast. Data that are extracted from electron microscope images are compared with simulation results regarding surface porosity and the autocorrelation function. The surfactants are modelled as a layer around the particles, and the effective thermal conductivity is calculated with a meshless numerical technique. Significant increase in conductivity is observed for small values of the fractal dimension and for large number density of particles in the aggregate. The simulations are in good agreement with experimental results. It is also concluded that prediction of the conductivity of such nanofluids requires the knowledge of the type and the amount of the surfactant added.

Research on a Simplified Model of an Aluminum Vapor Chamber in a Heat Dissipation System

With the rapid increase of power densities of electronic components, the traditional heat dissipation method of air forced convection has reached a heat transfer limit. As efficient phase change heat exchangers, vapor chambers have become an important guarantee for the development of high-power electronic components. Aluminum vapor chambers have become the future development trend because they are more lightweight and less expensive. In order to study the suitable simplified model of the aluminum vapor chamber in the radiating system, the testing system is established to test the thermal characteristics of the vapor chamber. First, six simplified models of the vapor chamber are proposed. Then, the thermal characteristics of the simplified models are simulated by STAR CCM+ software. Next, the error of the thermal resistance of the simplified model and the real vapor chamber is analyzed. Finally, a most suitable simplified model is obtained in the cooling system.

Modeling the Thermal Conductivity Inhomogeneities of Injection-Molded Particle-Filled Composites, Caused by Segregation

Many applications require new materials that have good thermal conductivity, are electrical insulators and can be processed easily and with relatively little energy. A new innovative solution for this problem is thermally conductive composites, which can replace metals in many cases. Many papers have focused on the prediction of their thermal conductivity. At the same time segregation has to be taken into account in the case of composites because it affects the distribution of thermally conductive particles, and thus local thermal conductivities. In this paper, we examined and modeled segregation during injection molding and its effect on thermal conductivity. We injection-molded samples from polypropylene with glass beads of different sizes and analyzed their filler content as a function of the flow path. We described the distribution of the filler with a mathematical model. Using this, we created a new, segregation-dependent model that describes the local thermal conductivity of polymer composites as a function of filler content with great accuracy.

Methods to Characterize Effective Thermal Conductivity, Diffusivity and Thermal Response in Different Classes of Composite Phase Change Materials

The phase change materials (PCMs) used in devices for thermal energy storage (TES) and management are often characterized by low thermal conductivity, a limit for their applicability. Composite PCMs (C-PCM), which combine active phase (proper PCM) with a passive phase with high conductivity and melting temperature have thus been proposed. The paper deals with the effect of length-scale on thermal characterization methods of C-PCM. The first part of the work includes a review of techniques proposed in the scientific literature. Up to now, special focus has been given to effective thermal conductivity and diffusivity at room or low temperature, at which both phases are solid. Conventional equipment has been used, neglecting length-scale effect in cases of coarse porous structures. An experimental set-up developed to characterize the thermal response of course porous C-PCMs also during active phase transition at high temperature is then presented. The setup, including high temperature-heat flux sensors and thermocouples to be located within samples, has been applied to evaluate the thermal response of some of the above C-PCMs. Experimental test results match Finite Elements (FE) simulations well, once a proper lattice model has been selected for the porous passive phase. FE simulations can then be used to estimate temperature difference between active and passive phase that prevents considering the C-PCM as a homogeneous material, to describe it by effective thermo-physical properties. In the engineering field, under these conditions, the design steps for TES systems design cannot be simplified by considering C-PCMs as homogeneous materials in FE codes.

Mechanism of Heat Transfer through Porous Media of Inorganic Intumescent Coating in Cone Calorimeter Testing

This work discusses the heat transfer process through a particular form of porous media: an inorganic-based intumescent coating in full-expansion state. Although the thermal mechanism in porous media has been vigorously studied for polymeric/ceramic/metallic foams, less information is available on its application with intumescent-type polymers. This examination demonstrates the procedure of (1) the optimisation of the coating’s internal multicellular structure for numerical modelling, based on topological analyses; (2) the finite element simulation for the coating-sample tested with cone calorimetry; and (3) the quantitative evaluation of the thermal insulation performance of its porous structure by adopting effective thermal conductivity. The modelling technique was verified using measurable data from the cone calorimeter tests. Consistent agreement between the numerical predictions and experimental measurements was achieved over the whole steel-substrate temperature history, based on the clarified thermal boundaries of the specimen and modelling of the combined conduction-radiation transfer. This numerical approach exhibits the impacts of porosity, pore-size, and external thermal load on the medium’s performance, as well as the individual contributions of the component heat transfer modes to the overall process. The full understanding of this thermal mechanism can contribute to the enhancement and optimisation of the thermal insulation performance of a porous-type refractory polymer.

Systematic Generation, Analysis, and Characterization of 3D Micro-architected Metamaterials

Controlling the unit-cell topology of microlattice structures can enable the customization of effective anisotropic material properties. A wide range of properties can be obtained by varying connectivity within the unit cell, which then can be further used to optimize structures specific to applications. A methodology for a systematic generation of microlattice structures is presented that focuses on controlling discrete topology instead of average porosity (as is done in conventional porous media). An algorithm is developed to create valid lattice structures without redundancies from a given set of template nodes. A set of possible permutations of structures from an eight-node cubic octant of a unit cell are generated for evaluation of the degree of anisotropy. Generic models are developed to calculate the effective thermal and mechanical properties as an effect of topology and porosity of the micro-architected structure. The thermal and mechanical anisotropies are investigated for the effective properties of micro-architected materials. A few of the structured materials are fabricated using 3D printing technology and their effective properties characterized. Structures are represented as graphs in the form of adjacency matrices. Effective thermal conductivity is analyzed using a resistance network model, and effective stiffness is evaluated using a self-consistent elastic model, respectively. A total of 160 000 structures are generated and compared to porous-metal foams in which porosity is one of the design variables. The results show that it is possible to obtain a wide range of properties spanning more than an order of magnitude in comparison to porous-metal structures. Structures with a maximum anisotropy ratios of 7.1 and 8.2 are observed for thermal and mechanical properties, respectively. Preliminary experimental results validated the anisotropy ratio for the thermal conductivity and stiffness.

Transient in-plane thermal transport in nanofilms with internal heating

Wide applications of nanofilms in electronics necessitate an in-depth understanding of nanoscale thermal transport, which significantly deviates from Fourier's law. Great efforts have focused on the effective thermal conductivity under temperature difference, while it is still ambiguous whether the diffusion equation with an effective thermal conductivity can accurately characterize the nanoscale thermal transport with internal heating. In this work, transient in-plane thermal transport in nanofilms with internal heating is studied via Monte Carlo (MC) simulations in comparison to the heat diffusion model and mechanism analyses using Fourier transform. Phonon-boundary scattering leads to larger temperature rise and slower thermal response rate when compared with the heat diffusion model based on Fourier's law. The MC simulations are also compared with the diffusion model with effective thermal conductivity. In the first case of continuous internal heating, the diffusion model with effective thermal conductivity under-predicts the temperature rise by the MC simulations at the initial heating stage, while the deviation between them gradually decreases and vanishes with time. By contrast, for the one-pulse internal heating case, the diffusion model with effective thermal conductivity under-predicts both the peak temperature rise and the cooling rate, so the deviation can always exist.