Values Of Effective Thermal Conductivity Research Articles

A novel method of accurately characterizing heat pipes using thermoelectric modules

This study details an experimental method for the accurate and rapid thermal characterization of low-intermediate temperature heat pipes (<200 °C). The aim of this work was to provide a method of measuring the thermophysical characteristics of heat pipes while providing definitive uncertainty values that enable accurate performance estimations prior to implementation into electronic systems. To achieve this, the apparatus used two heat flow meters, constituting cubic copper interface blocks and thermoelectric modules that measured heat flow rates across the specimen while precise, well-calibrated (±0.01 K) thermistors measured axial temperature drops along its length. The method was first demonstrated by characterizing a cylindrical copper reference specimen of known thermal conductivity, indicating a high degree of accuracy, with measured effective thermal conductivity values within ± 2 % of the correlated value. Concerning the thermal characterization of heat pipe specimens, thermal resistances and corresponding effective thermal conductivities of 0.122–0.08 K/W and 61.8–90.8 kW/m.K were measured for heat flow rates between 5 and 30 W. Uncertainties within the derived characteristics were less than 3.2 % for low heat flow rates (5–15 W) and less than 1.3 % for higher heat flow rates (15–30 W). Moreover, a 450 % reduction in characterization time was achieved compared to methods that employ thermally massive heat flow meters. In many aspects, the presented method achieved superior performance compared to those presented within the literature, proving its effectiveness for the accurate characterization and subsequent implementation of heat pipes into electronic systems.

Acoustic and thermal performance of luffa fiber panels for sustainable building applications

Sound-absorbing and thermal-insulating materials have a noteworthy role in the performance of buildings. In recent years, there has been a significant focus on utilizing natural fibers as construction materials for thermal insulation and sound absorption purposes. In this study, luffa, an abundant and eco-friendly natural fiber, was explored as a sustainable material with potential high acoustic and thermal performance due to its intrinsic hollow and sponge-like structure. The experiments were designed using the response surface method based on central composite design (RSM-CCD) to find the best combination of input variables including thickness, density, and binder content for optimal performance. The sound absorption average (SAA) and effective thermal conductivity (Keff) values of the panels ranged from 0.16-0.68, and 0.033–0.054 W/(mK), respectively. It was found that both the SAA and Keff are significantly influenced by thickness and density, while binder content does not have a significant effect. The optimal condition for fabricating luffa panels was found to be a thickness of 40 mm, density of 225 kg/m3, and binder content of 7.5 %. Furthermore, the acoustic characteristics of the optimized panels were examined in a conference hall using the Odeon software. The acoustic absorption properties of panels were also predicted using the Johnson–Champoux–Allard and Attenborough models.

Physics-informed neural networks for studying heat transfer in porous media

Numerous efforts have been devoted to studying heat transfer problems in porous media. Physics-based models, numerical methods and experiments are commonly adopted to obtain the temperature and heat flux fields, along with effective thermophysical properties like effective thermal conductivity for heat conduction, which exert significant impact on analyzing the heat transfer efficiency in porous systems. Recently, using data-driven machine learning methods to predict temperature/heat flux fields and effective thermal conductivity of porous media has gained attention, demonstrating the potential to achieve higher accuracy than physics-based models while requiring less computational time than numerical methods. However, machine learning approaches are commonly restricted by the requirement for sufficient labeled training data, which can be difficult and time-consuming to acquire. In this work, we apply physics-informed neural networks to investigate heat conduction in porous media. We show that, without any labeled training data, accurate predictions for temperature/heat flux fields in porous media can be achieved. The obtained effective thermal conductivity values for an ensemble of porous media samples have an average relative error of only 2.49%. Compared with numerical calculations, a computation acceleration of 5 orders of magnitude has been achieved. Compared with data-driven machine learning methods, this method offers enhanced flexibility since no labeled data is required. Furthermore, we also illustrate that physics-informed neural networks can be easily extended to predict nonlinear heat conduction in porous media. Our work demonstrates that physics-informed neural networks are promising tools for studying heat conduction problems and can also be possibly extended to study other complex heat transfer problems in porous media.

Effect of Working Fluid, Orientation, and Cooling Mode on Thermal Performance of Miniature Flat Heat Pipe

Abstract Flat heat pipes (FHPs) are commonly used as a passive cooling system in portable electronic gadgets due to their compact profile. The present study investigates the effect of different working fluids on the thermal performance of a miniature FHP under different orientations and condenser cooling mechanisms and the start-up performance of FHP. Deionized water, acetone, ethanol, and methanol are chosen as working fluids in the FHP. Five different inclinations (0 deg (horizontal), 30 deg, 45 deg, 60 deg, and 90 deg (vertical)) and two different condenser cooling methods (natural convection and forced convection with fan cooling) are considered in this experimental study. The FHP thermal performance is quantified in terms of overall temperature difference, thermal resistance, and effective thermal conductivity. The results indicate that comparatively higher effective thermal conductivity values are obtained for methanol and acetone heat pipes at low heat loads and under natural convection. At higher heat loads, the ethanol heat pipe had higher effective thermal conductivity values for the same condenser cooling method. For the case of the forced convection cooling mode, the methanol heat pipe had enhanced thermal performance as compared to the other three fluids for all heat load ranges and different inclinations. Due to the higher boiling point of water, as a working fluid water is not suitable in most of the experimental trials except at high heat load under forced convection cooling and in a horizontal orientation. The maximum effective thermal conductivity of 7846 W/mK is obtained for FHP filled with methanol at 24 W heat load and 90 deg orientation under forced convection condenser cooling.

Estimation of Out-of-Plane Effective Thermal Conductivity of Wire Mesh Using 3D Unit-Cell Model Incorporating Secondary Effects

AbstractThis study proposes a modified collocated parameter model to estimate the effective thermal conductivity (keff) of two-phase wire mesh materials. The study focused on the influence of parameters, primary as well as secondary, on the effective thermal conductivity of a wire mesh, namely, thermal conductivity ratio (α), concentration (γa), mesh number (M), and thermal contact conductance at the wire-to-wire interfacial area were all investigated in detail over a different temperature range in the normal to the wire mesh. The analytical expressions for effective thermal conductivity in the form of algebraic equations were derived by adopting the solid square cylinder unit-cell-based thermal resistance method for a three-dimensional spatially in-line touching periodic medium. The effective thermal conductivities of different wire mesh materials with solid–fluid combinations, possessing conductivity ratios of 1–10,000 and concentrations of 0–0.8, are predicted for various temperatures at constant pressure regimes using the developed model. Furthermore, the developed model was applied to estimate the effective thermal conductivities of brass and stainless-steel wire mesh, which are compared with values measured using the modified transient plane source (MTPS) instrument over the temperature range 283–460 K. For the brass and stainless-steel mesh—water, silicone oil, and air combinations, the estimated values of effective thermal conductivity obtained from the present model show an average deviation of fluids ± 2.6%, with air ± 4.4% in the z-direction, respectively, concerning the ones obtained from the experiments.

Prediction of Effective Elastic and Thermal Properties of Heterogeneous Materials Using Convolutional Neural Networks

The aim of this study is to develop a new method to predict the effective elastic and thermal behavior of heterogeneous materials using Convolutional Neural Networks CNN. This work consists first of all in building a large database containing microstructures of two phases of heterogeneous material with different shapes (circular, elliptical, square, rectangular), volume fractions of the inclusion (20%, 25%, 30%), and different contrasts between the two phases in term of Young modulus and also thermal conductivity. The contrast expresses the degree of heterogeneity in the heterogeneous material, when the value of C is quite important (C &gt;&gt; 1) or quite low (C &lt;&lt; 1), it means that the material is extremely heterogeneous, while C= 1, the material becomes totally homogeneous. In the case of elastic properties, the contrast is expressed as the ratio between Young’s modulus of the inclusion and that of the matrix (C = EiEm), while for thermal properties, this ratio is expressed as a function of the thermal conductivity of both phases (C = λiλm). In our work, the model will be tested on two values of contrast (10 and 100). These microstructures will be used to estimate the elastic and thermal behavior by calculating the effective bulk, shear, and thermal conductivity values using a finite element method. The collected databases will be trained and tested on a deep learning model composed of a first convolutional network capable of extracting features and a second fully connected network that allows, through these parameters, the adjustment of the error between the found output and the expected one. The model was verified using a Mean Absolute Percentage Error (MAPE) loss function. The prediction results were excellent, with a prediction score between 92% and 98%, which justifies the good choice of the model parameters.

FEM heat transfer modelling with tomography-based SiCf/SiC unit cell

Modern industry has become increasingly reliant on composite materials for a variety of applications, and the nuclear industry is no exception to this. Among the materials being researched as Enhanced Accident Tolerant Fuels, ceramic matrix composites such as SiC-fiber-reinforced SiC (SiCf/SiC) figure as some prime candidates due to their excellent high temperature performances. SiCf/SiC so far shows adequate nuclear, mechanical and chemical properties; still, the thermal properties need further investigation. The thermal behavior of a material is an important factor for its performance as a nuclear fuel cladding, i.e. the first barrier encapsulating the fuel pellets. Many features determine the resulting properties of composite materials, such as matrix and fiber reinforcement properties and orientation, void fraction, and pore morphology. This study establishes a methodology to study the physical properties of composite materials and applies it to SiCf/SiC. A FEM model is used to characterize the thermal properties of a fundamental SiCf/SiC element, referred to as a �unit cell�, with the objective of accurately predicting the thermal properties of this complex class of materials where experimental data is often difficult to obtain. The unit cell is built based on data acquired with high-resolution tomographic microscopy performed at the TOMCAT beamline of the Swiss Light Source. By using phase-retrieval prior to tomographic reconstruction, the pores, fibers and matrix that compose the material can be distinguished in the data analysis. The separated information is processed to obtain geometrical information about the individual pores and fibers, which is then used to parametrize them as cylindrical objects. This allows constructing a FEM model of a cubic unit cell that is used to extract the effective thermal properties of SiCf/SiC. The analysis scheme includes steady-state and dynamic thermal transport simulations, which yield directional effective thermal conductivity and diffusivity values, respectively. Both modes of analysis show isotropic thermal conductivity values in the range of 71 W/m/K at room temperature, more than three times that of currently employed nuclear cladding materials. Combining these results with the data on the larger structural features of the material will lead to realistic results on the macroscopic thermal properties.

Evaluating the effective thermal conductivity of cement mortar through x-ray scanning

The effective thermal conductivity of cement mortar as a porous material depends on its solid phase and void phase conductivity. Therefore, a sample's pore distribution can highly impact the value of effective thermal conductivity. For this reason, an x-ray CT scanner was applied to detect air-void content in mortars with different mix proportions. After 28 days of curing, the thermal conductivity of samples (40 × 40 × 80 mm3) was measured by the transient plane source (TPS) apparatus, TPS2500. Then, the samples were scanned by a Zeiss Metrotom 1500 CT scanner to detect the macro pore content. Finally, the VGStudio Max 3.0 software was applied to observe the voids and simulate the effective thermal conductivity. The results showed that the total macro void contents (in this study, voids bigger than 52 μm) were increased up to 15% and 33% for samples with the cement the sand ratio of 1:3 and 1:4 compared to the sample with the c:s ratio of 1:2. However, the results indicated that the void fraction is not uniform. Therefore, effective thermal conductivity can vary in different sample locations. The finding shows that x-ray scanning and image analysis is the proper method to precisely determine the effective thermal conductivity of cement-based materials.

Experimental Studies of the Effective Thermal Conductivity of Polyurethane Foams with Different Morphologies

Polyurethane foam (PUF) is actively used for thermal insulation. The main characteristic of thermal insulation is effective thermal conductivity. We studied the effective thermal conductivity of six samples of PUF with different types and sizes of cells. In the course of the research, heat was supplied to the foam using an induction heater in three different positions: above, below, or from the side of the foam. The studies were carried out in the temperature range from 30 to 100 °C. The research results showed that for all positions of the heater, the parameter that makes the greatest contribution to the change in thermal conductivity is the cell size. Two open-cell foam samples of different sizes (d = 3.1 mm and d = 0.725 mm) have thermal conductivity values of 0.0452 and 0.0287 W/m⸱K, respectively, at 50 °C. In the case of similar cell sizes for any position of the heater, the determining factor is the type of cells. Mixed-cell foam (d = 3.28 mm) at 50 °C has a thermal conductivity value of 0.0377 W/m⸱K, and open-cell foam (d = 3.1 mm) at the same temperature has a thermal conductivity value of 0.0452 W/m⸱K. The same foam sample shows different values of effective thermal conductivity when changing the position of the heater. When the heater is located from below the foam, for example, mixed-cell foam (d = 3.4 mm) has higher values of thermal conductivity (0.0446 W/m⸱K), than if the heater is located from above (0.0390 W/m⸱K). There are different values of the effective thermal conductivity in the upper and lower parts of the samples when the heater is located from the side of the foam. At 80 °C the difference is 40% for the open-cell foam (d = 3.1 mm).

Parametric Investigation of Closed Loop Pulsating Heat Pipe with Cerium Oxide Nanofluid

In this study, a closed-loop pulsating heat pipe experimental investigation was done with 1% wt concentration of cerium oxide/EG-water (60-40) nanofluid. The unsteady state measurement was done to determine the effect of heat input, filling ratio and evacuation pressure on the thermal performance of the closed-loop pulsating heat pipe. The thermal performance is assessed in terms of temperature variation, thermal resistance and effective thermal conductivity of the pulsating heat pipe. The most appropriate behaviour is observed at 0.0799 bar evacuation pressure with 50% FR. The lowermost thermal resistance of 0.116598K/W was observed at 0.0799 bar evacuation pressure and 50% FR. The effective thermal conductivity value was observed as 5078.34 W/mK at 50% FR, 0.0799 bar for 80W heat input which is 12 to 13 times better than pure copper. The pulsation action inside the pulsating heat pipe is verified with the power spectral density analysis. This study supports the better performance of the heat pipe at 50% FR with a lower evacuation pressure of 0.0799 bar.

Effective Thermal Conductivity of Structured Porous Medium: Numerical Study

The paper presents a numerical study of thermal conductivity of porous structures using the Ansys software package. Unlike the well-known porous materials used in construction and engineering, it is proposed to use porous materials with an ordered law of cavity placement. The porous material proposed is formed by dividing the volume into cubes of equal size with a spherical cavity placed in the center of each cube. The numerical calculation of an effective thermal conductivity coefficient of a porous medium is performed using the Ansys Mechanical computer modeling tool. The values obtained are compared with solutions based on classical methods for determining the effective thermal conductivity of porous materials. A dependency graph of effective thermal conductivity in a porous material based on pores geometric parameters (distance between cavities, diameter of cavities), as well as an analytical dependence to obtain the effective thermal conductivity value is presented. Additive technologies available today provide producing the proposed porous material with an ordered law of cavity placement with any accuracy and any pore geometric parameters. Such materials open up wide opportunities for engineers, especially in the field of thermal power engineering, because it has predictable thermophysical and mechanical properties.

Cost-efficient copper-nickel alloy for active cooling applications

It is recently proposed that active (Peltier) and passive (conductive) cooling could be combined to design highly-efficient thermal conductors as active heat sinks. We study the potential of low-cost copper-nickel alloys for active cooling applications. We show that for this application, copper-nickel alloys prepared using industrial-grade powders to lower the manufacturing cost, are as good as copper-nickel alloys made out of high purity metals. Furthermore, directed energy deposition, a type of additive manufacturing, can produce copper-nickel alloys which have about 10 percent lower thermoelectric power factor compared to mechanical-alloying methods but have larger thermal conductivity values, hence, possessing similar effective thermal conductivity values, reaching 403 Wm−1K−1at 390K. Much larger values are expected at higher temperatures. The usage of additive manufacturing enables complex geometries and large-scale production.

The effective thermal conductivity of virtual macroporous structures

Recent developments in macroporous structures featuring metallic bodies are becoming a focus of research due to their improved load-bearing capacity and high specific surface area, making them potential candidates for thermofluidic applications. Through numerical modelling and simulations at the pore-scale, this paper examines the ability of virtual macroporous structures generated by sphere-packing models to predict the effective thermal conductivity of “bottleneck-type” macroporous structures. Simulations of virtual macroporous structures with monosized pores show the relative impact of key pore structure-related properties on effective thermal conductivity in such structures. The extension of these findings to bimodal and adapted designs results in an increase in fluid transverse capacity of these structures, causing an increase in the original monomodal pore volume by 17 and 30% respectively, while varying their capillary radius from 10 to 80 μm only results in a 10% increase in pore volume fraction. Overall, this study demonstrated that pore structure-related properties played an important role in conductive heat transfer, with material porosity being an influential property and inversely (non-linear) correlated with the effective thermal conductivity of the foam–fluid system. The contributions tendered by material pores and specific surfaces were minimal, and mathematical relationships were proposed between effective thermal conductivity, pore-structure-related properties, and fluid and solid thermal conductivity values. Modelling techniques such as this one could serve to optimize the thermal performance of macroporous structures, as well as to gain the benefits of being ultra-lightweight and having a high Young modulus.

An innovative window heat recovery (WHR) system with heat pipe technology: Analytical, CFD, experimental analysis and building retrofit performance

This paper addresses the numerical and experimental performance analysis of a windows heat recovery system made of heat pipes. For modelling, the heat pipe is considered as a pseudo solid material with high value of effective thermal conductivity. An experimental investigation using a window heat recovery prototype was carried out to predict the value of effective thermal conductivity of the heat pipes and to validate the numerical model. After validation, a parametric analysis was conducted to investigate the performance of the recovery system for different working conditions (mass flow rate and temperature difference between exhausted and supplied air). Based on the performance obtained in the parametric analysis, energy performance in building and the impact on velocity and pressure distributions are also evaluated with the support of CFD analysis. It is found that the effectiveness of window heat recovery made of heat pipes depends on ventilation rate and temperature difference between exhausted and supplied air. Increasing ventilation rates and temperature differences decrease the effectiveness. For ventilation rate between 10–60 m 3/h and temperature difference 10–30 °C, effectiveness between 65%–95% and pressure drop 4–80 Pa are obtained. For performance in building, the power consumption can be reduced between 3%–24% and the thermal comfort increased.

Palm Leaf Fibers: A Potential Reinforcement for Enhancing Thermal Insulation Capability of Epoxy Based Composite

ABSTRACT The retardation of heat transfer plays an important role in the survival and efficient performance of machine components which is known as insulation. In the field of insulation, epoxy has a tremendous importance. To increase its insulation property, reinforcement of various insulative fibers produces a new class of fiber composites that greatly attracts the researchers. In the present investigation, the thermal behavior of a class of polymer composites with improved insulation capabilities, consisting of epoxy and short bismarckia palm fiber, has been reported. Epoxy composites incorporated with different loadings are prepared by solution casting method. Various thermodynamic properties of epoxy like thermal conductivity (K), glass transition temperature (Tg) and coefficient of thermal expansion were calculated for different fiber loadings. A model based on numerical method has been developed to calculate the value of effective thermal conductivity of the composites using finite element analysis. Then, the result is compared with existing analytical models and experimentally obtained values. The outcome shows that the insulative behavior of the composite has been greatly improved as we increase the fiber content in the epoxy. There is also a marked enhancement of Tg of the epoxy with the incorporation of fiber.

A computational investigation for the impact of particle size on the mechanical and thermal properties of teak wood dust (TWD) filled polyester composites

The prime object and novelty of this investigation are to analyze the effect of particle size on the mechanical and thermal properties of teak wood dust (TWD) reinforced polyester composites. Teak woods are ground into powder form and sieved to three different sizes (58, 110, and 155 μm) by a sieve shaker. Poly + TWD composite specimens are prepared with different filler content (0, 5, 10, 20, 25 wt%) and (0, 3.35, 6.52, 11.95, 17.33 vol%) separately for tensile and thermal conductivity test respectively. The effective thermal conductivity and tensile strength of the composites are measured by Unitherm™ Model 2022 and universal testing machine (UTM) respectively. Thermal conductivity and tensile of the composites decrease with increase in TWD content. The solution casting method is used for the fabrication of composites. It is observed that the tensile strength and thermal properties of the composites improve with an increase in the particle size of the teak wood dust. For computational investigation, multi finite element Digimat-FE software is also utilized to analyze the tensile and thermal behavior of the composites. The computational predicted results are also compared with the measured values for confirmation of findings. The presence of higher stress concentration is marked in the polymer matrix at a larger particle size of TWD particles. The finding of the simulated effective thermal conductivity (Keff) value of the composites is also compared with other theoretical models. It is found that the numerical predicted findings are nearly concurrent with the thermal conductivity value calculated by other models and experimental results.

Determination of the effective thermal conductivity of the porous media based on digital rock physics

The thermal properties of a porous medium have been emphasized for its essential value in rock thermal engineering. Traditional theoretical and experimental methods on thermal conductivity studies of porous media usually contain homogeneous and isotropic assumptions with subjective idealization. Starting from the Digital Rock Physics (DRP), this paper establishes a digital internal structure model of a homogeneous rock through the processes of noise reduction, threshold segmentation, algorithmic process and three-dimensional (3D) reconstruction based on a series of micro-CT (computer tomography) images of the rock. With high quality meshes compatible with the Computational Fluid Dynamics (CFD) software, the Effective Thermal Conductivity (ETC) values are numerically obtained in three Representative Element Volume (REV) blocks with consistent porosity property. The results show that there is a 5% variation between the highest and lowest ETC data along different heat conduction directions. In addition, the simulation results satisfactorily fit with the empirical self-consistent model for ETC prediction of porous media. It is expected the innovative method reported in the present work can provide a feasible and reliable alternative for analyzing the rock thermal and other physical properties based on the accurate description of the internal microscale structures of the porous media.

Determination of the effective thermal conductivity of particulate composites based on VO2 and SiO2

The effective thermal conductivity of composites made up of VO2 (SiO2) spherical particles randomly distributed and embedded in a SiO2 (VO2) matrix are numerically studied in a range of temperatures around the metal-insulator transition of VO2. This is done by means of three-dimensional finite element simulations for different concentrations and sizes of the particles as well as various interface thermal resistances. Our results are validated against the Mori-Tanaka analytical model. In addition, we develop a numerical method to calculate the heat storage capacity for composites with VO2 particles dispersed into a SiO2 matrix. It is shown that: i) The effective thermal conductivity of VO2/SiO2 composites increases with the VO2 particles' size, while the one of SiO2/VO2 composites is pretty much independent of the SiO2 particles' radius. ii) At the VO2 transition temperature (342.5 K), the effective thermal conductivity of VO2/SiO2 composites increases significantly at a rate of 2.7 × 10−3 Wm−1K−2, such that its value doubles up the SiO2 matrix thermal conductivity at the particle concentration of 40.2%. By contrast, the effective thermal conductivity of SiO2/VO2 composites decreases at a rate of 8.6 × 10−3 Wm−1K−2. iii) The effective thermal conductivity is strongly affected by the thermal resistance in VO2/SiO2 composites, by contrast the resistance effect does not play an important role for particle volume fractions of SiO2 up to 34.1% in SiO2/VO2 composites. The Mori-Tanaka model and our simulations predict the same trend of the effective thermal conductivity values of VO2/SiO2 composites. However, the analytical model fails when the matrix is made up of VO2 and the volumetric fraction of SiO2 exceeds 34.1%. The latent heat storage capacity of VO2/SiO2 composites increases with the VO2 particles’ concentration, such that at 40.2%, it takes the value of 24553 J kg−1 (486.7 cal mol−1), which is about half that of the pure VO2.

Fourier-like Thermal Relaxation of Nanoscale Explosive Hot Spots

We develop here an approach to directly determine the thermal transport properties of explosive hot spots with realistic initial structures through a combination of molecular dynamics (MD) and diffusive heat equation (HEq) modeling. Effective thermal conductivity values are determined by fitting HEq models to MD predictions of long timescale hot spot relaxation. The approach is applied to model hot spots in the molecular crystalline explosive TATB for a range of shock strengths and two limiting cases for impact orientation. Conductivity is found to be a strong function of density, which parametrically captures dependence on temperature, pressure, and material state. The approach provides a convenient foundation for determining the effective thermal conductivity for hot spot problems in other explosives and directly yields information on reasonable approximations that might be taken in higher-level models for those materials.

Multi-scale modelling on PEM-based electrolyte dehumidifier: Transient heat and mass transfer in anode catalyst layer with microstructures

As an independent and ultra-compact method, PEM-based dehumidifiers are promising, while accurate modelling of anode-side transport is still challenging. The theoretical model developed in this study is transient, two-dimensional and non-isothermal based on the lattice Boltzmann method, emphasizing the anode-side catalyst layer (CL) with reconstructed microstructures. Then the pore-scale CL modelling was coupled with the macro-scale simulation of air channel and diffusion layer (DL), and experimentally validated by a 3.5 cm2 dehumidifier. Results showed that the CL microstructure significantly affects the air outlet parameters of PEM dehumidifiers, especially in the beginning state. Although the uniform distribution of vapor concentration can be achieved immediately in CL, the time required for the air outlet to reach transient equilibrium is much longer, which is around 1000s for the moisture content and 7000s for the temperature. The moisture transfer resistance at the beginning state mainly exists in CL, then it moves to both CL and DL (close to the interface) when it reaches equilibrium. Due to the continuous heat generation, the temperature flux in CL dominates in the anode side, and increases by 5-6 times during dehumidification. The calculated values of effective mass diffusivity and thermal conductivity of CL are 7.71×10−6 m2/s and 0.063 W/m•K. The randomly arranged catalyst particles makes CL the hottest (2~3°C higher) and driest (half the DL concentration) part of PEM element, which severely limits the whole performance. This modelling reveals the interaction between anode-side CL with microstructures and the performance, considering the transient heat and mass transfer.