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

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

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

A hybrid nanofluid (HYNF) is an excellent working medium for improved heat transfer performance. To investigate the convective heat transfer performance of an HYNF of various particle mixing ratios (MR), at first samples of MgO-SiO2/water HYNF are prepared by varying the MR of MgO to SiO2 as 0:100, 40:60, 50:50, 60:40, and 100:0 at 0.05 mass % of concentration; then convective heat transfer characteristics are determined experimentally by exposing different mass flows of HYNF from 0.03–0.27 kg/s to a constant heat flux of 5630 W/m2. The results describe that the variation in MR strongly influences the Nusselt number (Nu) of HYNF. The maximum Nu, which is 1.94 times (~ 94.50%) higher than the same with base fluid, is obtained at MR of 60:40 and the mass flow rate of 0.13 kg/s. The variation of MR does not affect the friction factor of HYNF and it is nearly comparable to that of the base fluid. Further, a new concept has been proposed to determine the thermo-economic feasibility of HYNF in heat transfer applications. It was recommended to maintain the MR at 60:40 and 0.13 kg/s mass flow rate for optimum thermo-economic performance. [ABSTRACT FROM AUTHOR]

Thermal transport plays a pivotal role across diverse disciplines, yet the intricate relationship between amorphous network structures and thermal conductance properties remains elusive due to the absence of a reliable and comprehensive network's dataset to be investigated. In this study, we have created a dataset comprising multiple amorphous network structures of varying sizes, generated through a combination of the node disturbance method and Delaunay triangulation, to fine-tune an initially random network toward both increased and decreased thermal conductance C. The tuning process is guided by the simulated annealing algorithm. Our findings unveil that C is inversely dependent on the normalized average shortest distance L n o r m connecting heat source nodes and sink nodes, which is determined by the network topological structure. Intuitively, the amorphous network with increased C is associated with an increased number of bonds oriented along the thermal transport direction, which shortens the heat transfer distance from the source to sink node. Conversely, thermal transport encounters impedance with an augmented number of bonds oriented perpendicular to the thermal transport direction, which is demonstrated by the increased L n o r m . This relationship can be described by a power law C = L n o r m α , applicable to the diverse-sized amorphous networks we have investigated. [ABSTRACT FROM AUTHOR]

This study presents a one-dimensional mathematical model developed to simulate multi-zone thermal storage systems using phase change materials (PCMs). The model enables precise analysis of temperature distribution in the layered storage based on several PCM configurations and properties. It is distinguished by its adaptability to various tank geometries and the number of PCM capsules, enabling its application under diverse operating conditions. By simplifying the implementation of heat transfer processes that depend on the shape of the capsule and the thermal properties of the PCM, the computation time can be reduced to a level that makes simulations over longer periods feasible. Experimental validation confirmed the accuracy of the model, with deviations below 6%, underscoring its practical applicability. The study demonstrates that individual layering in the storage tank can be achieved by filling it with PCMs of different melting points without compromising the maximum storage capacity. It is shown that including a PCM layer can maintain the outlet temperature 20% longer while storing 14% more energy. The results point out the model's potential to improve the performance of thermal storage systems through targeted PCM layer configurations. The model serves as the basis for the planning and optimization of these systems. [ABSTRACT FROM AUTHOR]

The DIGIT experiment was launched at the Tournemire Underground Research Laboratory URL with the aim of determining the effects of temperature on the transfer of analogues of most mobile radionuclides (i.e., 36Cl, 129I and 79Se) in the Toarcian clay rock, the properties of which are close to host rocks being considered for future deep geological disposal of high-level (HL) radioactive wastes. The experimental principle involves the monitoring of an exchange between a test water traced with stable halides and deuterium at constant concentration and the porewater of the Toarcian clay rock submitted to various temperatures. This experiment seeks to partially address questions regarding the potential spread of contaminants during the thermal phase of High Level Waste (HLW) waste packages. Specifically, the in situ experiment aims to evaluate the role of scale effects and thermodiffusion, a process that combines Fick's law and the Soret effect, in the transfer of radionuclides. This paper presents the first steps of the study, including the scoping calculations, the experimental set-up and the first results obtained during the unheated phase. The study started with the acquisition of the initial parameters, including the rock thermal properties, the concentrations of the four tracers (chloride, bromide, iodide and deuterium) naturally present in the clay porewater and their diffusive transport parameters by using four diffusion exchange techniques (phase 0). A model coupling heat and mass transfers was then developed using Comsol Multiphysics®, integrating data acquired so far with existing literature data. A test water with a tracer concentration around 1000 times higher than those in the pore water was proposed with a temperature imposed at the test section wall of 70 °C. A large test zone of 50 cm height and 1 m in diameter and installed in a 3 m deep vertical well located in a sound zone at the URL was then proposed. The installation of the experiments required the realization of one shaft and of nine peripheral boreholes for the monitoring of temperature, water pressure and deformation. The experiment started with phase 1, involving a traced, unheated water start-up for a period of 5 months. Then, a core sampling was conducted in the emptied well, and the same diffusion exchange techniques were applied. The results of anionic tracers were compared to simulations based on initial parameters (phase 0), revealing that tracer penetration at the end of phase 1 exceeded simulated values by approximately 2 cm. This result is interpreted as an increase in the accessible porosity to tracers, possibly due to the excavation damaged zone. Future simulations should incorporate these adjusted diffusive transport parameters. Following phase 1, the heating system was activated, applying a temperature of 70 °C to the test zone. New data will enable the comparison of tracer penetration and assess the actual impact of temperature on tracer transfer. [ABSTRACT FROM AUTHOR]

Transient vehicle dynamics are deeply affected by the spatial distribution of tire temperature, heat generation, and heat dissipation capacity. However, the tire testing process involves numerous parameters, adding the complexity to monitoring the tire's thermal properties variation. To address this issue, this paper proposes a data-driven framework that combines the well-known Magic Formula with scaling factors and a long- and short-term memory (LSTM) neural network. The framework forecasts tire thermal properties and functionality under given conditions by learning from time-series data on tire operating conditions and mechanical characteristics. It predicts future spatially varying thermal properties of the tire, including temperatures, heat generation, and heat dissipation. This is achieved with a high degree of forecasting ability quantified by train size/test size ratios equaling 22 and 38% with an impressive Relative Root Mean Square Error (RRMSE) around 1−2%. Here, this framework is applied to quantify the impacts of various operating conditions, including vertical load, slip angle, and vehicle speed, on heat transfer for three crucial radial positions on the tire. Our results show that the vertical load is the primary influencing parameter among all operating conditions. The knowledge about transient tire dynamics will be advantageous to their performance optimization. [ABSTRACT FROM AUTHOR]

A fundamental requirement for analysing timber structures under fire is to consider the degradation of material properties with temperature. Therefore, the objective of this study is to propose a model that accounts for the variation of the thermo-physical properties, the development of char, and its evolution with temperature. This model integrates a sequential coupling of heat transfer analysis with structural response. The degradation of the material properties is accounted for through the regulatory approach recommended in Eurocode 5. The stress analysis employs an elasto-plastic model with nonlinear isotropic hardening. Implementation of the model is achieved within the Abaqus suite of finite element software using external subroutines. The model's predictions align well with experimental data, accurately reproducing both thermal and structural responses. Specifically, the model accurately predicts temperature profiles, displacements, and the depth of the charred layer, which initiates above 300 °C. Additionally, for rectangular sections, it was observed that exposure of all faces to fire results in a non-rectangular residual section. Furthermore, employing the temperature-dependent thermal property curves suggested by EC5 yields satisfactory results when predicting the fire resistance of softwood timber structures. [ABSTRACT FROM AUTHOR]

To enhance the heat-transfer performance of scramjet engines, a numerical simulation was conducted on the heat-transfer process of RP-3 aviation kerosene under supercritical pressure within a horizontal micro-fine circular tube. The intrinsic mechanism of the heat-transfer process was analyzed, summarizing the impacts of mass flux, inlet temperature, and gravitational acceleration. Furthermore, four commonly used buoyancy criterion numbers were compared and evaluated. The results indicate that the heat-transfer process can be divided into five phases: heating inlet phase, normal heat-transfer phase, heat-transfer deterioration phase, heat-transfer enhancement phase, and high-temperature normal heat-transfer phase. The heating inlet phase is significantly influenced by the inlet temperature, while the heat-transfer deterioration is affected both by the thermal property variations of the aviation kerosene and the buoyancy effects. Lower mass flux and hypergravity conditions all exacerbate heat-transfer deterioration. Inlet temperature, however, does not affect the heat-transfer pattern. Among the criteria, Grq/Grth provides the best prediction of buoyancy effects in horizontal circular tubes. [ABSTRACT FROM AUTHOR]

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

The use of nanofluids in heat transfer applications has significantly increased in recent times due to their enhanced thermal properties. It is therefore important to investigate the flow behavior and, thus, the rheology of different nanosuspensions to improve heat transfer performance. In this study, the viscosity of a BN-diamond/thermal oil hybrid nanofluid is predicted using four machine learning (ML) algorithms, i.e., random forest (RF), gradient boosting regression (GBR), Gaussian regression (GR) and artificial neural network (ANN), as a function of temperature (25–65 °C), particle concentration (0.2–0.6 wt.%), and shear rate (1–2000 s−1). Six different error matrices were employed to evaluate the performance of these models by providing a comparative analysis. The data were randomly divided into training and testing data. The algorithms were optimized for better prediction of 700 experimental data points. While all ML algorithms produced R2 values greater than 0.99, the most accurate predictions, with minimum error, were obtained by GBR. This study indicates that ML algorithms are highly accurate and reliable for the rheological predictions of nanofluids. [ABSTRACT FROM AUTHOR]

The thermal and electrical properties of photovoltaic cell (PVC) under linear phenomenological heat transfer law between it and the environment is studied through finite time thermodynamics and the volt-ampere characteristic equation. The properties of PVC are affected by heat transfer between PVC and environment. There are optimal solar radiation intensity and PVC output voltage (OV), which make the photoelectric conversion efficiency (PECE) of PVC reach the highest value. When OV and solar radiation intensity are 28.50 V and 700 W/m2, the maximum PECE is 0.156. There is also the best solar radiation intensity, which makes the open-circuit voltage (OCV) reach the maximum. When solar radiant intensity is 669 W/m2, the maximum OCV is 33.14 V. The values of power output and short-circuit current (SCC) are monotonically increasing with solar radiation intensity. Given solar radiation intensity, the power output and OV exhibit a parabolic shape. The operating temperature falls first and then grows with the OV. However, the change of operating temperature with OV is not much. Band gap is a decreasing function of operating temperature. This article can give theoretical support for the design and use of PVCs. [ABSTRACT FROM AUTHOR]

This work presents experimental investigation on specific heat capacity and thermal conductivity as a function of temperature and vertical density profile over the sample cross section of oriented strand board, particle board, medium density fibreboard, and plywood. A possibility of introducing an universal set of parameters describing various EWPs by averaging the parameters of the four studied materials obtained in this work is investigated. A one-dimensional heat transfer simulation sensitivity study is performed using the experimentally measured values. The study aims to investigate the effect of using spatial- and temperature-dependent properties compared to constant values and to study the importance of these parameters and their combination in predicting one-dimensional heat transfer below the thermal decomposition temperature of engineered wood products. [ABSTRACT FROM AUTHOR]

The prediction of the prevailing self-heat transfer parameters of a glass/epoxy composite coupon during fatigue testing in general and the distinction between viscoelastic- and frictional crack growth-related energy dissipation in particular, are not trivial problems. This work investigates the feasibility of predicting the convective film coefficient, the total work loss as well as the ratio between viscoelastic and fracture-induced damping from thermal images using Bayesian optimization in conjunction with 3D FE thermal analysis. To this end, glass fiber/epoxy biax coupons are pre-damaged by means of a drop weight impact machine and subsequently tested under uniaxial tension-tension high cycle fatigue conditions. IR images are taken of the self-heating thermal profile at steady-state conditions. Synthetic surface thermal images are generated by numerical thermal analysis of the damage distribution obtained by μ -CT scanning prior to testing. Bayesian optimization of the aforementioned parameters is conducted by minimizing the loss function between the as-measured and the synthetic IR image. The predicted work-loss is consequently validated against the measured hysteretic response, from which a very good agreement is found. [Display omitted] • Thermal heat transfer parameters are predicted via Bayesian optimization (BO). • FEM and BO are combined to find physically meaningful parameters. • The method efficiently handles 3D parameters compared to other approaches. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce a newly proposed operating principle for the heat transfer use sector, which originates in an ECHA restriction proposal on partially hydrogenated terphenyls.1 Heat transfer fluids are substances that enable processes to maintain desired temperature ranges accurately and are used in nearly every manufacturing sector. Heat transfer fluids achieve this through their thermal stability properties, and when operated in a closed system, can provide manufacturing processes with years of function with little maintenance. A new operating principle focused on this theme of closed system operation is called Strictly Controlled Closed Systems (SCCS). The SCCS will serve to standardise safe operation of heat transfer systems in the European Union by limiting exposures to workers and releases to the environment. The SCCS is a combination of several safety standards, principles, and guidelines from inside and outside the heat transfer use sector. This combination of resources has resulted in a novel inspection checklist that should aid in industrial compliance and review of enforcement agencies. In addition, SCCS should provide a regulatory path for hazardous heat transfer fluids to follow as a class. SCCS will allow even hazardous heat transfer fluids to have cradle-to-the-grave standardised management aimed at minimisation of environmental and human exposures. It is our expectation that adoption of this standard will be a role model to ensure the continued use of important chemistries in applications and manufacturing that drive the EU economy. [ABSTRACT FROM AUTHOR]

In this paper, hydrophilic cotton (CO) yarn and hydrophobic cross-section polyester (PET) filaments were used to prepare single-layer interwoven fabrics (CO/PET fabrics) with plain, 3/1 twill, and 8/5 satin to formulate a hydrophobicity–hydrophilicity gradient across the fabric for obtaining a good water transfer ability. The CO fabrics and PET fabrics were prepared for comparison. The contact angle and thermo-physiology properties of the fabrics, including the wicking property, moisture management ability, drying property, air permeability and water vapor permeability, thermal property, and dynamic cooling property, were investigated. The results show that the asymmetric hydrophobicity–hydrophilicity characteristic can be formed across CO/PET fabric with 3/1 twill and 8/5 satin. This can improve the water transfer ability from the inside to the outside of the fabrics, and the longer the floating length of the fabric is, the stronger the water transfer ability is. These two fabrics also exhibit excellent wicking properties, overall moisture management capability, and thermal comfort, and have good permeability, drying properties, and dynamic cooling properties compared to the corresponding fabrics. As a result, these two CO/PET interwoven textiles are more suitable for application as clothes worn in summer. The interweaving technique combining hydrophilic and hydrophobic yarns is an easy and cost-effective method to prepare fabrics that meet summer requirements. This work provides insight into the thermal and moisture comfort property of interwoven fabrics for summer garments. [ABSTRACT FROM AUTHOR]

Luminescence thermal quenching (TQ) is one of the most critical problems to be solved for further improvement of phosphors' applications in lighting and many other fields. Herein, a novel strategy is demonstrated to achieve outstanding anti‐TQ performance with a substantial enhancement of Eu3+ red emission at 613 nm from Sc2MO3O12:Tb3+,Eu3+ phosphor. Its anti‐TQ performance is endowed by dual energy transfer (ET) pathways and intensified by the negative thermal expansion (NTE) property of the Sc2MO3O12 host. Remarkably, the photoluminescence (PL) emission intensity of Eu3+ from Sc2MO3O12:20%Eu3+,2%Tb3+ phosphor at 648 K reaches 507.3% of the initial intensity taken at 298 K. The lifetime of Eu3+ emission at 613 nm elongates with increasing measurement temperature. The experimental data and density functional theory (DFT) calculations reveal that the host structure shrinkage via NTE leads to the thermally boosted Eu3+ red emission by intensifying the consecutive ET and confinement of the absorption light. The potential of this phosphor as a dual‐mode high temperature thermometer based on both emission lifetime and intensity ratio read‐out modes is realized. This work provides inspiration to combine multiple strategies to achieve broad and dramatic anti‐TQ phosphors with enhanced performance for various optical applications. [ABSTRACT FROM AUTHOR]

The present numerical study proposes a framework to determine the heat flow parameters—specific heat and thermal conductivity—of resin–graphene nanoplatelets (GNPs) (modified) as well as non-modified resin (with no GNPs). This is performed by evaluating the exothermic reaction which occurs during both the filling and post-filling stages of Liquid Composite Moulding (LCM). The proposed model uses ANSYS Fluent to solve the Stokes–Brinkman (momentum and mass), energy, and chemical species conservation equations to a describe nano-filled resin infusion, chemo-rheological changes, and heat release/transfer simultaneously on a Representative Volume Element (RVE). The transient Volume-of-Fluid (VOF) method is employed to track free-surface propagation (resin–air interface) throughout the computational domain. A User-Defined Function (UDF) is developed together with a User-Defined Scaler (UDS) to incorporate the heat generation (polymerisation), which is added as an extra source term into the energy equation. A separate UDF is used to capture intra-tow (microscopic) flow by adding a source term into the momentum equation. The numerical findings indicate that the incorporation of GNPs can accelerate the curing of the resin system due to the high thermal conductivity of the nanofiller. Furthermore, the model proves its capability in predicting the specific heat and thermal conductivity of the modified and non-modified resin systems utilising the computed heat of reaction data. The analysis shows an increase of ∼15% in the specific heat and thermal conductivity due to different mould temperatures applied (110–170 °C). This, furthermore, stresses the fact that the addition of GNPs (0.2 wt.%) improves the resin-specific heat by 3.68% and thermal conductivity by 58% in comparison to the non-modified thermoset resin. The numerical findings show a satisfactory agreement with and in the range of experimental data available in the literature.

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

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

This paper presents a detailed exploration of the development and characterization of multifunctional dual-purpose woven fabrics for thermal protective clothing. Through this research, 69 woven fabric prototypes have been carefully designed and produced, integrating various raw materials, yarn, and woven fabric construction parameters, with the aim of optimizing thermal protection properties while ensuring comfort and durability. The analysis led to the identification of two optimal woven fabric samples, which, upon further testing, exhibited exceptional dimensional stability, crease recovery, tear resistance, as well as abrasion and water resistance. Furthermore, the thermal properties were evaluated, demonstrating exceptional flame resistance, limited heat transmission, and high thermal insulation. Additionally, the study evaluated dynamic thermal properties, contact conductive heat transfer, air permeability, water vapour resistance, and thermal resistance of two clothing systems constructed from selected woven fabrics. Statistical analysis confirms significant differences between clothing systems, highlighting the influence of yarn composition and fabric structure on thermal performance and comfort, where one system exhibits better thermal insulation characteristics suitable for colder environments while the other excels in breathability for warmer climates. The developed woven fabrics meet high standards for protective clothing against heat and flame, surpassing currently available comparable woven fabrics on the market in terms of efficacy and performance. This research provides insights into the intricate balance between protection, comfort, and durability of woven fabrics, contributing to advancements in protective textile technology. [ABSTRACT FROM AUTHOR]

This study was conducted to apply the finite volume method (FVM) to solve the partial differential equation (PDE) governing the heat transfer process during meat cooking with convective surface conditions. For a one‐dimensional, round‐shaped food, such as meat balls, the domain may be divided into shells of equal thickness, with energy balance established for each adjacent shell using in the finite difference scheme (FDS) to construct a set of finite difference equations, which were then solved simultaneously using the FORTRAN language and the IVPAG subroutine of the International Mathematics and Statistics Library. The FDS is flexible for temperature‐dependent physical properties of foods, such as thermal conductivity (k), specific heat (Cp), thermal diffusivity (α), and boundary conditions, for example, surface heat transfer coefficient (h), to predict the dynamic temperature profiles in beef and chicken meat balls cooked in an oven. Once the FVM model was established and validated, it was used to simulate the dynamic temperature profiles during cooking, which were then used in combination with the general method to evaluate the thermal lethality of Shiga toxin‐producing Escherichia coli and Salmonella spp. using D and z values in ground meats during cooking. The method can be applied to design cooking processes that effectively inactivate foodborne pathogens while maintaining the quality of cooked meats and evaluate the adequacy of a cooking process. Practical Application: The temperature dependences of thermal conductivity (k) and thermal diffusivity (α) of raw ground beef and ground chicken meats were measured. These thermal properties were then used in numerical simulation to predict the dynamic heating temperature profile and thermal lethality of ground beef and chicken meat balls. The numerical simulation method may be used to optimize and evaluate thermal processes and ensure the inactivation of pathogens in meat products during cooking. [ABSTRACT FROM AUTHOR]

Nanofluids, characterized by the dispersion of nanoparticles in a base liquid, have attracted significant attention in recent years due to their exceptional thermal properties. Specifically, the specific heat capacity of nanofluids plays a crucial role in the design and optimization of heat transfer systems. Traditional experimental methods for determining the specific heat capacity of nanofluids are often limited in terms of cost, time, and operating condition ranges. To address these limitations, this research focuses on the development of a novel predictive model for estimating the specific heat capacity of nanofluids. This study aims to develop a machine learning regression model called Gradient Boost Regression (GBR) with Grid Search optimization (GSO) for accurately predicting the specific heat capacity of aluminum nitride (Al2N3) nanoparticles that are suspended in both water and an ethylene glycol (EG) solution. The GBR-GSO model capitalizes on the strengths of GBR, which can effectively capture complex relationships, and GSO, a metaheuristic optimization technique inspired by the law of gravity. By integrating these two approaches, we aim to create a robust and accurate predictive model for specific heat capacity in nanofluids. To develop and validate the GBR-GSO model, a diverse dataset based on experimental-specific heat capacity collected from the literature has been designed. The performance of the model has been evaluated by comparing its predictions with experimental data. The GBR-GSO model achieved 99.99% accuracy with the experimental data of specific heat capacity. This research contributes to the advancement of nanofluid-based heat transfer systems by providing an effective tool for predicting the specific heat capacity of nanofluids. The developed model can facilitate the design and optimization of various engineering applications, leading to the development of energy-efficient and sustainable technologies. [ABSTRACT FROM AUTHOR]

The effect of a twisting parameter on heat transport in a square-sectioned twisted tube was studied. One definition of a twisting parameter is the ratio of the hydraulic diameter to the length of the tube at the point when it completed a full 360-degree twist. The twist parameters that were selected are 5, and they were compared to the Double Twisted Square Tube heat exchanger (DTSTHE). Transient flow was taken into account when evaluating the sets of Reynolds numbers. On the other hand, there are four different concentration volumes of nano fluid amounts of 0.005, 0.01, 0.025, and 0.04 for turbulent flow, and the Reynolds number ranges from 5,000 to 25,000. An examination was conducted into the effect of the twist parameter on the convective heat transfer coefficient through turbulent flow. The finite volume approach and the conventional − turbulence model were used to conduct numerical simulations of three-dimensional, steady-state incompressible flow in body-fitted coordinates. It was found that as the twist parameter is decreased, the heat transmission coefficient increases. What this means for the created span-wise swirling flow is unclear. With increasing distance from the tube center towards the walls, the whirling causes the cross-flow velocity vectors to increase. The boundary layer has good thermal properties because it thins out at the tube wall as the near-wall velocity increases. Internal thermal balance is further improved by spinning since it increases the mixing process. A higher Reynolds number, along with larger velocity components, results in a higher heat transfer coefficient. [ABSTRACT FROM AUTHOR]

Thermal feedback plays an important role in tactile perception, greatly influencing fields such as autonomous robot systems and virtual reality. The further development of intelligent systems demands enhanced thermosensation, such as the measurement of thermal properties of objects to aid in more accurate system perception. However, this continues to present certain challenges in contact-based scenarios. For this reason, this study innovates by using the concept of semi-infinite equivalence to design a thermosensation system. A discrete transient heat transfer model was established. Subsequently, a data-driven method was introduced, integrating the developed model with a back propagation (BP) neural network containing dual hidden layers, to facilitate accurate calculation for contact materials. The network was trained using the thermophysical data of 67 types of materials generated by the heat transfer model. An experimental setup, employing flexible thin-film devices, was constructed to measure three solid materials under various heating conditions. Results indicated that measurement errors stayed within 10% for thermal conductivity and 20% for thermal diffusion. This approach not only enables quick, quantitative calculation and identification of contact materials but also simplifies the measurement process by eliminating the need for initial temperature adjustments, and minimizing errors due to model complexity. [ABSTRACT FROM AUTHOR]

This investigation focuses on the nanoparticle shape effect on the flow of conducting non-Newtonian Maxwell nanoliquid through an absorptive expanding surface. Insertion of inertial drag due to the Darcy–Forchheimer model and the dissipative heat in the flow phenomena supplements the work. The thermal properties of the fluid show its important role because of the use of various thermophysical models such as the Gharesim model of viscosity and the Mintsa model of thermal conductivity. The proposed mathematical model is designed by the implementation of suitable similarity transformations and then a numerical scheme is adopted to solve the transformed phenomena. In particular, shooting-based traditional Runge–Kutta (RK) fourth order is implemented for the solution. The behavior of the parameters that are participating in the flow phenomena is deployed via graphs and the computation of the engineering coefficients is displayed in tables. The impression of innumerable governing flow constraints is interpreted with the validation of these investigations with the earlier investigation. However, the major outcomes are; the particle concentration contributes its significant characteristics in enhancing the fluid velocity as well as temperature for the increasing shape. The dissipative heat formulated by the Eckert number also augments the fluid temperature in association with the thermal radiation whereas the unsteadiness parameter retards it significantly. [ABSTRACT FROM AUTHOR]

The present flow problem analyzes the impact of radiative heat and mass transfer with inclined magnetic field on thermal exchange and entropy production of two immiscible nature of electrically conducting couple stress fluid in a static porous saturated conduit. In this model, the lower and upper porous regions of the rectangular channel are occupied by two different types of couple stress fluids. The static horizontal parallel plates of the porous duct are completely isothermal and the flow of immiscible fluid through the porous duct develops because of a constant pressure gradient at the entry zone of the duct. The Brinkman model is utilized in the modeling of fluid flow through porous saturated domain and Rosseland's approximation is utilized to compute the radiative thermal exchange effect on nonmiscible couple stress fluid. In this work, authors have analyzed the effect of various thermo-physical parameters such as the Hartmann number (Ha), permeability parameter (Da), Schmidt number (Sc), Soret number (Sr) and couple stress parameter ( s i , i = 1 , 2) on the entropy generation characteristics, Bejan number distribution, thermal behavior, concentration distribution and flow characteristics of immiscible couple stress fluid which passes through the porous channel. The parameters Ha, Da, s i , Sc, Sr correspond to magnetic field effect, permeability of porous media, couple stress, mass diffusion and thermal diffusion, respectively. The most significant findings of this research work are as follows: • In a porous saturated channel, the immiscible couple stress fluid velocity, entropy production number and thermal profile get enhanced on increasing the couple stress parameter. • On increasing the Hartmann number and decreasing the permeability of porous region, the thermal properties and entropy production number both decrease. • The couple stress fluid's concentration field and Bejan number distribution get decreased on enhancing Soret number Sr and Schmidt number Sc. • The entropy generation near the wall of the channel rapidly increases on increasing the Schmidt number and Soret number. The emerging finding of this research work exhibits excellent agreement with previously published work. This research work can be utilized in food processing, petroleum products and chemical process. [ABSTRACT FROM AUTHOR]

In recent years, scientists have begun to search for more sustainable biomaterials. Although many studies have been conducted on different fiber-reinforced composites, much remains to be done. Using environmentally friendly composite materials for building insulation is a practical solution to reduce energy consumption. In this study, an advanced statistical approach using JMP software was adopted to manage a complex problem involving multiple parameters. This method was applied to optimize the thermal insulation characteristics of a bio-composite. By following a precisely designed experimental program. the study focuses on analyzing the impact of varying concentrations of date palm fibers (DPF) on the thermal properties of the material. The tested samples contained between 0% and 30% DPF. with a fiber length set at 7 mm. The findings of this study clearly illustrate that the thermal conductivity of the bio-composite decreases with an increase in the percentage of DPF. This phenomenon occurs because the incorporation of fibers into the composite enhances the porosity within the matrix. consequently, reducing its density. Thus. these results underscore the advantageous effect of DPF on the insulation properties of the material. [ABSTRACT FROM AUTHOR]

In this study, we introduced modifications to a prior existing enthalpic lattice Boltzmann method (LBM) tailored for simulating the conjugate heat transfer phenomena in non-homogeneous media with time-dependent thermal properties. Our approach is based upon the incorporation of the remaining terms of a conservative energy equation, excluding only the terms regarding flow compressibility and viscous dissipation, thereby accounting for the local and transient variations in the thermophysical properties. The solutions of verification tests, comprising assessments of both transient and steady-state solutions, validated the accuracy of the proposed model, further bolstering its reliability for analyzing heat transfer processes. The modified model was then used to perform an analysis on structured cavities under free convection, revealing compelling insights, particularly regarding transient regimes, demonstrating that the structured cavities exhibit a beneficial impact on enhancing the heat transfer processes, hence providing insights for potential design enhancements in heat exchangers. These results demonstrate the potential of our modified enthalpic LBM approach for simulating complex heat transfer phenomena in non-homogeneous media and structured geometries, offering valuable results for heat exchanger engineering and optimization. [ABSTRACT FROM AUTHOR]

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In this study, three randomly packed beds with varying tube-to-particle diameter ratios (D/d) are constructed using the discrete element method (DEM) and simulated via CFD under low pore Reynolds numbers (Rep < 100). An innovation of this research lies in the application of hydrogen in randomly packed beds, coupled with the consideration of its temperature-dependent thermal properties. The axial analysis of the heat transfer characteristics shows that PB−5 and PB−6 achieve thermal equilibrium 44% and 58% faster than PB−4, respectively, demonstrating enhanced heat transfer efficiency. However, at higher flow rates (0.8 m/s), the large-sized fluid channels in PB−6 severely impact the heat transfer efficiency, slightly reducing it compared to PB−5. Additionally, this study introduces a localized segmentation method for calculating the axial local Nusselt number, showing that the axial local Nusselt number (Nu) not only exhibits an inverse relationship with the axial porosity distribution, but also matches its amplitude fluctuations. The wall effect significantly impacts the flow and temperature distribution in the packed bed, causing notable velocity and temperature oscillations in the near-wall region. In the near-wall region, the average temperature is lower than in the core region, and the axial temperature profile exhibits more intense oscillations. These findings may provide insights into the use of hydrogen in randomly packed beds, which are vital for enhancing industrial applications such as hydrogen storage and utilization. [ABSTRACT FROM AUTHOR]

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

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

As a renewable energy source, geothermal energy has been widely used to provide space heating and cooling for buildings. The thermal performance of ground heat exchanger (GHE) is significant for the operating efficiency of the ground source heat pump (GSHP) systems. This paper presents a comprehensive review of developments and advances of three kinds of GHE, including vertical borehole GHE (VBGHE), Pile GHE (PGHE), and deep borehole GHE (DBGHE) which are currently popular in larger GSHP systems. Firstly, analytical models proposed to analyze heat transfer process of VBGHE with different geological conditions are summarized, such as homogenous or heterogeneous ground, with or without groundwater advection. Numerical and short-time step models and measures to improve GHE thermal performance are also reviewed. Secondly, a summary of research advances in PGHE is provided, which includes the heat transfer models of PGHE, the effects of geometric structure, operation modes, pile spacing, use of phase change material (PCM), thermal properties of PCM, thermo-mechanical behavior and/or thermal performance of PGHE. The effects of groundwater flow direction and velocity on PGHE are also summarized in brief. Lastly, models of three kinds of DBGHEs, i.e., deep coaxial GHE (DCGHE), deep U-bend GHE (DUGHE) and super-long gravity heat pipe (SLGHP), are reviewed. The physical bases of the different analytical models are elaborated and also their advantages and disadvantages are described. Advances in numerical modelling and improving numerical model calculation speed of DCBHE, DCBHE array, and DUBHE are summarized. The review provides a meaningful reference for the further study of GHEs. [ABSTRACT FROM AUTHOR]

AbstractWith the expansion of seabed gas hydrate exploitation, particle-reinforced cement is widely used in well cementing. However, the estimation of heat transfer and thermal conductivity for hollow cylinder composites reinforced with particles remains ambiguous and requires further clarification. This paper employs the inclusion-based boundary element method (iBEM) to calculate both the local field distribution and effective thermal properties of particle-reinforced hollow cylinder composites (PRHCC). Particle interactions are simulated through the introduction of the eigen-temperature gradient, and the boundary effect is modeled using the boundary element method. Numerical simulations explore the influence of interparticle interactions, particle volume fraction, particle properties, and particle distribution on the thermal properties of PRHCC. The results show that the particle interactions have distinct effects in different directions, significantly altering the temperature spread in the central and lower regions within the particles, and then affecting the overall thermal properties of PRHCC. This method serves as a valuable reference for the engineering industry, providing insightful guidance for optimizing design, fabrication processes, and material selection, particularly in components with thermal considerations. [ABSTRACT FROM AUTHOR]

Nanofluid is a specially crafted fluid comprising a pure fluid with dispersed nanometer-sized particles. Incorporation these nanoparticles into pure fluid results in a fluid with improved thermal properties in comparison of pure fluid. The enhanced properties of nanofluids make them highly sought after, in diverse applications, consisting of coolant of devices, heat exchangers, and thermal solar systems. In this study hybrid nanofluid consisting of copper, alumina and titanium nanoparticles on a curved sheet has investigated with impact of chemical reactivity, magnetic field and Joule heating. The leading equations have converted to normal equations by using appropriate set of variables and has then evaluated by homotopy analysis method. The outcomes are shown through Figures and Tables and are discussed physically. It has revealed in this study that Cu-nanofluid flow has augmented velocity, temperature, and volume fraction distributions than those of Al2O3-nanofluid and TiO2-nanofluid. Also, the Cu-nanofluid flow has higher heat and mass transfer rates than those of Al2O3-nanofluid and TiO2-nanofluid. [ABSTRACT FROM AUTHOR]

This present review explores the application of artificial intelligence (AI) methods in analysing the prediction of thermophysical properties of nanofluids. Nanofluids, colloidal solutions comprising nanoparticles dispersed in various base fluids, have received significant attention for their enhanced thermal properties and broad application in industries ranging from electronics cooling to renewable energy systems. In particular, nanofluids' complexity and non-linear behaviour necessitate advanced predictive models in heat transfer applications. The AI techniques, which include genetic algorithms (GAs) and machine learning (ML) methods, have emerged as powerful tools to address these challenges and offer novel alternatives to traditional mathematical and physical models. Artificial Neural Networks (ANNs) and other AI algorithms are highlighted for their capacity to process large datasets and identify intricate patterns, thereby proving effective in predicting nanofluid thermophysical properties (e.g., thermal conductivity and specific heat capacity). This review paper presents a comprehensive overview of various published studies devoted to the thermal behaviour of nanofluids, where AI methods (like ANNs, support vector regression (SVR), and genetic algorithms) are employed to enhance the accuracy of predictions of their thermophysical properties. The reviewed works conclusively demonstrate the superiority of AI models over the classical approaches, emphasizing the role of AI in advancing research for nanofluids used in heat transfer applications. [ABSTRACT FROM AUTHOR]

In the last few years, the efforts to increase the heat transfer rates have been concentrated on passive techniques in accordance with the aim of reducing the consumption of power and the establishment of more sustainable systems. One of these promising techniques is adding extended geometries (ribs) which serve as thermal conducting geometries and flow turbulators. Another technique is to add nanoparticles to enhance the overall thermal properties of working fluids. Researchers had investigated this approach from multiple different scientific points of view. Among these factors is the channel geometry, whether it is with a regular section or a microchannel. Two major types of channels can be noted: common or traditional channels with a macroscopic cross section and microchannels with much smaller sections. The investigation of a flow in a regular channel section includes the cases of laminar flow, turbulent flow, constant heat flux, constant temperature, as well as different working fluids. The current review tends to summarize the most remarkable efforts which had been done in the last few years according to the above-mentioned aspects. [ABSTRACT FROM AUTHOR]

Pool boiling heat transfer is recognized as an exceptionally effective method, widely applied across various industries. The adoption of non-azeotropic binary mixtures aligns with the environmental objectives of modern industrial development and enhances the coefficient of performance (COP) in numerous systems. Therefore, investigating the boiling heat transfer characteristics of these mixtures is crucial to improving their industrial usability. In this study, mixtures of ethylene glycol and deionized water (EG/DW) in varying concentrations were chosen as the working fluids. A comprehensive experimental setup was developed, followed by a series of experiments to assess their pool boiling performance. Simultaneously, the thermophysical parameters of these mixtures underwent detailed examination and analysis. The research revealed that the concentration of EG in the mixture markedly affects its thermal properties and temperature glide, both of which are crucial in influencing the heat transfer coefficient. Additionally, six established heat transfer coefficient prediction correlations, primarily designed for pure fluids, have been employed. However, their application to non-azeotropic mixtures under experimental conditions revealed significant deviations. To address this issue, the present study modified existing correlations with the temperature slip characteristics of non-azeotropic mixtures. This process involved recalibrating the wall superheat values in the correlations to reflect the local temperature differential at the boiling point, thereby customizing them for application to non-azeotropic mixtures. The modified correlations highlighted the unique behaviors of non-azeotropic mixtures in boiling heat transfer, demonstrating improved compatibility with these mixtures in a deviation within a permissible 20% range compared with experimental results. [ABSTRACT FROM AUTHOR]

Solar radiative heating andmelting of lake and sea ice is a geophysical problem that has attracted the attention of researchers for many years. This problem is important in connection with the current global change of the climate. Physical and computational models of the process are suggested in the paper. Analytical solutions for the transfer of solar radiation in lightscattering snow cover and ice are combined with numerical calculations of heat transfer in amultilayer system. The thermal boundary conditions take into account convective heat losses to the ambient air and radiative cooling in the mid-infrared window of transparency of the cloudless atmosphere. The study begins with an anomalous spring melting of ice on the large high-mountain lakes of Tibet. It was found that a thick ice layer not covered with snow starts to melt at the ice-water interface due to volumetric solar heating of ice. The results of the calculations are in good agreement with the field observations. The computational analysis showed a dramatic change in the process when the ice is covered with snow. A qualitative change in the physical picture of the process occurs when the snow cover thickness increases to 20-30 cm. In this case, the snow melting precedes ice melting and water ponds are formed on the ice surface. This is typical for the Arctic Sea in polar summer. Known experimental data are used to estimate the melting of sea ice under the melt pond. Positive or negative feedback related to the specific optical and thermal properties of snow, ice, and water are discussed. [ABSTRACT FROM AUTHOR]

Among earthen construction techniques, cob might be an interesting solution to mitigate greenhouse gases emissions and energy consumption of the building industry. One main issue encountered is that the cob material shows large variability of hygrothermal properties, which could consequently have an impact on the reliability of the estimation of the energy consumption of cob buildings. At the wall scale, the hygrothermal properties significantly influence the kinetics of moisture and heat transfers through the building shell, both being coupled. In order to measure the relative contribution of the variation of the hygrothermal properties, a sensitivity analysis of a coupled heat and moisture transfer model has been carried out on a cob wall. More specifically, a local sensitivity analysis has been performed (one model input wobbles around a reference value) and compared with a global sensitivity analysis, which may provide the potential interaction between model inputs. For the latter approach, the Morris method was used and allows to find the influence level of material properties and the relationships with model outputs. Two study cases have been performed: a static loading case, to find temperature and water vapour pressure profiles across the cob wall until the steady state and a dynamic loading case under a 2.5 years external dynamic loading (St-Nazaire meteorological data, France). As main results, the global approach showed in general a higher variability of properties, the sorption isotherms and the water vapour permeability were the most influential input parameters on humidity profiles while on temperature ones, the variability of both properties led up to 0.25 °C variation range. The influence of thermal properties was very sensitive to the daily-loading variation while that of the hygric properties was very sensitive to the seasonal-loading variation. [ABSTRACT FROM AUTHOR]

In regions characterized by continuous permafrost, hydrological modeling remains a complex activity, primarily due to constraints related to the prevailing climatic conditions and the specific behavior of the active layer. High-latitude regions receive less solar radiation; thus, most creeks are active only during summertime and stay frozen in the winter. To realistically simulate watersheds underlain by continuous permafrost, the heat transfer through the soil needs to be accounted for in the modeling process. In this study, a watershed located in a continuous permafrost zone in Russia is investigated. A model is proposed to integrate this heat transfer into an existing conceptual rain-flow transformation model, Hydrologiska Byråns Vattenbalansavdelning (HBV), to calculate the seasonal thaw depth and determine the components of water balance. The proposed integration is a novelty compared to the standard model, as it enables the physical and thermal properties of the soil to be taken into account. It was found that the proposed model, HBV-Heat, performs better than the stand-alone HBV model. Specifically, the average Nash–Sutcliffe efficiency (NSE) increases by 30% for the whole calibration period. In terms of the water balance components, the results are consistent with previous studies, showing that surface runoff represents 64% of the observed precipitation. [ABSTRACT FROM AUTHOR]

The present study is concerned with the analysis of unsteady heat conduction in regular bodies (large plane wall, long cylinder, and sphere) with constant initial temperature, prescribed surface heat flux and thermal properties of the solid that are invariant with temperature. Surprisingly, this important topic is absent in textbooks on heat transfer. The exact evaluation of the dimensionless surface temperatures varying with the dimensionless time in the regular bodies over the entire dimensionless time domain 0 < τ < ∞ is carried out with a symbolic algebra code. Thereafter, regression analysis is applied to the data gathered for the dimensionless surface temperatures varying with the dimensionless time of the regular bodies inside the dimensionless "small time" time sub-domains 0 < τ ≤ τ th . The dimensionless threshold time τ th is a decisive parameter that establishes the borderline between the "small time" sub-domain τ → 0 and the "large time" sub-domain τ ≫ 0 comprising the entire dimensionless time domain 0 < τ < ∞. Based on regression analysis, compact asymptotes are constructed for the dimensionless surface temperatures varying with the dimensionless time inside the dimensionless "small time" sub-domain 0 < τ ≤ τ th . At the end, agreements with the dimensionless exact, analytical surface temperature distributions (the baseline solutions) valid for the dimensionless time sub-domain 0 < τ < ∞. are considered of excellent quality. [ABSTRACT FROM AUTHOR]