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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]

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]

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]

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]

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]