Résumé: Currently, there is great interest in producing thermal energy (heat) from renewable sources and storing this energy in a suitable system. The use of a latent heat storage (LHS) system using a phase change material (PCM) is a very efficient storage means (medium) and offers the advantages of high volumetric energy storage capacity and the quasi-isothermal nature of the storage process. In recent years, phase change materials (PCMs) have become an interesting research area due to their advantages especially in thermal energy storage (TES). ...

Résumé: Solar energy is a crucial renewable resource, yet its efficient utilization remains a challenge because conventional heat transfer fluids have limited thermal conductivity. Parabolic Trough Collectors (PTCs) offer a viable solution for solar thermal energy conversion, but optimizing performance is essential for improving efficiency. This study investigates the performance of a PTC using a 0.3% TiO₂-water nanofluid compared to distilled water (DW) under real environmental conditions in Algeria’s arid climate. Two identical PTCs were tested outdoors at flow rates of 0.2, 0.3, and 0.4 L/min to evaluate heat transfer efficiency. The results demonstrated that the nanofluid consistently outperformed DW because of its superior thermal conductivity and heat retention. At 0.4 L/min, the nanofluid achieved 40% thermal efficiency, 9% higher than DW. However, as the flow rate decreased, the efficiency gap narrowed. These findings confirm the potential of nanofluids to enhance solar energy utilization, supporting sustainable energy solutions in high-irradiance regions.

Résumé: Spark plasma sintering (SPS) presents advantages over conventional sintering methods, notably high heating rates and reduced residence times. However, SPS suffers from uneven temperature and stress distributions due to suboptimal sintering parameters, such as uniaxial pressure and mold dimensions. These inconsistencies can result in microstructural inhomogeneities, adversely affecting the mechanical properties of the final products. To mitigate these issues, optimizing the sintering parameters is important for achieving a homogeneous polycrystalline material with desired mechanical characteristics. This study focuses on the numerical modeling of the thermoelectric and mechanical coupling behavior of alumina during SPS. Simulations were conducted using ANSYS software, and the model was integrated into a Box-Behnken Design of Experiment (BBD) to optimize three key factors: Factor 1 - uniaxial pressure (5 MPa to 20 MPa), Factor 2 - mold diameter (19 mm to 50 mm), and Factor 3 - mold height (4 mm to 8 mm). The results from the BBD optimization were analyzed using surface diagrams and ANOVA. The optimal SPS parameters identified for producing homogeneous alumina with desired mechanical properties are: uniaxial pressure of 20 MPa, mold height of 8 mm, and mold diameter of 34.5 mm, calculated for a current of 1000 A. This optimization approach effectively enhances the quality of SPS-sintered materials.

Résumé: This paper explores the incorporation of aramid fibers, recognized for their high mechanical flexibility and low thermal conductivity (TC), to serve as reinforcing agents within the highly porous aerogel matrix in order to overcome their fragility and weak mechanical structure that impose limitations on their practical utility especially in piping insulation. The thermal properties are determined using a micromechanical modeling approach that considers parameters such as temperature, fiber volume fraction, thermal conductivity, and porosity of the silica aerogel. For specific conditions, including an Aramid fiber radius of 6 microns, a silica aerogel thermal conductivity of 0.017 W.m-1.K-1, and a porosity of 95%, the resulting AFRA composite exhibits an Effective Thermal Conductivity (ETC) of 0.0234 W.m-1.K-1. Notably, this value is lower than the thermal conductivity of air at ambient temperature. The findings are further validated through experimental and analytical techniques. A response surface methodology (RSM) based on Box-Behnken design (BBD) is employed. This approach leads to the development of a quadratic equation intricately relating the key parameters to the ETC of the AFRA. The aim is optimization, identifying target optimal values for these parameters to further enhance the performance of AFRA composites.

Résumé: The investigation centers on the phase change processes integral to latent heat thermal energy storage (LHTES) units. These processes hold significant importance due to their capacity to store excess energy generated by both renewable and conventional power plants. The focus of the study lies in the intricate solid-liquid phase transitions, which are highly sensitive to alterations in thermal boundary conditions. In this specific study, a numerical simulation was carried out to analyze the behavior of kerosene, as a phase-change material, during the melting process inside a square cavity heated from one side and insulated from the other sides. The simulation was carried out using COMSOL Multiphysics software. The results of the study indicate that, initially, thermal conduction plays a dominant role in the melting process, while natural convection develops as melting progresses

Résumé: he storage capacity of surplus energy produced by renewable and conventional power plants makes latent heat storage units (LHSUs) using phase change materials (PCMs) relevant. The phase change processes are affected by the geometric shape of the PCM tank and the operating conditions to which it is subjected. This study explores the behavior of the melting process of paraffin (a PCM) in a square-shaped tank oriented in various orientations 0°, 30°, 60° and 90°. The results show that initially, the melting is dominated by thermal conduction, but as it progresses, natural convection takes over. Furthermore, the melting rate increases with the inclination angle. The total energy stored in a PCM during melting remains constant, although the time needed to store that energy can be considerably influenced by the orientation of the cavity.

Résumé: This paper investigates the heat transfer enhancement in a two-compartment heat exchanger using nanofluids, employing numerical simulations and deep learning. The study systematically examines the influence of key parameters: Rayleigh number (Ra=106−109), conductivity ratio (kr=1−15), nanoparticle volume fraction (φ=0−3%), nanofluid temperature (Temp=293-323K), and scaled heat exchanger wall thickness (0.02-0.05). The first compartment features internal heat generation, while the second incorporates baffles and nanofluids to optimize mixing and heat transfer. Computational Fluid Dynamics (CFD) is used to analyze Nusselt number, isotherms, streamlines, velocity vector magnitude, exergy loss, entropy generation, and the Bejan number. Deep learning models are developed to predict and optimize heat transfer performance based on these five input parameters. Results demonstrate that increasing the Rayleigh number and conductivity ratio significantly enhances heat transfer, while nanoparticles higher volume fractions improve performance, albeit with potential viscosity increases. Exergy analysis reveals opportunities for design optimization to minimize entropy generation. The integrated approach of CFD and deep learning provides a powerful tool for optimizing the design and operation of nanofluid-based heat exchangers for improved thermal management in various applications.

Résumé: This study investigates the application of Direct Current Atmospheric Plasma Spraying (DC-APS) for depositing zirconium dioxide (ZrO2) coatings to enhance component performance and durability in advanced applications. ZrO2's excellent thermal and mechanical properties make it ideal for industries like aerospace and renewable energy. We focus on optimizing critical process parameters, such as plasma jet temperature and particle injection velocity, to improve coating characteristics. Using the Jets&Poudres (JP) simulation code, we analyze particle dynamics and heat transfer, revealing how process variations influence coating morphology and adhesion. Additionally, we employ artificial neural networks (ANNs) to model the deposition process, achieving high accuracy in predicting performance metrics. Our findings confirm that DC-APS is an effective method for producing high-quality ZrO2 coatings and underscore the importance of process optimization and AI integration for enhancing thermal spray applications.