http//localhost:8080/jspui/handle/123456789/530 2026-05-08T19:29:50Z http//localhost:8080/jspui/handle/123456789/13686 Contribution à l’étude du comportement statique des structures en matériaux avancés sous chargements couplés non linéaires Meski, Khaled The aim of this work is to develop a new quasi-3D high order shear deformation theory to study the bending behavior of simply supported functionally graded sandwich beams (FGSBs), subjected to nonlinear hygro-thermo-mechanical loadings. The analysis focuses on symmetric and asymmetric sandwich beams, with skins made of functionally graded material. In addition, the influence of transverse stretching is considered in this study. The material properties, thermal expansion and moisture concentration coefficients vary gradually and continuously according to a power-law distribution (P-FGM) in terms of the constituent volume fractions. The proposed model adopts a new field of displacement that includes indeterminate integral variables and contains only four unknowns. The equilibrium equations are derived using the principle of virtual work and solved using the Navier's technique. Furthermore, the nonlinear hygro-thermo-mechanical coupling loading (NL-HTMCL) is integrated into these equations. The results obtained show a strong correlation between the non-dimensional stresses and displacements compared to those obtained from other 3D and quasi-3D theories, which demonstrates the efficiency and accuracy of the present theory. A parametric study is provided and discussed in detail in this thesis, quantifying dimensionless terms such as deflection, axial displacement, and stresses, while highlighting the effects of thermal and moisture loads. 2025-12-15T00:00:00Z http//localhost:8080/jspui/handle/123456789/13402 Energy Forecasting in Smart Grids: Toward an Artificial Intelligence-Driven Approach Bezzar, Nour El Houda This thesis focuses on developing an accurate electricity consumption forecasting model within smart grids by leveraging modern artificial intelligence techniques to enhance energy management and improve efficiency in residential environments. The study began with the preparation and preprocessing of detailed electricity consumption data for a single household, including measurements such as active power, reactive power, voltage, current intensity, and consumption from sub-metered appliances. Initially, a Long Short-Term Memory (LSTM) neural network model was applied for load forecasting. However, after comparative evaluation, the XGBoost model demonstrated superior accuracy and performance in reducing prediction errors. Consequently, efforts were concentrated on optimizing the XGBoost model through hyperparameter tuning and measure the features (using normalization or standardization), resulting in highly accurate forecasts with minimal error margins. To extend the scope of the research, the model was applied to six different households in a specific area, with hourly and daily predictions performed to provide a detailed and real-time view of electricity consumption patterns. This expansion allowed for a comprehensive understanding of consumption variability and enhanced the model’s predictive capability. In the final phase, an integrated energy management framework was developed based on the forecasting results. This framework optimizes the utilization of multiple energy sources including solar, wind, battery storage, and the electrical grid thus enabling efficient energy distribution and reducing reliance on the grid when possible. The framework represents a significant step towards building intelligent and sustainable energy systems capable of adapting to demand fluctuations and maximizing operational efficiency. This thesis makes a valuable scientific and practical contribution to the field of smart grids by combining AI techniques with energy management strategies. It opens new avenues for innovative solutions that support energy sustainability and help reduce environmental impacts in the future. 2025-10-14T00:00:00Z http//localhost:8080/jspui/handle/123456789/13251 Optimisation et Contrôle du Procédé de Traitement Thermochimique de Nitruration de l’Acier 32CDV13 Aggoune, Mohammed Salah Le travail present ́ e dans cette th ́ ese entre dans le cadre d’une recherche qui a pour ob- ` jectif d’optimiser le cycle de traitement thermochimique de nitruration en phase gazeuse de l’acier 32CDV13 en vue d’ameliorer ses propri ́ et ́ es m ́ ecaniques de la surface (bonne r ́ esistance ́ a` l’usure, au frottement et a la corrosion) et de la couche de diffusion de l’azote (bonne r ` esistance ́ a la fatigue et bonne t ` enacit ́ e ́ a cœur). Le manque de contr ` ole du proc ˆ ed ́ e de nitruration en ́ phase gazeuse n’a pas permis cependant la realisation de configurations de couches optimales ́ repondant aux cahiers des charges. La plupart des cycles de nitruration propos ́ es pour la nitru- ́ ration de cet acier ont conduit a des propri ` et ́ es m ́ ecaniques non souhait ́ ees par les utilisateurs. ́ Notre tache dans cette th ˆ ese a consist ` e d’abord, ́ a d` eterminer exp ́ erimentalement les param ́ etres ` operatoires de traitement (le d ́ ebit total de gaz NH ́ 3, N2, H2 et le potentiel de nitruration KN ) as- surant le transfert optimal de l’azote a l’interface gaz/solide. A la suite de plusieurs exp ` eriences ́ de nitruration realis ́ es sur le fer pur et l’acier 32CDV13, nous avons montr ́ e apr ́ es caract ` erisation ́ que les deux parametres cit ` es influent fortement sur la vitesse de diffusion de l’azote ́ a l’interface ` gaz/solide, la morphologie et la nature des couches de combinaison et de diffusion formees ́ a la ` surface de l’acier ainsi que sur les gradients de durete et de concentration en azote assurant le ́ durcissement de la couche de diffusion. La realisation d’essais de nitruration ́ a diff ` erents temps, ́ temperature et potentiel K ́ N (choisi constant et ou variable) et la caracterisation des ́ echantillons ́ nitrures par rayons X, microscopie optique, microsonde ́ electronique et microduret ́ e HV, nous a ́ permis de determiner les param ́ etres optimaux ` a partir desquels nous avons optimis ` e le proc ́ ed ́ e. ́ Nous avons montre qu’il ́ etait possible de r ́ ealiser : une couche de combinaison dense et non ́ poreuse a la surface d’ ` epaisseur de 5 ́ a` 6 μm environ, une profondeur de diffusion de l’azote voisine de 620 μm, un gradient de durete sur une profondeur de ́ 400 μm, assurant le durcisse- ment maximal de la couche de diffusion et enfin une faible densite de r ́ eseaux de nitrures et ́ de carbonitrures repartis aux joints de grains et dans la couche de diffusion. Les r ́ esultats de ́ validation du cycle sequenc ́ e propos ́ e confirment l’efficacit ́ e de la variante propos ́ ee et ouvre ́ ainsi la voie vers l’amelioration du proc ́ ed ́ e de nitruration dans l’industrie m ́ ecanique. ́ 2025-06-25T00:00:00Z http//localhost:8080/jspui/handle/123456789/13224 Advanced control structure based on the Frequency Separation Principle of a Wind Energy Conversion System (WECS) Brahmi, Oussama This PhD thesis addresses the challenges posed by the instantaneous fluctuations in wind and the non-linearity of wind turbines, which hinder the full exploitation of wind power with high efficiency and reliability. The primary objective of this research is to propose innovative control strategies for Wind Energy Conversion Systems (WECS) that balance the maximization of energy efficiency with the minimization of mechanical stress through the reduction of electromagnetic torque ripple. These strategies are grounded in the principle of frequency separation, which distinguishes between the short-term and long-term variations in wind dynamics. This separation enables the development of a dual-loop control architecture, optimizing both the high-frequency and lowfrequency dynamics inherent in the system. In the high-frequency loop, various controllers, including Linear Quadratic Regulator (LQR) and H∞ controllers, are implemented to manage the dynamics induced by turbulent wind speed. In the low-frequency loop, fractional-order proportional-integral (FOPI) controllers are employed, with an advanced approach incorporating a filtered fractional-order proportional-integral (FOPIF) controller optimized using the Particle Swarm Optimization (PSO) algorithm. These controllers aim to mitigate electromagnetic torque ripple, also known as the chattering problem, and ensure efficient Maximum Power Point Tracking (MPPT), thereby enhancing system performance under variable wind conditions. The proposed control strategies not only maximize the energy production of wind turbines but also reduce electromagnetic torque ripple, thereby minimizing mechanical stress, extending the operational lifespan, and lowering maintenance costs. By improving the dynamic behavior and reliability of the WECS, this research contributes significantly to advancing control technologies in wind energy systems, promoting the growth of efficient, scalable, and resilient renewable energy solutions. The findings of this work support the global transition toward sustainable energy, offering valuable insights into optimizing wind energy systems under challenging environmental conditions. 2025-06-30T00:00:00Z