El Alami Semma

In this work, we present a numerical study of mixed convection flows around large-scale heat sinks. It is based on the Cascade Lattice Boltzmann Method (LBM) for values of the Rayleigh number, in transitional regime, in the range 5×10 ⁷ ≤Ra≤5×10 ⁸ and for a Reynolds value fixed at Re=1000. The study is carried out in a rectangular cavity of dimension H subjected to periodic thermal and dynamic boundary conditions on its vertical walls. Two heat sources of (L', l', H/2) with a hot temperature T _h , are placed on the bottom wall of the cavity to simulate heat sinks. Fresh air (for cooling these heat sinks) is injected at a temperature T _c < T _h from the bottom of the cavity through two openings of length L''. The hot air is extracted through an opening (2L'' long) managed on the upper horizontal wall. The preliminary results, presented in this paper, are in the form of streamlines, isotherms and thermal profiles in the range of the Rayleigh number considered. Heat transfer is studied in terms of the average Nusselt number calculated over the entire surface of the two heat sources.

Partial shading, from obstacles such as buildings or trees, is a major challenge for photovoltaic systems, causing unpredictable fluctuations in solar energy production and underlining the need for advanced energy management strategies. In this paper, we propose an innovative approach that combines hybrid metaheuristic optimization with maximum power point tracking control (MPPT), using particle swarm optimization (PSO) in conjunction with the incremental conductance (IC) algorithm. We compare the proposed method with the conventional Perturb and Observation (P&O) algorithm. The choice of P&O as a comparison method is due to its simplicity, its familiarity with the scientific literature, its low cost of implementation. The main objective of swarm optimization combined with the IC algorithm lies in its ability to overcome the challenges posed by partial shading, ensuring accurate and efficient tracking of the point of maximum power, thanks to dynamic adaptation to variations in solar irradiation, thus enhancing the performance and resilience of the photovoltaic system. This approach is of crucial importance, offering considerable potential for solving the complex challenges associated with partial shading. Our results show that this hybrid MPPT algorithm offers superior tracking efficiency > 98% , faster convergence 500ms , better stability and increased robustness compared to traditional MPPT approaches. The system is composed of a PV and a boost converter that connects the input to the resistive load. The algorithms were implemented with MATLAB/Simulink as the simulation tool. These results not only reinforce the viability of sustainable energy solutions, but also open the way for the development of more sustainable energy solutions.The perspectives of this work are oriented towards a practical and extended integration of the proposed hybrid approach in real photovoltaic systems, with a particular emphasis on experimental validation.

Le but de ce travail est d’étudier numériquement la convection mixte dans une cavité simple et en forme de 'T' contenant des blocs chauffés à température constante TC et des ouvertures coaxiales aménagées sur les deux parois horizontales. Les blocs sont fixés sur la paroi inférieure adiabatique qui est soumise à un jet d’air vertical, celle du haut est maintenue froide à une température TF < TC. Les parois verticales sont rigides et adiabatiques. Les équations gouvernantes sont résolues à l’aide la méthode des volumes de contrôle. Notre intérêt est porté à l’effet de l’inclinaison sur la symétrie de la solution et le transfert de chaleur. Les principales paramètres de cette étude sont l’angle d’inclinaison 0<=<=90°, le nombre de Rayleigh Ra=105 , le nombre de Reynolds 100 <= Re <= 1000, le nombre de Prandtl Pr=0.72, la hauteur des blocs B=0.5, la largeur des ouvertures C=0.15 et la distance entre les blocs D=0.5. Les résultats sont présentés sous forme de lignes de courant et du champ de température.

This work intends to integrate artificial neural network (ANN) and data envelopment analysis (DEA) in a single framework to evaluate the performance of operations in the container terminal. The proposed framework is based on three steps. In the first step, a proposed identify the performance measures objectives and the indicators affecting the system. In the second step, the efficiency scores of the system are computed by using the Charnes Cooper and Rhodes (CCR) model (oriented inputs). In the last step, the Moth Search Algorithm (MSA) is employed as a new method for training the Feedforward Neural Network (FNN) to determine the efficiency scores. To demonstrate the efficacy of the proposed framework, two container terminals of Tangier and Casablanca are adopted to evaluate the performance.

The successful accomplishment of dry port-seaport system logistics activities is relatively difficult due to involvement of different risks. These risks and their respective sources have a tendency to disturb the system functioning. Risk management is one of the critical processes. An error can cost the dry port-seaport system too much. The aim of this paper is to analyze and assess risk factors within the dry port-seaport system using a multi-criteria approach. In this research, an effort has been made to collect the proces ’s risk factors. Our methodology is based on three steps. As results, risk factorsin rail connection are the most important and need a greater governmental concern. the findingswould be useful for industries in managing and reducing the consequences of the risks in dry port-seaport system.

The purpose of this paper is the modeling of a repository for modules and interfaces that must include all integrated information system management of a port terminal.Modules will provide a basic framework necessary for automatic management of internal operations and activities of all Port Terminals worldwide.

Mechanics & Industry, An International Journal on Mechanical Sciences and Engineering Applications