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To integrate the consideration of environmental issues in the design phase of a product eco-design is an ecological quality improvement and preventive approach of it while verifying that the resulting environmental gain is not at the expense of other environmental aspects and focusing on the preservation of its quality of use of its features or certain stages of its life cycle without losing the own global vision of eco-design. Following this vision, the use of bio-laden animal based polymers derived from a mode of production by implementing optimization practices respectful of natural resources sustainably, has become changing way of thinking largely replace the use of non-renewable resources for human activities, the depletion of fossil resources deposits and to fight against the impact of climate change generated by this practice. Our paper attempts to analyze first the mechanical behavior of a bio-load and then incorporated into a polymer material to test improved mechanical and thermal characteristics made to it after adding and optimizing subsequently, its use. This is a load in the form of powder or chips extracted from an animal sheath. Tensile tests are performed in a first step, in order to mechanically characterize the virgin material and eco-composite obtained after the combination, i.e. the maximum tensile strength, the relative deformation and the modulus of the elasticity, etc

This work highlights the use of artificial intelligence (AI) in fracture mechanics, in particular to solve complex problems such as the fracture of a corroded pipeline subjected to both internal pressure and axial compressive stress. The paper describes the use of artificial neural networks (ANN), a popular technique in AI, to replace the empirical expression of the DNV method with a system of simple equations based on weights and biases. The use of ANN avoids problems associated with theory, such as assumptions, boundary conditions and exact modeling. The choice of neural network structure was made on the basis of the required accuracy, measured by indicators such as the coefficient of determination R2 and the root mean square error MSE during the validation phase. The results obtained with this neural network model were satisfactory, showing good linear correlation with the target values and low divergence during the validation phase. The implementation of this model in computer applications facilitates the prediction of failure pressure without requiring in-depth expertise in finite element analysis.

Phase change materials (PCMs) hold promise for enhancing building energy efficiency. Smart walls with PCMs effectively control indoor temperatures, reducing heating and cooling energy use. This study examines five PCM configurations within a smart wall, including a reference wall, PCM1 wall, PCM2 wall, a dual PCM wall, and a novel configuration in Marrakech, Morocco. The primary aim is to find the most effective configuration for improving smart wall thermal performance. We employ numerical simulations in the ANSYS CFD environment to analyze the transient thermal behavior of walls with varying melting temperature PCMs.Results show that a novel dual-PCM wall significantly improves temperature stability and reduces energy consumption compared to the reference wall. This configuration excels in thermal energy storage and thermal conductivity. Overall, these findings highlight the effectiveness of a composite layer design for PCM-integrated smart walls. The new wall configuration with PCMs 1 and 2 reduces energy consumption by 89% in winter and 20.46% in summer. Additionally, a wall with two PCMs (1 and 2) in parallel layers reduces energy consumption by 89.5% in winter and 21.76% in the summer.

Major dependency on fossil energy resources and emission of greenhouse gases are common problems that have a very harmful impact on human communities. Thus, the use of renewable energy resources, such as wind power, has become a strong alternative to solve this problem. Nevertheless, because of the intermittence and unpredictability of the wind energy, an accurate wind speed forecasting is a very challenging research subject. This paper addresses a short-term wind speed forecasting based on Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The forecasting performances of the model were conducted using the same dataset under different evaluation metrics in terms of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) performance evaluation metrics. The obtained results denote that the used model achieves excellent forecasting accuracy.

The significant increase of cars and the population growth due to urban area development are considered as a challenge for the smart city last years. Resulting to traffic congestion, especially in the parking lot. One of the city major impacted infrastructures is the university campus. A parking management system saves time and resolves the traffic congestion. However, the rapid growth inside the university is not taking into consideration by the current parking management system. Also, the universities and other institutions are a hierarchical organism and have a specific priority and roles to be respected for spaces which include parking lot. The current paper addresses the parking management system inside universities with respect of the requirements of institution and proposes architecture based on IoT system to insure the flowless of the traffic inside the university. Also, a hybrid VRP and MDVRP solution to provide a fair distribution of parking lots between different visitors while respecting the priorities.

Wind power’s current growth rates are among the fastest in the world. Research on techniques to make wind farms more energy efficient is warranted for this reason. Optimizing the location of wind turbines within wind farms makes the use of wind energy more efficient and makes wind farms more competitive with other energy sources. The investment expenses for the only substations and electrical infrastructures of the offshore wind farms represent between 15 and 30% of the overall investment cost of the project, this leads us to study the optimization of the location of the substation. can reduce these expenses, which also reduces the total cable length inside the wind farm. Our objective is therefore to study the optimization of wind farms with two objective functions aimed at minimizing the costs of installing wind turbines and reducing connectivity between wind turbines using a metaheuristic PSO algorithm.

The infinite needs of humanity in several fields give birth to several innovations, in materials sciences, these needs are summed up in the creation of new composite materials. Obtaining a composite material from recycled material and a bio-load first of all makes it possible to recover the recycled material and to have new composite material. PVC is one of the most used plastics in recent years, the addition, after recycling, of a bio-load in the form of chicken feathers which are just lost in nature or incinerated, allows the birth of new composite material. In this article, we use neural networks which appear to be among the essential methods to solve and model complex systems, and in particular when it comes to non-linear problems. This method will be used in comparison with the finite element method to find the most adequate method which makes it possible to better model the behaviors of PVC bio-loaded by chicken feathers, and then predict the behaviors with different percentages of the bio-load. We took the results of the experiments carried out, with those of the study of finite elements, and with the results obtained by the neural networks which will be presented in this article, to find the model which makes it possible to better present and predict the behavior of the recycled PVC bio-loaded with an adequate percentage which improves the mechanical characteristics of this type of composite material. The error between the experimental results and those obtained by the neural networks is very small compared to the differences between the two experimental results and those obtained by the finite elements. Which proves that the neural network model is an important computer tool for modeling and predicting the results of mechanical experiments

The optimization of the size of wind farms is little studied in the literature. The objective of this study is to renew the existing wind farms by inserting new wind turbines with different characteristics. To evaluate our approach, a genetic algorithm was chosen to optimize our objective function, which aims to maximize the power of the wind farm studied at a reasonable cost, the Jensen wake model was chosen for the power calculation of the park. The results obtained from the simulation on the Horns-rev wind farm showed a significant increase in energy and a relatively reasonable cost of energy.

In this paper, a numerical study on the flow structure of non-Newtonian nanofluid in cylindrical enclosure with rotating end wall. The considered nanofluid, MWCNT-water, exhibits a strong power-law shear-thinning behavior with the increase in nanoparticles loading. The main focus in this study is the effect of nanoparticles concentration on the vortex breakdown phenomenon. The simulation results showed that adding a small amount of nanoparticle eliminate the vortex breakdown which is considered as a positive in mixing process. However, the increase in nanoparticles concentration as well as the enclosure aspect ratio promotes the apparition of secondary recirculation zone and stagnation zone.

This article presents an empirical research to classify a new renewable resource material, as opposed to eco-composites, it has been neglected by the materials specialist. This classification is based on the typology of elastic behavior demonstrated by tensile tests. In addition, some identifying criterions of the usefulness of this material were examined. To justify the relevance of this classification, curves from the extension of tests focusing on the virgin material, illustrate significant results of the review. Obtained from waste, having a significant recycling possibilities and potential from renewable resources, bio-mechanically characterized loads will be injected into polymeric materials of different categories. All in the perspective of promoting changes in thermomechanical properties, whether static or dynamic; such as resistance to corrosion, heat, wear… They result in functional changes such as security, relief, coatings and stability…

Nowadays, manufacturing industries are operating in a highly complex and unforeseeable economic context. Therefore, they tend, by necessity toward short term inter-firms coordination. Thus, found increasingly located at the intersection of corporate networks, to overcome their inability to quickly meet the needs of clients, facing thereby several complications related to the coordination between heterogeneous entities. Managing the flow of a network, made up of different links became a major challenge. The appeals to decision support tools and simulation are wished all the more to correlate the dynamics of the system and the targeted objectives, to assess the configurations effectiveness. Thus, enhance their performance. The aim of this paper was to establish a model of the industrial flow connectivity; Afterward, we’ve detailed a network configuration model based on the multi-agents systems, to study the interactions between all the actors and give a more realistic vision onto manufacturing coordination in the supply chain.