Publications Internationales
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13
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Item Relationship between structural and mechanical properties of polyethylene matrix nanocomposites(Faculty of Engineering, Khon Kaen University, 2024) Rahmaoui, Fath Eddine Zakaria; Belaidi, IdirThis study examined the impact of incorporating graphene nanoplatelets (GnP) into high-density polyethylene (PE) to create nanocomposites, with and without a compatibiliser. We specifically focused on the impact of structural crystallinity on the mechanical properties of the nanocomposites. These nanocomposites exhibited a much higher Young's modulus compared with pure PE. Specifically, the Young’s modulus increased exponentially with the addition of a compatibiliser and linearly without it. One explanation for this exponential rise in Young's modulus is that the crystal's compacted polymer chain structure improved its stiffness, facilitating effective load transfer. Additionally, a poor distribution of GnP in the nanocomposites with a filler content of 0.5 and 1 wt.%, both with and without a compatibiliser, led to a decreased stress and strain at break. However, at higher filler contents, well-distributed GnP play a key role in enhancing stress and strain at break.Item A review of emulsion flows, their characterization and their modeling in pumps(Institution of Chemical Engineers, 2024) Achour, Lila; Specklin, Mathieu; Asuaje, Miguel; Kouidri, Smaine; Belaidi, IdirIn the engineering field, emulsions and liquid–liquid two-phase flows within centrifugal pumps are generally unwanted as emulsions will have negative effects on pump operation. Besides, emulsions are usually formed when the oil and water phases are brought together in a process called emulsification, which is enhanced by high shear rates. This topic has been extensively researched over the past decades, with sophisticated theories regarding the phenomena involved in emulsions formation and characterization in pumps. Besides, given the complexity of the physics governing emulsions, studies on their modeling within pumps, based on empirical correlations or computational fluid dynamics models, are insufficient and remain limited. This review aims to provide a complete overview of investigations on liquid–liquid flow in centrifugal pumps. Characteristics of these mixtures, such as stability, phase inversion, droplet size distribution and rheological behavior, are discussed. Current approaches and techniques for analyzing pump performance handling emulsion and two-phase liquid–liquid flow are reviewed thoroughly. The limitations of the existing models are studied, and potential future developments are proposed.Item Prediction of the peak load and absorbed energy of dynamic brittle fracture using an improved artificial neural network(Elsevier, 2022) Oulad Brahim, Abdelmoumin; Belaidi, Idir; Fahem, Noureddine; Khatir, Samir; Mirjalili, Seyedali Jamal; Abdel Wahab, Magd M.In this paper, a robust technique is presented to predict the peak load and crack initiation energy of dynamic brittle fracture in X70 steel pipes using an improved artificial neural network (IANN). The main objective is to investigate the behaviour of API X70 steel based on two experimental tests, namely Drop Weight Tear Test (DWTT) and the Charpy V-notch impact (CVN), for steel pipe specimens. The mechanical properties in the brittle fracture behaviour of API X70 steel pipes are predicted utilizing numerical approaches with different crack lengths. Next, to simulate the impact of API X70 steel pipes at lower temperatures through a numerical approach, a cohesive approach using the extended Finite Element Method (XFEM) is used. The data obtained are used as input for the proposed IANN using Balancing Composite Motion Optimization (BCMO), Particle Swarm Optimization (PSO) and Jaya optimization algorithms, to predict the peak load values and crack initiation energy of dynamic brittle fractures in API X70 steel with different crack lengths. The results show the effectiveness of ANN-PSO and ANN-BCMO based on the convergence of the results and the accuracy of the prediction of the peak load and crack initiation energy.Item A new methodology to predict the sequence of GFRP layers using machine learning and JAYA algorithm(Elsevier, 2023) Fahem, Noureddine; Belaidi, Idir; Oulad Brahim, Abdelmoumin; Capozucca, Roberto; Thanh, Cuong Le; Khatir, Samir; Abdel Wahab, Magd M.In this paper, the best stacking sequence using experimental tests of GFRP composites is investigated. The main objective of this work is to determine the main specification of GFRP composite material, which is represented by its physics-mechanical properties, weight, and cost, before performing a series of experimental tests based on various stacking sequences. Our methodology is divided into three stages. The first stage is characterized by extracting the bending data from mechanical tests of some GFRP composites. In the second stage, the validated numerical model is used to simulate numerous cases of stacking sequences. In the last stage, the extracted data is used to determine the parameters for different stacking sequences using an inverse technique based on ANN and JAYA algorithm. The results provide a good prediction of parameters as well as a good orientation to make decisions on the best GFRP stacking sequence to be used, according to the required specifications of the manufacturer.Item The Optimal Values of Hashin Damage Parameters Predict Using Inverse Problem in a CFRP Composite Material(Springer, 2024) Fahem, Noureddine; Belaidi, Idir; Aribi, Chouaib; Zara, Abdeldjebar; Khatir, Tawfiq; Oulad Brahim, Abdelmoumin; Capozucca, RobertoThe ever-increasing demand for advanced composite materials in industries like aerospace and automotive has spurred the drive to address their inherent weaknesses. This pursuit is facilitated by the availability of numerical simulations and artificial intelligence, offering a cost-effective means to comprehensively study various phenomena without excessive reliance on experimentation. While existing models in the scientific realm provide a foundation for composite material modeling, achieving results closely aligned with experimental data is often challenging due to the variation of the parameters and conditions. This present study introduces an innovative approach aimed at optimizing composite material performance and minimizing discrepancies between experimental and numerical outcomes. This approach leverages sophisticated optimization algorithms to fine-tune the Hashin damage parameters, resulting in a highly accurate model. Furthermore, the incorporation of an Artificial Neural Network (ANN) via an inverse problem based on Jaya’s algorithm solving strategy facilitates the prediction of optimal parameters, ensuring a significant reduction in error. This novel methodology presents a promising avenue for elevating the efficiency and reliability of CFRP composite materials in practical applications.Item A reduced-order method with PGD for the analysis of dynamically loaded journal bearing(2022) Megdoud, Abdelhak; Manser, Belkacem; Belaidi, Idir; Bakir, Farid; Khelladi, SofianeMachine component design has become a prominent topic for researchers in recent years. The analysis of bearing systems has received considerable attention in order to avoid detrimental contact. Among the most important studies in this area are the transient problems of journal bearings, which are usually performed by coupling the Reynolds equation with the motion equations. Many techniques have been presented in the literature and are still being explored to ensure the accurate findings and efficient solution prediction of unsteady state Reynolds equation. In this paper, the Proper Generalized Decomposition (PGD) approach is expanded for the analysis of the lubricant behavior of dynamically loaded journal bearing considering Swift-Stieber boundary conditions. The PGD model is applied in this problem, seeking the approximate solution in its separated form of the partial differential Reynolds equation at each time step during the load applied cycle employing the alternating direction strategy. Compared to the classical resolution, the PGD solution has a considerably low computational cost. To verify the accuracy and efficiency of this approach, three cases have been considered, infinitely short, infinitely long and finite journal bearings under the dynamic load. The results of the suggested methodology when compared to the full discretized model (FDM) show that, the new scheme is more efficient, converges quickly, and gives the accurate solutions with a very low CPU time consumption.Item Damage detection in GFRP composite structures by improved artificial neural network using new optimization techniques(Elsevier, 2023) Zara, Abdeldjebar; Belaidi, Idir; Khatir, Samir; Oulad Brahim, Abdelmoumin; Boutchicha, Djilali; Abdel Wahab, MagdStructural damage identification has been researched for a long time and continues to be an active research topic. This paper proposes the use of the natural frequencies of a novel composite structures made of glass fibre reinforced polymer (GFRP). The proposed methodology consists of an improved Artificial Neural Network (ANN) using optimization algorithms to detect the exact crack length. In the first step, the characterization of fabricated material is provided to determine Young's modulus using an experimental static bending test, tensile test and modal analysis test. Next, numerical validation is performed using commercial software ABAQUS to extract more data for different crack locations in the structure. The comparison between experimental and numerical results shows a good agreement. ANN has been improved using recent optimization techniques such as Jaya, enhanced Jaya (E-Jaya), Whale Optimization Algorithm (WOA) and Arithmetic Optimization Algorithm (AOA) to calibrate the influential parameters during training. After considering several scenarios, the results show that the accuracy of E-Jaya is better than other optimization techniques. This study on crack identification using improved ANN can be used to investigate the safety and soundness of composite structuresItem Strength prediction of a steel pipe having a hemi-ellipsoidal corrosion defect repaired by GFRP composite patch using artificial neural network(Elsevier, 2023) Oulad Brahim, Abdelmoumin; Belaidi, Idir; Khatir, Samir; Le Thanh, Coung; Mirjalili, Seyedali; Magd, Abdel WahabLocal stress concentration occurs when faults are present in pipelines under pressure. An example of such defects is the problem of corrosion caused by the environment in the field of pipeline installation. In the first part of this paper, we attempt to model the corrosion in the hemi-ellipsoidal form in order to study the locations of stress concentration in the specimens by several experimental cases and their influence on the stress resistance. The Gurson-Tvergaard-Needleman (GTN) mesoscopic damage model is used to simulate the specimens with good accuracy. In the second part, the investigation is extended to a pipe under static pressure with and without the presence of a glass fibre reinforced polymer (GFRP) composite patch. The maximum stress and percent stress reduction in a defected pipe with a hemi-ellipsoidal defect are determined using a 3D finite element model. This part examines the impact of the geometry of the composite patches on the percentage reduction of the maximum stresses in a section of pipeline subjected to static pressure. In the third part, the stresses and the percentage reduction in the maximum stresses are predicted using an artificial neural network (ANN). An inverse problem using ANN and Jaya algorithm is proposed to predict the group level of different sizes of defects under composite patches based on the maximum stress and percentage reduction of stress that the pipe withstands. The new method relates directly to real-world pipeline construction and repair applications. It could be also used for structural safety monitoringItem Prediction of resisting force and tensile load reduction in GFRP composite materials using Artificial Neural Network-Enhanced Jaya Algorithm(Elsevier, 2023) Fahem, Noureddine; Belaidi, Idir; Oulad Brahim, Abdelmoumin; Noori, Mohammad; Khatir, Samir; Magd, Abdel WahabThis work presents an experimental and a numerical studies on the effect of the phenomenon of porosity on the mechanical properties of Glass Fiber Reinforced Polymer (GFRP). In a first part, material elaboration, as well as its characterization using a three-point bending test to extract the basic mechanical properties of the material, is considered. In a second part, a finite element model is created to simulate the problem of air bubbles broadly. Several cases of different shapes and sizes are simulated. The results show a significant effect on the reduction of load in both tensile and bending cases as the size of the bubbles increases. Furthermore, the second part includes the application of the Artificial Neural Network-Enhanced Jaya Algorithm (ANN-E JAYA) to predict the reduction of the tensile load as a function of different crack lengths obtained from an Extended Finite Element Method (XFEM) model. Next, to verify the accuracy of provided application, a comparison is made with two other applications such as Artificial Neural Network-Jaya Algorithm (ANN-JAYA) and Artificial Neural Network-Particle Swarm Optimization (ANN-PSO). The results of the three algorithms show good convergence, with a slight increase in accuracy for ANN-E JAYA. MATLAB code and data used in this article can be found at https://github.com/Samir-Khatir/GFRP-ANN-E-JAYA.gitItem Towards an accurate aerodynamic performance analysis methodology of Cross-Flow fans(MDPI, 2022) Himeur, Rania Majda; Khelladi, Sofiane; Ait Chikh, Mohamed Abdessamad; Vanaei, Hamid Reza; Belaidi, Idir; Bakir, FaridCross-flow fans (CFFs) have become increasingly popular in recent years. This is due to their use in several domains such as air conditioning and aircraft propulsion. They also show their utility in the ventilation system of hybrid electric cars. Their high efficiency and performance significantly rely on the design parameters. Up to now, there is no general approach that predicts the CFFs’ performance. This work describes a new methodology that helps deduce the performance of CFFs in turbomachinery, using both analytical modeling and experimental data. Two different loss models are detailed and compared to determine the performance–pressure curves of this type of fan. The efficiency evaluation is achieved by realizing a multidisciplinary study, computational fluid dynamics (CFD) simulations, and an optimization algorithm combined to explore the internal flow field and obtain additional information about the eccentric vortex, to finally obtain the ultimate formulation of the Eck/Laing CFF efficiency, which is validated by the experimental results with good agreement. This approach can be an efficient tool to speed up the cross-flow fans’ design cycle and to predict their global performance