Publications Internationales

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    Topological optimization of dimple distribution for enhanced performance in hydrodynamic porous self-lubricating journal bearings with sealed ends
    (Sage, 2025) Ifrah, Walid; Manser, Belkacem; Chellil, Ahmed; Ragueb, Haroun; Mechakra, Hamza; Khelladi, Sofiane; Belaidi, Idir
    This study numerically investigates the impact of optimal textures location on the performance of hydrodynamic porous self-lubricating journal bearings with sealed ends, subjected to a stationary load. The analysis employs a modified Reynolds equation coupled with Darcy’s law to model fluid flow in both the lubricating film and the porous matrix, considering the hydrodynamic self-lubrication problem. The governing nonlinear PDE systems were solved numerically using the finite difference method, combined with Reynolds boundary conditions and continuity conditions for velocity and pressure at the film-bush interface. A Binary Genetic Algorithm (BGA) is employed to optimize the topological distribution of square dimples in the textured porous layer to enhance bearing performance. The study investigates the influence of key parameters, including applied load, rotational speed, permeability, and texture depth, on bearing characteristics such as minimum film thickness and friction coefficient. Results show good agreement with benchmark data and indicate a positive enhancement in porous bearing performance. In addition, findings demonstrate that increasing the permeability of the porous structure reduces bearing performance (up to 25% in minimum film thickness and 8% in friction coefficient). However, the application of the optimization technique identified an optimal arrangement of textures that compensates for these performance losses, even under severe working conditions. Texturing the outlet region of the contact (beyond 180°) at the cavitation zone causes a micro-step bearing mechanism, generating localized pressure recovery within the textured area, significantly enhancing the minimum film thickness (up to 12%), reducing friction (up to 23%), and minimizing cavitation (up to 24%).
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    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, Roberto
    The 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.
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    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, Magd
    Structural 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 structures
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    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 Wahab
    Local 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 monitoring
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    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 Wahab
    This 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.git
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    Experimental crack identification of API X70 steel pipeline using improved artificial neural networks based on whale optimization algorithm
    (Elsevier, 2022) Oulad Brahim, Abdelmoumin; Belaidi, Idir; Khatir, Samir; Magagnini, Erica; Capozucca, Roberto; Abdel Wahab, Magd
    Intelligent systems have recently received recognition for their ability to solve extremely complicated and multidimensional problems. Artificial Neural Networks (ANN) has quite a lot of success in overcoming such issues, but some limitation can be found. The present study discusses in detail the application of the WOA-ANN hybrid model for predicting the crack length based on different input values, i.e. strains, stresses, and displacements, to test the accuracy of the presented technique. The proposed technique is compared with GA-ANN, AOA-ANN, and WOABAT-ANN. Coupled metaheuristic optimization algorithms with ANN aim to increase its effeciency. The connectivity between neurons carries some weight. Neurons are also connected to some biases. Connection weights and biases are modified to give the smallest possible error function based on the input values, and corresponding target output values supplied. Back Propagation (BP) is the usual name for this approach. The investigated approach is related to real engineering applications and controls the structures’ state. Standard ASTM test specimens are chosen to study the evolution of fracture mechanics parameters. Next, an analytical model is developed by simulating the tests using the Finite Element Method (FEM) and validated with experimental results. FEM is used to analyse the tensile failure process of the one-sided notch samples with the mesoscopic GTN damage model and extract the data required for WOA-ANN. After collecting the database, our model is ready for predicting different scenarios. The obtained results using WOA-ANN are efficient compared to other techniques
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    Analyse de la conception d'un montage d'usinage à l'aide d'éléments modulaires
    (2007) Zirmi, Said; Paris, Henri; Belaidi, Idir
    Un montage d'usinage est un outillage indispensable pour fixer la pièce solidement à la bonne position dans l'espace de travail de la machine outil. La conception du montage d'usinage joue un rôle important pour obtenir une pièce usinée de bonne qualité. Nous avons déjà formalisé les connaissances sur les prises de pièce de manière à pouvoir les gérer tout au long du processus de conception de la gamme d'usinage. Maintenant nous nous intéressons à la conception du montage d'usinage de manière à passer du modèle prise de pièce retenu à la solution technologique qui sera utilisée sur le poste de travail. Cet article aborde le problème de conception du montage d'usinage modulaire du point de vue technologique dans sa globalité ; c'est-à-dire le choix et le placement des éléments technologiques en tenant compte de toutes les contraintes de qualité, d'accessibilité et de comportement mécanique du système pièce-montage d'usinage-outil de coupe durant l'usinage
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    Multiobjective optimization of an agile machining type linear delta structure
    (2018) Mansouri, Khaled; Belaidi, Idir; Atia, Abdelmalek
    The innovative architectures design of agile machines dedicated to the machining at high speed requires the implementation of analytical and numerical models for the optimization of the kinematic, static and dynamic behavior of the machine, taking into account the elastic deformations and their compensation at level of machine control. In the context of multi-objective optimization, the first part is to identify the parameters and variables inherent to each constituent element of a DELTA robot type machine, the purpose is to optimize the essential elements of its structure. This requires a formulation of the multi-objective problem by expressing the objective functions, the constraints and the corresponding search spaces, as well as the resolution of the problem by the use of high-performance mathematical methods and tools (genetic algorithms, etc.).
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    Damage Modelling in Thermoplastic Laminates Reinforced with Steel and Glass Fibres under Quasi-Static Indentation Loading at Low-Velocity
    (Sage journals, 2019) Mahdad, M'Hamed; Ait Saada, Aicha; Belaidi, Idir
    This paper deals with experimental and numerical investigations of the composites damages with ductile and fragile reinforcement under quasi-static indentation loading. The main goal of the work is to increase the post-damage residual strength and ductility of thermoplastic composite. Two types of composite laminates with polypropylene (PP) matrix are tested: glass fibre laminate (GFPP) and steel fine wire mesh laminate (SWPP). The specimens are [0° 90°]2s stacking sequence and prepared by using a compression moulding technique. Quasi-static indentation tests were performed with two distinct penetration scales under low velocity (1.2 mm/min). The diameter of the hemispherical indenter is 16 mm. The failure mechanisms of composite layers were examined by the field emission scanning electron microscope (SEM). The results show that the failure mode of SWPP laminates is principally dominated by the plastic deformation component. In contrast, the GFPP laminate exhibits a fragile behaviour which is related to the fragile failure of glass fibres. In addition, the SEM shows that matrix cracking, fibre breakage, debonding and fibre pull out are the major damages observed around the indentation area. A model based on the combined use of plasticity, damage and fracture, was developed and applied to simulate quasi-static indentation behaviour and predict the resulting damage
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    Coupling of inverse method and cuckoo search algorithm for multiobjective optimization design of an axial flow pump
    (Sage journals, 2019) Abdessamad, Mohamed; Ait Chikh, Mohamed Abdessamad; Belaidi, Idir; Khelladi, Sofiane
    his work describes the application of a multiobjective cuckoo search method for turbomachinery design optimization of an axial pump. Maximization of the total efficiency and minimization of the required net positive suction head of the pump are the two objective functions considered for the optimization problem. The optimization process is carried out on a range of imposed volumetric flow rates, with taking into account at each discretized radius between the hub and tip of the rotor: the profile camber, rotor wall thickness, angular deviation, and the solidity, regarded as geometrical constraints and nominal flow rate as mechanical constraint. Two strategies are proposed in order to solve the problem. In the first one, three forms of mono-objective model with two variables, total efficiency and net positive suction head, are considered. In the second one, a multiobjective model with nondominated sorting scheme is adopted. A comparative evaluation of results obtained from the proposed approach with those of a reference machine and genetic algorithm allowed us to validate the present work