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RISS 인기검색어
- 검색키워드
- 저자 : T. Khatir (검색결과 3 건)
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T. Khatir,M. Bouchetara,M. Djafri,S. Khatir,M. Abdel Wahab 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.10
We studied the influence of balancing internal combustion engines on the performance of hydrodynamic plain bearings. A non-linear approach makes it possible to calculate the forces of pressure generated by the lubricant film. This approach is coupled with a dynamic calculation, which determines the inertia forces of the rod. The counterweight to balance the engine is applied to the heads of rods and not to the crankshaft. We chose three models of connecting rod (rod of an engine in series, rod with partial and rod with complete counterweight). To determine the lubricant pressure field in the bearing, the modified Reynolds equation was solved using the finite difference method, taking into account the boundary conditions of Reynolds. Since the bearing is subjected to a variable load, the mobility method was used to facilitate the resolution of the Reynolds equation. The proposed numerical simulation allowed us to analyze the influence of counterweight applied to the connecting rod head on the variation of the lubricant pressure field, the minimum film thickness, the axial flow and the friction torque in the big end bearing during the operating cycle.
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An efficient hybrid TLBO-PSO-ANN for fast damage identification in steel beam structures using IGA
S. Khatir,T. Khatir,D. Boutchicha,C. Le Thanh,H. Tran-Ngoc,T.Q. Bui,R. Capozucca,M. Abdel Wahab 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.25 No.5
The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (<i>nMSEDI</i>) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using <i>nMSEDI</i> to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from <i>nMSEDI</i> are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.
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Tran N. Hoa,S. Khatir,G. De Roeck,Nguyen N. Long,Bui T. Thanh,M. Abdel Wahab 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.25 No.4
This paper proposes a novel approach to model updating for a large-scale cable-stayed bridge based on ambient vibration tests coupled with a hybrid metaheuristic search algorithm. Vibration measurements are carried out under excitation sources of passing vehicles and wind. Based on the measured structural dynamic characteristics, a finite element (FE) model is updated. For long-span bridges, ambient vibration test (AVT) is the most effective vibration testing technique because ambient excitation is freely available, whereas a forced vibration test (FVT) requires considerable efforts to install actuators such as shakers to produce measurable responses. Particle swarm optimization (PSO) is a famous metaheuristic algorithm applied successfully in numerous fields over the last decades. However, PSO has big drawbacks that may decrease its efficiency in tackling the optimization problems. A possible drawback of PSO is premature convergence leading to low convergence level, particularly in complicated multi-peak search issues. On the other hand, PSO not only depends crucially on the quality of initial populations, but also it is impossible to improve the quality of new generations. If the positions of initial particles are far from the global best, it may be difficult to seek the best solution. To overcome the drawbacks of PSO, we propose a hybrid algorithm combining GA with an improved PSO (HGAIPSO). Two striking characteristics of HGAIPSO are briefly described as follows: (1) because of possessing crossover and mutation operators, GA is applied to generate the initial elite populations and (2) those populations are then employed to seek the best solution based on the global search capacity of IPSO that can tackle the problem of premature convergence of PSO. The results show that HGAIPSO not only identifies uncertain parameters of the considered bridge accurately, but also outperforms than PSO, improved PSO (IPSO), and a combination of GA and PSO (HGAPSO) in terms of convergence level and accuracy.
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