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- 저자 : Mokhtar Mohammadi (검색결과 2 건)
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Kiarasi, Faraz,Babaei, Masoud,Mollaei, Somayeh,Mohammadi, Mokhtar,Asemi, Kamran Techno-Press 2021 Advances in nano research Vol.11 No.4
Natural frequency analysis of functionally graded porous joined truncated conical-cylindrical shell reinforced by graphene platelet is investigated in this paper. The structure is consisting of a layered model with five kinds of distribution of graphene platelets in a metallic matrix containing open-cell interior pores. To calculate the effective properties of the porous nanocomposite joined shell, the generalized rule of mixture and the modified Halpin-Tsai equations are employed. Four different porosity distributions are assumed along the shell thickness: two kinds of symmetric functionally graded distributions, non-symmetric functionally graded distributions and uniform distribution of porosity. Graded finite element method (GFEM) based on Rayleigh-Ritz energy formulation has been used to solve 2D- axisymmetric elasticity equations. A parametric study is also conducted to show the effects of different geometric parameters, boundary conditions, weight fraction of graphene platelets, porosity coefficient, distribution of porosity and dispersion pattern of graphene platelets on the natural frequencies and mode shapes of the structure.
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Donkey and smuggler optimization algorithm: A collaborative working approach to path finding
Ahmed S. Shamsaldin,Tarik A. Rashid,Rawan A. Al-Rashid Agha,Nawzad K. Al-Salihi,Mokhtar Mohammadi 한국CDE학회 2019 Journal of computational design and engineering Vol.6 No.4
Swarm Intelligence is a metaheuristic optimization approach that has become very predominant over the last few decades. These algorithms are inspired by animals’ physical behaviors and their evolutionary perceptions. The simplicity of these algorithms allows researchers to simulate different natural phenom-ena to solve various real-world problems. This paper suggests a novel algorithm called Donkey and Smuggler Optimization Algorithm (DSO). The DSO is inspired by the searching behavior of donkeys. The algorithm imitates transportation behavior such as searching and selecting routes for movement by donkeys in the actual world. Two modes are established for implementing the search behavior and route-selection in this algorithm. These are the Smuggler and Donkeys. In the Smuggler mode, all the pos-sible paths are discovered and the shortest path is then found. In the Donkeys mode, several donkey behaviors are utilized such as Run, Face & Suicide, and Face & Support. Real world data and applications are used to test the algorithm. The experimental results consisted of two parts, firstly, we used the stan-dard benchmark test functions to evaluate the performance of the algorithm in respect to the most pop-ular and the state of the art algorithms. Secondly, the DSO is adapted and implemented on three real-world applications namely; traveling salesman problem, packet routing, and ambulance routing. The experimental results of DSO on these real-world problems are very promising. The results exhibit that the suggested DSO is appropriate to tackle other unfamiliar search spaces and complex problems.
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