Literature DB >> 34121517

Optimization design of curved outrigger structure based on buckling analysis and multi-island genetic algorithm.

Zhi-Hai Liu1, Shao-Lu Tian2, Qing-Liang Zeng2,3, Kui-Dong Gao2, Xin-Long Cui1, Cheng-Long Wang2.   

Abstract

In the present work, the working state of the crane leg is analyzed and discussed, and its structure is optimized. SolidWorks software is used for modeling; ANSYS software is used for finite element analysis. First of all, the constrained finite element method (CFEM) is used to analyze the linear eigenvalue buckling and geometric nonlinear buckling of outriggers with different cross-section shapes. Prove that the curved leg has certain advantages in buckling. At the same time, analyzing the leg along a different path of buckling condition and stress changes provide the basis for the design of the subsequent reinforcement. After selecting the best cross-section shape of the outrigger, the agent-based multi-island genetic algorithm is used to optimize the structural parameters of the outrigger under the transverse stiffened plate reinforced structure and the longitudinally stiffened plate reinforced structure respectively. It is proved that the outrigger with the transverse stiffened plate has a significant effect in improving the bearing capacity and in the lightweight of the structure. Finally, the gap between the movable leg and the fixed leg was changed, the stress of different gaps was analyzed by using the finite element method, and the appropriate gap value was selected according to the high-order fitting curve.

Entities:  

Keywords:  Finite element method; buckling analysis; clearance analysis; multi-island genetic algorithm; optimization

Mesh:

Year:  2021        PMID: 34121517     DOI: 10.1177/00368504211023277

Source DB:  PubMed          Journal:  Sci Prog        ISSN: 0036-8504            Impact factor:   2.774


  1 in total

1.  Hyper-Parameter Optimization of Stacked Asymmetric Auto-Encoders for Automatic Personality Traits Perception.

Authors:  Effat Jalaeian Zaferani; Mohammad Teshnehlab; Amirreza Khodadadian; Clemens Heitzinger; Mansour Vali; Nima Noii; Thomas Wick
Journal:  Sensors (Basel)       Date:  2022-08-18       Impact factor: 3.847

  1 in total

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