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Design optimization of truss structures using genetic algorithms
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Date
2012
Author
Ünalmış, Dilek
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Design optimization of truss structures is a popular topic in aerospace, mechanical, civil, and structural engineering due to benefits to industry. Common design problem for the structures is the weight minimization. Especially in aerospace engineering the minimization of the weight of the total structure gets the highest importance in the design. This study focuses on the design optimization of 2D and 3D truss structures. The objective function is the total mass of the structure which is subjected to stress and nodal displacement constraints. To optimize the design, Genetic Algorithm (GA) is preferred due to its efficiency in dealing with problems with discrete design variables as in the case of truss structures. This technique yields more realistic results than linear programming methods. In the thesis, a finite element code is developed for the analysis of planar and space truss structures. The developed finite element solver is coupled with a genetic algorithm optimization code which is also developed as a part of the thesis study. Different truss optimization case studies are performed to demonstrate the performance of the finite element solver and the genetic algorithm optimization code that are developed. It is shown that with the use of adaptive penalty function employing scaled fitnesses, the arbitrariness issue of the factor multiplying the error term in the augmented fitness function can be resolved. It is also shown that significant weight reduction can v be achieved by employing shape optimization together with size optimization compared to pure size optimization.
Subject Keywords
Trusses.
,
Genetic algorithms.
,
Combinatorial optimization.
,
Structural optimization.
,
Finite element method.
URI
http://etd.lib.metu.edu.tr/upload/12614932/index.pdf
https://hdl.handle.net/11511/21960
Collections
Graduate School of Natural and Applied Sciences, Thesis
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D. Ünalmış, “Design optimization of truss structures using genetic algorithms,” M.S. - Master of Science, Middle East Technical University, 2012.