TA367 : Structural Topology Optimization under Dynamic Loads by GESO Method
Thesis > Central Library of Shahrood University > Civil & Architectural Engineering > MSc > 2017
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Abstarct: Recently most of the studies and researches in optimization of topology are influenced by static loads, though in the real world the forces have dynamic identities. If these structures are influenced by dynamic loads, unpredictable tensions and deformations will be occurred in them. In the real world, most of the loads are applied as best structure optimization. Therefore, it is necessary that analysis and design should be performed baxsed on dynamic loads. It is necessary that structures also be optimized under dynamic loads in order that the real behavior of the constituent is modeled. Dynamic loading has a different effect on the structure response as the more the frequency of the applied load in relation to the structure frequency; the applied load will have a different effect on static state on the structure.
Evolutionary structural optimization (ESO) is baxsed on a simple idea that an optimal structure (with maximum stiffness but minimum weight) can be achieved by gradually removing ineffectively used materials from design domain. In general, the results from ESO are likely to be local optimums other than the global optimum desired. In this paper, the genetic algorithm (GA) is integrated with ESO to form a new algorithm called Genetic Evolutionary Structural Optimization (GESO), which takes the advantage of the excellent behavior of the GA in searching for global
optimums. For the developed GESO method, each element in finite element analysis is an individual and has its own fitness value according to the magnitude of its sensitivity number. Then, all elements in an initial domain constitute a whole population in GA. After a number of generations, undeleted elements will converge to the optimal result that will be more likely to be a global optimum than that of ESO
This task is performed by adding genetic algorithm operators like Mutation, Crossover in ESO. In fact, by this task, a new evolutionary algorithm called GESO is created that its power in searching the total responses is partially higher than that of ESO.
Keywords:
#GESO #Structure Evolutionary Optimization #Topology #Dynamic Loads #Genetic Algorithms Link
Keeping place: Central Library of Shahrood University
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Keeping place: Central Library of Shahrood University
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