MEANS AND EFFECTS OF CONSTRAINING THE NUMBER OF USED CROSS-SECTIONS IN TRUSS SIZING OPTIMIZATION (original) (raw)
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The use of continuous variables for cross-sectional dimensions in truss structural optimization gives solutions with a large number of different cross sections with specific dimensions which in practice would be expensive, or impossible to create. Even slight variations from optimal sizes can result in unstable structures which do not meet constraint criteria. This paper shows the influence of the use of discrete cross section sizes in optimization and compares results to continuous variable counterparts. In order to achieve the most practically applicable design solutions, Euler buckling dynamic constraints are added to all models. A typical space truss model from literature, which use continuous variables, is compared to the discrete variable models under the same conditions. The example model is optimized for minimal weight using sizing and all possible combinations of shape and topology optimizations with sizing.
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In this paper the effects of adding buckling constraints to truss sizing optimization for minimizing mass are investigated. Introduction of buckling testing increases the complexity of the optimization process as Euler buckling criteria changes with each iteration of the optimization process due to the changes in element cross section dimensions. The resulting models which consider this criteria are practically applicable. For the purposes of showing the effects of dynamic constraints for buckling, optimal parametric standard test models of 10 bar, 17 bar, and 25 bar trusses from the literature are tested for buckling and compared to the models with the added constraint. Models which do not consider buckling criteria have a considerable number of elements which do not meet buckling criteria. The masses of these models are substantially smaller than their counterparts which consider buckling.
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DISCRETE VARIABLE TRUSS STRUCTURAL OPTIMIZATION USING BUCKLING DYNAMIC CONSTRAINTS
Using continuous variables in truss structural optimization results in solutions which have a large number of different cross section sizes whose specific dimensions would in practice be difficult or expensive to create. This approach also creates optimal models which if varied, even slightly, result in structures which do not meet constraint criteria. This research proposes the discretization of cross section sizes to standard sizes of stock produced for the particular cross section and material, and a 1mm precision for node location when using shape optimization. Additionally, Euler buckling constraints are added to all models in order to achieve optimal solutions which can find use in practical application. Several standard test models of trusses from literature, which use continuous variables, are compared to the discrete variable models under the same conditions. Models are optimized for minimal weight using sizing, shape, topology, and combinations of these approaches.
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MATEC Web of Conferences, 2021
Truss optimization has the goal of achieving savings in costs and material while maintaining structural characteristics. In this research a 10 bar truss was structurally optimized in Rhino 6 using genetic algorithm optimization method. Results from previous research where sizing optimization was limited to using only three different cross-sections were compared to a sizing and shape optimization model which uses only those three cross-sections. Significant savings in mass have been found when using this approach. An analysis was conducted of the necessary bill of materials for these solutions. This research indicates practical effects which optimization can achieve in truss design.
IOP Conference Series: Materials Science and Engineering, 2019
This paper proposed a way to solve the problem of the optimum size of the truss, taking into account the local buckling constraint of compression elements of the truss. The consideration of dynamic constraint for buckling increases the complexity of the iterative algorithm to solve the truss optimization problem, because the dynamic constraint expresses condition involving the cross-sectional variable. The author has established an iterative algorithm to optimize trusses with stresses constraints (under strength conditions for tensile elements, buckling conditions for compression elements) and displacements. The iterative algorithm is established based on the correlation coefficients of internal forces between elements. The constraints of the problem are established on the basis of the results of internal forces, displacement and governing equation by finite element method. Based on the established algorithm, the authors had written the program to solve the optimization problem of p...
COMPARISON OF APPROACHES TO 10 BAR TRUSS STRUCTURAL OPTIMIZATION WITH INCLUDED BUCKLING CONSTRAINTS
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The complex problem of truss structural optimization, based on the discrete design variables assumption, can be approached through optimizing aspects of sizing, shape, and topology or their combinations. This paper aims to show the differences in results depending on which aspect, or combination of aspects of a standard 10 bar truss problem is optimized. In addition to standard constraints for stress, cross section area, and displacement, this paper includes the dynamic constraint for buckling of compressed truss elements. The addition of buckling testing ensures that the optimal solutions are practically applicable. An original optimization approach using genetic algorithm is verified through comparison with literature, and used for all the optimization combinations in this research. The resulting optimized model masses for sizing, shape, and topology or their combinations are compared. A discussion is given to explain the results and to suggest which combination would be best in a generalized example.
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Vietnam Journal of Mechanics, 2016
In this paper, the optimal sizing of truss structures is solved using a novel evolutionary-based optimization algorithm. The efficiency of the proposed method lies in the combination of global search and local search, in which the global move is applied for a set of random solutions whereas the local move is performed on the other solutions in the search population. Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. Here, the constraints and objective function are treated separately so that both function and constraint evaluations can be saved. The results show that the new algorithm can find optimal solution effectively and it is competitive with some recent metaheuristic algorithms in terms of number of structural analyses required.