Numerical Analysis of Free Vibration of Laminated Thin-Walled Closed-Section Shell Structures

Miller, Bartosz; Markiewicz, Barbara; Ziemiański, Leonard

doi:10.1007/978-3-030-17747-8_25

Cited by 1 publication

(2 citation statements)

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“…The metamodels DNN f MS 8000 and DNN P cr 8000 -CL2 were applied as tools to predict the values of parameters being minimized within a GA optimization procedure with the objective function given by Equation (7). After initial test the main GA parameters were selected as follows: floating-point coding of parameters, 100 individuals in each population, 500 generations, tournament selection function, adaptive feasible mutation function, constraints −90 ≤ λ i ≤ 90 applied on each lamination angle λ i (for details of Matlab implementation of GA see [29]).…”

Section: Multi-objective Optimization Of F 1 and P Crmentioning

confidence: 99%

“…Multilayer composite structures have a remarkable ease of forming various shapes, while each change of the composite structure topology may significantly change their dynamic behavior and/or buckling properties [5]. In case of layered composite material, created using the same matrix and reinforcement in each of the layers, the properties of the entire composite structure may vary [6][7][8][9]. A change of only the angles of reinforcement placement in subsequent composite material layers may cause the values of a structure natural frequencies and/or buckling forces increase or decrease significantly.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Dynamic and Buckling Behavior of Thin-Walled Composite Cylinder, Supported by Nature-Inspired Agorithms

Miller

Ziemiański

2020

Materials

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The paper presents the optimization of stacking sequence (the lamination angles in subsequent composite layers) of the composite cylinder in order to simultaneously maximize the values of the first natural frequency f1 and the first buckling force Pcr. The optimization problem involves either two objective functions or one which combines both problems using a coefficient whose optimal value is also being searched for. The main idea of the paper is the application of two neural network metamodels which substitute very time- and resource-consuming Finite Element (FE) calculations. The metamodels are created separately through a novel iterative procedure, using examples obtained through Finite Element Method (FEM). The metamodels, once ready, are able to assess the values of f1 and Pcr instantly and thus enable the application of nature-inspired Genetic Algorithm (GA) minimization with reasonable calculation times. Obviously, the maxima of f1 and Pcr may be located in different points of the design parameters (i.e., lamination angles) space, the considered optimization task is to find a solution for which both f1 and Pcr simultaneously reach values as close to their maxima as possible. All the investigated optimization examples are repeated several times and basic statistical analysis of the results is presented.

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Section: Multi-objective Optimization Of F 1 and P Crmentioning

confidence: 99%