Effects of sections added to multi-cell square tubes on crash performance

2020 ◽  
Vol 62 (5) ◽  
pp. 471-480 ◽  
Author(s):  
Emre İsa Albak
Keyword(s):  
Finite Element ◽  
Genetic Algorithms ◽  
Radial Basis Functions ◽  
Axial Loading ◽  
Basis Functions ◽  
Finite Element Analyses ◽  
Energy Absorber ◽  
Radial Basis ◽  
Crash Performance

Abstract In this study, the effects of sections added to multi-cell square tubes on crash performance are examined. Square, hexagonal and circular sections are added to multi-cell square tubes and their results are examined. Finite element analyses under axial loading are performed to examine the crash performance of the multi-cell tubes. Analyses show that by adding a section to the multi-cell square tubes. the crash behavior of the tubes is improved. According to the results, S5H multi-cell square tube reveals the best crash performance. The optimization of S5H is carried out by using genetic algorithms and radial basis functions. The S5H tube presents a good crashworthiness performance and could be used as an energy absorber.

scholarly journals Applying models of spatial interpolation for the approximation of responses in the refinement of parameters of finite element models

2018 ◽  
Vol 212 ◽  
pp. 01021
Author(s):  
Anatoly Pikhalov ◽  
Anton Zabelin
Keyword(s):  
Finite Element ◽  
Radial Basis Functions ◽  
Element Model ◽  
Optimization Method ◽  
Latin Squares ◽  
Basis Functions ◽  
Finite Element Models ◽  
Radial Basis ◽  
High Level ◽  
Parameter Values

The numerical experiment on refining the parameters of the finite element model of the beam by the method of approximating the responses is presented in the article. As mathematical models of joint-stock companies are used: linear combinations of radial-basis functions, and Kriging-models. These models are generated in the work on the basis of Latin squares and depend on the parameters to be refined (the moduli of elasticity of finite element groups of the beam). To obtain optimal values of the parameters, a genetic optimization method was used. The results of solving the optimization problem showed a high level of coincidence of the parameter values with a combination of response models obtained from dynamic and static types of calculations. It was also shown that when solving the problems of finite element models, it is sufficient to use models constructed only on the basis of radial-basis functions.

Upscaling 2D finite element analysis stress results using radial basis functions

2019 ◽  
Vol 220 ◽  
pp. 131-143 ◽  
Author(s):  
Andrea Chiappa ◽  
Pietro Salvini ◽  
Carlo Brutti ◽  
Marco Evangelos Biancolini
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Radial Basis Functions ◽  
Basis Functions ◽  
Element Analysis ◽  
Radial Basis

scholarly journals Stochastic Fracture Analysis Using Scaled Boundary Finite Element Methods Accelerated by Proper Orthogonal Decomposition and Radial Basis Functions

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaowei Shen ◽  
Haowen Hu ◽  
Zhongwang Wang ◽  
Xiuyun Chen ◽  
Chengbin Du
Keyword(s):  
Finite Element ◽  
Proper Orthogonal Decomposition ◽  
Radial Basis Functions ◽  
Orthogonal Decomposition ◽  
Basis Functions ◽  
Uncertain Variables ◽  
Computation Efficiency ◽  
Radial Basis ◽  
Proper Orthogonal ◽  
Scaled Boundary Finite Element

This paper presents a stochastic analysis method for linear elastic fracture mechanics using the Monte Carlo simulations (MCs) and the scaled boundary finite element method (SBFEM) based on proper orthogonal decomposition (POD) and radial basis functions (RBF). The semianalytical solutions obtained by the SBFEM enable us to capture the stress intensity factors (SIFs) easily and accurately. The adoption of POD and RBF significantly reduces the model order and increases computation efficiency, while maintaining the versatility and accuracy of MCs. Numerical examples of cracks in homogeneous and bimaterial plates are provided to demonstrate the effectiveness and reliability of the proposed method, where the crack inclination angles are set as uncertain variables. It is also found that the larger the scale of the problem, the more advantageous the proposed method is.

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