Optimization of thin-walled structures with geometric nonlinearity for maximum critical buckling load using optimality criteria

In this study, two optimality criteria are presented for shape optimization of thin-walled structures with geometric nonlinearity modeled by finite elements. The optimization problem considers the thickness and geometry design variables, and aims to maximize the critical load of the structure subjec...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Thin-walled structures Ročník 46; číslo 12; s. 1319 - 1328
Hlavní autori: Khosravi, Peyman, Ganesan, Rajamohan, Sedaghati, Ramin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 01.12.2008
New York, NY Elsevier Science
Predmet:
Buckling
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
Geometric nonlinearity
Optimality criterion
Physics
Shape optimization
Solid mechanics
Structural and continuum mechanics
Thickness optimization
Thin-walled structures
Thickness optimization
Thin-walled structures
Optimality criterion
Geometric nonlinearity
Shape optimization
Geometrical shape
Variable geometry
Sensitivity analysis
Optimization method
Quadratic programming
Modeling
Geometrical model
Optimization
Finite element method
Sequential method
Critical load
Non linear effect
Buckling
Gradient method
Thin wall
ISSN:0263-8231, 1879-3223
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:In this study, two optimality criteria are presented for shape optimization of thin-walled structures with geometric nonlinearity modeled by finite elements. The optimization problem considers the thickness and geometry design variables, and aims to maximize the critical load of the structure subject to constant total mass. Results of the optimization with optimality criteria are compared with those found by the gradient-based sequential quadratic programming method. It is shown that the optimum shape can be found using this method without performing the sensitivity analysis, and in less number of iterations compared to the standard gradient-based methods of optimization.
ISSN:0263-8231
1879-3223
DOI:10.1016/j.tws.2008.04.002