Composite wing structural optimization using genetic algorithms and response surfaces

1998 ◽  
Author(s):  
Boyang Liu ◽  
Raphael Haftka ◽  
Mehmet Akgun
Keyword(s):  
Genetic Algorithms ◽  
Structural Optimization ◽  
Response Surfaces ◽  
Composite Wing

Structural Optimization Design of an Aircraft Metal-Composite Wing

2013 ◽  
Vol 341-342 ◽  
pp. 519-523
Author(s):  
Ya Hui Zhang ◽  
Ji Hong Zhu ◽  
Jun Shuo Li ◽  
Wei Hong Zhang
Keyword(s):  
Structural Optimization ◽  
Optimization Design ◽  
Optimization Method ◽  
Design Solution ◽  
Metal Composite ◽  
Concept Design ◽  
Obvious Effect ◽  
Reinforced Composite ◽  
Three Phase ◽  
Composite Wing

The problem of metal-composite wing structural optimization is discussed and a strategy is presented. Topology optimization method is applied to provide load transferring path of structure for concept design. Size, shape and other optimization method are used to provide detailed design for individual components. A three-phase optimization method is discussed for fiber reinforced composite laminate skin. Optimal parameters include ply angle, percentage, thickness, layer shape and sequence. The design of laminate for ease of manufacture is based on a set of manufacturing constraints. This paper deals with a total optimal design solution for aileron structure of an aircraft. The result satisfies all the requirements of strength and stability, and has obvious effect of weight loss.

scholarly journals Structural optimization of a light aircraft composite wing

2020 ◽  
Vol 709 ◽  
pp. 044094 ◽  
Author(s):  
Phyo Wai Aung ◽  
Oleg Tatarnikov ◽  
Naing Lin Aung
Keyword(s):  
Structural Optimization ◽  
Composite Wing

Material identification in structural optimization using response surfaces

2004 ◽  
Vol 29 (2) ◽  
pp. 93-102 ◽  
Author(s):  
N. Stander ◽  
K.J. Craig ◽  
H. M�llersch�n ◽  
R. Reichert
Keyword(s):  
Structural Optimization ◽  
Response Surfaces ◽  
Material Identification

A Quantitative Study on Simultaneous Effects of Governing Parameters in Electrospinning of Nanofibers using Modified Neural Network using Genetic Algorithm

2017 ◽  
Vol 6 (1) ◽  
pp. 20-38
Author(s):  
Shayan Seyedin ◽  
Shima Maghsoodloo ◽  
Vahid Mottaghitalab
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Quantitative Study ◽  
Fiber Diameter ◽  
Volume Flow Rate ◽  
Solution Concentration ◽  
Electrospinning Process ◽  
Response Surfaces ◽  
Experimental Conditions

In this article, modified neural networks using genetic algorithms were employed to investigate the simultaneous effects of four of the most important parameters, namely; solution concentration (C); spinning distance (d); applied voltage (V); and volume flow rate (Q) on mean fiber diameter (MFD), as well as standard deviation of fiber diameter (StdFD) in electrospinning of polyvinyl alcohol (PVA) nanofibers. Genetic algorithm optimized neural networks (GANN) were used for modeling the electrospinning process. The results indicate better experimental conditions and more predictive ability of GANNs. Therefore, the approach of using genetic algorithms to optimize neural networks for modeling the electrospinning process has been successful. RSM could be employed when statistical analysis, quantitative study of the effects of the parameters and visualization of the response surfaces are of interest, whereas in the case of modeling the process and predicting new conditions, GANN is a more powerful tool and presents more desirable results.

Optimization of grounding grids by response surfaces and genetic algorithms

2003 ◽  
Vol 39 (3) ◽  
pp. 1301-1304 ◽  
Author(s):  
M. Caldora Costa ◽  
M. Leite Pereira ◽  
Y. Marechal ◽  
J. Coulomb ◽  
J.R. Cardoso
Keyword(s):  
Genetic Algorithms ◽  
Response Surfaces ◽  
Grounding Grids

scholarly journals Structural Optimization of Flat CRT by using Hybrid Genetic Algorithms

2006 ◽  
Vol 72 (716) ◽  
pp. 385-390 ◽  
Author(s):  
Hiroo SAKAMOTO ◽  
Shiro TAKADA ◽  
Junko ITOH ◽  
Masayuki MIYAZAKI ◽  
Toshihide MURAKAMI ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Structural Optimization ◽  
Hybrid Genetic Algorithms
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