13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference 2010
DOI: 10.2514/6.2010-9201
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Mission Adaptive Wing Optimization with Wind Tunnel Hardware in the Loop

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Cited by 5 publications
(4 citation statements)
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“…Cosin et al [18] adopted a genetic algorithm to optimize the camber geometry of a morphing wing aiming at minimizing the wing total drag. Smith et al [13] adopted a multi-objective genetic optimization aiming at improving adaptive cant angle of the wing tip for a commercial transport jet.…”
Section: Fig 2 Designed Morphing System Drawing [18]mentioning
confidence: 99%
See 1 more Smart Citation
“…Cosin et al [18] adopted a genetic algorithm to optimize the camber geometry of a morphing wing aiming at minimizing the wing total drag. Smith et al [13] adopted a multi-objective genetic optimization aiming at improving adaptive cant angle of the wing tip for a commercial transport jet.…”
Section: Fig 2 Designed Morphing System Drawing [18]mentioning
confidence: 99%
“…This design makes the camber morphing winglet feasible for real applications in the aeronautical industry. The morphing system used in this study is similar to the one used by Cosin et al [18]. Figure 2 shows an image from the constructed morphing system.…”
Section: Introductionmentioning
confidence: 99%
“…A 24.6% increase in aircraft range was obtained with camber morphing wing concept by (MARTINS;CATALANO, 2003). Performance improvement due to camber morphing wing concept proposed by Martins e Catalano (2003) was confirmed by experimental results obtained by Cosin et al (2010), that used a genetic algorithm (GA) for optimizing wing sections camber with wind tunnel on loop.…”
Section: Morphing Aircraftmentioning
confidence: 81%
“…The genetic algorithm (GA) is a gradient independent evolutionary optimization method based on the natural selection process. It is useful for multidisciplinary problems with unknown non-linear objective functions, being already used on wing geometry optimizations problems (CAYIROGLU; KILIC, 2017;COSIN et al, 2010;OYAMA et al, 1997). Good results were found using GA for aerodynamics optimization problem.…”
Section: Optimization Algorithmmentioning
confidence: 99%