The Potential of Genetic Algorithms for Conceptual Design of Rotor Systems

Crossley, William A.; Wells, Valana L.; Laananen, David H.

doi:10.1080/03052159508941191

Cited by 13 publications

(6 citation statements)

References 6 publications

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“…Crossley et al 120 and Crossley 121,122 studied rotor design for minimum weight and power for the design variables such as number of blades, airfoil sections, solidity, twist, tip speed, disk loading, and taper. Crossley 122 developed an algorithm for collapsing the constraints and objective functions into a single objective function using the K-S function.…”

Section: Nongradient Methodsmentioning

confidence: 99%

A Survey of Recent Developments in Rotorcraft Design Optimization

Ganguli

2004

Journal of Aircraft

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Section: Nongradient Methodsmentioning

confidence: 99%

A Survey of Recent Developments in Rotorcraft Design Optimization

Ganguli

2004

Journal of Aircraft

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“…Rotor design for minimum weight and power required were studied in Refs. [99] and [100] using the same design variables of Ref. [93].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Recent Applications of Design Optimization to Rotorcraft-A Survey

Celi

1999

Journal of Aircraft

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This paper presents a survey of applications of design optimization to helicopter problems carried out in the last decade. Helicopter optimization has not yet reached the same maturity as structural optimization, and some potential reasons are discussed first. Next, published optimization studies are reviewed, divided into sensitivity analysis studies, applications to rotor dynamics and aeroelasticity, applications to other helicopter problems, emerging technologies such as genetic algorithms and simulated annealing, and studies with experimental verification. The usefulness of design optimization for helicopter applications will increase with the availability of improved analyses, more efficient algorithms especially for sensitivity calculations, and further experimental verifications. The formulation of representative optimization test problem, including experimental verification, is also recommended.

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“…For selection, a method known as tournament selection is used (see Crossley (1994)) with a tournament size of 2. It works as follows: Two chromosomes are randomly selected without replacement from the current population and the best of the two is taken to be the first mate.…”

Section: Selection Crossover and Mutationmentioning

confidence: 99%