Using Pareto genetic algorithms for preliminary subsonic wing design

Anderson, M.; Gebert, Glenn

doi:10.2514/6.1996-4023

Cited by 26 publications

(8 citation statements)

References 4 publications

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“…While there is no guarantee that the GA will attain the global maximum, utilization of crossover, mutation, and niching have given the GA a much higher success rate for problems with a complex solution space. ‫ݖ‬ = 25ሺ‫݊݅ݏ‬ሺ‫ݔ‬ሻ ଶ + ‫݊݅ݏ‬ሺ‫ݕ‬ሻ ଶ ሻ + ‫ݔ‬ ଶ + ‫ݕ‬ ଶ (1) [11][12][13][14][15][16][17][18][19][20]. For this effort, the system performance model is a suite of codes used to predict flight performance for a generic multi-stage solid-fueled rocket motor (SRM) powered launch vehicle given a set of design parameters.…”

Section: A Genetic Algorithmmentioning

confidence: 99%

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Optimization of an Aero Assisted Launch Vehicle

Albarado

Hartfield

Burkhalter

2010

13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference

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A comprehensive simulation model for a three-stage solid-propellant launch vehicle has been combined with a genetic algorithm to show that aerodynamic assist during early flight of a launch vehicle can substantially improve vehicle performance. Three studies were completed which include a comparison model for the Minuteman-III intercontinental ballistic missile, an improvement to the suborbital phase of flight for a modern ICBM, and the design of a generic three-stage orbital launch vehicle. Significant performance enhancement was achieved by attaching wings to the first stage for providing aerodynamic assist. Optimization was performed by varying the geometric definition of the attached wings and core vehicle design parameters. Significant decreases in initial system weights and propellant mass fractions were achieved for a given payload with the addition of wing structure. These final results are presented in the form of effective specific impulse boost for the aerodynamically assisted vehicle. Nomenclature݃ = gravitational acceleration at Earth's surface ‫ܫ‬ ௦ = specific impulse ‫ܯ‬ = stage initial mass ‫ܯ‬ = stage burnout mass ‫ܯ‬ = stage payload mass ‫ܯ‬ = stage propellant mass ‫ܴܯ‬ = mass ratio ‫ܯ‬ ௦ = stage structural mass Δܸ = change in velocity ߝ = structural coefficient ߣ = payload coefficient

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Section: A Genetic Algorithmmentioning

confidence: 99%

“…Genetic algorithms have been proven to be a great tool for optimizing aerospace systems. Such systems include a myriad of aerospace topics including propellers 11 , wings and airfoils 12,13 , rockets 14 , missiles 15 , flight trajectories 16 , spacecraft controls 17,18 , turbines 19 , and inlets 20 .…”

Section: A Genetic Algorithmmentioning

confidence: 99%

Optimization of an Aero Assisted Launch Vehicle

Albarado

Hartfield

Burkhalter

2010

13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference

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“…For example, for the first station, the EI value (EI 1 ) is defined in the interval, as shown in Table 1. From the second to the eighth stations, respectively, the range is given by the previous value (EI 1 , in the case of EI 2 ) and a minimum value adopted as 70,000 Nm 2 . The same criteria is used for both bending and torsion stiffness values.…”

Section: Databasementioning

confidence: 99%

“…The optimization algorithm adopted in the MDO methodology is an important issue [28]. Due to inherent efficiency and simplicity of the evolutionary-based and genetic algorithms (GA), these were widely applied into many MDO schemes [1,2,17,18]. Another relevant aspect in modern design schemes is related to multiobjective targets.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective optimization of an aircraft wing design with respect to structural and aeroelastic characteristics using neural network metamodel

Caixeta

Marques

2018

J Braz. Soc. Mech. Sci. Eng.

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The multidisciplinary design optimization (MDO) is an important concept that focuses on bringing together disciplines involved with machine design. It is possible to use MDO in any stage of an aircraft design, that is in the conceptual, preliminary, or detailed design, as long as the numerical models are fitted to each of these stages. This work describes the development of a multidisciplinary design optimization applied to flexible aircraft wings, with respect to structural and aeroelastic characteristics. As tools for the aircraft designer, the numerical models must be fairly accurate and fast. Therefore, metamodels for the critical flutter speed prediction of aircraft wings were considered, thereby reducing significantly the computational cost of the optimization. For this purpose, artificial neural networks (NN) metamodeling is evaluated, based on their inherent properties in dealing with complex mappings. The NN metamodel is prepared using an aeroelastic code based on finite-element model coupled with linear potential aerodynamics. Results of the metamodel performance are presented, from where one can note that the NN is well suited for flutter prediction. Multiobjective optimization (MOO) using the genetic algorithm (GA) based on non-dominance approach is considered. The objectives were the maximization of critical flutter speed and minimization of structural mass. One case study is presented to evaluate the performance of the MOO, revealing that overall optimization process actually achieves the search for the Pareto frontier.

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“…The objective function was to maximize aerodynamic efficiency (lift-drag ratio). In 1996, Anderson and Gebert [10] introduced the using of Pareto genetic algorithms to determine high efficiency wing geometries, and demonstrates the capability of pareto genetic algorithms to determine highly efficient and robust wing designs given a variety of design goals and constraints. The design goals are maximizing lift-drag ratio and minimizing structure weight.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Design Optimization of Range Extension Kit of a Subsonic Flying Body

Elsherbiny¹,

Bayoumy

Elshabka³

et al. 2017

International Conference on Aerospace Sciences and Aviation Tec

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This paper aims to convert the aerodynamic shape of a conventional aerial subsonic flying body into a glide one by providing a range extension kit and fins. It describes the selection of configuration and airfoils depending on the tactical requirements and flight regimes. The wing and fins sizing is obtained using three different methods subjected to geometric constraints. The first method is an iterative optimization method using linear aerodynamic data. The second method is a genetic algorithm multi-objective function aims to maximize stability, controllability and lift-drag ratio within certain weights using linear aerodynamic data. The third method is a genetic algorithm optimization function integrated with MISSILE DATCOM aims to maximize lift-drag ratio. Then perform a direct uncontrolled six degree of freedom simulation for the three optimized designs and the conventional flying body. Comparing the results of ranges for these bodies reveals that the third method has the best range over the other designs including the conventional flying body.

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Using Pareto genetic algorithms for preliminary subsonic wing design

Cited by 26 publications

References 4 publications

Optimization of an Aero Assisted Launch Vehicle

Optimization of an Aero Assisted Launch Vehicle

Multiobjective optimization of an aircraft wing design with respect to structural and aeroelastic characteristics using neural network metamodel

Aerodynamic Design Optimization of Range Extension Kit of a Subsonic Flying Body

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