Transonic Wing Shape Optimization Based on Evolutionary Algorithms

Obayashi, Shigeru; Oyama, Akira; Nakamura, Takashi

doi:10.1007/3-540-39999-2_15

Cited by 4 publications

(3 citation statements)

References 12 publications

(9 reference statements)

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“…However, the drag reduction alone cannot be the optimization target if it can adversely affect the lift; in general, there is a tradeoff between lift and drag. 4 Several important criteria must be met in the optimization process to achieve not only a shock-free but also an efficient airfoil that has good drag characteristics, a buffet boundary high enough to permit cruising at design lift coefficient, no unsatisfactory offdesign performance, and so forth. 5 In the last several decades, many important contributions have been made to the design and optimization of shock-free airfoils.…”

Section: Introductionmentioning

confidence: 99%

Shape optimization of airfoils in transonic flow using a multi-objective genetic algorithm

Chen

Agarwal

2013

Proceedings of the Institution of Mechanical Engineers, Part G:

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Shape optimization of transonic airfoils requires creating an airfoil that reduces the drag due to transonic shocks by either eliminating them or reducing their strength at a given transonic cruise speed while maintaining the lift. The RAE 2822 and NACA 0012 airfoils are most widely used test cases for validation of computational modeling in transonic flow. This study employs a multi-objective genetic algorithm for shape optimization of RAE 2822 and NACA 0012 airfoils to achieve two objectives, namely eliminating shock and maintaining or increasing the lift at a given transonic Mach number and angle of attack. The commercially available software FLUENT is employed for calculation of the flow field using the Reynolds-averaged Navier–Stokes equations in conjunction with a two-equation turbulence model. It is shown that the multi-objective genetic algorithm can generate superior airfoils compared with the original airfoils by achieving both the objectives.

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Section: Introductionmentioning

confidence: 99%

Shape optimization of airfoils in transonic flow using a multi-objective genetic algorithm

Chen

Agarwal

2013

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…To deploy an aerodynamic shape optimization using GA, elaborative tradeoff between accuracy and cost should be considered at the beginning. Great efforts have been taken into the research of obtaining an affordable and acceptable optimal solution through GA. One strategy is to use a surrogate model to predict the results of expensive numerical CFD simulations [7][8][9]. Explicit functions were trained to capture a global approximation of the relation between design parameters and objectives.…”

Section: Introductionmentioning

confidence: 99%

Data fusion of multi-fidelity model and its application in low speed reflexed airfoil shape optimization

Wang

2011

2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC)

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In this paper, a data fusion method was presented to combine multi Computational Fluid Dynamics models together in a target to constitute a scheme which could give an accurate and expeditious prediction of CFD simulation and reduce the total computational cost in a global optimization using Genetic Algorithm. Numerical models were defined as lower and higher fidelity according to the accuracy and speed of simulation. The framework of artificial neural network was applied to map and fuse variable fidelity models together. The lower fidelity model was employed as a neuron on the network, and a mapping strategy mixed multiplicative and additive scaling between lower and higher fidelity was built. A low speed reflexed airfoil optimization was deployed with a target to maximize the lift-todrag ration under the constraints of consideration about stability margin using this technique. The optimal result proved that the method in this paper could decrease the computational cost and retain an acceptable accuracy of higher fidelity model at the same time.

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“…A Reynolds-averaged Navier-Stokes (RANS) aerodynamic solver has also been used by Chiba et al [23] and Obayashi et al [24] to optimize the wing of a proposed transonic regional jet using an adaptive range multi-objective genetic algorithm in collaboration with Mitsubishi Heavy Industries, Ltd.…”

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confidence: 99%

Preliminary Aerostructural Optimization of a Large Business Jet

Piperni

Abdo

Kafyeke

et al. 2007

Journal of Aircraft

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An overview of an industrial approach to the aerostructural optimization of a large business jet is presented herein. The optimization methodology is based on the integration of aerodynamic and structural analysis codes that combine computational, analytical, and semi-empirical methods, validated in an aircraft design environment. The aerodynamics subspace is analyzed with a three-dimensional transonic small disturbance code capable of predicting the drag of a complete, trimmed aircraft within engineering accuracy. The design of the wing structure is accomplished using a quasi-analytical method that defines the layout of the ribs and geometry of the spar webs, spar caps, and skin-stringer panels, and predicts the wing flexural properties and weight distribution. In addition, the prediction of operating economics as well as the integrated en route performance is coupled into the scheme by way of fractional change functional transformations. To illustrate the automated design system capabilities, the methodology is applied to the optimization of a large business jet comprising winglets, rear-mounted engines, and a T-tail configuration. The aircraft-level design optimization goal in this instance is to minimize a cost function for a fixed range mission assuming a constant maximum takeoff weight.Nomenclature A sk = cross-sectional area of skin, in: 2 A st = cross-sectional area of stiffener, in: 2 AR = aspect ratio C crew = hourly cost of the flight (and cabin) crew, CU=h C D = total drag coefficient C fees = landing fee charge based on maximum landing weight, CU=lb C fuel = price of fuel, CU=lb C L = operating lift coefficient C mnt = time-dependent airframe and engine maintenance cost, CU=h C mnt cyc = cyclic airframe maintenance cost, CU C mnt eng = cyclic engine maintenance component that is assumed to have a functional dependency with the engine derate level, CU E = relative or absolute error f = function; substitution parameter in modified integrated range model formulation g = acceleration due to gravity, ft=s 2 H = fuel calorific value, Btu=lb k aero = control factor used to regulate the extent of coupling (congruity or otherwise) between high-speed and intermediate-speed aerodynamic efficiency k clb = constant of proportionality required to predict all-up weight at top of climb k M = empirically derived coefficient used to establish a simplified relationship between overall power plant efficiency and Mach number k res = constant of proportionality establishing linear relationship between fractional change in total reserve fuel and fractional change in zero-fuel weight L=D = lift-to-drag ratio M = Mach number R = range, nm t = time, as in block or flight time, h V = forward speed [(knots, calibrated airspeed (KCAS) or knots, indicated airspeed (KIAS)] W= weight of a given component or assembly, all-up weight, lb x, y = arbitrary independent parameters or design variables z = arbitrary dependent parameters or objective functions = exponent, flight profile correction coefficient when useful load varies but payl...

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Transonic Wing Shape Optimization Based on Evolutionary Algorithms

Cited by 4 publications

References 12 publications

Shape optimization of airfoils in transonic flow using a multi-objective genetic algorithm

Shape optimization of airfoils in transonic flow using a multi-objective genetic algorithm

Data fusion of multi-fidelity model and its application in low speed reflexed airfoil shape optimization

Preliminary Aerostructural Optimization of a Large Business Jet

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