Aerodynamic shape optimization using efficient evolutionary algorithms and unstructured CFD solver

Jahangirian, A.; Shahrokhi, Ava

doi:10.1016/j.compfluid.2011.02.010

Cited by 48 publications

(20 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Marco and Lanteri (2000) used a parallel GA to reduce computation costs in airfoil optimum design. Zhang et al (2002) optimized airfoil and wing in subsonic and transonic regime by the implementation of GA. Jahangirian and Shahrokhi (2011) employed GA to optimize airfoil aerodynamic performance using CFD techniques. They also presented a fast method for aerodynamic optimization of transonic airfoil by means of GA and neural networks (Shahrokhi and Jahangirian 2010).…”

Section: Fig 1 Euroshock Presented Methods To Reduce Shock Wave Relmentioning

confidence: 99%

Flow Control of Transonic Airfoils using Optimum Suction and Injection Parameters

Moghadam

Jahangirian

2017

JAFM

View full text Add to dashboard Cite

In this paper, the application of the surface mass transfer optimization in shock wave-boundary layer interaction control at off-design conditions of transonic aircraft wing is presented. The suction or injection parameters include for example its position on the airfoil, its angle, the length of the hole and the rate of the injected or sucked flow. The optimization process is carried out using an efficient Genetic Algorithm (GA) method. The compressible viscous flow equations in Reynolds Averaged form are solved together with a twoequation k-epsilon turbulence model to accurately compute the objective function. Four different objective functions are introduced including maximum lift to drag ratio, minimum drag coefficient, maximum lift to drag ratio with no drag increment and minimum drag coefficient with no lift decrement. Effectiveness of each objective function is examined by comparing the optimum results in terms of the flow control parameters and flow characteristics.

show abstract

Section: Fig 1 Euroshock Presented Methods To Reduce Shock Wave Relmentioning

confidence: 99%

Flow Control of Transonic Airfoils using Optimum Suction and Injection Parameters

Moghadam

Jahangirian

2017

JAFM

View full text Add to dashboard Cite

show abstract

“…In this way, it is ensured that the design obtained is optimum with respect to the simulator system (CFD solver) and not only to the surrogate model. For more information about the SVMr, EAs and IES-SL readers can consult [6,7,10,11].…”

Section: Proposed Approachmentioning

confidence: 99%

“…Moreover, a combination of a generic algorithm (GA) and an artificial neural network (ANN) was applied in [7] to the shape optimization of an airfoil, parameterized by a modified PARSEC parameterization involving ten design variables. In [8] a surrogate based on Proper Orthogonal Decomposition (POD) was applied to the aerodynamic shape optimization of an airfoil geometry parameterized by sixteen design variables defined with Class Shape Transformation method (CST).…”

Section: Previous Workmentioning

confidence: 99%

Surrogate-Based Global Optimization of a Cylinder by the Use of Evolutionary Algorithms, Support Vector Machines and Non Uniform B-Splines

Andrés-Pérez¹,

González-Juárez²,

Martin-Burgos³

et al. 2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

View full text Add to dashboard Cite

show abstract

“…Due to the substantial cost of using a global optimization approach for an ASO problem, and the apparent lack of multimodality in a number of ASO problems, global optimizers have had only a small use in ASO, see [37][38][39][40][41] for example. However, to investigate the mutimodality of the ADODG multimodal benchmark problem, a state-of-the-art constrained global optimization framework [42] is employed here.…”

Section: Introductionmentioning

confidence: 99%

Global Optimization of Wing Aerodynamic Optimization Case Exhibiting Multimodality

Poole

Allen

Rendall

2018

Journal of Aircraft

View full text Add to dashboard Cite

An investigation into an aerodynamic optimization benchmark case that exhibits multimodality is presented using a global optimization approach. The recently suggested case 6 of the AIAA Aerodynamic Design Optimization Discussion Group involves the drag minimization of a rectangular NACA0012 wing subject to lift and root bending moment, as well as other geometric constraints. In this paper, optimization of that case using a state-of-the-art constrained population-based global optimization framework is presented.Shape control is achieved via a hierarchical application of the radial basis function domain element approach. Three different fidelity of optimization cases are performed to investigate the multimodality of various aspects of the full problem, with four independent runs of each case being performed. When considering chord optimization, the optimizer converges to two independently identifiable minima with very similar performance. When adding dihedral and sweep variation, further multimodality is introduced, though this multimodality is reasonably well defined, with minima including forward and rearward sweep, and an upward and downward winglet. Introducing thickness variables leads to a highly multimodal problem due to the coupling between the chord and thickness variables. The theoretical minimum drag for this case is 24.7 counts, which a number of the runs get close to achieving.

show abstract

Aerodynamic shape optimization using efficient evolutionary algorithms and unstructured CFD solver

Cited by 48 publications

References 9 publications

Flow Control of Transonic Airfoils using Optimum Suction and Injection Parameters

Flow Control of Transonic Airfoils using Optimum Suction and Injection Parameters

Surrogate-Based Global Optimization of a Cylinder by the Use of Evolutionary Algorithms, Support Vector Machines and Non Uniform B-Splines

Global Optimization of Wing Aerodynamic Optimization Case Exhibiting Multimodality

Contact Info

Product

Resources

About