Double Shock Control Bump design optimization using hybridised evolutionary algorithms

Lee, Dong-Seop; Périaux, Jacques; Pons‐Prats, Jordi; Bugeda, Gabriel; Oñate, Eugenio

doi:10.1109/cec.2010.5586379

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…For problems involving AFC applications, it is not possible to use (LP) for this same reason. Nonlinear optimisation methods appear to be more appropriate [47][48][49].…”

Section: Optimisation Of Afc Parametersmentioning

confidence: 99%

Active flow control optimisation on SD7003 airfoil at pre and post-stall angles of attack using synthetic jets

Tousi

Coma

Bergadà

et al. 2021

Applied Mathematical Modelling

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“…For problems involving AFC applications, it is not possible to use (LP) for this same reason. Nonlinear optimisation methods appear to be more appropriate [47][48][49].…”

Section: Optimisation Of Afc Parametersmentioning

confidence: 99%

Active flow control optimisation on SD7003 airfoil at pre and post-stall angles of attack using synthetic jets

Tousi

Coma

Bergadà

et al. 2021

Applied Mathematical Modelling

View full text Add to dashboard Cite

“…Details of RMOP can be found in references [38,39]. This optimization platform enables the analysis of single and multi-objective problems using the hybridized techniques that combine Pareto-game and Nashgames.…”

Section: Description Of the Genetic Algorithm Based Optimizermentioning

confidence: 99%

Turbulent separated shear flow control by surface plasma actuator: experimental optimization by genetic algorithm approach

et al. 2016

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The potential benefits of active flow control are no more debated. Among many others applications, flow control provides an effective mean for manipulating turbulent separated flows. Here, a non-thermal surface plasma discharge (dielectric barrier discharge) is installed at the step corner of a backward-facing step (U 0 =15 m/s, Re h =30000, Re θ =1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step (objective function #1) and also to measure the wall pressure fluctuation coefficients (objective function #2). An autonomous multi-variable optimization by genetic algorithm is implemented in an experiment for optimizing simultaneously the voltage amplitude, the burst frequency and the duty-cycle of the high voltage signal producing the surface plasma discharge. The single-objective optimization problems concern alternatively the minimization of the objective function #1 and the maximization of the objective function #2. The present paper demonstrates that when coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm can find the optimum forcing conditions in only a few generations. At the end of the iterative search process, the minimum reattaching position is achieved by forcing the flow at the shear layer mode where a large spreading rate is obtained by increasing the periodicity of the vortex street and by enhancing the vortex pairing process. The objective function #2 is maximized for an actuation at half the shear layer mode. In this specific forcing mode, time-resolved PIV shows that the vortex pairing is reduced and that the strong fluctuations of the wall pressure coefficients result from the periodic passages of flow structures whose size corresponds to the height of the step model.

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“…This approach was used for the optimization of aeronautic shape configurations in Lee et al [10] , Lee et al [11] and D. S. Lee and Srinivas [12]. This was applied in the optimization of composite structure design at Lee et al [13].…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Optimization Method, Application to a Single Objective Active Flow Control Test Case

et al. 2022

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Genetic Algorithms (GA) are useful optimization methods for exploration of the search space, but they usually have slowness problems to exploit and converge to the minimum. On the other hand, gradient based methods converge faster to local minimums, although are not so robust (e.g., flat areas and discontinuities can cause problems) and they lack exploration capabilities. This article presents a hybrid optimization method trying to combine the virtues of genetic and gradient based algorithms, and to overcome their corresponding drawbacks. The performance of the Hybrid Method is compared against a gradient based method and a Genetic Algorithm, both used alone. The rate of convergence of the methods is used to compare their performance. To take into account the robustness of the methods, each one has been executed more than once, with different starting points for the gradient based method and different random seeds for the Genetic Algorithm and the Hybrid Method. The performance of the different methods is tested against an optimization Active Flow Control (AFC) problem over a 2D Selig–Donovan 7003 (SD7003) airfoil at Reynolds number 6×104 and a 14 degree angle of attack. Five design variables are considered: jet position, jet width, momentum coefficient, forcing frequency and jet inclination angle. The objective function is defined as minus the lift coefficient (−Cl), so it is defined as a minimization problem. The proposed Hybrid Method enables working with N optimization algorithms, multiple objective functions and design variables per optimization algorithm.

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Double Shock Control Bump design optimization using hybridised evolutionary algorithms

Cited by 7 publications

References 14 publications

Active flow control optimisation on SD7003 airfoil at pre and post-stall angles of attack using synthetic jets

Active flow control optimisation on SD7003 airfoil at pre and post-stall angles of attack using synthetic jets

Turbulent separated shear flow control by surface plasma actuator: experimental optimization by genetic algorithm approach

A New Hybrid Optimization Method, Application to a Single Objective Active Flow Control Test Case

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