Robust active shock control bump design optimisation using parallel hybrid-MOGA

The paper investigates a robust optimisation for detail design of active shock control bump on a transonic Natural Laminar Flow (NLF) aerofoil using a Multi-Objective Evolutionary Algorithm (MOEA) coupled to Computational Fluid Dynamics (CFD) software. For MOEA, Robust Multi-objective Optimisation Platform (RMOP) developed in CIMNE is used. For the active shock control bump design, two different optimisation methods are considered; the first method is a Pareto- Game based Genetic Algorithm in RMOP (denoted as RMOGA). The second method uses a Hybridised RMOGA with Game-Strategies and a parallel computation for high performance computation. The paper not only shows how a shock control bump approach coupled to CFD improves aerodynamic performance of original transonic aerofoil but also it shows how high performance computation with applying Hybrid- Game and parallel computation increase the efficiency of optimisation in terms of computational cost and result accuracy.Postprint (published version

Optimization of the experimental set-up for a turbulent separated shear flow control by plasma actuator using genetic algorithms

Since 1947, when Schubauer and Skramstad established the basis of the technology with its revolutionary work about steady state tools and mechanisms for the flow management, the progress of the flow control technology and the development of devices have progressed constantly. Anyway, the applicability of such devices is limited, and only few of them have arrived to the assembly workshop. The problem is that the range of actuation is still limited. Despite their operability limitations, flow control devices are of great interest for the aeronautical industry. The number of projects investigating this technology demonstrates the relevance of in the Fluid Dynamic field. The scientific interest focus not only on the industrial applications and the improvement of the technology, but also on the deep understanding of the physical phenomena associated to the flow separation, turbulence formation associated to the final drag reduction aim. A clear example of what has been mentioned is the EC MARS research project (MARS project, FP7 project number 266326). Its objectives are aimed to a better understanding of the Reynolds Stress and turbulent flow related to both drag reduction and flow control. The research was carried out through the analysis of several flow control devices and the optimization of the parameters for some of them was an important element of the research. When solving a traditional fluid dynamics optimisation problem numerical flowanalysis are used instead of experimental ones due to their lower cost and shorter needed time for evaluation of candidate solutions. Nevertheless, in the particular case of the selected flow control plasma devices the experimental measurement of the performance of each candidate configuration has been much quicker than a numerical analysis. For this reason, the corresponding optimisation problem has been solved by coupling an evolutionary optimization algorithm with an experimental device. This paper discusses the design quality and efficiency gained by this innovative coupling.Peer ReviewedPostprint (author's final draft

Lift maximization with uncertainties for high lift devices optimization

In this paper, the aerodynamic shape optimization problems with uncertain operating conditions has been addressed. After a review of robust control theory and the possible approaches to take into account uncertainties, the use of Taguchi robust design methods in order to overcome single point design problems in Aerodynamics is proposed. Under the Taguchi concept, a design with uncertainties is converted into an optimization problem with two objectives which are the mean performance and its variance, so that the solutions are as less sensitive to the uncertainty of the input parameters as possible. Furthermore, the Multi-Criterion Evolutionary Algorithms (MCEAs) are used to capture a set of compromised solutions (Pareto front) between these two objectives. The flow field is analyzed by Navier-Stokes computation using an unstructured mesh. The proposed approach drives to the solution of a multi-objective optimization problem that is solved using a modification of a Nondominated Sorting Genetic Algorithm (NSGA). In order to reduce the number of expensive evaluations of the fitness function a Response Surface Modeling (RSM) is employed to estimate the fitness value using the polynomial approximation model. During the solution of the optimization problem a Semi-torsional Spring Analogy is used for the adaption of the computational mesh to all the obtained geometrical configurations. The proposed approach is applied to the robust optimization of the 2D high lift devices of a business aircraft by maximizing the mean and minimizing the variance of the lift coefficients with uncertain free-stream angle of attack at landing and takeoff flight conditions, respectively.Preprin

Drag reduction via turbulent boundary layer flow control

The final publication is available at Springer via http://dx.doi.org/10.1007/s11431-016-9013-6Turbulent boundary layer control (TBLC) for skin-friction drag reduction is a relatively new technology made possible through the advances in computational-simulation capabilities, which have improved the understanding of the flow structures of turbulence. Advances in micro-electronic technology have enabled the fabrication of active device systems able to manipulating these structures. The combination of simulation, understanding and micro-actuation technologies offers new opportunities to significantly decrease drag, and by doing so, to increase fuel efficiency of future aircraft. The literature review that follows shows that the application of active control turbulent skin-friction drag reduction is considered of prime importance by industry, even though it is still at a low technology readiness level (TRL). This review presents the state of the art of different technologies oriented to the active and passive control for turbulent skin-friction drag reduction and contributes to the improvement of these technologies.Peer ReviewedPostprint (author's final draft

EU-China research cooperation in aeronautics technologies: GRAIN 2

Postprint (published version

Lift maximization with uncertainties for high lift devices optimization

In this paper, the aerodynamic shape optimization problems with uncertain operating conditions has been addressed. After a review of robust control theory and the possible approaches to take into account uncertainties, the use of Taguchi robust design methods in order to overcome single point design problems in Aerodynamics is proposed. Under the Taguchi concept, a design with uncertainties is converted into an optimization problem with two objectives which are the mean performance and its variance, so that the solutions are as less sensitive to the uncertainty of the input parameters as possible. Furthermore, the Multi-Criterion Evolutionary Algorithms (MCEAs) are used to capture a set of compromised solutions (Pareto front) between these two objectives. The flow field is analyzed by Navier-Stokes computation using an unstructured mesh. The proposed approach drives to the solution of a multi-objective optimization problem that is solved using a modification of a Nondominated Sorting Genetic Algorithm (NSGA). In order to reduce the number of expensive evaluations of the fitness function a Response Surface Modeling (RSM) is employed to estimate the fitness value using the polynomial approximation model. During the solution of the optimization problem a Semi-torsional Spring Analogy is used for the adaption of the computational mesh to all the obtained geometrical configurations. The proposed approach is applied to the robust optimization of the 2D high lift devices of a business aircraft by maximizing the mean and minimizing the variance of the lift coefficients with uncertain free-stream angle of attack at landing and takeoff flight conditions, respectively

Multi-objective high lift systems design optimisation using hybridised evolutionary algorithm with Nash-game

This paper investigates the High Lift System (HLS) application of complex aerodynamic design problem using Genetic Algorithms (GAs) coupled to Game strategies. Two types of optimization methods are used; the first method is a standard GA based on Pareto dominance and the second method hybridises GA with a wellknown Nash Game strategies named Hybrid-GA. These optimization techniques are coupled to a pre/post processor GiD providing unstructured meshes during the optimisation procedure and a transonic analysis software PUMI. The computational efficiency and quality design obtained by GA and Hybrid-GA are compared. The numerical results for the multi-objective HLS design optimisation clearly shows the benefits of hybridising a GA with the Nash game and makes promising the above methodology for solving other more complex multi-physics optimisation problems in Aeronautics

Multi-objective high lift systems design optimisation using hybridised evolutionary algorithm with Nash-game

This paper investigates the High Lift System (HLS) application of complex aerodynamic design problem using Genetic Algorithms (GAs) coupled to Game strategies. Two types of optimization methods are used; the first method is a standard GA based on Pareto dominance and the second method hybridises GA with a wellknown Nash Game strategies named Hybrid-GA. These optimization techniques are coupled to a pre/post processor GiD providing unstructured meshes during the optimisation procedure and a transonic analysis software PUMI. The computational efficiency and quality design obtained by GA and Hybrid-GA are compared. The numerical results for the multi-objective HLS design optimisation clearly shows the benefits of hybridising a GA with the Nash game and makes promising the above methodology for solving other more complex multi-physics optimisation problems in Aeronautics.Postprint (published version