Aerodynamic/RCS Shape Optimisation of Unmanned Aerial Vehicles Using Hierarchical Asynchronous Parallel Evolutionary Algorithms

Dong, Lijie; González, Felipe; Srinivas, K.; Auld, Doug; Wong, KC

doi:10.2514/6.2006-3331

Cited by 27 publications

(10 citation statements)

References 3 publications

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“…Details of the FLO22 code validation can be found in author's previous work [23] and it is shown that the results obtained by FLO22 are in good agreement with experimental data [24]. FLO22 has capabilities to provide accurate results and solve the aerodynamic characteristics for 3D wings operating at transonic speeds.…”

Section: Potential Flow Solver (Flo22) and Frictionsupporting

confidence: 53%

Robust design optimisation using multi-objective evolutionary algorithms

et al. 2008

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Section: Potential Flow Solver (Flo22) and Frictionsupporting

confidence: 53%

Robust design optimisation using multi-objective evolutionary algorithms

et al. 2008

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“…Details of the FLO22 code validation can be found in author's previous work [36] where it is shown that the results obtained by FLO22 are in good agreement with experimental data. FLO22 has capabilities to provide accurate results for different geometries and solve the aerodynamic characteristics on transonic 3D wings.…”

Section: Potential Flow Solver (Flo22)supporting

confidence: 75%

Robust evolutionary algorithms for UAV/UCAV aerodynamic and RCS design optimisation

et al. 2008

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“…Details of the FLO22 code validation can be found in reference [19] and it is shown that the results obtained by FLO22 are in good agreement with experimental data [20]. FLO22 has capabilities to provide accurate results and to solve the aerodynamic characteristics for 3D wings operating at transonic speeds.…”

Section: Friction Drag Is Computed Externally Using the Friction Programmentioning

confidence: 62%

Fast reconstruction of aerodynamic shapes using evolutionary algorithms and virtual nash strategies in a CFD design environment

Périaux

Lee

Gonzalez³

et al. 2009

Journal of Computational and Applied Mathematics

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a b s t r a c tThis paper compares the performances of two different optimisation techniques for solving inverse problems; the first one deals with the Hierarchical Asynchronous Parallel Evolutionary Algorithms software (HAPEA) and the second is implemented with a game strategy named Nash-EA. The HAPEA software is based on a hierarchical topology and asynchronous parallel computation. The Nash-EA methodology is introduced as a distributed virtual game and consists of splitting the wing design variables -aerofoil sections -supervised by players optimising their own strategy. The HAPEA and Nash-EA software methodologies are applied to a single objective aerodynamic ONERA M6 wing reconstruction. Numerical results from the two approaches are compared in terms of the quality of model and computational expense and demonstrate the superiority of the distributed Nash-EA methodology in a parallel environment for a similar design quality.Crown

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Aerodynamic/RCS Shape Optimisation of Unmanned Aerial Vehicles Using Hierarchical Asynchronous Parallel Evolutionary Algorithms

Cited by 27 publications

References 3 publications

Robust design optimisation using multi-objective evolutionary algorithms

Robust design optimisation using multi-objective evolutionary algorithms

Robust evolutionary algorithms for UAV/UCAV aerodynamic and RCS design optimisation

Fast reconstruction of aerodynamic shapes using evolutionary algorithms and virtual nash strategies in a CFD design environment

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