In this work, a volume and longitudinal stability-constrained multiobjective aerodynamic shape optimization is conducted. The aerodynamic shapes of lifting bodies are parameterized by using class function/shape function transformation parameterization method for maximum design flexibility. Hypersonic aerodynamic objectives and constraints are analyzed by solving the Reynolds-averaged Navier–Stokes equations in conjunction with a two-equation turbulence model. The Kriging technique is adopted to construct surrogate models aiming at reducing the computational cost. A two-stage method of infill sampling combined with multiobjective optimization is proposed to improve the performance of the surrogate models. Multiobjective evolutionary algorithm based on decomposition (MOEA/D) combined with penalty function method is employed to handle the multiobjective optimization problem with nonlinear constraints. The optimization results reveal that the two-stage method based on surrogates can reduce the computational cost significantly, and the accuracy of the surrogates around the Pareto front is sufficient. The two objectives are competing such that a set of Pareto optimal solutions are obtained, which are the best tradeoffs among the objectives. The unconstrained multiobjective optimization problem is also investigated with MOEA/D and nondominated sorting genetic algorithm II (NSGA-II) respectively to make a further comparison. The results show that the Pareto sets based on MOEA/D are more excellent and distribute more evenly than that obtained by NSGA-II. The computational efficiency of MOEA/D is about six times faster than that of NSGA-II. Lastly, aerodynamic characters of typical shape of Pareto front are analyzed under different flight conditions, and the results reveal favorable robustness of this shape.
Hypersonic lifting body aerodynamic shape optimization based on the multiobjective evolutionary algorithm based on decomposition
2015-06-01
21 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
SAGE Publications | 2016
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