Using Whole Annealing Genetic Algorithms for the Turbine Cascade Inverse Design Problem

INTRODUCTIONInthelastfewyearstherehajsbeenagrowinginterestinthenumericalsolutionofconstrainedoptimizationproblemsofturbinegovernedbytheEulerorNavier-Stokesequations.Developmelltofturbinecascadeswithoptimumaerodynamicefficiencyhaslongbeenadesignchalle...

Turbine cascade optimum design, the typical non-convex optimal problem, has long been a design challenge in the engineering fields. The new type hybrid Genetic Algorithms-whole annealing GeneticAlgorithms have been developed in this paper. Simulated annealing selection and non-uniform mutation are adopted in the whole annealing Genetic Algorithms. Whole annealing Genetic Algorithmsoptimal performance have been tested through mathematical test functions. On this basis, turbinecascade inverse design using whole annealing Genetic Algorithms have been presented. The B-Splinefunction is applied to represent the cascade shape. C-type grid and Godunov scheme are adopted toanalysis the cascade aerodynamic performance. The optimal problem aims to obtain an cascade shapefrom different initial cascade through the given target pressure distribution. The optimum cascadeshape is in well agreement with the target cascade shape. The numerical results show that the wholeannealing Genetic Algorithms are the powerful optimum tools for turbine optimum design or othercomplex engineering design problems.