Using whole annealing genetic algorithms for the turbine cascade inverse design problem

Abstract

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 Genetic Algorithms have been developed in this paper. Simulated annealing selection and non-uniform mutation are adopted in the whole annealing Genetic Algorithms. Whole annealing Genetic Algorithms optimal performance have been tested through mathematical test functions. On this basis, turbine cascade inverse design using whole annealing Genetic Algorithms have been presented. The B-Spline function is applied to represent the cascade shape. C-type grid and Godunov scheme are adopted to analysis the cascade aerodynamic performance. The optimal problem aims to obtain an cascade shape from different initial cascade through the given target pressure distribution. The optimum cascade shape is in well agreement with the target cascade shape. The numerical results show that the whole annealing Genetic Algorithms are the powerful optimum tools for turbine optimum design or other complex engineering design problems.