Parallel genetic algorithm implementation in multidisciplinary rotor blade design

The present paper describes an adaptation of genetic algorithms in the design of large-scale multidisciplinary optimization problems. A hingeless composite rotor blade is used as the test problem, where the formulation of the objective and constraint functions requires the consideration of disciplines of aerodynamics, performance, dynamics, and structures. A rational decomposition approach is proposed for partitioning the large-scale multidisciplinary design problem into smaller, more tractable subproblems. A design method based on a parallel implementation of genetic algorithms is shown to be an effective strategy, providing increased computational efficiency, and a natural approach to account for the coupling between temporarily decoupled subproblems. A central element of the proposed approach is the use of artificial neural networks for identifying a topology for problem decomposition and for generating global function approximations for use in optimization. (AN)