In this paper, an efficient evolutionary optimisation of a turbine blade firtree root local profile is presented. The firtree geometry is designed using an intelligent rule-based computer-aided design system (ICAD) and analysed using an industrial-strength finite element code. A large number of geometric and mechanical constraints drawn from past experience are incorporated in the design of the model. The high computational cost associated with finding optimal designs using high-fidelity codes is addressed using a surrogate-assisted genetic algorithm. The initial surrogate model is first built based on points sampled with a design-of-experiment method. A database of designs analysed using the high-fidelity code is built and augmented while the genetic algorithm progresses. In the procedure for deciding whether the high-fidelity code should be run, a simple 3 ? principle is used instead of searching for the point with maximum expected improvement. This is combined with an appropriate ranking of the design points within the database. Some benchmark test problems are first used to illustrate the effectiveness and efficiency of the framework. When applied to the problem of local shape optimisation of a turbine blade firtree root, significant improvement is achieved using a limited computational budget
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