This paper describes a knowledge-based method for the automatic design of more efficient turbine blades. An Artificial Neural Network (ANN) is used to construct an approximate model (response surface) using a database containing Navier–Stokes solutions for all previous designs. This approximate model is used for the optimization, by means of Simulated Annealing (SA), of the blade geometry, which is then analyzed by a Navier–Stokes solver. This procedure results in a considerable speed-up of the design process by reducing both the interventions of the operator and the computational effort. It is also shown how such a method allows the design of more efficient blades while satisfying both the aerodynamic and mechanical constraints. The method has been applied to different types of two-dimensional turbine blades, of which three examples are presented in this paper.

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