Currently, most shape optimization activities for 2D blade sections focus on modifying the blade shape locally to get an optimum one, which implicitly assumes that the global shape is near optimum. Moreover, the common design parameters in most cases are not the variables used in shape optimization, hence the designer does not have control over the parameters that he or she uses in the design. In this work, the turbine blade shape at any given radial location, is represented with the MRATD model (Modified Rapid Axial Turbine Design), which is a low-order representation that describes the blade profile using a maximum of 17 aerodynamic design parameters that are given (and used) by the turbine designer, e.g. the blade axial chord, stagger, maximum thickness, throat, uncovered turning, inlet and exit blade and wedge angles, LE and TE radii etc... This representation is used in an optimization scheme to sweep the design space and identify the design parameters that would accomplish a certain optimization objective (e.g. maximum adiabatic efficiency) subject to some constraints (e.g. fixed throat area or minimum TE radius or maximum TE wedge angle or metal angles etc...). The optimization scheme uses evolutionary optimization algorithm, Genetic Algorithm(GA) and, to save computing time, Artificial Neural Network (ANN) is introduced to approximate the optimization objectives and constraints; it is trained and tested using a relatively small number of high fidelity CFD flow simulations. This approach to geometry representation is used to carry out a sensitivity study of the effect of the different design parameters on the blade performance of a highly efficient subsonic turbine blade. Its impact on the design process is also demonstrated.

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