Aero-structural optimization of gas turbine blades is a very challenging task, given e.g. three dimensional nature of the flow, stringent performance requirements, structural and manufacturing considerations, etc. The current research work addresses this challenge by development and implementation of structural shape optimization module and integrating it with an aerodynamic shape optimization module to form an automated aero-structural optimization procedure. The optimizer combines a Multi-Objective Genetic Algorithm (MOGA), with a Response Surface Approximation (RSA) of the Artificial Neural Network (ANN) type. During the optimization process, each objective function and constraint is approximated by an individual ANN, which is trained and tested using an aerodynamic as well as a structure database composed of a few high fidelity flow simulations (CFD) and structure analysis (CSD) that are obtained using ANSYS Workbench 11.0. Addition of this multiple ANN technique to the optimizer greatly improves the accuracy of the RSA, provides control over handling different design variables and disciplines. The described methodology is then applied to the aero-structural optimization of the E/TU-3 turbine blade row and stage at design conditions to improve the aerodynamic and structural performance of the turbomachinery blades by optimizing the stacking curve. The proposed methodology proved quite successful, flexible and practical with significant increase in stage efficiency and decrease in equivalent stress
