| Turbine performance directly affects engine specific impulse, thrust-to-weight ratio, and cost in a rocket propulsion system. This dissertation focuses on methodology and application of employing optimization techniques, with the neural network (NN) and polynomial-based response surface method (RSM), for supersonic turbine optimization. The research is relevant to NASA's reusable launching vehicle initiatives.;It is demonstrated that accuracy of the response surface (RS) approximations can be improved with combined utilization of the NN and polynomial techniques, and higher emphases on data in regions of interests. The design of experiment methodology is critical while performing optimization in efficient and effective manners.;In physical applications, both preliminary design and detailed shape design optimization are investigated. For preliminary design level, single-, two-, and three-stage turbines are considered with the number of design variables increasing from six to 11 and then to 15, in accordance with the number of stages. A major goal of the preliminary optimization effort is to balance the desire of maximizing aerodynamic performance and minimizing weight. To ascertain required predictive capability of the RSM, a two-level domain refinement approach (windowing) has been adopted. The accuracy of the predicted optimal design points based on this strategy is shown to be satisfactory. The results indicate that the two-stage turbine is the optimum configuration with the higher efficiency corresponding to smaller weights. It is demonstrated that the criteria for selecting the database exhibit significant impact on the efficiency and effectiveness of the construction of the response surface.;Based on the optimized preliminary design outcome, shape optimization is performed for vanes and blades of a two-stage supersonic turbine, involving O(10) design variables. It is demonstrated that a major merit of the RS-based optimization approach is that it enables one to adaptively revise the design space to perform multiple optimization cycles. This benefit is realized when an optimal design approaches the boundary of a pre-defined design space. Furthermore, by inspecting the influence of each design variable, one can also gain insight into the existence of multiple design choices and select the optimum design based on other factors such as stress and materials considerations.;The present work also investigates the issues related to accuracy of the RS approximations and explores possible ways of improving the RS model using appropriate treatments. For this purpose, an iteratively re-weighted least square technique, NN-enhanced RSM, and an approach placing higher emphasis on data belonging to a region of interests and improving the model performance in these critical areas are studied. |
