A Classifiers Based Optimization Technique for Designing Turbine Airfoils

The primary focus of this work is in the development of an evolutionary optimization technique which gets progressively 'smarter' during the optimization process by learning from computed domain knowledge. In the approach, the influence of the design variables on the problem solution is recognized, and the knowledge learned is then generalized to dynamically create or change design rules during optimization. This technique, when applied to a constrained optimization problem, shows progressive improvement in convergence of search, as successive generations of rules evolve by learning from the environment. This method is applied to a complex aerodynamic optimization problem involving turbine airfoil design. In this investigation, the 3D geometry of an airfoil is optimized by simultaneously optimizing multiple 2D slices of the airfoil. Results from the optimization of a low pressure turbine nozzle are presented in the paper. Results obtained using standard numerical optimization techniques are also presented for comparison purposes.