Robust Turbine Blade Optimization in the Face of Real Geometric Variations

Because of manufacturing variations, no real turbine blade exactly conforms to its nominal geometry. Even minimal deviations are known to affect aerodynamic performance, blade temperatures, and blade lifespan negatively. Rather than conventional deterministic design with its costly adherence to strict control of tolerance limits, robust design optimization aims to incorporate inevitable variations into the design process itself, so that both performance mean and scatter can be optimized simultaneously. Such a workflow is presented and applied in this paper to aerodynamically optimize an industrial turbine rotor blade against realistic manufacturing variations. A set of digitized three-dimensional laser scans from two turbofan engines forms the core of this study. On the basis of these deviations, the approach uses high-fidelity geometric models, nonintrusive uncertainty quantification, and efficient robust optimization with constraints to effectively locate Pareto-optimal designs. One selected robust blade is validated and shown to be desensitized to the observed manufacturing variability. The underlying measurement data are crucial to obtain realistic results and, as a consequence, are vital to design real robust turbine blades.