Aerodynamic Optimization of the Low-Pressure Turbine Module: Exploiting Surrogate Models in a High-Dimensional Design Space

摘要

Further improvement of state-of-the-art low-pressure (LP) turbines (LPTs) has become progressively more challenging. LP design is more than ever confronted to the need to further integrate complex models and to shift from single-component design to the design of the complete LPT module at once. This leads to high-dimensional design spaces and automatically challenges their applicability within an industrial context, where computing resources are limited and the cycle time is crucial. The aerodynamic design of a multistage LP turbine is discussed for a design space defined by 350 parameters. Using an online surrogate-based optimization (SBO) approach, a significant efficiency gain of almost 0.5pt has been achieved. By discussing the sampling of the design space, the quality of the surrogate models, and the application of adequate data mining capabilities to steer the optimization, it is shown that despite the high-dimensional nature of the design space, the followed approach allows to obtain performance gains beyond target. The ability to control both global as well as local characteristics of the flow throughout the full LP turbine, in combination with an agile reaction of the search process after dynamically strengthening and/or enforcing new constraints in order to adapt to the review feedback, not only illustrates the feasibility but also the potential of a global design space for the LP module. It is demonstrated that intertwining the capabilities of dynamic SBO and efficient data mining allows to incorporate high-fidelity simulations in design cycle practices of certified engines or novel engine concepts to jointly optimize the multiple stages of the LPT.