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ISSN Online: 2379-1748

ISBN Flash Drive: 978-1-56700-517-2

5-6th Thermal and Fluids Engineering Conference (TFEC)
May, 26–28, 2021 , Virtual

Co-Optimization of Turbine Blade Aero and Thermal Designs Based on Computational Fluid Dynamics (CFD) Models

Get access (open in a dialog) pages 177-186
DOI: 10.1615/TFEC2021.cmd.036762

Abstract

In the present study we present a co-optimization strategy applied to turbine blade aero and thermal design analyses. A typical product design cycle of a turbine engine features several key stages, such as aero design, thermal design, mechanical design, life assessment, and cycle study, to name a few. Each design stage involves conducting analyses and tests focused on the goals of the respective design stages. Throughout the design process, several major milestones and design stages occur in a relatively serial fashion. Often, optimization analysis is performed within each design stage, focusing on the objectives of the current stage. Some potential drawbacks of this approach include lack of information sharing between different design stages and prolonged overall design time. Therefore, in the present study, we present a co-optimization strategy that allows aero and thermal design analyses to run in parallel, while optimizing both the efficiencies of the aero design and thermal design and reducing the maximum temperature spots in the thermal design. The co-optimization strategy is applied on a 2D version of the NASA/GE E3 turbine blade CFD model. The blade profile is parameterized following the Class Shape Transformation (CST) formulation method. The film coolant hole diameter and orientation parameters are also varied for the thermal design. Simcenter HEEDS optimization tool was used to develop the co-optimization workflow. Two sets of studies have been conducted; the results show that the co-optimization study found better Pareto front designs than the other study in which the aero and thermal optimizations were conducted sequentially. Conducting this study in 2D provides a proof of concept and statements regarding value proposition to the time savings and fidelity of the optimization approach.