A Genetic Algorithm Based Multi-Objective Optimization of Squealer Tip Geometry in Axial Flow Turbines: A Constant Tip Gap Approach

Maral H., Senel C. B., Deveci K., ALPMAN E., Kavurmacıoğlu L. A., Camci C.

JOURNAL OF FLUIDS ENGINEERING-TRANSACTIONS OF THE ASME, vol.142, no.2, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 142 Issue: 2
  • Publication Date: 2020
  • Doi Number: 10.1115/1.4044721
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, Metadex, Civil Engineering Abstracts
  • Keywords: multi-objective optimization, artificial neural networks, genetic algorithm, tip leakage flow, squealer tip, axial turbine, ARTIFICIAL NEURAL-NETWORK, LEAKAGE FLOW, BLADE TIP, HEAT-TRANSFER, SIMULATION, PERFORMANCE, DESIGN
  • Istanbul Technical University Affiliated: Yes


Tip clearance is a crucial aspect of turbomachines in terms of aerodynamic and thermal performance. A gap between the blade tip surface and the stationary casing must be maintained to allow the relative motion of the blade. The leakage flow through the tip gap measurably reduces turbine performance and causes high thermal loads near the blade tip region. Several studies focused on the tip leakage flow to clarify the flow-physics in the past. The "squealer" design is one of the most common designs to reduce the adverse effects of tip leakage flow. In this paper, a genetic-algorithm-based optimization approach was applied to the conventional squealer tip design to enhance aerothermal performance. A multi-objective optimization method integrated with a meta-model was utilized to determine the optimum squealer geometry. Squealer height and width represent the design parameters which are aimed to be optimized. The objective functions for the genetic-algorithm-based optimization are the total pressure loss coefficient and Nusselt number calculated over the blade tip surface. The initial database is then enlarged iteratively using a coarse-to-fine approach to improve the prediction capability of the meta-models used. The procedure ends once the prediction errors are smaller than a prescribed level. This study indicates that squealer height and width have complex effects on the aerothermal performance, and optimization study allows to determine the optimum squealer dimensions.