Efficient aerodynamic optimization of turbine blade profiles: an integrated approach with novel HDSPSO algorithm

Abstract

Purpose

This paper delves into the aerodynamic optimization of a single-stage axial turbine employed in aero-engines.

Design/methodology/approach

An efficient integrated design optimization approach tailored for turbine blade profiles is proposed. The approach combines a novel hierarchical dynamic switching PSO (HDSPSO) algorithm with a parametric modeling technique of turbine blades and high-fidelity Computational Fluid Dynamics (CFD) simulation analysis. The proposed HDSPSO algorithm introduces significant enhancements to the original PSO in three pivotal aspects: adaptive acceleration coefficients, distance-based dynamic neighborhood, and a switchable learning mechanism. The core idea behind these improvements is to incorporate the evolutionary state, strengthen interactions within the swarm, enrich update strategies for particles, and effectively prevent premature convergence while enhancing global search capability.

Findings

Mathematical experiments are conducted to compare the performance of HDSPSO with three other representative PSO variants. The results demonstrate that HDSPSO is a competitive intelligent algorithm with significant global search capabilities and rapid convergence speed. Subsequently, the HDSPSO-based integrated design optimization approach is applied to optimize the turbine blade profiles. The optimized turbine blades have a more uniform thickness distribution, an enhanced loading distribution, and a better flow condition. Importantly, these optimizations lead to a remarkable improvement in aerodynamic performance under both design and non-design working conditions.

Originality/value

These findings highlight the effectiveness and advancement of the HDSPSO-based integrated design optimization approach for turbine blade profiles in enhancing the overall aerodynamic performance. Furthermore, it confirms the great prospects of the innovative HDSPSO algorithm in tackling challenging tasks in practical engineering applications.

Citation

Yan, C., Kang, E., Liu, H., Li, H., Zeng, N. and You, Y. (2024), "Efficient aerodynamic optimization of turbine blade profiles: an integrated approach with novel HDSPSO algorithm", Multidiscipline Modeling in Materials and Structures, Vol. 20 No. 4, pp. 725-745. https://doi.org/10.1108/MMMS-02-2024-0051