Cheng Yan, Enzi Kang, Haonan Liu, Han Li, Nianyin Zeng and Yancheng You
This paper delves into the aerodynamic optimization of a single-stage axial turbine employed in aero-engines.
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.
Details
Keywords
Nianyin Zeng, Hong Zhang, Yanping Chen, Binqiang Chen and Yurong Liu
This paper aims to present a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for path planning of intelligent robot…
Abstract
Purpose
This paper aims to present a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for path planning of intelligent robot when having obstacles in the environment.
Design/methodology/approach
The three-dimensional path surface of the intelligent robot is decomposed into a two-dimensional plane and the height information in z axis. Then, the grid method is exploited for the environment modeling problem. After that, a recently proposed switching local evolutionary PSO (SLEPSO) based on non-homogeneous Markov chain and DE is analyzed for the path planning problem. The velocity updating equation of the presented SLEPSO algorithm jumps from one mode to another based on the non-homogeneous Markov chain, which can overcome the contradiction between local and global search. In addition, DE mutation and crossover operations can enhance the capability of finding a better global best particle in the PSO method.
Findings
Finally, the SLEPSO algorithm is successfully applied to the path planning in two different environments. Comparing with some well-known PSO algorithms, the experiment results show the feasibility and effectiveness of the presented method.
Originality/value
Therefore, this can provide a new method for the area of path planning of intelligent robot.