Genetic particle swarm parallel algorithm analysis of optimization arrangement on mistuned blades. Issue 12 (2nd December 2017)

• Record Type: Journal Article
• Authors: Zhao, Tianyu; Yuan, Huiqun; Yang, Wenjun; Sun, Huagang
• Is Part Of: Engineering optimization. Volume 49:Issue 12(2017)
• Page Start: 2095
• Page End: 2116
• Publication Date: 2017-12-02
• Subjects: Mistuning blade arrangement -- mistuned parameter identification -- genetic particle swarm optimization algorithm -- GPU -- CUDA; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2017.1292422
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: This article introduces a method of mistuned parameter identification which consists of static frequency testing of blades, dichotomy and finite element analysis. A lumped parameter model of an engine bladed-disc system is then set up. A bladed arrangement optimization method, namely the genetic particle swarm optimization algorithm, is presented. It consists of a discrete particle swarm optimization and a genetic algorithm. From this, the local and global search ability is introduced. CUDA-based co-evolution particle swarm optimization, using a graphics processing unit, is presented and its performance is analysed. The results show that using optimization results can reduce the amplitude and localization of the forced vibration response of a bladed-disc system, while optimization based on the CUDA framework can improve the computing speed. This method could provide support for engineering applications in terms of effectiveness and efficiency.