Genetic particle swarm parallel algorithm analysis of optimization arrangement on mistuned blades |
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Authors: | Tianyu Zhao Wenjun Yang Huagang Sun |
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Affiliation: | 1. School of Mechanical Engineering &2. Automation, Northeastern University, Shenyang, PR China;3. Mechanical Technical Research Institute, Shijiazhuang, PR China |
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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. |
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Keywords: | Mistuning blade arrangement mistuned parameter identification genetic particle swarm optimization algorithm GPU CUDA |
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