Improving Centrifugal Compressor Performance by Optimizing the Design of Impellers Using Genetic Algorithm and Computational Fluid Dynamics Methods

Mohammad Omidi, Shu-Jie Liu, Soheil Mohtaram, Hui-Tian Lu and Hong-Chao Zhang
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Mohammad Omidi: School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
Shu-Jie Liu: School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
Soheil Mohtaram: Institute of Soft Matter Mechanics, College of Mechanics and Materials, Hohai University, Nanjing 210098, China
Hui-Tian Lu: Department of Construction & Operations Management, South Dakota State University, Brookings, SD 57007, USA
Hong-Chao Zhang: School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China

Sustainability, 2019, vol. 11, issue 19, 1-18

Abstract: It has always been important to study the development and improvement of the design of turbomachines, owing to the numerous uses of turbomachines and their high energy consumption. Accordingly, optimizing turbomachine performance is crucial for sustainable development. The design of impellers significantly affects the performance of centrifugal compressors. Numerous models and design methods proposed for this subject area, however, old and based on the 1D scheme. The present article developed a hybrid optimization model based on genetic algorithms (GA) and a 3D simulation of compressors to examine the certain parameters such as blade angle at leading and trailing edges and the starting point of splitter blades. New impeller design is proposed to optimize the base compressor. The contribution of this paper includes the automatic creation of generations for achieving the optimal design and designing splitter blades using a novel method. The present study concludes with presenting a new, more efficient, and stable design.

Keywords: genetic algorithm (GA); optimization; computational fluid dynamics (CFD); centrifugal compressor (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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