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

Author

Listed:
  • 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
    Department of Industrial Engineering, Texas Tech University, Lubbock, TX 79409, USA)

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.

Suggested Citation

  • Mohammad Omidi & Shu-Jie Liu & Soheil Mohtaram & Hui-Tian Lu & Hong-Chao Zhang, 2019. "Improving Centrifugal Compressor Performance by Optimizing the Design of Impellers Using Genetic Algorithm and Computational Fluid Dynamics Methods," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5409-:d:272177
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    References listed on IDEAS

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    1. Pierre Jop & Yoël Forterre & Olivier Pouliquen, 2006. "A constitutive law for dense granular flows," Nature, Nature, vol. 441(7094), pages 727-730, June.
    2. Mustafa Kaya, 2019. "A CFD Based Application of Support Vector Regression to Determine the Optimum Smooth Twist for Wind Turbine Blades," Sustainability, MDPI, vol. 11(16), pages 1-25, August.
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    Cited by:

    1. Silvio Barbarelli & Vincenzo Pisano & Mario Amelio, 2022. "Development of a Predicting Model for Calculating the Geometry and the Characteristic Curves of Pumps Running as Turbines in Both Operating Modes," Energies, MDPI, vol. 15(7), pages 1-28, April.
    2. Marco Bicchi & Michele Marconcini & Ernani Fulvio Bellobuono & Elisabetta Belardini & Lorenzo Toni & Andrea Arnone, 2023. "Multi-Point Surrogate-Based Approach for Assessing Impacts of Geometric Variations on Centrifugal Compressor Performance," Energies, MDPI, vol. 16(4), pages 1-21, February.
    3. Rong Xie & Muyan Chen & Weihuang Liu & Hongfei Jian & Yanjun Shi, 2021. "Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
    4. Muhammad Saeed & Abdallah S. Berrouk & Burhani M. Burhani & Ahmed M. Alatyar & Yasser F. Al Wahedi, 2021. "Turbine Design and Optimization for a Supercritical CO 2 Cycle Using a Multifaceted Approach Based on Deep Neural Network," Energies, MDPI, vol. 14(22), pages 1-27, November.
    5. Wei Li & Jisheng Liu & Pengcheng Fang & Jinxin Cheng, 2021. "A Novel Surface Parameterization Method for Optimizing Radial Impeller Design in Fuel Cell System," Energies, MDPI, vol. 14(9), pages 1-25, May.
    6. Mamdouh Alshammari & Fuhaid Alshammari & Apostolos Pesyridis, 2019. "Electric Boosting and Energy Recovery Systems for Engine Downsizing," Energies, MDPI, vol. 12(24), pages 1-33, December.
    7. Aridi, Rima & Faraj, Jalal & Ali, Samer & Lemenand, Thierry & khaled, Mahmoud, 2022. "A comprehensive review on hybrid heat recovery systems: Classifications, applications, pros and cons, and new systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    8. Yunren Sui & Zengguang Sui & Guangda Liang & Wei Wu, 2023. "Superhydrophobic Microchannel Heat Exchanger for Electric Vehicle Heat Pump Performance Enhancement," Sustainability, MDPI, vol. 15(18), pages 1-20, September.

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