IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i19p5409-d272177.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/19/5409/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/19/5409/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    6. 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.
    7. 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.
    8. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Khattri, Khim B. & Pudasaini, Shiva P., 2019. "Channel flow simulation of a mixture with a full-dimensional generalized quasi two-phase model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 165(C), pages 280-305.
    2. Cheng, Biyi & Yao, Yingxue, 2023. "Machine learning based surrogate model to analyze wind tunnel experiment data of Darrieus wind turbines," Energy, Elsevier, vol. 278(PA).
    3. Junnan Zhao & Xinyao Guo & Guodong Liu & Rui Wang & Huilin Lu, 2022. "A Review of the Continuum Theory-Based Stress and Drag Models in Gas-Solid Flows," Energies, MDPI, vol. 16(1), pages 1-22, December.
    4. Ji, Baifeng & Zhong, Kuanwei & Xiong, Qian & Qiu, Penghui & Zhang, Xu & Wang, Liang, 2022. "CFD simulations of aerodynamic characteristics for the three-blade NREL Phase VI wind turbine model," Energy, Elsevier, vol. 249(C).
    5. Samuel R. Wilson-Whitford & Jinghui Gao & Maria Chiara Roffin & William E. Buckley & James F. Gilchrist, 2023. "Microrollers flow uphill as granular media," Nature Communications, Nature, vol. 14(1), pages 1-6, December.
    6. Gianluca Martelloni & Franco Bagnoli, 2014. "Infiltration effects on a two-dimensional molecular dynamics model of landslides," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(1), pages 37-62, August.
    7. Matthias Rauter & Sylvain Viroulet & Sigríður Sif Gylfadóttir & Wolfgang Fellin & Finn Løvholt, 2022. "Granular porous landslide tsunami modelling – the 2014 Lake Askja flank collapse," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    8. Yi Liu & Zhaosheng Yu & Jiecheng Yang & Carl Wassgren & Jennifer Sinclair Curtis & Yu Guo, 2020. "Discrete Element Method Investigation of Binary Granular Flows with Different Particle Shapes," Energies, MDPI, vol. 13(7), pages 1-25, April.
    9. Juan Reyes, 2012. "Optimization of mixed variational inequalities arising in flow of viscoplastic materials," Computational Optimization and Applications, Springer, vol. 52(3), pages 757-784, July.
    10. Lhuillier, Daniel, 2007. "Constitutive relations for steady flows of dense granular liquids," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(2), pages 267-275.
    11. Rui Li & Yuliang Teng, 2022. "An improved DebrisInterMixingFoam for debris flow simulation: numerical investigation and application," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(3), pages 1925-1947, September.
    12. Mohammed Debbache & Messaoud Hazmoune & Semcheddine Derfouf & Dana-Alexandra Ciupageanu & Gheorghe Lazaroiu, 2021. "Wind Blade Twist Correction for Enhanced Annual Energy Production of Wind Turbines," Sustainability, MDPI, vol. 13(12), pages 1-17, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5409-:d:272177. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.