IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i10p3569-d814765.html
   My bibliography  Save this article

Computational Optimization of Free-Piston Stirling Engine by Variable-Step Simplified Conjugate Gradient Method with Compatible Strategies

Author

Listed:
  • Chin-Hsiang Cheng

    (Department of Aeronautics and Astronautics, National Cheng Kung University, No.1, University Road, Tainan 70101, Taiwan)

  • Yu-Ting Lin

    (Department of Aeronautics and Astronautics, National Cheng Kung University, No.1, University Road, Tainan 70101, Taiwan)

Abstract

This study aimed at the development of an algorithm for the computational optimization of free-piston Stirling engines. The design algorithm includes an optimization method and two compatible strategies. The optimization method is an improved version of traditional conjugate gradient method and is named the variable-step simplified conjugate gradient method (VSCGM). The free-piston Stirling engine is operable only in narrow-bounded parameter regions. Using the present approach, the operable variable combinations can be found efficiently. Two compatible strategies, the wake-up and backward-comparison strategies, are integrated with the VSCGM. The present design algorithm can handle multiple-parameter optimization with more flexible objective function definitions. Meanwhile, it features faster convergence as compared with the traditional conjugate gradient methods. Moreover, the feasibility of the VSCGM and the two compatible strategies is demonstrated in two test cases. It was found that the present approach can optimize the ten designed variables simultaneously, and the optimal designs can be yielded in a finite number of iterations. The results show that the inoperable initial designs were successfully optimized to reach a high power output.

Suggested Citation

  • Chin-Hsiang Cheng & Yu-Ting Lin, 2022. "Computational Optimization of Free-Piston Stirling Engine by Variable-Step Simplified Conjugate Gradient Method with Compatible Strategies," Energies, MDPI, vol. 15(10), pages 1-16, May.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3569-:d:814765
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/10/3569/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/10/3569/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chin-Hsiang Cheng & Yu-Ting Lin, 2020. "Optimization of a Stirling Engine by Variable-Step Simplified Conjugate-Gradient Method and Neural Network Training Algorithm," Energies, MDPI, vol. 13(19), pages 1-18, October.
    2. Cheng, Chin-Hsiang & Huang, Yu-Xian & King, Shun-Chih & Lee, Chun-I & Leu, Chih-Hsing, 2014. "CFD (computational fluid dynamics)-based optimal design of a micro-reformer by integrating computational a fluid dynamics code using a simplified conjugate-gradient method," Energy, Elsevier, vol. 70(C), pages 355-365.
    3. Kongtragool, Bancha & Wongwises, Somchai, 2003. "A review of solar-powered Stirling engines and low temperature differential Stirling engines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 7(2), pages 131-154, April.
    4. Huang, Yu-Xian & Wang, Xiao-Dong & Cheng, Chin-Hsiang & Lin, David Ta-Wei, 2013. "Geometry optimization of thermoelectric coolers using simplified conjugate-gradient method," Energy, Elsevier, vol. 59(C), pages 689-697.
    5. Zare, Sh. & Tavakolpour-Saleh, A.R., 2016. "Frequency-based design of a free piston Stirling engine using genetic algorithm," Energy, Elsevier, vol. 109(C), pages 466-480.
    6. Jörg Fliege & Benar Fux Svaiter, 2000. "Steepest descent methods for multicriteria optimization," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 51(3), pages 479-494, August.
    Full references (including those not matched with items on IDEAS)

    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. Chin-Hsiang Cheng & Yu-Ting Lin, 2020. "Optimization of a Stirling Engine by Variable-Step Simplified Conjugate-Gradient Method and Neural Network Training Algorithm," Energies, MDPI, vol. 13(19), pages 1-18, October.
    2. Chin-Hsiang Cheng & Yi-Han Tan, 2020. "Numerical Optimization of a Four-Cylinder Double-Acting Stirling Engine Based on Non-Ideal Adiabatic Thermodynamic Model and SCGM Method," Energies, MDPI, vol. 13(8), pages 1-19, April.
    3. Masoumi, A.P. & Tavakolpour-Saleh, A.R. & Rahideh, A., 2020. "Applying a genetic-fuzzy control scheme to an active free piston Stirling engine: Design and experiment," Applied Energy, Elsevier, vol. 268(C).
    4. Zhu, Shunmin & Yu, Guoyao & Liang, Kun & Dai, Wei & Luo, Ercang, 2021. "A review of Stirling-engine-based combined heat and power technology," Applied Energy, Elsevier, vol. 294(C).
    5. Kazemi, Abolghasem & Moreno, Jovita & Iribarren, Diego, 2023. "Economic optimization and comparative environmental assessment of natural gas combined cycle power plants with CO2 capture," Energy, Elsevier, vol. 277(C).
    6. Wang, Xiao-Dong & Wang, Qiu-Hong & Xu, Jin-Liang, 2014. "Performance analysis of two-stage TECs (thermoelectric coolers) using a three-dimensional heat-electricity coupled model," Energy, Elsevier, vol. 65(C), pages 419-429.
    7. Ellen H. Fukuda & L. M. Graña Drummond & Fernanda M. P. Raupp, 2016. "An external penalty-type method for multicriteria," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(2), pages 493-513, July.
    8. Karabulut, Halit & Yücesu, Hüseyin Serdar & ÇInar, Can & Aksoy, Fatih, 2009. "An experimental study on the development of a [beta]-type Stirling engine for low and moderate temperature heat sources," Applied Energy, Elsevier, vol. 86(1), pages 68-73, January.
    9. Thai Chuong, 2013. "Newton-like methods for efficient solutions in vector optimization," Computational Optimization and Applications, Springer, vol. 54(3), pages 495-516, April.
    10. Zare, Shahryar & Tavakolpour-Saleh, Alireza & Shourangiz-Haghighi, Alireza & Binazadeh, Tahereh, 2019. "Assessment of damping coefficients ranges in design of a free piston Stirling engine: Simulation and experiment," Energy, Elsevier, vol. 185(C), pages 633-643.
    11. Morovati, Vahid & Pourkarimi, Latif, 2019. "Extension of Zoutendijk method for solving constrained multiobjective optimization problems," European Journal of Operational Research, Elsevier, vol. 273(1), pages 44-57.
    12. Hadžiselimović, Miralem & Srpčič, Gregor & Brinovar, Iztok & Praunseis, Zdravko & Seme, Sebastijan & Štumberger, Bojan, 2019. "A novel concept of linear oscillatory synchronous generator designed for a stirling engine," Energy, Elsevier, vol. 180(C), pages 19-27.
    13. Konstantin Sonntag & Bennet Gebken & Georg Müller & Sebastian Peitz & Stefan Volkwein, 2024. "A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces," Journal of Optimization Theory and Applications, Springer, vol. 203(1), pages 455-487, October.
    14. Miglierina, E. & Molho, E. & Recchioni, M.C., 2008. "Box-constrained multi-objective optimization: A gradient-like method without "a priori" scalarization," European Journal of Operational Research, Elsevier, vol. 188(3), pages 662-682, August.
    15. Loau Al-Bahrani & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski, 2021. "Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations," Sustainability, MDPI, vol. 13(3), pages 1-26, January.
    16. Hu, Luoke & Peng, Chen & Evans, Steve & Peng, Tao & Liu, Ying & Tang, Renzhong & Tiwari, Ashutosh, 2017. "Minimising the machining energy consumption of a machine tool by sequencing the features of a part," Energy, Elsevier, vol. 121(C), pages 292-305.
    17. Cheng, Chin-Hsiang & Yu, Ying-Ju, 2012. "Combining dynamic and thermodynamic models for dynamic simulation of a beta-type Stirling engine with rhombic-drive mechanism," Renewable Energy, Elsevier, vol. 37(1), pages 161-173.
    18. Gaoju Xia & Huadong Zhao & Jingshuang Zhang & Haonan Yang & Bo Feng & Qi Zhang & Xiaohui Song, 2021. "Study on Performance of the Thermoelectric Cooling Device with Novel Subchannel Finned Heat Sink," Energies, MDPI, vol. 15(1), pages 1-14, December.
    19. Erik Alex Papa Quiroz & Nancy Baygorrea Cusihuallpa & Nelson Maculan, 2020. "Inexact Proximal Point Methods for Multiobjective Quasiconvex Minimization on Hadamard Manifolds," Journal of Optimization Theory and Applications, Springer, vol. 186(3), pages 879-898, September.
    20. Kely D. V. Villacorta & Paulo R. Oliveira & Antoine Soubeyran, 2014. "A Trust-Region Method for Unconstrained Multiobjective Problems with Applications in Satisficing Processes," Journal of Optimization Theory and Applications, Springer, vol. 160(3), pages 865-889, March.

    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:jeners:v:15:y:2022:i:10:p:3569-:d:814765. 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.