IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i4p818-d1335906.html
   My bibliography  Save this article

Multi-Objective Optimization of the Microchannel Heat Sink Used for Combustor of the Gas Turbine

Author

Listed:
  • Xiaoming Zhang

    (Shaanxi Special Equipment Inspection and Testing Institute, Xi’an 710049, China)

  • Tao Yang

    (Shaanxi Special Equipment Inspection and Testing Institute, Xi’an 710049, China)

  • Zhenyuan Chang

    (Shaanxi Special Equipment Inspection and Testing Institute, Xi’an 710049, China)

  • Liang Xu

    (School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Lei Xi

    (School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Jianmin Gao

    (School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Penggang Zheng

    (Shaanxi Special Equipment Inspection and Testing Institute, Xi’an 710049, China)

  • Ran Xu

    (Shaanxi Special Equipment Inspection and Testing Institute, Xi’an 710049, China)

Abstract

This research presents a surrogate model and computational fluid dynamic analysis-based multi-objective optimization approach for microchannel heat sinks. The Non-dominated Sorting Genetic Algorithm is suggested to obtain the optimal solution set, and the Kriging model is employed to lower the simulation’s computational cost. The physical model consists of a coolant chamber, a mainstream chamber, and a solid board equipped with staggered Zigzag cooling channels. Five design variables are examined in relation to the geometric characteristics of the microchannel heat sinks: the length of inlet of the cooling channels, the width of the cooling channels, the length of the “zigzag”, the height of the cooling channels, and the periodic spanwise width. The optimal geometry is established by choosing the averaged cooling effectiveness and coolant mass flow rate which enters the mainstream chamber through the microchannel heat sinks as separate objectives. From the Pareto front, the optimal microchannel heat sinks structures are obtained. Three optimized results are studied, including the maximum cooling effectiveness, minimum coolant mass flow rate, and a compromise between the both objectives; a reference case using the median is compared as well. Numerical assessments corresponding to the four cases are performed, and the flow and cooling performance are compared. Furthermore, an analysis is conducted on the mechanisms that impact the ideal geometric parameters for cooling performance.

Suggested Citation

  • Xiaoming Zhang & Tao Yang & Zhenyuan Chang & Liang Xu & Lei Xi & Jianmin Gao & Penggang Zheng & Ran Xu, 2024. "Multi-Objective Optimization of the Microchannel Heat Sink Used for Combustor of the Gas Turbine," Energies, MDPI, vol. 17(4), pages 1-18, February.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:818-:d:1335906
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/4/818/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/4/818/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    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. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    2. Wang, Zequn & Wang, Pingfeng, 2015. "A double-loop adaptive sampling approach for sensitivity-free dynamic reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 346-356.
    3. Puppo, L. & Pedroni, N. & Maio, F. Di & Bersano, A. & Bertani, C. & Zio, E., 2021. "A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    5. Wen, Zhixun & Pei, Haiqing & Liu, Hai & Yue, Zhufeng, 2016. "A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 170-179.
    6. Gaspar, B. & Teixeira, A.P. & Guedes Soares, C., 2017. "Adaptive surrogate model with active refinement combining Kriging and a trust region method," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 277-291.
    7. Palar, Pramudita Satria & Zuhal, Lavi Rizki & Shimoyama, Koji, 2023. "Enhancing the explainability of regression-based polynomial chaos expansion by Shapley additive explanations," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    8. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2017. "Simulation-based exploration of high-dimensional system models for identifying unexpected events," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 317-330.
    9. Wu, Zeping & Wang, Donghui & Okolo N, Patrick & Hu, Fan & Zhang, Weihua, 2016. "Global sensitivity analysis using a Gaussian Radial Basis Function metamodel," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 171-179.
    10. Liang Pei & Chunhui Wang & Liying Sun & Lili Wang, 2022. "Temporal and Spatial Variation (2001–2020) Characteristics of Wind Speed in the Water Erosion Area of the Typical Black Soil Region, Northeast China," IJERPH, MDPI, vol. 19(17), pages 1-17, August.
    11. Strang, Kenneth David, 2012. "Importance of verifying queue model assumptions before planning with simulation software," European Journal of Operational Research, Elsevier, vol. 218(2), pages 493-504.
    12. Jesús Martínez-Frutos & David Herrero-Pérez, 2016. "Kriging-based infill sampling criterion for constraint handling in multi-objective optimization," Journal of Global Optimization, Springer, vol. 64(1), pages 97-115, January.
    13. Wu, Xu & Kozlowski, Tomasz & Meidani, Hadi, 2018. "Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 422-436.
    14. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.
    15. Mert Edali & Gönenç Yücel, 2020. "Analysis of an individual‐based influenza epidemic model using random forest metamodels and adaptive sequential sampling," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(6), pages 936-958, November.
    16. Cremona, Marzia A. & Liu, Binbin & Hu, Yang & Bruni, Stefano & Lewis, Roger, 2016. "Predicting railway wheel wear under uncertainty of wear coefficient, using universal kriging," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 49-59.
    17. J P C Kleijnen & W C M van Beers, 2013. "Monotonicity-preserving bootstrapped Kriging metamodels for expensive simulations," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(5), pages 708-717, May.
    18. Kleijnen, J.P.C. & van Beers, W.C.M. & van Nieuwenhuyse, I., 2008. "Constrained Optimization in Simulation : A Novel Approach," Discussion Paper 2008-95, Tilburg University, Center for Economic Research.
    19. Aikaterini P. Kyprioti & Alexandros A. Taflanidis & Norberto C. Nadal-Caraballo & Madison O. Campbell, 2021. "Incorporation of sea level rise in storm surge surrogate modeling," 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. 105(1), pages 531-563, January.
    20. Kleijnen, Jack P.C., 2017. "Regression and Kriging metamodels with their experimental designs in simulation: A review," European Journal of Operational Research, Elsevier, vol. 256(1), pages 1-16.

    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:17:y:2024:i:4:p:818-:d:1335906. 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.