IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v88y2011i12p4441-4450.html
   My bibliography  Save this article

Optimization design of slotted fin by numerical simulation coupled with genetic algorithm

Author

Listed:
  • Wang, Yu
  • He, Ya-Ling
  • Mei, Dan-Hua
  • Tao, Wen-Quan

Abstract

Using a novel method that couples genetic algorithm (GA) with numerical simulation, the geometric configuration for a two-dimensional slotted fin has been optimized in this paper. The objective of optimization is to maximize the heat transfer capacity of slotted fin, and minimize the pressure drop penalty of fluid flow through the fin. The key of this method is the fitness function of GA, which were (j/j0)/(f/f0) and j/j0. In this complex multiparameter problem, the numerical simulation is a crucial step to calculate the Colburn factor j and friction factor f. The results showed that for two-dimensional slotted fin considered, the j factor is increased by 229.22%, the f factor is increased by 196.30%, and the j/f ratio was increased by 11.11% at Re=500 based on optimal integrated performance (j/j0)/(f/f0); the j factor is increased by 479.08% at Re=500 based on optimal heat exchange capacity j/j0. The feasibility of optimal designs was verified by the field synergy principle.

Suggested Citation

  • Wang, Yu & He, Ya-Ling & Mei, Dan-Hua & Tao, Wen-Quan, 2011. "Optimization design of slotted fin by numerical simulation coupled with genetic algorithm," Applied Energy, Elsevier, vol. 88(12), pages 4441-4450.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:12:p:4441-4450
    DOI: 10.1016/j.apenergy.2011.05.030
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261911003254
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2011.05.030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sanaye, Sepehr & Hajabdollahi, Hassan, 2010. "Thermal-economic multi-objective optimization of plate fin heat exchanger using genetic algorithm," Applied Energy, Elsevier, vol. 87(6), pages 1893-1902, June.
    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. Zhao, Xiaohuan & E, Jiaqiang & Zhang, Zhiqing & Chen, Jingwei & Liao, Gaoliang & Zhang, Feng & Leng, Erwei & Han, Dandan & Hu, Wenyu, 2020. "A review on heat enhancement in thermal energy conversion and management using Field Synergy Principle," Applied Energy, Elsevier, vol. 257(C).
    2. Liu, X.P. & Niu, J.L., 2014. "An optimal design analysis method for heat recovery devices in building applications," Applied Energy, Elsevier, vol. 129(C), pages 364-372.
    3. Maakala, Viljami & Järvinen, Mika & Vuorinen, Ville, 2018. "Optimizing the heat transfer performance of the recovery boiler superheaters using simulated annealing, surrogate modeling, and computational fluid dynamics," Energy, Elsevier, vol. 160(C), pages 361-377.
    4. Pourfattah, Farzad & Sabzpooshani, Majid, 2021. "On the thermal management of a power electronics system: Optimization of the cooling system using genetic algorithm and response surface method," Energy, Elsevier, vol. 232(C).
    5. Wang, Limin & Deng, Lei & Ji, Chenglong & Liang, Erkai & Wang, Changxia & Che, Defu, 2016. "Multi-objective optimization of geometrical parameters of corrugated-undulated heat transfer surfaces," Applied Energy, Elsevier, vol. 174(C), pages 25-36.

    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. De Bellis, Fabio & Catalano, Luciano A., 2012. "CFD optimization of an immersed particle heat exchanger," Applied Energy, Elsevier, vol. 97(C), pages 841-848.
    2. Wang, Zhe & Li, Yanzhong, 2016. "A combined method for surface selection and layer pattern optimization of a multistream plate-fin heat exchanger," Applied Energy, Elsevier, vol. 165(C), pages 815-827.
    3. Li, You-Rong & Du, Mei-Tang & Wu, Shuang-Ying & Peng, Lan & Liu, Chao, 2012. "Exergoeconomic analysis and optimization of a condenser for a binary mixture of vapors in organic Rankine cycle," Energy, Elsevier, vol. 40(1), pages 341-347.
    4. Jaroslaw Krzywanski, 2019. "A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods," Energies, MDPI, vol. 12(23), pages 1-32, November.
    5. Chantrelle, Fanny Pernodet & Lahmidi, Hicham & Keilholz, Werner & Mankibi, Mohamed El & Michel, Pierre, 2011. "Development of a multicriteria tool for optimizing the renovation of buildings," Applied Energy, Elsevier, vol. 88(4), pages 1386-1394, April.
    6. Reine, Alexandre & Bou Nader, Wissam, 2019. "Fuel consumption potential of different external combustion gas-turbine thermodynamic configurations for extended range electric vehicles," Energy, Elsevier, vol. 175(C), pages 900-913.
    7. Wei-Hsin Chen & Yi-Wei Li & Min-Hsing Chang & Chih-Che Chueh & Veeramuthu Ashokkumar & Lip Huat Saw, 2022. "Operation and Multi-Objective Design Optimization of a Plate Heat Exchanger with Zigzag Flow Channel Geometry," Energies, MDPI, vol. 15(21), pages 1-22, November.
    8. Li, Ming & Cao, Sunliang & Zhu, Xiaolin & Xu, Yang, 2022. "Techno-economic analysis of the transition towards the large-scale hybrid wind-tidal supported coastal zero-energy communities," Applied Energy, Elsevier, vol. 316(C).
    9. Sanaye, Sepehr & Dehghandokht, Masoud, 2011. "Modeling and multi-objective optimization of parallel flow condenser using evolutionary algorithm," Applied Energy, Elsevier, vol. 88(5), pages 1568-1577, May.
    10. Wang, Kun & He, Ya-Ling & Xue, Xiao-Dai & Du, Bao-Cun, 2017. "Multi-objective optimization of the aiming strategy for the solar power tower with a cavity receiver by using the non-dominated sorting genetic algorithm," Applied Energy, Elsevier, vol. 205(C), pages 399-416.
    11. Wang, Limin & Deng, Lei & Ji, Chenglong & Liang, Erkai & Wang, Changxia & Che, Defu, 2016. "Multi-objective optimization of geometrical parameters of corrugated-undulated heat transfer surfaces," Applied Energy, Elsevier, vol. 174(C), pages 25-36.
    12. Srikanth, R. & Nemani, Pavan & Balaji, C., 2015. "Multi-objective geometric optimization of a PCM based matrix type composite heat sink," Applied Energy, Elsevier, vol. 156(C), pages 703-714.
    13. Yonathan Heredia-Aricapa & Juan M. Belman-Flores & Jorge A. Soria-Alcaraz & Vicente Pérez-García & Francisco Elizalde-Blancas & Jorge A. Alfaro-Ayala & José Ramírez-Minguela, 2022. "Multi-Objective Optimization of a Multilayer Wire-on-Tube Condenser: Case Study R134a, R600a, and R513A," Energies, MDPI, vol. 15(17), pages 1-14, August.
    14. He, Senyu & Yin, Jianhua & Zhang, Bin & Wang, Zhaohua, 2018. "How to upgrade an enterprise’s low-carbon technologies under a carbon tax: The trade-off between tax and upgrade fee," Applied Energy, Elsevier, vol. 227(C), pages 564-573.
    15. Wang, Kun & Li, Ming-Jia & Guo, Jia-Qi & Li, Peiwen & Liu, Zhan-Bin, 2018. "A systematic comparison of different S-CO2 Brayton cycle layouts based on multi-objective optimization for applications in solar power tower plants," Applied Energy, Elsevier, vol. 212(C), pages 109-121.
    16. Dizaji, Hamed Sadighi & Pourhedayat, Samira & Aldawi, Fayez & Moria, Hazim & Anqi, Ali E. & Jarad, Fahd, 2022. "Proposing an innovative and explicit economic criterion for all passive heat transfer enhancement techniques of heat exchangers," Energy, Elsevier, vol. 239(PC).
    17. Cheng, Xianda & Zheng, Haoran & Dong, Wei & Yang, Xuesen, 2023. "Performance prediction of marine intercooled cycle gas turbine based on expanded similarity parameters," Energy, Elsevier, vol. 265(C).
    18. Zhang, Chengyu & Gümmer, Volker, 2020. "Multi-objective optimization and system evaluation of recuperated helicopter turboshaft engines," Energy, Elsevier, vol. 191(C).
    19. Ganjehkaviri, A. & Mohd Jaafar, M.N. & Hosseini, S.E. & Barzegaravval, H., 2017. "Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction," Energy, Elsevier, vol. 119(C), pages 167-177.
    20. Yin, Qian & Du, Wen-Jing & Ji, Xing-Lin & Cheng, Lin, 2016. "Optimization design and economic analyses of heat recovery exchangers on rotary kilns," Applied Energy, Elsevier, vol. 180(C), pages 743-756.

    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:eee:appene:v:88:y:2011:i:12:p:4441-4450. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.