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Parametric optimization of H-type finned tube with longitudinal vortex generators by response surface model and genetic algorithm

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  • Tang, Song-Zhen
  • Wang, Fei-Long
  • He, Ya-Ling
  • Yu, Yang
  • Tong, Zi-Xiang

Abstract

Low-low temperature electrostatic precipitator technology is one of the important ways for energy saving and emission reduction of coal-fired power plants. In order to further improve the dust removal efficiency and enhance waste heat recovery performance, a novel H-type finned elliptical tube heat exchanger with longitudinal vortex generators is proposed. To achieve the maximum heat transfer enhancement with the minimum friction factor augmentation, the response surface model and multi-objective genetic algorithm are adopted to optimize the design parameters. Firstly, combined with the finite volume method and the central composite design method, the second-order response surface model between the design parameters (the length, height, angle, and position of longitudinal vortex generators) and the objective functions (Nusselt number and friction factor) is established. Then, based on the response surface model, the Pareto optimal solution set is obtained by the multi-objective genetic algorithm. Finally, by comprehensively comparing Nusselt number, friction factor and performance evaluation criteria of Pareto optimal solutions, the optimal combination is determined. Compared with the H-type finned tube heat exchanger, the performance evaluation criteria of the optimized novel heat exchanger is improved by 48–55%, which contributes to improve the overall performance of low-low temperature electrostatic precipitator system. The findings of this paper may provide practical guidelines for researchers and designers to develop efficient heat exchangers.

Suggested Citation

  • Tang, Song-Zhen & Wang, Fei-Long & He, Ya-Ling & Yu, Yang & Tong, Zi-Xiang, 2019. "Parametric optimization of H-type finned tube with longitudinal vortex generators by response surface model and genetic algorithm," Applied Energy, Elsevier, vol. 239(C), pages 908-918.
  • Handle: RePEc:eee:appene:v:239:y:2019:i:c:p:908-918
    DOI: 10.1016/j.apenergy.2019.01.122
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    References listed on IDEAS

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    Cited by:

    1. Wang, Fei-Long & He, Ya-Ling & Tang, Song-Zhen & Kulacki, Francis A. & Tao, Yu-Bing, 2019. "Multi-objective optimization of a dual-layer granular filter for hot gas clean-up by using genetic algorithm," Applied Energy, Elsevier, vol. 248(C), pages 463-474.

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