Design and analysis of a novel multi-sectioned compound parabolic concentrator with multi-objective genetic algorithm

被引:23
|
作者
Xu, Jintao [1 ,2 ]
Chen, Fei [1 ,2 ]
Deng, Chenggang [1 ]
机构
[1] Kunming Univ Sci & Technol, Fac Chem Engn, Kunming 650500, Yunnan, Peoples R China
[2] Kunming Univ Sci & Technol, Inst Solar Energy Engn, Kunming 650500, Yunnan, Peoples R China
基金
中国国家自然科学基金;
关键词
Solar energy; Compound parabolic concentrator; Structure design; Genetic algorithm; Energy flux distribution; SOLAR; PERFORMANCE; EFFICIENCY; COLLECTOR;
D O I
10.1016/j.energy.2021.120216
中图分类号
O414.1 [热力学];
学科分类号
摘要
Currently, the high curvature curved reflective structure of standard compound parabolic concentrator (S-CPC) is expensive to manufacture, and the energy distribution on its absorber is extremely nonuniform. To solve these two problems simultaneously, a multi-objective optimization design method of multi-sectioned compound parabolic concentrator (M-CPC) based on the genetic algorithm is proposed, and a ray-path control experiment is conducted to verify the reliability of the calculation program. Several characteristics of the optimized concentrators and the basic model are studied comparatively. The energy distributions of different concentrators are visually displayed, the application value of M-CPC is discussed and the recommended design parameters with different types of absorbers are given. The results show that the obtained M-CPCs can significantly improve the uniformity of energy distribution on the absorber. In particular, the M-CPC with five reflective planes on the single side could reduce the average energy non-uniformity factor from 2.23 to 0.92, and the peak energy density on the absorber surface could be reduced from 46002.67W/m(2) to 2851.00W/m(2). The range of the initial acceptance angle and the working time are extended, and the problem of sudden cooling and heating on the absorber could be alleviated. (C) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Design optimization and optical performance analysis on multi-sectioned compound parabolic concentrator with plane absorber
    Li, Yongcai
    Jiao, Feng
    Chen, Fei
    Zhang, Zhenhua
    RENEWABLE ENERGY, 2021, 168 : 913 - 926
  • [2] An optimization design method and optical performance analysis on multi-sectioned compound parabolic concentrator with cylindrical absorber
    Xu, Jintao
    Chen, Fei
    Xia, Entong
    Gao, Chong
    Deng, Chenggang
    ENERGY, 2020, 197
  • [3] Model construction and optical properties investigation for multi-sectioned compound parabolic concentrator with particle swarm optimization
    Hu, Xin
    Chen, Fei
    Zhang, Zhenhua
    RENEWABLE ENERGY, 2021, 179 : 379 - 394
  • [4] A Novel Multi-Objective Genetic Algorithm for Clustering
    Kirkland, Oliver
    Rayward-Smith, Victor J.
    de la Iglesia, Beatriz
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2011, 2011, 6936 : 317 - 326
  • [5] A Novel Multi-Objective Electromagnetic Analysis Based on Genetic Algorithm
    Sun, Shaofei
    Zhang, Hongxin
    Dong, Liang
    Cui, Xiaotong
    Cheng, Weijun
    Khan, Muhammad Saad
    SENSORS, 2019, 19 (24)
  • [6] A micro multi-objective genetic algorithm for multi-objective optimizations
    Liu, G. P.
    Han, X.
    CJK-OSM 4: THE FOURTH CHINA-JAPAN-KOREA JOINT SYMPOSIUM ON OPTIMIZATION OF STRUCTURAL AND MECHANICAL SYSTEMS, 2006, : 419 - 424
  • [7] Design analysis of polymer filtration using a multi-objective genetic algorithm
    Fowler, K. R.
    Jenkins, E. W.
    Cox, C. L.
    McClune, B.
    Seyfzadeh, B.
    SEPARATION SCIENCE AND TECHNOLOGY, 2008, 43 (04) : 710 - 726
  • [8] Multi-objective design of reliable systems by genetic algorithm
    Echtle, K.
    Eusgeld, I.
    Hirsch, D.
    SAFETY AND RELIABILITY FOR MANAGING RISK, VOLS 1-3, 2006, : 1625 - +
  • [9] A multi-objective grouping genetic algorithm for modular design
    Tseng, Hwai-En
    Chang, Chien-Cheng
    Lee, Shih-Chen
    Li, Tzu-Hui
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2023, 237 (03) : 377 - 391
  • [10] Multi-objective Genetic Algorithm for Interior Lighting Design
    Plebe, Alice
    Pavone, Mario
    MACHINE LEARNING, OPTIMIZATION, AND BIG DATA, MOD 2017, 2018, 10710 : 222 - 233