IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v251y2019ic37.html
   My bibliography  Save this article

An analytical flux density distribution model with a closed-form expression for a flat heliostat

Author

Listed:
  • He, Caitou
  • Duan, Xiaoyue
  • Zhao, Yuhong
  • Feng, Jieqing

Abstract

Predicting the flux density distribution on the receiver surface is of significance for designing and deploying a central receiver system. In this paper, an analytical model with a closed-form expression is presented to accurately describe the flux density distribution that a flat heliostat reflects on the receiver plane. The flux spot is modeled as a two dimensional convolution between a uniform light flux density distribution over the heliostat effective reflection surface and a two dimensional quasi-Cauchy kernel. The convolution is solved analytically as a closed-form expression. The proposed model takes into account the sunlight direction, sun shape, heliostat position, size, orientation, slope error, and shadowing and blocking effects, etc. Extensive experiments and comparisons were conducted, and it shows that the proposed model is more accurate than the prevalent elliptical Gaussian model, in terms of total power and flux density distribution. Due to its closed-form expression, the proposed model can also be efficiently evaluated on a contemporary graphics processing unit to predict the flux spot of a heliostat within 2.8 ms. Thus this model has promising potential in the practical optimization applications.

Suggested Citation

  • He, Caitou & Duan, Xiaoyue & Zhao, Yuhong & Feng, Jieqing, 2019. "An analytical flux density distribution model with a closed-form expression for a flat heliostat," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
  • Handle: RePEc:eee:appene:v:251:y:2019:i:c:37
    DOI: 10.1016/j.apenergy.2019.113310
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919309729
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2019.113310?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. Cruz, N.C. & Salhi, S. & Redondo, J.L. & Álvarez, J.D. & Berenguel, M. & Ortigosa, P.M., 2018. "Hector, a new methodology for continuous and pattern-free heliostat field optimization," Applied Energy, Elsevier, vol. 225(C), pages 1123-1131.
    2. Huang, Weidong & Sun, Lulening, 2016. "Solar flux density calculation for a heliostat with an elliptical Gaussian distribution source," Applied Energy, Elsevier, vol. 182(C), pages 434-441.
    3. 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.
    4. Elsayed, M.M. & Fathalah, K.A., 1994. "Solar flux density distribution using a separation of variables/superposition technique," Renewable Energy, Elsevier, vol. 4(1), pages 77-87.
    5. Roldán, M.I. & Monterreal, R., 2014. "Heat flux and temperature prediction on a volumetric receiver installed in a solar furnace," Applied Energy, Elsevier, vol. 120(C), pages 65-74.
    6. Avila-Marin, Antonio L. & Fernandez-Reche, Jesus & Tellez, Felix M., 2013. "Evaluation of the potential of central receiver solar power plants: Configuration, optimization and trends," Applied Energy, Elsevier, vol. 112(C), pages 274-288.
    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. Song, Jifeng & Yang, Genben & Wang, Haiyu & Niu, Yisen & Hou, Hongjuan & Su, Ying & Wang, Qian & Zou, Zubing, 2022. "Influence of sunshape and optical error on spillover of concentrated flux in solar thermal power tower plant," Energy, Elsevier, vol. 256(C).
    2. Georgios E. Arnaoutakis & Dimitris Al. Katsaprakakis, 2021. "Concentrating Solar Power Advances in Geometric Optics, Materials and System Integration," Energies, MDPI, vol. 14(19), pages 1-25, September.
    3. He, Caitou & Zhao, Yuhong & Feng, Jieqing, 2019. "An improved flux density distribution model for a flat heliostat (iHFLCAL) compared with HFLCAL," Energy, Elsevier, vol. 189(C).
    4. He, Caitou & Zhao, Hanli & He, Qi & Zhao, Yuhong & Feng, Jieqing, 2021. "Analytical radiative flux model via convolution integral and image plane mapping," Energy, Elsevier, vol. 222(C).
    5. Lin, Xiaoxia & He, Caitou & Huang, Wenjun & Zhao, Yuhong & Feng, Jieqing, 2022. "GPU-based Monte Carlo ray tracing simulation considering refraction for central receiver system," Renewable Energy, Elsevier, vol. 193(C), pages 367-382.

    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. He, Caitou & Zhao, Hanli & He, Qi & Zhao, Yuhong & Feng, Jieqing, 2021. "Analytical radiative flux model via convolution integral and image plane mapping," Energy, Elsevier, vol. 222(C).
    2. Zeng, Zhichen & Ni, Dong & Xiao, Gang, 2022. "Real-time heliostat field aiming strategy optimization based on reinforcement learning," Applied Energy, Elsevier, vol. 307(C).
    3. Huang, Weidong & Yu, Liang & Hu, Peng, 2019. "An analytical solution for the solar flux density produced by a round focusing heliostat," Renewable Energy, Elsevier, vol. 134(C), pages 306-320.
    4. Huang, Weidong & Sun, Lulening, 2016. "Solar flux density calculation for a heliostat with an elliptical Gaussian distribution source," Applied Energy, Elsevier, vol. 182(C), pages 434-441.
    5. Lin, Xiaoxia & He, Caitou & Huang, Wenjun & Zhao, Yuhong & Feng, Jieqing, 2022. "GPU-based Monte Carlo ray tracing simulation considering refraction for central receiver system," Renewable Energy, Elsevier, vol. 193(C), pages 367-382.
    6. Ashley, Thomas & Carrizosa, Emilio & Fernández-Cara, Enrique, 2019. "Heliostat field cleaning scheduling for Solar Power Tower plants: A heuristic approach," Applied Energy, Elsevier, vol. 235(C), pages 653-660.
    7. 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.
    8. Bo Yang & Mohammad Mohsen Sarafraz & Maziar Arjomandi, 2021. "Thermal Performance Characteristics of a Microchannel Gas Heater for Solar Heating Applications," Energies, MDPI, vol. 14(22), pages 1-14, November.
    9. Mostafavi Tehrani, S. Saeed & Taylor, Robert A., 2016. "Off-design simulation and performance of molten salt cavity receivers in solar tower plants under realistic operational modes and control strategies," Applied Energy, Elsevier, vol. 179(C), pages 698-715.
    10. Avila-Marin, Antonio L. & Fernandez-Reche, Jesus & Carballo, Jose Antonio & Carra, Maria Elena & Gianella, Sandro & Ferrari, Luca & Sanchez-Señoran, Daniel, 2022. "CFD analysis of the performance impact of geometrical shape on volumetric absorbers in a standard cup," Renewable Energy, Elsevier, vol. 201(P1), pages 256-272.
    11. Arrif, Toufik & Hassani, Samir & Guermoui, Mawloud & Sánchez-González, A. & A.Taylor, Robert & Belaid, Abdelfetah, 2022. "GA-GOA hybrid algorithm and comparative study of different metaheuristic population-based algorithms for solar tower heliostat field design," Renewable Energy, Elsevier, vol. 192(C), pages 745-758.
    12. Wang, Kun & He, Ya-Ling & Qiu, Yu & Zhang, Yuwen, 2016. "A novel integrated simulation approach couples MCRT and Gebhart methods to simulate solar radiation transfer in a solar power tower system with a cavity receiver," Renewable Energy, Elsevier, vol. 89(C), pages 93-107.
    13. Guo, Jia-Qi & Li, Ming-Jia & Xu, Jin-Liang & Yan, Jun-Jie & Wang, Kun, 2019. "Thermodynamic performance analysis of different supercritical Brayton cycles using CO2-based binary mixtures in the molten salt solar power tower systems," Energy, Elsevier, vol. 173(C), pages 785-798.
    14. Huang, Weidong & Yu, Liang, 2018. "Development of a new flux density function for a focusing heliostat," Energy, Elsevier, vol. 151(C), pages 358-375.
    15. Laslett, Dean & Carter, Craig & Creagh, Chris & Jennings, Philip, 2017. "A large-scale renewable electricity supply system by 2030: Solar, wind, energy efficiency, storage and inertia for the South West Interconnected System (SWIS) in Western Australia," Renewable Energy, Elsevier, vol. 113(C), pages 713-731.
    16. Wang, Kun & He, Ya-Ling & Zhu, Han-Hui, 2017. "Integration between supercritical CO2 Brayton cycles and molten salt solar power towers: A review and a comprehensive comparison of different cycle layouts," Applied Energy, Elsevier, vol. 195(C), pages 819-836.
    17. Meybodi, Mehdi Aghaei & Beath, Andrew C., 2016. "Impact of cost uncertainties and solar data variations on the economics of central receiver solar power plants: An Australian case study," Renewable Energy, Elsevier, vol. 93(C), pages 510-524.
    18. Conroy, Tim & Collins, Maurice N. & Fisher, James & Grimes, Ronan, 2018. "Thermal and mechanical analysis of a sodium-cooled solar receiver operating under a novel heliostat aiming point strategy," Applied Energy, Elsevier, vol. 230(C), pages 590-614.
    19. Ma, Zhao & Li, Ming-Jia & Zhang, K. Max & Yuan, Fan, 2021. "Novel designs of hybrid thermal energy storage system and operation strategies for concentrated solar power plant," Energy, Elsevier, vol. 216(C).
    20. Du, Shen & Tong, Zi-Xiang & Zhang, Hong-Hu & He, Ya-Ling, 2019. "Tomography-based determination of Nusselt number correlation for the porous volumetric solar receiver with different geometrical parameters," Renewable Energy, Elsevier, vol. 135(C), pages 711-718.

    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:251:y:2019:i:c:37. 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.