Heat transfer and fluid flow analysis of pebble bed and its verification with artificial neural network

被引:5
|
作者
Sedani, Chirag [1 ]
Chaudhuri, Paritosh
Gupta, Manoj Kumar
机构
[1] Inst Plasma Res, Gandhinagar 382428, Gujarat, India
关键词
EFFECTIVE THERMAL-CONDUCTIVITY; SIMULATIONS; REACTORS;
D O I
10.1016/j.nme.2023.101439
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
The advancement of sophisticated packed beds has significant implications for the development of new equipment for associated industries. Determining the heat transfer and fluid flow properties of the functional material in the form of a pebble bed is crucial during the design phase of a solid-type ceramic breeding blanket in a fusion reactor. In order to efficiently construct and operate the breeder blanket, the goal of this study is to explore the features of heat transmission and fluid flow. Initially, the heat transfer and fluid flow analyses were carried out independently to benchmark the results using models and experiments using a stainless steel pebble bed with a diameter of 2 mm. Following that, a combined simulation analysis of heat transfer and fluid flow was carried out to demonstrate the system's effective operation for Li2TiO3. A model of an artificial neural network (ANN) has also been employed to forecast the results. The results of simulations are within 5% of the expected values made using ANN.
引用
收藏
页数:7
相关论文
共 50 条
  • [21] Effect of pebble diameters on the heat transfer characteristics of a structured pebble bed in an HTGR
    Chen, Leisheng
    Lee, Jaeyoung
    ENERGY, 2020, 212
  • [22] Numerical simulation of the fluoride salt flow and heat transfer in the pebble bed under pulsating flow conditions
    Zhang, Jiaming
    Liu, Limin
    Liu, Ziying
    Cong, Tenglong
    Gu, Hanyang
    APPLIED THERMAL ENGINEERING, 2025, 269
  • [23] Effect of coupling of heat transfer with fluid flow on cross-flow heat transfer in fixed bed
    Liu, Yu-Lan
    Xu, Zhi-Gang
    Chen, Jian-Chun
    Zhu, Zi-Bin
    Xie, Zai-Ku
    Lu, Wen-Kui
    Huadong Ligong Daxue Xuebao /Journal of East China University of Science and Technology, 2003, 29 (06):
  • [24] Heat transfer and flow analysis for square tube with oscillating electromagnetic field with experimental data by artificial neural network
    Vengsungnle, P.
    Naphon, N.
    Poojeera, S.
    Jongpluempiti, J.
    Srichat, A.
    Eiamsa-ard, S.
    Naphon, P.
    COGENT ENGINEERING, 2024, 11 (01):
  • [25] Develop artificial neural network numerical modeling to study fluid flow and heat transfer of dispersed nanoparticles through base liquid
    Alkanhal, Tawfeeq Abdullah
    INTERNATIONAL JOURNAL OF NUMERICAL METHODS FOR HEAT & FLUID FLOW, 2021, 31 (08) : 2733 - 2753
  • [26] Estimation of the heat transfer coefficient in a liquid-solid fluidized bed using an artificial neural network
    Manickaraj, J.
    Balasubramanian, N.
    ADVANCED POWDER TECHNOLOGY, 2008, 19 (02) : 119 - 130
  • [27] Heat transport in entropy-optimized flow of viscoelastic fluid due to Riga plate: analysis of artificial neural network
    Raja, M. Asif Zahoor
    Shoaib, M.
    El-Zahar, Essam Roshdy
    Hussain, Saddiqa
    Li, Yong-Min
    Khan, M. Ijaz
    Islam, Saeed
    Malik, M. Y.
    WAVES IN RANDOM AND COMPLEX MEDIA, 2022,
  • [28] Experimental investigation of flow and convective heat transfer on a high-Prandtl-number fluid through the nuclear reactor pebble bed core
    Liu, Limin
    Zhang, Dalin
    Li, Linfeng
    Yang, Yicheng
    Wang, Chenglong
    Qiu, Suizheng
    Su, G. H.
    APPLIED THERMAL ENGINEERING, 2018, 145 : 48 - 57
  • [29] Analysis on heat transfer and flow performance of pebble-bed fuel in three regular arrangements with multi-layers
    Wang, Zhichao
    Lu, Daogang
    Cao, Qiong
    Li, Zhen
    Cao, Feng
    PROGRESS IN NUCLEAR ENERGY, 2024, 175
  • [30] Experimental analysis of flow and convective heat transfer in the water-cooled packed pebble bed nuclear reactor core
    Liu, Limin
    Deng, Jian
    Zhang, Dalin
    Wang, Chenglong
    Qiu, Suizheng
    Su, G. H.
    PROGRESS IN NUCLEAR ENERGY, 2020, 122