化工学报 ›› 2021, Vol. 72 ›› Issue (9): 4649-4657.doi: 10.11949/0438-1157.20210156

• 流体力学与传递现象 • 上一篇    下一篇

基于GA-BP神经网络的超临界CO2传热特性预测研究

颜建国1(),郑书闽1,郭鹏程1(),张博2,毛振凯2   

  1. 1.西安理工大学西北旱区生态水利国家重点实验室,陕西 西安 710048
    2.中国电建集团西北勘测设计研究院有限公司,陕西 西安 710065
  • 收稿日期:2021-01-25 修回日期:2021-07-08 出版日期:2021-09-05 发布日期:2021-09-05
  • 通讯作者: 郭鹏程 E-mail:jgyan@xaut.edu.cn;guoyicheng@xaut.edu.cn
  • 作者简介:颜建国(1987—),男,博士,副教授,jgyan@xaut.edu.cn
  • 基金资助:
    国家自然科学基金项目(51839010);陕西省教育厅科研计划项目(21JY029);陕西高校青年科技创新团队项目(2020-29);清洁能源与生态水利工程研究中心项目(QNZX-2019-05)

Prediction of heat transfer characteristics for supercritical CO2 based on GA-BP neural network

Jianguo YAN1(),Shumin ZHENG1,Pengcheng GUO1(),Bo ZHANG2,Zhenkai MAO2   

  1. 1.State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an 710048, Shaanxi, China
    2.Power China Northwest Engineering Corporation Limited, Xi’an 710065, Shaanxi, China
  • Received:2021-01-25 Revised:2021-07-08 Published:2021-09-05 Online:2021-09-05
  • Contact: Pengcheng GUO E-mail:jgyan@xaut.edu.cn;guoyicheng@xaut.edu.cn

摘要:

超临界二氧化碳(S-CO2)动力循环在能源利用领域中拥有广阔的应用前景,其中超临界CO2的传热特性对其能量转换效率至关重要。开展了超临界CO2在水平小圆管内对流传热实验研究,并通过建立遗传算法优化的BP神经网络模型(GA-BP),对其在不同工况下的传热特性进行预测分析。实验参数范围:系统压力7.5~9.5 MPa,质量流速1100~2100 kg/(m2?s),热通量120~560 kW/m2。实验结果表明,超临界CO2传热系数随流体温度的升高先增大后减小,在拟临界温度附近达到最大值。GA-BP神经网络模型能有效地预测超临界CO2的传热系数,预测数据的决定系数R2为0.99662,超过95%的数据误差位于±10%范围内,平均误差为3.55%,为超临界流体传热预测提供新的思路。

关键词: 超临界二氧化碳, 对流, 传热, GA-BP神经网络, 传热预测

Abstract:

Supercritical carbon dioxide (S-CO2) power cycle has a broad application prospect in the field of energy utilization, in which the heat transfer of supercritical CO2 plays a significant role in its energy conversion efficiency. Therefore, an experiment has been conducted to determine the convective heat transfer characteristics of supercritical CO2 flowing in a mini horizontal circular tube, and a BP neural network optimized by genetic algorithm has been established to predict the heat transfer coefficient of supercritical CO2 under different conditions. The experimental parameters are as follows: system pressure 7.5—9.5 MPa, mass flux 1100—2100 kg/(m2?s), heat flux 120—560 kW/m2. The experimental results show that the heat transfer coefficient of supercritical CO2 increases first and then decreases with increasing fluid temperature, and reaches maximum near the pseudo-critical temperature. The model of the GA-BP neural network can effectively predict the heat transfer coefficient of supercritical CO2, the determinate coefficient of predicted data R2 is 0.99662, and more than 95% of the data are within the error range of ±10%, the average error is 3.55%. GA-BP neural network model provides a novel idea for heat transfer prediction for supercritical fluids.

Key words: supercritical carbon dioxide, convection, heat transfer, GA-BP neural network, prediction of heat transfer

中图分类号: 

  • TK 124

图1

超临界CO2流动传热实验系统"

图2

实验段示意图"

表1

实验工况"

实验参数数值
系统压力p/ MPa7.5,8.5,9.5
质量流速G/( kg/(m2?s))1100,1600,2100
实验段热通量q/( kW/m2)120,340,560
进口温度Tin/℃20~60

表2

参数不确定度"

实验参数不确定度/%
压力/ MPa0.14
流体温度/℃0.5
壁面温度/℃0.4
质量流速/(kg/(m2?s))0.75
热通量/(kW/m2)4.63
传热系数/(W/(m2·K))5.20

图3

BP神经网络示意图"

图4

GA-BP神经网络程序流程"

图5

神经网络预测值与实验值的对比"

图6

GA-BP神经网络预测误差"

图7

典型工况下的传热特性及其预测结果"

图8

不同压力下的传热特性"

图9

不同质量流速下的传热特性"

图10

不同热通量下传热特性"

图11

预测数据与独立工况实验数据对比"

图12

预测数据与文献中的实验数据对比"

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