Prediction of S-CO<sub>2</sub> flow resistance characteristics based on Bayesian-regularization BP neural network

WU Quan; ZHU Meng; CHEN Lei; QING Haoran; ZHOU Lingang; XU Kai; SU Sheng; HU Song; LIU Hui; XIANG Jun

doi:10.19666/j.rlfd.202112259

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Prediction of S-CO₂ flow resistance characteristics based on Bayesian-regularization BP neural network

更新时间：2025-09-28

- Prediction of S-CO₂ flow resistance characteristics based on Bayesian-regularization BP neural network
- Thermal Power Generation Vol. 51, Issue 6, Pages: 50-58(2022)
- 作者机构：
  
  1. 国家能源集团新能源技术研究院有限公司
  2. 华中科技大学煤燃烧国家重点实验室
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202112259
  CLC： TP183;TM611.3
- Published：2022
- 稿件说明：
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WU Quan, ZHU Meng, CHEN Lei, et al. Prediction of S-CO₂ flow resistance characteristics based on Bayesian-regularization BP neural network[J]. Thermal power generation, 2022, 51(6): 50-58.
DOI：

WU Quan, ZHU Meng, CHEN Lei, et al. Prediction of S-CO₂ flow resistance characteristics based on Bayesian-regularization BP neural network[J]. Thermal power generation, 2022, 51(6): 50-58. DOI： 10.19666/j.rlfd.202112259.

摘要

超临界二氧化碳（S-CO_2）流动摩擦阻力特性尚不明晰，现有阻力预测公式泛化能力不强。对S-CO_2在垂直上升光滑圆管中进行湍流流动的阻力特性进行了实验研究，系统分析了不同质量流速、压力和热流密度对S-CO_2流动摩擦阻力的影响，实验参数范围为质量流速750～2 200 kg/(m2·s)，流体压力10～20 MPa，热流密度200～340 kW/m2以及主流温度60～500℃（远离拟临界点）。实验结果表明：S-CO_2流动摩擦阻力随热流密度的增加而减小，这有利于S-CO_2发电的大规模工程应用；S-CO_2流动摩擦阻力随压力的增加而减小，主要原因在于压力增加导致CO_2流体密度增加。根据实验数据，构建了贝叶斯正则化BP神经网络模型，实现了对S-CO_2流动摩擦阻力的有效预测。预测值与实验值的相关系数R=0.998 6，超过98%的预测值均在10%的误差范围内，表明该模型拟合精度高，具有较好的泛化能力。

Abstract

The flow resistance characteristics of supercritical carbon dioxide(S-CO_2) are still unclear

and the generalization ability of existing resistance prediction formula is poor. The resistance characteristics of S-CO_2turbulent flow in a vertically ascending smooth circular tube were experimentally studied

and the effects of mass flow rate

pressure and heat flux on friction resistance of S-CO_2 were systematically analyzed. The experimental parameters' range are: 750～2 200 kg/(m2·s) of mass flow rate

10～20 MPa of fluid pressure

200～340 kW/m2 of heat flux and 60～500 ℃ of bulk temperature(away from the pseudo critical point). The experimental results show that

the friction resistance of S-CO_2 decreases with the increase of heat flux

which is beneficial to the engineering application of S-CO_2 power generation. The flow friction resistance of S-CO_2 decreases with the increase of pressure

which is mainly because the fluid density increases with the pressure. Furthermore

a Bayesianregularization BP neural network model was established to predict the flow friction resistance of S-CO_2 effectively.The correlation coefficient R between the predicted values and the experimental values is 0.998 6

and more than 98% of the predicted values are within the error range of 10%

indicating that the model has good generalization ability and high fitting accuracy.

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Prediction of S-CO2 flow resistance characteristics based on Bayesian-regularization BP neural network

DOI：10.19666/j.rlfd.202112259

摘要

Abstract

关键词

Keywords

references

Prediction of S-CO₂ flow resistance characteristics based on Bayesian-regularization BP neural network