Prediction of void fraction in gas-liquid two-phase flow under fluctuating vibration in horizontal and vertical pipes based on WOA-CNN-GRU-ATT

doi:10.11949/0438-1157.20250581

Abstract

Abstract:

Floating offshore nuclear power plant is considered a promising solution to address marine energy shortages. However, their oscillatory motion under wind and wave conditions causes fluctuations in the void fraction of gas-liquid two-phase flow within steam generators, posing challenges to operational safety. To overcome the limited applicability of existing models for calculating cross-sectional void fraction under fluctuating vibration, this study proposes a novel prediction model integrating convolutional neural networks (CNN), gated recurrent units (GRU), attention mechanisms, and the whale optimization algorithm (WOA). Experimental results demonstrate that the proposed model has good applicability for predicting void fractions in both horizontal and vertical upward pipes under fluctuating vibration. Additionally, the model shows robust performance across varying flow patterns. This research provides a new method for accurate void fraction prediction in fluctuating gas-liquid two-phase flows, offering theoretical support for design and operational optimization of steam generators of floating nuclear power plant.

Key words: gas-liquid two-phase flow, gas holdup, neural networks, fluctuating vibration, optimization algorithm

摘要：

海上漂浮核电站被认为是解决海洋能源短缺问题的有效措施之一，但在风浪的作用下处于运动状态，导致蒸汽发生器内气液两相流截面含气率变化，影响运行安全。针对起伏振动气液两相流截面含气率计算模型适用性有限的问题，基于卷积神经网络、门控循环单元、注意力机制和鲸鱼算法建立了起伏振动气液两相流截面含气率预测模型。预测结果表明，该模型对起伏振动水平和垂直上升管截面含气率有良好的适用性，且对流型具有良好的鲁棒性。研究结果为起伏振动下气液两相流截面含气率的精准预测提供了新的方法，为海上核电站蒸汽发生器的参数设计提供理论参考。

关键词: 气液两相流, 气含率, 神经网络, 起伏振动, 优化算法

CLC Number:

TL 334

Qichao LIU, Shibo ZHANG, Yuqing LI, Yunlong ZHOU, Yiwen RAN. Prediction of void fraction in gas-liquid two-phase flow under fluctuating vibration in horizontal and vertical pipes based on WOA-CNN-GRU-ATT[J]. CIESC Journal, 2025, 76(11): 5900-5910.

刘起超, 张世博, 李昱庆, 周云龙, 冉议文. 基于WOA-CNN-GRU-ATT的起伏振动水平和垂直管气液两相流截面含气率预测[J]. 化工学报, 2025, 76(11): 5900-5910.

Figures/Tables 11

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测量参数	相对不确定度
水流量	1%~6.667%
空气流量	0.556%~5%
截面含气率	0.47%~7.155%

测量参数	相对不确定度
水流量	1%~6.667%
空气流量	0.556%~5%
截面含气率	0.47%~7.155%

模型	RMSE	MAPE/%	R²
BPNN	0.0976	17.67	0.7465
SVM	0.0737	13.47	0.8552
CNN-GRT-ATT	0.0389	7.17	0.9676
WOA-C-G-A	0.0368	6.61	0.9709

模型	RMSE	MAPE/%	R²
BPNN	0.0976	17.67	0.7465
SVM	0.0737	13.47	0.8552
CNN-GRT-ATT	0.0389	7.17	0.9676
WOA-C-G-A	0.0368	6.61	0.9709

振频/Hz	振幅/m	气相折算速度/(m/s)	液相折算速度/(m/s)	截面含气率实验值/%	截面含气率预测值/%	相对误差/%
4	0.025	0.5	1	16.05	26.82	67.08
4	0.075	1	0.1	48.27	52.25	8.23
0	0	1	0.1	56.03	52.16	-6.90
4	0.025	1	0.5	46.60	42.39	-9.04