液固两相流物理场重构及数值模拟

The reconstruction of the physical field and numerical simulation of the liquid-solid two-phase flow

摘要

为了实现流动状态的实时获取,提出一种基于动态模态分解算法的液固两相流流动特征快速重构方法,基于搭建的液固两相流流型测试实验台,在拍摄流型时同步采集速度、压降等数据,将采集的数据作为边界条件,并通过数值模拟软件Fluent获取直管道不同阶段不同时刻的颗粒体积分数场、固相速度场、滑移速度场和两相壁面剪切应力,利用动态模态分解算法获取数据背后的模态,并用少量模态实现物理场的快速重构。结果表明:不同阶段的重构效果不同,匀速阶段的重构效果明显优于减速阶段的重构效果;动态模态分解算法适用于非线性较弱的情况,需对该算法进行改进以适应强非线性现象。

Abstract

In order to achieve the real-time acquisition of the flow state, a method for rapidly reconstructing the flow characteristics of liquid-solid two-phase flow based on the Dynamic Mode Decomposition Algorithm is proposed. Based on the built test rig for the flow patterns of liquid-solid two-phase flow, operating data such as velocity and pressure drop are synchronously collected while photographing the flow patterns. Using the collected data as boundary conditions, and based on the numerical simulation software Fluent, the particle volume fraction field, solid-phase velocity field, slip velocity field, and two-phase wall shear stress at different times in different stages of a straight pipeline are obtained. The Dynamic Mode Decomposition Algorithm is used to extract dominant modes from the collected data, and a small number of modes are used to achieve the rapid reconstruction of the physical field. The reconstruction results show that the reconstruction accuracy varies across different flow stages. The reconstruction effect in the uniform velocity stage is significantly better than that in the deceleration stage. The dynamic mode decomposition algorithm is suitable for cases with weak nonlinearity, and this algorithm needs to be improved to adapt to strong nonlinear phenomena.

关键词

动态模态分解 / 流体力学 / 数值模拟 / 液固两相流 / 流型

Key words

Dynamic Mode Decomposition / fluid mechanics / numerical simulation / liquid-solid two-phase flow / flow pattern

参考文献

[1] Kauffeld M, Wang MJ, Goldstein V, et al. Ice slurry applications[J]. International Journal of Refrigeration—revue Internationale Du Froid, 2010, 33(8): 1491-505.

[2] Giro RA, Bernasconi G, Giunta G, et al. Tagging and tracking oil—gas mixtures in multiphase pipelines[J]. Journal of Petroleum Science and Engineering, 2022, 218: 110982.

[3] 史雪薇, 谭超, 董峰 . 基于环形电导传感器的气液两相流流型识别与过程参数测量[J]. 化工进展, 2024, 43(2): 637-648.

[4] 张丝雨,

[5] Henry Miao, 吴浩达, 等 . 油井多相流计量技术研究进展[J]. 数码设计, 2017, 6(2): 21-27.

[6] 李熙, 陈可欣, 朱彦霖, 等 . 立式螺旋管内气—液两相流动参数测量与流型研究[J]. 工程热物理学报, 2024, 45(9): 2687-2695.

[7] 张文彪, 王港华, 邵丁, 等 . 基于多尺度熵分析的CO₂气液两相流流型识别 [J]. 计量学报, 2024, 45(7): 1024-1030.

[8] 张艳勇, 陈宝明, 李佳阳, 等 . 基于孔隙尺度的多孔骨架对固液相变的影响[J]. 山东建筑大学学报, 2019, 34(6): 56-62.

[9] 崔云杰, 陈宝明, 云和明, 等 . 梯度多孔介质腔体内固液相变过程数值研究[J]. 山东建筑大学学报, 2023, 38(5): 65-73.

[10] 张浩, 王艳静, 徐茜荣, 等 . 圆管内高聚物湍流减阻流动特性研究[J]. 山东建筑大学学报, 2023, 38(2): 40-48.

[11] 王萌, 张松, 施艳艳, 等 . 基于混合神经网络参数优化的两相流流型识别方法[J]. 河南师范大学学报(自然科学版), 2025, 53(3): 121-127.

[12] 王莹, 沈洋, 戚二帅, 等 . 基于深度学习的水下射流流型识别[J]. 江苏大学学报(自然科学版), 2023, 44(4): 437-443.

[13] 杨海潮, 胡红利, 陆程程, 等 . 采用多任务学习和电容层析成像的两相流参数测量方法[J]. 西安交通大学学报, 2023, 57(3): 202-211.

[14] Wang JH, Wang SG, Zhang TF, et al. Numerical and analytical investigation of ice slurry isothermal flow through horizontal bends[J]. International Journal of Refrigeration, 2018, 92: 37-54.

[15] 苗森春, 刘乐琪, 王晓晖, 等 . 双吸泵作液力透平叶轮内非定常流动DMD分析[J]. 农业机械学报, 2024, 55(6): 150-158.

[16] 欧玉琼, 刘琪麟, 赖焕新 . 中低雷诺数可压缩冲击射流的流动与传热特性[J]. 华东理工大学学报(自然科学版), 2025, 51(3): 419-427.

[17] 冯志鹏, 熊夫睿, 赵燮霖, 等 . 基于动态模态分解方法的正方形排列管束流体弹性不稳定性研究[J]. 核动力工程, 2023, 44(增刊2): 104-108.

[18] Spurin C, Armstrong RT, Mcclure J, et al. Dynamic mode decomposition for analysing multi—phase flow in porous media[J]. Advances in Water Resources, 2023, 175: 104423.

[19] Gidaspow D . Multiphase flow and fluidization: continuum and kinetic theory descriptions[M]. New York: Academic Press, 1994.

[20] Ekambara K, Sanders RS, Nandakumar K, et al. Hydrodynamic simulation of horizontal slurry pipeline flow using ANSYS—CFX[J]. Industrial & Engineering Chemistry Research, 2009, 48(17): 8159-8171.

[21] Schmid PJ . Dynamic mode decomposition of numerical and experimental data[J]. Journal of Fluid Mechanics, 2010, 656: 5-28.

[22] Zhao XT, Shen X, Geng LL, et al. Comparative study on the wake dynamics of pump—jet and ducted propeller based on dynamic mode decomposition[J]. Physics of Fluids, 2023, 35(11): 115135.

[23] Celik IB, Ghia U, Roache PJ, et al. Procedure for estimation and reporting of uncertainty due to discretization in CFD applications[J]. Journal of Fluids Engineering—Transactions of the Asme, 2008, 130(7): 078001.

[24] Wang XD, Sun LJ . Anti—circulant dynamic mode decomposition with sparsity—promoting for highway traffic dynamics analysis[J]. Transportation Research Part C: Emerging Technologies, 2023, 153: 104178.

基金资助

国家自然科学基金项目(52378090)