高浓度水煤浆管道气膜减阻两相流模拟及代理辅助优化

doi:10.11949/0438-1157.20230674

摘要/Abstract

摘要：

为解决高浓度水煤浆管道输送阻力大的问题，提出在水煤浆管道中通入气体，采用气膜减阻以降低管道的阻力损失。针对宾汉非牛顿流体水煤浆，基于流体体积（VOF）多相流模型，通过数值模拟研究分析了加气管关键参数对管道阻力系数的影响，并结合遗传聚合响应面模型与二次拉格朗日非线性规划（NLPQL）算法进行代理辅助优化。结果表明：在水煤浆管道中通入气体能够有效降低壁面剪切应力，气体速度与加气管直径对管道阻力系数影响较为显著，并且提升气体速度与增大加气管直径能够降低阻力系数，而管道阻力系数基本不受加气管道角度影响。优化以阻力系数最小化为目标函数，得出一组加气管参数，优化后管道阻力系数降低了0.0207，减阻率增加了16.90%，研究结果为高浓度水煤浆管道加气减阻机理及结构优化提供了理论指导。

关键词: 管输煤浆, 气膜减阻, 非牛顿流体, 数值模拟, 代理辅助优化, 两相流

Abstract:

To solve the problem of high-concentration coal-water slurry (CWS) pipeline transportation with large resistance, it was proposed to inject gas into the coal-water slurry pipeline and adopt gas film to reduce the resistance loss of the pipeline. For Bingham non-Newtonian fluid CWS, based on the volume of fluid (VOF) multiphase flow model, the effects of the key parameters of the gas pipe on the drag coefficient were analyzed through numerical simulation, and the influence of genetic aggregation response surface model and nonlinear programming by quadratic Lagrangian (NLPQL) algorithm were combined to perform surrogate-assisted optimization. The results show that the gas pumping inlet into the CWS pipeline can effectively reduce the wall shear stress, the gas velocity and the diameter of the gas pipe have a significant effect on the pipeline resistance coefficient. In addition, increasing the gas velocity and the diameter of the gas pipe can reduce the resistance coefficient, while the pipeline resistance coefficient is almost unaffected by the angle of the gas pipe. Taking the minimization of the resistance coefficient as the objective function, a set of gas pipe parameters is obtained. After optimization the resistance coefficient of the pipeline decreased by 0.0207, and the drag reduction rate is improved by 16.90%. The research results provide theoretical guidance for the mechanism and structural optimization of gas injection for drag reduction in high-concentration CWS pipelines.

Key words: coal-water slurry for pipe transportation, gas film drag reduction, non-Newtonian fluids, numerical simulation, surrogate-assisted optimization, two-phase flow

中图分类号:

TQ 536

何松, 刘乔迈, 谢广烁, 王斯民, 肖娟. 高浓度水煤浆管道气膜减阻两相流模拟及代理辅助优化[J]. 化工学报, 2023, 74(9): 3766-3774.

Song HE, Qiaomai LIU, Guangshuo XIE, Simin WANG, Juan XIAO. Two-phase flow simulation and surrogate-assisted optimization of gas film drag reduction in high-concentration coal-water slurry pipeline[J]. CIESC Journal, 2023, 74(9): 3766-3774.

图/表 11

图1 几何模型

Fig.1 Geometric model

图2 网格无关性验证

Fig.2 Verification of grid

图3 实验与数值模拟结果对比

Fig.3 Comparison of experiment and simulation results

图4 优化设计点

Fig.4 Optimization design points

图5 优化流程图

Fig.5 Optimization flowchart

图6 壁面气体体积分数分布云图

Fig.6 Wall air volume fraction contours

图7 壁面剪切应力分布云图

Fig.7 Wall shear contours

图8 不同加气管直径下的阻力系数随气体速度的变化曲线

Fig.8 Variation of resistance coefficient with gas velocity under different gas pipe diameters

图9 不同气体速度下的阻力系数随加气角度的变化曲线

Fig.9 Variation of resistance coefficient with air inlet angle under different gas velocities

图10 局部敏感性分析

Fig.10 Local sensitivity analysis

表1 优化结果

Table 1 Optimization results

结构	气体速度/ (m·s^-1)	加气管直径/mm	加气角度/(°)	阻力系数	减阻率/%	误差/%
原始结构	2.5	30	20	0.0921	24.30	—
CFD验证	2.5	30	20	0.0920	24.20	0.10
优化结构	3.5	40	90	0.0714	41.20	—
CFD验证	3.5	40	90	0.0715	41.15	0.08

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