物理
两相流
管(容器)
机械
振动
相(物质)
流量(数学)
声学
机械工程
量子力学
工程类
作者
Junzhe Zhao,Jin Wang,Maocheng Tian,Zheng Zhang
摘要
Gas–liquid two-phase flow frequently induces pipe vibrations when passing through bends, valves, tees, and similar structures due to inherent flow instability. Understanding the fluid–structure interaction characteristics between the fluid and U-tube under varying flow patterns is crucial for monitoring pipeline stability. This study establishes a gas–liquid two-phase flow-induced vibration test system of U-tubes, which can simultaneously measure the pressure and vibration acceleration signals at the inlet and outlet under different flow patterns. The research aims to elucidate the characteristics of vibration response induced by two-phase flow in the U-tube under the full flow pattern, and to investigate the variation laws of vibration response and differential pressure fluctuation under different flow patterns. The findings demonstrate that inlet and outlet differential pressure fluctuation characteristics and amplitude variation patterns correlate strongly with flow pattern transitions. In particular, U-tube elbows generate substantial impact vibrations in stratified plug flow and plug-bubble flow. Acceleration signals in the x-, y-, and z-directions reveal that the U-tube vibration mode primarily comprises a superposition of first-order and second-order modes. Probability density function statistics indicate that during the stratified-plug flow to slug-wavy flow pattern transition, impact vibration characteristics are most pronounced, with higher stability distribution fitting accuracy. The annular-dispersed flow demonstrates the least non-Gaussian behavior, resulting in higher normal distribution fitting accuracy. Gas–liquid two-phase flow-induced vibrations exhibit characteristic multifractal structures. Gas plug presence causes significant vibrational fractal structure unevenness, which decreases as the gas plug dissipates. Multifractal spectrum parameters serve as feature parameters for accurate flow pattern identification and classification.
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