Hydrodynamic characteristics of gas/liquid/fiber three-phase flows based on objective and minimally-intrusive pressure fluctuation measurements

Fibers;Flow regime identification;Three phase flow;Artificial neural networks;Pressure fluctuation;Power spectral density;Multiphase flow;Neural networks (Computer science)

Xie, Tao ; Mechanical Engineering

University:Georgia Institute of Technology

Department:Mechanical Engineering

关键词: Fibers;    Flow regime identification;    Three phase flow;    Artificial neural networks;    Pressure fluctuation;    Power spectral density;    Multiphase flow;    Neural networks (Computer science);   

Others  :  https://smartech.gatech.edu/bitstream/1853/4814/1/xie_tao_200412_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Flow regime identification in industrial systems that rely on complex multi-phase flows is crucial for their safety, control, diagnostics, and operation. The objective of this investigation was to develop and demonstrate objective and minimally-intrusive flow regime classification methods for gas/water/paper pulp three-phase slurries, based on artificial neural network-assisted recognition of patterns in the statistical characteristics of pressure fluctuations.Experiments were performed in an instrumented three-phase bubble column featuring vertical, upward flow. The hydrodynamics of low consistency (LC) gas-liquid-fiber mixtures, over a wide range of superficial phase velocities, were investigated. Flow regimes were identified, gas holdup (void fraction) was measured, and near-wall pressure fluctuations were recorded using high-sensitivity pressure sensors. Artificial neural networks of various configurations were designed, trained and tested for the classification of flow regimes based on the recorded pressure fluctuation statistics. The feasibility of flow regime identification based on statistical properties of signals recorded by a single sensor was thereby demonstrated. The transportability of the developed method, whereby an artificial neural network trained and tested with a set of data is manipulated and used for the characterization of an unseen and different but plausibly similar data set, was also examined. An artificial neural network-based method was developed that used the power spectral characteristics of the normal pressure fluctuations as input, and its transportability between separate but in principle similar sensors was successfully demonstrated. An artificial neural network-based method was furthermore developed that enhances the transportability of the aforementioned artificial neural networks that were trained for flow pattern recognition. While a redundant system with multiple sensors is an obvious target application, such robustness of algorithms that provides transportability will also contribute to performance with a single sensor, shielding effects of calibration changes or sensor replacements.