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REAL TIME AND QUASI-3D IMAGING OF GAS-LIQUID FLOW USING ELECTRICAL CAPACITANCE TOMOGRAPHY

机译:电容断层扫描的实时和拟气液流量的成像

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In this study, an electrical capacitance tomography (ECT) adopted with neural-network based multi-criterion optimization image reconstruction technique (NN-MOIRT) for real time and quasi-3D imaging of gas-liquid flow system is developed. The technique reconstruct the permittivity distribution (tomography image) from capacitance data obtained using 12-electrode twin-plane capacitance sensor. A combined series and parallel capacitance model is used convert the permittivity distribution into gas holdup distribution. Comparison of the overall gas holdups obtained by the ECT with those obtained from the pressure measurements shows a good agreement, validating the model proposed.The ECT is applied to study the hydrodynamic characteristics of the gas-liquid flow system including the bubbly flow structures, gas holdup profiles, and gas holdup variations along with the effect of the gas velocity.
机译:在本研究中,开发了用于基于神经网络的多标准优化图像重建技术(NN-MOITT)的电容断层扫描(ECT),用于实时和气液流动系统的准3D成像。该技术从使用12电极双面电容传感器获得的电容数据重建介电常数分布(断层摄影图像)。使用组合的串联和并联电容模型将介电常数分布转换为气体保持分布。由ECT获得的总体气体持有的比较与从压力测量结果中获得的那些表示良好的一致性,验证了所提出的模型。ECT应用于研究气液流动系统的流体动力学特性,包括泡沫流动结构,气体保持型材和气体持有变化以及气体速度的效果。

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