| Summary: | The double-sensor conductivity probe is one of the most commonly used techniques for obtaining local time-averaged parameters in two-phase flows. The uncertainty of this measurement technique has not been well understood in the past as it involves many different steps and influential factors in a typical measurement. This dissertation aims to address this gap by performing a systematic and comprehensive study on the measurement uncertainty of the probe. Three types of uncertainties are analyzed: that of measurands, of the model input parameters, and of the mathematical models. A Monte Carlo uncertainty evaluation framework closely simulating the actual measuring process is developed to link various uncertainty sources to the time-averaged two-phase flow quantities outputted by the probe. Based on the Monte Carlo uncertainty evaluation framework, an iteration method is developed to infer the true values of the quantities that are being measured. A better understanding of the uncertainty of the double-sensor conductivity probe is obtained.
Multiple advanced techniques, such as high speed optical imaging and fast X-ray densitometry, recently become mature and easily accessible. To further improve the accuracy of local two-phase flow measurement, a method is developed to integrate these techniques with the double-sensor conductivity probe by considering the measuring principles and unique advantages of each technique. It has been demonstrated that after processing and synergizing the data from different techniques using the current integration method, the final results show improved accuracy for void fraction, gas velocity and superficial gas velocity, compared to the original probe measurements.
High-resolution two-phase flow data is essential for the further development of various two-phase flow models and validation of two-phase CFD codes. Therefore, a comprehensive high-accuracy database of two-phase flows is acquired. The gas-phase information is obtained by the integration method developed in this dissertation, and the recently developed Particle Image Velocimetry and Planar Laser Induced Fluorescence (PIV-PLIF) technique is utilized to measure liquid-phase velocity and turbulence characteristics. Flow characteristics of bubbly flow, slug flow and churn-turbulent flow are investigated. The 1-D drift-flux model is re-evaluated by the newly obtained dataset. The distribution parameter model has been optimized based on a new void-profile classification method proposed in this study. The optimized drift-flux model has significant improvements in predicting both gas velocity and void fraction. === Doctor of Philosophy === The double-sensor conductivity probe is one widely used technique for measuring local time-averaged parameters in two-phase flows. Although a number of studies have been carried out in the past, a good understanding of the uncertainty of this technique is still lacking. This paper aims to address this gap by performing a systematic and comprehensive study on the measurement uncertainty of the probe. Three types of uncertainties are analyzed: that of measurands, of the model input parameters, and of the mathematical models. A better understanding of the uncertainty of the double-sensor conductivity probe has been obtained. Considering the unique measuring principles and advantages of multiple advanced techniques, a method is developed to integrate these techniques with the double-sensor conductivity probe to further improve the accuracy of local two-phase flow measurement. It has been demonstrated that the integration method significantly improves the accuracy of probe measurements. Realizing the needs of high-resolution two-phase flow data to the further development of various two-phase flow models and validation of two-phase CFD codes, a comprehensive database of two-phase flows is acquired. The gas-phase and liquid-phase information are acquired by the new integration method and the recently developed Particle Image Velocimetry and Planar Laser Induced Fluorescence (PIV-PLIF) technique, respectively. The classical 1-D drift-flux model is re-evaluated by the newly obtained dataset. The distribution parameter model has been optimized, resulting in significant improvements in predicting both gas velocity and void fraction.
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