Intube two-phase flow probabilities based on capacitance signal clustering

To study the objectivity in flow pattern mapping of horizontal two-phase flow in macroscale tubes, a capacitance sensor is developed for use with refrigerants. Sensor signals are gathered with R410A in an 8mm I.D. smooth tube at a saturation temperature of 15°C in the mass velocity range of 200 to 500kg/m²s and vapour quality range from 0 to 1 in steps of 0.025. A visual classification based on high speed camera images is made for comparison reasons. A statistical analysis of the sensor signals shows that the average and the variance are suitable for flow regime classification into slug flow, intermittent flow and annular flow by using a the fuzzy c-means clustering algorithm. This soft clustering algorithm perfectly predicts the slug/intermittent flow transition compared to our visual observations. The intermittent/annular flow transition is found at higher vapour qualities, but with the same trend compared to our observations and the prediction of [Barbieri et al., 2008, Flow patterns in convective boiling of refrigerant R-134a in smooth tubes of several diameters, 5th European Thermal-Sciences Conference, The Netherlands]. The intermittent/annular flow transition is very gradual. A probability approach can therefore better describe such a transition. The membership grades of the cluster algorithm can be interpreted as flow probabilities. These probabilities are further compared to time fraction functions of [Jassim et al., 2008, Prediction of refrigerant void fraction in horizontal tubes using probabilistic flow regime maps

Mapping of horizontal refrigerant two-phase flow patterns based on clustering of capacitive sensor signals

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Flow pattern mapping of horizontal evaporating refrigerant flow based on capacitive void fraction measurements

In daily life, air-conditioning devices are all around us. To optimize these devices, the prediction of two-phase flow heat transfer of evaporating refrigerants has to be improved. This heat transfer is strongly related to the two-phase flow patterns. Improving the current two-phase flow models should therfore focus on incorporating more flow phenomena into the models. In this work, two-phase flow patterns of horizontal evaporating refrigerant flow in smooth round macro-scale tubes are investigated. A macro-scale test facility for two-phase flow and heat transfer studies of HFCs was designed and constructed. Secondly, a capacitive void fraction sensor was developed to study the dynamic behavior of two-phase flows. The capacitance transducer can measure dynamic signals of fluids with the capacitance of the liquid smaller than 1pF and still achieve a SNR greater than 300. Capacitance signals of HFCs can thus be investigated. The dynamic capacitance signals in combination with high speed camera images were used to characterize horizontal two-phase flows in an 7.91mm ID smooth tube. Two datasets were gathered using R410A and R134a respectively. A detailed description of the two-phase flow phenomena was presented together wit a sensor signal charaterization using the time signals as well as PDFs and PSDs. All major two-phase flow phenomena were clearly represented in the sensor signals. To investigate the objectivity in the current flow pattern maps, several statistical parameters were analyzed in combination with the use of the fuzzy c-means clustering algorithm. The clustering in the selected feature space, groups the data points in clearly separable areas in a flow pattern map. Applying the technique to the HFC datasets, the slug flows could be easily separated from non-slug flows by using the variance of the sensor signal. The AVG and the F95 parameter were found most suitable for separating intermittent flows from annular flows. From the output of the soft-clustering algorithm a probabilistic flow pattern map was presentd for the HFC data. These maps clearly quantify the width of the transition zones and can be applied for probabilistic heat transfer and/or pressure drop modeling

Towards objective flow pattern mapping with the K-means clustering algorithm

Paper presented at the 6th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, South Africa, 30 June - 2 July, 2008.Two-phase flow regime prediction is of great importance for designing evaporators and condensers because the influence of the heat transfer coefficients is strongly related to the flow regimes. These flow regimes are often presented using flow pattern maps. As most flow pattern maps are based on visual observation, they lack objectively defined flow regime transition criteria. In order to add flow characteristics to the transitions boundaries, a sensor was developed which measures the capacitance of the two-phase flow. Due to the difference in dielectric constant of liquid and vapour and the dependency on the capacitance to the internal distribution of liquid and vapour in the cross-section of the tube, the sensor is able to characterize two-phase flow regimes. A large number of experiments was done with air-water flow. The setup was able to cover three main flow regimes for horizontal flow in a 9 mm tube, namely stratified, annular and intermittent flow. A multivariate analysis was performed to find characteristic signal parameters. The average signal value, together with the variance and a high frequency contribution factor were found suitable. These parameters were used as input features for the k-means clustering method, which groups the sensor data into a given number of classes. The influence of the weight parameters of the features was mapped, as well as the influence of the distance function. A comparison between the visual classification based on high speed camera images and the cluster classification shows a remarkable agreement.vk201