Romuald Mosdorf

Nonlinear analysis on fluctuation feature of two-phase flow through a T-junction

Abstract Several measurement methods of chaos dynamics were employed to analyze differential pressure fluctuations of two-phase flow through a T-junction with the aim to make clear the two-phase flow behavior splitting at a T-junction. These results may be significant for better understanding the flow structure and also for establishing valid models different from conventional viewpoints. These methods included: power spectral density and Hurst exponent, Lyapunov exponent, correlation dimension, pseudo-phase-plane trajectory. The experimental test section is a symmetrical and vertical impacting T-junction with 15 mm inner diameter for the main tube and two horizontal branches. Three kinds of flow pattern including bubble flow, churn flow and annular flow in the inlet tube, were investigated by detecting time series of differential pressure. It is demonstrated that two-phase flow splitting at a T-junction is a complicated nonlinear dynamic system. The Hurst exponents were larger than 0.5 showing that the flow behaviors studied are partly chaotic. The largest Lyapunov exponent greater than zero confirms the chaotic feature of two-phase flow at a T-junction in quality. Correlation dimensions were used to quantify the identified chaotic behavior.

Chaos in bubbling: nonlinear analysis and modelling

Abstract In the present paper, nonlinear features and analytical results for the chaotic bubbling from a submerged orifice are described. A chain of air bubbles was produced from the single orifice of 2 mm in diameter and micro-convection induced by the bubble generation was recorded using hot-probe anemometer located close to the orifice. The air flow rate was varied widely from q =100 to 2000 cc / min and the aspects of bubbling were observed by high-speed video. The nonlinear analysis is performed for the time series data of hot-probe anemometer especially in the range of q =435– 1500 cc / min . The calculated largest Lyapunov exponent shows that with increase of air volume flow rate, the time period for the process of liquid flow to lose stability becomes shorter and at high air flow rate such as q=1500 cc / min , it is shorter than the time period between subsequent bubbles. To explain such chaotic behaviors of bubbling, a simple model has been proposed. The model simulates the process of interaction between the elastic bubble wall and liquid. Simulation results compared well with the analytical results of experimental data. Summarizing, it is concluded that one of the reasons for chaos appearance is the nonlinear character of interaction between an elastic bubble wall and the liquid stream.

Self-aggregation Phenomenon and Stable Flow Conditions in a Two-Phase Flow Through a Minichannel

Abstract By increasing a water flow rate of the two-phase (air–water) flow through a minichannel, both the partitioning of air slugs into air bubbles of different sizes and small air bubbles aggregation into larger air bubbles were identified. These phenomena were studied in detail by using the corresponding sequences of light transmission time series recorded with a laser-phototransistor sensor. To distinguish any instabilities in air slugs along with their break-ups and aggregations, the recurrence plots and recurrence quantification analysis were applied.

Analysis of Non-stationary Signals by Recurrence Dissimilarity

We propose a new method for testing non-stationary and intermittent signals. Using a basic recurrence plot quantifier, i.e. a recurrence rate, we create a relative measure which is sensitive to changes in the nature of signal. Given the specificity of this measure, we call it recurrence dissimilarity (RD). First, we test it using well-known non-linear systems for which we generate signals with different intermittent characteristics. In addition, the generated signals are disturbed by noise. The effectiveness of our measure is verified by applying different variables and noise levels. The results allow us to draw a number of conclusions concerning the proposed method. Finally, we give examples of using this method for experimental data analysis. We report the results of detecting changes in flowing patterns in two-phase flows and of switching heart modes in the signals recorded in ECG Holter tests.

Dynamics of Two-Phase Flow through a Minichannel: Fourier and Multiscale Entropy Analyses

By changing a air flow rate of the two-phase (air-water) flow through a minichannel weidentified aggregation and partitioning of air bubbles and slugs of different sizes and air bubble arrangement into periodic patterns. The identification of these spatio-temporal behaviour was doneby digital camera. Simultaneously, we provide the detailed studies of these phenomena by using thecorresponding sequences of light transmission time series recorded by a laser-phototransistor sensor.To distinguish the instabilities in air slags and their breakups and aggregations we used the Fourierand multiscale entropy analysis.

Gas Bubbles and Slugs Crossover in Air–Water Two-phase Flow by Multifractals

Abstract Slugs and bubbles two-phase flow patterns dynamics in a minichannel are analysed. During the experiment, the volume flow rates of air and water were changed. We study transition of bubbles to slugs two-phase flow patterns using Fourier and multifractal approaches to optical transitivity signal. The sequences of light transmission time series are recorded by a laser-phototransistor sensor. Multifractal analysis helps to identify the two-phase structure and estimate the signal complexity. Especially, we discuss occurrence and identification of a self-aggregation phenomenon. These results are compared to corresponding Fourier spectra. The results indicate that the fractality is a an important factor influencing the distribution of the gas phase in water.

Dynamics and Noise Level Estimation in Two-Phase Flow through a Mini Channel

By changing air to water flow rates of a two-phase flow through a minichannel we identified aggregation and partitioning of air bubbles and slugs of various sizes. It was found that for some flowconditions air bubbles formed periodic patterns. The identification of the spatio-temporal behaviourswas performed with a laser transitivity sensor and confirmed by a digital camera. We used the Hurstexponent to distinguish instabilities in air slugs, their breakups and aggregations. In addition, we performed noise level estimation.

Detection of Two-Phase Flow Patterns in a Vertical Minichannel Using the Recurrence Quantification Analysis

Abstract The two-phase flow (water-air) occurring in square minichannel (3x3 mm) has been analysed. In the minichannel it has been observed: bubbly flow, flow of confined bubbles, flow of elongated bubbles, slug flow and semi-annular flow. The time series recorded by laser-phototransistor sensor was analysed using the recurrence quantification analysis. The two coefficients:Recurrence rate (RR) and Determinism (DET) have been used for identification of differences between the dynamics of two-phase flow patterns. The algorithm which has been used normalizes the analysed time series before calculating the recurrence plots.Therefore in analysis the quantitative signal characteristicswas neglected. Despite of the neglect of quantitative signal characteristics the analysis of its dynamics (chart of DET vs. RR) allows to identify the two-phase flow patterns. This confirms that this type of analysis can be used to identify the two-phase flow patterns in minichannels.

SYNCHRONIZATION OF DATA RECORDED USING ACQUISITION STATIONS WITH DATA FROM CAMERA DURING THE BUBBLE DEPARTURE

In this study the first part of the experimental data was recorded in a data acquisition station, and another one was recorded with a high speed camera. The data recorded using the acquisition station was recorded with higher frequency than the time between two subsequent frames of the film. During the analysis of the experimental data the problem was related to the synchronization of measurement from acquisition station and data recorded with a camera. In this paper the method of synchronization of experimental data has been shown. A laser- phototransistor system has been used. The data synchronization was required in scaling of sampling frequency in the investigated time series.