Recognition, Characterization, Categorization, and Forecasting of Counter-Current Gas-Liquid and Liquid-Liquid Two-Phase Flow-Structures: Experimental, Computational, And Numerical Attempts

Samal, Kumar (2022) Recognition, Characterization, Categorization, and Forecasting of Counter-Current Gas-Liquid and Liquid-Liquid Two-Phase Flow-Structures: Experimental, Computational, And Numerical Attempts. PhD thesis.

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The simultaneous flow of several phases or states is known as multiphase flow. Flow-regime or flow-structure is the morphological arrangement (interfacial structure) of the component phases. Transport phenomena like momentum transfer, mass transfer, or heat transfer in the multiphase flow strongly depend on the interfacial distribution (flow-structure). The time variation of pressure drop in the conduit of any multiphase flow system is oscillating in nature. This pressure oscillation may generate some unwanted vibrations in the flow systems. The frequency and amplitude of the said pressure oscillations strongly depend on its flow-structure. Therefore, proper analysis, understanding, and diagnosis of the flow-structures are essential to tackle any multiphase flow system and its safety issues. It is compulsory to monitor and track the flow-structures to optimize the performance of any steady or transient operations in the multiphase flow system. It is necessary to know the flow-structure to have smooth and safe operations in multiphase flow systems. It is not unexpected that copious attempts have so far been made to analyze the two-phase flow-structure or flow-regime. However, most of such attempts have been made for co-current gas-liquid and liquid-liquid two-phase flow only. Literature review reveals that sufficient research attempts have not been made on the diagnosis of flow-structures of counter-current two-phase flow (CCTPF). Characterization of CCTPF-regime has not received adequate attention to date. A few attempts have been found on the CCTPF-structure. Again, most of these few attempts have been made by considering the air-water fluid pair only. Attention is not paid to the CCTPF-regimes of other working fluid pairs. The flow-structure analysis of liquid-liquid CCTPF has rarely been attempted. The effect of flow-orientation on the CCTPF-structures has been explored up to a very small extent. Research attempts in CCTPF-patterns in smaller diameter tube is rarely found. Automatic online recognition, characterization, categorization, and forecasting of the CCTPF-structures based on objective signatures of the flow (using various sensors) are hardly found in the literature. Identification, characterization, parameterization, categorization, and forecasting of flow-structures are the prime issues in multiphase flow systems. Recognition of CCTPF is very challenging due to its inherent complexity and nonlinearity. The characterization of different flow structures poses huge difficulties, as the underlying process is filled with uncertainty, complexity, high nonlinearity, lack of knowledge, and fuzziness. The existence of these issues in CCTPF-structure is much more compared to that in co-current flow-structure as the CCTPF contains more significant variations in the relative velocity (between the phases) than the co-current two-phase flow. Situations of no solution or multiple solutions may frequently arise in CCTPF-hydrodynamics. Flooding may occur in CCTPF, which is generally undesirable in the flow system. However, gas-liquid and liquid-liquid CCTPF are very common and witnessed in several industrial applications. Thus, though it is challenging, the study of the CCTPF-structures under different flow situations is essentially required to design any CCTPF system and its safety issues. Thus, in the present thesis, at first, rigorous investigation (experimentations, characterization, parameterization, categorization, and forecasting) are made on gas-liquid CCTPF-structures in a 11 mm ID tube by varying the fluid pair and flow-orientations. Investigations are done for three different fluid pairs, namely: air-water, air-aqueous glycerine, and air-aqueous butanol. For each fluid pair, studies are conducted for three different flow-orientations: 0°, 45°, 90°. For each of the orientations, analyses are made for various combinations of the superficial velocities of the constituent phases covering a wide range. Secondly, extensive investigations on the gas-liquid CCTPF-structures in a conduit of 15 mm ID are made exactly in the same way as explained above (in the previous paragraph) to grasp the effect of the conduit ID on the developed gas-liquid CCTPF-structures. Here also the studies are made by considering the same three fluid pairs. Here also, a total of 3 flow-orientations (0°, 45.0°, 90.0°) are considered for each fluid pair, and various combinations of the superficial phase velocities are considered at each flow-orientation during investigations. Next, exhaustive investigations (experimentations, characterization, parameterization, and categorization) are made on the liquid-liquid CCTPF-structures in a conduit of 11 mm ID by varying the duct-orientations and combinations of the individual liquids’ superficial velocities. The kerosene-water fluid pair is considered for the present liquid-liquid CCTPF experimentations. A total of 7 tube-orientations (5°, 10°, 22.5°, 45°, 67.5°, 80°, 85°) are considered during investigations. During investigations, various combinations of the superficial velocities of the constituent phases covering a wide range are considered at each of the orientations. Then, exhaustive investigations on the liquid-liquid CCTPF-structures in a 15 mm ID tube are made exactly in the same way as explained in the previous paragraph to grasp the effect of the tube ID on the developed flow-structures. Here also, the studies are made by considering the kerosene-water fluid pair. A total of 5 tube orientations (0°, 22.5°, 45.0°, 67.5°, 90.0°) are considered during investigations. Analyses are made for various combinations of the superficial velocities of the constituent phases covering a wide range at each of the orientations, For each of the above four investigations, exhaustive experimental investigations are performed, and the CCTPF-structures are explored. High-resolution photographs of the flow-structures are captured and stored using a high-resolution DSLR camera to grasp the highly complex interfacial distributions in the flow-structures. High-speed videos of the flow-structures are recorded using a high-speed video camera (and then analyzed) to capture the rapidly time-varying nonlinear flow attributes. Sensors-based flow-regime identifiers are developed. Different sensors like differential pressure transmitters (DPT) and in-house fabricated conductivity probes are simultaneously employed to collect the flow-structures’ objective signatures in the form of time-series current and voltage signals, respectively. It is done to avoid possible confusion arising from subjective descriptions, which vary from subject to subject. The current signals of DPT are converted to corresponding ΔP signals. Statistical analysis of the normalized time-series voltage and ΔP signals is made using MATLAB coding to extract the non-parametric probability distribution (NPPD) functions and statistical parameters from those signals. Thus, the objective time-series data are converted into some statistical objective countable properties or parameters. The high-resolution photographs, high-speed videos (and the split snaps), time-series ΔP and voltage signals, and their NPPD function plots with statistical parameters are used together to recognize and distinguish the individual CCTPF-structures. Flow-structures are characterized and parameterized using those countable statistical parameters extracted from the objective time-series data. Computational intelligence (CI) based categorizer inspired by the EM algorithm is developed using MATLAB coding to categorize the developed gas-liquid and liquid-liquid CCTPF-structures automatically, considering various sets of those statistical parameters as inputs. CI-based forecaster employing a multilayer perception neural network is also developed by MATLAB coding to forecast different gas-liquid CCTPF-structures considering various sets of those statistical parameters as inputs. Some novel gas-liquid and liquid-liquid CCTPF-structures, which have not yet been reported in the literature, are found through the present investigation. The CCTPF-structures are successfully parameterized and characterized in terms of the NPPD plots and those statistical parameters of the objective signatures. These extracted NPPD plots and statistical parameters are found to be very useful for recognizing and distinguishing the individual flow-structures. The developed categorizer categorizes the generated gas-liquid and liquid-liquid flow-structures (under different working situations) automatically and successfully with high accuracy. The proposed forecaster forecasts the generated gas-liquid CCTPF-structures automatically and successfully with high accuracy in different working situations. Thus, the proposed categorizer and forecaster are robust enough and can be used for online automatic CCTPF-regime categorization and forecasting, respectively. Lastly, the concept of diagnosing the flow-structures using objective signatures is applied to a practical problem. The dynamics of CCTPF characteristics and CCFL mechanisms in the hot-leg of a PWR during the loss of coolant accident (LOCA) have not yet been revealed completely. Again, no such attempt is found in the literature regarding characterization, categorization, or prediction of flow-condition (flow-structure) in the hot-leg during LOCA. Numerical and computational attempts are made here to investigate and characterize the flow-condition in the hot-leg of a Pressurized Water Reactor (PWR) during the LOCA. 1/3rd model of German Konvoi PWR (Deendarlianto et al., 2008) is considered for the present numerical investigation. The finite Volume-based Volume of Fluid (VOF) model (with single momentum equation) is employed for transient numerical simulations of the two-phase hydrodynamics in hot-leg during LOCA. The turbulence effect is captured using ‘k-w’ model. Effect of individual fluid-flowrates and stored water (initial water-level), on the developed CCTPF-structures in hot-leg are extensively studied. Variations of the spatial distribution of phases with time are evaluated. Flow-structures in the hot-leg are characterized and parameterized in terms of statistical parameters extracted from the time variation of pressure drop (PD) and volume fraction (VF) across the flow domain. CI-based hybrid methodology employing neuro-biological schemes with the evolutionary-based algorithm (GA tuned MLFFNN with BPA) is developed using ‘C’ programming language to predict the flow-situation (occurrence or absence of plugging/blocking) in the hot-leg using the extracted statistical parameters as input. A computational approach is also developed to relate the flow-situation (occurrence or absence of plugging) in the hot-leg with the working conditions of PWR. Plugging is found to be most responsive to the gas-flowrate. Plugging/blocking easily occurs at a high gas-flowrate. It may occur even at moderate gas-flowrate when the initial stored water-level in hot-leg crosses the threshold limit. It is found that the developed methodology is able to perfectly capture the relationship between the flow-condition in hot-leg and the said statistical parameters and predict the flow-situation with a high success rate. The complex nonlinear relation between the flow-situation in the hot-leg and the working conditions is also perfectly captured by the developed computational approach.

Item Type:Thesis (PhD)
Uncontrolled Keywords:Gas-liquid CCTPF-structures; Liquid-liquid CCTPF-structures; Statistical parameters of the objective signatures; NPPD plots; Characterization; CI-based categorizer; CI-based forecaster; MLP neural network; Plugging/blocking in PWR hot-leg; Finite Volume-based VOF; GA tuned MLFFNN
Subjects:Engineering and Technology > Mechanical Engineering > Mechatronics
Engineering and Technology > Mechanical Engineering > Thermodynamics
Engineering and Technology > Mechanical Engineering > Computational Fluid Dynamics
Engineering and Technology > Mechanical Engineering > Structural Analysis
Divisions: Engineering and Technology > Department of Mechanical Engineering
ID Code:10485
Deposited By:IR Staff BPCL
Deposited On:16 Apr 2024 15:30
Last Modified:16 Apr 2024 15:30
Supervisor(s):Ghosh, Suman

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