scholarly journals Data-Driven-Based Forecasting of Two-Phase Flow Parameters in Rectangular Channel

2021 ◽  
Vol 9 ◽  
Author(s):  
Qingyu Huang ◽  
Yang Yu ◽  
Yaoyi Zhang ◽  
Bo Pang ◽  
Yafeng Wang ◽  
...  
Keyword(s):  
Random Forest ◽  
Two Phase Flow ◽  
Interfacial Area ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Support Vector ◽  
Phase Flow ◽  
Random Forest Regression ◽  
Two Phase ◽  
Flow Parameters

In the current nuclear reactor system analysis codes, the interfacial area concentration and void fraction are mainly obtained through empirical relations based on different flow regime maps. In the present research, the data-driven method has been proposed, using four machine learning algorithms (lasso regression, support vector regression, random forest regression and back propagation neural network) in the field of artificial intelligence to predict some important two-phase flow parameters in rectangular channels, and evaluate the performance of different models through multiple metrics. The random forest regression algorithm was found to have the strongest ability to learn from the experimental data in this study. Test results show that the prediction errors of the random forest regression model for interfacial area concentrations and void fractions are all less than 20%, which means the target parameters have been forecasted with good accuracy.

Axial Development of Local Flow Parameters in Bubbly Two-Phase Flow in Normal and Microgravity Conditions

2009 ◽  
Author(s):  
Yutaka Takata ◽  
Dong Chang Xing ◽  
Yutaka Fukuhara ◽  
Tatsuya Hazuku ◽  
Tomoji Takamasa ◽  
...  
Keyword(s):  
Two Phase Flow ◽  
Interfacial Area ◽  
Axial Distance ◽  
Phase Flow ◽  
Two Phase ◽  
Flow Measurements ◽  
Local Flow ◽  
Flow Parameters ◽  
Microgravity Conditions ◽  
Axial Development

In relation to the development of the interfacial area transport equation, a precise database of the axial development of void fraction profile, interfacial area concentration and Sauter mean bubble diameter in an adiabatic nitrogen-water bubbly flow in a 9 mm-diameter pipe was constructed for normal and microgravity conditions using stereo image-processing. The flow measurements were performed at four axial locations (axial distance from the inlet normalized by the pipe diameter, z/D = 5, 20, 40 and 60) and with various flows: superficial gas velocity of 0.00840–0.0298 m/s, and superficial liquid velocity of 0.138–0.914 m/s. The effect of gravity on radial distribution of bubbles and the axial development of two-phase flow parameters is discussed in detail based on the obtained database and visual observation.

Local Interfacial Structure in Downward Two-Phase Bubbly Flow

2002 ◽  
Author(s):  
Hiroshi Goda ◽  
Seungjin Kim ◽  
Sidharth S. Paranjape ◽  
Joshua P. Finch ◽  
Mamoru Ishii ◽  
...  
Keyword(s):  
Two Phase Flow ◽  
Bubbly Flow ◽  
Interfacial Area ◽  
Interfacial Structure ◽  
Phase Flow ◽  
Two Phase ◽  
Interfacial Area Concentration ◽  
Flow Parameters ◽  
Local Parameters ◽  
Axial Development

The local interfacial structure for vertical air-water co-current downward two-phase flow was investigated under adiabatic conditions. A multi-sensor conductivity probe was utilized in order to acquire the local two-phase flow parameters. The present experimental loop consisted of 25.4 mm and 50.8 mm ID round tubes as test sections. The measurement was performed at three axial locations: L/D = 13, 68 and 133 for the 25.4 mm ID loop and L/D = 7, 34, 67 for the 50.8 mm ID loop, in order to study the axial development of the flow. A total of 7 and 10 local measurement points along the tube radius were chosen for the 25.4 mm ID loop and the 50.8 mm ID loop, respectively. The experimental flow conditions were determined within bubbly flow regime. The acquired local parameters included the void fraction, interfacial area concentration, bubble interface frequency, bubble Sauter mean diameter, and interfacial velocity.

Two-Phase Flow Regime Identification Using Fluctuating Force Signals Under Machine Learning Techniques

2018 ◽  
Author(s):  
Yuta Saito ◽  
Shuhei Torisaki ◽  
Shuichiro Miwa
Keyword(s):  
Machine Learning ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Phase Flow ◽  
Two Phase ◽  
Learning Techniques ◽  
Flow Regime Identification

For rational design of industrial machineries such as nuclear power plants and heat exchanging devices, understanding of the two-phase flow regime is crucial. In this study, a new method of flow regime identification using the two-phase fluctuating force signals is proposed. Unlike the existing methodologies to measure two-phase flow parameters, the advantageous feature of utilizing the fluctuating force signal is that the measurement can be conducted under completely intrusive environment. Experiments were conducted using the tri-axial force transducers installed at the 90 degrees pipe bend of the vertical upward flow. For signal classification, machine learning techniques were utilized to identify flow regime, and four types flow regimes, namely, bubbly, slug, churn-turbulent, and annular flows were considered. From the obtained fluctuating force database, the features that characterize the signal were selected in the time and the frequency domain. In the current study, three types of machine learning algorithms such as the artificial neural network (ANN), support vector machine (SVM), and decision tree were examined and results obtained by each learning technique was compared.

Local Interfacial Structures in Horizontal Bubbly Flow with 90-Degree Bend

2006 ◽  
Author(s):  
Seungjin Kim ◽  
Jung Han Park ◽  
Gunol Kojasoy ◽  
Joseph M. Kelly
Keyword(s):  
Two Phase Flow ◽  
Bubbly Flow ◽  
Interfacial Area ◽  
Phase Flow ◽  
Two Phase ◽  
Interfacial Area Concentration ◽  
Tube Cross Section ◽  
Bubble Interactions ◽  
Flow Parameters ◽  
Geometric Effects

Present study investigates the geometric effects of flow obstruction on the distribution of local two-phase flow parameters and their transport characteristics in horizontal two-phase flow. The round glass tubes of 50.3mm in inner diameter are employed as test sections, along which a 90-degee elbow is located at L/D = 206.6 from the two-phase mixture inlet. In total, 15 different flow conditions are examined within the air-water bubbly flow regime. The detailed local two-phase flow parameters are acquired by the double-sensor conductivity probe at four different axial locations. The effect of elbow is found to be evident in both the distribution of local parameters and their development. The elbow clearly promotes bubble interactions resulting in significant changes in interfacial area concentration. It is also found that the elbow-effect propagates to be more significant further downstream (L/D = 250) than immediate downstream (L/D = 225) of the elbow. Furthermore, it is shown that the elbow induces significant oscillations in the flow in both vertical and horizontal directions of the tube cross-section. Characteristic geometric effects due to the existence of elbow are also shown clearly on the axial development of one-dimensional interfacial area concentration and void fraction.

A Physically Based, One-Dimensional Two-Fluid Model for Direct Contact Condensation of Steam Jets Submerged in Subcooled Water

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
David Heinze ◽  
Thomas Schulenberg ◽  
Lars Behnke
Keyword(s):  
Direct Contact ◽  
Two Phase Flow ◽  
Fluid Model ◽  
Interfacial Area ◽  
Phase Flow ◽  
Two Phase ◽  
One Dimensional ◽  
Two Fluid Model ◽  
Contact Condensation ◽  
Two Fluid

A simulation model for the direct contact condensation of steam in subcooled water is presented that allows determination of major parameters of the process, such as the jet penetration length. Entrainment of water by the steam jet is modeled based on the Kelvin–Helmholtz and Rayleigh–Taylor instability theories. Primary atomization due to acceleration of interfacial waves and secondary atomization due to aerodynamic forces account for the initial size of entrained droplets. The resulting steam-water two-phase flow is simulated based on a one-dimensional two-fluid model. An interfacial area transport equation is used to track changes of the interfacial area density due to droplet entrainment and steam condensation. Interfacial heat and mass transfer rates during condensation are calculated using the two-resistance model. The resulting two-phase flow equations constitute a system of ordinary differential equations, which is solved by means of the explicit Runge–Kutta–Fehlberg algorithm. The simulation results are in good qualitative agreement with published experimental data over a wide range of pool temperatures and mass flow rates.

Estimation of the fluid-fluid interfacial area using kinetic interface sensitive tracers in dynamic porous media experiments

2021 ◽  
Author(s):  
Alexandru Tatomir ◽  
Huhao Gao ◽  
Hiwa Abdullah ◽  
Martin Sauter
Keyword(s):  
Porous Medium ◽  
Porous Media ◽  
Two Phase Flow ◽  
Organic Liquid ◽  
Column Experiment ◽  
Interfacial Area ◽  
Breakthrough Curves ◽  
Water Soluble ◽  
Phase Flow ◽  
Two Phase

<p>Fluid-fluid interfacial area (IFA) in a two-phase flow in porous media is an important parameter for many geoscientific applications involving mass- and energy-transfer processes between the fluid-phases. Schaffer et al. (2013) introduced a new category of reactive tracers termed kinetically interface sensitive (KIS) tracers, able to quantify the size of the fluid-fluid IFA. In our previous experiments (Tatomir et al., 2018) we have demonstrated the application of the KIS tracers in a highly-controlled column experiment filled with a well-characterized porous medium consisting of well-sorted, spherical glass beads.</p><p>In this work we investigate several types of glass-bead materials and natural sands to quantitatively characterize the influence of the porous-medium grain-, pore-size and texture on the mobile interfacial area between an organic liquid and water. The fluid-fluid interfacial area is determined by interpretation of the breakthrough curves (BTCs) of the reaction product of the KIS tracer. When the tracer which is dissolved in the non-wetting phase meets the water, an irreversible hydrolysis process begins leading to the formation of two water-soluble products. For the experiments we use a peristaltic pump and a high precision injection pump to control the injection rate of the organic liquid and tracer.</p><p>A Darcy-scale numerical model is used to simulate the immiscible displacement process coupled with the reactive tracer transport across the fluid-fluid interface. The results show that the current reactive transport model is not always capable to reproduce the breakthrough curves of tracer experiments and that a new theoretical framework may be required.</p><p>Investigations of the role of solid surface area of the grains show that the grain surface roughness has an important influence on the IFA. . Furthermore, a linear relationship between the mobile capillary associated IFA and the inverse mean grain diameter can be established. The results are compared with the data collected from literature measured with high resolution microtomography and partitioning tracer methods. The capillary associated IFA values are consistently smaller because KIS tracers measure the mobile part of the interface. Through this study the applicability range of the KIS tracers is considerably expanded and the confidence in the robustness of the method is improved.</p><p> </p><p> </p><p>Schaffer M, Maier F, Licha T, Sauter M (2013) A new generation of tracers for the characterization of interfacial areas during supercritical carbon dioxide injections into deep saline aquifers: Kinetic interface-sensitive tracers (KIS tracer). International Journal of Greenhouse Gas Control 14:200–208. https://doi.org/10.1016/j.ijggc.2013.01.020</p><p>Tatomir A, Vriendt KD, Zhou D, et al (2018) Kinetic Interface Sensitive Tracers: Experimental Validation in a Two-Phase Flow Column Experiment. A Proof of Concept. Water Resources Research 54:10,223-10,241. https://doi.org/10.1029/2018WR022621</p>

Modeling Two-Phase Flow in Porous Media Including Fluid-Fluid Interfacial Area

2008 ◽  
Author(s):  
Jennifer Niessner ◽  
S. Majid Hassanizadeh ◽  
Dustin Crandall
Keyword(s):  
Porous Media ◽  
Capillary Pressure ◽  
Two Phase Flow ◽  
Interfacial Area ◽  
Flow In Porous Media ◽  
Extended Model ◽  
Phase Flow ◽  
Two Phase ◽  
Specific Interfacial Area ◽  
Macro Scale

We present a new numerical model for macro-scale two-phase flow in porous media which is based on a physically consistent theory of multi-phase flow. The standard approach for modeling the flow of two fluid phases in a porous medium consists of a continuity equation for each phase, an extended form of Darcy’s law as well as constitutive relationships for relative permeability and capillary pressure. This approach is known to have a number of important shortcomings and, in particular, it does not account for the presence and role of fluid–fluid interfaces. An alternative is to use an extended model which is founded on thermodynamic principles and is physically consistent. In addition to the standard equations, the model uses a balance equation for specific interfacial area. The constitutive relationship for capillary pressure involves not only saturation, but also specific interfacial area. We show how parameters can be obtained for the alternative model using experimental data from a new kind of flow cell and present results of a numerical modeling study.

scholarly journals Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

2019 ◽  
Vol 11 (11) ◽  
pp. 3222 ◽  
Author(s):  
Pascal Schirmer ◽  
Iosif Mporas
Keyword(s):  
Random Forest ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Regression ◽  
Nearest Neighbours ◽  
Energy Disaggregation ◽  
Vector Machines ◽  
Non Linear ◽  
Load Monitoring ◽  
Sinusoidal Current

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.

Interfacial Structures and Regime Transition in Co-Current Downward Bubbly Flow

2004 ◽  
Vol 126 (4) ◽  
pp. 528-538 ◽  
Author(s):  
S. Kim ◽  
S. S. Paranjape ◽  
M. Ishii ◽  
J. Kelly
Keyword(s):  
Two Phase Flow ◽  
Bubbly Flow ◽  
Superficial Velocity ◽  
Phase Flow ◽  
Two Phase ◽  
Drift Flux Model ◽  
Drift Flux ◽  
Flow Parameters ◽  
Interfacial Structures ◽  
Flux Model

The vertical co-current downward air-water two-phase flow was studied under adiabatic condition in round tube test sections of 25.4-mm and 50.8-mm ID. In flow regime identification, a new approach was employed to minimize the subjective judgment. It was found that the flow regimes in the co-current downward flow strongly depend on the channel size. In addition, various local two-phase flow parameters were acquired by the multi-sensor miniaturized conductivity probe in bubbly flow. Furthermore, the area-averaged data acquired by the impedance void meter were analyzed using the drift flux model. Three different distributions parameters were developed for different ranges of non-dimensional superficial velocity, defined by the ration of total superficial velocity to the drift velocity.

Sign in / Sign up

Export Citation Format

Share Document