Experimental Investigation for Flow Regime Identification Using Probability Density Function of Void Fraction Signals

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Min-Song Lin ◽  
Shao-Wen Chen ◽  
Feng-Jiun Kuo ◽  
Yen-Shih Cheng ◽  
Pei-Syuan Ruan ◽  
...  
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Flow Regime ◽  
Void Fraction ◽  
Flow Conditions ◽  
Two Phase ◽  
Specific Flow ◽  
Wide Range ◽  
Flow Regime Identification

Abstract In this study, upward air–water two-phase flow tests were carried out in a 3 cm diameter pipe under atmospheric pressure, and over 3000 data points were collected from a wide range of superficial gas and liquid velocities (⟨jg⟩ ≈ 0.02–30 m/s and ⟨jf⟩ ≈ 0.02–2 m/s) for the investigation of flow regime identification. The probability density function (PDF) of transient void fraction signals and its full-width at half-maximum (FWHM) were obtained and used for analysis and data classification. Considering the features of PDF profiles, the flow conditions can be classified into four regions, which show a fair agreement with the existing flow regime maps in general trends. Furthermore, by examining the FWHM distributions, two more regions with high-FWHM (HF) values were identified as the transitions of higher-flow bubbly-to-slug and slug-to-churn flows as well as most portion of churn flow, and a valley region next to the HF regions can express the transition of churn-to-annular flows. Overall, six groups of flow conditions can be classified based on the present methodology, and each group can be corresponding to specific flow regimes or transition regions. This study can provide a simple and efficient way for flow regime identification.

scholarly journals A numerical study of entropy and residual entropy estimators based on smooth density estimators for non-negative random variables

2021 ◽  
Vol 54 (2) ◽  
pp. 99-121
Author(s):  
Yogendra P. Chaubey ◽  
Nhat Linh Vu
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Numerical Study ◽  
Random Variables ◽  
Random Variable ◽  
Residual Entropy ◽  
Density Estimator ◽  
Wide Range ◽  
Smooth Density

In this paper, we are interested in estimating the entropy of a non-negative random variable. Since the underlying probability density function is unknown, we propose the use of the Poisson smoothed histogram density estimator to estimate the entropy. To study the per- formance of our estimator, we run simulations on a wide range of densities and compare our entropy estimators with the existing estimators based on different approaches such as spacing estimators. Furthermore, we extend our study to residual entropy estimators which is the entropy of a random variable given that it has been survived up to time $t$.

Measurement of Void Fraction and Pressure Drop of Air-Oil Two-Phase Flow in Horizontal Pipes

2004 ◽  
Vol 126 (1) ◽  
pp. 107-118 ◽  
Author(s):  
J. L. Pawloski ◽  
C. Y. Ching ◽  
M. Shoukri
Keyword(s):  
Pressure Drop ◽  
Flow Regime ◽  
Void Fraction ◽  
High Speed ◽  
Two Phase Flow ◽  
Flow Regimes ◽  
Phase Flow ◽  
Two Phase ◽  
Wide Range ◽  
Flow Regime Maps

The void fractions, flow regimes, and pressure drop of air-oil two-phase flow in a half-inch diameter pipe over a wide range of test conditions have been investigated. The flow regimes were identified with the aid of a 1000 frames per second high-speed camera. A capacitance sensor for instantaneous void fraction measurements was developed. The mean and probability density function of the instantaneous void fraction signal can be used to effectively identify the different flow regimes. The current flow regime data show significant differences in the transitional boundaries of the existing flow regime maps. Property correction factors for the flow regime maps are recommended. The pressure drop measurements were compared to the predictions from four existing two-phase flow pressure drop models. Though some of the models performed better for certain flow regimes, none of the models were found to give accurate results over the entire range of flow regimes.

scholarly journals Mechanical Integrity of 3D Rough Surfaces during Contact

Coatings ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 15
Author(s):  
Maxence Bigerelle ◽  
Franck Plouraboue ◽  
Frederic Robache ◽  
Abdeljalil Jourani ◽  
Agnes Fabre
Keyword(s):  
Plastic Deformation ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Contact Pressure ◽  
Rough Surfaces ◽  
Morphological Structure ◽  
Random Variable ◽  
Wide Range ◽  
Weierstrass Functions

Rough surfaces are in contact locally by the peaks of roughness. At this local scale, the pressure of contact can be sharply superior to the macroscopic pressure. If the roughness is assumed to be a random morphology, a well-established observation in many practical cases, mechanical indicators built from the contact zone are then also random variables. Consequently, the probability density function (PDF) of any mechanical random variable obviously depends upon the morphological structure of the surface. The contact pressure PDF, or the probability of damage of this surface can be determined for example when plastic deformation occurs. In this study, the contact pressure PDF is modeled using a particular probability density function, the generalized Lambda distributions (GLD). The GLD are generic and polymorphic. They approach a large number of known distributions (Weibull, Normal, and Lognormal). The later were successfully used to model damage in materials. A semi-analytical model of elastic contact which takes into account the morphology of real surfaces is used to compute the contact pressure. In a first step, surfaces are simulated by Weierstrass functions which have been previously used to model a wide range of surfaces met in tribology. The Lambda distributions adequacy is qualified to model contact pressure. Using these functions, a statistical analysis allows us to extract the probability density of the maximal pressure. It turns out that this density can be described by a GLD. It is then possible to determine the probability that the contact pressure generates plastic deformation.

scholarly journals Flow Pattern and Resistance Characteristics of Gas–Liquid Two-Phase Flow with Foam under Low Gas–Liquid Flow Rate

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3722
Author(s):  
Bin Wang ◽  
Jianguo Hu ◽  
Weixiong Chen ◽  
Zhongzhao Cheng ◽  
Fei Gao
Keyword(s):  
Stainless Steel ◽  
Probability Density Function ◽  
Probability Density ◽  
Flow Pattern ◽  
Density Function ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Liquid Ratio ◽  
Two Phase ◽  
Foam Flooding

To reduce the cost of arranging air foam flooding equipment at each wellhead, a method of establishing centralized air foam flooding injection stations is proposed. The flow pattern and resistance characteristics of air foam flooding mixtures in different initial conditions are studied. Experimental results indicate that the probability density function of stratified flow is obtained by comparing stainless steel and transparent pipes. If the gas–liquid ratio is kept constant, then the shape of the probability density function remains unchanged in both stainless steel and transparent tubes. Meanwhile, the flow pattern under the gas–liquid ratio is determined by comparing the image recognition results with the probability density function, and a formula for calculating the resistance and pressure drop of the gas and liquid two-phase flow in the horizontal and upward pipes is established. Compared with the experiments, the error results of the calculation are small. Thus, the proposed equations can be used to predict the flow resistance of real air foam flooding.

Stochastic estimation of organized turbulent structure: homogeneous shear flow

1988 ◽  
Vol 190 ◽  
pp. 531-559 ◽  
Author(s):  
Ronald J. Adrian ◽  
Parviz Moin
Keyword(s):  
Probability Density Function ◽  
Shear Flow ◽  
Probability Density ◽  
Density Function ◽  
Deformation Tensor ◽  
Stochastic Estimation ◽  
Correlation Tensor ◽  
Homogeneous Shear ◽  
Wide Range ◽  
Homogeneous Shear Flow

The large-scale organized structures of turbulent flow can be characterized quantitatively by a conditional eddy, given the local kinematic state of the flow as specified by the conditional average of u(x’, t) given the velocity and the deformation tensor at a point x: 〈u(x’, t)|u(x, t), d(x, t)〉. By means of linear mean-square stochastic estimation, 〈u’|u, d〉 is approximated in terms of the two-point spatial correlation tensor, and the conditional eddy is evaluated for arbitrary values of u(x, t) and d(x, t), permitting study of the turbulent field for a wide range of local kinematic states. The linear estimate is applied to homogeneous turbulent shear flow data generated by direct numerical simulation. The joint velocity-deformation probability density function is used to obtain conditions corresponding to those events that contribute most to the Reynolds shear stress. The primary contributions to the second-quadrant and fourth-quadrant Reynolds-stress events in homogeneous shear flow come from flow induced through the ‘legs’ and close to the ‘heads’ of upright and inverted ‘hairpins’, respectively.The equation governing the joint probability density function of fu,d (u, d) is derived. It is shown that this equation contains 〈u’/u, d〉 and that the equations for second-order closure can be derived from it. Closure requires approximation of 〈u’/u, d〉.

Sign in / Sign up

Export Citation Format

Share Document