scholarly journals An Efficient Model for Predicting the Segregation Profile of Binary Fluidized Beds

2018 ◽  
Vol 63 (1) ◽  
pp. 147-159
Author(s):  
Mohamed Sobhi Al-Agha ◽  
Pál Szentannai
Keyword(s):  
Concentration Profile ◽  
Fluidized Beds ◽  
Vertical Axis ◽  
3D Models ◽  
Data Sets ◽  
Two Phase ◽  
Main Work ◽  
Fitting Procedure ◽  
Segregation Profile ◽  
Vertical Concentration Profile

In most cases, the stationary fluidized beds are composed of two different particle classes (inert and active particles), and the concentration profile of these binary beds along the vertical axis is crucial regarding the effectiveness of the reactor. The present study introduces a semi-empirical 1D mathematical model for predicting the vertical concentration profile of binary fluidized beds. The proposed model is a developed and applicable version of the so-called Gibilaro and Rowe two-phase model, in which the differential equations describing the jetsam movement in the bulk and wake phases were solved numerically. The main work was to determine the parameters of the basic model, which was carried out by means of an advanced multi-step parameter fitting procedure. A more general form was established, which is based on direct linkage with the operating parameters that can be directly set and measured on the system. Comparisons with very diverse measured data sets available in the literature prove the accuracy of this model. Additional comparisons pointed out that the realization of this model is numerically inexpensive as it is several orders of magnitude faster than the available 2D and 3D models.

Aerodynamic and aeroacoustic performance investigations on modified H-rotor Darrieus wind turbine

2021 ◽  
pp. 0309524X2110039
Author(s):  
Amgad Dessoky ◽  
Thorsten Lutz ◽  
Ewald Krämer
Keyword(s):  
Wind Turbine ◽  
High Speed ◽  
Cfd Simulation ◽  
Vertical Axis ◽  
3D Models ◽  
Power Coefficient ◽  
Design Parameters ◽  
Second Phase ◽  
Vertical Axis Wind Turbine ◽  
Guide Vanes

The present paper investigates the aerodynamic and aeroacoustic characteristics of the H-rotor Darrieus vertical axis wind turbine (VAWT) combined with very promising energy conversion and steering technology; a fixed guide-vanes. The main scope of the current work is to enhance the aerodynamic performance and assess the noise production accomplished with such enhancement. The studies are carried out in two phases; the first phase is a parametric 2D CFD simulation employing the unsteady Reynolds-averaged Navier-Stokes (URANS) approach to optimize the design parameters of the guide-vanes. The second phase is a 3D CFD simulation of the full turbine using a higher-order numerical scheme and a hybrid RANS/LES (DDES) method. The guide-vanes show a superior power augmentation, about 42% increase in the power coefficient at λ = 2.75, with a slightly noisy operation and completely change the signal directivity. A remarkable difference in power coefficient is observed between 2D and 3D models at the high-speed ratios stems from the 3D effect. As a result, a 3D simulation of the capped Darrieus turbine is carried out, and then a noise assessment of such configuration is assessed. The results show a 20% increase in power coefficient by using the cap, without significant change in the noise signal.

Statistical Uncertainty in Long-Term Distributions of Significant Wave Height

1996 ◽  
Vol 118 (4) ◽  
pp. 284-291 ◽  
Author(s):  
C. Guedes Soares ◽  
A. C. Henriques
Keyword(s):  
Probability Distribution ◽  
Wave Height ◽  
Significant Wave Height ◽  
Data Sets ◽  
Monthly Basis ◽  
Significant Wave ◽  
Fitting Procedure ◽  
The One ◽  
Homogeneous Data

This work examines some aspects involved in the estimation of the parameters of the probability distribution of significant wave height, in particular the homogeneity of the data sets and the statistical methods of fitting a distribution to data. More homogeneous data sets are organized by collecting the data on a monthly basis and by separating the simple sea states from the combined ones. A three-parameter Weibull distribution is fitted to the data. The parameters of the fitted distribution are estimated by the methods of maximum likelihood, of regression, and of the moments. The uncertainty involved in estimating the probability distribution with the three methods is compared with the one that results from using more homogeneous data sets, and it is concluded that the uncertainty involved in the fitting procedure can be more significant unless the method of moments is not considered.

scholarly journals Day-time identification of summer hailstorm cells from MSG data

2013 ◽  
Vol 1 (5) ◽  
pp. 5453-5498 ◽  
Author(s):  
A. Merino ◽  
L. López ◽  
J. L. Sánchez ◽  
E. García-Ortega ◽  
E. Cattani ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Deep Convection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Phase Method ◽  
Data Sets ◽  
Liquid Water Path ◽  
Two Phase ◽  
Logistic Regression Models ◽  
Ebro Valley

Abstract. Identifying deep convection is of paramount importance, as it may be associated with extreme weather that has significant impact on the environment, property and the population. A new method, the Hail Detection Tool (HDT), is described for identifying hail-bearing storms using multi-spectral Meteosat Second Generation (MSG) data. HDT was conceived as a two-phase method, in which the first step is the Convective Mask (CM) algorithm devised for detection of deep convection, and the second a Hail Detection algorithm (HD) for the identification of hail-bearing clouds among cumulonimbus systems detected by CM. Both CM and HD are based on logistic regression models trained with multi-spectral MSG data-sets comprised of summer convective events in the middle Ebro Valley between 2006–2010, and detected by the RGB visualization technique (CM) or C-band weather radar system of the University of León. By means of the logistic regression approach, the probability of identifying a cumulonimbus event with CM or a hail event with HD are computed by exploiting a proper selection of MSG wavelengths or their combination. A number of cloud physical properties (liquid water path, optical thickness and effective cloud drop radius) were used to physically interpret results of statistical models from a meteorological perspective, using a method based on these "ingredients." Finally, the HDT was applied to a new validation sample consisting of events during summer 2011. The overall Probability of Detection (POD) was 76.9% and False Alarm Ratio 16.7%.

Dimensionality Reduction with Unsupervised Feature Selection and Applying Non-Euclidean Norms for Classification Accuracy

2011 ◽  
pp. 91-109
Author(s):  
Amit Saxena ◽  
John Wang
Keyword(s):  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Fitness Function ◽  
Synthetic Data ◽  
Feature Subset Selection ◽  
Second Phase ◽  
Data Sets ◽  
Feature Subset ◽  
K Nearest Neighbor ◽  
Two Phase

This paper presents a two-phase scheme to select reduced number of features from a dataset using Genetic Algorithm (GA) and testing the classification accuracy (CA) of the dataset with the reduced feature set. In the first phase of the proposed work, an unsupervised approach to select a subset of features is applied. GA is used to select stochastically reduced number of features with Sammon Error as the fitness function. Different subsets of features are obtained. In the second phase, each of the reduced features set is applied to test the CA of the dataset. The CA of a data set is validated using supervised k-nearest neighbor (k-nn) algorithm. The novelty of the proposed scheme is that each reduced feature set obtained in the first phase is investigated for CA using the k-nn classification with different Minkowski metric i.e. non-Euclidean norms instead of conventional Euclidean norm (L2). Final results are presented in the paper with extensive simulations on seven real and one synthetic, data sets. It is revealed from the proposed investigation that taking different norms produces better CA and hence a scope for better feature subset selection.

The Onset of Gas Entrainment From a Stratified Two-Phase Region Through a Single Side-Oriented Branch

2002 ◽  
Author(s):  
M. Ahmed ◽  
K. Lenard ◽  
I. Hassan ◽  
N. Esmail
Keyword(s):  
Inclination Angle ◽  
Density Ratio ◽  
Phase Region ◽  
Data Sets ◽  
Critical Height ◽  
Two Phase ◽  
Gas Entrainment ◽  
Single Side ◽  
Inclination Angles ◽  
Predicted Values

A new theoretical investigation has been conducted for the prediction of the critical height at the onset of gas entrainment during single discharge from a stratified two-phase region through a branch installed on an inclined flat wall. Two models have been developed; a simplified point-sink model and a more-accurate finite-branch. The predicted critical height at the onset of gas entrainment was proven to be a function of Froude number (Fr) and density ratio of the interface fluids. The results of the predicted critical height at the onset of gas entrainment, at low values of Fr (<10), were found to be more accurate when using the finite-branch analysis compared to the results found using the pink-sink analysis. Whereas, with increasing Fr, the predicted values of both models converged to the same value. Furthermore, the point-sink analysis was demonstrated to be independent of wall inclination angle, while the finite-branch analysis showed a slight decrease in the value of the critical height with increasing wall inclination angle. Three different experimental data sets at wall inclination angles of zero, 45 and 90 degrees (i.e. side, inclined and bottom branches) were used in the following study for the comparisons between the experimental and theoretically predicted results. A good concurrence was illustrated between the experimental and theoretical values.

scholarly journals A joint ALMA–Bolocam–Planck SZ study of the pressure distribution in RX J1347.5−1145

2019 ◽  
Vol 487 (3) ◽  
pp. 4037-4056 ◽  
Author(s):  
Luca Di Mascolo ◽  
Eugene Churazov ◽  
Tony Mroczkowski
Keyword(s):  
Strong Shock ◽  
Galaxy Cluster ◽  
Joint Analysis ◽  
Data Sets ◽  
X Ray ◽  
Cluster Galaxies ◽  
Fitting Procedure ◽  
The Galaxy ◽  
The Rich ◽  
Rich Data

ABSTRACT We report the joint analysis of single-dish and interferometric observations of the Sunyaev–Zeldovich (SZ) effect from the galaxy cluster RX J1347.5−1145. We have developed a parametric fitting procedure that uses native imaging and visibility data, and tested it using the rich data sets from ALMA, Bolocam, and Planck available for this object. RX J1347.5−1145 is a very hot and luminous cluster showing signatures of a merger. Previous X-ray-motivated SZ studies have highlighted the presence of an excess SZ signal south-east of the X-ray peak, which was generally interpreted as a strong shock-induced pressure perturbation. Our model, when centred at the X-ray peak, confirms this. However, the presence of two almost equally bright giant elliptical galaxies separated by ∼100 kpc makes the choice of the cluster centre ambiguous, and allows for considerable freedom in modelling the structure of the galaxy cluster. For instance, we have shown that the SZ signal can be well described by a single smooth ellipsoidal generalized Navarro–Frenk–White profile, where the best-fitting centroid is located between the two brightest cluster galaxies. This leads to a considerably weaker excess SZ signal from the south-eastern substructure. Further, the most prominent features seen in the X-ray can be explained as predominantly isobaric structures, alleviating the need for highly supersonic velocities, although overpressurized regions associated with the moving subhaloes are still present in our model.

scholarly journals “Complexity–entropy” diagrams and their application to the study of coal tectonic disturbance

2019 ◽  
Vol 129 ◽  
pp. 01016 ◽  
Author(s):  
Olga Malinnikova ◽  
Dmitry Uchaev ◽  
Denis Uchaev ◽  
Vasiliy Malinnikov ◽  
Catherine Ulyanova
Keyword(s):  
Electron Microscope ◽  
Quantitative Description ◽  
Structural Complexity ◽  
Vertical Axis ◽  
Complexity Measure ◽  
Entropy Measure ◽  
Data Sets ◽  
Two Dimensional ◽  
Outburst Hazard ◽  
Scanning Electron

It is investigated the possibility of using so–called "complexity–entropy" diagrams for the quantitative description of the degree of coal disturbance using coal images obtained by means of scanning electron microscope (SEM). These diagrams plot structural complexity measure (vertical axis) versus entropy measure (horizontal axis) for distribution of probability given in some way on the image. In this paper, the values of both measures were calculated on the basis of the shearlet transform, and the Jensen divergence was used as the basic divergence for calculating the complexity measure. All calculations were performed for more than 140 images of coal specimens with various degrees of disturbance, obtained from the quiet zone of the seam and the outburst zone. As a result of research, it was found that two-dimensional distributions for measures of complexity and entropy in most cases are informative data sets for differentiating coals by degree of complexity. Moreover, such characteristics of these distributions as mathematical expectation and, to a less degree, mode can be used as simple quantitative descriptors of coals with various degrees of disturbance. These characteristics can be used to show the closeness of the spatial structure for the analyzed coal specimens to strictly periodic or absolutely chaotic ones. On the basis of the obtained results, conclusions about the possibility to separate coals according to the degree of their outburst hazard were done.

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