Comparative Study of Mixture Model and Eulerian Model used in Hydro Cyclone with the Help of CFD Simulation

2020 ◽  
Author(s):  
Indrashis Saha ◽  
Tathagata Mukherjee ◽  
Richa Pandey
Keyword(s):  
Comparative Study ◽  
Mixture Model ◽  
Cfd Simulation ◽  
Eulerian Model

Hyperspectral Blind Unmixing and Multiple Target Detection Using Linear Mixture Model

2012 ◽  
Vol 488-489 ◽  
pp. 1224-1228
Author(s):  
Qaisar Mushtaq ◽  
Ihsan Ul Haq ◽  
Muhammad Ahmad ◽  
Muhammad Sohaib
Keyword(s):  
Comparative Study ◽  
Blind Source Separation ◽  
Mixture Model ◽  
Target Detection ◽  
Hyperspectral Imagery ◽  
Component Analysis ◽  
Separation Technique ◽  
Multiple Target ◽  
Linear Mixture Model ◽  
Vertex Component Analysis

In this paper a blind source separation technique Joint Approximate Diagonalization of Eigen-matrices (JADE) is investigated to unmixing and multiple target detection for hyperspectral imagery data. Our targeted minerals are Alunite, Buddingtonite, Calcite and Kaolinite in ‘Cuprite’ scene data that has been widely used for research experiments in hyperspectral imagery. A comparative study is conducted to show the effectiveness of the JADE with Vertex Component Analysis. The results are evaluated with both full and reduced bands.

scholarly journals Comparative Study of Mixture Model and Eulerian Model Used in Hydrocyclone with the Help of CFD Simulation

2020 ◽  
Author(s):  
Indrashis Saha ◽  
Tathagata Mukherjee
Keyword(s):  
Fluid Flow ◽  
Mixture Model ◽  
Particle Motion ◽  
Numerical Study ◽  
Research Work ◽  
Tangential Velocity ◽  
Volume Of Fluid ◽  
Slight Variation ◽  
Multiphase Model ◽  
Eulerian Model

Due to the accuracy of numerical calculation of fluid flow inside a hydrocyclone can be obtained using Computational Fluid Dynamics (CFD), highly modified super computers are used to simulate the fluid flow and track particle motion inside a hydrocyclone. This paper deals with the numerical study using three multiphase models viz. Volume of fluid, Mixture and Eulerian model. The dimensions of the hydrocyclone taken into consideration for numerical analysis is same as considered by Rajamani. Validation of axial and tangential velocities at different strategically decided axial stations, RMS axial and tangential velocity profiles of the hydrocyclone is done using Reynolds Stress Model (RSM). The hydrocyclone model has been designed in Creo 3.0 using the same dimensions which later was imported to CFD for meshing. Fine hexagonal mesh numbering up to 5 lacs were constructed to obtain optimum results. Fluid flow was allowed to be developed in ANSYS FLUENT 16.2. Entire simulation took 96 hours to generate results and track particle movements inside the hydrocyclone. The particle tracking has been done using three multiphase model. The first being the volume of fluid was used for validation purposes and the comparison of the Mixture and Eulerian model are the basic focus of this research work. Conclusive results indicate that usage of different multiphase model does not result in variation in particle motion. The slight variation in grade efficiency values are hardly noticeable. The Mixture model and Eulerian model predict lower separation efficiency as compared with Volume of fluid multiphase model.

A Mie-Grüneisen mixture Eulerian model for underwater explosion

2014 ◽  
Vol 31 (3) ◽  
pp. 425-452 ◽  
Author(s):  
Zongduo Wu ◽  
Zhi Zong ◽  
Lei Sun
Keyword(s):  
Mixture Model ◽  
Mass Fraction ◽  
Equations Of State ◽  
Underwater Explosion ◽  
Eulerian Model ◽  
Fem Model ◽  
Content Type ◽  
Fluid Field ◽  
Structure Problem ◽  
Multi Phase

Purpose – The purpose of this paper is to provide an improved Mie-Grüneisen mixture model to simulate underwater explosion (UNDEX). Design/methodology/approach – By using Mie-Grüneisen equations of state (EOS) to model explosive charge, liquid water and solid structure, the whole fluid field is considered as a multi-phases mixture under Mie-Grüneisen EOS. Then by introducing auxiliary variables in Eulerian model and using mass fraction to establish a diffusion balance, a new improved Mie-Grüneisen mixture model is presented here. For the new reconstructed mixture model, a second order MUSCL scheme with TVD limiter is employed to solve the multi-phase Riemann problem. Findings – Numerical examples show that the results obtained by Mie-Grüneisen mixture model are quite closed to theoretical and empirical data. The model can be also used in 2-D fluid-structure problem of UNDEX effectively and it is proved that the deformation of structure can be clearly described by mass fraction. Research limitations/implications – The FVM model based on mass fraction can only describe the motion of compressible material under impact. Material failure or large deformation needs a modification about the EOS or implementations of other models (i.e. FEM model). Originality/value – An improved non-oscillation Mie-Grüneisen mixture model, which based on mass fraction, is given in the present paper. The present Mie-Grüneisen mixture model provides a simplified and efficient way to simulate UNDEX. The feasibility of this model to simulate the detonation impacts on different mediums, including water and other metal mediums, is tested and verified here. Then the model is applied to the simulation of underwater contact explosion problem. In the simulation, deformation of structure under explosion loads, as well as second shock wave, are studied here.

A RANK-TWO NMF CLUSTERING: APPLICATION TO GLIOBLASTOMAS CHARACTERIZATION AND COMPARATIVE STUDY

2019 ◽  
Vol 31 (03) ◽  
pp. 1950019
Author(s):  
Aymen Bougacha ◽  
Jihene Boughariou ◽  
Ines Njeh ◽  
Omar Kammoun ◽  
Kheireddine Ben Mahfoudh ◽  
...  
Keyword(s):  
Comparative Study ◽  
Mixture Model ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Qualitative Assessment ◽  
Unsupervised Segmentation ◽  
Linear Mixture Model ◽  
Tumor Region ◽  
Four Levels ◽  
Competitive Efficiency

This paper explores a novel clustering approach for multimodal Glioblastomas (GBM) characterization using the magnetic resonance image (MRI) modality. We define our segmentation problem as a linear mixture model (LMM). In every segmentation process, we generate a non-negative matrix with GLCM features from every MRI slice and a rank-two NMF (Non Negative Matrix Factorization) is applied. Our method process in four levels of segmentation. In the first one, the LMM matrix for the whole brain was generated from FLAIR modality to extract whole tumor region, which considered as the region of Interest (ROI). In the second level, we extract the ROI from T1c modality and the LMM matrix was generated from only this ROI to extract necrosis region. The principle will be the same for the other two levels to extract the enhanced and the non-enhanced region. Quantitative and qualitative assessment over the publicly dataset from MICCAI 2015 challenge (BRATS 2015) demonstrated that the proposed method could generate a competitive efficiency for high grade Glioblastomas characterization among several competing method. In order to highlight the performance of our method, we propose a comparative study with unsupervised segmentation methodologies (K-means, fuzzy C-means (FCM), gaussian mixture model (GMM) and hierarchical non-negative factorization (hNMF)) over the publicly BRATS 2015 dataset by computing validation metrics (the sensitivity, the dice and the specificity). The obtained results could attest the performance of the proposed algorithm compared to the unsupervised segmentation methodologies.

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