scholarly journals Dispersed phase volume fraction, weak acids and Tween 80 in a model emulsion: Effect on the germination and growth of Bacillus weihenstephanensis KBAB4 spores

2018 ◽  
Vol 109 ◽  
pp. 288-297 ◽  
Author(s):  
Lucie Léonard-Akkari ◽  
Stéphanie Guégan ◽  
Fabienne Courand ◽  
Olivier Couvert ◽  
Jean-François Lepage ◽  
...  
Keyword(s):  
Volume Fraction ◽  
Tween 80 ◽  
Dispersed Phase ◽  
Phase Volume ◽  
Weak Acids ◽  
Phase Volume Fraction ◽  
Bacillus Weihenstephanensis ◽  
Germination And Growth ◽  
Model Emulsion

Prediction of Frictional Pressure Gradient in Horizontal Oil/Water Dispersion Flow

SPE Journal ◽  
2010 ◽  
Vol 16 (01) ◽  
pp. 148-154 ◽  
Author(s):  
Jany Carolina Vielma ◽  
Ovadia Shoham ◽  
Ram S. Mohan ◽  
Luis E. Gomez
Keyword(s):  
Pressure Gradient ◽  
Volume Fraction ◽  
Average Error ◽  
Dispersed Phase ◽  
Phase Volume ◽  
Water Dispersion ◽  
Phase Volume Fraction ◽  
Law Of The Wall ◽  
Oil Water ◽  
The Law

Summary A novel model has been developed for the prediction of frictional pressure gradient in unstable turbulent oil/water dispersion flow in horizontal pipes. This model uses the friction-factor approach, based on the law of the wall, to predict the pressure gradient. Modification of both the von Karman coefficient κ' and the parameter B' have been carried out in the law of the wall to include the effect of the dispersed phase—namely, the dispersed-phase volume fraction and the characteristic-droplet-size diameters. The developed model applies to both dilute and dense flows, covering the entire range of water cuts. Model predictions have been compared with a comprehensive experimental database collected from literature, resulting in an absolute average error of 9.6%. Also, the comparisons demonstrate that the developed model properly represents the physical phenomena exhibited in unstable turbulent oil/water dispersions. These include drag reduction, increase in frictional pressure gradient with increasing dispersed-phase volume fraction, and the peak in the frictional pressure gradient at the oil/water phase-inversion region.

Numerical simulation of a concentrated emulsion in shear flow

1996 ◽  
Vol 321 ◽  
pp. 395-419 ◽  
Author(s):  
M. Loewenberg ◽  
E. J. Hinch
Keyword(s):  
Shear Flow ◽  
Volume Fraction ◽  
Flow Direction ◽  
Continuous Phase ◽  
Boundary Integral ◽  
Dispersed Phase ◽  
Phase Volume ◽  
Phase Volume Fraction ◽  
Rigid Particles ◽  
Concentrated Emulsion

A three-dimensional computer simulation of a concentrated emulsion in shear flow has been developed for low-Reynolds-number finite-capillary-number conditions. Numerical results have been obtained using an efficient boundary integral formulation with periodic boundary conditions and up to twelve drops in each periodically replicated unit cell. Calculations have been performed over a range of capillary numbers where drop deformation is significant up to the value where drop breakup is imminent. Results have been obtained for dispersed-phase volume fractions up to 30% and dispersed- to continuous-phase viscosity ratios in the range of 0 to 5. The results reveal a complex rheology with pronounced shear thinning and large normal stresses that is associated with an anisotropic microstructure that results from the alignment of deformed drops in the flow direction. The viscosity of an emulsion is only a moderately increasing function of the dispersed-phase volume fraction, in contrast to suspensions of rigid particles or undeformed drops. Unlike rigid particles, deformable drops do not form large clusters.

scholarly journals Electrical Conductivity of Field-Structured Emulsions

Fluids ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 74
Author(s):  
Arthur R. Zakinyan ◽  
Ludmila M. Kulgina ◽  
Anastasia A. Zakinyan ◽  
Sergey D. Turkin
Keyword(s):  
Magnetic Field ◽  
Electrical Conductivity ◽  
Volume Fraction ◽  
Low Frequency ◽  
Maximal Response ◽  
Dispersed Phase ◽  
Phase Volume ◽  
Disperse Systems ◽  
Phase Volume Fraction ◽  
The Magnetic Field

The structure formation influence on various macroscopic properties of fluid–fluid disperse systems is poorly investigated. The present work deals with the experimental study of the charge transfer in emulsions whose dispersed phase droplets are arranged into chainlike structures under the action of an external force field. The emulsions studied are the fluid system in which water droplets are dispersed in a hydrocarbon-based magnetic fluid. Under the effect of an external uniform magnetic field, anisotropic aggregates form from the emulsion dispersed phase drops. The low-frequency electrical conductivity of emulsions has been measured. It is demonstrated that the emulsions’ conductivity grows several times under the effect of magnetic field parallel to the measuring electrical field. The anisotropic character of the emulsion electrical conductivity in the presence of magnetic field has been demonstrated. It is revealed that the maximal response of conductivity on the magnetic field action takes place at the dispersed phase volume fraction of about 20%. The dynamics of the conductivity variation is analyzed in dependence on the magnetic field strength and the dispersed phase volume fraction. The obtained results may be of interest in the development of potential applications of disperse systems with magnetic-field-controllable properties.

scholarly journals Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Wook Kim ◽  
Seong-Hoon Kang ◽  
Se-Jong Kim ◽  
Seungchul Lee
Keyword(s):  
High Performance ◽  
Materials Science ◽  
Volume Fraction ◽  
Training Data ◽  
Phase Fraction ◽  
Phase Volume ◽  
Generative Adversarial Network ◽  
Phase Volume Fraction ◽  
Original Dataset ◽  
Multi Phase

AbstractAdvanced high strength steel (AHSS) is a steel of multi-phase microstructure that is processed under several conditions to meet the current high-performance requirements from the industry. Deep neural network (DNN) has emerged as a promising tool in materials science for the task of estimating the phase volume fraction of these steels. Despite its advantages, one of its major drawbacks is its requirement of a sufficient amount of training data with correct labels to the network. This often comes as a challenge in many areas where obtaining data and labeling it is extremely labor-intensive. To overcome this challenge, an unsupervised way of learning DNN, which does not require any manual labeling, is proposed. Information maximizing generative adversarial network (InfoGAN) is used to learn the underlying probability distribution of each phase and generate realistic sample points with class labels. Then, the generated data is used for training an MLP classifier, which in turn predicts the labels for the original dataset. The result shows a mean relative error of 4.53% at most, while it can be as low as 0.73%, which implies the estimated phase fraction closely matches the true phase fraction. This presents the high feasibility of using the proposed methodology for fast and precise estimation of phase volume fraction in both industry and academia.

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