On multiphase turbulence models for collisional fluid–particle flows

2014 ◽  
Vol 742 ◽  
pp. 368-424 ◽  
Author(s):  
Rodney O. Fox

AbstractStarting from a kinetic theory (KT) model for monodisperse granular flow, the exact Reynolds-averaged (RA) equations are derived for the particle phase in a collisional fluid–particle flow. The corresponding equations for a constant-density fluid phase are derived from a model that includes drag and buoyancy coupling with the particle phase. The fully coupled macroscale/hydrodynamic model, rigorously derived from a kinetic equation for the particles, is written in terms of the particle-phase volume fraction, the particle-phase velocity and the granular temperature (or total granular energy). As derived from the hydrodynamic model, the RA turbulence model solves for the RA particle-phase volume fraction, the phase-averaged (PA) particle-phase velocity, the PA granular temperature and the PA turbulent kinetic energy of the particle phase. Thus, unlike in most previous derivations of macroscale turbulence models for moderately dense granular flows, a clear distinction is made between the PA granular temperature $\Theta _\textit {p}$, which appears in the KT constitutive relations, and the particle-phase turbulent kinetic energy $k_\textit {p}$, which appears in the turbulent transport coefficients. The exact RA equations contain unclosed terms due to nonlinearities in the hydrodynamic model and we briefly discuss the available closures for these terms. Finally, we demonstrate by comparing model predictions with direct numerical simulation results that even for non-collisional fluid–particle flows it is necessary to provide separate models for $\Theta _\textit {p}$ and $k_\textit {p}$ in order to correctly account for the effect of the particle Stokes number and mass loading.

Author(s):  
Jesse Capecelatro ◽  
Olivier Desjardins ◽  
Rodney O. Fox

Starting from the kinetic theory (KT) model for monodisperse granular flow, the exact Reynolds-average (RA) equations were recently derived for the particle phase in a collisional gas-particle flow by Fox [1]. The turbulence model solves for the RA particle volume fraction, the phase-average (PA) particle velocity, the PA granular temperature, and the PA particle turbulent kinetic energy (TKE). A clear distinction is made between the PA granular temperature, which appears in the kinetic theory constitutive relations, and the particle-phase turbulent kinetic energy, which appears in the turbulent transport coefficients. Mesoscale direct numerical simulation (DNS) can be used to assess the validity of the closures proposed for the unclosed terms that arise due to nonlinearities in the hydrodynamic model. In order to extract meaningful statistics from simulation results, a separation of length scales must be established to distinguish between the PA particle TKE and the PA granular temperature. In this work, we introduce an adaptive spatial filter with an averaging volume that varies with the local particle-phase volume fraction. This filtering approach ensures sufficient particle sample sizes in order to obtain meaningful statistics while remaining small enough to avoid capturing variations in the mesoscopic particle field. Two-point spatial correlations are computed to assess the validity of the filter in extracting meaningful statistics. The filtering approach is applied to fully-developed cluster-induced turbulence (CIT), where the production of fluid-phase kinetic energy results entirely from momentum coupling with finite-size inertial particles. Simulation results show a strong correlation between the local volume fraction and granular temperature, with maximum values located just upstream of clusters (i.e., where maximum compressibility of the particle velocity field exists), and negligible particle agitation is observed within clusters.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Wook Kim ◽  
Seong-Hoon Kang ◽  
Se-Jong Kim ◽  
Seungchul Lee

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.


2014 ◽  
Vol 224 ◽  
pp. 3-8 ◽  
Author(s):  
Sebastian Kamiński ◽  
Marcel Szymaniec ◽  
Tadeusz Łagoda

In this work an investigation of internal structure influence on mechanical and fatigue properties of ferritic-pearlitic steels is shown. Ferrite grain size and phase volume fraction of three grades of structural steel with similar chemical composition, but different mechanical properties, were examined. Afterwards, samples of the materials were subjected to cyclic bending tests. The results and conclusions are presented in this paper


2016 ◽  
Vol 74 (10) ◽  
pp. 2454-2461
Author(s):  
Qiang Bi ◽  
Juanqin Xue ◽  
Yingjuan Guo ◽  
Guoping Li ◽  
Haibin Cui

The recycling of copper and nickel from metallurgical wastewater using emulsion liquid membrane (ELM) was studied. P507 (2-ethylhexyl phosphonic acid-2-ethylhexyl ester) and TBP (tributyl phosphate) were used as carriers for the extraction of copper and nickel by ELMs, respectively. The influence of four emulsion composition variables, namely, the internal phase volume fraction (ϕ), surfactant concentration (Wsurf), internal phase stripping acid concentration (Cio) and the carrier concentration (Cc), and the process variable treat ratio on the extraction efficiencies of copper or nickel were studied. Under the optimum conditions, 98% copper and nickel were recycled by using ELM. The results indicated that ELM extraction is a promising industrial application technology to retrieve valuable metals in low concentration metallurgical wastewater.


SPE Journal ◽  
2010 ◽  
Vol 16 (01) ◽  
pp. 148-154 ◽  
Author(s):  
Jany Carolina Vielma ◽  
Ovadia Shoham ◽  
Ram S. Mohan ◽  
Luis E. Gomez

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.


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