Stochastic Lagrangian Modeling for Large Eddy Simulation of Dispersed Turbulent Two-Phase Flows

by

Abdallah S. Berrouk

DOI: 10.2174/97816080529671110101
eISBN: 978-1-60805-296-7, 2011
ISBN: 978-1-60805-377-3



Indexed in: Scopus, EBSCO.

Understanding the dispersion and the deposition of inertial particles convected by turbulent flows is a domain of research of consider...[view complete introduction]
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Large Eddy Simulation of Particle-Laden Flows

- Pp. 17-25 (9)

Abdallah Sofiane Berrouk

Abstract

<p>In this chapter, the importance of the dispersed two-phase turbulent flows for industrial and environmental applications is highlighted. Both Eulerian and Lagrangian descriptions of particle-laden flows are reviewed. Since the book's focus is on the stochastic modeling for particle transport by subfilter motion in a Lagrangian framework, more emphasis is put on the Lagrangian description. Though the stochastic modeling is so far only tested in a context of one-way coupling between dispersed and carrier phases, the importance that subfilter motion may have in predicting turbulence modulation is also explained.</p> <p>Success in simulating particle-laden turbulent flows relies heavily on a greater understanding of the interaction of the two phases. This can lead undoubtedly to increases in performance, reduction in cost and/or improved safety in systems where they are encountered. It also increases the quality of predictions of the effectiveness of natural flow phenomena for dispersing particulate pollutants to acceptable concentration levels.</p> <p>In principle, the direct numerical simulation (DNS) of turbulent flows, involving a large number of particles, with appropriate boundary and initial conditions would describe completely the two-phase flows. Due to the high computational cost of DNS, both the velocity field of the carrier phase and trajectories of particles can be calculated through Large Eddy Simulations (LES). Yet another method, Stochastic Modeling (SM) coupled to RANS calculations can be used. The aim of RANS/SM is to reduce the computational effort through generating a synthetic turbulent flow field with statistical properties of interest identical to that of the real turbulent flow.</p>

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