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Developing theory of probability density function for stochastic modeling of turbulent gas-particle flows

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  • Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China

Received date: 2017-09-07

  Revised date: 2017-12-21

  Online published: 2018-07-01

Supported by

Project supported by the National Natural Science Foundation of China (No. 51390493)

Abstract

Turbulent gas-particle flows are studied by a kinetic description using a probability density function (PDF). Unlike other investigators deriving the particle Reynolds stress equations using the PDF equations, the particle PDF transport equations are directly solved either using a finite-difference method for two-dimensional (2D) problems or using a Monte-Carlo (MC) method for three-dimensional (3D) problems. The proposed differential stress model together with the PDF (DSM-PDF) is used to simulate turbulent swirling gas-particle flows. The simulation results are compared with the experimental results and the second-order moment (SOM) two-phase modeling results. All of these simulation results are in agreement with the experimental results, implying that the PDF approach validates the SOM two-phase turbulence modeling. The PDF model with the SOM-MC method is used to simulate evaporating gas-droplet flows, and the simulation results are in good agreement with the experimental results.

Cite this article

Lixing ZHOU . Developing theory of probability density function for stochastic modeling of turbulent gas-particle flows[J]. Applied Mathematics and Mechanics, 2018 , 39(7) : 1019 -1030 . DOI: 10.1007/s10483-018-2344-8

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