Campus Units

Chemical and Biological Engineering, Ames Laboratory, Mechanical Engineering

Document Type

Article

Research Focus Area

Computational Fluid Dynamics

Publication Version

Accepted Manuscript

Publication Date

2-1-2021

Journal or Book Title

Chemical Engineering Journal

Volume

405

First Page

127020

DOI

10.1016/j.cej.2020.127020

Abstract

The quadrature-based semi-analytical solution for the conditional moment closure (SA-CMC) given in (A. D. Ilgun, A. Passalacqua, and R. O. Fox, “A quadrature-based conditional moment closure for mixing-sensitive reactions,” Chem. Eng. Sci., 226, 2020) eliminates the additional conditioning-space discretization in CMC applications by assuming that the mixture-fraction PDF is well represented by a β-PDF. A Gaussian quadrature provides the mixture-fraction abscissae, and the conditional scalar mean is expressed in terms of Jacobi polynomials. Here, by preserving the computational efficiency of SA-CMC, a novel quadrature-based moment method (QBMM-CMC) is developed for CMC applications, which does not assume the form of the mixture-fraction PDF. Remarkably, by solving the closed forms for the micromixing terms from CMC, exact expressions result for the mixture-fraction moments of any order. Thus, QBMM-CMC covers cases where the mixture-fraction PDF cannot be well represented by a β-PDF and can be applied to disperse multiphase flows with mass transfer (e.g., droplet evaporation). For single-phase and multiphase pure-mixing problems, the QBMM-CMC mixture-fraction moments are observed to deviate from the β-PDF. For single-phase mixing with and without dispersed-phase mass transfer, QBMM-CMC predictions for mixing-sensitive competitive-consecutive and parallel reactions are investigated parametrically.

Comments

This is a manuscript of an article published as Ilgun, A. D., R. O. Fox, and A. Passalacqua. "Solution of the first-order conditional moment closure for multiphase reacting flows using quadrature-based moment methods." Chemical Engineering Journal (2020): 127020. DOI: 10.1016/j.cej.2020.127020. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier B.V.

Language

en

File Format

application/pdf

Available for download on Thursday, September 15, 2022

Published Version

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