A1 Journal article (refereed), original research

Comparison of Moment-Based Methods for Representing Droplet Size Distributions in Supersonic Nucleating Flows of Steam


Publication Details
Authors: Afzalifar Ali, Turunen-Saaresti Teemu, Grönman Aki
Publisher: American Society of Mechanical Engineers (ASME)
Publication year: 2018
Language: English
Volume number: 140
Issue number: 2
ISSN: 0098-2202
eISSN: 1528-901X
JUFO-Level of this publication: 1
Open Access: Not an Open Access publication

Abstract

This
paper investigates the performance of moment-based methods and a
monodispersed model (Mono) in predicting the droplet size distribution
and behavior of wet-steam flows. The studied moment-based methods are a
conventional method of moments (MOM) along with its enhanced version
using Gaussian quadrature, namely the quadrature method of moments
(QMOM). The comparisons of models are based on the results of an
Eulerian–Lagrangian (E–L) method, as the benchmark calculations,
providing the full spectrum of droplet size. In contrast, for the MOM,
QMOM, and Mono an Eulerian reference frame is chosen to cast all the
equations governing the phase transition and fluid motion. This choice
of reference frame is essential to draw a meaningful comparison
regarding complex flows in wet-steam turbines as the most important
advantage of the moment-based methods is that the moment-transport
equations can be conveniently solved in an Eulerian frame. Thus, the
moment-based method can avoid the burdensome challenges in working with a
Lagrangian framework for complicated flows. The main focus is on the
accuracy of the QMOM and MOM in representing the water droplet size
distribution. The comparisons between models are made for two supersonic
low-pressure nozzle experiments reported in the literature. Results
show that the QMOM, particularly inside the nucleation zone, predicts
moments closer to those of the E–L method. Therefore, for the test case
in which the nucleation is significant over a large proportion of the
domain, the QMOM provides results in clearly better agreements with the
E–L method in comparison with the MOM.


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Last updated on 2019-10-10 at 13:58