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Title: Calibration of drag models for mesoscale simulation of gas–liquid flow through packed beds

Abstract

We propose a systematic approach for calibrating the interphase drag models used in mesoscale CFD simulations of gas-liquid flow through packed beds. The method uses packing specific liquid holdup and pressure drop data to find model coefficients that both minimize relative error in the two-phase momentum equations and ensure correct limiting single phase behavior of the model. The approach is demonstrated for three phenomenological drag models from the literature using data from a random Bialecki ring packing under counterflow conditions. Calibration results in a 2-3 fold reduction in the mean relative error for predicted pressure gradient/liquid holdup, relative to predictions with “standard” Ergun coefficients, when an idealized set of momentum equations are solved. The calibrated models are then implemented into a two-fluid CFD solver and used to perform a two-dimensional time accurate simulation of the same packed bed at high gas and liquid flowrates. The predicted mean flow properties are in very good agreement with macroscale experimental data, while the instantaneous liquid distributions show significant transient, axial and lateral nonuniformities.

Authors:
;
Publication Date:
Research Org.:
National Energy Technology Lab. (NETL), Pittsburgh, PA, and Morgantown, WV (United States). In-house Research
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1467010
Report Number(s):
NETL-PUB-21036
Journal ID: ISSN 0009-2509; PII: S0009250917304682
Resource Type:
Journal Article
Journal Name:
Chemical Engineering Science
Additional Journal Information:
Journal Volume: 172; Journal Issue: C; Journal ID: ISSN 0009-2509
Country of Publication:
United States
Language:
English
Subject:
20 FOSSIL-FUELED POWER PLANTS; 97 MATHEMATICS AND COMPUTING; 99 GENERAL AND MISCELLANEOUS; mesoscale cfd, gas-liquid flow, packed bed, drag model, calibration

Citation Formats

Finn, Justin R., and Galvin, Janine E.. Calibration of drag models for mesoscale simulation of gas–liquid flow through packed beds. United States: N. p., 2017. Web. doi:10.1016/j.ces.2017.07.022.
Finn, Justin R., & Galvin, Janine E.. Calibration of drag models for mesoscale simulation of gas–liquid flow through packed beds. United States. doi:10.1016/j.ces.2017.07.022.
Finn, Justin R., and Galvin, Janine E.. Wed . "Calibration of drag models for mesoscale simulation of gas–liquid flow through packed beds". United States. doi:10.1016/j.ces.2017.07.022. https://www.osti.gov/servlets/purl/1467010.
@article{osti_1467010,
title = {Calibration of drag models for mesoscale simulation of gas–liquid flow through packed beds},
author = {Finn, Justin R. and Galvin, Janine E.},
abstractNote = {We propose a systematic approach for calibrating the interphase drag models used in mesoscale CFD simulations of gas-liquid flow through packed beds. The method uses packing specific liquid holdup and pressure drop data to find model coefficients that both minimize relative error in the two-phase momentum equations and ensure correct limiting single phase behavior of the model. The approach is demonstrated for three phenomenological drag models from the literature using data from a random Bialecki ring packing under counterflow conditions. Calibration results in a 2-3 fold reduction in the mean relative error for predicted pressure gradient/liquid holdup, relative to predictions with “standard” Ergun coefficients, when an idealized set of momentum equations are solved. The calibrated models are then implemented into a two-fluid CFD solver and used to perform a two-dimensional time accurate simulation of the same packed bed at high gas and liquid flowrates. The predicted mean flow properties are in very good agreement with macroscale experimental data, while the instantaneous liquid distributions show significant transient, axial and lateral nonuniformities.},
doi = {10.1016/j.ces.2017.07.022},
journal = {Chemical Engineering Science},
issn = {0009-2509},
number = C,
volume = 172,
place = {United States},
year = {2017},
month = {11}
}