Hydrodynamics of trickle bed reactors at high pressure: Two-phase flow model for pressure drop and liquid holdup, formulation and experimental validation

In this paper, two-phase pressure drop data were obtained for boiling in horizontal rectangular microchannels with a hydraulic diameter of 0.55 mm for R-134a over mass velocities from 790 to 1122, heat fluxes from 0 to 31.08 kW/m2 and vapor qualities from 0 to 0.25. The experimental results show that the Chisholm parameter in the separated flow model relies heavily on the vapor quality, especially in the low vapor quality region (from 0 to 0.1), where the two-phase flow pattern is mainly bubbly and slug flow. Then, the measured pressure drop data are compared with those from six separated flow models. Based on the comparison result, the superficial gas flux is introduced in this paper to consider the comprehensive influence of mass velocity and vapor quality on two-phase flow pressure drop, and a new equation for the Chisholm parameter in the separated flow model is proposed as a function of the superficial gas flux . The mean absolute error (MAE ) of the new flow correlation is 16.82%, which is significantly lower than the other correlations. Moreover, the applicability of the new expression has been verified by the experimental data in other literatures.

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Two-phase flow distribution in 2D trickle-bed reactors

Chemical Engineering Science ◽

10.1016/s0009-2509(98)00360-1 ◽

1999 ◽

Vol 54 (13-14) ◽

pp. 2409-2419 ◽

Cited By ~ 28

Author(s):

Y. Jiang ◽

M.R. Khadilkar ◽

M.H. Al-Dahhan ◽

M.P. Dudukovic

Keyword(s):

Two Phase Flow ◽

Flow Distribution ◽

Phase Flow ◽

Two Phase ◽

Trickle Bed Reactors ◽

Trickle Bed

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Development of a hybrid pressure drop and liquid holdup phenomenological model for trickle bed reactors based on two‐phase volume averaged equations

The Canadian Journal of Chemical Engineering ◽

10.1002/cjce.23892 ◽

2020 ◽

Author(s):

Binbin Qi ◽

Sebastián Uribe ◽

Omar Farid ◽

Muthanna Al‐Dahhan

Keyword(s):

Pressure Drop ◽

Phenomenological Model ◽

Phase Volume ◽

Liquid Holdup ◽

Two Phase ◽

Trickle Bed Reactors ◽

Averaged Equations ◽

Trickle Bed

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Discussion of paper ‘a theoretical solution of the lockhart and martinelli flow model for calculating two-phase flow pressure drop and hold-up’

International Journal of Heat and Mass Transfer ◽

10.1016/0017-9310(73)90267-6 ◽

1973 ◽

Vol 16 (1) ◽

pp. 225-226 ◽

Cited By ~ 2

Author(s):

D. Chisholm

Keyword(s):

Pressure Drop ◽

Flow Model ◽

Two Phase Flow ◽

Theoretical Solution ◽

Phase Flow ◽

Two Phase ◽

Flow Pressure

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Predicting Two Phase Flow Pressure Drop With CFD and ANN

Volume 7: Fluid Flow, Heat Transfer and Thermal Systems, Parts A and B ◽

10.1115/imece2010-37364 ◽

2010 ◽

Author(s):

I˙smail Teke ◽

O¨zden Ag˘ra ◽

Hakan Demir ◽

S¸. O¨zgu¨r Atayılmaz

Keyword(s):

Pressure Drop ◽

Flow Model ◽

Two Phase Flow ◽

Saturation Temperature ◽

Smooth Tube ◽

Phase Flow ◽

Two Phase ◽

Analytical Formulas ◽

Flow Pressure ◽

Good Agreement

In this study, the several well known two-phase viscosity models were used for predicting two-phase flow pressure drop in a smooth tube using Computational Fluid Dynamics (CFD) software at homogenous flow conditions. Pressure drop for two different mass flux values (300 and 650 kg/m2s) for R134a with a saturation temperature of 45 °C in a smooth tube has been modeled according to the homogenous flow model and the results have been compared with the analytical formulas and experimental data from the literature. Three different average viscosity correlations were used. It is seen that the numerical results are in a good agreement with the homogenous flow model and fall in ± 30% band. Also, the results derived from the average viscosity expression are in a good agreement with the results calculated using separated two-phase flow correlations. In addition to this, Artificial Neural Networks (ANNs) were employed for predicting the pressure drop in a horizontal smooth pipe. The trained network gives the best values over the correlations with less than 1% mean relative error.

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