A conservative fully implicit algorithm for predicting slug flows

Krasnopolsky, Boris; Lukyanov, Alexander A.

doi:10.1016/j.jcp.2017.11.032

Cited by 11 publications

(8 citation statements)

References 34 publications

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“…We take advantage of this fact by providing to the solver a sparsity pattern consisting of an array of ones, indicating the elements in the Jacobian that are nonzero. This significantly reduces the computational time (e.g., see [22]).…”

Section: Numerical Aspectsmentioning

confidence: 99%

A One-Dimensional Mechanistic Model for Tracking Unsteady Slug Flow

Padrino

Srinil

Kurushina

et al. 2021

Volume 10: Fluids Engineering

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A novel one-dimensional slug tracking mechanistic model for unsteady, upward gas-liquid slug flow in inclined pipes is presented. The model stems from the first principles of mass and momentum conservation applied to a slug unit cell consisting of a slug body of liquid and a region of stratified flow containing an elongated bubble and a liquid film. The slug body front and rear are treated as surfaces of discontinuity where mass and momentum balances or “jump laws” are prescribed. The former is commonly applied in mechanistic models for slug flow, whereas the latter is typically overlooked, thereby leading to the assumption of a continuous pressure profile at these points or to the adoption of a pressure drop due to the fluid acceleration on a heuristic basis. Our analysis shows that this pressure change arises formally from the momentum jump law at the slug body front. The flow is assumed to be isothermal, the gas is compressible, the pressure drop in the elongated bubble region is accounted for, the film thickness is considered uniform, and weight effects in the pressure from the interface level are included. Besides specifying momentum jump laws at both borders of the slug body, another novel feature of the present model is that we avoid adopting the quasi-steady approximation for the elongated bubble-liquid film region, and thus the unsteady terms in the mass and momentum balances are kept. The present model requires empirical correlations for the slug body length and the elongated bubble nose velocity. The non-linear equations are discretized and solved simultaneously for all the slug unit cells filling the pipe. Time-space variation of the slug body and film lengths, liquid holdup and void fraction, and pressures, among other quantities, can be predicted, and model performance is evaluated by comparing with data in the literature.

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Section: Numerical Aspectsmentioning

confidence: 99%

A One-Dimensional Mechanistic Model for Tracking Unsteady Slug Flow

Padrino

Srinil

Kurushina

et al. 2021

Volume 10: Fluids Engineering

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“…We consider the incompressible form of the TFM, for (separated) stratified flow, assuming a hydrostatic balance between the fluids 5 . One reason this model is of interest, is its potential to dynamically simulate the transition from stratified flow to slug flow 6,7 . This stands in contrast to for example the drift‐flux model, where the two velocities are not modelled separately: instead a closure relation is introduced for their difference 8,9 .…”

Section: Introductionmentioning

confidence: 99%

“…5 One reason this model is of interest, is its potential to dynamically simulate the transition from stratified flow to slug flow. 6,7 This stands in contrast to for example the drift-flux model, where the two velocities are not modelled separately: instead a closure relation is introduced for their difference. 8,9 The transition to slug flow can occur through hydrodynamic instabilities that arise naturally for the TFM.…”

Section: Introductionmentioning

confidence: 99%

Energy‐consistent formulation of the pressure‐free two‐fluid model

Buist

Sanderse

Dubinkina

et al. 2023

Numerical Methods in Fluids

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The pressure‐free two‐fluid model (PFTFM) is a recent reformulation of the one‐dimensional two‐fluid model (TFM) for stratified incompressible flow in ducts (including pipes and channels), in which the pressure is eliminated through intricate use of the volume constraint. The disadvantage of the PFTFM was that the volumetric flow rate had to be specified as an input, even though it is an unknown quantity in case of periodic boundary conditions. In this work, we derive an expression for the volumetric flow rate that is based on the demand for energy (and momentum) conservation. This leads to PFTFM solutions that match those of the TFM, justifying the validity and necessity of the derived choice of volumetric flow rate. Furthermore, we extend an energy‐conserving spatial discretization of the TFM, in the form of a finite volume scheme, to the PFTFM. We propose a discretization of the volumetric flow rate that yields discrete momentum and energy conservation. The discretization is extended with an energy‐conserving discretization of the source terms related to gravity acting in the streamwise direction. Our numerical experiments confirm that the discrete energy is conserved for different problem settings, including sloshing in an inclined closed tank, and a traveling wave in a periodic domain. The PFTFM solutions and the volumetric flow rates match the TFM solutions, with reduced computation time, and with exact momentum and energy conservation.

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“…The test case of multiphase flow in a pipe is particularly challenging due to the difficulties such as the space-time evolution of multiphase flow patterns (stratified, bubbly, slug, annular), the turbulent phase-tophase interactions, the drag, inertia and wake effects that arise for the HFM from the high aspect ratio (length to diameter) of the domain of a typical pipe. Many address this by developing one dimensional (flow regime-dependent or -independent) models for long pipes [78][79][80]. Nevertheless, such models contain some uncertainties as they rely on several closure or empirical expressions [81] under the limited experimental data [82] in describing, for example, the 3D space-time variations of interfacial frictional forces with phase distributions (the bubble/drop entrainment, the bubble-induced turbulence, the phase interfacial interactions), depending on the flow pattern, flow direction and pipe physical properties (inclination, diameter, length).…”

Section: Introductionmentioning

confidence: 99%

An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes

Heaney,

Wolffs,

Tómasson

et al. 2022

Preprint

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The modelling of multiphase flow in a pipe presents a significant challenge for high-resolution computational fluid dynamics (CFD) models due to the high aspect ratio (length over diameter) of the domain. In subsea applications, the pipe length can be several hundreds of kilometres versus a pipe diameter of just a few inches.Approximating CFD models in a low-dimensional space, reduced-order models have been shown to produce accurate results with a speed-up of orders of magnitude. In this paper, we present a new AI-based nonintrusive reduced-order model within a domain decomposition framework (AI-DDNIROM) which is capable of making predictions for domains significantly larger than the domain used in training. This is achieved by (i) using a domain decomposition approach; (ii) using dimensionality reduction to obtain a low-dimensional space in which to approximate the CFD model; (iii) training a neural network to make predictions for a single subdomain; and (iv) using an iteration-by-subdomain technique to converge the solution over the whole domain. To find the low-dimensional space, we explore several types of autoencoder networks, known for their ability to compress information accurately and compactly. The performance of the autoencoders is assessed on two advection-dominated problems: flow past a cylinder and slug flow in a pipe. To make predictions in time, we exploit an adversarial network which aims to learn the distribution of the training data, in addition to learning the mapping between particular inputs and outputs. This type of network has shown the potential to produce realistic outputs. The whole framework is applied to multiphase slug flow in a horizontal pipe for which an AI-DDNIROM is trained on high-fidelity CFD simulations of a pipe of length 10 m with an aspect ratio of 13:1, and tested by simulating the flow for a pipe of length 98 m with an aspect ratio of almost 130:1. Statistics of the flows obtained from the CFD simulations are compared to those of the AI-DDNIROM predictions to demonstrate the success of our approach.

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A conservative fully implicit algorithm for predicting slug flows

Cited by 11 publications

References 34 publications

A One-Dimensional Mechanistic Model for Tracking Unsteady Slug Flow

A One-Dimensional Mechanistic Model for Tracking Unsteady Slug Flow

Energy‐consistent formulation of the pressure‐free two‐fluid model

An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes

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