A conservative fully implicit algorithm for predicting slug flows
- 1. Schlumberger Moscow Research, 109147 Moscow, Pudovkina st. 13 (Russian Federation)
- 2. Institute of Mechanics, Lomonosov Moscow State University, 119192, Moscow, Michurinsky ave. 1 (Russian Federation)
- 3. Schlumberger–Doll Research, One Hampshire street, Cambridge, 02139 MA (United States)
Description
An accurate and predictive modelling of slug flows is required by many industries (e.g., oil and gas, nuclear engineering, chemical engineering) to prevent undesired events potentially leading to serious environmental accidents. For example, the hydrodynamic and terrain-induced slugging leads to unwanted unsteady flow conditions. This demands the development of fast and robust numerical techniques for predicting slug flows. The presented in this paper study proposes a multi-fluid model and its implementation method accounting for phase appearance and disappearance. The numerical modelling of phase appearance and disappearance presents a complex numerical challenge for all multi-component and multi-fluid models. Numerical challenges arise from the singular systems of equations when some phases are absent and from the solution discontinuity when some phases appear or disappear. This paper provides a flexible and robust solution to these issues. A fully implicit formulation described in this work enables to efficiently solve governing fluid flow equations. The proposed numerical method provides a modelling capability of phase appearance and disappearance processes, which is based on switching procedure between various sets of governing equations. These sets of equations are constructed using information about the number of phases present in the computational domain. The proposed scheme does not require an explicit truncation of solutions leading to a conservative scheme for mass and linear momentum. A transient two-fluid model is used to verify and validate the proposed algorithm for conditions of hydrodynamic and terrain-induced slug flow regimes. The developed modelling capabilities allow to predict all the major features of the experimental data, and are in a good quantitative agreement with them.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.jcp.2017.11.032Additional details
Identifiers
- DOI
- 10.1016/j.jcp.2017.11.032;
- PII
- S0021999117308707;
Publishing Information
- Journal Title
- Journal of Computational Physics (Print)
- Journal Volume
- 355
- Journal Page Range
- p. 597-619
- ISSN
- 0021-9991
- CODEN
- JCTPAH
INIS
- Country of Publication
- Netherlands
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 52118879
- Subject category
- S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
- Descriptors DEI
- ACCIDENTS; ACCOUNTING; ALGORITHMS; CHEMICAL ENGINEERING; FLUIDS; HELMHOLTZ INSTABILITY; HYDRODYNAMICS; IMPLEMENTATION; LINEAR MOMENTUM; NUCLEAR ENGINEERING; OILS; SIMULATION; UNSTEADY FLOW
- Descriptors DEC
- ENGINEERING; FLUID FLOW; FLUID MECHANICS; INSTABILITY; MATHEMATICAL LOGIC; MECHANICS; ORGANIC COMPOUNDS; OTHER ORGANIC COMPOUNDS; PLASMA INSTABILITY; PLASMA MACROINSTABILITIES
Optional Information
- Copyright
- Copyright (c) 2017 Elsevier Inc. All rights reserved.