Two phase flow

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Revision as of 15:11, 1 October 2007

1 Introduction
- 1.1 Importance of two phase flow in industrial configurations
- 1.2 Overview of the different available approach
2 Lagrangian dispersed two-phase flow modelling
- 2.1 Eddy Interaction Model
- 2.2 Time Correlated Model
3 Mixture model for two phase flow
4 Eulerian Two fluids approach
5 Conclusion
6 References

Introduction

article in progress

Importance of two phase flow in industrial configurations

Two phase flow phenomena occur in various industrial application in all fluid mechanics application fiels. Aerospace, automotive, nuclear applications, etc. In all this domain, prediction of two phase behaviour is important. Prediction of liquid spray in an internal combustion engine should enable us to have a better control on combustion process and then to reduce pollutant emissions. Controlling water - steam equilibrium in a coller system enable to prevent from industrial accident, de-icing/anti-icing of aircraft on the ground etc. Any other examples can be quoted here.

Overview of the different available approach

Two main family can be distinguished to model two phase flow, depending of the two phase configuration approach. In case of dispersed configuration a lagrangian approach is suitable. Such an approach consists in following dropplets (or bubbles) during then movement. This is done by applying external force on the particle and solving acceleration, then velocity and finally position. On the other hand, two phase flow can be solve with an eulerien approach. As in all eulerian framework, this approach consists in considering inlet and outlet flux in a given volume. In such an eulerian approach, two family can be distinguished : Mixture model and Two fluids model, those two approach will be detailed in corresponding section bellow.

Lagrangian dispersed two-phase flow modelling

article in progress

The main goal in Lagrangien approaches is to statiscally particle history in given flow fields. The starting point in Lagrangien approach is the fundamental law of dynamics:

$m_p\frac{dV_p}{dt} = F(t)$

$\frac{dx_p}{dt} = V_p$

Where ${F(t)}$ stands for the resulting force on the particle.

Resolution of these equations requires the knowledge of the instantaneous velocity of the fluid at particle position. The problem is thus to track fluid particles along the discrete particle trajectory. A fluid particle instantaneously owns the velocity of the surrounding fluid and the simulation of its trajectory relies on a quite simple equation such as: ${x_i(t+}{\delta}{t)}={x_i(t)}+{u_i}{\delta}{t}$

The instantaneous fluid velocity ${u_i}$ is decomposed into a mean part which is known (from turbulence model prediction) and a fluctuating part ${u_i}^{'}$ . So generating fluctuating part of the fluid velocity is the core of the problem. Generation of the fluid particle velocity fluctuations is based on a Gaussian PDF for the fluid velocities, but different random schemes can then be used to guess the fluid velocity, which are characterized by the underlying fluid Lagrangian correlation function ${R_{fl}}$ .

${R_{fl}(\tau)}=\frac{\overline{{u}^{'}(t){u}^{'}(t+\tau)}}{{\sqrt{\overline{{u}^{'2}(t)}}}{\sqrt{\overline{{u}^{'2}(t+\tau)}}}}$

The first approach was proposed by Gosman and Ioannides (1981) and has been called “eddy life time”. Each fluid velocity fluctuation is kept constant on a time step which is equal to the Lagrangian integral time scale $\tau_l$ . The resulting Lagrangian correlation function is linearly decreasing from 1 to 0 in a time delay equal to $2\tau_l$ :

A first extension has been proposed by Ormancey & Martinon (1984) and is commonly used in the Lagrangian community. In that scheme, a Poisson distribution of the time interval is introduced: each fluid velocity fluctuation is kept constant until a random number (uniformly distributed between 0 and 1) is smaller than ${\delta}{t} / {\tau_l}$ . The resulting fluid Lagrangian correlation function is exponentially decreasing from 1 to 0:

Later, Desjonqueres (1987) proposed a process which can handle any given correlation function. This can be done through a correlation matrix, as described by Berlemont et al (1990) or Boughattas et al. (2006). Moreover, Desjonqueres (1987) introduced the Frenkiel family of correlation function (1948) which are defined by:

Where m is a loop parameter, giving the exponential decrease for m=0. Let us note that this function presents negative loops and m=1 is the value used by Picart (1981) during confrontations of simulations with the experimental data.

It is important to keep in mind that particle dispersion is roughly proportional to the turbulence time/length scales.

Eddy Interaction Model

Time Correlated Model

Mixture model for two phase flow

Basics of the mixture model

MAC approach

VOF method

Eulerian Two fluids approach

Basics of the two fluids approach

Interfacial exchange closures

Turbulence modelling in such a context

Conclusion

References

Gosman A. D., Ionnides I. E. (1981), "Aspects of computer simulation of liqued fuelled combustors", AIAA aerospace sciences meeting, paper 81-0323, St.louis,MO.
Boughattas N., Gazzah M. H., Said R. (2006), "Effects of a co-flow on particles or droplets dispersion and on droplets vaporization in turbulent air flow", ICAMEM2006, Hammamet, Tunisia.
Boughattas N., Gazzah M. H., Said R. (2007), "Lagrangian prediction of particulate two-phase flow", Fifth Mediterranean Combustion Symposium, Monastir, Tunisia.

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