Modeling of dispersed phase by Lagrangian approach in Fluent


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1 Lappeenranta University of Technology From the SelectedWorks of Kari Myöhänen 2008 Modeling of dispersed phase by Lagrangian approach in Fluent Kari Myöhänen Available at:
2 Theory and simulation of dispersedphase multiphase flows, Autumn 2007 Spring 2008 Modeling of Dispersed Phase by Lagrangian Approach in Fluent 11 March 2008 Kari Myöhänen
3 Presentation Outline Introduction Modeling options and limitations in Fluent Model theory Solution strategies Example calculation
4 Introduction The discrete phase model (DPM) in Fluent follows the EulerLagrange approach. The fluid phase (gas or liquid, continuous phase ) is treated as a continuum by solving the timeaveraged NavierStokes equations (Eulerian reference frame). The dispersed phase is solved by tracking a number of particles through the calculated flow field of continuous phase (Lagrangian reference frame). The particles may be taken to represent solid particles in gas or liquid, liquid droplets in gas or bubbles in liquid. The dispersed phase can exchange momentum, mass and energy with the fluid phase.
5 Discrete Phase Modeling Options in Fluent Fluent provides the following discrete phase modeling options: Calculation of the particle trajectories using a Lagrangian formulation that includes: Discrete phase inertia Hydrodynamic drag Force of gravity Other forces pressure gradient, thermophoretic, rotating reference frame, brownian motion, Saffman lift, and user defined forces Steady state and transient flows. Turbulent dispersion of particles. Heating and cooling of the discrete phase. Vaporization and boiling of liquid droplets. Combusting particles, including volatile evolution and char combustion to simulate coal combustion. Optional twoway coupling of the continuous phase flow and the discrete phase. Wall film modeling. Spray model (droplet collision and breakup).
6 Limitations in Fluent Particleparticle interactions are neglected. Assumption: dispersed phase is sufficiently dilute. Fluent manual provides a hand rule volume fraction usually less than 1012%. In general, this limit is far too high and does not fulfill the requirement of ratio between the momentum response time and collisional time V / C < 1 (see lecture notes, session 1). The DPM model is however often used for dense dispersed flows as well. Care should be taken when interpreting the results. The steady state DPM model cannot be applied for continuous suspension of particles The particle streams should have welldefined entrance and exit conditions. For cases, in which the particles are suspended indefinetely in the continuum (e.g. stirred tanks), the unsteady DPM modeling should be used instead. If the dispersed phase model is used with EulerianEulerian multiphase model the coupling is defined with the primary phase only. Several restrictions when using DPM model with other Fluent models Limitations with parallel computing, streamwise periodic flows, combustion models, sliding meshes, etc. See Fluent manual for details.
7 Regimes of Dispersed TwoPhase Flows fluid particle fluid particle fluid particle particle Sommerfeld (2000), based on Elghobashi (1994).
8 Momentum Equation The force balance of particle in Lagrangian reference frame defines the movement of the particles. The momentum equation for idirection: Acceleration Drag Gravity Additional acceleration due to other forces (force/unit particle mass)
9 Drag Coefficient For smooth spherical particles, Fluent uses equation by Morsi and Alexander (1972): The constants a 1, a 2 and a 3 are determined for different ranges of Re: For nonspherical particles, the equation by Haider and Levenspiel (1989) is used: Shape factor Surface area of sphere with same volume Actual surface area
10 Comparison of Drag Coefficient Equations
11 Coupling The discrete phase and the continuous phase can be coupled in a number of ways. In Fluent, the oneway or twoway coupling are possible to model. Oneway coupling The continuous phase affects the discrete phase, but there is no reverse effect. In Fluent, this is referred as uncoupled approach. The discrete phase is solved once after the continuous phase flow has been solved. Twoway coupling Both phases affect each other (exchange of momentum, mass and energy). In Fluent, this is referred as coupled approach. The continuous phase flow field is impacted by the discrete phase and the calculations of the continuous phase and dispersed phase equations are alternated until the solution is converged (hopefully). Threeway coupling Particle disturbance of the fluid locally affects another particle s motion, e.g. drafting of a trailing particle. Fourway coupling Particle collisions affect motion of individual particles.
12 TwoWay Coupling in Fluent Momentum exchange Drag Other interaction forces Heat exchange (without chemical reactions) Vaporization and pyrolysis Sensible heat Mass exchange
13 Particle Types and Laws in Fluent Particle type Description Requirements Laws activated Inert inert/heating or cooling Available for all models 1, 6 Droplet heating/evaporation/ boiling Energy equation. Minimum two chemical species or the nonpremixed or partially premixed combustion model. Gas phase density by ideal law. 1, 2, 3, 6 Combusting heating; evolution of volatiles/swelling; heterogeneous surface reaction Energy equation. Minimum three chemical species or the nonpremixed combustion model. Gas phase density by ideal law. 1, 4, 5, 6 Multicomponent multicomponent droplets/particles Energy equation. Min. two chemical species. Use volume weighted mixing law to define define particle mixture density. 7 Law 1: Particle temperature below vaporization temperature. Law 2: Droplet vaporization. Law 3: Droplet boiling. Law 4: Devolatilization of combusting particle. Law 5: Surface combustion. Law 6: Volatile fraction of the particle consumed. Law 7: Multicomponent particle definition
14 Example of Laws Applied for a Drying Droplet Temperature Different energy and mass transfer equations are applied during different laws. T bp T vap T injection Law 1: Inert heating before vaporization Law 2: Vaporization Law 3: Boiling Law 6: Volatile fraction consumed Particle time
15 Mass and Energy Transfer of Drying Droplet Law 1:Inert heating before vaporization Law 6: Volatile fraction consumed Heat transfer Law 2: Vaporization Convection Radiation Mass transfer (molar flux of vapor) Diffusion coefficient given by user Vapor concentration at droplet surface / bulk gas Vapor pressure must be correctly defined Heat transfer Evaporation Law 3: Boiling Mass transfer without radiation with radiation Particle temperature is constant. Energy required for vaporization appears as energy sink for gas phase
16 ParticleWall Interaction Different particle boundary conditions can be defined for walls, inlets and outlets: volatile fraction flashes to vapor Escape Reflect Trap For particle reflection, a restitution coefficient e is specified: Normal component: Tangential component:
17 Turbulent Dispersion of Particles In Fluent, the dispersion of particles due to continuous phase turbulence can be modeled by a stochastic tracking model (random walk model, eddy interaction model), or a particle cloud model. In the random walk model, the instantaneous continuous phase velocity is formed of mean velocity and fluctuating component: The fluctuating component varies randomly during a particle track. Each particle injection is tracked repeatedly in order to generate a statistically meaningful sampling. The cloud model uses statistical methods to trace the turbulent dispersion of particles about a mean trajectory Mean trajectory is calculated from the ensemble average of the equations of motion for the particles represented in the cloud. Distribution of particles inside the cloud is represented by a Gaussian PDF.
18 Eddy Interaction Model The stochastic tracking model in Fluent is based on eddy interaction model. The discrete particle is assumed to interact with a succession of eddies. Each eddy is characterized by a Gaussian distributed random velocity fluctuation u i a time scale (life time of eddy) e a length scale (size of eddy) L e During interaction, the fluctuating velocity is kept constant. The interaction lasts until time exceeds the eddy lifetime or the eddy crossing time. Literature presents several theories for determining the above values (see Graham and James (1996)). The following presents the equations used in Fluent with k turbulence model. Fluid Lagrangian integral time Coefficient C L defined by user. Default value C L = Characteristic life time of eddy Eddy length scale Le C (based on Karema(2008)) Eddy crossing time L k 3 / 2 or alternatively random variation: e T L ln r ln r = uniform random number [0...1]. Notice: e L Notice: in literature, the length scale and life time are often linked: L In Fluent, this seems to be: Le 1 e k e 2 2 p d Velocity response time p 18 r 1 T e 2k 3 Fluctuating velocity For k turbulence model: = Gaussian distributed random number (standard normal distribution)
19 Injection Setup Particle injections can be defined by various methods: Single: a particle stream is injected from a single point. Group: particle streams are injected along a line. Cone: streams are injected in a hollow conical pattern. Solid cone. Surface: particle streams are injected from a surface (one stream from each cell face). Atomizer: streams are injected by using various predefined atomizer models. File: injection locations and initial conditions are defined in an external file. For each injection, the following data are defined: Particle type (inert, droplet, combusting, multicomponent) Material (from database) Initial conditions (particle size, velocity, etc.) Destination species for reacting particles. Evaporating material for combusting particles.
20 DPM Concentration Fluent can report a DPM concentration in a coupled calculation. This is a total concentration of the discrete phase in a continuous cell. The mass flow of a particle track is determined based on particle mass and mass flow at the particle injection and particle mass at current location. The particle mass can change due to evaporation and other phase changes. The discrete phase concentration inside a cell can be determined from the residence time and mass flow. Inside a cell, the particle stream is tracked with n particle time steps. The residence time of one particle track is the sum of these time steps. The total concentration is summed over all particle tracks. The particleparticle interaction is neglected, thus when multiple particle tracks cross the cell, the calculated concentration can exceed the bulk density of solids or even solid density (volume fraction of solids above 1). These results are not physically sensible but they can show areas, where the particle loading is high and the assumption of dilute flow is not valid. t 0 m p t N
21 Solution Strategies: Particle Tracking The particle tracks are calculated in steps. The step length factor determines approximately the number of steps per fluid cell. The default value is 5, but it should preferably be higher: Increasing the step length factor (i.e. decreasing the step length) can improve stability of heat and mass exchange (e.g. when calculating vaporization). The max. number of steps limits the number of calculated time steps. This should be large enough so that the particles can travel from entrance to exit. If particles remain suspended in the model (tracking incomplete), then steady state solution is questionable and transient tracking should be used instead. The transient calculations in Fluent can be performed in a number of ways and combinations. This presentation is focused on steady state calculation.
22 Solution Strategies: TwoWay Coupling The solution of the continuous field without coupling is usually the starting point. In most cases, the continuous flow does not have to be fully converged before the coupling is started, because the particle tracks will have a large effect on the continuous flow. In a coupled calculation, additional source terms appear in discretized flow equations of continuous phase. During particle tracking, each particle is seeing a fresh cell and makes no notice of particles already visited and marked the cell with their source terms. This leads to overprediction of the source terms and bad convergence behaviour with evaporation, combustion and radiation. Use solution limits to limit the temperature in the domain. Increasing the number of trajectories (especially with random walk model) will smooth the particle source terms, which should help convergence. The discrete phase source terms can be underrelaxed (e.g. 0.5). The flow equations may need to be underrelaxed as well (energy and species). The number of continuous phase calculations between the trajectory calculations can either be small (< 3) or high (>15). In the first choice, the dispersed and continuous flow are closer coupled and the solution of both should slowly convergence. In the second choice, the flows are decoupled and the solution of continuous field remains better converged and the calculation is more stable. In the latter case, the continuous phase may appear to be converged, but the discrete phase is not. If the dispersed phase is not dilute, then convergence is very difficult to achieve in coupled calculations. Calculate continuous phase Calculate particle tracks Update source terms
23 Modeling Example The model geometry is shown below. Hot air flows in a 200 mm diameter duct. Wet limestone particles are injected from the top of the duct (inlet d = 50 mm) at location 500 mm before a 90 bend. Air inlet: 10 m/s, 270 C, D= 0.2 m Particle inlet: 0.1 kg/s, 0.1 m/s, d p =200 µm, p =2700 kg/m3, H 2 O=30% Average volume fraction of solids in the duct: dilute, twoway coupling (but only as average)
24 Mesh
25 Gas Properties
26 Solid Properties (Limestone)
27 Model Parameters
28 Solution of Continuous Phase The continuous phase was first solved without the particles. The convergence was good.
29 Uncoupled Mean Particle Tracks The mean particle tracks were solved without twoway coupling. The particle tracks are thus calculated only once after the continuous phase was solved. The following images present particle tracks colored by mass, which indicates evaporation. Initial mass Fully evaporated 1.13E8 kg 7.92E9 kg
30 Uncoupled Turbulent Tracks Random walk model with 50 stochastic tracks (total 2400) was used with default C L = Uncoupled solution, ie. oneway coupled calculation of dispersed phase. Turbulence effects are fairly small, but can be noticed in the track images. Initial mass Fully evaporated 1.13E8 kg 7.92E9 kg
31 Solution of Coupled Calculation Twoway coupled solution did not converge well. Different step length factors, underrelaxation parameters and number of continuous phase iterations were tried. In the final calculations, the step length factor was 20 and the number of continuous phase iterations between dispersed phase calculations was 20. The residuals were indicating poor convergence.
32 Coupled Particle Tracks The particle tracks show that some of the particle streams circulate for long times before reaching the outlet. The solution of flow is much different from uncoupled solution. The images do not show all particle tracks. Initial mass Fully evaporated 1.13E8 kg 7.92E9 kg
33 Effect on Continuous Flow Field In the coupled calculation, the particle tracks affect the continuous phase flow. In this case, the effect is considerable.
34 DPM Concentration The DPM concentration shows the total concentration of dispersed phase. Results indicate that in the bend, the dispersed phase is not dilute ( max = 0.094). Reaching a converged solution in this case would be impossible.» The results should be utilized with caution.
35 Visualization of Results Different process variables can be easily visualized: pressure, velocities, temperature, concentration of species, turbulence variables,...
36 Summary The DPM model in Fluent can be used for studying oneway or twoway coupled dilute dispersed flows, including effects of turbulence. The basic model is easy to use and physics are clear and simple. The limitations of the DPM model should be carefully considered when analyzing the results. The model neglects particleparticle interaction, thus it is valid for dilute dispersed phase only. The oneway coupling is valid for very dilute flow only. The twoway coupled solution can be much different from the oneway coupled solution. The average flow can be dilute, but it can contain regions, in which the dispersed phase is dense. In these regions, the model results are false. Moreover, the convergence is poor, if the dispersed phase is dense and the momentum, mass and energy exchange to continuous phase is strong. Despite the limitations, the DPM model can be (and is) successfully used for modeling various applications.
37 References Bakker, A. (2006). Lecture notes, Computational Fluid Dynamics, Dartmouth College. Elghobashi, S. (1994). On predicting particleladen turbulent flows, Appl. Sci. Res. 52, pp Fluent 6.3 Documentation (2008). Fluent Training Material (2008). Graham D. I. and James P.W. (1996). Turbulent dispersion of particles using eddy interaction models. Int. J. Multiphase Flow, 221, pp Haider, A. and Levenspiel, O. (1989). Drag Coefficient and Terminal Velocity of Spherical and Nonspherical Particles.Powder Technology, 58, pp Jalali, P. (2007). Lecture notes, Theory and simulation of dispersedphase multiphase flows, Lappeenranta University of Technology. Karema, H. (2008). Discussions with Hannu Karema (Process Flow), January Loth, E. (2008). Computational Fluid Dynamics of Bubbles, Drops and Particles (draft). Morsi, S. and Alexander A. (1972), An investigation of particle trajectories in twophase flow systems, Journal of Fluid Mechanics 55, pp Sommerfeld, M. (2000). Theoretical and Experimental Modelling of Particulate Flows. Lecture Series , von Karman Institute for Fluid Dynamics.
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