Multiphase Flows. Mohammed Azhar Phil Stopford


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1 Multiphase Flows Mohammed Azhar Phil Stopford 1
2 Outline VOF Model VOF Coupled Solver Free surface flow applications Eulerian Model DQMOM Boiling Model enhancements Multifluid flow applications Coupled CFD/DEM 2
3 VOF Model: New in FLUENT 14 Coupled VOF Solver Solves the momentum, pressure based continuity and volume fraction equations together. Coupled VOF solver aims to achieve faster steady state solution compared to segregated method of solving equations. 3
4 Coupled VOF Solver Select Coupled scheme as Pressurevelocity Coupling Enable Coupled with Volume Fraction option Volume Fraction Courant Number provides the additional implicit underrelaxation for VOF equation. (could help for numerically sensitive cases) TUI Recommendations for higher order momentum schemes  Lower underrelaxation for momentum  Disable high order RhieChow flux through solve >set > numerics 4
5 Free Surface Flow Around the Container Ship Cutcell mesh was created by TGrid 8.533M cells half model 5
6 Free Surface Flow Around the Container Ship Segregated VOF Coupled VOF Segregated VOF converges in 1450 iterations Coupled VOF converges in 500 iterations 6
7 Free Surface Flow Around the Container Ship The free surface level plot clearly shows that segregated VOF run matches well with coupled VOF run and experimental result after 1450 iterations but not after 500 iterations. 7
8 Eulerian Model: News in FLUENT 14 DQMOM for population balance models Critical Heat flux (CHF) model 8
9 DQMOM usage Problem Cumulative size distribution of droplets at inlet available It is desired to convert the size distribution into inputs required by DQMOM (volume fraction and moment4 values) Solution Use the Generate DQMOM Values to obtain relevant inputs for DQMOM Target application Ease of use for DQMOM problems 9
10 Modeling spray injection using DQMOM Problem Diesel type spray from Madsen thesis Nhexane injected into nitrogen gas Injection velocity = 127 m/s Nozzle diameter = 127 mum Modeling details Population balance model in Fluent WAVE breakup model for breakage frequency Equisized binary breakage or parabolic breakage pdf 10
11 Mesh 2 cells in the nozzle exit 218x55 cells kepsilon per phase turbulence model with turbulent dispersion turned on 11
12 Comparison between homogeneous and inhomogenous model 12
13 Results Shape of the spray Velocity vectors for the spray 13
14 Comparison with experiments Comparison with experiments at x/d=400 Wu et al 14
15 Boiling models in Fluent14 We have three different models available in R14 RPI Boiling model Applicable to subcooled nucleate boiling Nonequilibrium Boiling Extension of RPI to take care of saturated boiling Critical Heat Flux Extension of RPI to take care of boiling crisis 15
16 Testing the CHF model CHF model of Fluent Experimental data from Hoyer Area of influence Kenning Bubble departure frequency Cole Turbulent drift force  Simonin 16
17 RPI paper validation case Vertical pipe Length: 2 m Diameter: 15.4 mm Heat Flux: 570 kw/m 2 Mass Flux: 900 kg/m 2 s Operating pressure: 4.5 Mpa Results from Bartolomei experiments Experiments ANSYS CFD (Fluent) RPI_paper 0.1 Void fraction Temperature in K Axial distribution of Average void fraction 17
18 default settings for boiling model 18
19 Results with default settings Experiments RPI_paper F14 F14_adapted F14_2level_adapted F Axial distribution of Average void fraction 19
20 Results with various sub models other than defaults F14 default settings F14, const. AOI =2 F14, Unal, Const. AOI=2 F14, Kocamustafa, Const. AOI=2 RPI paper Experiements Axial distribution of Average void fraction 20
21 Observations 1. Mesh dependency 2. Excessive vapor generation close to Onset of boiling 21
22 Reason for Mesh dependency q wall q f q e q q q f = Single phase heat transfer to liquid Grid independent as it uses a heat transfer coefficient for liquid calculated by Fluent internally q e = Evaporation heat flux, f(t wall T sat ) Grid independent as it does not use liquid temperature from the cell next to the wall q q = Quenching heat flux, f(t wall T liq ) Grid dependent component 22
23 Solution used in CFX at R12 q q = Quenching heat flux, f(t wall T liq ) In CFX, an approach was developed to make this component grid independent Based on the temperature in the cell next to the wall and its Y+, they estimate liquid temperature at Y+=250 and use this in the above correlation This option is available in Fluent at R14 as Quenching Correction Model 23
24 Quenching correction options in Fluent14 24
25 Results with various options for quenching F14 default settings F14 with fixed y+=250 F14 with fixed temp = (Tsat  3) K Axial distribution of Average void fraction 25
26 Checking grid independence for quenching correction option Y+= Experiments RPI_paper F14, fixed Y+ = Axial distribution of Average void fraction F14_adapted, fixed Y+=250 F14 2 level adapted, fixed Y+=250 26
27 Results with Wall lubrication Experiments RPI_paper F14, fixed Y+ = 250 F14_adapted,fixed Y+=250,WL F14 2 level adapted, fixed Y+=250, WL Axial distribution of Average void fraction 27
28 Coupled Fluid and DEM At high volume fraction of particles particleparticle interaction becomes important. With or Without Interaction with Fluid Flow 28
29 Theory: Soft Sphere DEM Discrete Element Method: DEM Cundall and Strack (1979) Soft Sphere Approach Contact forces computed from deformation. Overlap of ideal spheres used as the measure for deformation. Newtons 2 nd law integrated in time. Allows for Nbody interaction. Rigidity of materials determines time scale for integration. Particle 1: mass m 1 position x 1 velocity v 1 Particle 2: mass m 2 position x 2 velocity v 2 r 1 r 2 overlap* * not to scale, greatly exaggerated 29
30 Theory: Soft Sphere DEM (cont d) Forces in Newtons 2 nd law collected from pairwise interaction. Collision laws defined for pairs of collision partners. Implemented Force Laws Spring SpringDashpot Friction These forces enter the equation of motion for the particle through F other Particle 1: mass m 1 position x 1 velocity v 1 Particle 2: mass m 2 position x 2 velocity v 2 r 1 r 2 F 2 30 Spring Model F F 1 ( kn ) n n x x j j F x x i i loss m 2 t 2 1 coll f Springdashpot Model F 1 k n 2 ln 12 ln 2 m 12 m1m 2 m m 1 2 t coll f loss m K F 1 12 overlap* * not to scale, greatly exaggerated
31 31 Fluidized Bed: Base Case Dimension 0.2 * 0.2 * 0.4 m cube. 16K Hex cells. BC Bottom: Velocity Inlet=0.5 m/s Top: Pressure Outlet=1 atm DPM Particle diameter = 750 micron 15K Parcels Volume Ratio of single mesh cell to single parcel = 5.5 DEM + DDPM SpringDashpot: K = 100 Eta = 0.8 (particleparticle), 1 (particleboundary) Friction: Mustick = 0.5, Muglide = 0.2, Mulimit = 0.1, Velglide = 4.6, Vellimit = 20, Slopelimit = 2 Node based averaging (Beta) Particle time step size = 2e4 sec No. of continuous phase iterations per DPM iteration = 200 Update DPM sources every flow iteration Drag Law = WenYu
32 Fluidized Bed: Base Case Solver Settings PC SIMPLE Node based Gradients Bounded Second Order Implicit Momentum, Volume Fraction: QUICK URF: Pressure = 0.9, Momentum = 0.2, Volume Fraction, DPM = 1 32
33 Fluidized Bed: Base Case Simulation results Pressure drop balanced by the weight of the bed Realistic bubbling frequency No need for carefully tweaking of parameters: Robust results. Note Postprocessing: Showing only a slice from the full bed. 33
34 Fluidized Bed: Spring Constant Variation Compare K = 100 N/m K = 1000 N/m Time step: 5e5 s Results independent of K K = 100 N/m sufficient 34
35 Fluidized Bed: NETL Challenge 2011 A blind challenge problem on modeling a bubbling fluidized bed of FCC particles with a Particle Size Distribution (PSD). Cf. Dimensions: 0.91m x 0.91 m x 7.41m Particle content: 1351kg initially Fine Particles have diameter < 45 μm Comparison: 12% fine particles 3% fine particles PSDs have similar mean average diameter of 85μm and 89μm. Fluidization differences from details of PSD. 35
36 Mesh: 91k cells Fluidized Bed: NETL Challenge 2011 Fluid/Particle Time Step: 0.5 ms 12% fines: initially about 512k parcels 3% fines: ditto SpringDashpot and Friction forces with default values for particleparticle and particleboundary collisions. Able to simulate 16s of flow time in a day on 12 processors with a complete PSD % fines 3% fines
37 Proppant Transport Proppants are used to prop open artificial fractures created in the rock of gas fields. The placement of proppants at appropriate locations is essential for a successful fracturing operation. * 37 Case Dimensions: 3m x 0.3m x 0.03m (width x height x depth) Particle content: 444k parcels, 8.9kg at 10s. SpringDashpot and friction force with default values except K = 100 N/m for particleparticle and particlewall collisions. Fluid Time Step: 2e3s, Particle Tracking Time Step: 2.5e4s Particle volume fraction at injection surface about Using staggering in time and space on injection surface. * colored by VOF
38 Summary ANSYS is committed to providing and supporting best in class technology in dense and dilute multiphase Continue to improve speed and fidelity through experimental validation of results. Have the most comprehensive and advanced collection of multiphase tools of any commercially available CFD code. 38
39 THANK YOU! Questions? Please contact:
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