Advanced Applications of STAR- CCM+ in Chemical Process Industry Ravindra Aglave Director, Chemical Process Industry

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1 Advanced Applications of STAR- CCM+ in Chemical Process Industry Ravindra Aglave Director, Chemical Process Industry

2 Outline Notable features released in 2013 Gas Liquid Flows with STAR-CCM+ Packed Bed Reactors: Beyond porous media approach Optimization: A paradigm shift

3 Eulerian Multiphase Multiple Granular phases Simulation of mixtures with 2 or more granular phases Granular temperature model extended Previously algebraic equation solved Solving full transport equation Chemical reactions Intraphase reactions Interphase reactions Reynolds Stress Model with EMP Rotating, swirling and anisotropic flows Multicomponent Boiling Model for EMP Calculates the mass, energy and momentum transfer between a continuous and a dispersed multicomponent phase Interface Momentum Dissipation Model Reduces unphysical parasitic currents

4 Lagrangian/DEM Stochastic Secondary Droplet (SSD) breakup model Efficient and accurate method compared to other approaches Passive Scalars Passive scalars may now be used with Lagrangian/DEM Scalars may transfer between particles continuous phase New multiphase interaction method Particle-wall conductive heat transfer Forces Drag torque Spin lift force Choice of rolling friction models Force proportional Constant torque Displacement damping Lattice and random injectors can use geometry parts Improved speed, convenience

5 Reacting Flow Diesel engines, boilers, coal-powered plants Soot Two-Equation Model for non-premixed combustion aka the Moss Brookes Hall soot model Two additional transport equations solved for increased accuracy Surface Chemistry Model Chemical reactions on surfaces without requiring DARS-CFD add-on. The Homogenous Reactor The Eddy Break-Up (EBU) model The Non-reacting model with Segregated Species

6 Reacting Flow Threaded PPDF table construction Enhanced user experience and performance GUI can still be used during operation Three stream PVM Progress Variable Model Can now model two fuel streams and one oxidizer stream Previously only one fuel stream allowed Soot Two Equation Model Moss-Brookes-Hall soot model can now work with the Eddy Break Up (EBU) model widening applicability to non-premixed flames Addition of PAH sub-model for nucleation for soot prediction with higher hydrocarbon fuels such as kerosene Sandia Flame EBU Soot Volume Fraction User Defined Char Oxidation Model User defined char oxidation rate for coal combustion Soot Modeling Coal Combustion

7 Gas Liquid Flows

8 General Setup Gas Outlet 3D Model 0.45m x 0.2m x 0.05m hexahedral cells Water does not enter or leave domain Velocity inlet K-e turbulence model Time step size = 1e s Bubble size dp = 2 mm monodisperse Three Different Set-up I : Degassing boundary II: Degassing boundary wih additional forces III: Flow split /gas pocket at top Gas Inlet

9 Case I: Pfleger Setup Outlet: Degassing BC Drag Force (Cd = 0.66) Turb. Disp. Force Vgas = 48 l/h v sup = m/s

10 Case I: Results: Plume after 1 sec

11 Case I: Plume Oscillation

12 Case II: Enhanced Pfleger Setup Drage Force: Tomiyama Lift Force: Tomiyama Turb. Disp. Force Bubble Induced Turbulence (Troshko&Hassan) Virtual Mass Force Diaz et al. (2008), Chem. Eng. J. 139, Ziegenhein (2013), CIT, accepted manuscript

13 Case II: Results

14 Case II: Results

15 Case II: Results Averaged over 100s Snapshot at t = 220s

16 Case II: Results

17 Case III: Air Buffer Setup Setup like Case I Flow-split outlet dt ~ s Inner Iteration = Reaching convergence within each timestep is important!

18 Conclusion Simulation with degassing BC: Robust and accurate All kind of forces can be considered Simulation with air buffer: Startup has to be monitored carefully (each time step has to be converged) Lift force can not be taken into account

19 Power of Optimization: A paradigm shift

20 Problem Statement To design an Heater ducting for furnaces for use in the refining/petrochemical industry Goal is to minimize the mass flow variation through burner throats With the minimal Pressure drop possible A variety of geometric parameters can be changed The Heater consists of a central duct connected to the burners via short cylindrical legs 20

21 Parameters Radius of connector Height of duct Width of duct Connector Dia Taper Taper

22 22 Base Case Results

23 Parametric CAD Robustness Study CAD variations explored 148 evaluations performed 40 mins on 8 cores for baseline 32 hrs for entire project on 40 cores CD-adapco PowerTokens provide ultimate flexibility for DSE by allowing the user to decide what combination of parallel evaluations and solver cores is most efficient for them Metrics used Delta Mass Flow = Q max Q min (Performance) Q ideal Delta Pressure = P max in the system (Fan/Damper limit) 23

24 Meshing Mesh Continuum Models Base Size Surface Size ( min / target ) Prism Layer Mesher (layers / stretching / total thickness) Surface Remesher, Polyhedral Mesher, Prism Layer Mesher 10.0 mm 4.0 mm / 10.0 mm Block: 1.6 m / 1.6 m 3 / 1.3 / 2.5 mm Block Floor: 5 / 1.3 / 100 mm 24

25 Results Design 158 Design 40 25

26 Process Automation Parametric CAD Geometry STAR-CCM+ CFD Analysis Design Variables Simulation Responses Input & Output Files Are Defined Program Execution is Automated Design Variable are Identified and Tagged in Files Complete Process is Executed from 1 Button or Script 26

27 Mixing tank geometry Geometry created within 3D CAD Specific dimensions set as design parameters

28 Optimization setup: Pareto front Objectives o Maximize volume averaged turbulent kinetic energy (proportional to mixing) o Minimize moment on impeller blades and shaft (indicative of torque/power consumption) Variables Variable name Minimum Maximum Increment Baffle length m m m Baffle numbers Impeller blade pitch angle 0 90 o 5 o Number of impellers 1 5 1

29 Computational Summary Single Phase, Water # of Cells = 200K (varies with geometry) # Possible designs ~ # of Designs = 153 Parametric geometry creation = 2-3 hrs Optimate setup time = 30 mins 5 simultaneous on 12 cores (60 cores) = 10 hrs clock time Total compute hours = 5 x 10 = 600 hrs # of power tokens = 5x12 = 60

30 Results: Pareto Front (# of Designs 20) Pressure on impeller blades Turbulent kinetic energy

31 Pareto Front (# of Designs = 20)

32 THANK YOU