Advanced Applications of STAR- CCM+ in Chemical … Applications of STAR-CCM+ in Chemical Process...
Transcript of Advanced Applications of STAR- CCM+ in Chemical … Applications of STAR-CCM+ in Chemical Process...
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Advanced Applications of STAR-
CCM+ in Chemical Process Industry Ravindra Aglave
Director, Chemical Process Industry
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Notable features released in 2013
Gas – Liquid Flows with STAR-CCM+
Packed Bed Reactors: Beyond porous media approach
Optimization: A paradigm shift
Outline
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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
Eulerian Multiphase
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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
Lagrangian/DEM
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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
Reacting Flow
Diesel engines, boilers, coal-powered
plants
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Reacting Flow
Threaded PPDF table construction – Enhanced user experience and performance
– GUI can still be used during operation
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
User Defined Char Oxidation Model – User defined char oxidation rate for coal combustion
Three stream PVM
Sandia Flame EBU
Soot Volume Fraction
Soot Modeling
Coal Combustion
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Gas – Liquid Flows
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3D Model
– 0.45m x 0.2m x 0.05m
– 40.000 hexahedral cells
– Water does not enter or leave domain
Velocity inlet
– K-e turbulence model
– Time step size = 1e-3 - 0.1 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
General Setup
Gas Inlet
Gas Outlet
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Outlet: Degassing BC
Drag Force (Cd = 0.66)
Turb. Disp. Force
Vgas = 48 l/h
vsup=0.00133 m/s
Case I: Pfleger Setup
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Case I: Results: Plume after 1 sec
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Case I: Plume Oscillation
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Drage Force: Tomiyama
Lift Force: Tomiyama
Turb. Disp. Force
Bubble Induced Turbulence (Troshko&Hassan)
Virtual Mass Force
Case II: Enhanced Pfleger Setup
Diaz et al. (2008), Chem. Eng. J. 139, 363-379
Ziegenhein (2013), CIT, accepted manuscript
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Case II: Results
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Case II: Results
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Case II: Results
Averaged over 100s Snapshot at t = 220s
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Case II: Results
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Case III: Air Buffer Setup
• Setup like Case I • Flow-split outlet • dt ~ 0.001 - 0.01 s • Inner Iteration = 40 - 200
Reaching
convergence within
each timestep is
important !
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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
Conclusion
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Power of Optimization: A paradigm
shift
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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
Problem Statement
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Radius of connector
Height of duct
Width of duct
Parameters
Taper
Connector Dia
Taper
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Base Case Results
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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
𝑄𝑖𝑑𝑒𝑎𝑙 (Performance)
– Delta Pressure = ∆𝑃𝑚𝑎𝑥 in the system (Fan/Damper limit)
Parametric CAD Robustness Study
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Meshing
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Mesh Continuum Models
Surface Remesher, Polyhedral
Mesher, Prism Layer Mesher
Base Size 10.0 mm
Surface Size ( min / target ) 4.0 mm / 10.0 mm
Block: 1.6 m / 1.6 m
Prism Layer Mesher
(layers / stretching / total thickness)
3 / 1.3 / 2.5 mm
Block Floor: 5 / 1.3 / 100 mm
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Results
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Design 158
Design 40
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Process Automation
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Parametric CAD Geometry STAR-CCM+ CFD Analysis
Simulation Responses
Design Variables
• 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
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Mixing tank geometry
• Geometry created within 3D CAD
• Specific dimensions set as design
parameters
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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 0.005 m 0.012 m 0.0005 m
Baffle numbers 0 9 1
Impeller blade pitch angle 0 90o 5o
Number of impellers 1 5 1
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Computational Summary
Single Phase, Water
# of Cells = 200K (varies with geometry)
# Possible designs ~ 16000
# 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
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Results: Pareto Front (# of Designs 20)
Turbulent kinetic energy Pressure on impeller blades
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Pareto Front (# of Designs = 20)
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THANK YOU