CCM+ in Chemical Process Industry Ravindra Aglave Director, - - PowerPoint PPT Presentation

Advanced Applications of STAR- CCM+ in Chemical Process Industry

Ravindra Aglave Director, Chemical Process Industry

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

Outline

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

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

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

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

Gas – Liquid Flows

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

Outlet: Degassing BC Drag Force (Cd = 0.66)

• Turb. Disp. Force

Vgas = 48 l/h vsup=0.00133 m/s

Case I: Pfleger Setup

Case I: Results: Plume after 1 sec

Case I: Plume Oscillation

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

Case II: Results

Case II: Results

Case II: Results

Averaged over 100s Snapshot at t = 220s

Case II: Results

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 !

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

Power of Optimization: A paradigm shift

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

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 =

𝑅 𝑛𝑏𝑦−𝑅 𝑛𝑗𝑜 𝑅𝑗𝑒𝑓𝑏𝑚

(Performance) – Delta Pressure = ∆𝑄

𝑛𝑏𝑦 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

Results

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Design 158 Design 40

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

Mixing tank geometry

• Geometry created within 3D CAD
• Specific dimensions set as design

parameters

Optimization setup: Pareto front

Objectives

• Maximize volume averaged turbulent kinetic energy (proportional to mixing)
• 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 9 1 Impeller blade pitch angle 0 90o 5o Number of impellers 1 5 1

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

Results: Pareto Front (# of Designs 20)

Turbulent kinetic energy Pressure on impeller blades

Pareto Front (# of Designs = 20)

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