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

20

Radius of connector

Height of duct

Width of duct

Parameters

Taper

Connector Dia

Taper

Base Case Results

22

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

23

Meshing

24

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

25

Design 158

Design 40

Process Automation

26

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

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

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)

THANK YOU