CD-adapco’s vision of STAR-CCM+

in the Chemical Process Industry Ravindra Aglave

CPI Sector Manager

Agenda

Success with CFD: Automotive and

Chemical Industries

STAR-CCM+ in the CPI

– Demands and Fulfillment

CFD Simulation space topology

Looking Forward: Future

developments

Similarities

– Value addition in engineering process

– Experimental validation necessary

– Long & Expensive experimentation

– Lower fidelity models can be still useful

Commonly cited differences

– Multiphase modeling

– Complex Physics and material properties

– Designs/projects are “one-off”

– A step wise approach to address complexity

– No repetitive/quick changes

…but only perceived

Comparison with automobile industries

Tank sloshing, spray injection,

internal combustion, NOx

formation, film on windshields,

SCR evaporation, reactions,

heat transfer (boiling,

condensation), soiling

Mo

ve

Ground/Air/Water transport

Consumables

Fuels Additives Lubricants

Components

Structure

Body

Steel

Rubber

Foams, Plastic

Interior

Furnishings Control Electronics materials

Stay

Home, Office, Theater, Hotel

Material

Cement, glass Carpets, Textiles

Paper, Ink Electronics materials

People

Health & personal care

Pharma

Cleaning

Nutrition

Food Beverage

Ceramics Utensils

Water treatment

Stay

Pu

t

Comfort & Safety

Environment Pollution control Waste treatment

Defense

Risk Mitigation Explosion Leaks, Dispersions

Civilization and Chemical Industry

Shortened product-process development cycles

Optimization to improve yield and efficiency

Efficient design of new products and processes

Improvements in health, safety, and environment

Demands of the Chemical Industry of Future

© December 1996 by The American Chemical Society,

American Institute of Chemical Engineers,

The Chemical Manufacturers Association,

The Council for Chemical Research, and

The Synthetic Organic Chemical Manufacturers Association

CFD identified as key enabling technology

Major issues/barriers clearly identified

How is CD-adapco / STAR-CCM+ conceptualized to address these issues?

Barriers to CFD in Chemical industry

“Incorporate complex geometry”

“Excessive time required for set up”

“In-house codes developed by industry are typically usable only by specialists”

“Lack of models describing appropriate physics”

New Product

New Process

Innovation

Effic

iency

Inn

ovati

on

E

fficie

ncy

Simulation Demands

New Product/ “Producing Machine”

• Faster time to market

• Safer

• Reduce number of reacting steps

New Process

• Less downtime

• Safer

• Increased capacity/yield

Inn

ova

tio

n

Efficiency

Innovation

Efficien

cy

Maturity Objective Simplest Physics

Simple Physics

Complex More Complex

Most Complex

Level 1 Understand Level 2 Troubleshoot Level 3 Predict Level 4 Explore Level 5 Optimize

Maturity Objective Simplest Physics

Simple Physics

Complex More Complex

Most Complex

Level 1 Understand Level 2 Troubleshoot Level 3 Predict Level 4 Explore Level 5 Optimize

Simulation Topology

Innovation

Efficien

cy

Expertise (Research, Academic)

Do

llars/Euro

s

Chemical Process Industry Breakdown

Reactor feed systems: Distribution • Ducts, pipes, tees, injectors

• Distributors, collectors, reactor heads

Stirred & unstirred reactors, bubble columns • Single phase hydrodynamics • G-L, S-L phase distributions, Coalescence & Break-up, • Interphase transfer, reactions, G-L-S systems

Packed bed reactors • Hydrodynamics • Heat transfer • Surface and gas phase chemistry

Fluidized bed reactors • Minimum fluidization, pressure drop

• Various fluidization regimes

• Reactions

High Temperature • Burner design, Heater design, Heater ducts, Cracking furnaces

• Waste incinerators

Separation Equipment • Stripping, Distillation

• Complex Thermodynamics

Stirred Vessel Reactor Roadmap

1 2 3 4 5 6 7

Hdrodynamics (MRF) • Power consumption,

pumping capacity, free surface shape

• Turn-Around: 1-2 Days

1

Unsteady (RBM) • Free surface shape • Mixing time • 2-3 days

2

Gas-Liquid flows • Fixed distribution of bubble size • Coalescence – breakup • 1 week

4

Gas-Liquid flows • Gas injection (single bubble size) • Complex geometry • Gas holdup • 3-4 days

3 Gas-Liquid flows • Adaptive distribution of

bubbles (Adaptive MUSIG) • Detailed coalescence-breakup • > 1Week

5

Solid-Liquid flows • Minimum suspension rpm • Suspension height • Structural analysis (vibration) • 1-2 Weeks

6

Gas-Solid-Liquid • E.g Catalysed hydrogenation • Coalescence-breakup • Other methods for solid

behaviour • > Few weeks

7

8

OPTIMIZATION • Complete design space, automated

• 16000 if manual • Single or multiple objectives • Hybrid optimization algorithm • Pareto front

8

Note: Times are estimated on past projects. The times are constantly decreasing, or model is increasing in complexity.

Stirred Vessel Reactor Roadmap

1 2 3 4 5 6 7

Hdrodynamics (MRF) • Power consumption,

pumping capacity, free surface shape

• Turn-Around: 1-2 Days

1

Unsteady (RBM) • Free surface shape • Mixing time • 2-3 days

2

Gas-Liquid flows • Fixed distribution of bubble size • Coalescence – breakup • 1 week

4

Gas-Liquid flows • Gas injection (single bubble size) • Complex geometry • Gas holdup • 3-4 days

3 Gas-Liquid flows • Adaptive distribution of

bubbles (Adaptive MUSIG) • Detailed coalescence-breakup • > 1Week

5

Solid-Liquid flows • Minimum suspension rpm • Suspension height • Structural analysis (vibration) • 1-2 Weeks

6

Gas-Solid-Liquid • E.g Catalysed hydrogenation • Coalescence-breakup • Other methods for solid

behaviour • > Few weeks

7

8

OPTIMIZATION • Complete design space, automated

• 16000 if manual • Single or multiple objectives • Hybrid optimization algorithm • Pareto front

8

Note: Times are estimated on past projects. The times are constantly decreasing, or model is increasing in complexity.

Understand

Troubleshoot

Predict

Explore

Optimize

Have I solved all level 1 or level 2 problems throughout my company?

– Is every batch stirred reactor operating optimally in all plants?

– If not where is the hurdle? Is doing nothing a good option?

Is this engineers tool or an experts tool?

– Is there a reason to confine it to CFD groups

Or a research tool?

– Going far from bread & butter?

What is the rest of the industry /world doing?

– 1-2 simulations for a project versus a set/optimization?

Is Simulation looked upon as “first choice” or “last resort”?

Your simulation deployment roadmap

Liquid – Liquid Micromixing

Polymerization Framework

Adaptive MUSIG Model

Alpha-Pressure coupling

EMP with Porous Media

Rheology

– Turbulent Non-Newtonian Flow

– Temperature dependent Viscosity

– Property averaging of liquid mixtures

– Emulsion and Suspension

Reacting Channel

CD-adapco Roadmap for 2014

Sauter Mean Diameter

Operational Efficiency

• Easy to use

• Cost efficient

Physics

• Rapid Expansion

• Co-simulation

Workflow

• Integrated / Repeatable

• Parameterized

• Conserves expertise: Simulation assistant

Framework

• Modern

• Object Oriented

• Pipeline

• Multi-disciplinary

STAR-CCM+ Conceptualization

Operational Efficiency

• Easy to use

• Cost efficient

Physics

• Rapid Expansion

• Co-simulation

Workflow

• Integrated / Repeatable

• Pipeline

• Parameterized Framework

• Modern, Object Oriented

• Multi-disciplinary

• Expertise: Simulation Assistant

In our vision

“Incorporate complex geometry”

“Lack of models describing appropriate physics”

“Excessive time required for set up”

“In-house codes developed by industry are typically usable only by specialists”

Final thoughts

Expertise (Research, Academic)

Do

llars

/Eu

ros

If y

ou

th

ink

go

od

de

sig

n is

ex

pe

ns

ive

,

yo

u s

ho

uld

lo

ok

at

the

co

st

of

ba

d d

es

ign

.

Dr.

Ral

ph

Sp

eth

, C

EO J

agu

ar

…because doing

nothing was even a

bigger risk

Dr.-Ing. Norbert Reithofer, CEO BMW AG

If you think good

design is expensive,

you should look at the

cost of bad design.

Dr. Ralph Speth, CEO Jaguar

Thank You!

Eddy Contact Model (based on Froney and Nafia, 2000)

Kolmogorov (corresponds to Engulfment type of mixing)

Classical Scalar Mixing

– Numerically Efficient

– Reasonable Accuracy

– Easy to use for multiple reactions

– Steady or Unsteady

Liquid – Liquid Micromixing

Case 1: EBU A= 4.0

Case 2: Classical

A + B = R

R + B = S

Example

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Method of Moments

Users to have options to input kinetic parameters for – Initiation

– Propagation

– Termination Reactions

Each of the – Initiator

– Radical

– Monomer

– Solvent Species assigned to appropriate liquid species for transport

Choice for Quasi-steady state for radical species

In future users can be given a choice of specifying user-defined moments sources.

Polymerization

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Adaptive MUSIG Model

1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8

d

d

At the end of a calculation step

– Mass and number density are redistributed between neighbour groups

– Each group has the same mass but new diameters.

Adaptive MUSIG Mode

Each size group is treated as a phase, solving standard equations plus a number density equation.

Adaptive MUSIG - Droplet breakup through an

orifice

Sauter Mean Diameter

Breakup Rate (log scale)

Turbulent-induced breakup

Shear-induced breakup

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