© Fraunhofer SCAI
Dr. Carsten Brodbeck, Bettina Landvogt Fraunhofer SCAI
Martin Schamberg Hennecke Polyurethane Technology
Design and Optimization of Plants and Components
for the Production of Polyurethane Foams using
STAR-CCM+
STAR European Conference 2010, London
©Hennecke ©Hennecke
© Fraunhofer SCAI
Project Sponsor and Partners
The project is funded by German Federal Ministry for Economy and
Technology
It is a cooperation project of the research facility “Fraunhofer SCAI” and the
industrial company “Hennecke Polyurethane Technology”
Hennecke is a medium sized company, part of Adcuram Group AG and
producer of polyurethane machines and plants with a large variety of products
© Fraunhofer SCAI
Content
Introducing Project Partner “Hennecke”
Introducing “Fraunhofer SCAI”
Description of Slab Stock Foam Plants
Description of Mix Head Injectors
CFD Approach in STAR-CCM+
Optimization Approach with DesParO
Mesh Adaption Approach in STAR-CCM+
First Results Slab Stock Foam
First Results Mix Heads
© Fraunhofer SCAI
Product variety of Hennecke
Metering machines
Mix Heads
Gas loading technology, blowing agent metering units
Elastomer lines
PU spraying methods
Moulding lines
Lines for refrigerated appliances
Sandwich panel lines
Slabstock lines
Recycling technologies
Tank farms ©Hennecke
© Fraunhofer SCAI
Fraunhofer SCAI
Multiphysics Software: MpCCI, SCAIMapper
Optimization Software: Autonester, PackAssistant, DesParO
Crash Software: DiffCrash, DesParO, FEMZIP
Research: Simulation Engineering, Numerical Methods,
Bioinformatics, Optimization
© Fraunhofer SCAI
Slab Stock Foam Plant
Slab stock foams are produced amongst others as standard, hypersoft, high load bearing or visco-elastic foams
Hennecke offers facilities for high and small quantities as continuous and discontinuous lines
Hennecke wants to offer facilities with a reduced production rate. Many customers request due to investment costs and logistical problems a smaller piece number produced per time.
Question is:How do we have to change the inlet domain geometry and flow parameters to obtain a line with a reduced production rate in order to assure a smooth operation and a high quality foam?
The fluid has to pass critical point before blowing starts
The fluid has to spread all over the sheet system
The fluid may not re-circulate
© Fraunhofer SCAI
Slab Stock Foam Plant
©Hennecke
©Hennecke
©Hennecke
Film Hennecke
© Fraunhofer SCAI
Mix Heads – Mix Chambers
Mix heads are the key elements of any PU production line
Hennecke offers mix heads types for different applications, small and very
large shot weights, mix injection even in challenging positions
Question is:
How can we modify the mix head geometries and the mix head chambers to
obtain a desired mixing quality by spending less energy?
Mix heads have to operate for different densities and mass flows
There are geometrical restrictions due to production methods and
cost reduction
To reduce energy expended low-pressure stirrers are preferable, but
for maintenance and cleaning high-pressure mixing is better. Is the
result a combination of both?
Time scale is very small as one shot takes only milliseconds
© Fraunhofer SCAI
Mix Heads – Mix Chambers
©Hennecke
©Hennecke
© Fraunhofer SCAI
Numerical Approach
Slab stock foam:
VOF, user defined density/viscosity (planned), residence time, java
scripting for batch execution, steady and unsteady, mesh adaption
Variation of inlet geometry
Mix heads:
single fluid (filled chamber), power law density, turbulent, with or without
cavitation (VOF), stationary and transient, java scripting, mesh adaption
Variation of mix head and mixing chamber geometry and viscosity
Optimization for both:
Using SCAI’s software DesParO for multi-objective optimization
Define parameters (geometry, etc.), boundary conditions (densities,
viscosities, etc.) and criteria (mean age, efficiency).
© Fraunhofer SCAI
Optimization Approach – Robust Design
Design-of-
Experiment
Methods
Non-linear Metamodeling
(Radial Basis Functions ++)
Adaptive, hierarchical
meta-models:
“iterative refinement“
Incorporation of scatter
+ global and fully local
tolerance estimation
Sensitivity and robustness analysis with an efficient reduction of the design
space
Meta modeling (response surface modeling with radial basis functions) and
advanced design-of-experiment techniques
Multi-objective robust design-parameter optimization (target function,
sensitivity analysis, Pareto-front determination)
© Fraunhofer SCAI
Optimization Approach –
Software for interactive, multi-objective robust design-parameter studies and
optimization
http://www.scai.fraunhofer.de/fileadmin/images/nuso/DesParO/RobustDesign_2009.pdf
© Fraunhofer SCAI
• Wrap surface if
it contains
non-manifold
edges / vertices
Mesh Adaption in STAR-CCM+ - WorkflowBatch automated via Perl scripts writing STARCCM+ java scripts
• Simulation on
coarse mesh• Flag cells for adaption, iterative
modification of sensitivity to match selected
percentage of cells
= pressure / velocity / volume fraction gradient
md = mean deviation; sd = standard deviation
α = sensitivity; th = threshold
1
2
n
n
i
sd
i
md
sdmdth
Realized by reports and
field functions
• Split regions by
Function, remove
small cell groups to
decrease non-
contiguous regions
then further split by
Non-Contiguous
• Extract boundary
surface of regions with
flagged cells
• Export extracted
surfaces as Nastran
• Import surfaces
as parts and use
as volumetric
controls for
meshing
© Fraunhofer SCAI
Mesh Adaption Process – Example Mix Heads
initial grid
128,539 cells
adapted grid
1,447,400 cells
• several cycles possible
• volumes of previous adaptions could be kept
• stop process if too many cells are flagged
© Fraunhofer SCAI
Mesh Adaption Process – Example Slab Stock
• adaption to volume fraction and velocity gradient
• limit adaption to cells in PU fluid
• limit of non-contiguous splitting to fifty was needed -> otherwise splitting failed
• multi-regional adaption supported
initial grid adapted grid
© Fraunhofer SCAI
Slab Stock Foam – Model Geometry
Dispenser
Fluid basin, generating
back pressure
Moving upper wall
Gap width Sp
Distance L3
Gap width s1
Inclination γ
Distance L1
PU
Side viewAngular view
Dispenser
Fluid basin (back pressure)
Moving upper wall
Moving lower wallOutlet height s2
© Fraunhofer SCAI
Slab Stock Foam – Mesh
• trimmed mesh (size ~4 mil. cells)
• prism layer mesh
• imprint mesh of injector
• volumes shapes are adapted to geometrical parameters
© Fraunhofer SCAI
Slab Stock Foam – Evaluating Results
Before starting the optimization process a parameter study was launched with
variation of gap sizes, down-grade and inlet length, inclination.
The results are evaluated by:
• printing minimum, maximum, average velocity, PU volume fraction and
mean age for closest position and for outlet of channel
• exporting scene files of several planes x or z = const.
• exporting streamline animation file
automated by Java scripts
Closest
position
Outlet of
channel
© Fraunhofer SCAI
Slab Stock Foam – Bad/Better Result
backflow Fluid not spread Fluid widely spread
Not so good Looks better
Film Film
© Fraunhofer SCAI
Slab Stock Foam – Optimization Parameters
Boundary conditions
mass flow
density
viscosity
distinct dispenser
Parameters
gap width
inclination
dispenser position
down-grade height
outlet height
Criteria
homogeneous residence time
distribution (range, standard
deviation, maximum) in outlet
homogeneous velocity
distribution (no backflow, range,
mean deviation) in outlet
Dispenser
Dispenser position
Gap width
Inclination
Down-grade height
PU
Outlet height
© Fraunhofer SCAI
Mix Heads – Model Geometry
Mix Head Injector Mixing Chamber
Inlets
Diffusor
Nozzle Gap
Nozzle Angle
Nozzle
Diameter
Outlet
Injector
Nozzles
Rounded
Wall to
Ejector
top viewside view
Nozzle Pin
© Fraunhofer SCAI
Mix Head – Mesh
• trimmed mesh (size ~1 mil. cells)
• volumes shapes are adapted to geometrical parameters
© Fraunhofer SCAI
The efficiency of different mix head geometries is measured by mass flow averaged
velocities (at different distances from the nozzle) of the fluid for varying gap width (=
mass flow rates).
Numerical Results - Mix Heads
D = nozzle diameter (var)
angle = nozzle angle (fix)
gap width [mm]
ma
ss-a
ve
rage
d v
elo
city [m
/s]
© Fraunhofer SCAI
Numerical Results - Mix Heads
A total of about 80 “numerical experiments” with different parameters (geometrical
parameters, material properties, boundary conditions) were simulated and analyzed with
DesParO.
D = nozzle diameter (var)
angle = nozzle angle (fix)
© Fraunhofer SCAI
Mix head – Optimization Parameters
Boundary conditions
inlet pressure
density
viscosity
mass flow / nozzle gap width
Parameters
nozzle disk cone grading
nozzle disk widths/heights
nozzle pin widths/heights
nozzle diffusor
Criteria
mass-averaged velocity
(momentum of fluid) in three
planes behind injector
Nozzle Gap
Nozzle disk
Nozzle
Diameter
Nozzle Pin
Nozzle disk
Nozzle Diffusor
© Fraunhofer SCAI
Summary
Today I presented:
Basic features of slab stock foam facilities and high-pressure mixing devices
for polyurethane applications
Numerical approach for CFD and optimization
Automated mesh adaption process for STAR-CCM+
Numerical results for slab stock foam and mixing heads
© Fraunhofer SCAI
Outlook
General:
make a precise definition of all parameters, boundary conditionsand criteria for the optimization process
develop further java scripts to enhance automation and flexibility
Slab stock foam:
include residence-time dependent density and viscosity in model
integrate simulation into optimization process and apply adaption process to all simulation runs
Mix heads:
integrate mixing chamber and a agitator device to simulation model
launch more DoE controlled simulations for mixing chamber and mixing heads applying mesh adaption process
expand optimization result visualization
© Fraunhofer SCAI
Thanks for your attention!
