The document discusses using finite element modeling in thermal packaging design. It describes the advantages of including thermal modeling in the design process, such as not needing prototypes and being able to test multiple variations. The presentation introduces finite element analysis and heat transfer concepts. It also provides examples of simulating standard designs and using simulation as part of the development process through case studies.

1. The Use of Finite Element Modeling in
Thermal Packaging Design
Presented By:
Landon Halloran
Finite Element Analysis Engineer
lhalloran@cryopak.com
October 2011
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2. Overview
• Advantages of Thermal Modeling in the
Design Process
• Introduction to Finite Element Analysis
• Heat Transfer
• Thermal Simulation
• Simulation of Standard Designs
• Simulation as Part of the Development
Process: Case Studies
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3. Advantages of Including Thermal
Modeling in the Design Process
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4. Why Simulate?
• No prototyping required
• No inventory required
• Saves on chamber time
• Limitless number of temperature probes
• Can identify susceptible locations in the pack-out
• Accelerates the design process
• Multiple variations can be tested and compared
• Graphic rendering for customers
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5. Introduction to Finite Element Analysis
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6. A Wide Variety of Applications
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7. What is “Finite Element Analysis”?
• A computational method for performing analysis
of a physical system.
• A tool for simulation of the mechanical, thermal,
rheological, electronic or other response of a
system.
• Involves the division of a body into smaller
domains or “elements”.
• The applicable equations describing the physics
of the system can then be evaluated for each of
the elements.
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8. A Simple Example in 2-D: I
-10 W/m2
40 C
10 C
10 W/m2
An odd-shaped sheet of HDPE is A CAD model of the sheet is divided into
subjected to a heat input and output, as smaller components by meshing.
well as constant temperatures at two
places.
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9. A Simple Example in 2-D: II
-10 W/m2
40 C
10 C
10 W/m2
Constraints such as fixed temperatures The simulation algorithm runs with
are defined in the simulation set-up. multiple iterations and calculates the
resulting temperature throughout the
object.
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10. Heat Transfer
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11. Heat Transfer: I
• There are three methods of heat transfer between
two objects:
– Conduction
• Transfer of thermal energy through direct contact
• Fourier’s Law: q = kAΔT/Δx
– Convection
• Transfer of thermal energy through a fluid
• Can be free or forced
• q = hcAΔT
– Radiation
• Transfer of energy through electromagnetic radiation emitted by all objects
with temperature > 0 K (-273 C)
• Hotter objects emit more power: P = σT4
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12. Heat Transfer: II
• In thermal packaging, the primary method of
heat transfer is conduction
• Free convection by air cells in a packaging
configuration also have a significant effect
• To properly treat both mechanisms of heat
transfer, a hybrid thermal-fluid solver is
required
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13. Thermal Simulation
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14. Thermal Simulation: I
• In order for thermal simulation to be an
integral part of the design process, simulation
results must be validated.
• In thermal/flow simulation, there are multiple
user parameters and options that govern the
thermodynamics of the modelled system.
• Many of these can be measured directly,
however many must be determined through
simulated experiment
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15. Thermal Simulation: II
• Finding the correct simulation parameters and
making the appropriate geometric
approximations present the biggest challenges
in obtaining simulation results that accurately
replicate test results.
• A standard set of processes, options and
parameters has been developed leading to
acceptable simulation results for existing and
new thermal packaging designs.
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16. Models of Individual Components
• Database of part drawings
• Can be used as
components in assembly
models and simulations.
• Geometry is generally
simplified for simulation
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17. Simulation Parameters and Options
• Density, specific heat capacity, and thermal
conductivity
• Convective heat transfer multiplier
• Radiative and convective thermal coupling to ambient
environment
• Conductive contact between tangent faces
• Solver parameters
• Others
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18. Parameterization Simulation
Effect of Convective Heat Transfer Multiplier (TS-48 Large Canadian
with ATP Summer 48H Profile)
12
11
10
9
Temperature (C)
8 Test - Probe 7
Test - Probe 10
7
Simulation - CHTM=1
6
Simulation - CHTM=0.5
5 Simulation - CHTM=0.25
4 Simulation - CHTM=0.125
3
2
0 12 24 36 48
Time (h)
The extent to which the convective heat transfer multiplier affects results can be readily seen here.
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19. Materials: I
• For valid simulation results, accurate values of several
physical properties of each material are required:
– Density (ρ)
– Specific heat capacity (cp)
– Thermal conductivity (k)
• For PCMs, values for these properties are required both
above and below the phase-change temperature, as well
as:
– Latent heat capacity (L)
– Phase change temperature (TPC)
• Some of these material properties exhibit a significant
variation over the temperature range of interest and
must be programmed as temperature-dependent tables.
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20. Materials: II
• PCMs • Insulators
– Water-based Cryogels – Expanded Polystyrene
– Phase-5 (EPS)
– Phase-22 – Low, medium and high
density
– Phase-27
– 20-Below Gel – Polyurethane (PUR)
– Dry Ice – Polyisocyanurate (PIR)
– Vacuum Insulated Panels
• Containers
– Corrugated Cardboard • Others
– HDPE/LDPE
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21. Simulation of Standard Designs
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22. Simulations of Standard Designs
• TimeSaver 24 Small (US)
• TimeSaver 48 Premium Medium (US)
• TimeSaver 48 Large (Canada)
• TimeSaver 96 PUR Small (US)
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23. TimeSaver-24 Small
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24. Design Details
• 12 CG1181 gel packs.
• Was simulated both with and
without a convective heat
transfer coefficient multiplier to
effectively model the
configuration with and without
bubble wrap.
• Gel packs were modeled with
a simplified geometry and with
an averaged conductive heat
transfer coefficient.
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25. Summer Simulation Results
40
35
30
25
Temperature (°C)
20
15
10
5
0
0 6 12 18 24
Time (h)
Test #1 Test #2 Test #3 Solution 1 SIM (with HTCM=1/8) Solution 3 SIM ISTA Summer 24H
“w/ bubble-wrap” “w/o bubble-wrap”
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26. Winter Simulation Results
20
15
10
Temperature (°C)
5
0
0 6 12 18 24
-5
-10
-15
Time (h)
Test #1 Test #2 Test #3 Solution 2 SIM (with HTCM=1/8) Solution 4 SIM ISTA Winter 24H
“w/ bubble-wrap” “w/o bubble-wrap”
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27. TimeSaver-48 Medium Premium
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28. Model Details
• Presence of two different PCMs, Phase-5
and water-based gels, presents additional
challenges in obtaining accurate simulation
results.
• To account for attenuated convective heat
transfer due to blockage from bubble-wrap
, a convective heat transfer coefficient
multiplier was used.
• Conduction assumed perfect between EPS
layers
Cut-away view of 48H Winter Simulation
• Effective conductive transfer coefficient
used for other surface-to-surface contact
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29. Simulation Results - Winter
TimeSaver 48 Medium Premium - Winter Minimum Load
20
15
10
Temperature (C)
5
0.61 C
0
0 12 24 36 48
-5
-10
-15
Time (h)
Qualification Test #1 Qualification Test #2 Qualification Test #3 Simulation Payload T ISTA Winter 48H
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30. TimeSaver-48 Large Canadian
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31. Model Details
• Customer expressed interest in using
the design but as concerned about the
performance against their own
temperature profile.
Final Temperature - Winter • In order to facilitate the conversation
the shipper was modeled and tested
against the profile in question.
• Gel packs are the sole PCMs. Modeled
geometry was idealized.
• Effective conductive heat transfer
coefficient used
• Effect of convective heat transfer
multiplier was extensively investigated
Final Temperature - Summer
with this model.
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32. Simulation Results - Summer
Timesaver-48 Large Canadian Assembly 001 – Summer – SIM 001:
Solution 10 - ATP Canadian Summer 48H
40
35
30
25
Temperature (C)
20 Payload Test Temperature
Payload Simulation Temperature
15
Temperature Profile
10
5
0
0 12 24 36 48
Time (h)
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33. Simulation Results - Winter
Timesaver-48 Large Canadian Assembly 001 - SIM 001:
Solution 2 - ATP Canadian Winter 48H
25
20
15
10
Temperature (C)
5
0
0 6 12 18 24 30 36 42 48
-5
-10
-15
-20
-25
Time (h)
Temp Probe 1 (C) Temp Probe 2 (C) Payload Simulation (C) External Profile Temperature
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34. Simulation Results – Winter
(Customer-supplied profile)
Timesaver-48 Large Canadian Assembly 001 - SIM 001:
Solution 3 - Lynden Winter 51H
25
20
15
10
5
Temperature (C)
0
0 12 24 36 48
-5
-10
-15
-20
-25
-30
Time (h)
External Temperature Profile Payload Center Temperature
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35. TimeSaver-96 Small PUR
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36. Model Details
• PCMs are Phase-5
bottles and gel
bottles.
• Again, effective
conductive heat
transfer coefficient
used to model
imperfect
conduction
between surfaces
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37. Simulation Results
TimeSaver-96 Small PUR - Winter
20
15
10
Temperature (C)
5
0
0 12 24 36 48 60 72 84 96
-5
-10
-15
Time (h)
Qualification Test #1 Qualification Test #2 Qualification Test #3
Payload (Sol'n 1) Payload (Sol'n 3) ISTA Winter 96H
Convection Heat Transfer Multiplier : 1/8 (sol’n 1) & 1/16 (sol’n 3)
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38. Simulation as Part of the Development
Process: Case Studies
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39. Modifications to Existing
Pallet Shipper Design
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40. Model Details
• In order make the REPAK 120
design more robust, the
performance of new bottle
configurations was examined.
• We wanted to examine the
effect of adding an extra bottle
to each sleeve.
• By simulating this new
configuration, significant
prototyping labour and
chamber time were avoided.
• Other options may be further
explored by modifying the
model.
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41. New Panels
3-Bottle Panel 4-Bottle Panel
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42. Simulation Results - Summer
55
50
45
40
• Simulation results
35 from new designs
Temperature (°C)
30 compared with test
25 results from old
20
design
15
10
5
0
REPAK 12- Bottom Corner REPAK 13-Bottom Center
0 24 48 Time (h) 72 96 120
REPAK 14-Top Corner 3-Bottle SIM - Payload Center
REPAK 15-Top Center 4-Bottle SIM - Payload Center
REPAK Summer 120H
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43. Simulation Results - Winter
25
20
15 • Simulation
10
results from
5
new designs
compared with
Temperature (°C)
0
0 24 48 72 96 120
test results from
-5 old design.
-10 • Notably, the
-15 initial payload
-20
temperature in
the test run was
-25
less than 5 C.
-30
Time (h)
3-Bottle SIM - Payload Center 4-Bottle SIM - Payload Center REPAK 8-Top Corner
REPAK 6-Bottom Corner REPAK 7-Bottom Center REPAK 9-Top Center
REPAK Winter 120H
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44. Pallet Shipper Concept
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45. Overview of Project
• A large distributor of medical products expressed
interest in a custom pallet shipper capable of
transporting a wide variety of temperature-sensitive
products for at least 48 hours.
• Customer was very concerned with volume
optimization and reusability.
• To help the customer make a more informed
decision, the effect of varying PIR wall thickness was
modelled.
• Results were obtained within 2 weeks with no
prototyping cost.
• Prototype construction and testing would be very
costly and take long to complete. Simulation allows us
to rapidly evaluate the feasibility of various designs.
The results can then be used to help us improve the
¼ view used to exploit symmetry
and decrease calculation time design.
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46. Simulation Parameters
• Wall layers:
• 1/4" HDPE
• different thicknesses of PIR were tested (40, 60, and 80 mm)
• 1/2" Phase-5
• 1/4" HDPE
• Simulated payload: idealized non-fluid material with thermal
properties of a bulk air/water blend.
• Temperature profile is a constant 20 C (68 F) for 3 days
• Worst-case (top corner) and best-case (center of XY face)
points chosen from the payload surface for post-simulation
analysis
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47. Simulation Example
80mm PIR walls, Temperature after 72 hours
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48. Temperature Response of Payload
12.00
in 20°C (68°F) Flat-line Simulation
11.00
10.00
Temperature (C)
9.00
8.00
7.00
6.00
5.00
0.00 8.00 16.00 24.00 32.00 40.00 48.00 56.00 64.00 72.00
Time (hour)
40mm Top Corner 40mm Middle of XZ Face 60mm Top Corner
60mm Middle of XZ Face 80mm Top Corner 80mm Middle of XZ Face
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49. Effect of PIR Wall Thickness on
Performance
PIR Wall Payload Top Payload Center of
Thickness Thickness x ID y ID z ID Volume Volume Corner Failure Face Failure Time
(mm) (inches) (in) (in) (in) (in3) (L) time (h) (h)
40 2.57 35.85 43.85 72.85 114524 1876 30 47
60 3.36 34.28 42.28 71.28 103279 1692 34 55
80 4.15 32.70 40.70 69.70 92768 1520 62 72+
• Increased PIR thickness has the expected effect of prolonging the period that the
payload can be transferred at its safe temperature range of 2 – 8 C.
• The trade-off for longer effectiveness is a decreased payload volume.
• By shipping products with lower initial temperatures, the period of effectiveness
can be prolonged.
• Many factors, especially the dimensions and the composition of the payload, can
affect these times.
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50. Conclusions
• The results of finite element analysis have been
demonstrated and verified with existing designs.
• The use of finite element thermal analysis in the
design process presents multiple advantages to our
customers:
– Comparison of multiple design variations
– Rapid analysis of changes to designs
– Identification of susceptible locations in the pack-out
– Robust design and simulation of large or complex pack-
outs that would entail large cost or time to prototype
– Graphical output of models and results
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51. Thank-you for your attention!
Any questions?
Landon Halloran
lhalloran@cryopak.com
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