Integrate Laser Scan Data into FEA Model to Perform Level 3 Fitness-for- Service Assessment of Critical Assets in Refinery & Process Industries.

1. Integrating Laser Scan Data into FEA
Model to Perform Level 3 FFS
Assessment
www.vias3d.com
Georgiy Makedonov, FEA Engineer
Glenn Larson, Senior Design Engineer
Arindam Chakraborty, PhD, PE, CTO
USA Canada India Mexico
16000 Park Ten Place, Suite 301, Houston, TX 77084
Phone : +1 (832) 301-0881
May 11, 2022
Contact: achakraborty@vias3d.com

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4. 4
Guru Gobind Singh Refinery, India (2012)
“Disaster at Pittsburgh”
Oil Tank Collapse (1988)
“Oil and gas industry is playing a significant role in the growth of Storage Tank Market”
“The Global Storage Tank Market appraised at USD 12.56 billion in 2019 is expected to touch USD 16.87 billion by 2027”, –
Global Storage Market Size, Report, 2020
Since 1965:
• ~74% of storage tank accidents occurred in petroleum refineries or oil terminals.
• ~30% of all storage tank accidents worldwide occurred due to poor operation and maintenance
Design Codes
API 650 (2019) - Welded Tanks for Oil Storage
API 579 (2016) - Fitness for Service
ASME BPVC VIII, Division 2 - Alternative Rules
ASME BPVC, Part D (Properties)

5. Objective
Integrate Laser Scan Data into FEA
Model to Perform Level 3 Fitness-for-
Service Assessment of Critical Assets
in Refinery & Process Industries
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6. What is FFS?
• Methodology and procedures determines whether an existing engineering
structure is able to perform its intended function under operating loads.
• There are calculation methods and procedures to assess whether a
structure is operable as it is, needs repair or should be replaced altogether.
• FFS assessments usually require a standard list of information such as
original design conditions, materials of construction, and operation and
maintenance history.
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7. Benefits of FFS Assessment
OPERATIONAL AND
MAINTENANCE REDUCED
COSTS
MAINTAIN MECHANICAL
INTEGRITY OF ASSETS
EXTENSION OF ASSETS
SERVICE LIFE
COMPLIANCE OF REGULATION
FOR A SAFE OPERATION
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8. How can we use 3DEXPERIENCE
and SIMULIA/Abaqus to integrate
laser scan data into FEA?
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9. Workflow
Overview
Importing Scan
Data and Navigate
Cleaning Data Shape Recreation
Geometry import
to Abaqus
Assigning Material
Properties and
Sections
Meshing
Application of
Loads and BCs
Analysis
Post-processing /
Integrity
Assessment

10. Workflow (Steps 1 and 2)
Step 1: Import and Navigate
Step 2: Cleaning Data
Step 1 – Import and Navigate
• Import 10mm x 10mm filtered laser scan data.
• Original data is in ASCII format.
• Scale and trim the data using Digital Shape Preparation app.
Step 2 – Cleaning Data
• The surface generated was based on the down-sampling of the
point cloud data to achieve a smother surface with least deviation.
• The laser scan data is so accurate that you can see the heat
radiation from welding.
• Trim and clean the data to remove erroneous points.
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11. • Catia Digital Shape Editor uses an algorithm to homogenize the data to find the tangent results needed to
create the surface
• This method removes points that cannot possibly be part of a manufactured surface.
• The result is checked against the original unfiltered point cloud for deviation.
• This ensures that you can see what we are removing, making a clean surface that represents the actual
manufactured surface.
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Tank Surface Generation Analysis
The deviation analysis in shows that
the majority of points on the surface
deviate from the point cloud data
between 0 to 25 mm.

12. Workflow (Steps 3 and 4)
Step 3 – Shape Recreation
• Easily transform a 3D scan into a clean surface.
• The Reverse Engineering role contains also all the features
needed to work with cloud points and scan, such as smoothing,
surface network, etc.
Step 4 – Geometry Import to Abaqus
• Tank surface was imported in Abaqus/CAE as a .STP file to
• Nozzles, appurtenances and rafters were not included in the
FEA.
• Bottom plate added to the tank bottom as a shell extension
Step 3: Shape Recreation
Step 4: Geometry Import to Abaqus
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13. Workflow (Steps 5 and 6)
Step 5 – Assigning Material Properties and Section
• Assign the section to the part with appropriate thickness (cloud
thickness data can be used)
• Assign the material properties (SA-387)
• Elastic modulus =200 GPa
• Yield Strength = 245 MPa
Step 6 – Meshing
• Assign the mesh to the part.
• Refine the mesh in the areas of high concern (create partitions
if necessary).
• Number of surface elements S4R, shell reduced integration,
quadrilateral) ~ 350,000
Step 5: Assigning Material Properties and Section
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Step 6: Meshing

14. • Level 3 FFS assessment requires plastic collapse, local failure and nonlinear buckling checks for mechanical integrity of a
structure
• Plastic collapse – structure’s global capacity to sustain the applied loads
• Criteria – complete convergence of analysis (structure can sustain the applied loads)
• Local failure – a measure of a structure’s plastic strain against a plastic strain limit.
• Criteria – equivalent plastic strain must be lower the plastic strain limit.
• Nonlinear buckling – determines the local cross-sectional buckling capacity of the structure under the given loads
• Criteria – a load proportionality factor (LPF) greater than 1.0 indicates that the structure will not buckle until load greater
the applied loads is achieved.
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Evaluation Criteria
Analysis Case Analysis LF (D,P,Ps) LF (W) LF (Ss) LF (E)
Case 1 – Plastic Collapse Linear Elastic 1.0 1.0 1.0 N/A
Case 2a – Plastic Collapse Elastic-Plastic 2.2 N/A N/A N/A
Case 2b – Plastic Collapse Elastic-Plastic 2.0 0.8 2.5 N/A
Case 2c – Plastic Collapse Elastic-Plastic 2.0 1.5 0.8 N/A
Case 2d – Plastic Collapse Elastic-Plastic 2.0 1.5 0.8 N/A
Case 2e – Plastic Collapse Elastic-Plastic 2.0 N/A 0.8 1.5
Case 3 – Local Failure Elastic-Plastic 1.5 N/A N/A N/A
Case 4 – Nonlinear Buckling Elastic-Plastic 2.0 1.5 0.8 N/A

15. Workflow (Steps 7 and 8)
Step 7 – Application of Boundary Conditions
• Apply the appropriate boundary conditions
• Bottom surface is fixed (no translation, no rotation)
Step 8 – Application of Loads
• Force is applied at a reference point coupled to inner surface of tank​
• Apply the appropriate loading conditions (in addition to gravity):
• Wind design speed
• Wind pressure load
• Internal Hydrostatic Pressure
• Design Temperature
• Vacuum Pressure
• Snow Load
• Seismic load 15
Steps 7 and 8: Application of Loads and BC
Load Cases

16. Workflow (Step 9)
Step 9 – Results Analysis
• The results show that the tank passes plastic collapse, local failure and buckling
checks assessment as stipulated in API 579.
• Some plastically deformed areas​ are observed in tank shell (shown by red
colors).
• The governing load for plastic collapse turned out to be the hydrostatic load
case.
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Plastic Collapse (Elastic-Plastic) Bottom Ring
Local Failure (Elastic-Plastic)
NL Buckling Analysis
Plastic Collapse (Linear Elastic)

17. Conclusions
• The tank shell passes the FFS criteria of API 579 code (plastic
collapse, local failure, buckling). ​
• Tank is fit for service at design conditions.
• No repairs or reinforcements are required to operate the tank
based on assessed conditions.
• It is recommended that non-destructive testing (NDT) of high
distortion points throughout the tank be performed.
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Analysis Case Analysis Result
LC 2a (Dead weight,
pressure)
Pass
LC 2b (Dead weight,
pressure, snow, wind)
Pass
LC 2c (Dead weight,
pressure, snow, wind)
Pass
LC 2d (Dead weight, external
pressure, snow, wind)
Pass
LC 2e (Dead weight, external
pressure, snow, seismic)
Pass
Local Failure Pass
Nonlinear Buckling Pass

18. Summary
Accurately capturing surface distortion through laser scan
data is of paramount importance.
Multiple load combinations can be assessed efficiently to
determine worst combination of directional loads and surface
distortions.
Using 3DEXPERIENCE platform, geometrical surface derived
from point cloud data can be seamlessly integrated into the
FEA workflow to perform high fidelity FFS assessment using
Abaqus.
The workflow presented here can be used for reliability
studies by introducing variances in measured data and or by
introducing surface profiles for areas lacking measurements.
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