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RBDO OF PRIMARY SHIELD STRUCTURE
UNDER SEISMIC LOADING
S. K. Radha, A. Chakraborty
Virtual Integrated Analytics Solutions (VIAS)
K. C. Sener, A. H. Verma
Civil Engineering, Purdue University
October 30, 2018
1400 Broadfield Blvd. Suite 325, Houston TX 77084
Phone : +1 (832) 301-0881
www.viascorp.com
Contact Person:
Dr. A. Chakraborty – achakraborty@viascorp.com
SIMULIA Technology Day, Toronto
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Agenda
• VIAS Introduction
• Background
• Problem Statement
• Test Setup and Results
• FEA Model & Results
• Reliability based Design Optimization
• Optimization Results
▪ Plate Thickness
▪ Reliability
• Conclusions
• Future Work
2
© 2018 Virtual Integrated Analytics Solutions Inc.
Who We Are
Consulting
Training
Development
Software
• Provider of Engineering Solutions with experience in multiple industries
• Technical team consists of experts in the fields of
• Platinum Partner of Dassault Systèmes
• Headquartered in Houston with presence throughout North America
• Energy, Process & Utilities • Aerospace • Automotive • Marine & Offshore
• Industrial Machinery • High Tech • Nuclear • Medical Devices
• Design • Manufacturing • Systems Integration • Simulation • Data Analytics
3
© 2018 Virtual Integrated Analytics Solutions Inc.
PLM Services
• Sales, support and implementation of enabling technologies for product development and
manufacturing.
• Interdisciplinary process definition and execution to fulfill client needs related to:
➢ Systems Development and Integration
➢ Virtual Product Authoring, Synthesis and Management
➢ Concurrent Engineering
➢ Design Validation & Producibility
➢ Model Based Enterprise for Manufacturing
➢ Regulatory and International Trade Compliance
➢ Supplier/Customer Collaboration
➢ Engineering/IT Business Growth
4
© 2018 Virtual Integrated Analytics Solutions Inc.
Simulation Capabilities
Design Analysis and
Validation using Simulation
Fatigue & Durability /
Fracture / Damage
Mechanics
Optimization and Reliability
Multi-physics Simulations
(CFD, Thermal Analysis)
FFS and Code Based Design
by Analysis
Composite Structures
Modelling
Structural Analysis / Plant
Engineering
Crack Propagation Modelling
Simulation Automation
Verification and Validation
Local-Global Modeling
5
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Introduction
• PSW provides support and protection to reactor
pressure vessel (RPV) and critical reactor internals of
PWR
• Consists of several structural members like bearing
walls, shear walls, slabs, etc.
• Seismic activity can damage critical structural/
shielding components
• Modular steel-plate composite (SC) PSW with
concrete infills increasingly used
• SC structures exhibits excellent structural
performance in terms of stiffness, strength and
ductility under seismic loading
RPV
PSW
Steel
Concrete
Steel-Plate
Composite PSW
Outer
Shield
Building
6
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Introduction (Cont.)
• Typical SC structure consists of:
▪ Thick concrete walls (infill between plates)
▪ 3 layers of steel plates (interior and exterior
surfaces and 1 middle layer to provide
additional reinforcement)
▪ Intermediate web plates
• SC modules fabricated in production
environment and transported to construction
site
• Erected, welded and finally filled with concrete
on-site thereby reducing construction critical
path time
Exterior Plates
Interior Plates Web Plates
Concrete
Infill
Cavity
7
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Background
• An experimental test setup of a 1/6th scale PSW
specimen and the seismic load-deformation
behavior tested
• 3D FEA model was developed and validated for
deterministic numerical study
Current work extends to reliability based design optimization (RBDO) study
8
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Problem Statement
• Objective:
▪ Minimize T and t (minimizes inertia under
seismic and cost)
▪ Maximize Reliability (applied stress < yield)
• Design Variables:
▪ T: Steel plate thickness at openings
▪ t: Typical steel plate thickness
Steel plate thickness at openings
(T): Brown plates
Typical steel plate thickness (t):
Red, Green, Blue, and Black plates
Initial Design Dimensions
T= 0.17 in (4.32 mm)
t= 0.10 in (2.54 mm)
9
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Test Setup
• Experimental test conducted in
Japan of 1/6th scale version of the
APWR PSW
• Polygonal SC structure with three
steel liner plates and web plates
• Free-standing test specimen
rigidly embedded in reinforced
concrete base
PSW Cross-section and Isometric view of PSW ( no concrete infill)
Horizontal Section Cut through
Specimen (with concrete infill)
Reinforced concrete base
(T)
(t)
10
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Test Setup (Cont.)
• Test setup configured as a free
standing structure with top
connected to upper concrete
loading block and fixed base
• Lateral force applied on structure
hydraulically
• Quasi-static cyclic loading applied
• Subjected to three load-controlled
cycles in elastic range of response
• Final cycle until ultimate failure
11
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Test Results
• Ultimate strength controlled by
combination of flexure, shear, and axial
forces in wall segments
• Local buckling and yielding occurred in
steel plates adjacent to openings
• Specimen showed substantial post yield
ductility
• Measured shear strains in inner, middle
and outer steel plates were relatively
equal
• Demonstrates that all three steel plates
contribute to system strength
12
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
FEA Model
• 3-D half model used since geometry and
loading are mirror symmetric
• All of the PSW steel plates are modeled using
4-node shell elements (S4R) and multi-axial
plasticity material model
• Concrete infill is modeled with continuum 3-D
8-node reduced integration (C3D8R) brick
elements and elastic fracture (brittle cracking)
material model
• Composite action between steel and concrete
is modeled with non-linear connector elements
• Monotonic and cyclic loading conditions are
simulated
Exploded view of part instances and mesh
13
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
FEA Model: Results
von Mises stress at applied loads of 1000,
2000, 3000 and 4000 kips on inner steel
plate
von Mises stress at applied loads of 1000, 2000, 3000
and 4000 kips on outer steel plate
von Mises stress at applied loads of 1000, 2000, 3000
and 4000 kips on middle steel plate
Red indicates yielding
(>67.3 ksi)
14
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
FEA Model: Results (Cont.)
• Analytical load-displacement response
behavior compares well with experimental
results
• Comparisons also indicate that cyclic load
response and behavior of specimen are similar
to monotonic analysis results
• Cyclic loading is not expected to have major
influence on results due to non-ductile shear
failure of specimen
• Hence only monotonic loading considered in
the RBDO study
Displacement gradient for monotonic loading
Comparison of Experimental and Analytical results
15
© 2018 Virtual Integrated Analytics Solutions Inc.
Requires Design Process Automation
DV2
Feasible
Region
DV1
(Sample points for DOE)
active constraint
inequality constraint
inactive constraint
Minimized
Objective Function
Contour
Reliable and
Robust Design
Deterministic Optimum
[High Failure Rate]
RBDO [Very Low Failure Rate]
Initial
Design from
DOE
(Approximation)
RBDO Process
16
© 2018 Virtual Integrated Analytics Solutions Inc.
RBDO Process (Cont.)
Requires Approximation of Response
17
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Stochastic Analysis
• Maximum Von Mises stress (σvm) induced in steel
plate considered the most important criteria to
evaluate lateral load capacity
• T, t – design variables (to be within 20% of T = 0.17 in
and t = 0.1 in)
• T , t, σy, and u considered as normal random variables
with mean values and 10 % as their coefficient of
variation (COV)
• Performance function defined as g(X)= σvm (T, t, σy,
u) - σy
• Reaching the yield strength of steel (σy) is considered
as structural failure (i.e g(X) >0) (Failure Criteria)
• Target reliability 0.99
Normal Random Variable Mean
Thickness of steel plate at opening (T)
0.17 in
(4.32 mm)
Thickness of typical steel plate (t)
0.10 in
(2.54 mm)
Yield stress of Steel (𝛔 𝐲)
67 ksi
(462 MPa)
Applied Displacement (𝐮)
0.10 in
(2.54 mm)
18
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Response Approximation
• Response Surface Method (RSM) is used for
optimization and reliability
• Response approximated as quadratic function that
provided good correlation (mean error< 5%)
60
62
64
66
60 62 64 66
Actual
Predicted
Actual vs. Predicted values of approximation.
25 FEA runs: For Approximation Function
10 FEA runs: For Error Computing
Typical FEA Runtime – 16hrs
19
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Pareto Effects
• U, t has the most pronounced effect on von Mises
stress
• Higher order and correlation effects are small
0 10 20 30
σ_y
T
t
u
% effect on maximum von mises stress (Linear)
0 10 20 30
σ_y and T
σ_y and u
(σ_y)^2
T and u
t and u
u^2
% effect on maximum von Mises stress (2nd order
& correlated)
20
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Optimization Workflow (Isight)
• RBDO workflow setup in Isight based on FEA model
response approximations
• Second Order Reliability Method (SORM) and Monte Carlo
Simulation (MCS) reliability techniques used
• Sequential Quadratic Programming (SQP) and Hooke-
Jeeves (HJ) direct search approaches used for optimization
Sample Size for Optimization –
DO : ~ 60
RBDO : ~ 100,000
21
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Results: Optimized Plate Thickness
Reliability Method Optimization Method
T
(in)
t
(in)
SORM
SQP 0.16 0.12
HJ 0.16 0.12
MCS
SQP 0.14 0.12
HJ 0.14 0.12
RBDO:
Optimization Method
T
(in)
t
(in)
SQP 0.13 0.09
HJ 0.15 0.09
SQP 0.13 0.09
HJ 0.15 0.09
DO:
MCS results better
suited for non-
linear response
Original Values
T= 0.17 in
t= 0.10 in
22
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Results: Reliability
Reliability
Method
Optimization Method DO RBDO
SORM
SQP 0.48 0.64
HJ 0.49 0.64
MCS
SQP 0.55 0.68
HJ 0.55 0.68
Original Model Reliability
SORM: 0.56
MCS: 0.62
23
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Conclusions
• DO has lowered both thickness values from initial
• However, RBDO has lowered the thickness of steel plate at opening (T) by 17.64 % and
increased the thickness of typical steel plate (t) by 20 % from the original values
• RBDO achieved a reliability of 68 % and increased the reliability of the PSW structure
by 9.6 % and 23.6 % when compared to the original reliability and reliability from DO,
respectively.
24
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Future Work
• Extension for a better RBDO of PSW which considers
• Additional failure criteria (concrete casting pressure, thermal loading, etc)
• Additional design and stochastic variables
25
© 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc.
Thank you
26

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RBDO of Primary Shield Structure Under Seismic Loading

  • 1. RBDO OF PRIMARY SHIELD STRUCTURE UNDER SEISMIC LOADING S. K. Radha, A. Chakraborty Virtual Integrated Analytics Solutions (VIAS) K. C. Sener, A. H. Verma Civil Engineering, Purdue University October 30, 2018 1400 Broadfield Blvd. Suite 325, Houston TX 77084 Phone : +1 (832) 301-0881 www.viascorp.com Contact Person: Dr. A. Chakraborty – achakraborty@viascorp.com SIMULIA Technology Day, Toronto
  • 2. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Agenda • VIAS Introduction • Background • Problem Statement • Test Setup and Results • FEA Model & Results • Reliability based Design Optimization • Optimization Results ▪ Plate Thickness ▪ Reliability • Conclusions • Future Work 2
  • 3. © 2018 Virtual Integrated Analytics Solutions Inc. Who We Are Consulting Training Development Software • Provider of Engineering Solutions with experience in multiple industries • Technical team consists of experts in the fields of • Platinum Partner of Dassault Systèmes • Headquartered in Houston with presence throughout North America • Energy, Process & Utilities • Aerospace • Automotive • Marine & Offshore • Industrial Machinery • High Tech • Nuclear • Medical Devices • Design • Manufacturing • Systems Integration • Simulation • Data Analytics 3
  • 4. © 2018 Virtual Integrated Analytics Solutions Inc. PLM Services • Sales, support and implementation of enabling technologies for product development and manufacturing. • Interdisciplinary process definition and execution to fulfill client needs related to: ➢ Systems Development and Integration ➢ Virtual Product Authoring, Synthesis and Management ➢ Concurrent Engineering ➢ Design Validation & Producibility ➢ Model Based Enterprise for Manufacturing ➢ Regulatory and International Trade Compliance ➢ Supplier/Customer Collaboration ➢ Engineering/IT Business Growth 4
  • 5. © 2018 Virtual Integrated Analytics Solutions Inc. Simulation Capabilities Design Analysis and Validation using Simulation Fatigue & Durability / Fracture / Damage Mechanics Optimization and Reliability Multi-physics Simulations (CFD, Thermal Analysis) FFS and Code Based Design by Analysis Composite Structures Modelling Structural Analysis / Plant Engineering Crack Propagation Modelling Simulation Automation Verification and Validation Local-Global Modeling 5
  • 6. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Introduction • PSW provides support and protection to reactor pressure vessel (RPV) and critical reactor internals of PWR • Consists of several structural members like bearing walls, shear walls, slabs, etc. • Seismic activity can damage critical structural/ shielding components • Modular steel-plate composite (SC) PSW with concrete infills increasingly used • SC structures exhibits excellent structural performance in terms of stiffness, strength and ductility under seismic loading RPV PSW Steel Concrete Steel-Plate Composite PSW Outer Shield Building 6
  • 7. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Introduction (Cont.) • Typical SC structure consists of: ▪ Thick concrete walls (infill between plates) ▪ 3 layers of steel plates (interior and exterior surfaces and 1 middle layer to provide additional reinforcement) ▪ Intermediate web plates • SC modules fabricated in production environment and transported to construction site • Erected, welded and finally filled with concrete on-site thereby reducing construction critical path time Exterior Plates Interior Plates Web Plates Concrete Infill Cavity 7
  • 8. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Background • An experimental test setup of a 1/6th scale PSW specimen and the seismic load-deformation behavior tested • 3D FEA model was developed and validated for deterministic numerical study Current work extends to reliability based design optimization (RBDO) study 8
  • 9. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Problem Statement • Objective: ▪ Minimize T and t (minimizes inertia under seismic and cost) ▪ Maximize Reliability (applied stress < yield) • Design Variables: ▪ T: Steel plate thickness at openings ▪ t: Typical steel plate thickness Steel plate thickness at openings (T): Brown plates Typical steel plate thickness (t): Red, Green, Blue, and Black plates Initial Design Dimensions T= 0.17 in (4.32 mm) t= 0.10 in (2.54 mm) 9
  • 10. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Test Setup • Experimental test conducted in Japan of 1/6th scale version of the APWR PSW • Polygonal SC structure with three steel liner plates and web plates • Free-standing test specimen rigidly embedded in reinforced concrete base PSW Cross-section and Isometric view of PSW ( no concrete infill) Horizontal Section Cut through Specimen (with concrete infill) Reinforced concrete base (T) (t) 10
  • 11. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Test Setup (Cont.) • Test setup configured as a free standing structure with top connected to upper concrete loading block and fixed base • Lateral force applied on structure hydraulically • Quasi-static cyclic loading applied • Subjected to three load-controlled cycles in elastic range of response • Final cycle until ultimate failure 11
  • 12. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Test Results • Ultimate strength controlled by combination of flexure, shear, and axial forces in wall segments • Local buckling and yielding occurred in steel plates adjacent to openings • Specimen showed substantial post yield ductility • Measured shear strains in inner, middle and outer steel plates were relatively equal • Demonstrates that all three steel plates contribute to system strength 12
  • 13. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. FEA Model • 3-D half model used since geometry and loading are mirror symmetric • All of the PSW steel plates are modeled using 4-node shell elements (S4R) and multi-axial plasticity material model • Concrete infill is modeled with continuum 3-D 8-node reduced integration (C3D8R) brick elements and elastic fracture (brittle cracking) material model • Composite action between steel and concrete is modeled with non-linear connector elements • Monotonic and cyclic loading conditions are simulated Exploded view of part instances and mesh 13
  • 14. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. FEA Model: Results von Mises stress at applied loads of 1000, 2000, 3000 and 4000 kips on inner steel plate von Mises stress at applied loads of 1000, 2000, 3000 and 4000 kips on outer steel plate von Mises stress at applied loads of 1000, 2000, 3000 and 4000 kips on middle steel plate Red indicates yielding (>67.3 ksi) 14
  • 15. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. FEA Model: Results (Cont.) • Analytical load-displacement response behavior compares well with experimental results • Comparisons also indicate that cyclic load response and behavior of specimen are similar to monotonic analysis results • Cyclic loading is not expected to have major influence on results due to non-ductile shear failure of specimen • Hence only monotonic loading considered in the RBDO study Displacement gradient for monotonic loading Comparison of Experimental and Analytical results 15
  • 16. © 2018 Virtual Integrated Analytics Solutions Inc. Requires Design Process Automation DV2 Feasible Region DV1 (Sample points for DOE) active constraint inequality constraint inactive constraint Minimized Objective Function Contour Reliable and Robust Design Deterministic Optimum [High Failure Rate] RBDO [Very Low Failure Rate] Initial Design from DOE (Approximation) RBDO Process 16
  • 17. © 2018 Virtual Integrated Analytics Solutions Inc. RBDO Process (Cont.) Requires Approximation of Response 17
  • 18. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Stochastic Analysis • Maximum Von Mises stress (σvm) induced in steel plate considered the most important criteria to evaluate lateral load capacity • T, t – design variables (to be within 20% of T = 0.17 in and t = 0.1 in) • T , t, σy, and u considered as normal random variables with mean values and 10 % as their coefficient of variation (COV) • Performance function defined as g(X)= σvm (T, t, σy, u) - σy • Reaching the yield strength of steel (σy) is considered as structural failure (i.e g(X) >0) (Failure Criteria) • Target reliability 0.99 Normal Random Variable Mean Thickness of steel plate at opening (T) 0.17 in (4.32 mm) Thickness of typical steel plate (t) 0.10 in (2.54 mm) Yield stress of Steel (𝛔 𝐲) 67 ksi (462 MPa) Applied Displacement (𝐮) 0.10 in (2.54 mm) 18
  • 19. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Response Approximation • Response Surface Method (RSM) is used for optimization and reliability • Response approximated as quadratic function that provided good correlation (mean error< 5%) 60 62 64 66 60 62 64 66 Actual Predicted Actual vs. Predicted values of approximation. 25 FEA runs: For Approximation Function 10 FEA runs: For Error Computing Typical FEA Runtime – 16hrs 19
  • 20. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Pareto Effects • U, t has the most pronounced effect on von Mises stress • Higher order and correlation effects are small 0 10 20 30 σ_y T t u % effect on maximum von mises stress (Linear) 0 10 20 30 σ_y and T σ_y and u (σ_y)^2 T and u t and u u^2 % effect on maximum von Mises stress (2nd order & correlated) 20
  • 21. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Optimization Workflow (Isight) • RBDO workflow setup in Isight based on FEA model response approximations • Second Order Reliability Method (SORM) and Monte Carlo Simulation (MCS) reliability techniques used • Sequential Quadratic Programming (SQP) and Hooke- Jeeves (HJ) direct search approaches used for optimization Sample Size for Optimization – DO : ~ 60 RBDO : ~ 100,000 21
  • 22. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Results: Optimized Plate Thickness Reliability Method Optimization Method T (in) t (in) SORM SQP 0.16 0.12 HJ 0.16 0.12 MCS SQP 0.14 0.12 HJ 0.14 0.12 RBDO: Optimization Method T (in) t (in) SQP 0.13 0.09 HJ 0.15 0.09 SQP 0.13 0.09 HJ 0.15 0.09 DO: MCS results better suited for non- linear response Original Values T= 0.17 in t= 0.10 in 22
  • 23. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Results: Reliability Reliability Method Optimization Method DO RBDO SORM SQP 0.48 0.64 HJ 0.49 0.64 MCS SQP 0.55 0.68 HJ 0.55 0.68 Original Model Reliability SORM: 0.56 MCS: 0.62 23
  • 24. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Conclusions • DO has lowered both thickness values from initial • However, RBDO has lowered the thickness of steel plate at opening (T) by 17.64 % and increased the thickness of typical steel plate (t) by 20 % from the original values • RBDO achieved a reliability of 68 % and increased the reliability of the PSW structure by 9.6 % and 23.6 % when compared to the original reliability and reliability from DO, respectively. 24
  • 25. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Future Work • Extension for a better RBDO of PSW which considers • Additional failure criteria (concrete casting pressure, thermal loading, etc) • Additional design and stochastic variables 25
  • 26. © 2018 Virtual Integrated Analytics Solutions Inc.© 2018 Virtual Integrated Analytics Solutions Inc. Thank you 26