Design Optimization of Safety Critical Component for Fatigue and Strength Using Simulation and Data Analytics

Design Optimization of Safety Critical
Component for Fatigue and Strength Using
Simulation and Data Analytics
Arindam Chakraborty, PhD, PE
Virtual Integrated Analytics Solutions (VIAS)
achakraborty@viascorp.com
www.viascorp.com
June 07, 2018

© 2018 Virtual Integrated Analytics Solutions Inc.
Outline
• Overview of VIAS
• Strength of Simulation
• BOP Design Optimization using Simulation
• Machine Learning in Design and Simulation
• Summary
2

Company Overview
4
• Multiple Industry Experience
• Aerospace & Defense
• Automotive
• Energy, Process & Utilities
• Machinery & Equipment
• Nuclear
• Marine & Offshore
• Medical Devices
• High-tech
• Presence in Houston, Chicago, Cincinnati, LA, Portland
• Provides Engineering Consultancy, Automation and
Customization, Training
• Team consists of PhD’s and MSc/MTech’s in Design,
Manufacturing, Solid Mechanics, Fatigue & Fracture,
Composites, Thermal & Fluid, Materials & Corrosion, Numerical
Analysis, Optimization & Reliability, Data Analytics, System and
Hardware Architecture
• Dassault Systèmes PLM Platinum Partner (CATIA, SIMULIA,
DELMIA, ENOVIA, 3DEXPERIENCE)
• Provide AM Simulation and 3D Printing Services
Engineering
Consultancy
Training
Automation &
Customization
Software

VIAS Capabilities
5
Advanced FEA – Nonlinear, 3D Geometry, Complex Loads
Fatigue-Fracture / Damage Mechanics
Reliability and Optimization
CFD and Multi-physics Simulation
Fitness-for-Service and Root Cause Analysis
Data Analytics
CAD and PLM Services

Strength of Simulation
Realistic
prediction for
harsh conditions
Usage of
optimization
and fatigue
techniques
Gain safer
products
in less time
7

Strength of Simulation Example: BOP Design
BOP Body - Quarter Model
• Mechanical device designed to seal off
wellbore, safely control and monitor oil and
gas well in case of blowout
• High Pressure High Temperature (HPHT)
conditions for Deepwater Wells
• Highest safety and quality standards are
mandatory
• Challenge of optimizing the design – weight
reduction
• Need for efficient simulation process to
reduce design time and cost
8
To seal drill pipe
OD and shear the
main body

BOP DESIGN OPTIMIZATION –
MULTIPHYSICS SIMULATION

BOP Design Optimization
1
5
Response Surface
Approximation
Fatigue and
Strength
3
Capturing
Reality in
Simulation2
Exploring
Design Space
through DOE 4
6
Design Optimization
Product
Requirements
and Problem
Statement
11

SIMULIA Software – Power of Portfolio
BOP Design Optimization using Isight and fe-safe
•FEA of structural response
under applied mechanical loadsAbaqus
•Process Automation
•Workflow Customization
•Design Exploration
•Optimization
Isight
•Fatigue Analysis
•Life Prediction
fe-safe
12

Simulation Driven Design Process – Step 1
1
Product
Requirements
and Problem
Statement
13

Problem Statement – Step 1
Initial Design Dimensions
Radius = 2.5 in Cavity Height = 10 in
Cavity Width = 13.75 in
Top Beam = 19 in
Lower Beam = 19 in
Side Wall = 23 in
14
18-3/4 – 20k BOP Design
Product Requirements
• Sufficient design life
under cyclic loading
• Weight reduction

Problem Statement – Step 1
Design VariablesObjective:
Maximize fatigue life
Design Variables (Deterministic):
▪ Cavity Height(CH): 8" ≤ H ≤ 12"
▪ Cavity Width (CW): 11" ≤ W ≤ 16.5
▪ Top Beam(TB): 11.4" ≤ TB ≤ 26.6“
▪ Lower Beam (LB): 11.4" ≤ LB ≤ 26.6“
▪ Side Wall (SW): 16.1" ≤ SW ≤ 29.9“
▪ Radius (R): 2.235" ≤ R ≤ 5.215“
Constraint:
➢ Maximum Displacement ≤ 0.040 inches
➢ Mass ≤ 80% of initial mass
➢ Cavity Height (CH) should be greater than 2 times
the Radius (R)
➢ Cavity Width (CW) should be greater than 10 inches
plus Radius (R)
R CH
CW
TB
LB
SW
15

2
16
Capturing Reality
in Simulation

Material, Loads, BCs – Step 2
Linear Elastic Material Properties for Steel AISI 4130:
▪ Young’s Modulus: 29,000 ksi
▪ Yield Strength: 66.7 ksi
Loads (Cycling from 0 to Max.):
Internal Pressure = 20 ksi
Vertical Load = 150 kips
17
Z-Symmetry
X-Symmetry
Y-Fixed

Meshing – Step 2
10-node quadratic
tetrahedron
elements (C3D10)
8-node linear brick
elements with reduced
integration (C3D8R)
Parameter Value
Element Type C3D8R / C3D10
No. of Elements 36938
Computational Cost 5 minutes/run
18

Fatigue and
Strength 3
19

FEA for Strength – Step 3 (Abaqus)
von Mises Stress due to Internal Pressure von Mises Stress due to Vertical Lift Load
Node 2 for Y-Displacement Constraint
Initial Design
20
Mass
(lb)
Y-Disp
Node 1 (in)
Y-Disp
Node 2 (in)
15497.5 0.0439 0.0333
Output Node 1 for Y-Displacement
Constraint

Fatigue Life – Step 3 (fe-safe)
Initial Design
22
Lowest fatigue cyclesElement/node with lowest life
588.8
Neuber Correction
Internal Pressure = 20 ksi Vertical Load = 150 kips
Pressure Cycles = 100 cycles Vertical Load Cycles = 500 cycles
ONE BLOCK

Exploring
Design Space
through DOE 4
23

Simulation Workflow – Step 4 (Isight)
Initial Design
Design of
Experiments
(DOE)
Response
Surface
Optimized
Design
24
fe-safe
DOE (Optimal Latin Hypercube – 76 Runs)

Design of Experiments – Step 4 (Isight)
Pareto Plot
Radius and Cavity Width are the design parameters that have higher effect on the fatigue life of the BOP
25

5
Response Surface
Approximation
27

Initial Design
Design of
Experiments
(DOE)
Response
Surface
Optimized
Design
28
fe-safe

Approximation – Step 5 (Isight)
Elliptical Basis Function (EBF)
Error Analysis - Fatigue
Responses R-Squared
Mass 0.99789
Y Displacement – Node 1 0.99295
Y Displacement – Node 2 0.99335
Fatigue Cycles 0.94123
29
Error Analysis – Disp N 1

6
Design Optimization
31

Initial Design
Design of
Experiments
(DOE)
Response
Surface
Optimized
Design
32
fe-safe
Sequential Quadratic Programming
– 57 Iterations

Deterministic Optimum Design – Step 6
Case CH
(in)
CW
(in)
R
(in)
LB
(in)
TB
(in)
SW
(in)
Fatigue
Cycles
Mass
(lb)
Y-Disp
Node 1 (in)
Y-Disp
Node 2 (in)
Initial Design
(Mean)
10.0 13.75 3.725 19.0 19.0 23.0 588.8 15497.5 0.0439 0.0333
Optimum
Design
8.0 13.02 3.02 22.05 17.45 16.1 1348.9 12501.8 0.040 0.0287
R CH
CW
TB
LB
SW
Input Output
Initial Design Optimum Design
• Fatigue life is increased 2.3 times from the initial design
• Geometry, weight and displacement constraints are satisfied
34

• Machine learning can help in design simulations by generating
predictive models to estimate output given initial parameters.
• We approximate the output of a simulation using deep network
architectures for regression.
• The idea is to capture compact, high-order
representations in an efficient and iterative
manner.
• Learning takes place by combining non-
linear combinations of inputs on many
layers of abstraction.
• Low levels concepts are the foundation for
high level concepts.
Machine Learning in Design and
Simulation
35

Empirical results using BOP design data showing Mean Squared Error
for three algorithms
Machine Learning to Predict Output of
Simulations
Deep Learning Network Shows Less Error
36
Simple Linear
Regression
Deep Learning Single Layer
Neural Network

Projection on two variables showing actual data (black) and
approximation using deep learning (red).
Machine Learning to Predict Output of
Simulations
Good Approximation of Predicted Values vs. Actual Values
37

Machine Learning to Minimize the
Number of Simulations
Machine learning can additionally help to minimize the number
of simulations by using a technique known as “active learning”.
In active learning the algorithm point to those instances (parameter
vectors) that are “most informative” to increase the accuracy in the
predictions.
Active
Learning
38

Sample Estimation vs True Performance.
Machine Learning to Minimize The
Number of Simulations
40

Summary
• Optimizing BOP Design using SIMULIA Power of Portfolio
Software Concludes:
• fatigue life is increased by 2.3 times from the initial design;
• weight of the BOP is reduced by 20%;
• maximum allowable displacement of 0.04 inches is satisfied;
• automation of the design and simulation process helps to decrease
the cost and reduce the time;
• Mathematics based as compared to heuristic design approach.
• Using Machine Learning in BOP Design and Simulation
Helps to:
• predict the output of simulations much faster than traditional
FEA type models;
• minimize the number of simulations through active learning.
41

Design Optimization of Safety Critical Component for Fatigue and Strength Using Simulation and Data Analytics

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