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2. Aug 2018•0 gefällt mir•73 views

2. Aug 2018•0 gefällt mir•73 views

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Ingenieurwesen

The current work focuses on simulation based optimization of a complex, safety critical component where it is prohibitively expensive to carry out finite element analysis (FEA) simulations for all possible sample realizations and therefore requires statistical or machine learning techniques for a timely yet accurate solution. The applicability of machine learning further brings the opportunity of performing in-service monitoring using sensor data and thereby performing predictive maintenance.

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- 1. 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
- 2. © 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
- 4. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 5. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 7. © 2018 Virtual Integrated Analytics Solutions Inc. Strength of Simulation Realistic prediction for harsh conditions Usage of optimization and fatigue techniques Gain safer products in less time 7
- 8. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 9. BOP DESIGN OPTIMIZATION – MULTIPHYSICS SIMULATION
- 10. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 11. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 12. © 2018 Virtual Integrated Analytics Solutions Inc. Simulation Driven Design Process – Step 1 1 Product Requirements and Problem Statement 13
- 13. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 14. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 15. © 2018 Virtual Integrated Analytics Solutions Inc. Simulation Driven Design Process – Step 2 2 16 Capturing Reality in Simulation
- 16. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 17. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 18. © 2018 Virtual Integrated Analytics Solutions Inc. Simulation Driven Design Process – Step 3 Fatigue and Strength 3 19
- 19. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 20. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 21. © 2018 Virtual Integrated Analytics Solutions Inc. Simulation Driven Design Process – Step 4 Exploring Design Space through DOE 4 23
- 22. © 2018 Virtual Integrated Analytics Solutions Inc. Simulation Workflow – Step 4 (Isight) Initial Design Design of Experiments (DOE) Response Surface Optimized Design 24 fe-safe DOE (Optimal Latin Hypercube – 76 Runs)
- 23. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 24. © 2018 Virtual Integrated Analytics Solutions Inc. Simulation Driven Design Process – Step 5 5 Response Surface Approximation 27
- 25. © 2018 Virtual Integrated Analytics Solutions Inc. Simulation Workflow – Step 5 (Isight) Initial Design Design of Experiments (DOE) Response Surface Optimized Design 28 fe-safe
- 26. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 27. © 2018 Virtual Integrated Analytics Solutions Inc. Simulation Driven Design Process – Step 6 6 Design Optimization 31
- 28. © 2018 Virtual Integrated Analytics Solutions Inc. Simulation Workflow – Step 6 (Isight) Initial Design Design of Experiments (DOE) Response Surface Optimized Design 32 fe-safe Sequential Quadratic Programming – 57 Iterations
- 29. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 30. © 2018 Virtual Integrated Analytics Solutions Inc. • 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
- 31. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 32. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 33. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 34. © 2018 Virtual Integrated Analytics Solutions Inc. Sample Estimation vs True Performance. Machine Learning to Minimize The Number of Simulations 40
- 35. © 2018 Virtual Integrated Analytics Solutions Inc. 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
- 36. Q&A THANK YOU!