CM&S and VVUQ of COMPLEX SYSTEMS using DIGITAL TWIN and DIGITAL THREAD FRAMEWORKS

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1 CM&S and VVUQ of COMPLEX SYSTEMS using DIGITAL TWIN and DIGITAL THREAD FRAMEWORKS Sanjeev Kulkarni, Animesh Dey, and Robert Tryon VEXTEC Corporation, Nashville TN

2 ICME and Material Genome AFRL Digital Thread Update, ERS Annual Technology Meeting 2014, Pam Kobryn, Structures Technology Branch, Aerospace Systems Directorate 2

3 Digital Thread and Digital Twin Digital Thread Advanced modeling and simulation tools that link materials, design, processing, and manufacturing information Provide the agility and tailorability needed for rapid development and deployment, while also reducing risk. Digital Twin - Virtual representation of the System integration of data, models, and analysis tools applied over the entire life cycle on an unique tail-number. Modeling and simulation tools will optimize manufacturability, inspectability, and sustainability from the outset. Data captured from legacy and future systems to refine and update models that enable component and system-level prognostics. AFRL Digital Thread Update, ERS Annual Technology Meeting 2014, Pam Kobryn, Structures Technology Branch, Aerospace Systems Directorate 3

4 Digital Thread and Digital Twin Use ALL AVAILABLE INFORMATION in analyses Use PHYSICS to inform analyses Use PROBABILISTIC METHODS to quantify program risks CLOSE THE LOOP from the beginning to the end and back to the beginning of the AFRL lifecycle Digital Thread Update, ERS Annual Technology Meeting 2014, Pam Kobryn, Structures Technology Branch, Aerospace Systems Directorate 4

5 Motivation: Why do we need Uncertainty Management? Simulation-based design and certification is fundamentally about making decisions with uncertainty. The goal is to decide efficiently: What is the actual uncertainty in the simulation results? How will changing the scale and fidelity of the analysis impact the uncertainty in the results? What does this mean for the product reliability? Uncertainty exists : How to best manage its impact on reliability 5

6 Sources of Uncertainty and Errors Uncertainty of a computational model comes from several sources: Physical variability Statistical uncertainty Limited data Errors/Approximations can be categorized as: Discretization error in FEA Limited number of simulations Approximation to the correct physics 6

7 Digital Thread and Digital Twin Virtual Life Management (VLM) uses uncertainty propagation across multiple levels of a system: All available data and knowledge Physics-based computational analysis Probabilistic analysis to explicitly propagate statistical uncertainty Updating when new data/knowledge becomes available Model 1 Model 2 Model 3 7

8 Digital Thread and Digital Twin Virtual Twin of a Component/System/Assembly with Cyclic Loading Estimate fatigue life subject to Sensitivity of uncertainty in input variables and Sensitivity of modeling approximations. Flow of the design analysis: Manufacturing process model provides residual stress Structural analysis provides stresses model Microstructural material model predicts fatigue Sources of uncertainty: Manufacturing process uncertainty Predicted residual stress uncertainty Structural geometry uncertainty Loads and boundary conditions uncertainty FEA mesh size uncertainty Material microstructure uncertainty Cumulative Probability Distribution Function (CDF): Cycles to failure considering all uncertainties vs. actual test results Fatigue durability results for a large population and at the fleet level. Simulations performed at and correlated and calibrated to values of manufacturing process output such as residual stress 8

9 Digital Thread and Digital Twin 9

10 Meshing FEA & Material Initial Element Superimpose Natural Substructure Map FEA Stress to Substructure Translate Microstress to each Grain Component Design Configuration Material Configuration VLM Computational Processing Mapping the Elements Component Simulation FLEET Simulation 10

11 Grain Level Processing Grain characteristics and stress used in damage equations Grain lives statistically combined into element life and repeated for all FEA elements 11

12 Microstructural Properties and Defects Damage mechanisms used to determine potential crack nucleation sites Grains, Inclusions, Voids Grain Boundaries, Triple Points Damage element available for each mechanism SVE populated with damage elements Void Grains Inclusion Boundaries 12

13 Damage Mechanisms 13

14 Damage Mechanisms The physics governing damage process changes as the damage accumulates Mathematical models (equations) exist to describe the physics at each stage VLM contains a library of damage models and stage transition rules that are applied as appropriate to the materials being simulated 14

15 Grain Level Processing Grain characteristics and stress used in damage equations Grain lives statistically combined into element life and repeated for all FEA elements 15

16 Element Component System Fleet 27,943 Tooth Life: 15,932 cycles Failure Cause: Defects Component Life: 14,334 cycles 22,113 18,961 22,229 25,342 17,561 24,793 Integrate VLM Results with FEA VLM Integration for Entire Component Repeat Sequence for Each Tooth 1 st Virtual Twin Gear Simulated VT 1, VT 2, VT 3 VT 1,000 Run 1,000 Simulations 16

17 Digital Thread and Digital Twin 17

18 Simulated Fatigue Tests Windows desktop tool Wide range of applications Output VEXTEC s VPS-MICRO Software Stand-alone tool for simple specimen geometry models Integrate FEA models for complex geometry of full-scale components Simulated S-N Curve Virtual fracture surface Detailed statistical analysis Customizable Software Product Interface with Standard FEA software Predict risk of failure from complex inservice loading spectrums Software Partners Simulated S-N Curve 18

19 Multi-Disciplinary Example: Airframe Longeron Stick Position, g-loads Aerodynamics Model Applied loads Airframe Geometry Global Airframe FEA Model Cut-bdry displacements Detailed Geometry Local CSL FEA Model Mission cycle profile Microstructural & Crack Growth Parameters Fatigue Model POF What are the POF bounds due to uncertainty? 19

20 Long Stick Force (lbs) Aerodynamic Maneuver and Sources of Uncertainty Longitudinal Stick Force Peak time of Stick Force Mach # Altitude Time (s) ChangeLongF Original ChangeTime 20

21 Nodal Vector Load (lbs) Global Airframe FEM with Maneuver Loads Case 1 Case 4 Case 7 Case 10 Case 13 Case 16 Case 19 Case 22 Case 25 Case 28 Case Case Case Load Set Case 40 Case 43 Case 46 Case 49 Case 52 Case 55 Case 58 Case Case

22 VonMises Stress Uncertainty in Aerodynamics leads to Uncertainty in Global Stresses VonMises Stress Standard Delayed Peak Early Peak Increased LongF Decreased LongF RomOff ICFlag6,M0.9,20K 22

23 Orientation of the Local FEA 23

24 Fatigue Analysis for 7XXX Aluminum Assumes damage starts at an inclusion If the damage can grow, progress to small flaw fracture mechanics (SFFM) and grow the damage for each cycle of the mission. Continue with SFFM until the average microstructural properties at the crack tip are equal to the bulk average material properties. If damage can still grow, progress to LEFM, Paris Law. Continue cycle-by-cycle damage growth until DK > DK IC 26

25 Monte Carlo Simulation Sequence Outer Loop Input: Maneuver Input: Particle size (Mean is Uncertain) Inner Loop Aerodynamics, FEA, Fatigue Model (Model error) Output: POF Exceeding K Ic (Uncertainty bounds) 27 27

26 POF Result: Bounds on POF Uncertainty in inclusion size can impact POF 100% 90% 80% 70% 60% Mean Defect= 1.8 mils Mean Defect = 0.18 mils 50% 40% 30% 20% 10% 0% Log10(Cycles) 30 30

27 Digital Thread and Digital Twin - Healthcare Digital Thread An Ecosystem that enables a Data Driven, Physics and Probabilistics Model possible that includes the above ingredients. With insights generated include Prediction of Performance for a broad population, Risk of Recall,.and with the ability to update the model when more data is available Virtual Twin is the actual model Digital Twin A Patient Specific model with a particular traceable device ID and procedure. That is tracked as a digital replicate including additional procedures, quality control and such -. Virtual Patient Device or Therapy Physician or Process 31 31

28 Digital Thread and Digital Twin Pacemaker Lead Cond1: A +/-a Cond2: B +/-b Condition 1: A Von Mises Stress; Max Stress=XX ksi Simulation of two test conditions Displacements A and B Cycling between maximum displacement and 0 displacement Cycling to 1E10 cycles and Runout (suspension) if no failure Measured difference in maximum Von-Mises stress between Conditions 1 and 2 Condition 2: B Von Mises Stress; Max Stress=YY ksi 32 32

29 Digital Thread and Digital Twin Pacemaker Lead Summary Simulated fatigue buckling test under 2 load conditions Virtual DOE consisted of 9600 individual coil simulations Outcomes / Next Steps Sensitivity study around particle size, density and residual stress Determined residual stress to be a calibrated value - new knowledge Developed Insights - Design alternatives, Material substitution, Vendor management Potential - Sensitivity analysis, Design trade studies, Supplier controls, Design optimization Add Realism Coiling Simulation for Residual Stresses VDOE Results for Residual Stress 33 33

30 Digital Thread and Digital Twin Additive M Process Integration Identification of Key Processing Parameters Identify Location Specific Microstructure Develop Required Mechanical Properties Microstructural Damage Evaluation Identify Microstructural Mechanisms Physics-Based Microstructural Damage Algorithms NDE Integration and Stress FE Analysis Create Microstructural Model Microscale Material Model Voids and NMIs Microstructural Volume Element Multi-scale Fatigue Damage Model Architecture for Component Durability Simulation Develop Algorithm for Durability Prediction 34 34

31 Example: AM Aluminum Cold Spray Microstructure of AA 6061 AM Microstructure of AA 6061 extruded bar 35 35

32 Example: AM Aluminum Cold Spray Extruded bar test data AM simulation AM test data (Testing performed after simulation) 36 36

33 Example: AM Repair Fastener Traditional structural analysis is performed using FE. VLM performed to determine durability of structure VLM used to optimize design for AM 37 37

34 Example: AM Repair Fastener Life Prediction AM part using original design geometry Part geometry optimized for AM 38 38

35 Benefits of Uncertainty Propagation Model Sensitivity of the uncertainty in the analysis prediction to each uncertainty/approximation can be estimated Once a Computational Model is built, the methodology can be used to continually update based on new information to arrive at most robust predictions. A more judicious allocation of computational fidelity and resources can be made without sacrificing accuracy. 39

36 VEXTEC Introduction Software-enabled provider of solutions which test, assess and predict product fatigue/corrosion life Software Enabled Solution Value- Added Knowledge to Clients Patented applied materials science algorithms fuel the software to quickly analyze and predict failure timelines and rates applicable from simple levers to complex machines The software is a sophisticated computer simulation with inputs that can easily be varied to replace extensive and expensive physical testing programs Analysis enables VEXTEC s clients to forecast product durability and manage product life cycle risk Knowing the life cycle of any given part or machine, the client can better manage utilization and replacement rates a significant economic benefit Applicable Across Multiple Industries Aerospace & Defense Automotive & Transportation Industrial Equipment Medical Implants 40

37 Thank You! Founded in 2000 in Nashville Software backed by 7 Patents: Virtual Life Management (VLM ) & VPS- MICRO Value Proposition: Help companies assure product reliability and reduce cost New products to market quickly Leverage physical testing for increased confidence Forecast product durability and manage product life cycle risk Business Model: Hybrid consulting services, software licensing and training Please visit our Booth#