Isight - parametric optimization and automation. Marko Vrh SIMULIA seminar

Transcription

1 Isight - parametric optimization and automation Marko Vrh SIMULIA seminar Ljubljana,

2 Agenda What is Isight Licesing What can Isight do for (or instead) of you? How to work with Isight Design Exploration &Optimization Isight applications 2

3 What is Isight? 3

4 Isight as a part of portfolio 4

5 Licensing Abaqus solvers (1 core) Abaqus/CAE, Isight Gateways, fe-safe GUI Interactive Abaqus/CAE Extended(QAX) concurrent license for SIMULIA s graphical user interfaces Tosca Structure w/ linear Abaqus/Foundation (1core) Isight simflows fe-safe analyses (1 node) Abaqus solvers (4 cores) Abaqus/CAE, Isight Gateways, fe-safe GUI Compute Extended Tokens (QXT) accessing the Power of the Portfolio computation engines Tosca Structure nonlinear (use with 3 rd party FEA) Tosca Fluid (up to 8 cores) (use with 3 rd party CFD) Isight simflows (e.g. w/ Abaqus/Standard) fe-safe analyses (1 node) Abaqus solvers (4 cores) Abaqus/CAE, Isight Gateways, fe-safe GUI Tosca Structure nonlinear w/ Abaqus/Standard (1 core) Tosca Fluid (up to 32 cores) (use with 3 rd party CFD) Isight complex simflows (e.g. DOE w/ Tosca Structure and Abaqus/Foundation) fe-safe analyses (1 node) Abaqus solvers (8 cores) Abaqus/CAE, Isight Gateways, fe-safe GUI Tosca Structure nonlinear w/ Abaqus/Standard (4+ cores) Tosca Structure w/ Abaqus/Standard & fesafe Tosca Fluid (128+ cores) (use with 3 rd party CFD) Isight more complex simflows (e.g. w/ Abaqus/Standard, fe-safe) fe-safe analyses (2+ nodes) Abaqus solvers (16+ cores) Abaqus/CAE, Isight Gateways, fe-safe GUI 5 Extended Tokens 8 Extended Tokens 9 Extended Tokens 13 Extended Tokens 16+ Extended Tokens 1 /CAE Extended Seat 1 /CAE Extended Seat 1 /CAE Extended Seat 1 /CAE Extended Seat 1 /CAE Extended Seat 5

6 Isight Software Robot Automated Process Engineer defines simulation process and design goals Automate input generation, run, and output extraction Systematic search of the space Suitable for: Frequently used applications Design Concept Build Computer Model Choose Strategy Adjust Input File(s) Run Model Software Robot Review Output File(s) Meets Requirements? N Y Final Design 6

7 Isight Benefits Reduce design time A template for frequently used design processes Automate parallel submission Improve quality Design optimization Account for variation in materials, loads, tolerances, and operating conditions Understand your design Which model parameters influence design targets 7

8 How to work with Isight? 8

9 Components Building Blocks of Sim-Flow Abaqus Adams Car Adams Chassis ANSA ANSYS ANSYS Workbench Approximation AVL AVL Boost Calculator CATIA V5 COM Data Exchanger Data Matching Database DateTime DLL Driver Dymola Elapsed Time Excel Femap GT-Power Isight Isight Advanced File Parser Isight Fast File Parser LS-DYNA MADYMO Mail Mathcad MATLAB DOE Exporation Loop Monte Carlo MSC.Adams MSC.Nastran MSC.Patran NEiNastran NXNastran OSCommand PAMCRASH Pause Pro/E Reference Script Simcode Noise Analysis Optimization SDI Six Sigma Solidworks Simulation STAR-CCM+ Unigraphics NX Word XML Parser Taguchi RD Target Solver Task 9

10 Example: Abaqus Component Set up link with Abaqus model in 3 steps 1. Modification of Abaqus input deck (.INP or.cae) 2. Execution of the Abaqus solver or CAE & solver 3. Extraction of outputs from.odb and.dat files

11 Example: Abaqus Component Automatic data exchange with CAE Supports the.cae input file for geometry features, material properties, section properties, loads, and boundary conditions At runtime, the componet remeshes the FE model, and produces a run-ready simulation Automatic data exchange with Abaqus input files Numerous Abaqus keywords: Beam General Section, Boundary, CLOAD, DLOAD, Density, Elastic, Mass, Plastic, Shell Section, Solid Section, Spring, and more Automatic data exchange with Abaqus.ODB and.dat files Numerous ABAQUS formatted output headers: Node (multiple variations), Node_Set, Element (multiple variations), EIGENVAL, MASS, Moment_of_Inertia, coords, etc. 11

12 Example: Simcode Component Interacts with an arbitrary simulation code by: Modify/update input file(s) Execute the simulation code via command line Read the results file(s) General way to wrap any simulation code Requires no modification to the simulation code itself The Input and Output tabs are complete Data Exchanger component editors 12

13 Parameter Mapping To pass data from one component to another, parameters in one component must be mapped to parameters in another component View and edit mappings on the Mapping tab Isight will auto-map parameters with the same name and type Manually map by dragging from one parameter to another Drag a parameter to a component to have it created and mapped 13

14 Design Exploration & Optimization 14

15 The Big Picture - Design Exploration Shop for the best design Y 2 Outputs DOE: Critical Factors and Initial Design Constraint Boundary X 2 Inputs Initial Best Design Feasible (safe) Infeasible (failed) Y 1 X 1 15

16 DOE Perform trade-offs and understand the design space Capabilities Determine which input variables have the most influence on your simulation outputs Parallel execution Use to build approximation models Estimate of an Optimal Design Types Parameter studies Orthogonal Arrays Box-Behnken Full / Fractional Factorial Arrays Optimal/Latin Hypercube Central Composite Import Outside Experiments 16

17 Approximations Build from DOE samples, speed up calculationss Types Response Surface Model with term selection (to 4th order) Radial Basis Function Elliptical Basis Functions Chebychev polynominals Kriging Capabilities Automatic setup Automatic error estimation Effects graphs Interactive tradeoffs Simulation Surrogate 17

18 The Big Picture Optimization Y 2 Outputs Optimization & Design Exploration Improve Design Performance Optimized Design Constraint Boundary Optimization (Approximations) Initial Design from DOE Feasible (safe) Infeasible (failed) Y 1 18

19 Optimization Drive toward a target performance Capabilities Formulate variables, constraints, and multiple objectives Parallel execution Multi-objective Pareto fronts Types Gradient: NLPQL,LSGRG2 Multi-Objective: NSGAII, NCGA, AMGA, particle swarm Pattern: Hooke-Jeeves and Downhill Simplex Exploratory: Multi-Island Genetic Algorithm (MIGA), Adaptive Simulated Annealing (ASA) Automatic Optimization: Pointer II Automatically configures NLPQL, particle swarm, Downhill Simplex, and a linear solver Sim approximation surrogate 19

20 The Big Picture Robustness and Reliability Y 2 Improve Design Quality Outputs % Reliable % Unreliable Design for Six Sigma Robustness and Reliability Analysis and Optimization Robust and Reliable Design Feasible (safe) Constraint Boundary Infeasible (failed) Y 1 20

21 Six Sigma Drive designs toward target performance and quality Capabilities input uncertainty and variation 9 Distribution types: Normal, Weibull Truncated Tails Robustness & reliability Analysis and optimization Parallel execution Types MVM, FORM, SORM, Monte Carlo, DOE Design Variables R1 X1, X2, X3 R2 Random Variables Abaqus Robust, Reliable Solution Reliability, R Probability of Failure, P f Y1 Mean Constraint 21

22 Post-Processing & Visualization Enabling innovation and customer collaboration by allowing engineers to discover alternatives and make design tradeoffs Plots with one-click virtual prototype visualization in Abaqus/Viewer Correlation maps show the impact of model parameters on target Interactive engineering data mining Design Space Visualization for real-time attribute tradeoffs Statistical post processing Robustness/reliability graphs Real-time history plots Click! Design Parameter Correlation Statistical Processing Data Mining Interactive Real-time Plots 22

23 Isight applications 23

24 Isight Calibration of a Bonded Puck Testing often has friction present, bulging of specimen occurs Force to compress is significantly larger exciting bulk modulus It is experimentally difficult to exclude friction at the specimen loading interface 24

25 Isight Calibration of a Bonded Puck Create Process Capture in Isight Start with these three files puck_bonded.inp puck_bonded.odb RF2_Test_Data.txt 25

26 Isight Calibration of a Bonded Puck Optimization Techniques: Hooke-Jeeves Objectives: Sum_YSqrDiff_force 26

27 Isight Calibration of a Bonded Puck Results Initial SqDiff = 6.208E9 Final SqDiff = Yeoh Coefficients are: coefficient right values Start values Isight values C C C

28 Data Matching Application Example Desired Response:Different stiffness desired in each principal direction. 28

29 Data Matching Application Example Isight parameters: Abaqus/CAE Geometry 14 independent parameterized geometry variables 29

30 Data Matching Application Example A quality fit is found within 50 evaluations 30

31 Thermal Optimization vent hole radius vent hole segment vent hole angle 31

32 Thermal Optimization Optimization results Parameter Reference Optimum Vent hole radius 4.0 mm 2.4mm Number of Hole Segments 6 24 Vent hole angle Maximum nodal temperature 286 C 256 C Maximum temperature of the disc was reduced by 30 C, while leaving sufficient surface area for efficient braking. 32

33 Thank you!