isight - parametric optimization and...

Isight - parametric optimization and automation

Marko Vrh

SIMULIA seminar

Ljubljana, 12.4.2016

SIMULIA Isight www.cadcam-group.eu2

Agenda

What is Isight Licesing

What can Isight do for (or instead) of you?

How to work with Isight

Design Exploration &Optimization

Isight applications


What is Isight?


Isight as a part of portfolio


Licensing

• Tosca Structure w/ linearAbaqus/Foundation

(1core)

• Isight simflows

• fe-safe analyses (1 node)• Abaqus solvers (4 cores)

• Abaqus/CAE, Isight Gateways, fe-safe GUI

• Tosca Structure nonlinearw/ Abaqus/Standard (1

core)

• Tosca Fluid (up to 32 cores) (use with 3rd party CFD)

• Isight complex simflows (e.g. DOE w/ Tosca Structure and Abaqus/Foundation)

• fe-safe analyses (1 node)• Abaqus solvers (8 cores)


8 Extended Tokens

1 /CAE Extended Seat

9 Extended Tokens


13 Extended Tokens


16+ Extended Tokens


• Tosca Structurenonlinear (use with 3rd

party FEA)

• Tosca Fluid (up to 8 cores) (use with 3rd party CFD)

• Isight simflows(e.g. w/ Abaqus/Standard)

• fe-safe analyses (1 node)

• Abaqus solvers (4 cores)


• Tosca Structure nonlinear w/ Abaqus/Standard(4+ cores)

• Tosca Structure w/Abaqus/Standard & fe-

safe

• Tosca Fluid (128+ cores)(use with 3rd party CFD)

• Isight more complexsimflows (e.g. w/Abaqus/Standard, fe-safe)

• fe-safe analyses (2+ nodes)

• Abaqus solvers (16+ cores)


ComputeExtended Tokens

(QXT)accessing the Power of the

Portfolio computation

engines

InteractiveAbaqus/CAE

Extended(QAX)concurrent license for SIMULIA’s

graphical user interfaces

• Abaqus solvers (1 core)


5 Extended Tokens



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

Final Design

Design Concept

Adjust Input File(s)

Meets

Requirements?

N

Run Model

Review Output File(s)

Y

Build Computer Model

Software

Robot

Choose Strategy


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

Isight Benefits


How to work with Isight?


Components – Building Blocks of Sim-Flow

ANSYS

Workbench

Calculator

Abaqus

Approximation COM

Data

ExchangerData

Matching

Database

ExcelIsight Isight Advanced

File Parser

Isight Fast

File ParserMail MATLAB

OSCommand

PauseReference Script

Simcode

Word

Adams Car

MSC.Adams

ANSYS

AVL AVL

Boost

CATIA V5

DateTime DLL Driver

Elapsed TimeFemap

GT-Power

LS-DYNA MADYMOMathcad

MSC.NastranMSC.Patran NEiNastran NXNastran

PAMCRASH Pro/E

Solidworks

Simulation

STAR-CCM+Unigraphics NX XML Parser

Adams Chassis

Dymola

ANSA

Task

DOE Monte CarloLoop

Target Solver

Exporation

SDI

Taguchi RD

Six SigmaOptimizationNoise Analysis


Example: Abaqus Component

Set up link with Abaqus model in 3 steps1. 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

1

2

3


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.

Example: Abaqus 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

Example: Simcode Component


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

Parameter Mapping


Design Exploration&

Optimization


The Big Picture - Design Exploration

Y1

Constraint

Boundary

Y2

Initial Best Design

Feasible Infeasible

(safe) (failed)

X2

X1

Outputs

Inputs

DOE:

Critical Factors

and Initial Design

“Shop” for the best design


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

DOE


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

Approximations


Y1

Constraint

Boundary

Y2

Initial Design

from DOE

Feasible Infeasible

(safe) (failed)

Outputs

Improve Design

Performance

Optimization

(Approximations)

Optimized Design

Optimization & Design Exploration

The Big Picture – 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

Optimization


The Big Picture – Robustness and Reliability

Y1

Constraint

Boundary

Y2

Feasible Infeasible

(safe) (failed)

Outputs

Improve Design Quality

Design for Six SigmaRobustness and Reliability

Analysis and OptimizationRobust and Reliable

Design

% Unreliable% Reliable


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

Abaqus

Y1Mean

R2R1

X1, X2, X3Design

Variables

Random Variables

Robust,Reliable Solution

Reliability, R

Constraint

Probability of Failure, Pf

Six Sigma


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

Data MiningStatistical ProcessingDesign Parameter Correlation

Interactive Real-time Plots

Click!

Post-Processing & Visualization


Isight applications


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

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




Optimization Techniques: Hooke-JeevesObjectives: Sum_YSqrDiff_force


Results

Initial SqDiff = 6.208E9 Final SqDiff = 607.48

Yeoh Coefficients are:

coefficient “right” values Startvalues

Isightvalues

C10 1.1855 1.9244 1.1799

C20 0.0899 0.3686 0.0938

C30 0.0006 0.1523 0.0



• Desired Response:Different stiffness desired in each principal direction.

Data Matching – Application Example


Isight parameters: Abaqus/CAE Geometry

14 independent parameterized geometry variables




A quality fit is found within 50 evaluations


vent hole segmentvent hole radius vent hole angle

Thermal Optimization


Maximum temperature of the disc was reduced by 30C, while leaving sufficient surface area for efficient braking.

Optimization results

Parameter Reference Optimum

Vent hole radius4.0 mm 2.4mm

Number of Hole Segments 6 24

Vent hole angle8.0 7.0

Maximum nodaltemperature 286 C 256 C

Thermal Optimization

Thank you!

www.cadcam-group.eu

isight - parametric optimization and...

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