Webinar OptiSLang4 Ansys WB

1

Webinar

optiSLang 4 & ANSYS Workbench

Dynardo GmbH

2Webinar optiSLang 4 and ANSYS Workbench

Excellence of optiSLang

• optiSLang is an algorithmic toolbox for

sensitivity analysis, optimization,

robustness evaluation, reliability analysis

and robust design optimization (RDO)

• optiSLang is the commercial tool that has

completed the necessary functionality of

stochastic analysis to run real world

industrial applications in CAE-based

robust design optimizations

• optiSLang offers the beginner and

expert users an easy and reliable

application by means of predefined

workflows, algorithmic wizards and

robust default settings


Start

CAE process (FEM, CFD, MBD, Excel, Matlab, etc.)

Robust Design Optimization

Optimization

Sensitivity Study

Single & Multi objective (Pareto) optimization

Robust Design

Variance based Robustness Evaluation

Probability based Robustness Evaluation, (Reliability analysis)


Graphical User Interface


optiSLang 4: Graphical User Interface

Build, Run and Analyze your flow using• Wizards• Comfortable Drag&Drop• Dialogs and Tables• Postprocessing


Nodes

• Input

• Process / Properties

• Output

Connections

• Data flow

• Triggering

Graphical Programming


Process Integration


Process Integration

Parametric model as base for

• User defined optimization (design) space

• Naturally given robustness (random) space

Design variablesEntities that define the design space

Response variablesOutputs from the system

The CAE processGenerates the results according to the inputs

Scattering variablesEntities that define the robustness space


Signals in optiSLang

• Signals are vector outputs having an abscissa (e.g. time axis)

and several output channels (e.g. displacements, velocities)

• Comprehensive library of signal functions enables the user to extract

local and statistical quantities and to analyze differences between

several signal channels e.g. for calibration tasks

• Automatic mapping of non-consistent abscissa discretizations for the

signals of each design and of the reference curves

• Direct access to signal plots in the optiSLang postprocessing and

interactive connection to the statistic/optimization postprocessing


optiSLang 4 Integrations

Direct integrations� Matlab� Excel� Python� SimulationX� Ansys Workbench

Supported connections� Ansys� Abaqus� Adams� …

Arbitary connection ofASCII file based solvers


Full integration of optiSLang in Ansys Workbench

• optiSLang modules Sensitivity, Optimization and

Robustness are directly available in ANSYS Workbench


The Workbench Integration

• The workbench node directly connects to

the workbench project and gets the inputs

and outputs from the parameter set


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Sensitivity Analysis


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Scanning the Design Space

Inputs Design of Experiments Solver evaluation Outputs

• Uniform distribution of inputs is represented by Latin Hypercube Sampling

• Minimum number of samples should represent statistical properties, cover the input space optimally and avoid clustering

• For each design all responses are calculated


Metamodel of Optimal Prognosis (MOP)

• Approximation of solver output by fast surrogate model

• Reduction of input space to get best compromise between available

information (samples) and model representation (number of inputs)

• Determination of optimal approximation model

• Assessment of approximation quality

• Evaluation of variable sensitivities

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© Dynardo GmbH

Multi-Disciplinary Optimization


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Multidisciplinary Optimization with optiSLang

CAD and CAE Parameter definition

Sensitivity study – identify the most important parameters and check variation/COD of response values

minimize

Define optimization goal and optimize

Validate optimized design in CAE and CAD


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optiSLang Optimization Algorithms

Gradient-based Methods

• Most efficient method if gradients are accurate enough

• Consider its restrictions like local optima, only continuous variablesand noise

Adaptive Response Surface Method

• Attractive method for a small set of continuous variables (<20)

• Adaptive RSM with default settings is the method of choice

Nature inspired Optimization

• GA/EA/PSO imitate mechanisms of nature to improve individuals

• Method of choice if gradient or ARSM fails

• Very robust against numerical noise, non-linearity, number of variables,…

Start


Pareto Optimization

• Find a set of solutions close to the Pareto-optimal solutions

• Find solutions which are diverse enough to represent the whole front

Strength Pareto Evolutionary Algorithm

• Elitism is applied by using an archive

of non-dominated individuals

• Fitness assignment is based on the

dominance criterion

• Preservation of population diversity

is realized by density estimation

• Suitable start population significantly

improves convergence

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Decision Tree for Optimizer Selection

• optiSLang automatically suggests an optimizer depending on the

parameter properties, the defined criteria and user specified settings

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© Dynardo GmbH

Robustness Analysis


© Dynardo GmbH© Dynardo GmbH

Variance based Robustness Analysis

1) Define the robustness space using scatter range, distribution and correlation

2) Scan the robustness space by producing and evaluating ndesigns

3) Check the variation 4) Check the

explainability of the model

5) Identify the most important scattering variables


Training Program


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Training

optiSLang 4 Basics

• 3 day introduction to process integration (ASCII, Matlab, Excel, Python),

sensitivity, optimization, calibration and robustness analysis

optiSLang inside ANSYS Workbench

• 2 day introduction seminar to parameterization in ANSYS Workbench

and sensitivity analysis and optimization via optiSLang inside ANSYS WB

optiSLang 4 and ANSYS Workbench

• 1 day introduction to the integration of ANSYS Workbench projects in a

optiSLang 4 solver chain, parameterization of signals via APDL output

optiSLang 4 Update

• 1 day update seminar introducing process integration and automation

with optiSLang 4 (for optiSLang 3 users)


Examples


Optimization of a Hook

How to change the hook, so that

• The v.-Mises stress will not exceed 300MPa,

• The mass will be as minimal as possible and

• Certain geometry parameters will be in predefined bounds?


DS_Angle (120-150°)

DS_Thickness (15-25 mm)

DS_LowerRadius (45-55 mm)

DS_Depth (15-25 mm)

Optimization of a Hook

Design parameters (at DesignModeler)


Solver: ANSYS® Mechanical

• Open the ready to use Workbench project hook.wbpz

• In ANSYS Workbench ANSYS Mechanical is used as solver

1.


Results of the Sensitivity Analysis

• The approximation quality is excellent for both output variables

• The influence of the angle and the lower radius is relatively small


Initial vs. Optimal Design

Initial Design Optimal Design

Mass = 752g Mass = 613g

Equivalent Stress = 460MPa Equivalent Stress = 299MPa


Damped Oscillator

• Single degree-of-freedom system

excited with initial kinetic energy

• Equation of motion of free vibration:

• Un-damped and damped eigen-frequency

• Time-dependent displacement function


• Optimization goal: Minimize maximum

amplitude after 5s free vibration:

• Restricted damped eigen-frequency

as optimization constraint:

• Mass and stiffness as optimization

variables, damping ratio and

kinetic energy as constant

The Optimization Task


• Robustness evaluation at

the deterministic optimum

• Mass m, damping ratio D, stiffness k and initial kinetic energy as

normally distributed random variables

Robustness Analysis


Solver: MS Excel

• Open the ready to use Workbench project via the start menu:

� All Programs/optiSLang/Ansys Workbench/Examples/oscillator.wbpz

• In ANSYS Workbench Microsoft Office Excel is used as a solver


Cantilever Beam

• Finite Element model in ANSYS Workbench

• Elastic material behavior

• Cantilever beam is deformed by a predefined external displacement

• Reaction forces at deformed beam end are monitored depending on

deformation and written to text output file via APDL


Problem Definition

• Identification of the geometry

parameters that a given

reference force displacement

function is achieved

• Parameter bounds:

Thickness 0.8 – 1.2 mm

Radius 32.0 – 37.0 mm

Depth 2.5 – 3.5 mm

Height 5.0 – 15.0 mm

• Objective function is the sum of squared errors between the reference

and the calculated force values


The Solver Chain

• The solver chain contains the workbench node and an additional text

output node to read the APDL output file

• The reference is obtained by another text output node


Results of the Sensitivity Analysis


Results of the Optimization

Webinar OptiSLang4 Ansys WB

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