dynardo • © dynardo gmbh 2015 time to market cost efficient ways for optimal … ·...

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Dr.-Ing. Johannes WillCEO DYNARDO GmbH

Time to MarketCost efficient ways for optimal and robust products

DYNARDO • © Dynardo GmbH 2015

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Dynardo

• Founded: 2001 (Will, Bucher,

CADFEM International)

• More than 50 employees,

offices at Weimar and Vienna

• Leading technology companies

Daimler, Bosch, E.ON, Nokia,

Siemens, BMW are supported

Software Development

Dynardo is your engineering specialist

for CAE-based sensitivity analysis,

optimization, robustness evaluation

and robust design optimization

• Mechanical engineering

• Civil engineering &

Geomechanics

• Automotive industry

• Consumer goods industry

• Power generation

CAE-Consulting


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• Simulation is necessary for time to market and cost efficiency

• Hardware trial and error needs to be reduced, test needs to be placed late in the product development

• CAE-based optimization and CAE-based robustness evaluation becomes more and more important in virtual product development

– Optimization is introduced into virtual prototyping since 20 years

– Robustness evaluation is the key methodology for safe, reliable and robust products

– The combination of optimizations and robustness evaluation will lead to robust design optimization strategies

Challenges in Virtual Prototyping

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Robust DesignRobust Design Optimization (RDO) optimize the design performance

with consideration of scatter of design (optimization) variables as well as other tolerances or uncertainties.

As a consequence of uncertainties the location of the optima as well as the contour lines of constraints scatters.

To proof Robust Designs safety distances are quantified with variance or probability measurements using stochastic analysis.


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Robust Design Optimization

Pareto Optimization

Adaptive Response Surface

Evolutionary Algorithm


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


Optimization

Sensitivity Analysis

Single & multi objective (Pareto) optimization

Robust DesignVariance based

Robustness evaluation

Probability based Robustness valuation(Reliability Analysis)

Start


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Sensitivity Analysis Workflow

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Deterministic DoE

• Complex scheme required to detect multivariate dependencies

• Exponential growth with dimension

• Full factorial:

• Koshal linear:

Advanced Latin Hypercube Sampling

• Reduced sample size for statistical estimates

compared to plain Monte Carlo

• Reduces unwanted input correlation

How to Generate a Design of Experiments


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• Deterministic designs use maximum 3 levels for each variable

• No. of solver calls grows exponentially with dimension

• LHS with N samples produces N levels for each variable: better scan

• Reducing the variable space does not loose the information of removed

variables in LHS

Example: 4 minor and 1 major important input variables:

LHS, 100 samples Full factorial, 243 designs

Why do we prefer Stochastic Sampling?


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Identifying important parameters

From tornado chart of linear correlations to the Coefficient of Prognosis (CoP)

Will, J.; Most, T.: Metamodel of optimized Prognosis (MoP) – an automatic approach for user friendly design optimization; Proceedings ANSYS Conference 2009, Leipzig, Germany,www.dynardo.de


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Correlation Measurements

• Coefficients of pairwise linear/quadratic correlation is the simplest correlation measurement

• Multi-dimensional non-linear correlation can be detected using advanced meta models (Neural networks, Moving least squares,..)

Goodness of fit Measurements (CoD)

• Goodness of Fit (Coefficient of Determination CoD) summarize correlations on the meta models

Statistical measurements


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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)

• Advanced filter technology to obtain candidates of optimal subspace

• Determination of optimal approximation model (polynomials, MLS, …)

• Assessment of approximation quality (Coefficient of Prognosis, CoP)

MOP algorithm solves 3 important tasks:

• Best variable subspace

• Best meta-model

• Estimation of prediction quality


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Which optimization algorithm?

Gradient-Based

Algorithms


Pareto Optimization


global Response Surface

Optimization Algorithms:

Sensitivity Analysis allows best choice!

Which one is the best?


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Optimization of see hammer

Dynamic performance optimization under weight and stress constraints using 30 CAD-parameter. With the help of sensitivity study and optimization (ARSM), the performance of a deep sea hammer for different pile diameters was optimized.

Design Evaluations: 200 times 4 loadcaseCAE: ANSYS workbenchCAD: ProEngineer

Initial Design valid for two pile diameter

Optimized design valid for four pile diameter

weight=4365 kg weight=5155 kg


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• Optimization of the total weight of two load cases with constrains (stresses)

• 30.000 discrete Variables

• Self regulating evolutionary strategy

• Population of 4, uniform crossover for reproduction

• Active search for dominant genes with different mutation rates

Solver: ANSYS

Design Evaluations: 3000

Design Improvement: > 10 % 0

Optimization of a Large Ship VesselEVOLUTIONARY ALGORITHM

Riedel, J.: Gewichtsoptimierung eines Passagierschiffes, Bauhaus Universität Weimar, Institutskolloquium, 2000, Germany, www.dynardo.de]


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Optimization






Start


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Robustness Evaluation Workflow


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1. Reliable Input Definition Distribution function Correlations Random fields

2. Reliable stochastic analysis variance-based robustness evaluation using optimized LHS suitable portfolio of Reliability Analysis

3. Reliable Post Processing Coefficient of Prognosis Reliable variation and correlation measurements easy and safe to use

Successful Robustness Evaluation need the balance between

Acceptance of method/result documentation/communication!


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Definition of Uncertainties

Correlation is an important characteristic of stochastic variables.

Distribution functions define variable scatter

Correlation of single uncertain values

Spatially correlated field values:Statistics on Structures

Translate know how about uncertainties into proper scatter definition

Tensile strength

Yie

ld s

tress


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Implementation of Random Field Parametric

4. Running Robustness Evaluation including Random Field effects

3. Generation of multiple imperfect structures using Random Field parametric

Introduction of spatial correlated scatter to CAE-Parameter (geometry, thickness, plastic values)

1. Input: multiple process simulation or measurements

2. Generation of scatter shapes using Random Field parametric, quantify scatter shape importance


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Robustness Post-processing

Traffic light plot

Overview of scatter range

Histogram & Statistical Data

Estimate sigma level

MOP/CoP Sensitivities

Monitor local forecast quality / non-linearities

Cause analysis


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Robustness check of optimized designs • With the availability of parametric modeling environments like

ANSYS workbench an robustness check becomes very easy!

• Menck see hammer for oil and gas exploration (up to 400m deep)

• Robustness evaluation against tolerances, material scatter and working and environmental conditions

• 60 scattering parameter

Design Evaluations: 100Process chain: ProE-ANSYS workbench- optiSLang


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Reliability Analysis

• Robustness evaluation reliable estimate relatively high probabilities (2, like 1% of failure)

• Reliability analysis verify rare event probabilities

(≥3, smaller then 1 out of 1000)

Monte Carlo Sampling is the safest and most robust way to calculate small event probabilities, but at the same time prohibitive expensive

There is no one magic algorithm to estimate probabilities with “minimal” sample size.

All “effective” algorithms will try to learn about the failure domain and have the risk to learn unreliable information

Therefore it is recommended to use two different algorithms to verify rare event probabilities


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Gradient-based algorithms = First Order Reliability algorithm (FORM)

Adaptive Response Surface Method

Latin Hypercube Sampling

Reliability Analysis AlgorithmsISPUD Importance Sampling using Design Point

Monte Carlo Sampling Directional Sampling

X1

X2


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Robustness & Reliability Algorithms

How choosing the right algorithm?

Robustness Analysis provide the knowledge to choose the

appropriate algorithm


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Reliability Analysis of turbo machines

Basic Design Objectives:

• Life of the disc

• Mass of the discThermal AnalysisStress Analysis

Analysis Disciplines:

Basic Costumer Requirements

• Lifecycles High

• Mass Low

3D Model of HPT-Disc

Performance needs to be proven which a given Probability of Failures (POF)


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Limit state (Lifecycles)

Probability of Failure (POF)

Design 1 Design 2

3.0*10-5

30000 cycles

1.3*10-8

28000 cycles

Requirement not fulfilled

Requirement fulfilled

For example: requirement ofPOF for predicted life < 1.0*10-6

Check reliability of predicted life

Solver: ANSYS WorkbenchMethod: ARSM30..50 Solver evaluations


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Optimization






Start


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Pareto Optimization




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3) From optiSLang Robustness Evaluation safety margins are derived.

4) Three steps of optimization using optiSLang ARSM and EAoptimizer improve the design to an optiSLang Six sigma design.

2) The design was checked in the space of 36 scattering variables using optiSLang Robustness evaluation. Some Criteria show high failure probabilities!

5) Reliability proof using ARSMto account the failure probability did proof six sigma quality.

Start: Optimization using 5 Parameter, then customer asked: How save is the design?

by courtesy of

Iterative RDO of a Connector


Source: Roos, D. and R. Hoffmann (2008). Successive robust design optimization of an electronic

connector, Proceedings Weimarer Optimization and Stochastic Days 5.0, Weimar, Germany

1) From the 31 optimization parameter the most effective one are selected with optiSLang Sensitivity analysis.

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RDO - best practise

• Substantial robustness evaluation and robustness measures are a necessary first step

• Best available translation of all available information about uncertainties is crucial

• the careful planning of a suitable algorithmic RDO workflow and careful checking of suitable measures for design robustness is recommended

• Consequently, it is recommended to start with an iterative RDO approach using decoupled optimization and robustness steps.

• This iterative approach helps to better understand scattering variable importance in order to adjust the necessary safety margins.


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• For serial use RDO software tools needs to support process automation and integration together with “easy and safe to use” modules, algorithmic wizards and post processing for

– large number of parameter

–non linear, noisy, imperfect (loss of designs) variation spaces

– including process integration and automation functionality

–minimization of necessary solver runs

• The CAX Tools need to support parametric modelling

• Thanks to the modern job control programs, cloud and CPU improvements job load, hardware and software recourses are significant, but no general bottle neck

Challenges of CAE based RDO


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optiSLang– general purpose tool for variation analysis

• optiSLang is an algorithmic toolbox for

sensitivity analysis,

model calibration

optimization,

robustness evaluation,

reliability analysis and

robust design optimization (RDO)

• optiSLang offers high end process

automations and integration functionality for

workflow design

Easy and safe to use:

• optiSLang offers the beginner and

expert users an easy and reliable

application by means of predefined

workflows, algorithmic wizards and

robust default settings


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optiSLang process automation and systemintegration

Adams


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Seamless integration intopowerful Parametric Modeling EnvironmentoptiSLang inside ANSYS

minimize

• Including process automation, third party CAE integration, bidirectional CAD interfaces, parallel computing

• Easy parametrization via parameter manager

• With this technology ANSYS Workbench is ready to address RDO task‘s


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Modules Sensitivity, Optimization and Robustness provide „best practice“ optiSLang workflows

optiSLang inside ANSYS Workbench


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RDO Booklet

The RDO Booklet aims

- to explain the CAE-based Robust Design Optimization approach to managers, engineers & designers

- try as simple as possible to introduce all relevant pieces of technology from a practical point of view

- to illustrate the RDO approach with and engineering example.

Introduction Weimar Optimization and Stochastic days 2015


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Dr.-Ing. Johannes WillCEO DYNARDO GmbH


Time to MarketCost efficient ways for optimal products

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offers to the market:

• Software

• Parametric CAX modelling environment like ANSYS Workbench together with general purpose variation analysis tool optiSLang to support virtual prototyping & product optimization

• Consulting service to establish virtual product optimization at your company at different levels

• establish parametrized CAX models & CAX process automation and integration

• establish parametrized CAE workflows (vertical applications) and calibrated CAX models to be used in variation studies

• establish functional relationships (MOP’s) to approximate variation windows of optimization parameters or uncertainties based on simulation or/and tests

Simulation is the key for time to market


40The Dynardo Hydraulic Fracturing workflow


41Dynardo‘s Hydraulic fracturing Simulation Toolbox


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By using cost function and Dynardo meta models (MOP) we can produce Pareto Frontier between conflicting goals Cost reduction and EUR optimization.

Stay with cost, optimize EUR

Reduce cost, stay with EUR

optimal EUR, highest costs

Workflow over all relevant disciplines

We needed to include all relevant disciplines being able to convince the asset teams.

Current frac design


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• Software

• Classic approach: Deliver the software (ANSYS, Dynardo HF Extension, optiSLang) environment to the customer

– But to establish the complex CAX workflow requires implementation time and team of experts

• Consulting service to establish the workflow

• Prepare a vertical application: Deliver calibrated, parametrized reservoir models including software environment to the customer to continue with variation studies

• Upfront simulation: Deliver MOP’s to approximate variations windows of operational parameters and reservoir uncertainties and combine with cost functions in EXCEL

Example hydraulic fracturing in Oil and Gas


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Use of MOP


• Integrate advanced cost model via EXCEL node and couple to production model using MOP’s in optiSLang

• MOP models are updated per reservoir in regard of possible variation window of operational parameter

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MOP- key to generate best possible functional models

• During a sensitivity analysis, MOPs (Metamodel of Optimal Prognosis) functional models are determined by optiSLang to approximate and understand as best as possible the correlation between input parameter variation and response variation.


Latin Hypercube Sampling (LHS)uniform scanning of extensive design spaces

DoE

Solver

MOP

Approximation models to understand the correlation between input parameter and response variation


The Power of using MOP

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Include MOP‘s in your web services toprovide customer best possibleproduct selection



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1. Generate parametric CAE models to calculate product performance

2. Validate the CAE models with available measurements

3. Scan possible product assemblies in advance using LHS sampling

4. Generation and Verification of Meta models to have functional representation of possible variants

5. Use MOP‘s in Customer‘s sales tool to support product selection and assembly

MOP Service



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Optimization Goal: Module requirements like m² panel, solar surface, module arrangement

System Loading:Dead weight, wind load, snow load, structure/foundation interaction)


System Optimization using MOP

BeamRafter

GS 13/58 GS 19/63 GS 25/65 GS 31/69

Module library

Search for cost-efficient configuration of System (minimized material consumption) from existing profile libraryMOP

Solar module


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Robust Design Optimization (RDO)

in virtual product development

optiSLang enables you to:

• Quantify risks

• Identify optimization potentials

• Adjust safety margins without limitation of input parameters

• Secure resource efficiency

• Improve product performance

• Save time to market


Read more about theory, applications and customer stories at our

internet library: www.dynardo.com

dynardo • © dynardo gmbh 2015 time to market cost efficient ways for optimal … ·...

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