new features in the algorithmic setup, workflow automation ... · 1 new features in the algorithmic...

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New Features in the Algorithmic

Setup, Workflow Automation

and Postprocessing

Dynardo GmbH

9th Annual Weimar Optimization

and Stochastic Days

November 29-30, 2012

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New Features in the Algorithmic Setup, Workflow Automation & Postprocessing

Dynardo GmbH

© Dynardo GmbH 2012

Outline

New features in

• Sensitivity analysis

• Metamodel of Optimal Prognosis

• Sensitivity flow and post processing

• Signal extraction

• Multi-disciplinary optimization

• Decision tree

• Advanced optimization flows

• New optimization algorithms

• Robustness evaluation

• Definition of input scatter

• Advanced robustness flow

• Post processing

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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

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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 (CoP)

MOP solves 3 important tasks:

• Best variable subspace

• Best meta-model

• Estimation of prediction quality

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The Sensitivity Flow

• The MOP is now fully inte-

grated in the sensitivity flow

• User-interaction may be

enabled before the MOP is built

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Neglection of Outliers

• Outliers or (physical) meaningless designs may be deactivated and the

reduced data set is taken automatically for the MOP generation

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Post Processing

CoP Matrix:

• CoP values of full models and single variable contribution is shown

in a new post processing window

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Optimization on the MOP

• Optimization on the MOP is now

directly linked to the sensitivity flow

• Validation of best design is directly per-

formed with predefined solver template

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Optimization on the MOP

• Reduced parameter set (output of the MOP) is automatically linked to

the optimization on MOP

• Optimization with direct solver calls can import the MOP parameter

manager as well

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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

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Example: Optimization of Fuel Consumption

• Minimization of total fuel consumption of a train over a given distance

• Constraints are maximum time, maximum speed, maximum and minimum accelerations

• Simulation tool showed numerical problems in calculation of minimum acceleration during the last time steps:

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• Optimizer converged very well for objective, time and maximum acceleration constraints

Previous sensitivity analysis indicated excellent CoP values

• Objective: Total fuel consumption:

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• Due to the numerical errors in the minimum acceleration values, the optimizer did not converge for this requirement

Final design did not fulfill the constraints

Sensitivity analysis indicated a low CoP value

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Reliable extraction of minimum acceleration required

Extraction before critical time steps was successful

Optimization converged with fulfilled constraints

Full interval 1m reduced interval 9m reduced interval

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Generating Signal Objects and Functions

• Vectors and matrices of the solver output and reference curves

can be merged to signals using the Calculator

• Full ETK functionality as well as optiSLang 3 signal functions are

available

• User can define own functions as macros

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Generating Signal Objects and Functions

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Multidisciplinary 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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Optimization Algorithms

Gradient-based

Biological Algorithms: - Genetic algorithms - Evolutionary strategies - Particle Swarm Optimization

Start

Pareto Optimization

Adaptive Response Surface Method

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Decision Tree for Optimizer Selection

• An optimizer is automatically suggested depending on the parameter

properties, the defined criteria as well as user specified settings

• Preoptimized reference without failed

or noisy solver responses -> NLPQL

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• Single-objective optimization with no user defined settings

ARSM is the default method

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• For multi-objective optimization the global Evolutionary Algorithm

is recommended

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Advanced Optimization Flow

• In optiSlang 4 different optimization steps can be connected by

automatically importing the best design of e.g. a global optimization to

a following local optimization

• Such flows can be defined

before any solver is called

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© Dynardo GmbH 2012 © Dynardo GmbH 2012

Memetic Algorithm

• Solving optimization tasks with continuous and nominal discrete

parameters like different variants of models or systems

• Each variant may have different input parameters

• Combination of combinatorial and continuous optimization methods

• Genetic algorithm + local search

• Cultural Evolution

• Dynamic parameter spaces

• Available in optiSLang 4.1

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Simplex Algorithm

• Very robust local optimization method

• Good convergence for low

number of design variables

• Extension for constraints

and failed designs

• Available in optiSLang 4.1

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Parallel Coordinates Plot

• Enables the investigation of dependencies between inputs and outputs

by a 2D plot

• All designs of a DOE are plotted by lines within the specific ranges

for each input/output

• By analyzing the flow through

the plot the user can detect

mechanisms and relations

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Parallel Coordinates Plot

• Automatic clustering with respect to a single input/output helps to

better understand the flow (available in optiSLang 4.1)

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Robustness Evaluation

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Definition of Input Scatter

• The definition of the random variable properties is implemented in

table format by automatically taken the reference values as mean

• Probability density functions for all random variables are plotted

corresponding to the defined variable properties

• Standard deviation or Coefficient of Variation can be specified

• Reference design can be imported from arbitrary flows or result files

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Advanced Optimization/Robustness Flow

• Similar to hierarchical optimization steps, single-objective optimization

and robustness analysis can be connected e.g. by automatically

considering the best design of an optimization as nominal design

(mean values) for a following robustness analysis

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Post Processing

• The safety margin can be expressed

by means of the sigma level

• If a limit is specified, the sigma

level and probabilities to exceed

and the deceed the limit

are shown in the post processing

Thank you for your attention!

new features in the algorithmic setup, workflow automation ... · 1 new features in the algorithmic...

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