June 1-3, 2015 in Dolní Morava, Czech Republic

Conference for numerical simulation

Jun 6-8, 2016 in Würzburg

Conference for technical aspects in the design and use of modern connectors

Jun 7-8, 2016 in Munich

Computer-aided engineering conference in the automotive industry.

Jun 12-15, 2016 in Linz

Challenges in Forming High-Strength Sheets

Jun 6, 2016 in Winterthur

Conference for numerical simulation


Library RDO Journal Weimar Optimization and Stochastic Days Newsletter Quarter 2_2016 Dynardo...

Dynardo GmbH is proud to announce that Dynardo’s optiSLang, leading edge software for CAE-based...

Release note optiSLang 5.0.0 more information about optiSLang 5 more information about optiSLang...



Jun 6-8, 2016 in Weimar

Jun 9 in Vienna
Aug 10, 2016 in Lein.-Echt.
Sep 7, 2016 in Renens
Nov 16, 2016 in Berlin
Nov 22, 2016 in Aadorf

Jun 22 in Hanover
Sep 30, 2016 in Lein.-Echt.
Dec 9, 2016 in Grafing

Aug 12, 2016 in Lein.-Echt.
Nov 18, 2016 in Berlin
Nov 24, 2016 in Aadorf

Training: Integration of Maxwell & optiSLang

This training unit will introduce you to optiSLang's basic functionality allowing the process integration, sensitivity analysis, and optimization of Maxwell-Models.

Any Maxwell simulation model can be coupled to optiSLang via standard functions for text-based integration. On the output side, the handling of scalar parameters and signals in both Maxwell and optiSLang's calculator environment represents a central aspect. On the output side, the handling of scalar parameters and signals in both Maxwell and optiSLang's calculator environment represents a central aspect. The definitions made here determine the database upon which the visualizations of the statistics and metamodel postprocessing build up. You will learn how to conduct a sensitivity analysis in optiSLang and to interpret the graphically displayed results, e.g. to identify the most influential model parameters and to bring design conflicts into clear view. Next, you will use the gained knowledge for efficient design optimization. The procedure will be exemplified through a case study of a 2D electric motor model.


Metamodel of Optimal Prognosis (MOP) for the torque ripple amplitude
Reference geometry (left) / optimized geometry (right)
Optimized controller signal
Optimized torque signal in comparison with reference case

1. Overview of the graphical user interface of optiSLang

2. Process integration

  • Direct integration of a Maxwell model via text files
  • Extraction of result variables and signal processing
  • Integration via ANSYS-Workbench 

3. Application of a sensitivity analysis

  • Definition of parameter spaces and Design of Experiments
  • Statistics postprocessing: visualization of correlations (e.g. torque-efficiency conflict)
  • Metamodel postprocessing: interpretation of visualizations and discussion of physical causes

4. Solution of an exemplary optimization task

  • Definition of objective and constraint functions taking into account knowledge gained during the sensitivity analysis
  • Exploiting the metamodel for optimization
  • Direct optimization of the Maxwell model

5. Outlook: parallelization via Large-Scale DSO

  • GUI-based DSO versus Large-Scale-DSO
  • Basic concept of using Large-Scale-DSO with optiSLang
  • Function with DoE algorithms and iterative optimizers
  • Speed-up behavior of different optimizers in comparison

As a prerequisite, basic knowledge about statistics and optiSLang is required which can be obtained by attending the training "optiSLang basics".


Dipl.-Phys. Markus Stokmaier

to be announced
Please register not later than 14 days prior to the event date.

1 Day Seminar from 9am-5pm

min. 5 to max. 12 participants 

500 Euro (excluding VAT)

50% for students
30% for university members / PHD's

We recommend the following hotels with special conditions for your stay in Weimar:
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