Python-Based eSES/MB Infrastructure
The Python-based extended System Entity Structure / Model Base (eSES/MB) infrastructure has been developed
by the research group Computational Engineering and Automation (CEA) at Wismar University of Applied Sciences.
It is intended to automatically execute a number of simulation experiments using the components in the figure
below. It extends the ideas of the SES/MB framework introduced by B.P. Zeigler with new modeling features,
methods, and components. These extensions allow the automatic processing of SES as well as automatic model
generation and execution.
Extended SES/MB Infrastructure for Modeling and Simulation.
Today's simulation softwares provide powerful numerical methods for simulation, but lack comprehensive support for the
conceptual modeling phase. In order to tackle this lack, the research group CEA developed the
SES Toolbox for MATLAB/Simulink.
The toolbox implements the components and methods of the eSES/MB framework and supports model generation for
MATLAB/Simulink models, but lacks general interfaces for an Execution Unit and an Experiment Control as well as support
for different simulation software. The Python toolset offered on this site provides general interfaces with special focus
on the possibilty to support different simulation environments.
The Python-based eSES/MB infrastructure consists of following tools available as OpenSource software on GitHub:
The tools have a comprehensive documentation in the file "doc.pdf" in the main directory of each tool (except for SESViewEl).
In the documentation of SESToPy an introduction and background information to the eSES/MB infrastructure is given.
Currently SESMoPy supports the simulators Simulink, OpenModelica and Dymola natively. A general interface supported by a number of
simulators is the Functional Mock-up Interface (FMI). SESMoPy supports model generation via FMI. Advantage of using FMI is that a general
non simulator specific modelbase can be used to support all simulators. Please see the documentation of SESMoPy for more information.
Detailed information are provided in the documentation of the respective tool (except for SESViewEl).
Quick start information for the software are provided in the readme files of the respective tool.
For using this infrastructure the following steps need to be taken:
- Different system variants are modeled in a System Entity Structure (SES) using the software SESToPy. SESViewEl can be
connected to SESToPy in order to view the SES modeled in SESToPy as tree.
- A modelbase (MB) organizing basic models is created with a supported simulation software.
- The experiment to execute is defined in the software SESEcPy.
- SESEcPy is started.
Folkerts, H., Pawletta, T., Deatcu, C., and Hartmann, S. (2019). A Python Framework for
Model Specification and Automatic Model Generation for Multiple Simulators. In: Proc. of
ASIM Workshop 2019 - ARGESIM Report 57, ASIM Mitteilung AM 170. ARGESIM/ASIM Pub.
TU Vienna, Austria, 02/2019, 69-75. (Print ISBN 978-3-901608-06-3)
Folkerts, H., Deatcu, C., Pawletta, T., Hartmann, S. (2019). Python-Based eSES/MB
Framework: Model Specification and Automatic Model Generation for Multiple Simulators.
SNE - Simulation Notes Europe Journal, ARGESIM Pub. Vienna, SNE 29(4)2019, 207-215.
(DOI: 10.11128/sne.29.tn.10497),(Selected EUROSIM 2019 Postconf. Publ.)
Folkerts, H., Pawletta, T., Deatcu, T. (2019). An Integrated Modeling,
Simulation and Experimentation Environment in Python Based on SES/MB and DEVS.
Proc. of the 2019 Summer Simulation Conference, ACM Digital Lib.,
2019 July 22-24, Berlin, Germany, 12 pages.
Folkerts, H., Pawletta, T., Deatcu, C., Zeigler, B. (2020). Automated, Reactive Pruning
of System Entity Structures for Simulation Engineering. SCS SpringSim'20, May 19-May 21,
2020, Virtual Conference (Corona pand.), 12 pages.
Folkerts, H., Pawletta, T., Deatcu, C. (2020). Model Generation for Multiple Simulators
Using SES/MB and FMI. 25. ASIM Symposium Simulationstechnik (SST), Oct. 14.-15.,
Virtual Conference, Germany, ARGESIM Report 59 (ISBN 978-3-901608-93-3), p 13-20,
2020/10, H. Folkerts