Irene Hafner

Investigating communication and step-size behaviour for co-simulation of hybrid physical systems

Abstract This paper discusses the method of cooperative simulation of discrete and continuous models with the Building Controls Virtual Test Bed, a software environment that allows coupling different simulation programs. In the course of a project aiming the energy optimization in cutting factories, models of machines of differing complexity and a building containing them have to be implemented to further simulate the thermal processes. Since all partial models require individual modelling approaches, solver time steps, solvers or even simulators, the method of co-simulation is considered. The partial models will be implemented with Modelica, MATLAB, Simulink and Simscape and accessed with the co-simulation tool BCVTB. The simulation results show that this method of co-simulation can be sufficient for the needs of describing thermal systems with large time constants but has to be found insufficient for simulations requiring high accuracy and variable step solvers in the overall simulation.

MMT—An e-learning system based on computer numeric system for teaching mathematics and modelling

Abstract This paper introduces a tool enabling e-learning of modelling and simulation in a user-friendly and unique way – the MMT system. MMT stands for Mathematics, Modelling and Tools. One of the things taught with the help of the MMT system is lectures of basic mathematics, where students can experiment with MMT examples concerning Analysis and Linear Algebra to strengthen their understanding of the current subject of the lecture. The most important use of the MMT server lies in the support of teaching modelling and simulation. By varying the input parameters of models on the MMT server students can find out in which way they affect the simulation. To enable profound understanding of the models, all source codes are offered to be downloaded from the MMT system. The paper starts with a simple example to become acquainted with the MMT environment and understand the interaction of the MMT example with the corresponding function. One of the latest achievements within the MMT system has been the successful inclusion of examples accessing several m-files and further even Simulink models. The communication between the MMT server and a Simulink model is realised in a m-file exchanging parameters and output among the model and the respective MMT example. The last part of the paper discusses the use of the MMT system in exams, which is realised by the e-learning platform TUWEL. Via this tool students are asked to find out specific data coordinates or input values for a given problem on the MMT server.

Learning Petri Net Dynamics through a Matlab Web Interface

Modelling and simulation are two promising methodologies in attempts to manage the ever increasing complexity of modern technical systems. The knowledge of the inherent continuous and discrete-event dynamics of systems improves the understanding of systems operation and provides the base for designing management and control strategies. Modelling and simulation are therefore vital components of engineering and other technology related curricula. Petri nets are suitable for modelling of discrete-event systems with highly parallel and cooperating activities. Teaching of Petri net basics can contribute to understanding of discrete-event dynamics through a well defined framework supported by formal analysis techniques. As such it is an important part of courses on modelling and simulation. The paper presents an approach to teach basics of Petri net dynamics and related analysis techniques by the help of an object oriented Mat lab m-script based Petri net tool, which was developed to implement Petri net examples within MMT-server e-learning environment. The tool structure is briefly described, a learning process proposal is presented, and an overview of basic examples on Petri net dynamics and analysis is provided.

On the terminology and structuring of co-simulation methods

The need for holistic simulation of complex systems becomes more and more apparent, arising from different fields of application and approached by different scientific methods. This paper addresses the differences in terminology arising from these various origins, the levels on which they meet as well as an attempt on the classification and structuring of current state-of-the-art methods.

Discussion of two Case Studies on DAEs using Different Approaches for Regularisation

The object-oriented model description of physical or mechanical systems leads to differentialalgebraic equations. In general the numerical solution of such equation systems is very complex, numerically extensive or may even be impossible. Therefore it is important to find methods for solving given equation system, this leads to the so-called index reduction and regularization methods. This paper gives a short overview of common methods of index reduction. Additionally a classification of these different approaches is made. Afterwards each approach is presented in detail and the advantages and disadvantages of the different methods are discussed. In order to compare the different index reduction methods, the methods described above are demonstrated by various examples. For the comparability of the different methods the obtained numerical solutions and the deviation from the constraint equations are displayed graphically. Therefore the distinct approaches can be compared with regard to their numerical solutions. The two examples are mechanical systems with differential index three. The equations of motion of a pendulum on a circular path in Cartesian coordinates and the motion of the double pendulum in Cartesian coordinates, which shows a chaotic behaviour, are used as case studies. Introduction • • • 1 Basic Definitions C Poell et al. Case Studies on Different DAE Regularization Methods 180 SNE 24(3-4) – 12/2014 TN 2 Regularisation Methods 2.1 Differentiation and substitution of the constraint 2.2 Baumgarte-Method 2.3 Pantelides Algorithm • • • • • • C Poell et al. Case Studies on Different DAE Regularization Methods SNE 24(3-4) – 12/2014 181 T N 2.4 Orthogonal Projection method • •