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The Use of SimulationX in Teaching
Prof. Kurt Hug
Bern University of Applied Sciences’ Department of Engineering and
Information Technology (BFH-TI), Automotive Engineering Division,
Biel/Bienne, Switzerland
Zusammenfassung
Die Automobilabteilung der Berner Fachhochschule BFH-TI hat Wege gesucht, wie sie
in der Lehre mit einem möglichst holistischen Ansatz die Brücke zwischen den langlebigen Grundlagen der Technik und den im Vergleich dazu kurzlebigeren Technologien
herstellen kann. Sie hat sich zu diesem Zwecke bei ITI neben dem Softwarepacket SimulationX auch ein Simulationsmodell eines sogenannten HiL-Simulators (HiL: Hardware in the Loop) beschafft. Dieser wurde bereits ausführlich an einem Vorgängersymposium präsentiert [1]. Deshalb wird an dieser Stelle auf die Anlage selbst nur dort
eingegangen, wo es nötig erscheint; Abb. 1. zeigt sie. Mit ihr bietet sich die Gelegenheit, den Studierenden das moderne und komplexe Konzept eines HiL-Simulators in
seinen Grundzügen näherzubringen. Gleichzeitig gewährt der gewählte Ansatz der
„Physik der dynamischen Systeme“ (SD: System Dynamics) den Studierenden einen
ganzheitlichen und vertieften Einblick in physikalische Zusammenhänge.
Summary
The Automotive Engineering Division of the Bern University of Applied Sciences’ Department of Engineering and Information Technology (BFH-TI) has been investigating
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ways of developing an approach to its teaching that is as comprehensive as possible,
so as to bridge the gap between the enduring principles of technical engineering and
the shorter-lived technologies which apply them. To that end, in addition to purchasing ITI’s SimulationX software application, it has also acquired a simulation model of
a socalled HiL (hardware in the loop) simulator from this same firm. Since this simulator has already been presented in detail at an earlier symposium [1], this article will
refer to the system itself, shown in figure 1, only where necessary for the topics being
examined here. The simulator enables students to familiarise themselves with the fundamental characteristics of the complex concepts on which a modern HiL simulator is
based. Adopting a System Dynamics (SD) approach to examining the physics of dynamic systems also enables the students to gain a comprehensive, in-depth appreciation
of physical causes and effects within complex systems.
Figure 1: HiL simulator. The buggy’s actions are simulated in real time. Remote control
is carried out by real-time hardware. For animation purposes the real buggy is run
synchronously with the simulation.
What is our task?
The need to recognise and accept that our available resources, with regard to both
time and materials, are becoming ever scarcer is by no means new. That scarcity,
whether attributable to finite supplies of certain material substances or to political
considerations, is universal. It applies as much to universities as to anyone else. In a
constantly diminishing number of foundation courses, schools are required to teach
their students ever greater quantities of ever more ephemeral knowledge. This trend
is mirrored in industry, where development cycles are becoming ever shorter. How can
teaching keep pace?
For many years, the emphasis was on experiments which usually took a long time to
conduct and were generally costly. The curriculum and its laboratory exercises were
designed accordingly. Industry had to devote substantial resources to designing and
building prototypes. It was not uncommon for very minor errors to result not only
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in massive increases in costs, but also in significant dangers during the experimental
phase.
What is needed today are methods which avoid these difficulties and which make it
possible to predict the efficiency and feasibility of a new system before it is actually
developed, thus ensuring that the planning process is reliable. This is achieved by
applying mathematical models which replicate the real physical attributes of a system
and its processes as precisely as possible.
Where do the problems occur?
Students of Electrical Engineering have no difficulty in accepting the idea that the
behaviour of an electronic component can be described in terms of the voltage across
it or the current flowing through it. Those properties describe the two fundamentally
different attributes with which nature provides us for describing electrical components
and thus for carrying out our work as engineers. It is equally self-evident to these
students that, when these components are connected to each other, this creates a
self-contained system, in this case an electronic circuit. If we then go on to mention
that these attributes are generally referred to as Kirchhoff’s circuit laws, in honour of
their inventor Gustav Kirchhoff, then everyone is delighted to learn that they now
know and can apply a powerful set of tools for analysing electric circuits.
It is shortly after this, when one goes on to point out that, having now grasped this
principle, students would have no difficulty in switching from Electrical Engineering
to its mechanical counterpart, that heads begin to shake and doubts are expressed.
At this point, in general it is not helpful when one tells the students that all they need
to remember is that instead of talking about an electrical tension u, professors of
Mechanical Engineering will refer to a velocity v, and that they will substitute their
remarks about an electrical current i with observations on a force they describe as F.
Of course, it does not really help here, that the students are well aware that changing
their course of study cannot really be as easy as that, not least because it would hardly
be in the best interests of the teaching faculties concerned.
However, since closer observation does indeed show that the properties of the components and connections set out above do indeed apply to all fields of engineering
and physics, it would be worthwhile for us to make the most of these analogies. In
fact, they also apply in a number of other fields beyond that of engineering. And
yet, despite this, experience shows that these analogies are seldom put to use systematically. Object-oriented modelling and simulation provides ideal opportunities for
achieving this.
In order to avoid excessive complexity, and without looking in greater detail at some
of the key principles of interface design, let us limit ourselves here to considering subsystems of an entire domain of physics, as was demonstrated in the example above
by reference to electrical engineering. As was mentioned earlier, systems of this kind
acquire their overall, uniform characteristics thanks to the connections between their
individual components. It is where such connections exist, at the interfaces which link
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them together, that the operation of the overall system depends on the necessary
information being transferred between its components.
Let us now introduce the concept of two general types of interface variables. The
value of one of these variables within a system will always remain the same. The literature describes this as an intensive variable or potential value, one which is independent of the quantity of material being considered. All other variables are described as
extensive, i.e. ones which are proportional to the quantity of material concerned. The
fundamental principles of conservation of energy state that the sum of an extensive
variable within a closed system must always be zero.
We can generalise this by defining a proportional value IM and a potential value ϕΜ
for the primary quantity M present in any domain.
Examples of this are:
Primary quan- Intensive variable:
potential value
tity
(M)
(ϕM)
ThermoEntropy
Temperature
dynamics
(J/K)
(K)
Electricity
Electric charge
Electric tension
(As)
(V)
Translational Momentum
Velocity
mechanics
(Ns)
(m/s)
Rotational
Angular
Angular velocity
mechanics
momentum
(rad/s)
(Nms)
Hydraulics
Volume
Pressure
(m3)
(N/m2)
Physical
domain
Extensive variable:
proportional value
(IM)
Heat flow
(J/s)
Electric current
(A)
Force
(N)
Torque
(Nm)
Volumetric flow rate
(m3/s)
Table 1: Primary quantities, intensive and extensive variables for various domains
In order to implement and use object-oriented modelling and simulation, we of course
also need to take a closer look at the components of a system as well as its variables.
These components act as the proportionality factor that links the extensive and intensive variables within a physical domain.
Let us begin with a general observation about energy storage. The general principle
here is that physical systems permit two fundamentally different forms of energy storage - potential energy storage and kinetic energy storage.
In the literature, components with the capacity to store potential energy are often
described as i components, with i in this case designating inertia. Conversely, components which store kinetic energy are described as c components, c designating
capacity. To complete the picture of these passive elements we also need a resistance
component, unsurprisingly referred to as r, to account for any losses. Each of these
components will obviously have a specific significance in each of the different domains.
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Clearly, a theory of this kind will not be complete without sources for the intensive
and extensive variables concerned.
These generalisations enable us to develop a uniform description which can be applied to all the flows of energy occurring within a given physical system. If the same
mathematical formulas can be used to describe physical processes in different domains, then the flows of energy involved must also be the same. It is also self-evident
that the law of energy conservation will also apply to all these systems.
This provides an elegant generalisation of three essential laws familiar to every electrical engineer: the law of induction, the law of capacitance and the law of resistance.
The law of induction describes the relationship between the rate of change of the flow
variable dIM/dt and the potential difference ΔϕM. This proportional variable is termed
inductance LM and this makes it possible to store the potential value ϕM. In linear
terms, the relationship is as follows:
The law of capacitance describes the relationship between the stored primary quantity M and the potential value ϕM. This proportional variable is the capacity CM of the
primary quantity and this makes it possible to store the extensive variable IM. In linear
terms, the relationship is as follows:
∆ϕ M =
∆M
CM
The law of resistance describes the relationship between the extensive variable IM and
the potential difference ΔϕM. This proportional variable is termed resistance RM. In
linear terms, the relationship is as follows:
∆ϕ M = RM ⋅ I M
Irrespective of the domain under consideration, it is the case that the product of the
extensive variable IM and the potential difference ΔϕM will be equal to the process
output PM of the domain concerned.
PM = ∆ϕ ⋅ I M
What we have expressed here in a manner which is not entirely scientifically exact
(because it is formulated in linear terms only and does not take the special properties
of thermodynamics into account), are the fundamental principles underlying the physics of dynamic systems. This corresponds to the application of the system dynamics
(SD) used in engineering, a methodology which enables us to construct comprehensive models to simulate complex dynamic systems [2].
The physics of dynamic systems comprise only three pillars, they provide the foundation on which the entire theoretical structure rests [3]. These are:
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•• the balance of the seven primary quantities: mass, volume, electric charge,
momentum, torque, entropy, amount of substance
•• the constitutive equations
•• the role of energy
How do we solve this problem?
I have often wondered whether it is didactically more efficient to give a child the basic
building blocks of the Lego range or whether the child would learn more instead from
having a themed package, such as a ready-made Lego castle. It is certainly true that
this is a matter of considerable controversy in the field of pedagogy. Those teaching
at universities must ask themselves the same question. Is an engineering curriculum
more sustainable if it teaches enduring fundamental principles or short-lived modern
concepts? In other words, is an engineering student’s body of knowledge of greater
long-term use if I teach the fundamental principles of Ohm’s Law or if I examine the
protocol of a standard which is currently used in every new vehicle but which may
well be outmoded in five years’ time. While both approaches are undoubtedly desirable, the problem, as stated above, is that there is not enough time to cover them
both. I imagine the reader may have observed that I have good reasons for preferring
the approach based on fundamental principles. Industry, however, will probably be
more interested in hiring an engineer who is familiar with the most modern and current methods.
The physics of dynamic systems enable us to perform the difficult task of meeting the
needs of both approaches. Software-based System Dynamics provide an ideal basis
for delivering up-to-date education in natural sciences and engineering.
Once the reasons for acquiring a simulation application of this kind had been set out,
the question then arose as to how we intended to deploy such a powerful tool. ITI has
presented an infrastructure [1] which meets all my requirements, not least with regard
to electric vehicles, my research field.
The implementation of new drive concepts will be one of the most far-reaching tasks involved in vehicle development in the years ahead. By working on mechatronic
systems of this degree of complexity, comprising an electric engine, as well as its actuator and mechanical drive train, engineers will face many kinds of new challenges,
such as those relating to how the various parts will work with each other dynamically
when the vehicle is in motion. HiL simulators with complete vehicle models able to
function in real time will be needed for the functional development of control devices,
both in the early stages of their development and ultimately for testing the software
used to control them. The equipment which has been purchased provides an exemplary illustration of how simulations of this kind can be carried out. A precise description of the HiL simulator, as well as details of its technical specifications, can be found in
[1]. While these data are not replicated here, they can be found in the slides presented
at that symposium. Figure 2 provides an overview of the SimulationX model.
In addition to using what is admittedly a highly complex model to familiarise themselves with SD methodology, the students also of course construct mechatronic mo280
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dels of their own. It is always a joy to witness how the lecture hall then transforms
itself into a virtual tuning hangar and the students naturally start to compete with
each other. Teaching under these circumstances is really fun.
Figure 2: Overall view of a SimulationX model. The yellow background designates the
axes, the chassis and steering. The light red background denotes the drive train and the
electrical components are shown against a green background.
Conclusion
The physics of dynamic systems makes it possible to describe nature in a coherent
fashion. By applying appropriate tools to this process it is possible to gain an understanding of the more complex sub-processes involved. Overall representations of
entire mechatronic systems become tangible, thus reinforcing understanding of the
underlying physics. Analogous conclusions also provide access to domains far removed from the exact natural sciences, thus making it possible to gain insights into
areas which were previously unexplored. This approach also enables us to engage
in ever-more-important interdisciplinary work, so that we can leave isolated thinking
behind us.
References
[1]
Real-time modeling of an electrically driven model vehicle with SimulationX
and NI VeriStand; Conference Proceedings of the 13th ITI Symposium, International Congress Center Dresden, Dresden, November 24 – 25 2010, published by: ITI GmbH
[2]
http://systemdesign.ch/index.php/Physik_der_dynamischen_Systeme
accessed on 2.9.2013
[3]
https://home.zhaw.ch/~maur/SystemPhysik/ , accessed on 2.9.2013
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