Page 17 - Mechatronic Systems Modelling and Simulation with HDLs

6 2 PRINCIPLES OF MODELLING AND SIMULATION

• In some cases the ‘time constants’ of the experiment and observer are
incompatible, such as the investigation of elementary particles or galaxies.

• In some cases an experiment is ruled out for moral reasons, for example exper-
iments on humans in the field of medical technology.

However, these benefits are countered by some disadvantages:

• Each virtual experiment requires a complete, validated and verified modelling
of the system.

• The accuracy with which details are reproduced and the simulation speed of
the models is limited by the power of the computer used for the simulation.

In many cases the benefits outweigh the disadvantages and virtual experiments
can be used advantageously. The repeatability guaranteed by the computer is partic-
ularly beneficial if the virtual experiment is systematically planned and performed
as part of an optimisation.
In what follows we will define a range of terms relating to modelling and
simulation. This will allow us to move from a general consideration to the systems
investigated in this work, thus providing a good structure to the discussion. The
following representation relates to the work of the SCS Technical Committee on
Model Credibility, see [362].
Reality is initially an entity, situation or system to be investigated by simulation.
Its modelling can be viewed as a two-stage process, as shown in Figure 2.1. In the
first stage, reality is analysed and modelled using verbal descriptions, equations,
relationships or laws of nature, which initially establishes a conceptual model. A
field of application then has to be defined for this conceptual model, within which
the model should provide an acceptable representation of reality. Furthermore,
the degree of correspondence between conceptual model and reality that should be
achieved for the selected field of application, has to be defined. A conceptual model
is adequately qualified for a predetermined field of application if it produces the
required degree of correspondence with reality. In the second stage of modelling the
conceptual model is transformed into an executable, i.e. simulatable, model as part
of implementation. This primarily consists of a set of instructions that describe the
system’s response to external stimuli. The instructions can be processed manually
or using a computer. The latter is called simulation and permits the processing
of significantly greater data quantities, and thus the consideration of significantly
more complex problems.
The development of models for simulation is a difficult process, and thus prone
to errors. On the other hand, the reliability of a simulation is crucially dependent
upon the quality of the model. So methods and tools are required that are capable
of validating and verifying the models. Let us now define these two terms, valida-
tion and verification, more closely, see Figure 2.1. Model verification investigates
whether the executable model reflects the conceptual model within the specified
limits of accuracy. Verification transfers the conceptual model’s field of application