|InfoVis.net>Magazine>message nº 152||Published 2004-09-13|
|También disponible en Español|
The digital magazine of InfoVis.net
The increasing complexity of technology and the market, together with the need to take informed decisions in an agile way knowing the probable results of our actions, has led to the appearance of many simulators. Other highly complex phenomena like meteorology or nuclear physics make it necessary to use simulators in order to predict behaviour, especially in unusual or dangerous situations
You can find many examples ranging from flight simulators, used for the professional training or commercial and military pilots or to play (X-plane , MS Flight Simulator ); through to market and financial simulators, manufacturing process or urban growth (http://empact.geog.kent.edu/slt_county.html), up to scientific simulators that deal with the prediction of such practical things as meteorology prediction or as exotic as the simulation of the collision between two galaxies.
The concept of simulation is strongly bound to that of a model, which is the core of any simulation. In our context a simulation is the execution (usually computerised) of a model that reproduces the behaviour of a system under some pre-determined conditions that possibly change with time. The model is a theoretical scheme, usually of a mathematical nature, that represents the behaviour and evolution of a system defined through a series of parameters.
For example, in a flight simulator, the mathematical model of fluid dynamics allows you, once you’ve defined the geometry of the plane and the conditions of speed, heading, atmospheric pressure, wind, etc. to compute the resulting forces operating on the plane in each moment, thus deriving its behaviour in response to our actions on the controls.
Complexity is another of the signs of identity of simulations, bound usually to phenomena with a large quantity of associated parameters. For this reason the result of a simulation tends to be a set of data that changes with time, whose comprehension becomes difficult just by looking at the data tables.
Correspondingly most simulators also have in common the use of graphical representations that allow the user to visualise the, some times complex, result of the simulation in a simple way. Many times the visualisation includes a realistic representation, for example flight simulators build the views, sounds and movements that you can feel in the plane. Urban growth simulators represent the changes in the geography through the use of maps.
Financial and market simulators make use of bar charts and other usual business graphics. Scientific simulations usually show two or three dimensional diagrams of the studied phenomenon. In the end, the visualisation of simulations uses basically the usual tools of graphic representation, with an important difference: most of the visualisations are dynamic, i.e. they represent variation in time.
Dynamic visualisation takes advantage of the natural capability of the human perceptive system to pursue objects in a continuously moving field, finding cause-effect relation between them. The most elementary stratum is that of creating a “movie” where the result is shown for every unit of time, then visualising it as a continuum. Nevertheless dynamic visualisation offers many more possibilities.
Dynamic visualisations have the potential to be modified “on the fly” through the use of geometric transformations like rotations, zoom or translation. You can dynamically insert or eliminate parts of the structure that we are visualising to enhance the understanding of the phenomenon. For example in a flight simulation we can add a line that shows the past 3D trajectory of the plain or we can hide the control panel to better see what’s going on.. In a collision between galaxies we can hide the halo of outer stars to better see the interaction of the nucleus of the galaxies.
Combined with the immersive experience of virtual reality, dynamic simulation allows us a higher level of interaction.
Simulations are increasingly used for their predictive capability and, possibly even more so because of their possibilities in training and learning in many fields where complexity is important.
Dynamic visualisation is an important element of advanced simulations that takes advantage of the natural human perceptive capabilities to facilitate the understanding of the complex and changing results of simulations. Two fields, visualisation and simulation, with a great future to come.
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