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OpenSIMPLY Free open source simulation software
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Scientific software

Contents:

Why use modeling?

Why and when is modeling necessary in scientific research?

Modeling is used to obtain system characteristics when there is no formula, or the formula takes too long to compute.

In general, modeling can be physical (for example, mechanical construction modeling) or mathematical. Here we talk about mathematical modeling.

Simulation modeling

Mathematical modeling that uses simulation to describe the behavior of the system under study is called simulation modeling.

One of the most widely used simulation modeling approaches is discrete-event simulation.

It is used across natural sciences, engineering, social sciences — from NASA to small business warehouses, and from call centers to fast-food restaurants.

Discrete-event simulation is widely implemented as computer simulation software.

Statistical experiments

Discrete-event simulation belongs to the class of statistical approaches to computer simulation. During a simulation run, a series of statistical experiments take place inside the model.

The number of these experiments is not large for a simple system, but this number increases significantly for large, complex systems. Since every such experiment takes some modeling time, a larger number of them results in longer modeling time.

Runs of simulation

One simulation run gives only one value for the system's characteristic. This is not enough to describe a function.

Even two values can only describe a linear function. Since most functions are non-linear and often complex, a small number of values usually gives an inaccurate picture.

Only a sufficiently large enough number of values can describe the function accurately. In practice, many values are required for a smooth and reliable characteristic.

Reliable results

Reliable results appear only after the model reaches steady state (when the system's behavior becomes sufficiently stable). Therefore, statistics gathering should begin only after steady state is reached.

To reach the steady state, the model must run long enough — especially for large or complex systems. So even one reliable result requires a sufficiently large number of statistical experiments.

Fast modeling runtime

Each value of a characteristic requires its own simulation run, so the scientific software should run fast. Otherwise results take too long. For large or complex systems, especially when accounting for steady state, fast modeling time is very important.

So, minimizing simulation runtime is one of the key requirements for scientific research.

Multi-run simulation

To get results for different inputs, run the model many times — best automatically. To avoid performance loss in graphical mode, scientific software should also run in terminal mode. Multi-run results should be output in different ways: as tables, in CSV format, or stored in a database.

OpenSIMPLY as scientific software

OpenSIMPLY meets all the requirements listed above:
  • OpenSIMPLY implements global, selective, and deferred statistics gathering modes. The deferred mode takes steady state into account and helps avoid accuracy problems.

    Another feature for improving accuracy is the ability to suspend statistics gathering while the model is still in steady state.
  • Block simulation in OpenSIMPLY runs very fast, while Simula-like simulation runs especially fast (extremely fast in many cases).
  • OpenSIMPLY provides flexible output of both single and tabular data in various file formats or databases, for both graphical and console modes.
  • The project can simulate sufficiently complex and large systems.
  • The simulation process can be visualized using charts or graphical elements.
  • OpenSIMPLY is ready for remote simulation. A model can be run on a remote computer using Samba or similar data transfer protocols, while model progress or visualization of selected parameters can be observed on the local machine.
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