Virtual world, digital twin, modeling – simulation has many different names. What can be simulated and what are the benefits of simulation?
Almost anything can be simulated. For example, in the industrial sector, the world and machines are simulated. Simulating the world includes modeling the environment and conditions. Simulating a machine, on the other hand, involves the modeling of a machine and its mechanical or physical properties.
Simulation requires data, which can be collected with the aid of various sensors. In the real world, the sensors are physically attached to a machine, whereas in simulated environment, the sensors need to be simulated, as well. Simulated sensors function as an interface between the software being tested and the simulation world. Thus, the sensors provide data on the interaction between the modeled machine and the world.
Simulation can be carried out, for example, by combining open-source software and commercial software. Nowadays, there are numerous complete frameworks specializing in specific situations and tasks. In order for the modeling to succeed as planned, it is important to choose the right framework for the situation.
What, then, are the benefits of simulation?
Simulation enables agile development. With the aid of simulation, testing can be carried out close to the developer, which brings savings and speeds up the testing process. Simulation does not require complicated processes, and the simulated device does not have to be completed when it enters the testing phase. The simulation environment can be set up as part of the development environment, which enables simultaneous testing and iteration during development.
Duplication provides certainty for testing
Simulation does not always necessary require a real machine. When the simulation is implemented entirely with software, testing and duplication are cheaper. The larger the scale on which the software is tested, the more reliable the results. Furthermore, if there are several developers, simulation enables convenient simultaneous testing.
Simulation benefits machine learning and model training, as it enables the accumulation of useful data for training a machine-learning model. Simulation also enables the production of synthetic data. For example, the optical properties of a real camera can be modeled and used to create synthetic images of a simulated world. The images can then be used to train and test pattern recognition algorithms.
Testing of complicated and rare situations
Complicated and rare situations involving extreme conditions are often difficult to test in a real environment and with real equipment. Simulations, on the other hand, can be used to create an unlimited number of different situations that can be repeated.
In the industrial sector, it is important that the machines work appropriately. Simulation helps create various dangerous situations in which the automated machine deviates from its route or crashes. With the aid of testing, risky situations can be avoided in real life.
However, the simulation of rare situation may also lead to an enormous number of various test scenarios. For instance, in the automotive industry, testing is affected by factors such as weather conditions, other traffic and speed. In order for the test results to be reliable, simulation quality must be sufficiently high and the simulation must correspond with a real-life situation.
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