The vast majority of all analysis performed is deterministic in that all the input values are constant so if you run the analysis 100 times you will get the same answer 100 times. In reality, if you manufacture 100 items and subject them to the same test you will get 100 different answers, even if the differences are small.
Incorporating random variation into your analysis and then running it many times to get an idea of the average and standard deviation of key responses can be described as stochastic simulation.
Why is this important?
Take the example of a car body. In the simplest terms, the noise and vibration characteristics of a car can be tied to the natural frequencies. These frequencies are a function of stiffness and mass, both of which can vary from car to car. The mass can vary due to fluctuations in material density, the thickness of panels and the quantities of e.g. adhesive used to bond components. …
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