Uncertainty quantification

The design of robust electrical machines requires a deep insight into the device’s electromagnetic field distribution. Today, this is achieved by deploying computer simulations rather than physical prototypes. On the other hand, the available input data, e.g. material curves, include uncertainties, e.g. unknown errors due to measurements. The influence of these errors can be characterized by uncertainty quantification as shown in in the Figure below. In the mathematical models, the corresponding parameters are substituted by random variables to describe the uncertainties.

Uncertainty in the inrush current in the simulation of the startup phase of an electrical machine.

Straightforward approaches such as Monte-Carlo simulations simulations are computationally too expensive. More sophisticated approaches, for example the generalized Polynomial Chaos approach, allow faster convergence and hence less computational effort.


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