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Existing models for the analysis of offshore wind turbines account for the aerodynamic action on the turbine rotor in detail, requiring a high computational price.
The MHE approach has been shown to provide the most reliable estimates in several example problems; albeit at a high computational price.
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The inclusion of nonlinearities potentially increases the model accuracy, but at a higher computational price.
In addition, we suggest an accelerated implementation of the method based on hypothesis filtering as in [5], which shows superior results for the price of high computational cost.
More specifically, the method based on probabilistic assumptions showed superior results for the price of very high computational cost.
However, the price of the superlinearly convergent MFD for achieving superlinearity is rather high computational cost per single iteration.
In fact, some recent works (see, e.g. [14]) have shown tha, using a nonlinear convolutional model, excellent results can be obtained, although at the price of a higher computational cost.
The price is a higher computational cost.
This second complication can probably be completely overcome by adding more points to the estimation of the empirical distribution, but the addition of more points comes at the price of a higher computational cost.
This is at the price of a higher computational cost and the availability of fine resolution data, whereas the metapopulation approach is less dependent on detailed data and is computationally cheaper.
However, the price to pay is a higher computational cost per time step, higher discretization errors and lower parallel scalability.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com