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Today's advanced simulators facilitate thorough studies on VANETs but are hampered by the computational effort required to consider all of the important influencing factors.
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The effort of the present study is to reduce the number of sensors per bearing pedestals by enhancing the computational effort in signal processing.
Mechanical nonlinearities introduced by the contact among rolls and by bearings increase the computational effort, but their role is somewhere overestimated in the literature.
By using the multistep approach, the computational effort was quite small (about 20 min. on a computer with Intel Core i5, 64-bit, 3.3 GHz processor).
In order to show the relevant speedup achieved by coagSODA, we present here the comparison of the computational effort required by GPU and CPU for the simulation of the BCC model.
The method reduces the computational effort by several orders of magnitude compared to published methods using the same model.
Using trajectory sensitivities to obtain V-CCT minimizes the computational effort by avoiding repetitive trial-and-error time-domain simulations.
Several problem instances with different number of batches demonstrate that the proposed approach reduces the computational effort by orders of magnitude.
The resulting low-dimensional spectrum of significant eigenvalues is mapped to a coarse-grid representation of the periodic solution, reducing the computational effort by one order of magnitude compared to standard methods.
This fast local level set method reduces the computational effort by one order of magnitude, works in as much generality as the original one, and is conceptually simple and easy to implement.
Thereby, we can focus the computational effort by stratifying only the most influential variables, which allows to better deal with high-dimensional spaces in the estimation step of the ADS method.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com