ARRID: ANN-based Rotordynamics for Robust and Integrated Design
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Abstract (arXiv)
The purpose of this study is to introduce ANN-based software for the fast evaluation of rotordynamics in the context of robust and integrated design. It is based on a surrogate model made of ensembles of artificial neural networks running in a Bokeh web application. The use of a surrogate model has sped up the computation by three orders of magnitude compared to the current models. ARRID offers fast performance information, including the effect of manufacturing deviations. As such, it helps the designer to make optimal design choices early in the design process. The designer can manipulate the parameters of the design and the operating conditions to obtain performance information in a matter of seconds.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 1 of its 17 references that are in the map. First posted on arXiv 25 Aug 2022.
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- 2021Ensemble deep learning: A reviewcited by 284
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