ARRID: ANN-based Rotordynamics for Robust and Integrated Design
August 25, 2022 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Soheyl Massoudi, Jรผrg Schiffmann
arXiv ID
2208.12640
Category
cs.NE: Neural & Evolutionary
Cross-listed
cs.AI
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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.
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