English

ARRID: ANN-based Rotordynamics for Robust and Integrated Design

Neural and Evolutionary Computing 2022-08-29 v1 Artificial Intelligence

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.

Keywords

Cite

@article{arxiv.2208.12640,
  title  = {ARRID: ANN-based Rotordynamics for Robust and Integrated Design},
  author = {Soheyl Massoudi and Jürg Schiffmann},
  journal= {arXiv preprint arXiv:2208.12640},
  year   = {2022}
}

Comments

Submitted to Machine Learning in Computational Design Workshop of the 39th International Conference on Machine Learning, 2022, 4 pages, 1 figure

R2 v1 2026-06-25T02:00:17.536Z