Local reduced order modeling and iterative sampling for parametric analyses of aero-icing problems

Zhao Zhan, Wagdi G. Habashi, Marco Fossati

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

A framework of local reduced-order modeling using machine learning algorithms is presented together with an approach to optimally select the snapshots for strongly nonlinear problems. By using an unsupervised learning algorithm, solutions are grouped into clusters of similar features. The input parameter space is divided into subregions by decision boundaries based on a supervised learning algorithm. Local reduced-order bases are extracted on each cluster, for which the solutions are represented as a linear combination of the basis vectors from their corresponding subregion. The proposed methodology is employed to conduct a comprehensive, exploration of the in-flight icing certification envelopes.
Original languageEnglish
Pages (from-to)2174 - 2185
Number of pages10
JournalAIAA Journal
Volume53
Issue number8
Early online date24 Apr 2015
DOIs
Publication statusPublished - 31 Aug 2015

Keywords

  • machine learning algorithms
  • in-flight icing
  • vectors

Fingerprint Dive into the research topics of 'Local reduced order modeling and iterative sampling for parametric analyses of aero-icing problems'. Together they form a unique fingerprint.

Cite this