Prediction and uncertainty propagation of correlated time-varying quantities using surrogate models

Irene Tartaruga*, Jonathan Cooper, Mark H Lowenberg, Pia N Sartor, S Coggon, Y Lemmens

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

11 Citations (Scopus)
324 Downloads (Pure)


The identification of correlated quantities is of particular interest in several fields of engineering and physics, for example in the development of reliable structural designs. When ‘time-varying’ quantities are analysed, pairs of correlated interesting quantities (IQs), e.g. bending moments, torques, etc., can be displayed by plotting them against each other, and the critical conditions determined by the extreme values of the envelope (convex hull). In this paper, a reduced order singular value-based modelling technique is developed that enables a fast computation of the correlated loads envelope for systems where the effect of variation of design parameters needs to be considered. The approach is extended to efficiently quantify the effects of uncertainty in the system parameters. The effectiveness of the method is demonstrated by consideration of the gust loads occurring from the aeroelastic numerical model of a civil jet airliner.
Original languageEnglish
Pages (from-to)29-42
Number of pages14
JournalCEAS Aeronautical Journal
Issue number1
Early online date22 Oct 2015
Publication statusPublished - 1 Mar 2016

Structured keywords



  • Correlated loads
  • Prediction
  • Uncertainty quantification
  • Surrogate models
  • Singular value decomposition
  • Kriging surrogates


Dive into the research topics of 'Prediction and uncertainty propagation of correlated time-varying quantities using surrogate models'. Together they form a unique fingerprint.

Cite this