Uncertainty Quantification with dependent inputs wind and waves

Authors
Publication date 2018
Host editors
  • R. Owen
  • R. de Borst
  • J. Reese
  • C. Pearce
Book title Proceedings of the 6th. European Conference on Computational Mechanics (Solids, Structures and Coupled Problems ECCM 6, 7th. European Conference on Computational Fluid Dynamics ECFD 7, Glasgow, Scotland, UK, June 11-15, 2018
ISBN (electronic)
  • 9788494731167
Event 7th European Conference on Computational Fluid Dynamics
Pages (from-to) 4099-4110
Publisher Barcelona: International Center for Numerical Methods in Engineering
Organisations
  • Faculty of Science (FNWI) - Korteweg-de Vries Institute for Mathematics (KdVI)
Abstract A framework for performing uncertainty quantification is presented which is
well-suited for systems with dependent inputs with unknown distributions. The multivariate input is given as a dataset whose variables can have strong, nonlinear dependencies. For each of the elements in the framework (dependency analysis, sample selection and sensitivity analysis), we recently developed new methods, which are here combined for the first time. The framework is tested on an example involving a wind farm simulation with offshore weather conditions as input.
Document type Conference contribution
Language English
Published at http://www.eccm-ecfd2018.org/frontal/docs/Ebook-Glasgow-2018-ECCM-VI-ECFD-VII.pdf
Other links http://www.eccm-ecfd2018.org
Permalink to this page
Back