In this work, the uncertainties concerning the knowledge of the cloud properties within an icing encounter are forward propagated through a numerical model for an Anti-Ice Electro-Thermal Ice Protection System (AI-ETIPS) into two representative quantities of runback ice formations. The goal is to characterize the uncertainty of the selected parameters, which are of the utmost relevance for characterizing possible runback ice formations. Monte Carlo Sampling methods (MCS) are the preferred technique because the targeted quantities suffer from a non-smooth discontinuous dependency on the cloud properties. Besides, and despite the mentioned irregularities, generalized Polynomial Chaos Expansions (gPCE) are also implemented to assess whether it would be possible to obtain the low order statistical quantities of the runback ice-related quantities. This work aims to evaluate whether it is possible to attain reasonably accurate estimates of the low order statistics at a significantly reduced computational cost.
Cloud Uncertainty Quantification for Runback Ice Formations in Anti-Ice Electro-Thermal Ice Protection Systems
Space Technol.Proceedings
International Conference on Uncertainty Quantification & Optimisation ; 2020 ; Brussels, Belgium November 17, 2020 - November 20, 2020
Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications ; Kapitel : 2 ; 21-34
2021-07-16
14 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch