Temporal coherence importance sampling for wind turbine extreme loads estimation

Peter A. Graf, Ignas Satkauskas, Ryan King, Katherine Dykes, Julian Quick, Levi Kilcher, Jennifer Rinker

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Estimating long return period extreme wind turbine loads is made especially difficult by the large response variability for “the same” environmental conditions. To alleviate this, we have “opened up the black box” of the turbulent wind generation stage of the simulations. Exploiting the notion of “temporal coherence” allows us to manipulate the turbulent inflow to target extreme wind conditions, while at the same time quantifying “how probable these are”. The resulting importance sampling load estimates achieve a significantly lower exceedance probability (i.e., they represent much longer return periods) than estimates using the same number of samples (i.e., the same computational resources) but only a standard Monte Carlo estimate. This paper presents the underlying methodology and some preliminary results. We find that for some loads the method works remarkably well, but for other loads challenges remain.
Original languageEnglish
Title of host publicationProceedings of the AIAA Scitech 2019 Forum
PublisherAerospace Research Central (ARC)
Publication date2019
Article number1798
DOIs
Publication statusPublished - 2019
EventAIAA Scitech 2019 Forum - San Diego, United States
Duration: 7 Jan 201911 Jan 2019
https://arc.aiaa.org/doi/book/10.2514/MSCITECH19

Conference

ConferenceAIAA Scitech 2019 Forum
CountryUnited States
CitySan Diego
Period07/01/201911/01/2019
Internet address
SeriesAiaa Scitech 2019 Forum

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