Effects of Finite Sampling on Fatigue Damage Estimation of Wind Turbine Components: A Statistical Study

Shadan Mozafari, Katherine Dykes, Jennifer Rinker, Paul Veers

Research output: Contribution to journalArticlepeer-review

2 Scopus Citations

Abstract

The variability of the wind turbine loads complicates fatigue assessment in the design phase, as performing simulations covering the entire lifetime is computationally expensive. The current work provides important information for assessing the uncertainty in fatigue damage estimation due to finite data. We study the sample size effect on mean, variance, and skewness of damage in each wind bin, identify the important wind bins, and study the uncertainty propagation from each wind bin to the lifetime damage using 3600 aeroelastic simulations and bootstrapping. To achieve less than 1% error in the damage estimation across all load channels in the current case study, at least 100 turbulence seeds are needed. Damage in different wind bins follows a lognormal distribution when using the conventional approach of six seeds. The provided insights and information allow the designer to achieve a specific level of accuracy for a given computational cost using strategic bin sampling.

Original languageAmerican English
Pages (from-to)799-820
Number of pages22
JournalWind Engineering
Volume47
Issue number4
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© The Author(s) 2023.

NREL Publication Number

  • NREL/JA-4A00-86257

Keywords

  • central limit theorem
  • fatigue damage variance
  • finite-sampling error
  • Gaussian fatigue damage
  • Wind turbine fatigue

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