Details
| Original language | English |
|---|---|
| Pages (from-to) | 1747-1763 |
| Number of pages | 17 |
| Journal | Wind Energy Science |
| Volume | 9 |
| Issue number | 8 |
| Publication status | Published - 20 Aug 2024 |
Abstract
Uncertainty quantification (UQ) is a well-established category of methods to estimate the effect of parameter variations on a quantity of interest based on a solid mathematical foundation. In the wind energy field most UQ studies focus on the sensitivity of turbine loads. This article presents a framework, wrapped around a modern Python UQ library, to analyze the impact of uncertain turbine properties on aeroelastic stability. The UQ methodology applies a polynomial chaos expansion surrogate model. A comparison is made between different wind turbine simulation tools on the engineering model level (alaska/Wind, Bladed, HAWC2/HAWCStab2, and Simpack). Two case studies are used to demonstrate the effectiveness of the method to analyze the sensitivity of the aeroelastic damping of an unstable turbine mode to variations of structural blade cross-section parameters. The code-to-code comparison shows good agreement between the simulation tools for the reference model, but also significant differences in the sensitivities.
ASJC Scopus subject areas
- Energy(all)
- Renewable Energy, Sustainability and the Environment
- Energy(all)
- Energy Engineering and Power Technology
Sustainable Development Goals
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In: Wind Energy Science, Vol. 9, No. 8, 20.08.2024, p. 1747-1763.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Uncertainty quantification of structural blade parameters for the aeroelastic damping of wind turbines
T2 - A code-to-code comparison
AU - Verdonck, Hendrik
AU - Hach, Oliver
AU - Polman, Jelmer D.
AU - Schramm, Otto
AU - Balzani, Claudio
AU - Müller, Sarah
AU - Rieke, Johannes
N1 - Publisher Copyright: © 2024 Hendrik Verdonck et al.
PY - 2024/8/20
Y1 - 2024/8/20
N2 - Uncertainty quantification (UQ) is a well-established category of methods to estimate the effect of parameter variations on a quantity of interest based on a solid mathematical foundation. In the wind energy field most UQ studies focus on the sensitivity of turbine loads. This article presents a framework, wrapped around a modern Python UQ library, to analyze the impact of uncertain turbine properties on aeroelastic stability. The UQ methodology applies a polynomial chaos expansion surrogate model. A comparison is made between different wind turbine simulation tools on the engineering model level (alaska/Wind, Bladed, HAWC2/HAWCStab2, and Simpack). Two case studies are used to demonstrate the effectiveness of the method to analyze the sensitivity of the aeroelastic damping of an unstable turbine mode to variations of structural blade cross-section parameters. The code-to-code comparison shows good agreement between the simulation tools for the reference model, but also significant differences in the sensitivities.
AB - Uncertainty quantification (UQ) is a well-established category of methods to estimate the effect of parameter variations on a quantity of interest based on a solid mathematical foundation. In the wind energy field most UQ studies focus on the sensitivity of turbine loads. This article presents a framework, wrapped around a modern Python UQ library, to analyze the impact of uncertain turbine properties on aeroelastic stability. The UQ methodology applies a polynomial chaos expansion surrogate model. A comparison is made between different wind turbine simulation tools on the engineering model level (alaska/Wind, Bladed, HAWC2/HAWCStab2, and Simpack). Two case studies are used to demonstrate the effectiveness of the method to analyze the sensitivity of the aeroelastic damping of an unstable turbine mode to variations of structural blade cross-section parameters. The code-to-code comparison shows good agreement between the simulation tools for the reference model, but also significant differences in the sensitivities.
UR - http://www.scopus.com/inward/record.url?scp=85202178363&partnerID=8YFLogxK
U2 - 10.5194/wes-9-1747-2024
DO - 10.5194/wes-9-1747-2024
M3 - Article
AN - SCOPUS:85202178363
VL - 9
SP - 1747
EP - 1763
JO - Wind Energy Science
JF - Wind Energy Science
SN - 2366-7443
IS - 8
ER -