Uncertainty quantification analysis in the blade element momentum method

Blade element momentum (BEM) theory is a widely used non-linear model for the efficient evaluation of wind turbine performance and design. The aim of this paper is to quantify the uncertainty related to BEM inputs and sub-models, and investigate how these propagate through the model. Uncertainties associated with viscous dissipation in the wake, aerofoil force coefficients, and tip-loss models are considered. The uncertainty quantification (UQ) of these parameters is analysed using non-intrusive polynomial chaos expansion, which provides a structured method for uncertainty propagation and global sensitivity quantification. Sobol’s indices are employed to rank the relative importance of each factor to the overall uncertainty in the system, with a focus on rotor performance and spanwise load distributions. Two BEM implementations with and without tip-loss correction are used to simulate the NREL 5 MW and DTU 10 MW reference wind turbines. Global sensitivity quantification shows that the different rotors may exhibit different levels of sensitivity to input parameters. The effect of viscous mixing in the turbine wake is found to have a significant impact on predicted rotor performance. Uncertainty in tip-loss model coefficients is also found to be generally important, particularly when evaluating spanwise variations in rotor loads. The Sobol’s indices are also observed to depend on the tip speed ratio (TSR), with the most significant uncertainty factors converging for T S R ≥ 6 . We additionally compare global sensitivity analysis to local analysis based on partial derivatives of the uncertainty parameters. The non-linear nature of BEM means that a local analysis does not always capture the interactions between different factors, potentially leading to misleading evaluations of parameter importance. UQ has the potential to improve the understanding of BEM, and provide guidance on the importance of sub-model improvements. • This paper quantifies the uncertainty for the Blade Element Momentum (BEM) theory. • Uncertainty quantification (UQ) is implemented using polynomial chaos expansion. • Global sensitivity quantification determines the significant uncertainty parameters. • Different rotors at different tip speed ratios show different levels of sensitivity. • Local analysis potentially leads to misleading evaluations of parameter importance.

Authors

Institutions

Publication Details

Journal
Renewable Energy
Published
2025-05-01
DOI
https://doi.org/10.1016/j.renene.2025.123181
Primary Topic
Probabilistic and Robust Engineering Design
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Uncertainty quantification analysis in the blade element momentum method

Zheni Fei, Christopher Vogel, Guiyu Cao
Renewable Energy
Probabilistic and Robust Engineering Design
article

Uncertainty quantification analysis in the blade element momentum method

Zheni Fei, Christopher Vogel, Guiyu Cao
article en

Abstract

Blade element momentum (BEM) theory is a widely used non-linear model for the efficient evaluation of wind turbine performance and design. The aim of this paper is to quantify the uncertainty related to BEM inputs and sub-models, and investigate how these propagate through the model. Uncertainties associated with viscous dissipation in the wake, aerofoil force coefficients, and tip-loss models are considered. The uncertainty quantification (UQ) of these parameters is analysed using non-intrusive polynomial chaos expansion, which provides a structured method for uncertainty propagation and global sensitivity quantification. Sobol’s indices are employed to rank the relative importance of each factor to the overall uncertainty in the system, with a focus on rotor performance and spanwise load distributions. Two BEM implementations with and without tip-loss correction are used to simulate the NREL 5 MW and DTU 10 MW reference wind turbines. Global sensitivity quantification shows that the different rotors may exhibit different levels of sensitivity to input parameters. The effect of viscous mixing in the turbine wake is found to have a significant impact on predicted rotor performance. Uncertainty in tip-loss model coefficients is also found to be generally important, particularly when evaluating spanwise variations in rotor loads. The Sobol’s indices are also observed to depend on the tip speed ratio (TSR), with the most significant uncertainty factors converging for T S R ≥ 6 . We additionally compare global sensitivity analysis to local analysis based on partial derivatives of the uncertainty parameters. The non-linear nature of BEM means that a local analysis does not always capture the interactions between different factors, potentially leading to misleading evaluations of parameter importance. UQ has the potential to improve the understanding of BEM, and provide guidance on the importance of sub-model improvements. • This paper quantifies the uncertainty for the Blade Element Momentum (BEM) theory. • Uncertainty quantification (UQ) is implemented using polynomial chaos expansion. • Global sensitivity quantification determines the significant uncertainty parameters. • Different rotors at different tip speed ratios show different levels of sensitivity. • Local analysis potentially leads to misleading evaluations of parameter importance.

Renewable EnergyVol. 250
University of Oxford (GB)
UK Research and Innovation
Climate action
Openalex Percentile: Top 91%
Probabilistic and Robust Engineering Design
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.