A Fast Simulation Method for Wind Turbine Blade Icing Integrating Physical Simulation and Statistical Analysis

Simulating wind turbine blade icing quickly is important for wind farms to issue early warnings and effectively deal with the adverse effects of cold weather. However, current numerical simulation methods suffer from high computational costs and lack straightforward acceleration techniques for practical ice prediction. Here, we developed a fast and simple blade icing simulation method via an integrated physical simulation and statistical analysis method. This method consists of two steps: firstly, numerical simulation with CFD, and secondly, table look-up calculations. Over 10,000 sets of wind turbine blade icing simulations based on FENSAP-ICE and an NACA64-A17 wing were conducted to develop this method and analyze the influences of environmental factors on blade icing. The results show that ice thickness generally increases with an increase in wind speed, a decrease in temperature, and an increase in liquid water content (LWC), but there is a nonlinear relationship between them. For example, ice thickness has a linear relationship with the LWC within a certain range but hardly changes with a LWC beyond that range. The validation results show that the fast simulation method established in this paper has good consistency with the original numerical simulation method. It can greatly improve the computational efficiency of icing simulations while retaining the accuracy of numerical simulations. It takes less than 1 s to complete over 1000 sets of icing simulations, which offers potential for the fast prediction of wind turbine blade icing in the future.

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Publication Details

Journal
Energies
Published
2024-11-20
DOI
https://doi.org/10.3390/en17225785
Citations
6
Primary Topic
Icing and De-icing Technologies
Type
article
Field-Weighted Citation Impact
7.79
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article

A Fast Simulation Method for Wind Turbine Blade Icing Integrating Physical Simulation and Statistical Analysis

Long Ma, Jinbo Wu, Ming Qin, Wei Yong Jiang et al.
6 citations
Energies
Icing and De-icing Technologies
7.79
article

A Fast Simulation Method for Wind Turbine Blade Icing Integrating Physical Simulation and Statistical Analysis

Long Ma, Jinbo Wu, Ming Qin, Wei Yong Jiang, Jun Guo, Guohan Zhao, Renqiang Wen
article en
6 citations

Abstract

Simulating wind turbine blade icing quickly is important for wind farms to issue early warnings and effectively deal with the adverse effects of cold weather. However, current numerical simulation methods suffer from high computational costs and lack straightforward acceleration techniques for practical ice prediction. Here, we developed a fast and simple blade icing simulation method via an integrated physical simulation and statistical analysis method. This method consists of two steps: firstly, numerical simulation with CFD, and secondly, table look-up calculations. Over 10,000 sets of wind turbine blade icing simulations based on FENSAP-ICE and an NACA64-A17 wing were conducted to develop this method and analyze the influences of environmental factors on blade icing. The results show that ice thickness generally increases with an increase in wind speed, a decrease in temperature, and an increase in liquid water content (LWC), but there is a nonlinear relationship between them. For example, ice thickness has a linear relationship with the LWC within a certain range but hardly changes with a LWC beyond that range. The validation results show that the fast simulation method established in this paper has good consistency with the original numerical simulation method. It can greatly improve the computational efficiency of icing simulations while retaining the accuracy of numerical simulations. It takes less than 1 s to complete over 1000 sets of icing simulations, which offers potential for the fast prediction of wind turbine blade icing in the future.

EnergiesVol. 17(22)
China Three Gorges Corporation (China) (CN), Huazhong University of Science and Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 3%
Icing and De-icing Technologies
7.79
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