An Experimental Study to Characterize the Effects of Ice Accretion on the Aerodynamic Performance of an Offshore Wind Turbine Blade Model

Sista, Harsha; Wang, Jincheng; Hu, Haiyang; Hu, Hui

doi:10.2514/6.2023-3524

AIAA AVIATION 2023 Forum 2023

DOI: 10.2514/6.2023-3524

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An Experimental Study to Characterize the Effects of Ice Accretion on the Aerodynamic Performance of an Offshore Wind Turbine Blade Model

Harsha Sista

Jincheng Wang

Haiyang Hu

et al.

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2024

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Cited by 1 publication

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An XAI Framework for Predicting Wind Turbine Power under Rainy Conditions Developed Using CFD Simulations

Syed Ahmed Kabir,

Gajendran,

Taslim

et al. 2024

Atmosphere

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Renewable energy sources are essential to address climate change, fossil fuel depletion, and stringent environmental regulations in the subsequent decades. Horizontal-axis wind turbines (HAWTs) are particularly suited to meet this demand. However, their efficiency is affected by environmental factors because they operate in open areas. Adverse weather conditions like rain reduce their aerodynamic performance. This study investigates wind turbine power prediction under rainy conditions by integrating Blade Element Momentum (BEM) theory with explainable artificial intelligence (XAI). The S809 airfoil’s aerodynamic characteristics, used in NREL wind turbines, were analyzed using ANSYS FLUENT and symbolic regression under varying rain intensities. Simulations at a Reynolds number (Re) of 1 × 106 were performed using the Discrete Phase Model (DPM) and k–ω SST turbulence model, with liquid water content (LWC) values of 0 (dry), 10, 25, and 39 g/m3. The lift and drag coefficients were calculated at various angles of attack for all the conditions. The results indicated that rain led to reduced lift and increased drag. The innovative aspect of this research is the development of machine learning models predicting changes in the airfoil coefficients under rain with an R2 value of 0.97. The proposed XAI framework models rain effects at a lower computational time, enabling efficient wind farm performance assessment in rainy conditions compared to conventional CFD simulations. It was found that a heavy rain LWC of 39 g/m3 could reduce power output by 5.7% to 7%. These findings highlight the impact of rain on aerodynamic performance and the importance of advanced predictive models for optimizing renewable energy generation.

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An XAI Framework for Predicting Wind Turbine Power under Rainy Conditions Developed Using CFD Simulations

Syed Ahmed Kabir,

Gajendran,

Taslim

et al. 2024

Atmosphere

View full text Add to dashboard Cite

show abstract

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An Experimental Study to Characterize the Effects of Ice Accretion on the Aerodynamic Performance of an Offshore Wind Turbine Blade Model

Cited by 1 publication

References 17 publications

An XAI Framework for Predicting Wind Turbine Power under Rainy Conditions Developed Using CFD Simulations

An XAI Framework for Predicting Wind Turbine Power under Rainy Conditions Developed Using CFD Simulations

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