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Combining Computational Fluid Dynamics and Gradient Boosting Regressor for Predicting Force Distribution on Horizontal Axis Wind Turbine
The blades of the horizontal axis wind turbine (HAWT) are generally subjected to significant forces resulting from the flow field around the blade. These forces are the main contributor of the flow-induced vibrations that pose structural integrity challenges to the blade. The study focuses on the application of the gradient boosting regressor (GBR) for predicting the wind turbine response to a combination of wind speed, angle of attack, and turbulence intensity when the air flows over the rotor blade. In the first step, computational fluid dynamics (CFD) simulations were carried out on a horizontal axis wind turbine to estimate the force distribution on the blade at various wind speeds and the blade’s attack angle. After that, data obtained for two different angles of attack (4◦ and 8◦) from CFD acts as an input dataset for the GBR algorithm, which is trained and tested to obtain the force distribution. An estimated variance score of 0.933 and 0.917 is achieved for 4◦ and 8◦, respectively, thus showing a good agreement with the force distribution obtained from CFD. High prediction accuracy and less time consumption make GBR a suitable alternative for CFD to predict force at various wind velocities for which CFD analysis has not been performed. ; publishedVersion
Combining Computational Fluid Dynamics and Gradient Boosting Regressor for Predicting Force Distribution on Horizontal Axis Wind Turbine
The blades of the horizontal axis wind turbine (HAWT) are generally subjected to significant forces resulting from the flow field around the blade. These forces are the main contributor of the flow-induced vibrations that pose structural integrity challenges to the blade. The study focuses on the application of the gradient boosting regressor (GBR) for predicting the wind turbine response to a combination of wind speed, angle of attack, and turbulence intensity when the air flows over the rotor blade. In the first step, computational fluid dynamics (CFD) simulations were carried out on a horizontal axis wind turbine to estimate the force distribution on the blade at various wind speeds and the blade’s attack angle. After that, data obtained for two different angles of attack (4◦ and 8◦) from CFD acts as an input dataset for the GBR algorithm, which is trained and tested to obtain the force distribution. An estimated variance score of 0.933 and 0.917 is achieved for 4◦ and 8◦, respectively, thus showing a good agreement with the force distribution obtained from CFD. High prediction accuracy and less time consumption make GBR a suitable alternative for CFD to predict force at various wind velocities for which CFD analysis has not been performed. ; publishedVersion
Combining Computational Fluid Dynamics and Gradient Boosting Regressor for Predicting Force Distribution on Horizontal Axis Wind Turbine
Bagalkot, Nikhil (author) / Keprate, Arvind (author) / Orderløkken, Rune (author)
2022-01-24
cristin:1988265
Vibration ; 4 ; 1 ; 248-262
Article (Journal)
Electronic Resource
English
Slime Mould Algorithm Enhanced Gradient Boosting Regressor for Prediction of Slope Stability
| Springer Verlag | 2024