Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake
| Autor: | Shantanu Purohit, Ijaz Fazil Syed Ahmed Kabir, Eddie Y. K. Ng |
|---|---|
| Přispěvatelé: | School of Mechanical and Aerospace Engineering |
| Rok vydání: | 2022 |
| Předmět: |
Turbulence Intensity
Artificial neural network Series (mathematics) Renewable Energy Sustainability and the Environment Computer science business.industry Phase (waves) Computational fluid dynamics Wake Machine learning computer.software_genre Turbine Physics::Fluid Dynamics Support vector machine Wake Velocity Turbulence kinetic energy Mechanical engineering [Engineering] Artificial intelligence business computer Algorithm |
| Zdroj: | Renewable Energy. 184:405-420 |
| ISSN: | 0960-1481 |
| Popis: | In this paper, three machine learning (ML) algorithms, Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Extreme Gradient Boosting (XGBoost), are validated to estimate the velocity and turbulence intensity of a wind turbine's wake at distinct downstream distances. To this end, a series of high-fidelity numerical simulations for the NREL Phase VI wind turbine is carried out to generate training and test datasets for the three machine learning algorithms. The predicted wake velocity and turbulence intensity from the ML models are also contrasted with significant existing analytical wake models. Machine learning algorithms estimate velocity and turbulence intensity in the wake in a way commensurate to the Computational Fluid Dynamics (CFD) simulations while running at a similar pace as low-fidelity wake models. The results demonstrate that machine learning-based algorithms can predict velocity and turbulence intensity better with higher precision than the traditional analytical wake models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect