CFD-based surrogate modelling of urban wind farms using artificial neural networks: double rotor arrangements
| Autor: | Abdolrahim Rezaeiha, Hamid Montazeri, Sadra Sahebzadeh |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Journal of Physics: Conference Series. 2042:012093 |
| ISSN: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/2042/1/012093 |
| Popis: | Extensive characterization studies are required to identify optimal wind farm layouts and achieve high power density, i.e., power per land area. Performing such studies using experimental or high-fidelity numerical methods can be timely and computationally expensive. To alleviate this obstacle, surrogate models can be developed to mimic the behavior of the simulation/experiment. In this paper, a shallow feed-forward artificial neural network (ANN) surrogate model is developed. The Levenberg-Marquardt algorithm is used to train a model with 3 layers and 10 hidden nodes. The model correlates the arrangement of a double-rotor vertical axis wind turbine array, as the fundamental generating cell of the wind farm, with its overall power performance. The inputs are the relative distance (R) and angle (®) between the rotors, and the output is the overall power coefficient of the array. In total, 96 CFD-simulated arrangements are used as data points to train, validate and test the model. The trained model has a mean square error of 2.10 × 10-5 and R-squared of 0.99, indicating its accuracy and generalizability. The average and maximum errors are 3% and 10%, respectively. The employed method can be expanded to accommodate more rotors towards optimal urban wind farm layout design. |
| Databáze: | OpenAIRE |
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