Savonius wind turbine blade design and performance evaluation using ANN-based virtual clone: A new approach
Abdullah Al Noman, Zinat Tasneem, Sarafat Hussain Abhi, Faisal R. Badal, Md Rafsanzane, Md. Robiul Islam, Firoz Alam
Abstract
The drag based Savonius wind turbine (SWT) has shown immense potential for renewable power generation in built-up areas under complex urban wind conditions. While a series of studies have been conducted on improving SWT's efficiency, optimal performance has yet to be achieved using traditional design approaches such as experimental and/or computational fluid dynamics methods. Recently, artificial intelligence and machine learning have been widely used in design optimization. As such, an ANN-based virtual clone can be an alternative to traditional design methods for wind turbine performance determination. Therefore, the main goal of this study is to investigate whether ANN-based virtual clones are capable of determining the performance of SWTs with a shorter timeframe and minimal resources compared to traditional methods. To achieve the objective, an ANN-based virtual clone model is developed. Two sets of data (computational and experimental) are used to validate and determine the efficacy of the proposed ANN-based virtual clone model. Using experimental data, the model's fidelity is over 98%. The proposed model produces results in one-fifth the time of the existing simulation (based on the combined ANN + GA metamodel) method. The model also reveals the location of the dataset's optimized point for augmenting the turbine's performance.