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Exploded view of bladeless wind turbine mechanism

Exploded view of bladeless wind turbine mechanism

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A bladeless wind turbine utilizes vortex formation to extract energy from the wind. Vortex formation are small swirls of air which occur as a result of the geometric shape of the device. This study designed a bladeless wind turbine which incorporates a structural support at a distance offset from the center axis of the cylindrical mast. Springs wer...

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... Where airborne devices like wings [7], kites [8], or aerial vehicles [9] are connected through tethers to ground control units to produce energy through the tension produced by the airborne device to the tether. Other innovations include producing energy through bladeless wind turbines which utilize the vortex formation for wind energy production [10], [11]. ...
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... This type of turbine does not have the problems of the previous generation turbines, so to optimize VBT [7], many researchers around the world have started experimental and research studies. Antony and Boucher [8] have studied the effective parameters on the output power of the bladeless turbine. They showed that for higher and lower wind speeds, the turbine vibration parameters are constant. ...
Preprint
Energy harvesting from wind turbines has been explored by researchers for more than a century from conventional turbines up to the latest bladeless turbines. Amongst these bladeless turbines , vortex bladeless wind turbine (VBT) harvests energy from oscillation of a turbine body. Due to the novelty of this science and the widespread researches around the world, one of the most important issues is to optimize and predict produced power. To enhance the produced output electrical power of VBT, the fluid-solid interactions (FSI) were analyzed to collect a dataset for predicting procedure. Long short-term memory (LSTM) method has been used to predict the produced power of VBT from the collected data. The reason of choosing LSTM from various artificial neural network methods is that the parameters of VBT study are all time-dependent and the LSTM is one of the most accruable algorithms for predicting time series data. In order to find the relationship between the parameter and the variables used in this research, a correlation matrix was presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and its prediction shows that the LSTM method is very accurate for these types of research. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of 2 and a half hours to two minutes. Also, one of the most important achievements of this study is to suggest a mathematical relation of VBT output power which helps to extend it in a different size of VBT with a high range of parameter variations.
... This type of turbine does not have the problems of the previous generation turbines, so to optimize VBT [7], many researchers around the world have started experimental and research studies. Antony and Boucher [8] have studied the effective parameters on the output power of the bladeless turbine. They showed that for higher and lower wind speeds, the turbine vibration parameters are constant. ...
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From conventional turbines to cutting-edge bladeless turbines, energy harvesting from wind has been well explored by researchers for more than a century. The vortex bladeless wind turbine (VBT) is considered an advanced design that alternatively harvests energy from oscillation. This research investigates enhancing the output electrical power of VBT through simulation of the fluid-solid interactions (FSI), leading to a comprehensive dataset for predicting procedure and optimal design. Hence, the long short-term memory (LSTM) method, due to its time-series prediction accuracy, is proposed to model the power of VBT from the collected data. To find the relationship between the parameters and the variables used in this research, a correlation matrix is further presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and their predictions indicates that the LSTM method is suitable for modeling. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of two and a half hours to two minutes. In addition, one of the most important achievements of this study is to suggest a mathematical relation of output power, which helps to extend it in different sizes of VBT with a high range of parameter variations.
... Examples of airborne wind energy (AWE) are the kite wind power systems (Dief, et al. 2018) (Rushdi, et al. 2020), tethered wing power systems (Kakavand and Nikoobin 2021), and airborne vehicles (Makani n.d.). Some other trends also include bladeless wind turbines (Adeyanju and Boucher 2020) (Francis, Umesh, and Shivakumar 2021). However, wind turbines are still the most common and most installed wind power systems. ...
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