Accurate forecasts of ocean waves energy can not only reduce costs for investment, but it is also essential for the management and operation of electrical power. This paper presents an innovative approach based on long short-term memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analysis is provided by collecting the experimental data from another study and the exerted data from a numerical simulation of Searaser. The simulation is performed with Flow-3D software, which has high capability in analyzing fluid–solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study, wind speed and output power are related with an LSTM method. Moreover, it can be inferred that the LSTM network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement, and the root mean square is 0.49 in the mean value related to the accuracy of the LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of the LSTM method.

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.

No abstract

The new types of bladeless wind turbines and generating electricity using them is one of the most interesting topics for engineering nowadays. Electricity generation using the structural vibration due the resonance phenomenon is the concept behind a vortex bladeless turbine. The present study numerically investigates the effects of the drag force on the body frequency of an oscillating bladeless wind turbine. A 2-D numerical simulation was performed for a cylinder with a semi-circular cross-section in cross-flow in two different cases. The research was conducted for both uncontrolled and controlled oscillating cylinders. The controlling process was performed using a pair of ring magnets as a spring with a variable coefficient. The flow field, vibration, vortex shedding and structural frequencies, and the resonance phenomenon are studied in this research. Finally, the controlled and uncontrolled frequencies of the cylinder are explored, and the power spectrum for various velocities is analyzed in two different states, namely with and without a tuning system. From the results, it can be concluded that the usage of the controlling system in these turbines can greatly regulate the oscillations and increase the frequency value by limiting the vibration amplitude. According to this principle, it can be inferred that increasing the frequency of fluctuations greatly increases the production capacity of these turbines.