[01477] Optimization of a submerged piezoelectric device using an ANN Model
Session Time & Room : 3D (Aug.23, 15:30-17:10) @E811
Type : Contributed Talk
Abstract : The design of a submerged piezoelectric wave energy converter (PWEC) device has been analyzed to optimize the power generated by the PWEC device. An artificial neural network (ANN) is adopted to optimize the geometric parameters of the device. First, a numerical model is introduced using the boundary element methodology (BEM). The input database for the modeling of the ANN model is generated using the Latin Hypercube Sampling method, and the output database for the modeling of the ANN model is simulated using the numerical model based on BEM. Four hundred samples are used to model the ANN with data taken in a 70:30 ratio for training and validation of the model. The prediction of the optimal parameter values for the design of the PWEC device is carried out using a database containing 3000 sample points generated randomly using the LHS method. The developed ANN model shows a good agreement between the training accuracy and the validation accuracy. Also, the model forecast provides a range for the geometric parameters of the PWEC device to optimize power generation.
