Development of a novel wind to electrical energy converter of passive ferrofluid levitation through its parameter modelling and optimization. (December 2021)

• Record Type: Journal Article
• Authors: Pathak, Saurabh; Zhang, Ran; Bun, Khemrith; Zhang, Hui; Gayen, Bishakhdatta; Wang, Xu
• Is Part Of: Sustainable energy technologies and assessments. Volume 48(2021)
• Publication Date: 2021-12
• Subjects: Wind energy harvester -- Design optimization -- Central composite design -- Response surface method modelling -- Genetic algorithm -- Artificial neural network; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2021.101641
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: The exclusive properties of passive levitation and ultra-low friction surface formation of ferrofluids have been utilized to design a highly efficient wind energy harvester. A novel design of passive ferrofluid levitation wind energy converter has been proposed and laboratory prototype has been developed. The simulation model has been developed using the ANSYS Workbench Maxwell electromagnetic software and validated by the experimental results. In this paper, different approaches such as response surface method (RSM), central composite design (CCD) and artificial neural network (ANN) modelling have been applied to relate the system output power to the input design parameters of the wind energy harvester system. The sensitivity analysis of design parameters and their interactions have been conducted. The CCD and RSM modelling have been applied for predicting the power output from the design parameters for the energy harvester where the design parameters has been optimized for the maximum power output using the genetic algorithm (GA). Also, the RSM modelling is validated by the analysis of variance. As a result, the optimal design parameter combination to produce the highest power output can be identified. The optimization result will be compared with and verified by the prediction result of the ANN model. It is found that within the range of the design parameters, the maximum power output of the wind energy harvester obtained through the RSM with the GA is close to that predicted by the ANN model. The design optimization method can be extended to develop an upscale model of the wind energy harvester for larger power output. The proposed magnetic levitation technology has many more potential applications in sensors and actuators, cooling and mechanical bearings and in health sectors.