A pareto optimal front of fluidic diode for a wave energy harnessing device. (15th September 2022)

• Record Type: Journal Article
• Authors: Hithaish, Doddamani; Siddique, M. Hamid; Samad, Abdus
• Is Part Of: Ocean engineering. Volume 260(2022)
• Publication Date: 2022-09-15
• Subjects: Wave energy -- Fluidic diode -- Oscillating water column -- Multi-objective optimization -- Artificial neural network -- Pareto optimal front; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2022.111821
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: A twin-turbine or turbine duo (TUD) is constructed from a pair of unidirectional turbines. Flow reversal hampers the performance of these units. A Fluidic diode (FD) that offers a variable resistance to the flow can be used with TUD to prevent flow reversal, and its performance is governed by diodicity. With the increase in diodicity, flow blockage improves, but the resistance across the turbines increases too. As these turbines operate under a smaller pressure drop, the FD used with them should have higher diodicity with lesser fluid resistance across them. This study presents a multi-objective shape optimization of an FD to maximize diodicity and minimize pressure drop. The shape optimization is performed by simultaneously varying six design parameters. The performance of FD was evaluated by solving the 3D Reynolds Averaged Navier-Stokes equations. The popular evolutionary search algorithm (NSGA-II) with an artificial neural network produced a set of non-dominated optimal solutions (Pareto optimal set). Compared to the base model, the optimal design set shows improved diodicity from 17.2 to 21.57% and pressure drop from −2.535% to 78.67%, respectively. Further, the flow analyses of optimal designs show that the nozzle angle and the toroidal cup radius affect more than the other variables. Highlights: A fluidic diode for the wave energy converter is optimized numerically. Pareto optimum designs were produced using an evolutionary algorithm (NSGA-II) to obtain higher diodicity and lower pressure drop across the fluidic diode. An increase in the geometrical dimensions of the nozzle angle and toroidal cup radius affected the performance of the fluidic diode in both flow directions. The overall efficiency of the twin turbines improved with optimized fluidic diode models.