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.
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