Multi-objective optimization study of wave elimination and electricity generation performance of Savonius hydrokinetic turbine array based on metamodel
Year: 2025
Type: article
Abstract: Deep-sea cages represent a significant approach to harnessing marine energy in China; however, harsh sea conditions characterized by high waves and strong currents pose considerable threats to both farmed fish and aquaculture equipment. With the optimization objectives of enhancing power generation performance and wave dissipation capability, this study introduces the Savonius rotor (referred to as the S-rotor), which offers advantages such as simple structure, excellent self-starting performance, and effective wave and current dissipation. The proposal involves using S-rotor arrays to improve the wave and current dissipation capacity of ship-shaped cages. Sample data were generated through CFD simulations, and a coupled solution was achieved by integrating CFD simulations with the Kriging meta-model and the Nondominated Sorting Genetic Algorithm II (NSGA-II), which were selected via systematic comparison. Two optimization structures were derived using two different decision criteria, resulting in improvements in wave dissipation efficiency and power generation performance by 52.9 % and −16.9 %, and 41.8 % and 20.9 %, respectively, thereby enhancing the overall performance of deep-sea cages. • The efficient optimization scheme of “Kriging + NSGA-II” was determined through a systematic comparison of three metamodeling techniques and three optimization algorithms. • Yielding two sets of optimization results , the overall performance demonstrated a significant improvement. • Investigate the magnitude of each parameter’ s influence on the optimized structure and elucidate the interaction mechanisms among the parameters. • Through flow field analysis of the optimized array, the overlap of high-viscosity regions, high-vorticity regions, and high-efficiency wave elimination regions was discovered.
Source: Ocean Engineering
Authors: Ruiyin Song, Haihan Yu, Congjie Ren, Xi Cui, Fengshen Li, Lei Zhang, Gao Bai, Kai Chen, Da Liu
Institutions: NingboTech University, Zhejiang University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,150
OpenAlex ID: W4416773600
