Hydrokinetic turbine design through performance prediction and hybrid metaheuristic multi-objective optimization

• Published in: Energy conversion and management 2021-06, Vol.238, p.114169, Article 114169
• Main Authors: Cavalari Labigalini, Luan, Salvo, Ricardo de Vasconcelos, Sene de Lima, Rafael, Corrêa da Silva, Rodrigo, de Marchi Neto, Ismael
• Format: Article
• Language: English
• Subjects: Algorithms; BEM; Cavitation; Demand analysis; Design; Electric power demand; Heuristic methods; Hydrokinetic turbine; Layouts; Metaheuristic algorithms; Multi-objective optimization; Multiple objective analysis; Optimization; Performance prediction; Power curve; Renewable resources; Sustainable yield; Turbine performance validation; Turbines; Wind power; Wind turbines
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2021.114169

•Correlations validation from each turbine parameters according to experimental data.•Power and thrust coefficient estimated with an error of 1.40% and 1.78%, respectively.•Hydrokinetic turbine optimization of its power coefficient and blade inertia.•Input variables are rotor diameter, number of blades and rotational speed.•Procedure found a turbine power coefficient’s 17.92% lower than Betz limit. Small wind turbines (SWT) and hydrokinetic turbines (HT) are affordable ways to distribute power generation from renewable resources. In order to better harness such available sources and to design future equipment as efficient as possible, this paper aims to develop a layout optimization. Firstly, the Blade Element Momentum (BEM) method is applied to validate the aerodynamic models from literature with experimental data. It intends to replicate turbine operation and its performance. The power and thrust curves could be well-predicted according to tip-speed ratio (TSR) variation. Secondly, meta-heuristic algorithms based on natural behaviour are used to find the best hydrokinetic turbine design in a hypothetical environment subjected to a single person's electricity demand. Later, an analysis of a higher power supply will also be made. The optimization was carried out concerning power and blade inertia, and the layout parameters used as input were the rotor diameter, the number of blades, and the rotational speed. Cavitation effect was taking into account and tried to be avoided at chord calculation. The most efficient algorithm could find a turbine with a power coefficient 18% lower than the Betz Limit.