Multi objective optimization of horizontal axis tidal current turbines, using Meta heuristics algorithms

Tahani, Mojtaba; Babayan, Narek; Astaraei, Fatemeh Razi; Moghadam, Ali Mokhtari

doi:10.1016/j.enconman.2015.06.086

Cited by 30 publications

(9 citation statements)

References 37 publications

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“…Therefore, there is an urgent need to develop new energy resources to adapt to future development. [2][3][4] At present, many new concepts and devices have emerged with the rapidly developing of tidal current technology but horizontal axis tidal current turbine is one of the hottest researches. During the last few decades, technologies that directly capture the kinetic energy from tidal-driven marine currents have been developed.…”

Section: Introductionmentioning

confidence: 99%

“…3,[23][24][25][26][27][28][29] Vianna et al 23 designed wind turbine blade geometry based on multi-objective optimization using metaheuristics, and the results showed that the output of the design methodology is feasible for manufacturing. 3,[23][24][25][26][27][28][29] Vianna et al 23 designed wind turbine blade geometry based on multi-objective optimization using metaheuristics, and the results showed that the output of the design methodology is feasible for manufacturing.…”

Section: Introductionmentioning

confidence: 99%

“…1 Tidal current turbine is an effective device which can directly obtain energy from the ocean with less harmful environmental effects. [2][3][4] At present, many new concepts and devices have emerged with the rapidly developing of tidal current technology but horizontal axis tidal current turbine is one of the hottest researches. 5 The design method and theory of tidal current turbine mainly comes from horizontal axis wind turbine.…”

Section: Introductionmentioning

confidence: 99%

“…Multi-objective optimization algorithm is widely used in the design of hydraulic turbine and wind turbine. 3,[23][24][25][26][27][28][29] Vianna et al 23 designed wind turbine blade geometry based on multi-objective optimization using metaheuristics, and the results showed that the output of the design methodology is feasible for manufacturing. Gao et al 30 selected the nondominated sorting genetic algorithm (NSGA-II) to conduct the optimizations, and better optimization results are obtained.…”

Section: Introductionmentioning

confidence: 99%

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Prediction and multi‐objective optimization of tidal current turbines considering cavitation based on GA‐ANN methods

Sun

Fan

et al. 2019

Energy Science & Engineering

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As a new type of energy, tidal current energy is increasingly gaining attention worldwide. However, the tidal current energy development field faces a key technical problem of how to improve the generation efficiency and service life of tidal current generators. In this study, an innovative optimized design method of the horizontal axis tidal turbine was applied to the rotor blade design. To be specific, blade cavitation was considered as a factor for optimization and then was improved using a modern optimization method that combined artificial neural networks and genetic algorithms, solving for a numerical solution. In the optimization program, the annual energy output and the superficial area of the rotor blade served as the optimization goals, and the chord length and the twist angle served as the optimization variables. The cavitation constraint was different from the traditional method in that the judgment criterion of occurrence of cavitation was no longer the minimum pressure coefficient but the maximum stress coefficient. The combination of the artificial neural network and the genetic algorithm not only realized high optimization accuracy but also greatly saved computation time. To verify the validity of the optimization method, a 1 MW turbine was designed, and the optimal solution was chosen from the formed Pareto optimal solution set for modeling, and the new model was compared with the model quoted in the reference. The simulation result shows that the turbine designed using the optimization method not only reduces the surface area of the rotor blade and improves the annual energy output, but also remarkably improves the blades' cavitation resistance. The optimization analysis and comparison using different cavitation judgment criteria shows that the cavitation resistance of blades obtained using the maximum stress coefficient as the judgment criterion is more advantageous.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction and multi‐objective optimization of tidal current turbines considering cavitation based on GA‐ANN methods

Sun

Fan

et al. 2019

Energy Science & Engineering

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show abstract

“…Lam and Chen [14] proposed two equations to predict the wake characteristics based on the fundamental works of ship propeller jet. Tahani et al [15] used the multi objective optimization of the horizontal-axis tidal current turbine to achieve its optimum performance. Lam and Chen [16] also used CFD simulation to investigate the slipstream between marine current turbine and seabed.…”

Section: Introductionmentioning

confidence: 99%