Optimization Method for the Hydrodynamic Performance of Tidal Turbines Considering Blockage Effect

Abstract

As crucial components in the advancement of tidal energy, tidal turbines are progressively transitioning from large standalone units to multi-unit arrays. In the context of tidal arrays, the blockage effect has become a key factor that necessitates careful consideration due to its substantial impact on turbine performance. This paper introduces an innovative optimization methodology aimed at improving the hydrodynamic performance of tidal turbines while specifically addressing the environmental blockage effect. The proposed approach integrates the blade element momentum—computational fluid dynamics hydrodynamic model, radial basis function neural networks, and the nondominated sorting genetic algorithm III optimization algorithm. A case study is used to demonstrate this methodology, focusing on the optimization of a tidal turbine under varying blockage intensity levels. Comparative analyses of optimal turbine configurations indicate that strategic modifications in design parameters can substantially improve hydrodynamic performance in response to varying blockage intensity. Specifically, increasing the root chord length and decreasing the tip chord length effectively reduce the turbine’s load while maintaining operational efficiency as blockage intensity increases. Conversely, the blade pitch angle exhibits minimal sensitivity to the blockage effect. Moreover, the optimized turbines demonstrate lower velocity deficits in their near-field wake compared to the prototype. The case study not only validates the effectiveness of the proposed optimization method but also highlights the substantial impact of blockage effects on optimal turbine configurations. This research provides valuable theoretical insights for designing turbines intended for deployment in tidal arrays.

Keywords

Tidal current turbine / Blade optimization / Blockage effect / BEM—CFD / RBF neural networks / NSGA-III

References

[1] BadshahM, VanZwietenJ, BadshahS, JanS. CFD study of blockage ratio and boundary proximity effects on the performance of a tidal turbine. Iet Renewable Power Generation, 2019, 13(5): 744-749

[2] BahajAS, MollandAF, ChaplinJR, BattenWMJ. Power and thrust measurements of marine current turbines under various hydrodynamic flow conditions in a cavitation tunnel and a towing tank. Renewable Energy, 2007, 32(3): 407-426

[3] ChenJH, WangXC, LiH, JiangCH, BaoLJ. Design of the blade under low flow velocity for horizontal axis tidal current turbine. Journal of Marine Science and Engineering, 2020, 8(12): 1-13

[4] DebK, JainH. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 2014, 18(4): 577-601

[5] EdenhoferO, MadrugaRP, SokonaY. Renewable energy sources and climate change mitigation. Intergovernmental Panel on Climate Change, Geneva, Switzerland, 2011

[6] HeltonJC, DavisFJ. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliability Engineering & System Safety, 2003, 81(1): 23-69

[7] KimSJ, SinghPM, ChoiYD. Hydrofoil selection and design of a 50W class horizontal axis tidal current turbine model. Journal of the Korean Society of Marine Engineering, 2015, 39(8): 856-862

[8] KohWXM, NgEYK. A CFD study on the performance of a tidal turbine under various flow and blockage conditions. Renewable Energy, 2017, 107: 124-137

[9] KolekarN, BanerjeeA. Performance characterization and placement of a marine hydrokinetic turbine in a tidal channel under boundary proximity and blockage effects. Applied Energy, 2015, 148: 121-133

[10] LaurensJM, Ait-MohammedM, TarfaouiM. Design of bare and ducted axial marine current turbines. Renewable Energy, 2016, 89: 181-187

[11] LiG, ZhuWD. Tidal current energy harvesting technologies: A review of current status and life cycle assessment. Renewable & Sustainable Energy Reviews, 2023, 179: 113269

[12] LiZQ, LiGN, DuL, GuoHP, YuanWX. Optimal design of horizontal axis tidal current turbine blade. Ocean Engineering, 2023, 271: 1-19

[13] LiuHW, ZhouHB, LinYG, LiW, GuHG. Design and test of 1/5th scale horizontal axis tidal current turbine. China Ocean Engineering, 2016, 30(3): 407-420

[14] MalkiR, WilliamsAJ, CroftTN, TogneriM, MastersI. A coupled blade element momentum-Computational fluid dynamics model for evaluating tidal stream turbine performance. Applied Mathematical Modelling, 2013, 37(5): 3006-3020

[15] MastersI, MalkiR, WilliamsAJ, CroftTN. The influence of flow acceleration on tidal stream turbine wake dynamics: A numerical study using a coupled BEM-CFD model. Applied Mathematical Modelling, 2013, 37(16–17): 7905-7918

[16] MoforL, GoldsmithJ, JonesF Ocean energy technology readiness, patents, deployment status and outlook, 2014 Abu Dhabi, The United Arab Emirates International Renewable Energy Agency

[17] OlczakA, StallardT, FengT, StansbyPK. Comparison of a RANS blade element model for tidal turbine arrays with laboratory scale measurements of wake velocity and rotor thrust. Journal of Fluids and Structures, 2016, 64: 87-106

[18] PerezL, CossuR, GrinhamA, PenesisI. An investigation of tidal turbine performance and loads under various turbulence conditions using Blade Element Momentum theory and highfrequency field data acquired in two prospective tidal energy sites in Australia. Renewable Energy, 2022, 201: 928-937

[19] PerezL, CossuR, GrinhamA, PenesisI. Tidal turbine performance and loads for various hub heights and wave conditions using high-frequency field measurements and Blade Element Momentum theory. Renewable Energy, 2022, 200: 1548-1560

[20] SchluntzJ, WilldenRHJ. The effect of blockage on tidal turbine rotor design and performance. Renewable Energy, 2015, 81: 432-441

[21] SunZ, LiD, MaoY, FengL, ZhangY, LiuC. Anti-cavitation optimal design and experimental research on tidal turbines based on improved inverse BEM. Energy, 2022, 239: 1-20

[22] VogelCR, WilldenRHJ. Improving tidal turbine performance through multi-rotor fence configurations. Journal of Marine Science and Application, 2019, 18(1): 17-25

[23] WangS, TangJ, LiC, JiR, Fernandez-RodriguezE. Performance evaluation and fast prediction of a pitched horizontalaxis tidal turbine under wave-current conditions using a variablespeed control strategy. Journal of Fluids and Structures, 2025, 133: 104265

[24] WangXC, LiH, ChenJH, JiangCH, BaoLJ. Research on solidity of horizontal-axis tidal current turbine. Energies, 2023, 16(8): 1-17

[25] YiJ, LiXY, XiaoM, XuJN, ZhangL. Construction of nested maximin designs based on successive local enumeration and modified novel global harmony search algorithm. Engineering Optimization, 2017, 49(1): 161-180

[26] ZhuFW, DingL, HuangB, BaoM, LiuJT. Blade design and optimization of a horizontal axis tidal turbine. Ocean Engineering, 2020, 195: 1-10

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Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature