Advances in the hydraulic and mechanical optimization of hydrokinetic turbines for renewable energy generation: a systematic literature review following the PRISMA 2020 methodology
DOI:
https://doi.org/10.70577/asce.v5i4.1136Keywords:
Fluid dynamics, Water resource management, Mechanical engineering, Artificial intelligence, Renewable resourcesAbstract
Hydrokinetic turbines have emerged as an attractive option for harnessing the kinetic energy present in rivers, estuaries, and tidal currents, without the need for large-scale hydraulic infrastructure. However, the performance of these systems depends on a complex interaction between hydraulic and mechanical variables. Despite an increase in research in this field, existing knowledge remains fragmented across various disciplines, which hinders an integrated understanding of the current state of the technology and the most effective strategies for enhancing its efficiency. Objective. To analyze recent scientific advancements related to the hydraulic and mechanical optimization of hydrokinetic turbines designed for renewable energy generation. Methodology. A systematic review was conducted following the PRISMA 2020 guidelines. The search was performed across Scopus, Web of Science, ScienceDirect, SpringerLink, IEEE Xplore, MDPI, and Wiley Online Library, including scientific articles published between 2021 and 2026. Results. The evidence showed a trend toward multidisciplinary approaches that integrate mechanical design, hydraulic optimization, and CFD simulation. Key advancements included the geometric optimization of rotors and blades, the use of ducts to improve energy capture, and the extensive application of CFD for the design and validation of turbines. Conclusions. Combining hydraulic and mechanical strategies yields the greatest performance gains in hydrokinetic turbines. CFD has established itself as the dominant analysis tool, while artificial intelligence is emerging as a promising resource for predictive design and advanced optimization; however, there remains a lack of validation under actual operating conditions.
Downloads
References
Ai, K., Wang, M., Wang, D., Wang, H., Cao, R., & Avital, E. (2021). Numerical Study of A Generic Tidal Turbine Using BEM Optimization Methods. China Ocean Engineering, 35(3), 344-351. https://doi.org/10.1007/s13344-021-0032-1 DOI: https://doi.org/10.1007/s13344-021-0032-1
Alhousni, F. K., Nwokolo, S. C., Meyer, E. L., Alsenani, T. R., Alhinai, H. A., Ahia, C. C., Okonkwo, P. C., & Ahmed, Y. E. (2025). Multi-scale computational fluid dynamics and machine learning integration for hydrodynamic optimization of floating photovoltaic systems. Energy Informatics, 8(1), 103. https://doi.org/10.1186/s42162-025-00567-9 DOI: https://doi.org/10.1186/s42162-025-00567-9
Betancur Gómez, J. D., Ardila Marín, J. G., & Chica Arrieta, E. L. (2023). Numerical and experimental performance analysis of a horizontal axis hydrokinetic turbine. Journal of Applied Research and Technology, 21(6), 965-974. https://doi.org/10.22201/icat.24486736e.2023.21.6.1996 DOI: https://doi.org/10.22201/icat.24486736e.2023.21.6.1996
Boretti, A., & Castelletto, S. (2025). Advancements and challenges in tidal stream and oceanic current turbines: An overview of current technologies and future prospects. Marine Development, 3(1), 10. https://doi.org/10.1007/s44312-025-00054-5 DOI: https://doi.org/10.1007/s44312-025-00054-5
Bowman, J., Bhushan, S., Burgreen, G., & Dettwiller, I. (2024). A Machine-Learned Actuator Line Model for Hydrokinetic Turbines. Volume 2: Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC); Flow Visualization, V002T05A028. https://doi.org/10.1115/FEDSM2024-131429 DOI: https://doi.org/10.1115/FEDSM2024-131429
Fekiač, J. J., Krbata, M., Kohutiar, M., Janík, R., Kakošová, L., Breznická, A., Eckert, M., & Mikuš, P. (2025). Comprehensive Review: Optimization of Epoxy Composites, Mechanical Properties, & Technological Trends. Polymers, 17(3), 271. https://doi.org/10.3390/polym17030271 DOI: https://doi.org/10.3390/polym17030271
Gong, X., Ge, W., Yan, J., Zhang, Y., & Gongye, X. (2020). Review on the Development, Control Method and Application Prospect of Brake-by-Wire Actuator. Actuators, 9(1), 15. https://doi.org/10.3390/act9010015 DOI: https://doi.org/10.3390/act9010015
Gu, Y., Zou, T., Liu, H., Lin, Y., Ren, H., & Li, Q. (2024). Status and Challenges of Marine Current Turbines: A Global Review. Journal of Marine Science and Engineering, 12(6), 884. https://doi.org/10.3390/jmse12060884 DOI: https://doi.org/10.3390/jmse12060884
Ileberi, G. R., & Li, P. (2023). Integrating Hydrokinetic Energy into Hybrid Renewable Energy System: Optimal Design and Comparative Analysis. Energies, 16(8), 3403. https://doi.org/10.3390/en16083403 DOI: https://doi.org/10.3390/en16083403
Jiang, B., Amini, M. R., Liao, Y., Martins, J. R. R. A., & Sun, J. (2022). Control Co-Design of a Hydrokinetic Turbine With Open-Loop Optimal Control. Volume 8: Ocean Renewable Energy, V008T09A006. https://doi.org/10.1115/OMAE2022-81006 DOI: https://doi.org/10.1115/OMAE2022-81006
Lakatos, G. D., Albu (Druța), R. M., Rhazzali, A. L., Ferenci, S., Cioca, L. I., Munteanu, R. A., & Szabó, L. (2026). The Role of Hydraulic Turbines in the Energy Transition: A Systematic Review of Methods for Evaluating and Optimizing Hydropower Plant Operation. Processes, 14(5), 841. https://doi.org/10.3390/pr14050841 DOI: https://doi.org/10.3390/pr14050841
Liu, X., Hu, J., Zhang, Y., Yu, H., Shen, W., Xu, Y., & Zheng, J. (2025). Structural Optimization of a Pipeline Savonius Hydro Turbine Based on Broad Learning. Applied Sciences, 15(16), 9181. https://doi.org/10.3390/app15169181 DOI: https://doi.org/10.3390/app15169181
Liu, Z., Zheng, Y., Zhang, Y., & Xu, J. (2025). MLP-Optimized Duct Design for Enhanced Hydrodynamic Performance in Tidal Turbines. Water, 17(18), 2691. https://doi.org/10.3390/w17182691 DOI: https://doi.org/10.3390/w17182691
Nekahi, A., Anil Kumar, M. R., Deng, S., Li, X., Petropoulos, A., Nanda, J., & Zaghib, K. (2025). Toward Green Renewable Energies and Energy Storage for the Sustainable Decarbonization and Electrification of Society. Electrochemical Energy Reviews, 8(1), 12. https://doi.org/10.1007/s41918-025-00247-y DOI: https://doi.org/10.1007/s41918-025-00247-y
Park, J., Knight, B. G., Liao, Y., Mangano, M., Pacini, B., Maki, K. J., Martins, J. R. R. A., Sun, J., & Pan, Y. (2023). CFD-based design optimization of ducted hydrokinetic turbines. Scientific Reports, 13(1), 17968. https://doi.org/10.1038/s41598-023-43724-4 DOI: https://doi.org/10.1038/s41598-023-43724-4
Restrepo-Zapata, K., Betancour-Osorio, J., & Rubio-Clemente, A. (2025). Turbinas hidrocinéticas: Características, principio de funcionamiento e impactos ambientales asociados. 24(4), 91-106.
Salinas-León, C. D., & Ochoa-Correa, D. (2025). Multicriteria analysis for the site selection of hydrokinetic turbines-HKT for renewable energy generation: A case study in Ecuador. Renewable Energies, 3(1), 27533735251340200. https://doi.org/10.1177/27533735251340200 DOI: https://doi.org/10.1177/27533735251340200
Song, K., Yao, J., Huan, H., Wei, L., & Liu, Q. (2026). Research Progress of Archimedes Spiral Hydrokinetic Turbines in Free-Flow Conditions: A Comprehensive Review. Journal of Marine Science and Engineering, 14(15), 1449. https://doi.org/10.3390/jmse14151449 DOI: https://doi.org/10.3390/jmse14151449
Velásquez, L., Rengifo, J., Saldarriaga, A., Rubio-Clemente, A., & Chica, E. (2025). Optimization of Vertical-Axis Hydrokinetic Turbines: Study of Various Geometric Configurations Using the Response Surface Methodology and Multi-Criteria Decision Matrices. Processes, 13(7), 1950. https://doi.org/10.3390/pr13071950 DOI: https://doi.org/10.3390/pr13071950
Yang, R., Cui, T., Chen, Z., Ma, S., Ping, H., Wei, F., Gao, Z., Lu, G., Liu, H., & Zhang, L. (2026). Review on Dynamic Inflow Sensing Layout Optimization for Large-Scale Wind Farms: Wake Modeling, Data-Driven Prediction, and Multi-Objective Uncertainty Optimization. Energies, 19(3), 810. https://doi.org/10.3390/en19030810 DOI: https://doi.org/10.3390/en19030810
Zemanová, L., & Rudolf, P. (2020). Flow Inside the Sidewall Gaps of Hydraulic Machines: A Review. Energies, 13(24), 6617. https://doi.org/10.3390/en13246617 DOI: https://doi.org/10.3390/en13246617
Zhong, S., Jin, G., Chen, Y., Ye, T., & Zhou, T. (2025). Review of Vibration Analysis and Structural Optimization Research for Rotating Blades. Journal of Marine Science and Application, 24(1), 120-136. https://doi.org/10.1007/s11804-024-00405-6 DOI: https://doi.org/10.1007/s11804-024-00405-6
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Juan Francisco Durango Grisales, Ramón Gabriel Aguilar Vega, Juan Carlos Cerón Vinueza, Vily Dick Mamani Osco, Franklin César Ramírez Baquerizo
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Eres libre de:
- Compartir : copiar y redistribuir el material en cualquier medio o formato
- Adaptar : remezclar, transformar y desarrollar el material
- El licenciante no puede revocar estas libertades siempre y cuando usted cumpla con los términos de la licencia.
En los siguientes términos:
- Atribución : Debe otorgar el crédito correspondiente , proporcionar un enlace a la licencia e indicar si se realizaron cambios . Puede hacerlo de cualquier manera razonable, pero no de ninguna manera que sugiera que el licenciante lo respalda a usted o a su uso.
- No comercial : no puede utilizar el material con fines comerciales .
- CompartirIgual — Si remezcla, transforma o construye sobre el material, debe distribuir sus contribuciones bajo la misma licencia que el original.
- Sin restricciones adicionales : no puede aplicar términos legales ni medidas tecnológicas que restrinjan legalmente a otros hacer algo que la licencia permite.
