Técnicas actuales de optimización de generación de energía en hidroturbinas: una revisión
DOI:
https://doi.org/10.24850/j-tyca-2025-05-10Palabras clave:
entropía local, hidroturbinas, metaheurísticas, simulación, optimizaciónResumen
Se abordan las técnicas de optimización para la generación de energía en hidroturbinas con un enfoque en algunos de los métodos metaheurísticos y el método de generación de entropía local. Entre los métodos metaheurísticos se incluyen los que se basan en las aplicaciones del algoritmo genético, el enjambre de partículas y recocido simulado, entre otros. Se discuten las ventajas y desventajas de cada método, y se analiza su rendimiento en diferentes estudios en contraste con el método de generación de entropía local con el objetivo de determinar cuál método resulta el más apropiado para su utilización en una metodología de diseño.
Citas
Abd-Elaziz, M., Elsheikh, A. H., Oliva, D., Abualigah, L., Lu, S., & Ewees, A. A. (2021). Advanced Metaheuristic Techniques for Mechanical Design Problems: Review. Archives of Computational Methods in Engineering, 29(1), 695-716. DOI: 10.1007/s11831-021-09589-4
Amine, K. (2019). Multiobjective simulated annealing: Principles and algorithm variants. Advances in Operations Research. DOI: 10.1155/2019/8134674
Aponte, R. D., Teran, L. A., Grande, J. F., Coronado, J. J., Ladino, J. A., Larrahondo, F. J., & Rodríguez, S. A. (2020). Minimizing erosive wear through a CFD multi-objective optimization methodology for different operating points of a Francis turbine. Renewable Energy, 145, 2217-2232. DOI: 10.1016/j.renene.2019.07.116
Arabnia, M., & Ghaly, W. (2010). A strategy for multi-point shape optimization of turbine stages in three-dimensional flow. In: Proceedings of the ASME Turbo Expo, 7(PART A) (pp. 489-502). DOI: 10.1115/GT2009-59708
Ardizzon, G., Cavazzini, G., & Pavesi, G. (2015). Adaptive acceleration coefficients for a new search diversification strategy in particle swarm optimization algorithms. Information Sciences, 299, 337-378. DOI: 10.1016/j.ins.2014.12.024
Arnone, A., Bonaiuti, D., Focacci, A., Pacciani, R., Scotti-Del-Greco, A., & Spano, E. (2008). Parametric optimization of a high-lift turbine vane. In: Proceedings of the ASME Turbo Expo 2004, 5 B (pp. 1469-1479). DOI: 10.1115/GT2004-54308
Barricelli, N. A. (1957). Symbiogenetic evolution processes realized by artificial methods. Methodos, 9, 143-182.
Barricelli, N. A. (1954). Esempi numerici di processi di evoluzione. Methodos, 6(21-22), 45-68.
Bejan, A. (1996). Entropy generation minimization: The new thermodynamics of finite‐size devices and finite‐time processes. Journal of Applied Physics, 79(3), 1191-1218. DOI: 10.1063/1.362674
Bhattarai, S., Dahal, K., Vichare, P., & Mishra, B. (2018). CFD based stochastic optimization of Pelton turbine bucket in stationery condition. In: Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018 (pp. 53-57). DOI: 10.1109/ICMAE.2018.8467607
Carr, J. (2014). An introduction to genetic algorithms. Senior Project, 1(40), 7.
Chatila, J. G., & Danageuzian, H. R. (2022). PIV and CFD investigation of paddle flocculation hydrodynamics at low rotational speeds. Scientific Reports, 12(1), 1-16. DOI: 10.1038/s41598-022-23935-x
Chen, N. X., Zhang, H. W., Xu, Q., & Huang, W. G. (2010). Application of simple gradient-based method and multi-section blade parameterization technique to aerodynamic design optimization of a 3D transonic single rotor compressor. In: Proceedings of the ASME Turbo Expo, 7(PART A) (pp. 503-512). DOI: 10.1115/GT2009-59734
Chen, N., Zhang, H., Ning, F., Xu, Y., & Huang, W. (2008). An effective turbine blade parameterization and aerodynamic optimization procedure using an improved response surface method. In: Proceedings of the ASME Turbo Expo, 6 PART B (pp. 1169-1180). DOI: 10.1115/GT2006-90104
Chen, Y., & Roux, B. (2015). Generalized metropolis acceptance criterion for hybrid non-equilibrium molecular dynamics-Monte Carlo simulations. Journal of Chemical Physics, 142(2). DOI: 10.1063/1.4904889
Delahaye, D., Chaimatanan, S., & Mongeau, M. (2019). Simulated annealing: From basics to applications. International Series in Operations Research and Management Science, 272, 1-35. DOI: 10.1007/978-3-319-91086-4_1
Dowsland, K. A., & Díaz, A. (2003). Heuristic design and fundamentals of the Simulated Annealing. Revista Iberoamericana de Inteligencia Artificial, (19), 93-102. DOI: 10.4114/ia.v7i19.718
Duan, Y., Zheng, Q., Jiang, B., Lin, A., & Zhao, W. (2020). Implementation of three-dimensional inverse design and its application to improve the compressor performance. Energies, 13(20), 5378. DOI: 10.3390/en13205378
Fang, H., Chen, L., & Shen, Z. (2011). Application of an improved PSO algorithm to optimal tuning of PID gains for water turbine governor. Energy Conversion and Management, 52(4), 1763-1770. DOI: 10.1016/j.enconman.2010.11.005
Ghasemi, E., McEligot, D. M., Nolan, K. P., Crepeau, J., Tokuhiro, A., & Budwig, R. S. (2013). Entropy generation in a transitional boundary layer region under the influence of freestream turbulence using transitional RANS models and DNS. International Communications in Heat and Mass Transfer, 41, 10-16. DOI: 10.1016/j.icheatmasstransfer.2012.11.005
Ghorani, M. M., Sotoude-Haghighi, M. H., & Riasi, A. (2020). Entropy generation minimization of a pump running in reverse mode based on surrogate models and NSGA-II. International Communications in Heat and Mass Transfer, 118, 104898. DOI: 10.1016/j.icheatmasstransfer.2020.104898
Giannakoglou, K. C. (2002). Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence. Progress in Aerospace Sciences, 38(1), 43-76. DOI: 10.1016/S0376-0421(01)00019-7
Gong, R., Wang, H., Chen, L., Li, D., Zhang, H., & Wei, X. (2013). Application of entropy production theory to hydro-turbine hydraulic analysis. Science China Technological Sciences, 56(7), 1636-1643. DOI: 10.1007/s11431-013-5229-y
Gong, R. Z., Qi, N. M., Wang, H. J., Chen, A. L., & Qin, D. Q. (2017). Entropy production analysis for S-characteristics of a Pump turbine. Journal of Applied Fluid Mechanics, 10(6), 1657-1668. DOI: 10.29252/jafm.73.245.27675
Guzmán-Avalos, P., Molinero-Hernández, D., Galván-González, S., Herrera-Sandoval, N., Solorio-Díaz, G., & Rubio-Maya, C. (2023). Numerical design and optimization of a hydraulic micro-turbine adapted to a wastewater treatment plant. Alexandria Engineering Journal, 62, 555-565. DOI: 10.1016/j.aej.2022.07.004
Han, Y., Zhou, L., Bai, L., Shi, W., & Agarwal, R. (2021). Comparison and validation of various turbulence models for U-bend flow with a magnetic resonance velocimetry experiment. Physics of Fluids, 33(12), 125117. DOI: 10.1063/5.0073910
Herwig, H., Gloss, D., & Wenterodt, T. (2008). A new approach to understanding and modelling the influence of wall roughness on friction factors for pipe and channel flows. Journal of Fluid Mechanics, 613, 35-53. DOI: 10.1017/S0022112008003534
Holland, J. H. (1975). Adaptation in natural and artificial systems (2nd ed.). Michigan, USA: MIT Press (1992), University of Michigan Press, Ann Arbor.
Holland, J. H. (1992). Holland. Genetic algorithms. Scientific American, 267(1), 44-50. DOI: 10.1038/scientificamerican0792-66
Hou, H., Zhang, Y., Li, Z., Jiang, T., Zhang, J., & Xu, C. (2016). Numerical analysis of entropy production on a LNG cryogenic submerged pump. Journal of Natural Gas Science and Engineering, 36, 87-96. DOI: 10.1016/j.jngse.2016.10.017
Hu, Z., Zhu, B., Liu, X., Ma, Z., & Xue, C. (2019). Multiobjective optimization design of ultrahigh-head pump turbine runners with splitter blades. IOP Conference Series: Earth and Environmental Science, 240(7), 072036. DOI: 10.1088/1755-1315/240/7/072036
Ishibuchi, H., & Murata, T. (1998). A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 28(3), 392-403. DOI: 10.1109/5326.704576
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, 4, 1942-1948. DOI: 10.1109/ICNN.1995.488968
Kennedy, J. (2006). Swarm intelligence. In: Zomaya, A. Y. (ed.). Handbook of nature-inspired and innovative computing (pp. 187-219). Boston, USA: Springer. Recuperado de https://doi.org/10.1007/0-387-27705-6_6
Kim, J. H., Choi, J. H., & Kim, K. Y. (2010). Design optimization of a centrifugal compressor impeller using radial basis neural network method. In: Proceedings of the ASME Turbo Expo, 7(PART A) (pp. 443-451). DOI: 10.1115/GT2009-59666
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680. DOI: 10.1126/science.220.4598.671
Kock, F., & Herwig, H. (2004). Local entropy production in turbulent shear flows: A high-Reynolds number model with wall functions. International Journal of Heat and Mass Transfer, 47(10-11), 2205-2215. DOI: 10.1016/j.ijheatmasstransfer.2003.11.025
Leguizamón, S., & Avellan, F. (2020). Open-source implementation and validation of a 3D inverse design method for Francis turbine runners. Energies, 13(8). DOI: 10.3390/en13082020
Liu, D., Xiao, Z., Li, H., Liu, D., Hu, X., & Malik, O. P. (2019). Accurate parameter estimation of a hydro-turbine regulation system using adaptive fuzzy particle swarm optimization. Energies, 12(20), 3903. DOI: 10.3390/en12203903
McEligot, D. M., Nolan, K. P., Walsh, E. J., & Laurien, E. (2008). Effects of pressure gradients on entropy generation in the viscous layers of turbulent wall flows. International Journal of Heat and Mass Transfer, 51(5-6), 1104-1114. DOI: 10.1016/j.ijheatmasstransfer.2007.05.008
Melzer, A. P., & Pullan, G. (2019). The role of vortex shedding in the trailing edge loss of transonic turbine blades. DOI: 10.1115/1.4041307
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21(6), 1087. DOI: 10.1063/1.1699114
Mitchell, M. (1998). An introduction to genetic algorithms. Cambridge, USA: MIT Press.
Mrope, H. A., Abeid, Y., Jande, C., & Kivevele, T. T. (2021). A review on computational fluid dynamics applications in the design and optimization of crossflow hydro turbines. Journal of Renewable Energy, 2021, 1-13. DOI: 10.1155/2021/5570848
Park, H. J., Cho, S. W., & Lee, C. (2021). Particle swarm optimization algorithm with time buffer insertion for robust berth scheduling. Computers & Industrial Engineering, 160, 107585. DOI: 10.1016/j.cie.2021.107585
Pierret, S., & Van den Braembussche, R. A. (1999). Turbomachinery blade design using a Navier–Stokes solver and artificial neural network. Journal of Turbomachinery, 121(2), 326-332. DOI: 10.1115/1.2841318
Pinelli, L., Amedei, A., Meli, E., Vanti, F., Romani, B., Benvenuti, G., Fabbrini, M., Morganti, N., Rindi, A., & Arnone, A. (2022). Innovative design, structural optimization, and additive manufacturing of new-generation turbine blades. Journal of Turbomachinery, 144(1). DOI: 10.1115/1.4051936/1115179
Qin, S., Wang, S., Sun, G., Zhong, Y., & Cao, B. (2021). New approach of inverse design of transonic compressor rotor blade via prescribed isentropic Mach distributions without modification of governing equations. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 236(7), 1422-1438. DOI: 10.1177/09544100211032489
Safari, A., Hajikolaei, K. H., Lemu, H. G., & Wang, G. G. (2016). A high-dimensional model representation guided PSO methodology with application on compressor airfoil shape optimization. Proceedings of the ASME Turbo Expo, 2C-2016. DOI: 10.1115/GT2016-56741
Shieber, S., & Rapaport, W. (September, 2005). The Turing test: Verbal behavior as the hallmark of intelligence. Computational Linguistics, 31(3). DOI: 10.1162/089120105774321127
Shirzadi, M., Mirzaei, P. A., & Naghashzadegan, M. (2017). Improvement of k-epsilon turbulence model for CFD simulation of atmospheric boundary layer around a high-rise building using stochastic optimization and Monte Carlo Sampling technique. Journal of Wind Engineering and Industrial Aerodynamics, 171, 366-379. DOI: 10.1016/j.jweia.2017.10.005
Shrestha, U., & Choi, Y. D. (2020). A CFD-based shape design optimization process of fixed flow passages in a Francis hydro turbine. Processes, 8(11), 1392. DOI: 10.3390/pr8111392
Shrestha, U., & Choi, Y. D. (2021). Suppression of flow instability in the Francis hydro turbine draft tube by J-groove shape optimization at a partial flow rate. Journal of Mechanical Science and Technology, 35(6), 2523-2533. DOI: 10.1007/s12206-021-0523-2
Siddique, N., & Adeli, H. (2016). Simulated annealing, its variants and engineering applications. International Journal on Artificial Intelligence Tools, 25(6). DOI: 10.1142/S0218213016300015
Soesanto, Q. M. B., Widiyanto, P., Susatyo, A., & Yazid, E. (2019). Cascade optimization of an axial-flow hydraulic turbine type propeller by a genetic algorithm. International Journal of Technology, 10(1), 200-211. DOI: 10.14716/ijtech.v10i1.1744
Tesfaye-Woldemariam, E., Lemu, H. G., & Wang, G. G. (2018). CFD-driven valve shape optimization for performance improvement of a micro cross-flow turbine. Energies, 11(1), 248. DOI: 10.3390/en11010248
Tiow, W. T., Yiu, K. F. C., & Zangeneh, M. (2005). Application of simulated annealing to inverse design of transonic turbomachinery cascades. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 216(1), 59-74. DOI: 10.1243/095765002760024845
Torres-Sánchez, A., Santos-Oliván, D., & Arroyo, M. (2020). Approximation of tensor fields on surfaces of arbitrary topology based on local Monge parametrizations. Journal of Computational Physics, 405, 109168. DOI: 10.1016/j.jcp.2019.109168
Turing, A. M. (October, 1950). Computing machinery and intelligence. Mind, New Series, 59(236), 433-460. DOI: 10.1093/mind/LIX.236.433
van Laarhoven, P. J. M., & Aarts, E. H. L. (1987). Simulated annealing. Simulated Annealing: Theory and Applications, 7-15. DOI: 10.1007/978-94-015-7744-1_2
Wang, W., Han, Z., Pei, J., Pavesi, G., Gong, X., & Yuan, S. (2023). Energy efficiency optimization of water pump based on heuristic algorithm and computational fluid dynamics. Journal of Computational Design and Engineering, 10(1), 382-397. DOI: 10.1093/jcde/qwac142
Xing, X. Q., & Damodaran, M. (2002). Optimal design of transonic fan blade leading edge shape using CFD and simultaneous perturbation stochastic approximation method. Recuperado de https://www.researchgate.net/publication/37595334_Optimal_Design_of_Transonic_Fan_Blade_Leading_Edge_Shape_Using_CFD_and_Simultaneous_Perturbation_Stochastic_Approximation_Method
Xue, P., Liu, Z. P., Lu, L., Tian, Y. J., Wang, X., & Chen, R. (2019). Research and optimization of performances of a pump turbine in pump mode. IOP Conference Series: Earth and Environmental Science, 240(7), 072012. DOI: 10.1088/1755-1315/240/7/072012
Yang, F., Chang, P., Cai, Y., Lin, Z., Tang, F., & Lv, Y. (2022). Analysis of energy loss characteristics of vertical axial flow pump based on entropy production method under partial conditions. Entropy, 24(9), 1200. DOI: 10.3390/e24091200
Yu, A., Tang, Y., Tang, Q., Cai, J., Zhao, L., & Ge, X. (2022). Energy analysis of Francis turbine for various mass flow rate conditions based on entropy production theory. Renewable Energy, 183, 447-458. DOI: 10.1016/j.renene.2021.10.094
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2025 Tecnología y ciencias del agua
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Por Instituto Mexicano de Tecnología del Agua se distribuye bajo una Licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional. Basada en una obra en https://www.revistatyca.org.mx/. Permisos que vayan más allá de lo cubierto por esta licencia pueden encontrarse en Política editorial
