DOE-ANOVA to optimize hydrokinetic turbines for low velocity conditions

The need of reducing the dependence of fossil fuels and CO2 emissions have motivated the diversification of energy matrix. Among the Renewables, the hydropower shows better characteristics compared to solar, wind, biomass and geothermal, because its low CO2 emissions, higher density and others techn...

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Autores:
Rueda-Bayona, Juan Gabriel
Paez, Natalia
Cabello Eras, Juan José
Sagastume Gutierrez, Alexis
Tipo de recurso:
Article of investigation
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/10764
Acceso en línea:
https://hdl.handle.net/11323/10764
https://repositorio.cuc.edu.co/
Palabra clave:
CFD
DOE-ANOVA
Hydrokinetic microturbine
Multiple regression
Optimization
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
id RCUC2_8124f1589488165f140f84a83a96f873
oai_identifier_str oai:repositorio.cuc.edu.co:11323/10764
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv DOE-ANOVA to optimize hydrokinetic turbines for low velocity conditions
title DOE-ANOVA to optimize hydrokinetic turbines for low velocity conditions
spellingShingle DOE-ANOVA to optimize hydrokinetic turbines for low velocity conditions
CFD
DOE-ANOVA
Hydrokinetic microturbine
Multiple regression
Optimization
title_short DOE-ANOVA to optimize hydrokinetic turbines for low velocity conditions
title_full DOE-ANOVA to optimize hydrokinetic turbines for low velocity conditions
title_fullStr DOE-ANOVA to optimize hydrokinetic turbines for low velocity conditions
title_full_unstemmed DOE-ANOVA to optimize hydrokinetic turbines for low velocity conditions
title_sort DOE-ANOVA to optimize hydrokinetic turbines for low velocity conditions
dc.creator.fl_str_mv Rueda-Bayona, Juan Gabriel
Paez, Natalia
Cabello Eras, Juan José
Sagastume Gutierrez, Alexis
dc.contributor.author.none.fl_str_mv Rueda-Bayona, Juan Gabriel
Paez, Natalia
Cabello Eras, Juan José
Sagastume Gutierrez, Alexis
dc.subject.proposal.eng.fl_str_mv CFD
DOE-ANOVA
Hydrokinetic microturbine
Multiple regression
Optimization
topic CFD
DOE-ANOVA
Hydrokinetic microturbine
Multiple regression
Optimization
description The need of reducing the dependence of fossil fuels and CO2 emissions have motivated the diversification of energy matrix. Among the Renewables, the hydropower shows better characteristics compared to solar, wind, biomass and geothermal, because its low CO2 emissions, higher density and others technical factors. Within the Hydropower, the Hydrokinetic turbines (HT) are considered as a promising technology because can provide electricity during low flow velocity conditions (< 2 m/s) and is able to operate in shallow waters < 8 m and in secluded areas without access to the energy network. In this sense, the present study incentivizes the research in Hydropower and proposes and new application of DOE-ANOVA combined with Computational Fluid Dynamics (CFD) modelling for the HT design and optimization. Accordingly, this work evaluated the performance of a HT with 1.9 m of rotor diameter operating in a water flow of 1.5 m/s through a 23 factorial design with 9 modelling cases (MC). The results showed that the increment of outlet diameters increased the downstream velocity and the hydrodynamic pressure over the HT, and the reduction of the blade tip edge distance generated an increment of the response of the HT hydraulic and mechanical properties.
publishDate 2022
dc.date.issued.none.fl_str_mv 2022-08-31
dc.date.accessioned.none.fl_str_mv 2024-02-21T22:29:53Z
dc.date.available.none.fl_str_mv 2024-02-21T22:29:53Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.issn.spa.fl_str_mv 2369-0739
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/10764
dc.identifier.doi.none.fl_str_mv 10.18280/mmep.090415
dc.identifier.eissn.spa.fl_str_mv 2369-0747
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC – Repositorio CUC
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identifier_str_mv 2369-0739
10.18280/mmep.090415
2369-0747
Corporación Universidad de la Costa
REDICUC – Repositorio CUC
url https://hdl.handle.net/11323/10764
https://repositorio.cuc.edu.co/
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Mathematical Modelling of Engineering Problems
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[2] Rueda-Bayona, J.G., Guzmán, A., Eras, J.J.C., SilvaCasarín, R., Bastidas-Arteaga, E., Horrillo-Caraballo, J. (2019). Renewables energies in Colombia and the opportunity for the offshore wind technology. Journal of Cleaner Production, 220: 529-543. https://doi.org/10.1016/j.jclepro.2019.02.174
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[4] Salleh, M.B., Kamaruddin, N.M., Mohamed-Kassim, Z. (2019). Savonius hydrokinetic turbines for a sustainable river-based energy extraction: A review of the technology and potential applications in Malaysia. Sustainable Energy Technologies and Assessments, 36: 100554. https://doi.org/10.1016/j.seta.2019.100554
[5] Xu, J., Ni, T., Zheng, B. (2015). Hydropower development trends from a technological paradigm perspective. Energy Conversion and Management, 90: 195-206. https://doi.org/10.1016/j.enconman.2014.11.016
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[17] Alipour, R., Alipour, R., Fardian, F., Koloor, S.S.R., Petrů, M. (2020). Performance improvement of a new proposed Savonius hydrokinetic turbine: A numerical investigation. Energy Reports, 6: 3051-3066. https://doi.org/10.1016/j.egyr.2020.10.072
[18] Shashikumar, C.M., Vijaykumar, H., Vasudeva, M. (2021). Numerical investigation of conventional and tapered Savonius hydrokinetic turbines for low-velocity hydropower application in an irrigation channel. Sustainable Energy Technologies and Assessments, 43: 100871. https://doi.org/10.1016/j.seta.2020.100871
[19] Kirke, B. (2020). Hydrokinetic turbines for moderate sized rivers. Energy for Sustainable Development, 58: 182-195. https://doi.org/10.1016/j.esd.2020.08.003
[20] Chawdhary, S., Angelidis, D., Colby, J., Corren, D., Shen, L., Sotiropoulos, F. (2018). Multiresolution large-Eddy simulation of an array of hydrokinetic turbines in a fieldscale River: The Roosevelt Island Tidal Energy Project in New York City. Water Resources Research, 54(12): 10-188. https://doi.org/10.1029/2018WR023345
[21] Verdant_Power. (2020). Verdant Power’s Roosevelt Island Tidal Energy (RITE) Project. https://www.verdantpower.com/projects.
[22] Ocean Renewable Power Company (ORPC). (2012). Tidal Energy Maine Project. https://www.energy.gov/articles/maine-project-takeshistoric-step-forward-us-tidal-energy-deployment.
[23] Fernández-Jiménez, A., Cruz, D.F.D.L., Ruiz-Torres, J., Perrino-Blanco, J.L., Jimeno-Almeida, R. (2018). Harnessing the energy of tidal currents: State-of-the-art and proposal of use in EV Charging Points. Multidisciplinary Digital Publishing Institute Proceedings, 2(23): 1504. https://doi.org/10.3390/proceedings2231504
[24] Posa, A., Broglia, R. (2021). Characterization of the turbulent wake of an axial-flow hydrokinetic turbine via large-eddy simulation. Computers & Fluids, 216: 104815. https://doi.org/10.1016/j.compfluid.2020.104815
[25] John, B., Thomas, R.N., Varghese, J. (2020). Integration of hydrokinetic turbine-PV-battery standalone system for tropical climate condition. Renewable Energy, 149: 361- 373. https://doi.org/10.1016/j.renene.2019.12.014
[26] Posa, A., Broglia, R. (2021). Momentum recovery downstream of an axial-flow hydrokinetic turbine. Renewable Energy, 170: 1275-1291. https://doi.org/10.1016/j.renene.2021.02.061
[27] Saini, G., Saini, R.P. (2020). Comparative investigations for performance and self-starting characteristics of hybrid and single Darrieus hydrokinetic turbine. Energy Reports, 6: 96-100. https://doi.org/10.1016/j.egyr.2019.11.047
[28] Chimakurthi, S.K., Reuss, S., Tooley, M., Scampoli, S. (2018). ANSYS workbench system coupling: A state-ofthe-art computational framework for analyzing multiphysics problems. Engineering with Computers, 34(2): 385-411. https://doi.org/10.1007/S00366-017- 0548-4
[29] Ramadan, A., Hemida, M., Abdel-Fadeel, W.A., Aissa W.A., Mohamed, M.H. (2021). Comprehensive experimental and numerical assessment of a drag turbine for river hydrokinetic energy conversion. Ocean Engineering, 227: 108587. https://doi.org/10.1016/j.oceaneng.2021.108587
[30] Khaled, F., Guillou, S., Méar, Y., Hadri, F. (2021). Impact of the blockage ratio on the transport of sediment in the presence of a hydrokinetic turbine: Numerical modeling of the interaction sediment and turbine. International Journal of Sediment Research, 36(6): 696- 710. https://doi.org/10.1016/j.ijsrc.2021.02.003
[31] Lee, J., Kim, Y., Khosronejad, A., Kang, S. (2020). Experimental study of the wake characteristics of an axial flow hydrokinetic turbine at different tip speed ratios. Ocean Engineering, 196: 106777. https://doi.org/10.1016/j.oceaneng.2019.106777
[32] Alizadeh, H., Jahangir, M.H., Ghasempour, R. (2020). CFD-based improvement of Savonius type hydrokinetic turbine using optimized barrier at the low-speed flows. Ocean Engineering, 202: 107178. https://doi.org/10.1016/j.oceaneng.2020.107178
[33] Sarma, N.K., Biswas, A., Misra, R.D. (2014). Experimental and computational evaluation of Savonius hydrokinetic turbine for low velocity condition with comparison to Savonius wind turbine at the same input power. Energy Conversion and Management, 83: 88-98. https://doi.org/10.1016/j.enconman.2014.03.070
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[35] Espina-Valdés, R., Fernández-Jiménez, A., Francos, J.F., Marigorta, E.B., Álvarez-Álvarez, E. (2020). Small cross-flow turbine: Design and testing in high blockage conditions. Energy Conversion and Management, 213: 112863. https://doi.org/10.1016/j.enconman.2020.112863
[36] Tovar, A.M.R., Lopez, Y.U., Laín, S. (2017). Design and prototype of a micro hydrokinetic vertical turbine. Renewable Energy and Power Quality Journal, 1: 903- 910. https://doi.org/10.24084/repqj15.512
[37] Muratoglu, A., Tekin, R., Ertuğrul, Ö.F. (2021). Hydrodynamic optimization of high-performance blade sections for stall regulated hydrokinetic turbines using Differential Evolution Algorithm. Ocean Engineering, 220: 108389. https://doi.org/10.1016/j.oceaneng.2020.108389
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[39] Bouvant, M., Betancour, J., Velásquez, L., RubioClemente, A., Chica, E. (2021). Design optimization of an Archimedes screw turbine for hydrokinetic applications using the response surface methodology. Renewable Energy, 172: 941-954. https://doi.org/10.1016/j.renene.2021.03.076
[40] Montgomery, D.C. (2019). Design and Analysis of Experiments. Tenth Edit, John Wiley & Sons, Inc., Hoboken, NJ, USA. https://www.wiley.com/enus/Design+and+Analysis+of+Experiments%2C+10th+E dition-p-9781119492443.
[41] Rueda-Bayona, J.G., Gil, L., Calderón, J.M. (2021). CFD-FEM modeling of a floating foundation under extreme hydrodynamic forces generated by low sea states. Mathematical Modelling of Engineering Problems, 8: 888-896. https://doi.org/10.18280/MMEP.080607
[42] Sarpkaya, T. (1993). Offshore hydrodynamics. Journal of Offshore Mechanics and Artic Engineering, 115: 2-5. https://doi.org/10.1115/1.2920085
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[44] Rueda-Bayona, J.G. (2017). Identificación de la influencia de las variaciones convectivas en la generación de cargas transitorias y su efecto hidromecánico en las estructuras offshore. Universidad Del Norte. http://hdl.handle.net/10584/7629.
[45] Rueda-Bayona, J.G., Horrillo-Caraballo, J., Chaparro, T.R. (2020). Modelling of surface river plume using setup and input data files of Delft-3D model. Data in Brief, 31: 105899. https://doi.org/10.1016/j.dib.2020.105899
[46] Ragheb, M., Ragheb, A.M. (2011). Wind turbines theory-the betz equation and optimal rotor tip speed ratio. Fundamental and Advanced Topics in Wind Power, 1(1): 19-38. https://doi.org/10.5772/21398
[47] Hossam, S., Aleem, E.A. (2014). Mathematical analysis of the turbine coefficient of performance for tidal stream turbines. https://bura.brunel.ac.uk/bitstream/2438/11044/1/Fullte xt.pdf.
[48] Power, H.E., Gharabaghi, B., Bonakdari, H., Robertson, B., Atkinson, A.L., Baldock, T.E. (2019). Prediction of wave runup on beaches using Gene-Expression Programming and empirical relationships. Coastal Engineering, 144: 47-61. https://doi.org/10.1016/j.coastaleng.2018.10.006
[49] Young, D.L., Scully, B.M. (2018). Assessing structure sheltering via statistical analysis of AIS data. Journal of Waterway, Port, Coastal, and Ocean Engineering, 144: 04018002. https://doi.org/10.1061/(ASCE)WW.1943- 5460.0000445
[50] Rueda-Bayona, J.G., Guzmán, A., Cabello, J.J. (2020). Selection of JONSWAP spectra parameters for waterdepth ans sea-state transitions. Journal of Waterway, Port, Coastal, and Ocean Engineering. https://doi.org/10.1061/(ASCE)WW.1943- 5460.0000601
[51] Kotroni, V., Lagouvardos, K., Lykoudis, S. (2014). High-resolution model-based wind atlas for Greece. Renewable and Sustainable Energy Reviews, 30: 479- 489. https://doi.org/10.1016/j.rser.2013.10.016
[52] Qasim, A., Nisar, S., Shah, A., Khalid, M.S., Sheikh, M.A. (2015). Optimization of process parameters for machining of AISI-1045 steel using Taguchi design and ANOVA. Simulation Modelling Practice and Theory, 59: 36-51. https://doi.org/10.1016/j.simpat.2015.08.004
[53] Derschum, C., Nistor, I., Stolle, J., Goseberg, N. (2018). Debris impact under extreme hydrodynamic conditions part 1: Hydrodynamics and impact geometry. Coastal Engineering, 141: 24-35. https://doi.org/10.1016/j.coastaleng.2018.08.016
[54] Fragasso, J., Moro, L., Lye, L.M., Quinton, B.W. (2019). Characterization of resilient mounts for marine diesel engines: Prediction of static response via nonlinear analysis and response surface methodology. Ocean Engineering, 171: 14-24. https://doi.org/10.1016/j.oceaneng.2018.10.051
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spelling Atribución 4.0 Internacional (CC BY 4.0)© 2024 IIETA. All Rights Reserved.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Rueda-Bayona, Juan GabrielPaez, NataliaCabello Eras, Juan JoséSagastume Gutierrez, Alexis2024-02-21T22:29:53Z2024-02-21T22:29:53Z2022-08-312369-0739https://hdl.handle.net/11323/1076410.18280/mmep.0904152369-0747Corporación Universidad de la CostaREDICUC – Repositorio CUChttps://repositorio.cuc.edu.co/The need of reducing the dependence of fossil fuels and CO2 emissions have motivated the diversification of energy matrix. Among the Renewables, the hydropower shows better characteristics compared to solar, wind, biomass and geothermal, because its low CO2 emissions, higher density and others technical factors. Within the Hydropower, the Hydrokinetic turbines (HT) are considered as a promising technology because can provide electricity during low flow velocity conditions (< 2 m/s) and is able to operate in shallow waters < 8 m and in secluded areas without access to the energy network. In this sense, the present study incentivizes the research in Hydropower and proposes and new application of DOE-ANOVA combined with Computational Fluid Dynamics (CFD) modelling for the HT design and optimization. Accordingly, this work evaluated the performance of a HT with 1.9 m of rotor diameter operating in a water flow of 1.5 m/s through a 23 factorial design with 9 modelling cases (MC). The results showed that the increment of outlet diameters increased the downstream velocity and the hydrodynamic pressure over the HT, and the reduction of the blade tip edge distance generated an increment of the response of the HT hydraulic and mechanical properties.10 páginasapplication/pdfengInternational Information and Engineering Technology AssociationCanadahttps://www.iieta.org/journals/mmep/paper/10.18280/mmep.090415DOE-ANOVA to optimize hydrokinetic turbines for low velocity conditionsArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Mathematical Modelling of Engineering Problems[1] Eras, J.J.C., Morejón, M.B., Gutiérrez, A.S., GarcÃa, A.P., Ulloa, M.C., MartÃnez, F.J.R., Rueda-Bayona, F.G. (2019). A look to the electricity generation from nonconventional renewable energy sources in Colombia. International Journal of Energy Economics and Policy, 9(1): 15-25. https://doi.org/10.32479/ijeep.7108[2] Rueda-Bayona, J.G., Guzmán, A., Eras, J.J.C., SilvaCasarín, R., Bastidas-Arteaga, E., Horrillo-Caraballo, J. (2019). Renewables energies in Colombia and the opportunity for the offshore wind technology. Journal of Cleaner Production, 220: 529-543. https://doi.org/10.1016/j.jclepro.2019.02.174[3] International Energy Agency (IEA). (2019). World Energy Outlook 2019 – Analysis - IEA, World Energy Outlook 2019. https://www.iea.org/reports/worldenergy-outlook-2019.[4] Salleh, M.B., Kamaruddin, N.M., Mohamed-Kassim, Z. (2019). Savonius hydrokinetic turbines for a sustainable river-based energy extraction: A review of the technology and potential applications in Malaysia. Sustainable Energy Technologies and Assessments, 36: 100554. https://doi.org/10.1016/j.seta.2019.100554[5] Xu, J., Ni, T., Zheng, B. (2015). Hydropower development trends from a technological paradigm perspective. 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Ocean Engineering, 171: 14-24. https://doi.org/10.1016/j.oceaneng.2018.10.05198897949CFDDOE-ANOVAHydrokinetic microturbineMultiple regressionOptimizationPublicationORIGINALDOE-ANOVA to Optimize Hydrokinetic Turbines for Low Velocity Conditions.pdfDOE-ANOVA to Optimize Hydrokinetic Turbines for Low Velocity Conditions.pdfArtículoapplication/pdf1430913https://repositorio.cuc.edu.co/bitstreams/48dcb68e-802d-40d4-a3b1-bbd0f189fc14/download4b9b7d915840c54b810767814aea8a84MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/868cac0a-5e8c-4a58-92e1-0e7442b273cc/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTDOE-ANOVA to Optimize Hydrokinetic Turbines for Low Velocity Conditions.pdf.txtDOE-ANOVA to Optimize Hydrokinetic Turbines for Low Velocity Conditions.pdf.txtExtracted texttext/plain48637https://repositorio.cuc.edu.co/bitstreams/c6d65366-085c-4366-8cf4-1302a9c8c90c/downloadf5ccb2b7a9b28672c851ae51f1743fdfMD53THUMBNAILDOE-ANOVA to Optimize Hydrokinetic Turbines for Low Velocity Conditions.pdf.jpgDOE-ANOVA to Optimize Hydrokinetic Turbines for Low Velocity Conditions.pdf.jpgGenerated Thumbnailimage/jpeg17571https://repositorio.cuc.edu.co/bitstreams/4fa4a529-4912-457f-9f0b-f2a4170e189a/download702a0e7306ac1a690c65edf7e3a774d4MD5411323/10764oai:repositorio.cuc.edu.co:11323/107642024-09-17 11:02:56.151https://creativecommons.org/licenses/by/4.0/© 2024 IIETA. 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.


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