La importancia del dato en la simulación fluidodinámica de plataformas flotantes para energías renovables marinas
- Jesús María Blanco 1
- Ángela Bernardini 2
- Lander Galera Calero 1
-
1
Universidad del País Vasco/Euskal Herriko Unibertsitatea
info
Universidad del País Vasco/Euskal Herriko Unibertsitatea
Lejona, España
- 2 NAITEC
ISSN: 0422-2784
Año de publicación: 2022
Título del ejemplar: Economía del dato
Número: 423
Páginas: 53-66
Tipo: Artículo
Otras publicaciones en: Economía industrial
Resumen
Este artículo trata sobre la relevancia de la calidad de los datos, en su aplicación a la eólica marina flotante, una de las tecnologías offshore más prometedoras. La acción de las olas afecta en gran medida al rendimiento de la turbina, aumentando su coste de energía nivelado. Se propone un modelo para investigar su comportamiento, el cual tiene un coste computacional prohibitivo debido a la ingente cantidad de datos a tratar, por lo que se optó por una solución de computación en la nube
Referencias bibliográficas
- AI for EARTH, MICROSOFT, 2020: https://www.microsoft. com/en-us/ai/ai-for-earth (last accessed Nov. 2021)
- Anaconda-SODS-Report-2020: https://know.anaconda.com/rs/387-XNW-688/images/Anaconda-SODS-Report-2020-Final.pdf (last accessed Nov. 2021)
- Banko, M., Brill, E. Mitigating the paucity-of-data problem: exploring the effect of training corpus size on classifier performance for natural language processing. Proc. 1st Internal Conference on human language technology research, 1-5, San Diego, CA, (2001).
- Batini, C. Cappiello, C. Francalanci, C., Maurino, A., Methodologies for data quality assessment and improvement, ACM Computing Surveys (CSUR), 41, pp. 16-23, (2009).
- Bernardini, A., Asensio, J., Olazagoitia, J.L., Biera, J., Evolutionary Neural Networks for Product Design Tasks, Hybrid Artificial Intelligent Systems Lecture Notes in Computer Science,7208, 2012, pp 421-428.
- Blanco, J.M.; Vazquez, L.; Peña, F.; Díaz, D., New investigation on diagnosing steam production systems from multivariate time series applied to thermal power plants, Applied Energy, 101, 2013: pp. 589-599. doi: 10.1016/j.apenergy.2012.06.060.
- Catarci, T., Scannapieco, M. Data quality under the computer science perspective. Archivi Computer, 2, 2002, pp. 1-15.
- Christoph E., GrgiÄ-HlaÄa, N. Machine Advice with a Warning about Machine Limitations: Experimentally Testing the Solution Mandated by the Wisconsin Supreme Court, Journal of Legal Analysis, 13, 1, 2021, pp 284-340.
- Galera-Calero, L.; Blanco, J.M.; Izquierdo, U.; Esteban, G.A. Performance Assessment of Three Turbulence Models Validated through an Experimental Wave Flume under Different Scenarios of Wave Generation. J. Mar. Sci. Eng. 2020, 8, 881, doi: 10.3390/jmse8110881.
- Han, J., Kamber, M., Pei, J., Data Preprocessing, Data Mining (Third Edition), pp. 83-124, 2012.
- Huang, G., Bryden, KM, McCorkle, DS, Interactive Design using CFD and Virtual Engineering, Actas de la 10ª Conferencia de optimización y análisis multidisciplinario de AIAA / ISSMO, AIAA-2004-4364, Albany, (2004).
- Izquierdo, U.; Galera-Calero, L.; Albaina, I.; Vázquez, A.; Esteban, G.A.; Blanco, J.M. Experimental and Numerical Determination of the Optimum Configuration of a Parabolic Wave Extinction System for Flumes. Ocean Eng. 2021, 238, 109748, doi: 10.1016/j.oceaneng.2021.109748.
- Jacobson, M.Z.; Delucchi, M.A.; Bauer, Z.A.F.; Goodman, S.C.; Chapman, W.E.; Cameron, M.A.; Bozonnat, C.; Chobadi, L.; Clonts, H.A.; Enevoldsen, P.; et al. 100% Clean and Renewable Wind, Water, and Sunlight All-Sector Energy Roadmaps for 139 Countries of the World. Joule, 2017, 1, pp. 108- 121, doi: 10.1016/j.joule.2017.07.005.
- Loshin, D., Data Quality, Business Intelligence (Second Edition), pp. 165-187, (2013).
- MARIA project, 2020: https://www.energias-renovables.com/eolica/microsoft-presta-su-inteligencia-artificial-a-un-20200518 (last accessed Nov. 2021)
- McCorkle, DS, Bryden, KM, Using the Semantic Web to Enable Integration with Virtual Engineering Tools, Actas del 1er Taller Internacional de Fabricación Virtual (27), Washington, DC, (2006).
- Parra, C., Olazagoitia, J.L., Biera, J., Development of intelligent tools to eliminate squeal noise in brake systems, 6th European Conference on Braking JEF 2010, Lille, France, (2010).
- Pipino, L.L., Lee, Y.W., Wang, R.Y., Data quality assessment, Communications of the ACM, 45, pp. 211-218, (2002).
- Rahm, E., Do, H., Data Cleaning: Problems and Current Approaches, Computer Science IEEE Data Eng. Bull., 2000.
- Scott Mayer M., Marcin Sienieky Shravya S., International evaluation of an AI system for breast cancer screening, Nature, 577, 2020, pp 89-94.
- Smart City Index 2020 by IMD Business School: https://www. imd.org/smart-city-observatory/Home/ (last accessed Nov. 2021)
- Vázquez, L.; Blanco, J.M.; Ramis, R.; Peña, F.; Díaz, D., Robust methodology for steady state measurements estimation based framework for a reliable long term thermal power plant operation performance monitoring, Energy, 93, 1, 2015: pp. 923-944. doi: 10.1016/j.energy.2015.09.044
- Vinuesa, R.; Brunton, S.L. The Potential of Machine Learning to Enhance Computational Fluid Dynamics. ArXiv abs/211002085 Phys. 2021.
- Windt, C.; Faedo, N.; García-Violini, D.; Peña-Sanchez, Y.; Davidson, J.; Ferri, F.; Ringwood, J.V. Validation of a CFD-Based Numerical Wave Tank Model of the 1/20th Scale Wavestar Wave Energy Converter. Fluids, 2020, 5, 3, 112, doi: 10.3390/fluids5030112.