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+34 946 567 842
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+34 946 567 842
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ngorostidi@bcamath.org
Information of interest
- Orcid: 0000-0002-0629-659X
Bsc Hons Mechanical Engineering. University of Oviedo, Spain. 2015-19.
MSc Computational Fluid Dynamics. Cranfield University, United Kingdom. 2019-20.
Research interests include offshore wind engineering, ocean wave mechanics, machine learning, computational fluid dynamics, wind turbine aerodynamics and automotive aerodynamics.
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Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders
Gorostidi, N.
; Pardo, D.; Nava, V. (2023-11-01)
Floating offshore wind turbines (FOWTs) show promise in terms of energy production, availability, and sustainability, but remain unprofitable due to high maintenance costs. This work proposes a deep learning algorithm to ...
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On building physics-based AI models for the design and SHM of mooring systems
Nava, V.; Aristondo, A.; Varo, V.; Esteras, M.; Touzon, I.; Boto, F.; Mendikoa, I.; Ruiz-Minguela, P.; Gil-Lopez, S.; Gorostidi, N.; Pardo, D. (2023-01-01)
Expert systems in industrial processes are modelled using physics-based approaches, data-driven models or hybrid approaches in which however the underlying physical models generally constitute a separate block with respect ...
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MONITORING MOORING (MONIMOOR) LINES OF FLOATING STRUCTURES USING DEEP LEARNING-BASED APPROACHES
Sharma, S.; Nava, V.; Gorostidi, N. (2023)
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Predictive Maintenance of Floating Offshore Wind Turbine Mooring Lines using Deep Neural Networks
Gorostidi, N.; Nava, V.; Aristondo, A.; Pardo, D. (2022)
The recent massive deployment of onshore wind farms has caused controversy to arise mainly around the issues of land occupation, noise and visual pollution and impact on wildlife. Fixed offshore turbines, albeit beneficial ...