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Analysis of institutional authors

Medina-Manuel, AntonioAuthorSanchez, Rafael MolinaAuthorSouto-Iglesias, AntonioCorresponding Author

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October 15, 2024
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AI-Driven Model Prediction of Motions and Mooring Loads of a Spar Floating Wind Turbine in Waves and Wind

Publicated to: Journal of Marine Science and Engineering. 12 (9): 1464- - 2024-09-01 12(9), DOI: 10.3390/jmse12091464

Authors:

Medina-Manuel, Antonio; Sanchez, Rafael Molina; Souto-Iglesias, Antonio
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Affiliations

Univ Politecn Madrid UPM, CEHINAV, DACSON, ETSIN, Ave Memoria 4, Madrid 28040, Spain - Author
UPM, CEHINAV, DITTU, ETSI Caminos Canales & Puertos, Madrid 28040, Spain - Author

Abstract

This paper describes a Long Short-Term Memory (LSTM) neural network model used to simulate the dynamics of the OC3 reference design of a Floating Offshore Wind Turbine (FOWT) spar unit. It crafts an advanced neural network with an encoder-decoder architecture capable of predicting the spar's motion and fairlead tensions time series. These predictions are based on wind and wave excitations across various operational and extreme conditions. The LSTM network, trained on an extensive dataset from over 300 fully coupled simulation scenarios using OpenFAST, ensures a robust framework that captures the complex dynamics of a floating platform under diverse environmental scenarios. This framework's effectiveness is further verified by thoroughly evaluating the model's performance, leveraging comparative statistics and accuracy assessments to highlight its reliability. This methodology contributes to substantial reductions in computational time. While this research provides insights that facilitate the design process of offshore wind turbines, its primary aim is to introduce a new predictive approach, marking a step forward in the quest for more efficient and dependable renewable energy solutions.
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Keywords

Data-driven modelFloating offshore wind turbine (fowt)Fully coupled numerical simulationsLstm neural networksMooring loads predictioMooring loads predictionSeakeeping motions predictionTime-series prediction

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Journal of Marine Science and Engineering due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 7/25, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Engineering, Marine. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Ocean Engineering.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-25:

  • Google Scholar: 1
  • WoS: 10
  • Scopus: 11
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-25:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 15.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 13 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 5.
  • The number of mentions on the social network X (formerly Twitter): 7 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/86621/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 139
  • Downloads: 42
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (MEDINA MANUEL, ANTONIO) and Last Author (SOUTO IGLESIAS, ANTONIO).

the author responsible for correspondence tasks has been SOUTO IGLESIAS, ANTONIO.

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Awards linked to the item

The authors acknowledge the funding received from the Ministry of Science and Innovation of Spain through projects FOWT-DAMP2 (reference: PID2021-123437OB-C21) and FOWT-PLATE-MOOR (reference: TED2021-130951B-I00).
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