1,720,969 research outputs found
On mooring line tension and fatigue prediction for offshore vertical axis wind turbines: a comparison of lumped-mass and quasi-static approaches
No full textDespite several potential advantages, relatively few studies and design support tools have been developed for floating VAWTs. Due to the substantial aerodynamics differences, the analyses of VAWT on floating structures cannot be easily extended from what have been already done for HAWTs. Therefore, the main aim of the present work is to compare the dynamic response of the FOWT system adopting two different mooring dynamics approaches. Two version of the in-house aero-hydro-mooring coupled model of dynamics for VAWT (FloVAWT) have been used, using: a mooring quasi-static model, which solves the equations using an energetic approach, and a modified version of FloVAWT, which instead couples with the lumped mass mooring line model MoorDyn. The results, in terms of mooring line tension, fatigue and response in frequency have been obtained and analysed, based on a 5MW Darrieus type rotor supported by the OC4-DeepCwind semisubmersible
On the comparison of the dynamic response of an offshore floating VAWT system when adopting two different mooring system model of dynamics: quasi-static vs lumped mass approach
No full textThe interest in floating offshore wind turbines (FOWT) has
been growing substantially over the last decade and, after a
number of prototypes deployed [1], the first offshore floating
wind farms have been approved and are being developed. While
a number of international research activities have been
conducted on the dynamics of offshore floating HAWT systems
(e.g. OC3-Phase IV2, OC4-Phase II3), relatively few studies have
been conducted on floating VAWT systems, despite their
potential advantages [2]. Due to the substantial differences
between HAWT and VAWT aerodynamics, the analyses on
floating HAWT cannot be extended to floating VAWT systems.
The main aim of the present work is to compare the dynamic
response of the FOWT system adopting two different mooring
dynamics approaches. Two version of the in-house aero-hydromooring
coupled model of dynamics for VAWT “FloVAWT” [3]
are used: one which adopts a mooring quasi-static model, and
solves the equations using an energetic approach [4], and a
modified version of FloVAWT, which uses instead the lumpedmass
mooring line model “MoorDyn” [5]. The floating VAWT
system considered is based on a 5MW Darrieus type rotor
supported by the OC4-Phase II3 semi-submersible.
The results for the considered metocean conditions show
that MoorDyn approach estimate larger translational
displacements of the platform, compared to the quasi-static rigid
approach previously implemented in FloVAWT. As expected, the
magnitudes of the forces along the lines are lower, being part of
the energy employed for the elastic deformation of the cables. A
systematic comparison of the differences between the two
approaches is presented.
1 Previous affiliation: University of Main
A damage detection and location scheme for offshore wind turbine jacket structures based on global modal properties
Get PDFAbstract Structural failures of offshore wind substructures might be less likely than failures of other equipments of the offshore wind turbines, but they pose a high risk due to the possibility of catastrophic consequences. Significant costs are linked to offshore operations, like inspections and maintenance activities, thus remote monitoring shows promise for a cost-efficient structural integrity management. This work aims to investigate the feasibility of a two-level detection, in terms of anomaly identification and location, in the jacket support structure of an offshore wind turbine. A monitoring scheme is suggested by basing the detection on a database of simulated modal properties of the structure for different failure scenarios. The detection model identifies the correct anomaly based on three types of modal indicators, namely, natural frequency, the modal assurance criterion between mode shapes, and the modal flexibility variation. The supervised Fisher's linear discriminant analysis is applied to transform the modal indicators to maximize the separability of several scenarios. A fuzzy clustering algorithm is then trained to predict the membership of new data to each of the scenarios in the database. In a case study, extreme scour phenomena and jacket members' integrity loss are simulated, together with variations of the structural dynamics for environmental and operating conditions. Cross-validation is used to select the best hyperparameters, and the effectiveness of the clustering is validated with slight variations of the environmental conditions. The results prove that it is feasible to detect and locate the simulated scenarios via the global monitoring of an offshore wind jacket structure
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Failure diagnosis for offshore wind turbines with low availability of run-to-failure data
Get PDFDespite the efforts to achieve a through-life reliable design and the attempts to control the failures of wind turbines, some system failures are inevitable. The inherent requirement for cost, material, and weight optimisation, together with the extreme operating conditions, can lead to unexpected failures. This is true for land-based turbines and has an even greater impact on offshore wind systems, where the harsh environment and the high cost of the assets and logistics increase the importance of a proactive approach to the system’s maintenance.The smart management of an asset starts with the identification of the health status of its systems, to take cost effective decision on how and when maintain it. The first level of the detection of an anomality in the system comprises the recognition only of the failed status of the asset (level I). Following, the location of the failure should be identified (level II), followed by the detection of its degree of severity (level III) and consequences (level IV). Depending on the availability of continuous monitoring data, historical databases, and advanced numerical models, different frameworks can be established for the failure diagnostics and prognostics. This thesis investigates on the use data-driven, model-based, and digital twin solutions to support the diagnosis of failure events of offshore wind turbine systems characterised by a low availability of run-to-failure data. This topic is of major concern for either the current installations - for which the collection of data is restrained either to only few assets or to more cost-effective temporary monitoring campaign – and the new offshore wind technologies (e.g., floating wind, large-MW structures), for which no or only a limited amount of operating data has been gathered. The mechanical failure of the components of the offshore wind speed conversion system can have a significant impact to the operational expenditure and can be associated to a significant loss of production of the offshore wind farm. The detection of their incipiency has been extensively investigated by machine and deep learning techniques on big sets of condition monitoring and operational data. By contrast, this research explores the implementation of transfer learning to detect anomalies in an offshore wind gearbox with low availability of representative failure data. To move towards the quantification of the consequences of such a failure (level IV), a case of study is used to explore then most suitable the model-reduction techniques to be applied to a full aero-servo-elastic model of the offshore wind turbine. Such a numerical model is the basis for the development of digital twin technology; it is aimed at capturing the only the essential dynamics while targeting the degree(s) of freedom indicating the presence of the failure mode. The presence of a damage in the offshore wind foundation is not commonly recorded, yet structural failures can either lead to catastrophic consequence or considerably increase the cost of maintenance for the planning of expensive subsea inspections. In particular, the fatigue-driven offshore wind jacket foundation designs are sensitive to extreme site conditions, and their expected lifetime can decrease considerably if exposed for a long time to undetected phenomena such as scour and corrosion. This research demonstrates the feasibility of a vibration-based diagnosis (level II) of several damage scenarios for a jacket substructure of an offshore wind turbine. Considering than only a percentage of the assets in the farm are likely to be instrumented with a high-frequency structural health monitoring system, the feasibility of the detection (level I) of a structural failure mode via low-resolution operational data is additionally explored. These virtual monitoring frameworks are supported by the deployment of the digital twin technologies for their setup and their future field application.Despite the efforts to achieve a through-life reliable design and the attempts to control the failures of wind turbines, some system failures are inevitable. The inherent requirement for cost, material, and weight optimisation, together with the extreme operating conditions, can lead to unexpected failures. This is true for land-based turbines and has an even greater impact on offshore wind systems, where the harsh environment and the high cost of the assets and logistics increase the importance of a proactive approach to the system’s maintenance.The smart management of an asset starts with the identification of the health status of its systems, to take cost effective decision on how and when maintain it. The first level of the detection of an anomality in the system comprises the recognition only of the failed status of the asset (level I). Following, the location of the failure should be identified (level II), followed by the detection of its degree of severity (level III) and consequences (level IV). Depending on the availability of continuous monitoring data, historical databases, and advanced numerical models, different frameworks can be established for the failure diagnostics and prognostics. This thesis investigates on the use data-driven, model-based, and digital twin solutions to support the diagnosis of failure events of offshore wind turbine systems characterised by a low availability of run-to-failure data. This topic is of major concern for either the current installations - for which the collection of data is restrained either to only few assets or to more cost-effective temporary monitoring campaign – and the new offshore wind technologies (e.g., floating wind, large-MW structures), for which no or only a limited amount of operating data has been gathered. The mechanical failure of the components of the offshore wind speed conversion system can have a significant impact to the operational expenditure and can be associated to a significant loss of production of the offshore wind farm. The detection of their incipiency has been extensively investigated by machine and deep learning techniques on big sets of condition monitoring and operational data. By contrast, this research explores the implementation of transfer learning to detect anomalies in an offshore wind gearbox with low availability of representative failure data. To move towards the quantification of the consequences of such a failure (level IV), a case of study is used to explore then most suitable the model-reduction techniques to be applied to a full aero-servo-elastic model of the offshore wind turbine. Such a numerical model is the basis for the development of digital twin technology; it is aimed at capturing the only the essential dynamics while targeting the degree(s) of freedom indicating the presence of the failure mode. The presence of a damage in the offshore wind foundation is not commonly recorded, yet structural failures can either lead to catastrophic consequence or considerably increase the cost of maintenance for the planning of expensive subsea inspections. In particular, the fatigue-driven offshore wind jacket foundation designs are sensitive to extreme site conditions, and their expected lifetime can decrease considerably if exposed for a long time to undetected phenomena such as scour and corrosion. This research demonstrates the feasibility of a vibration-based diagnosis (level II) of several damage scenarios for a jacket substructure of an offshore wind turbine. Considering than only a percentage of the assets in the farm are likely to be instrumented with a high-frequency structural health monitoring system, the feasibility of the detection (level I) of a structural failure mode via low-resolution operational data is additionally explored. These virtual monitoring frameworks are supported by the deployment of the digital twin technologies for their setup and their future field application
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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