Nonlinear meta-models for conceptual seakeeping design of fishing vessels

Sayli, Ayla; Alkan, Ahmet; Ganiler, Onur

doi:10.1016/j.oceaneng.2010.02.005

“…The results show that the prediction of seakeeping at the design concept stage is very satisfying. In the next article, Sayli, et al, [3], developed a nonlinear meta model to predict the behavior of seakeeping fishing vessels. Seakeeping data of fishing vessels on regular head waves is used to develop meta models of the heave, pitch and vertical acceleration transfer functions using non-linear analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of Monohull Fish Processing Vessel Hullform to Support Domestic Fishing Activities in Indonesia

Zakki*,

Windyandari,

Chrismianto

et al. 2019

IJITEE

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This research is focused on the development of the design and prototype of monohull fish processing vessel hullform to support fishing activities such as catching fish, collecting fish from the smaller boats and processing fish catches. The extensive on-board facilities in the fish processing vessel are maintaining the freshness of the catch and improving the quality of catch fish than traditional fishing boat. The initial stage of this study is determining the principal dimension and exploring of the configurations of monohull hull forms. Furthermore, the investigation of resistance characteristics, intact stability and seakeeping behavior was carried out using strip theory and computational fluid dynamic methods. Regarding the performance of the hulls resistance, the analysis indicates that the hull form with smaller block coefficients showed better resistance characteristics. In the case of intact stability, the results of the analysis showed that the model 2 of monohull hull form showed better intact stability characteristics compared to the other models

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“…In addition, this data set has motions of vessels in terms of RAOs of Heave, Pitch and Vertical Absolute Acceleration at stern. The RAO computations have been carried out by means of a two-dimensional computer code based on Frank close-fit method [8,9]. …”

Section: Vessel Databasementioning

confidence: 99%

“…Determination of relational classification among hull form parameters Ayla Sayli and ship motions performance for a set of small vessels Ahmet Dursun Alkan, Merve Aydın The correlation analysis among the vessels in Cluster 1 (Vessel 01, 06, 09 and 11) for heave motion has showed a highly close approximation compared to the output of Simple Model [8,9] which was obtained by the correlation analysis of all 39 vessel variants, for 13 vessels with three loading conditions. In Simple Model, the ship motions were represented in function of hull form parameters of length to beam ratio, beam to draught ratio, length to volume ratio (LPP/B, B/T, L/ 1/3 ), speed to length ratio (Froude number, Fn) and wave length to ship length ratio (/L).…”

mentioning

confidence: 97%

Determination of Relational Classification Among Hull Form Parameters and Ship Motions Performance for a Set of Small Vessels

Sayli

¹

,

Alkan

²

,

Aydın³

2016

Brod

Self Cite

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Data science for engineers is the most recent research area which suggests to analyse large data sets in order to find data analytics and use them for better designing and modelling. Ship design practice reveals that conceptual ship design is critically important for a successful basic design. Conceptual ship design needs to identify the true set of design variables influencing vessel performance and costs to define the best possible basic design by the use of performance prediction model. This model can be constructed by design engineers. The main idea of this paper comes from this crucial idea to determine relational classification of a set of small vessels using their hull form parameters and performance characteristics defined by transfer functions of heave and pitch motions and of absolute vertical acceleration, by our in-house software application based on K-Means algorithm from data mining. This application is implemented in the C# programming language on Microsoft SQL Server database. We also use the Elbow method to estimate the true number of clusters for K-Means algorithm. The computational results show that the considered set of small vessels can be clustered in three categories according to their functional relations of their hull form parameters and transfer functions considering all cases of three loading conditions, seven ship speeds as non-dimensional Froude numbers (Fn) and nine wave-length to ship-length values (λ/L).

show abstract

“…In addition, this data set has motions of vessels in terms of RAOs of Heave, Pitch and Vertical Absolute Acceleration at stern. The RAO computations have been carried out by means of a two-dimensional computer code based on Frank close-fit method [8,9]. The values of motions are based on Froude numbers and λ/L values at loading conditions.…”

Section: Vessel Databasementioning

confidence: 99%

Untitled

2016

Brod

0

View full text Add to dashboard Cite

Data science for engineers is the most recent research area which suggests to analyse large data sets in order to find data analytics and use them for better designing and modelling. Ship design practice reveals that conceptual ship design is critically important for a successful basic design. Conceptual ship design needs to identify the true set of design variables influencing vessel performance and costs to define the best possible basic design by the use of performance prediction model. This model can be constructed by design engineers. The main idea of this paper comes from this crucial idea to determine relational classification of a set of small vessels using their hull form parameters and performance characteristics defined by transfer functions of heave and pitch motions and of absolute vertical acceleration, by our in-house software application based on K-Means algorithm from data mining. This application is implemented in the C# programming language on Microsoft SQL Server database. We also use the Elbow method to estimate the true number of clusters for K-Means algorithm. The computational results show that the considered set of small vessels can be clustered in three categories according to their functional relations of their hull form parameters and transfer functions considering all cases of three loading conditions, seven ship speeds as non-dimensional Froude numbers (Fn) and nine wavelength to ship-length values (λ/L).

show abstract

Nonlinear meta-models for conceptual seakeeping design of fishing vessels

Cited by 12 publications

References 2 publications

Development of Monohull Fish Processing Vessel Hullform to Support Domestic Fishing Activities in Indonesia

Development of Monohull Fish Processing Vessel Hullform to Support Domestic Fishing Activities in Indonesia

Determination of Relational Classification Among Hull Form Parameters and Ship Motions Performance for a Set of Small Vessels

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