A new vessel motion based method for parametric estimation of the waves encountered by the ship in a seaway

Zago, Lariuss; Simos, Alexandre N.; Kawano, Alexandre; Kogishi, Andre M.

doi:10.1016/j.apor.2023.103499

Cited by 6 publications

(2 citation statements)

References 51 publications

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“…In this sense, the main take-away from this initial assessment is that good wave spectrum estimates can be expected, when the WBA is applied under “controlled” conditions. In fact, similar observations have been made in several other studies, where (fully) dedicated experimental datasets, including model tests, are analysed 16 , 18 , 24 , 40 – 43 . Note that by controlled conditions should be understood that the detailed hull geometry is available for computation of the transfer functions, and, furthermore, that the uncertainties in operational parameters (i.e., ship speed and loading condition) are small.…”

Section: Resultssupporting

confidence: 84%

Estimating waves via measured ship responses

Nielsen,

Bingham,

Brodtkorb

et al. 2023

Sci Rep

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Optimisation of energy efficiency and operational performance as well as assessment of safety levels and emissions of marine operations require detailed information about the acting wave system. It is possible—with an analogy to classical wave buoys—to estimate the directional wave spectrum by processing sensor measurements of wave-induced responses (e.g., motions and structural responses) from a ship. Compared to other sources of wave data (e.g., buoys, satellites, third-generation wave models), estimation concepts using the ship itself as a buoy provide the wave spectrum at the exact spatio-temporal point, potentially increasing accuracy and with minimal associated cost. This paper gives an overview of the technology, discusses associated uncertainties, and highlights new developments made for estimating waves via measured ship responses.

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Section: Resultssupporting

confidence: 84%

Estimating waves via measured ship responses

Nielsen,

Bingham,

Brodtkorb

et al. 2023

Sci Rep

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“…Zago et al proposed a new parametric approach for wave estimation based on ship motion, giving a parametric description of the encounter wave spectrum, which allowed the algorithm to perform wave inference in the ship's frame of ref. [57].…”

Section: Introductionmentioning

confidence: 99%

Identification of Multi-Innovation Stochastic Gradients with Maximum Likelihood Algorithm Based on Ship Maneuverability and Wave Peak Models

Liu,

Zhang,

Wang

et al. 2024

JMSE

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This paper investigates the problem of real-time parameter identification for ship maneuvering parameters and wave peak frequency in an ocean environment. Based on the idea of Euler discretion, a combined model of ship maneuvering and wave peak frequency (ship–wave) is made a discretion, and a discrete-time auto-regressive moving-average model with exogenous input (ARMAX) is derived for parameter identification. Based on the ideas of stochastic gradient identification and multi-innovation theory, a multi-innovation stochastic gradient (MI-SG) algorithm is derived for parameter identification of the ship–wave discretion model. Maximum likelihood theory is introduced to propose a maximum likelihood-based multi-innovation stochastic gradient (ML-MI-SG) algorithm. Compared to the MI-SG algorithm, the ML-MI-SG algorithm shows improvements in both parameter identification accuracy and identification convergence speed. Simulation results verify the effectiveness of the proposed algorithm.

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