Wave Period Distribution in Mixed Sea-States

Rodrı́guez, Germán; Soares, C. Guedes; Pacheco, Mercedes

doi:10.1115/1.1643387

Cited by 15 publications

(8 citation statements)

References 18 publications

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“…Based on the ocean spectrum we can see that the long period waves have smaller energy spectrum than the shorter waves. The measured frequency spectrum for a heavy sea shows the high peak for the frequency band [44]. Similar peaks we see in the spectrum of the vessel's motion signals (vessel's roll and pitch) are associated with the sea conditions (see Fig.…”

Section: Electrical Power Analysis Based On Power Signal Decompossupporting

confidence: 78%

Data-Driven Methodology for the Analysis of Operational Profile and the Quantification of Electrical Power Variability on Marine Vessels

Swider

Pedersen

2019

IEEE Trans. Power Syst.

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Measurements from the on-board systems of marine vessels are increasingly available for data analysis and are growing in importance as the ship industry enters a phase of digitalization. The purposes of the data analysis from vessels in operation include the verification of the power system design in general and improvement of the electrical power load analysis (EPLA) in particular. In this paper, we show how to extract valuable information from the data-driven operational profile analysis, which reveals the real power demand, and how the vessel was operated. Using the real power range analysis, we emphasize the significance of rarely occurring high power demands, which are critical for power system design and optimization. We propose a methodology for the quantification of variability in the generated power, which explains the tails of probability distributions of a power signal based on signal decomposition. The proposed methodology makes use of the data and it can facilitate the selection of the optimal size, number, and configuration of generators or batteries when designing new power systems. Measurements from a data collection system are used to demonstrate the methodology for dynamic positioning (DP) mode of the operation of a platform supply vessel (PSV). Index Terms-electric power load analysis (EPLA), power variability quantification, load variability, power system design.

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Section: Electrical Power Analysis Based On Power Signal Decompossupporting

confidence: 78%

Data-Driven Methodology for the Analysis of Operational Profile and the Quantification of Electrical Power Variability on Marine Vessels

Swider

Pedersen

2019

IEEE Trans. Power Syst.

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“…The distribution of wave periods is narrower than that of wave heights in wind seas (Goda, 2000) and intrinsically difficult to determine (Rodríguez et al., 2004). Most of the models proposed for the distribution of wave periods were derived as the marginal distribution of the joint probability distribution of wave heights and periods.…”

Section: Short‐term Statistical Distributionsmentioning

confidence: 99%

“…Rodríguez et al. (2004) investigated the performance of two prominent period distribution models, viz. Those developed by Cavanie´ et al.…”

Section: Short‐term Statistical Distributionsmentioning

confidence: 99%

Investigation of Wave Height Distributions and Characteristic Wave Periods in Coastal Environments

James

Panchang

2022

JGR Oceans

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The short‐term statistics of wave conditions in coastal waters around the United Kingdom have been investigated using over 40,000 half‐hour long sea state records with significant wave height greater than 3 m. The extensive data set facilitates an assessment of various wave height and period distribution models in shallow and intermediate waters. The results reveal that the relative wave height Hs/D (where Hs is the significant wave height and D the water depth) can serve as a key indicator in choosing the distribution with least error in a given sea state. The Naess model is found to be the most accurate in describing the tail of the wave height distribution in a sea state for low relative wave heights (Hs/D < 0.2), and the depth‐dependent van Vledder model for high relative heights (Hs/D > 0.4). In between these sea states, a transition in the performance of the deep‐water and the depth‐dependent models is visible. When details of the spectrum are not available, the Weibull distribution is the most accurate amongst applicable models, in spite of considerable variability in its parameter values. While the spectral bandwidth appears to have minimal impact on the distribution of wave heights in a sea state, it does appear to influence the relationship between characteristic wave periods. Based on measurements, these are found to deviate from empirical relationships proposed, for example, by the US Army Coastal Engineering Manual. Improved formulas that incorporate the spectral width are therefore proposed.

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“…Wist (2003) modelled successive wave periods with the Nataf transformation using a two-parameter Weibull distribution and a generalized gamma distribution, and the latter was in good agreement with the data. Of the various probabilistic models considered for comparison with zero up-crossing wave periods in Gaussian combined sea states, the distribution recommended by Myrhaug and Slaattelid (1999), which is an amalgamation of two, two-parameter Weibull functions represents adequately the observed distributions of wave periods (Rodríguez et al, 2004). Anyway, no attempt appeared to have been made to gauge other wave period statistics from these models.…”

Section: Introductionmentioning

confidence: 98%

“…The marginal distributions of wave periods, even under the assumption that waves are linear and have narrow band frequency spectrum (Rodríguez et al, 2004) are difficult to determine probably because often sea states have a certain percentage of cases with two wave systems (Guedes Soares, 1984), in which case the distribution of periods will appear bimodal and will not fit the distribution appropriate to single sea states. One approach to determining the distribution of wave periods is to derive it as the marginal distribution of the joint distribution of individual wave heights (H) and periods (T).…”

Section: Introductionmentioning

confidence: 98%