Sea state estimation using multiple ships simultaneously as sailing wave buoys

Nielsen, Ulrik Dam; Brodtkorb, Astrid H.; Sørensen, Asgeir J.

doi:10.1016/j.apor.2018.12.004

Cited by 45 publications

(22 citation statements)

References 28 publications

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“…(2) the low initial investment required 1 and (3) the advantage that ships and offshore platforms can work together to provide worldwide information about the sea conditions, even from remote areas, at anytime. Several works (e.g., [4][5][6]) have shown the accuracy of the estimations of the sea conditions obtained through the VMBs. However, in some sporadic cases, the authors also observed large discrepancies between the inferred directional sea spectra and the measurements adopted as the paradigm for assessing the performance of the VMB.…”

Section: Motivationmentioning

confidence: 96%

Assessing an Improved Bayesian Model for Directional Motion Based Wave Inference

Mas-Soler

Souto-Iglesias²,

Simos

2020

JMSE

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An innovative Bayesian motion-based wave inference method is derived and assessed in this work. The evaluation of the accuracy of the proposed prior distribution has been carried out using the results obtained during a dedicated experimental campaign with a scale model an Oil and Gas (O&G) semisubmersible platform. As for the Bayesian statistical inference approaches, the features of the proposed novel prior distribution, as well as the hypotheses adopted, are discussed. It has been found that significant improvements can be obtained if the new approach is adopted to estimate the sea conditions from measured vessel motions. Finally, it is possible to highlight a substantial reduction of the computing time when the sea conditions are estimated by means of the improved Bayesian method, if compared with the conventional approaches for motion-based wave inference.

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Section: Motivationmentioning

confidence: 96%

Assessing an Improved Bayesian Model for Directional Motion Based Wave Inference

Mas-Soler

Souto-Iglesias²,

Simos

2020

JMSE

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“…A global network of directional spectral wave drifters, supplemented as feasible by targeted campaigns such as deploying wave drifters in advance of large storms including hurricanes, should be implemented over the next decade. This network may include new platforms, including ships of opportunity (Nielsen et al, 2019), once their errors are known. Adding many doubtful data should not be thought as a replacement for high quality measurements that are particularly needed for long term climate monitoring and statistics on extreme events.…”

Section: In Situ Observational Systemsmentioning

confidence: 99%

Observing Sea States

et al. 2019

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Sea state information is needed for many applications, ranging from safety at sea and on the coast, for which real time data are essential, to planning and design needs for infrastructure that require long time series. The definition of the wave climate and its possible evolution requires high resolution data, and knowledge on possible drift in the observing system. Sea state is also an important climate variable that enters in air-sea fluxes parameterizations. Finally, sea state patterns can reveal the intensity of storms and associated climate patterns at large scales, and the intensity of currents at small scales. A synthesis of user requirements leads to requests for spatial resolution at kilometer scales, and estimations of trends of a few centimeters per decade. Such requirements cannot be met by observations alone in the foreseeable future, and numerical wave models can be combined with in situ and remote sensing data to achieve the required resolution. As today's models are far from perfect, observations are critical in providing forcing data, namely winds, currents and ice, and validation data, in particular for frequency and direction information, and extreme wave heights. In situ and satellite observations are particularly critical for the correction and calibration of significant wave heights to ensure the stability of model time series. A number of developments are underway for extending the capabilities of satellites and in situ observing systems. These include the generalization of directional measurements, an easier exchange of moored buoy data, the measurement of waves on drifting buoys, the evolution of satellite altimeter technology, and the measurement of directional wave spectra from satellite radar instruments. For each of these observing systems, the stability of the data is a very important issue. The combination of the different data sources, including numerical models, can help better fulfill the needs of users.

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“…Moored buoys are reliable and are the primary means of collecting wave data. This issue of such devices exists because of the performance and maintenance complexity, low-resolution properties, as well as their price and high sensitivity to system errors and weather damage [1][2][3]11,24]. Due to the disadvantages of conventional devices, DWS estimation methods have been extended to process measurements of ship motions (especially the roll, pitch, and heave motions) and their couplings.…”

Section: Estimation Of Dwsmentioning

confidence: 99%

“…Therefore, the connection between encounter and true-wave frequencies is described as a triple-valued function. In other words, the problem exists in the transformation of encountered frequencies into the true-wave frequencies because of the Doppler effect (also known as the triple-valued function problem or the speed-of-advance problem) [3]. The deep-water relationship between the encounter and true wave frequency is given by:…”

Section: Estimation Of Dwsmentioning

confidence: 99%

“…This is significant because of reducing the possibility of critical situations, both for passengers and cargo on board, as well as providing a sense of stability and safety. In addition to moored buoy, satellite, and radar measurements, which have been in use for many years, a DWS estimation based on measured ship motions has proved to be much easier in terms of maintenance [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

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Improving the Performance of Dynamic Ship Positioning Systems: A Review of Filtering and Estimation Techniques

Lerga

Prpić-Oršić

et al. 2020

JMSE

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Various operations at sea, such as maintaining a constant ship position and direction, require a complex control system. Under such conditions, the ship needs an efficient positioning technique. Dynamic positioning (DP) systems provide such an application with a combination of the actuators mechanism, analyses of crucial ship variables, and environmental conditions. The natural forces of induced nonlinear waves acting on a ship’s hull interfere with the systems. To generate control signals for actuators accurately, sensor measurements should be filtered and processed. Furthermore, for safe and green routing, the forces and moments acting on the ship’s hull should be taken into account in terms of their prediction. Thus, the design of such systems takes into account the problem of obtaining data about the directional wave spectra (DWS). Sensor systems individually cannot provide high accuracy and reliability, so their measurements need to be combined and complemented. Techniques based on the recursive Kalman filter (KF) are used for this purpose. When some measurements are unavailable, the estimation procedure should predict them and, based on the comparison of theoretical and measured states, reduce the error variance of the analyzed signals. Different approaches for improving estimation algorithms have evolved over the years with the indication of improvement. This paper gives an overview of the state-of-the-art estimation and filtering techniques for providing optimum estimation states in DP systems.

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Sea state estimation using multiple ships simultaneously as sailing wave buoys

Cited by 45 publications

References 28 publications

Assessing an Improved Bayesian Model for Directional Motion Based Wave Inference

Assessing an Improved Bayesian Model for Directional Motion Based Wave Inference

Observing Sea States

Improving the Performance of Dynamic Ship Positioning Systems: A Review of Filtering and Estimation Techniques

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