Conference Paper

Reducing Operational Risks by On-Board Phase Resolved Prediction of Wave Induced Ship Motions

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Abstract

For numerous offshore operations, wave induced vessel motions form a limitation for operability: Installation of wind turbines, removal and placements of top sites on/from jackets, landing of helicopters etc. can only be done safely in relatively benign wave conditions. In many cases the actually critical phase takes no more than some tens of seconds. An on-board prediction of vessel motions would enable crew to anticipate on these near future vessel motions and avoid dangerous situations resulting from large ship motions. This paper presents results from a field campaign in which non-coherent raw X-band navigation radar data was used as input for a procedure that inverts the radar data into a phase resolved estimation of the wave elevation. In combination with a wave propagation and vessel response model, this procedure can compute a prediction of phase resolved vessel motions, some tens of seconds up to minutes into the future, depending on radar range and sea state. We compare predictions obtained this way with actual measurements of a well intervention vessel that were obtained during a sea trial performed at the North Sea. It was concluded that the method results in very accurate predictions: correlations between 0.8–0.9 were obtained for predicted ship motions of/around the COG and the vertical motions of the helicopter deck. Copyright © 2016 by ASME Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

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... The corresponding prediction capacity is limited by the accuracy of the wave forecast and the applied linear transfer functions, i.e., response amplitude operators (RAOs). In recent decades, research about ODSS has been mainly focused on improving vessel motion prediction by improving the wave prediction for the near future by: (1) processing of coherent wave radar signals [11,12]; (2) using non-coherent wave radar signals combined with ship motion measurements [13][14][15]; (3) applying ''ship as a wave buoy'' analogy [16,17] assuming stationary sea states and predicting the future sea state by extrapolation; (4) or improving the accuracy of the wave analysis model [18][19][20]. ...
... For periods from 3 s to 5 s and from 25 s to 26 s, was set to 0.25 s; and for periods from 26 s to 40 s, is 0.5 s. Consequently, the frequency intervals applied in Eq. (14) were unevenly distributed, thus avoiding time record repetition. ...
... When short-crested waves are considered, Eqs. (13) and (14) are substituted by: ...
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... Over the recent years, the first successes with wave and ship motion prediction systems using radar as remote wave sensing technique have been reported. ([1]- [3]) Next Ocean is a start-up with the mission to develop and commercialize a vessel motion prediction system existing of low cost COTS (commercial off-the-shelve) hardware components combined with high performance analysis software, capable of providing on-board real time predictions of ship motions and waves, presented to the crew via an intuitive user interface that helps making go / no go decisions during motion critical offshore operations. ...
... Since this radar back scatter is not directly physically related to any sea wave property, it leaves a scaling issue which will be briefly discussed in the proceeding. A brief summary of the approach as previously presented in [3] is given here. The analysis exists of mainly two separate branches, being executed simultaneously during real-time operation, being the spectral branch and the deterministic branch: A classical 3D FFT approach combined with dispersion filtering based on [4] is applied in order to obtain the directional spectrum ( ) to select a subset of k , referred to as solve k , representing the major fraction of the total energy represented by the spectrum. ...
... -Xsense 6 DOF Motion sensor, providing vessel motions for all 6 degrees of freedom -JRC JRL-31 GPS compass, providing GPS position and vessel heading All signals were recorded synchronically, sharing one and the same time base and stored in files, one file per radar antenna revolution. Earlier field tests were reported on in [3]. The purpose of the test presented in this paper was mainly to assess the system's performance under very different circumstances. ...
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... Researches on ODSSs in the last two decades mostly focused on developing and applying onboard wave measuring systems to ensure timely and sufficiently accurate wave forecast for real-time vessel and structural response predictions. For examples, waves can be measured on board by 1) coherent Doppler marine radar systems [10,11]; 2) non-coherent nautical radar systems, e.g., WaMoS II system [3,12]; 3) special cameras based on light detection and ranging (LIDAR) technology [13,14]; 4) using vessel responses and applying "ship as a wave buoy" analogy [4,15]; and 5) deploying wave buoys near the operating location and connecting to the floater directly. The WAP module should also be able to acquire historical wave data from other instruments or Module Input Output WAP a) 1) Measured historical waves (time records); 2) Historical waves by wave model analysis (S ζ ζ (ω, β W ) or θ θ θ ); 3) Measured forecasted waves (time records); 4) Wave forecast by wave model analysis (S ζ ζ (ω, β W ) or θ θ θ ); 5) The measuring or analysis uncertainties. ...
... The nonlinearity of vessel roll motion is well-known due to the dominated nonlinear damping terms [35]. Therefore, it is often challenging to get acceptable quality of roll motion prediction when linear roll RAO is applied and the additional linearized damping term cannot be sufficiently tuned based on the full-scale measurements [10][11][12]25]. Better correlation between the extreme responses from the prediction and the measurement of roll motion has been normally observed. ...
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... With the development in sensor technology and computational process capacity during the last two decades, many research-oriented onboard decision support systems (ODSS) for marine and offshore activities have been developed aiming at improving vessel motion predictions. Examples are: 1) SeaSense system ; 2) CASH system (Clauss et al., 2012); 3) OWME project (Onboard Wave and Motion Estimator) applying non-coherent WaMoS II radar (Dannenberg et al., 2010;Naaijen et al., 2016Naaijen et al., , 2018; 4) ESMF project (Environment and Ship Motion Forecasting) applying coherent wave radar systems Kusters et al., 2016;Alford et al., 2015). On-site full-scale tests have been performed for validation of the different proposed methods (Naaijen et al., 2016(Naaijen et al., , 2018Connell et al., 2015;Alford et al., 2015). ...
... Examples are: 1) SeaSense system ; 2) CASH system (Clauss et al., 2012); 3) OWME project (Onboard Wave and Motion Estimator) applying non-coherent WaMoS II radar (Dannenberg et al., 2010;Naaijen et al., 2016Naaijen et al., , 2018; 4) ESMF project (Environment and Ship Motion Forecasting) applying coherent wave radar systems Kusters et al., 2016;Alford et al., 2015). On-site full-scale tests have been performed for validation of the different proposed methods (Naaijen et al., 2016(Naaijen et al., , 2018Connell et al., 2015;Alford et al., 2015). Challenges on roll motion prediction based on the vessel being modelled as a linear transfer function, known as response amplitude operator (RAO), have been reported in all the relevant tests. ...
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A directional hybrid wave model (DHWM) has been developed for deterministic prediction of short-crested irregular ocean waves. In using the DHWM, a measured wave field is first decomposed into its free-wave components based on as few as three point measurements. Then the wave properties are predicted in the vicinity of the measurements based on the decomposed free-wave components. Effects of nonlinear interactions among the free-wave components up to second order in wave steepness are considered in both decomposition and prediction. While the prediction scheme is straightforward, the decomposition scheme is innovative and accomplished through an iterative process involving three major steps. The extended maximum likelihood method is employed to determine the directional wave spreading; the initial phases of directional free-wave components are determined using a least-square fitting scheme; and nonlinear effects are computed using both conventional and phase modulation methods to achieve fast convergence. The free-wave components are obtained after the nonlinear effects being decoupled from the measurements. Variety of numerical tests have been conducted, indicating that the DHWM is convergent and reliable.
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Common marine X-Band radars can be used as a sensor to survey ocean wave fields. The wave field images provided by the radars are sampled and analysed by a wave monitoring system (called WaMoS II) developed by the German research institute GKSS. This measuring system can be mounted on a ship, on offshore stations or at coastal locations. The measurement is based on the backscatter of microwaves from the ocean surface, which is visible as `sea clutter' on the radar screen. From this observable sea clutter, a numerical analysis is carried out. The unambiguous directional wave spectrum, the surface currents and sea state parameters such as wave periods, wave lengths, and wave directions can be derived. To provide absolute wave heights, the response of the nautical radar must be calibrated. Similar to the wave height estimations for Synthetic Aperture Radars, the so-called `Signal to Noise Ratio' leads to the determination of the significant wave height (HS). In this paper, WaMoS II results are compared with directional buoy data to show the capabilities of nautical microwave radars for sea state measurements.
Article
An empirical inversion method is presented for determination of time series of ocean surface elevation maps from nautical radar-image sequences. The method is based on the determination of the surface tilt angle in antenna look direction at each pixel of the radar images. Thereby in situ sensors are not required. An external calibration is not necessary. A conventional nautical X-band radar, operating at grazing incidence and horizontal polarization in transmit and receive, is used as a sensor. Radar-image sequences, with their high spatial resolution and large coverage, offer a unique opportunity to derive and study individual waves and wave fields in space and time and therefore allow the measurement of individual wave parameters and wave groups. For validation of the inversion scheme, the significant wave heights derived from the inverted radar data sets and from colocated wave records are compared. It is shown that the accuracy of the radar-retrieved significant wave height is within the accuracy of the in situ sensors. Furthermore, a wave elevation time series is directly compared to a buoy record to show the capabilities of the proposed method.
Shallow Angle Wave profiling LI-DAR
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