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Reducing Operational Risks by On-Board Phase Resolved Prediction of Wave Induced Ship Motions
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.
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... In the case of the PM, the most exploited and effective methods for SM and QP predictions are the Linear Potential Flow Models (PL) [49,55,57,66], which, even if they make strong assumptions (i.e., wave motions can be decomposed into independent sinusoidal components and the ship is a linear filter), well represent the ship's response in the majority of the cases and are able to give near real-time predictions. Other exploited models, much for predictions of sea-keeping performances than for QP prediction, are Non-Linear Potential Flow models (PNL) [67] and computational fluid dynamics (CFD) techniques (where software tools are used to perform the calculations required to simulate the flow of the ocean surface) [56,62]. ...
... Physics-based sea-keeping models are focused on the response of the ship to the wave actions and, specifically, on the prediction of the SMs and the wave loads acting on the ship structures. Though viscous effects certainly play a role in the behaviour of a ship in waves, classical methods used for SM and QP prediction, based on potential flow theory, ignore these effects, and nevertheless provide in general accurate predictions of the SMs, except for the roll motion, for which viscous effects play a dominant role [49,66]. ...
... One rather common approach to classification is to consider PL models, PNL models and CFD models. PL models [49,55,57,66] are well established and widely used. The main simplification introduced by such models is to assume the hull wet surface to be defined by the ratio between the mean position of the hull and the mean free surface, which allows the linearization of the boundary conditions of the potential flow problem. ...
... 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: ...
It is essential for a safe and cost-efficient marine operation to improve the knowledge about the real-time onboard vessel conditions. This paper proposes a novel algorithm for simultaneous tuning of important vessel seakeeping model parameters and sea state characteristics based on onboard vessel motion measurements and available wave data. The proposed algorithm is fundamentally based on the unscented transformation and inspired by the scaled unscented Kalman filter, which is very computationally efficient for large dimensional and nonlinear problems. The algorithm is demonstrated by case studies based on numerical simulations, considering realistic sensor noises and wave data uncertainties. Both long-crested and short-crested wave conditions are considered in the case studies. The system state of the proposed tuning framework consists of a vessel state vector and a sea state vector. The tuning results reasonably approach the true values of the considered uncertain vessel parameters and sea state characteristics, with reduced uncertainties. The quantification of the system state uncertainties helps to close a critical gap towards achieving reliability-based marine operations.
... 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. ...
The workability of various types of operations offshore are largely affected by waves and wave induced motions. Examples are crew transfer from crew transfer vessels or service operation vessels to offshore wind turbines for maintenance, landing of helicopters in (navy) vessels and various crane operations. Over the recent years quite some effort has been put in technology aiming to provide a real time on-board prediction of approaching waves and wave induced vessel motions some minutes in advance. Enabling crew to anticipate, thus enhancing the safety and operability of these operations. This paper addresses the performance during a field test of the system as being under development by Next Ocean enabling such predictions, based on using an off-the-shelve (non-coherent) navigation radar system as a remote wave observer. Briefly summarizing (earlier publications on) the technical approach, focus will be on results obtained from a field test where the system was validated. Good agreements between ship motions as measured by an on-board motion reference unit and predictions obtained by the wave and motion prediction system during a field test on the North Sea near the Dutch coast on a 42 m patrol vessel will be shown in the results section, from which the usefulness of the system for operational decision support can be concluded.
... 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. ...
Digital twins have attracted significant attention across different domains for decades. In the maritime and the energy industries, digital twins have been mainly used for system condition monitoring, project visualization, crew training, real-time decision making/support, and predictive maintenance based on onsite measurement data from onboard sensors. Such a digital twin normally presumes the vessel’s operational condition by assistance from sensors and engineering judgement. However, a vessel’s operational condition and loading state may shift quite often due to the frequently changing operational scenarios, tasks, and environmental conditions. In addition, the true vessel state (e.g., inertia distribution) may deviate from the intended one according to planning due to possible engineering errors. Even though there are sensors helping to monitor vessel condition such as draft monitoring systems and ballast systems, several important vessel parameters are difficult to measure directly, e.g., moment of inertia, center of gravity, and nonlinear hydrodynamic damping. This paper proposes a framework for monitoring vessel condition and providing decision support based on quantitative risk assessment, through a vessel state observer which is able to self-tune the important but uncertain vessel parameters by utilizing the available prior knowledge, vessel measurements, and information about the associated sea states. The tuned vessel parameters improve the information about the real-time vessel condition and consequently assist to improve the prediction accuracy of vessel seakeeping performance in the near future for the emerging wave conditions. Furthermore, the tuned results and the response prediction can then be applied to a decision support system, quantitatively evaluating potential risk and providing suggestions. The framework consists of 5 modules, i.e., wave data acquisition and processing, vessel data acquisition and processing, vessel seakeeping model tuning, real-time vessel motion and critical structural response prediction, and risk awareness and avoidance. Details of each module are described in the paper. The proposed framework can also assist in the development of autonomous ships.
... 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. ...
Vessel and wave hydrodynamics are fundamental for vessel motion prediction. Improving hydrodynamic model accuracy without compromising computational efficiency has always been of high interest for safe and costeffective marine operations. With continuous development of sensor technology and computational capacity, an improved digital twin concept for vessel motion prediction can be realized based on an onboard online adaptive hydrodynamic model. This article proposes and demonstrates a practical approach for tuning of important vessel hydrodynamic model parameters based on simulated onboard sensor data of vessel motion response. The algorithm relies fundamentally on spectral analysis, probabilistic modelling and the discrete Bayesian updating formula. All case studies show promising and reasonable tuning results. Sensitivities of the approach with respect to its key parameters were also studied. Sensor noise has been considered. The algorithm is found to be computationally efficient, robust and stable when tuning the values of hydrodynamic parameters and updating their uncertainties, within reasonable sensor noise levels.
... The X-band marine radar has been widely used for ocean wave remote sensing tasks due to its ability to image the sea surface with high temporal and spatial resolutions. Naaijen et al. [1] used X-band radar data to estimate wave elevation while combining a wave propagation and vessel response model to predict future vessel motions. Lyzenga et al. [2] employed X-band Doppler radar data to inverse phase-resolved wave field. ...
... It is believed that it plays a key role in a future framework of onboard decision support [21]. Realworld case studies have been performed in several research projects such as CASH [9], ESMF [10], [11], and OWME [12], [13] projects. In these projects, physics-based transfer functions map wave-radar observations into ship motions. ...
... In the joint industry project, Onboard Wave and Motion Estimator (OWME), the ocean environment was evaluated using the FFT technique, and the phases of the discretized components were optimized for the predefined wave directionalities (Dannenberg et al., 2010). By combining the wave propagation model and pre-computed response amplitude operator (RAO) model, the incident waves and the resulting ship motions were forecasted (Naaijen et al., 2016(Naaijen et al., , 2018. For the decision support system, Computer-Aided Ship Handling (CASH; Clauss et al., 2012), predictions were also performed on the wave-induced motion and hydrodynamic pressure distribution using a 3D FFT analysis. ...
This paper introduces a real-time digital twin for ship operations in seaways. The concept of the digital twin is becoming popular, and it is adopted for ship operation systems in this study. In particular, this paper introduces a new and innovative concept of the digital twin to predict ocean waves and hydrodynamic performances, such as seakeeping and maneuvering, which enables the risk and optimum route to be forecast in real time. An essential element in the realization of such a real-time digital twin is the real-time prediction of ocean waves. Hence, a sophisticated algorithm for wave reconstruction using measured wave-radar images is developed, which is extended to predicting the future evolution of a three-dimensional wave field in front of a ship within a time window of the order of 10 min. As another essential element, an analysis program to solve the coupled seakeeping-maneuvering problem is developed. This analysis can also be used in real time. By combining this with wave prediction software, the future occurrence of ocean waves and ship responses can be predicted. By extending this approach, the risk and performance of ships in various ocean environments can be predicted. In this paper, concepts, approaches, and examples are introduced.
... It is of major international maritime interest to increase the sea state under which a wide range of launch and recovery operations can be safely undertaken Ferrier et al. 2000;Crossland et al. 2009;Ferrier et al. 2013;Naaijen et al. 2016). Typical tasks include the launch and recovery of fixed and rotary wing aircraft; small, surface, or submersible vehicles, such as rigid-hulled inflatable boats (RHIBs); and cargo, fuel, and personnel transfer systems between vessels and from vessels onto offshore structures. ...
A number of maritime operations can benefit from a short-term deterministic sea wave prediction (DSWP). Conventional X-band radars have recently been shown to provide a low-cost convenient source of two-dimensional wave profile information for DSWP purposes. However, such rotating radars typically introduce temporal smearing into the data, which introduces errors when traditional Fourier transform–based wave prediction methods are used. The authors report on a new approach for DSWP that avoids such errors. Furthermore, it is not susceptible to the condition number problems that arise with any form of direct or indirect inversion-based approaches. Extensive numerical analyses are conducted to illustrate the effect of the mixed space–time nature of the data on DSWP and the efficiency of the proposed technique to handle it.
Model-based prediction of vessel response is valuable for planning and execution of marine operations. Response-based operation criteria are expected to give less downtime and cheaper and safer operations than criteria based on wave height and wave period. At least this holds if the response model has sufficient accuracy. The accuracy can in principle be improved by tuning the model using measured inputs and outputs. It is envisioned that an advisory system for planning and execution of marine operations will contain a module for continuous model improvement based on on-site measurements of excitation and response. A premise for this is that the measurements be of sufficiently high quality. To test the potential of automatic model tuning an established numerical vessel model is subjected to tuning with high-precision data from a model test with wave disturbance only. This may give information of how well modelling can be achieved using tuning under favourable conditions and serve as a benchmark for tuning under noisy conditions. In addition the results may give suggestions for improvement of the mathematical model. A prototype tuning software is written in Matlab. The tuning principle is to minimize the output error, i.e. the difference between measured and simulated response, by adjusting the model's parameters. For the minimization a quasi-Newton method is used. The tuning software is tested with data from the model test and found to function as intended. Examples of tuning are given.
This article studies a procedure that facilitates short-time, deterministic predictions of the wave-induced motion of a marine vessel, where it is understood that the future motion of the vessel is calculated ahead of time. Such predictions are valuable to assist in the execution of many marine operations (crane lifts, helicopter landings, etc.), as a specific prediction can be used to inform whether it is safe, or not, to carry out the particular operation within the nearest time horizon. The examined prediction procedure relies on observations of the correlation structure of the wave-induced response in study. Thus, predicted (future) values ahead of time for a given time history recording are computed through a mathematical combination of the sample autocorrelation function and previous measurements recorded just prior to the moment of action. Importantly, the procedure does not need input about the exciting wave system, and neither does it rely on off-line training. In the article, the prediction procedure is applied to experimental data obtained through model-scale tests, and the procedure's predictive performance is investigated for various irregular wave scenarios. The presented results show that predictions can be successfully made in a time horizon corresponding to about 8–9 wave periods ahead of current time (the moment of action).
Military sea basing operations include mooring ships together offshore and transferring cargo and equipment between them. A newly developed Environmental and Ship Motion Forecasting (ESMF) System will facilitate these operations by providing predictions of ship motions in waves. Coherent forecasts of the ship motions are provided through remote sensing of the ambient waves and using these waves as input to a predictive ship motion simulation. Key technologies developed in support of the ESMF system include: a custom-built wave sensing radar; a least squares inverse wave retrieval algorithm; a ship motion model for performing rapid seakeeping simulations; and a robust peer-to-peer system architecture. The ESMF system was tested extensively in a demonstration aboard the R/V Melville with very good results, often achieving correlations of forecast-to-realized signals of better than 80% over 30 minute intervals.
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For a number of maritime tasks there is a short time period, typically only a few tens of seconds, where a critical event occurs that defines a limiting wave height for the whole operation. Examples are the recovery of fixed and rotary winged aircraft, cargo transfers, final pipe mating in fluid transfer operations, and launch/recovery of small craft. The recovery of a 30-t rescue submersible onto a mother ship in the North Atlantic Treaty Organization (NATO) Submarine Rescue System is a prime example. In such applications short-term deterministic sea wave prediction (DSWP) can play a vital role in extending the sea states under which the system can be safely deployed. DSWP also has great potential in conducting experimental sea wave research at full scale. This report explores the feasibility of using data from an experimental wave profiling radar in achieving DSWP. The report includes theory, simulation, and field testing. Two forms of DSWP are employed: a fixed point system based upon a restricted set of wave directions from which some success is obtained and the other a fully two-dimensional technique that requires further development. The main finding is that using wave profiling radar for DSWP offers promise but requires improvements both to the spatial reliability and the resolution of the wave profiling radar and to the temporal resolution of its sweep before the technique can be considered to be viable as a usable tool.
Vessels and floating structures are subject to wave induced motions affecting and often even preventing their operation. A system to predict short periods of quiescent vessel motions a few minutes in advance could reduce the costly 'Waiting on Weather'. The Joint Industry Project 'On board Wave and Motion Estimator (OWME)' developed a system capable of predicting the vessel motions on board in real time up to two minutes in advance. It measures remote wave profiles by means of nautical radar, uses linear wave theory to propagate the waves in time and space and estimates the ship response to the predicted wave field. This paper describes the concept and shows results of a first field trial aboard an offshore support vessel in mild sea conditions. © 2010 by The International Society of Offshore and Polar Engineers (ISOPE).
We discuss the spatio-temporal domain, here referred to as the predictable zone, in which waves can be predicted deterministically based on an observation in a limited spatial or temporal domain. A key issue is whether the group or phase speed of the observed waves governs the extent of the predictable zone. We have addressed this issue again using linear wave theory on both computer-generated synthetic wave fields and laboratory experimental observations. We find that the group speed adequately indicates the predictable zone for forecasting horizons relevant for offshore and maritime applications.
Real-time sea state measurements could be used to greatly enhance the safety and efficiency of offshore operations. In this paper, a variational data assimilation scheme is developed to improve ocean wave field estimates from marine navigation radars. The assimilation scheme minimizes a cost function which is defined as the difference between radar observations and predictions from a nonlinear wave evolution model over a time interval which is referred to as assimilation interval. An adjoint method is used to calculate the gradient of the cost function with respect to control variables, and the descent direction for minimization is obtained from tangent linear model. The proposed assimilation scheme is validated using synthetic and field data.
This paper is the natural follow-up of the paper [6] also presented at OMAE 2012 by the authors. In [6] the authors describe various linear reconstruction methods for short-crested seas. Each of them is based on a specific analysis technique for the spectral components obtained when applying a 3D Fourier Transform to a series of snap shots of the ocean surface. The efficiency and the accuracy of each method is investigated in [6] for the reconstruction of synthetic images of linear short-crested wave fields.
The purpose of the second part of the paper is to test the most accurate methods described in [6] for the prediction of synthetic linear waves. The predicted wave fields are calculated within a specific area in space and time called the prediction zone, which is determined theoretically. The method for determining the theoretical prediction zone is also described in this part of the paper.
This article explores the feasibility of using a 3-dimensional Fast Fourier Transform (3D FFT) to obtain a frequency domain description of a spatio temporal measured short crested wave field. As 3D FFT is also the basic technique behind wave measurements by navigational X-band radars, the frequency components obtained by these radars could be used as initialization of a wave propagation model, enabling deterministic prediction of wave elevation on board of ships / offshore structures. Different methods are presented to use the dispersion relation to filter wave components obtained by the 3D FFT. The effect on the accuracy of data windowing and temporal measurement domain size are explored by simulations with linear synthetic wave data: It is investigated how well a synthetic wave field reconstructs after inverse transforming the filtered frequency components obtained by 3D FFT. A second paper [1] will consider the prediction outside the measurement domain by using the filtered 3D FFT components.
The presented research is part of the development of an onboard wave and motion estimation system that aims to predict wave elevation and vessel motions some 60–120 s ahead, using wave elevation measurements by means of X-band radar. In order to validate the prediction model, scale experiments have been carried out in short crested waves for 3 different sea states with varying directional spreading, during which wave elevation and vessel motions were measured. To compare predicted and measured wave elevation, three wave probes were used at different distances from a large set of wave probes that was used as input to the model. At one of the prediction locations, also tests were performed to measure vessel motions. This setup allowed validation of a method that was used for initializing the linear wave prediction and ship motion prediction model. Various observations and conclusions are presented concerning optimal combinations of prediction model parameters, probe set-up and sea state.
We report on the development of free surface reconstruction algorithms to predict ocean waves, based on spatial observations made with a high frequency ASC Flash LIDAR camera. We assume that the camera is mounted on a vessel, in a forward looking position, and is pointing at some distance ahead of its path. In its current design, the camera gener-ates a 64 x 64 matrix of laser rays, providing as many simultaneous mea-surements of the distance to the ocean surface, every 0.05-0.25 s, in an angular sector of 15-20 by 9-15 deg. (depending on design assumptions). From this data and the camera's location and orientation, the coordinates of the measured surface points can be generated as a function of time; this yields a sample of spatio-temporal wave elevation data. Due to wave shadowing effects, the density of measurement points gradually decreases (i.e., becomes sparse) with the distance to the camera. Free surface re-construction algorithms were first developed and validated for linear 1D and 2D irregular surface models, whose amplitude coefficients are esti-mated based on minimizing the mean square error of simulated surface elevations to measurements, over space and time (for a specified time ini-tialization period). In the validation tests reported here, irregular ocean surfaces are generated based on a directional Phillips or JS spectrum, and simulated LIDAR data sets are constructed by performing geometric in-tersections of laser rays with each generated surface. Once a nowcast of the ocean surface is estimated from the (simulated) LIDAR data, a forecast can be made of expected waves ahead of the vessel, for a time window that depends both on the initialization period and the resolved wavenumbers in the reconstruction. The process can then be repeated for another prediction window, and so forth. To reconstruct severe sea states, however, nonlinear effects must be included in the sea surface rep-resentation. This is done, here, by representing the ocean surface using the efficient Lagrangian model Choppy, expressed in a new explicit for-mulation, which was demonstrated to approximate second-order effects in waves (Nouguier et al., 2009). In the paper, we develop and validate the 1D surface generation and reconstruction of irregular sea states using Choppy.
In this paper we describe the development of a low cost, high power coherent-on-receiver radar. The unit has a 25kW peak transmit power and is capable of accurately measuring velocity as well as range. The design of the radar is optimized for marine surveillance, although the techniques developed have general application. The radar system is validated through a series of tests that culminate with sea surface measurements.
We report on the development of free surface reconstruction algorithms to predict ocean waves, based on spatial observations made with a high-frequency Flash light detection and ranging camera. We assume that the camera is mounted on a vessel, in a forward looking position, and is pointing at some distance ahead of its path yielding a sample of spatiotemporal wave elevation data. Because of the geometry, the density of measurement points gradually decreases (i.e., becomes sparse) with the distance to the camera. Free surface reconstruction algorithms were first developed and validated for linear 1-D and 2-D irregular surface models, whose amplitude coefficients are estimated on the basis of minimizing the mean square error of simulated surface elevations to measurements, over space and time (for a specified time initialization period). In the validation tests reported here, irregular ocean surfaces are generated on the basis of a directional Pierson-Moskowitz or Elfouhaily spectrum, and simulated LIDAR data sets are constructed by performing geometric intersections of laser rays with each generated surface. Once a nowcast of the ocean surface is estimated from the (simulated) LIDAR data, a forecast can be made of expected waves ahead of the vessel, for a time window that depends both on the initialization period and the resolved wavenumbers in the reconstruction. The process can then be repeated for another prediction window, and so forth. To reconstruct severe sea states, however, nonlinear effects must be included in the sea surface representation. This is done, here, by representing the ocean surface using the efficient Lagrangian choppy wave model .
One of the most relevant parameters to characterize the severity of ocean waves is the significant wave height (H
s
). The estimate of H
s
from remotely sensed data acquired by non-coherent X-band marine radars is a problem not completely solved nowadays. A method commonly used in the literature (standard method) uses the square root of the signal-to-noise ratio (SNR) to linearly estimate H
s
. This method has been widely used during the last decade, but it presents some limitations, especially when swell-dominated sea states are present. To overcome these limitations, a new non-linear method incorporating additional sea state information is proposed in this article. This method is based on artificial neural networks (ANNs), specifically on multilayer perceptrons (MLPs). The information incorporated in the proposed MLP-based method is given by the wave monitoring system (WaMoS II) and concerns not only to the square root of the SNR, as in the standard method, but also to the peak wave length and mean wave period. Results for two different platforms (Ekofisk and FINO 1) placed in different locations of the North Sea are presented to analyze whether the proposed method works regardless of the sea states observed in each location or not. The obtained results empirically demonstrate how the proposed non-linear solution outperforms the standard method regardless of the environmental conditions (platform), maintaining real-time properties.
A series of spatial wave images recorded by a conventional marine radar is analyzed to determine the three-dimensional E(kx, ky, omega) spectrum. In the absence of a surface current the spectral energy in this three-dimensional wave number frequency space will lie on a shell defined by the dispersion relationship. Any deviation from the expected dispersion relationship can be interpreted as being due to a current induced Doppler shift of the wave frequency. A least squares curve fitting technique is used to determine the surface current required to account for the observed Doppler shift. A comparison of the radar-determined spectra and surface currents with ground truth data indicates that the radar system and analysis technique produces results consistent with conventional instrumentation.
A method to estimate sea surface elevation maps from marine radar image sequences is presented. This method is the extension of an existing inverse modeling technique to derive wave spectra from marine radar images, which assumes linear wave theory with temporal stationarity and spatial homogeneity of the observed sea surface elevation. The proposed technique to estimate wave elevation maps takes into account a modulation transfer function (MTF), which describes the radar imaging mechanisms at grazing incidence and horizontal polarization. This MTF is investigated and empirically determined by wave measurements and numerical simulations. The numerical simulations show that shadowing is the dominant effect in the radar imaging mechanism at grazing incidence and horizontal polarization. Further comparisons of wave spectra, as well as comparisons of the wave height probability distributions obtained by the wave elevation maps and the corresponding buoy measurements with the theoretical Rayleigh distribution, confirm the applicability of the proposed method.
The radar backscatter from the ocean surface, called sea clutter,
is modulated due to the surface wave field. The modulation introduces a
spatio-temporal correlation of the sea clutter signal. A
three-dimensional wavenumber frequency spectrum of the sea clutter is
calculated from a time series of radar images with an FFT algorithm.
Because of the non-linearity of the imaging process the image spectrum
contains harmonics in addition to the linear fundamental mode. These
modes are localized at distinct surfaces in the wave number frequency
space corresponding to the scaled dispersion relation of surface gravity
waves. Because of the localization of the spectral energy on dispersion
shells, the aliasing effect due to a temporal undersampling can be
overcome. The dispersion shells are reconstructed over the Nyquist
frequency barrier and for negative frequencies. The dispersion shells
are used as spectral filters to separate the modes from the clutter
noise pedestal of radar image spectra. This pedestal shows the effect of
the impulse response due to the finite spatial resolution of the radar.
The spectral energy of the wavenumber frequency spectrum is integrated
over the frequency coordinate axis, with the positive solution of the
dispersion relation of surface gravity waves as signal filter. With this
spectral filter method an unambiguous wave number image spectrum is
selected. The fundamental image spectrum is related to the surface wave
spectrum by an image transfer function. An empirical calibration
procedure is presented. This procedure is based on the correlation of
the signal to noise ratio with the significant wave height. The
applicability of the method is shown using a radar and buoy data set.
The spectral energies of fundamental mode, first harmonic, and the
clutter component are compared
OceanWaveS GmbH has been developing a prototype system, based on standard non-coherent X-band navigational radars, capable of predicting future sea surface elevations. Combined with a vessel hydrodynamic simulator, this system will forecast ship motions. Applications for such a system include offshore operations, e.g. crane lifts, LNG, cargo and personnel transfers. The forecasts may be assimilated into automated control systems; e.g. floating wind turbines or dynamic positioning systems, or used within Decision Support Systems. This article presents correlation analysis results between the predicted sea surface elevations and vessel-mounted reference sensors for three independent sea trials. Despite neglecting vessel hydrodynamics, correlations of forecasts to measured references of 80% are achieved for T+60 seconds predictions for all sea trials. The prediction horizon is observed up to 180 seconds of forecast.
The University of Michigan is leading a team that includes subcontractors Ohio State University, Aquaveo, LLC, and Woods Hole Oceanographic Institute to design, implement, and test an Environmental and Ship Motion Forecasting (ESMF) system. The system has application to many challenges associated with offshore operations, including skin-to-skin transfer of cargo/personnel and extreme wave/response prediction. Briefly, the system uses a modified commercial-off-the-shelf (COTS) Doppler marine radar to determine the wave field surrounding the vessel; nonlinear wave theory to propagate the wave surface forward in time; and seakeeping theory to predict future vessel motions. A major challenge is that all computations must be done in real time. This paper will briefly describe the system and show an example application of predicting extreme waves and motions for a floating offshore type platform.
Copyright © 2015 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
The reduced order model at the heart of the APS Environmental and Ship Motion Forecasting system must retain the accuracy of a higher fidelity seakeeping code while simultaneously meeting computational speed required to provide motion forecasts minutes into the future for two or more ships operating in close proximity. We describe the mathematical formulation of the reduced order model and efficient modeling techniques to construct databases of wave force Response Amplitude Operators and Impulse Response Functions before presenting comparisons between the reduced order model, a higher fidelity seakeeping code called AEGIR, and experimental data for the R/V Melville and two multi-ship configurations.
Copyright © 2015 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
Wave induced motions limit the working time and safety of many offshore operations, e.g. crane lifts, LNG transfers, and helicopter landings. This paper details recent efforts to deterministically predict ocean surface waves using measurements from the WaMoS II. The WaMoS II derives full 3-dimensional sea surface elevation maps from nautical X-band radar images, yielding both sufficient resolution and range for a useful prediction horizon. Validation of these surface elevation fields shows they are in close agreement with sea surface elevation timeseries from wave buoys and motion reference units. Results from a simple propagation model are presented.
A method is presented for the inversion of images of the sea surface taken by nautical radar into wave elevation that is specifically suitable for the prediction of the wave elevation outside the observation domain covered by the radar. By means of a beamwise analysis of the image obtained by a scanning radar, the image information is translated into wave elevation. Subsequently a 2D FFT is applied in order to obtain the directional wave components required for a linear propagation of the wave field. Assuming knowledge of the significant wave height, a method to obtain the correct scaling of the wave prediction is proposed. The proposed method is verified using synthetic radar images which are modelled by applying shadowing and tilt effect to synthesised short crested linear waves.
This paper presents a time-domain decision support system based on deterministic, non-linear wave forecast and motion prediction for short-term offshore operations. The system consists of three individual constituents: sea state registrations, non-linear wave forecast and motion prediction. Surface elevation snapshots taken continuously by a ship board radar at great distance ahead the operational area are preprocessed by the wave monitoring system WaMoS II and used as input for the wave forecast tool. The non-linear wave propagation is modelled by applying the Higher Order Spectral Method (HOSM), which offers high accuracy and fast calculation time at once. The predicted surface elevation at the location of operation are used for the evaluation of the corresponding offshore structure response. For this purpose, Impulse Response Functions are implemented, which enable the fast determination of the response in time
domain.
The focus of the present study lies on the evaluation of the non-linear wave forecast and motion prediction part of the proposed decision support system. For illustrating the efficiency of the process, an offshore crane operation is investigated. The accuracy of the predicted (future) surface elevation as well as response of the multi-body problem is evaluated against model tests in the controlled environment of a seakeeping basin.
An existing analysis technique for separation of incident and reflected wave trains (e.g. Goda and Suzuki 1976; Mansard and Funke 1980; Zelt and Skjelbreia 1992) is extended towards a 2DH formulation, for constant depth. Combined with a phase-averaged technique (MLM), a phase resolving analysis of multidirectional wave trains is performed. The ability of the algorithm to separate directional components in a short-crested sea is tested against a numerical simulation and a laboratory observation of a short-crested wave field. The investigations show, that the directionality of a short-crested wave train can successfully be approximated in a phase resolving manner, by a superposition of multiple long-crested waves, propagating in various (main) directions.
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.
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.
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
- M R Belmont
- J M K Horwood
- R W F Baker
- J Baker
Belmont, M. R., Horwood, J. M. K., Baker, R. W. F.,
and Baker, J., 2007. "Shallow Angle Wave profiling LI-DAR". Journal of Atmosperic and Oceanic Technology,
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OceanWave Reconstruction Algorithms Based on Spatiotemporal Data Acquired by a Flash LIDAR Camera
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A Real-Time System for Forecasting Extreme Waves and Vessel Motions
- L K Alford
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- O Nwogu
- A Zundel
Alford, L. K., Beck, R. F., Johnson, J. T., Lyzenga, D.,
Nwogu, O., and Zundel, A., 2015. "A Real-Time System
for Forecasting Extreme Waves and Vessel Motions". In
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pp. 1-8.
Design , Implementation , and Evaluation of a System for Environmental and Ship Motion Forecasting
- L K Alford
- R F Beck
- J T Johnson
- D Lyzenga
- O Nwogu
- A Zundel
Alford, L. K., Beck, R. F., Johnson, J. T., Lyzenga, D.,
Nwogu, O., and Zundel, A., 2014. "Design, Implementation, and Evaluation of a System for Environmental and
Ship Motion Forecasting". In 30th Symposium on Naval
Hydrodynamics, no. November, pp. 2-7.