Conference Paper

Validation of a Deterministic Wave and Ship Motion Prediction System

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Abstract

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.

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... Research on ODSSs [5][6][7][8][9] in recent decades has mainly focused on reducing the wave forecast uncertainty by, e.g., (1) developing high-fidelity wave forecast models [10] for forecasts of a few hours up to some days in advance; (2) calibrating the local alpha factor [1] with wave-measuring instruments deployed near the floater [11]; (3) measuring the wave field in front of the vessel and predicting the encountered waves through noncoherent or coherent radar systems or special cameras [8,[12][13][14]; and (4) estimating the wave spectrum by applying the ''ship as a wave buoy'' analogy [15,16] and predicting the sea state by extrapolation. Similar to the design of marine operations, such an ODSS predicts wave-induced vessel responses based on the presumed deterministic vessel condition in terms of, e.g., the load distribution and linearized viscous damping, which may deviate from the real condition at the operation execution phase. ...
... Similar to the design of marine operations, such an ODSS predicts wave-induced vessel responses based on the presumed deterministic vessel condition in terms of, e.g., the load distribution and linearized viscous damping, which may deviate from the real condition at the operation execution phase. These uncertainties of the vessel condition can significantly contribute to the errors of the predicted vessel motions and the consequent decision making [5,8,9]. Therefore, it is important to identify the on-site vessel conditions based on the information available on board and from the operation design phase. ...
... Eqs. (7) and (9) to (12) should be treated as a complete set of the measurement functions to calculate the predicted measurement vector +1, at each sigma point, while Eqs. (7) to (9) provide the procedure to calculate the measurement vector +1 . The measurement functions are very difficult to express in a compact mathematical formulation because (1) many different response characteristics can be included in the measurement space (e.g., and ); (2) it involves seakeeping simulations, rigid body motion transformations, derivative calculations, etc.; and (3) the applied RAOs again depend on the state and subsequent selection of the sigma points. ...
Article
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Wave-induced vessel motion prediction plays a critical role in ensuring safe marine operations. The operational limiting criteria can usually be calculated by applying presumed linearized vessel motion transfer functions based on the specified vessel loading condition, which may deviate from the real vessel condition when the operation is executed. Reducing the uncertainties of the onboard vessel loading condition can therefore improve the accuracy of vessel motion prediction and hence improve the safety and cost-efficiency for marine operations. However, parameters related to the onboard vessel loading condition can be difficult to measure directly, such as the center of gravity and moments of inertia. In addition, the hydrodynamic viscous damping terms are always subject to significant uncertainties and sometimes become critical for accurate vessel motion predictions. A very promising algorithm for the tuning of these important uncertain vessel parameters based on the unscented Kalman filter (UKF) that uses onboard vessel motion measurements and synchronous wave information was proposed and demonstrated previously by application to synthetic data. The present paper validates the UKF-based vessel seakeeping model tuning algorithm by considering measurements from model-scale seakeeping tests. Validation analyses demonstrate rational tuning results. The observed random errors and bias in relation to the measurement functions due to the applied simplification and linearization in the seakeeping simulations can lead to biased tuning. The importance of designing the state space and the measurement space is demonstrated by case studies. Due to the nonlinear relationship between the uncertain vessel parameters and the vessel motions, the tuning is shown to be sensitive to the mean state vector and selection of the surrounding sigma points.
... Les systèmes opérationnels fournissant des prédictions déterministes de champ de vagues en temps réel (comme ceux proposés par Hilmer & Thornhill (2015), Kusters et al. (2016) ou Naaijen et al. (2018)) utilisent des images de radars à bande X et des modèles physiques linéarisés. Ces choix proviennent de la capacités des radars nautiques à mesurer certaines propriétés locales de la surface océanique sur un large domaine (jusqu'à ∼ 3-4 km 2 ) autour de la structure sur laquelle ils sont montés, et de la contrainte du temps réel restreignant fortement l'utilisation v d'une modélisation complexe pour les processus physiques liés à la propagation vagues et à leur interaction avec la structure. ...
... Operational systems that provide real-time deterministic predictions of ocean wave fields (e.g., Hilmer & Thornhill, 2015;Kusters et al., 2016;Naaijen et al., 2018) rely on X-band radar images and linearized physical models. These choices come from the capacity of nautical radars to measure some local properties of the ocean surface over a large domain (up to ∼ 3-4 km 2 ) around the structure upon which they are mounted, and on the real-time constraint that strongly restrains from using a complex physical modeling of the wave propagation and wave/structure interaction processes. ...
... This way, radars are able to generate large spatio-temporal instantaneous datasets of wave elevations surrounding the structure upon which they are mounted, with a typical space resolution (limited by their range resolution) of about 5-10 m at sampling frequency 0.5-1 Hz. This technology has been implemented in commercial products such as WaMoS II developed by OceanWaveS GmbH TM (Hilmer & Thornhill, 2015), the prediction systems of Next Ocean TM (Naaijen et al., 2018), or FutureWaves TM (Kusters et al., 2016). A selection of radar images, together with the dispersion relation, can be used to estimate the directional wave spectrum. ...
Thesis
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Researches conducted in this thesis address the problem of deterministic prediction of ocean wave fields around a marine structure, a key parameter for the analysis and control of a vast range of offshore operations, on the basis ofdatasets acquired remotely by an optical sensor. Efforts focus on the inclusion, at low computational cost, of the modeling of nonlinear hydrodynamic phenomena, preserving the reliability the surface representation in case of severe sea state.A weakly nonlinear Lagrangian approach (ICWM), whose hydrodynamic properties are evaluated by inter-comparison with reference wave models, is selected for the description of the free surface. The prediction problem is then formulated as an inverse problem that aims at fitting the solution described by the wave model to observations, here composed of free surface elevation datasets generated by a synthetic, yet realistic, lidar sensor scanning the ocean surface at grazing angle. Predictions are then issued through the propagation in time and space of the parameterized wave model. The applicability of the methodology is validated using observations of both unidirectional and directional wave fields, obtained at differentinstants to compensate for their strong spatial non-uniformity. The relative performance comparison between ICWM and lower-order wave models highlights the improvements due to the modeling of wave nonlinearities, especially those pertaining to the correction of the dispersion relation. A demonstration of the usefulness of ICWM is then provided by meansof a procedure that is fully validated experimentally in a wave tank.
... Only a few methods are capable due to the contrary specifications of very fast calculation time and high accuracy at once. The fastest method but also simplest one is the linear theory, which has already been applied for wave prediction applications with promising findings [2,[7][8][9][10][11][12][13][14][15]. A Shipboard Routing Assistance system (SRA) based on the continuous ship's X-band radar measurements of the surrounding seaway were presented by Payer and Rathje [7]. ...
... They showed that for short forecast duration and small to moderate wave steepness, the accuracy of the linear approach is sufficient. In addition, Naaijen and Huijsmans [9,11] as well as Naaijen et al. [10,15] applied linear wave evolution equations for real time wave prediction and ship motion estimation in long as well as short crested waves concluding "that a 60s accurate forecast of wave elevation is very well feasible for all considered wave conditions and motion predictions are even more accurate". Due to the simpleness and robustness, the linear approach is an integral part of commercially available prediction system (e.g., FutureWaves TM [13,14], Next Ocean TM [6]). ...
... with W = Φ z | z=ζ as vertical velocity at the free surface. Using Equation (14) and Equation (15) as free surface boundary conditions, the boundary value problem is now exclusively related to the vertical velocity W(x, ζ(x, t), t) (in space domain) and the solution for W(x, ζ(x, t), t) in terms of ζ(x, t) and Ψ(x, t) can be determined by series expansion. The procedure proposed by West et al. [31] starts from the formal expression that the velocity potential Ψ(x, t) and vertical velocity W(x, ζ(x, t), t) can be represented as Taylor series expansion a z = 0. Assuming that Ψ(x, t) and ζ(x, t) are quantities of O(ζ n ), φ(x, t) and W(x, t) are expanded by perturbation series, with ζ as ordering parameter and M = m + 1 is the order of approximation of non-linearity. ...
Article
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This paper discusses the potential of deterministic wave prediction as one basic module for decision support of offshore operations. Therefore, methods of different complexity—the linear wave solution, the non-linear Schrödinger equation (NLSE) of two different orders and the high-order spectral method (HOSM)—are presented in terms of applicability and limitations of use. For this purpose, irregular sea states with varying parameters are addressed by numerical simulations as well as model tests in the controlled environment of a seakeeping basin. The irregular sea state investigations focuses on JONSWAP spectra with varying wave steepness and enhancement factor. In addition, the influence of the propagation distance as well as the forecast horizon is discussed. For the evaluation of the accuracy of the prediction, the surface similarity parameter is used, allowing an exact, quantitative validation of the results. Based on the results, the pros and cons of the different deterministic wave prediction methods are discussed. In conclusion, this paper shows that the classical NLSE is not applicable for deterministic wave prediction of arbitrary irregular sea states compared to the linear solution. However, the application of the exact linear dispersion operator within the linear dispersive part of the NLSE increased the accuracy of the prediction for small wave steepness significantly. In addition, it is shown that non-linear deterministic wave prediction based on second-order NLSE as well as HOSM leads to a substantial improvement of the prediction quality for moderate and steep irregular wave trains in terms of individual waves and prediction distance, with the HOSM providing a high accuracy over a wider range of applications.
... Marine radars and LIDAR systems measure the wave field before waves approaching to vessel. Therefore, they can also be used as wave forecast information in a very short time ahead (e.g., up to few minutes) for real-time vessel and structural response prediction [25]. ...
... 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. ...
Conference Paper
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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. ...
Article
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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.
... In the latter case, the cost-function approach relies on the approximation of the potential through lower-order formulations, impacting the physical consistency of the solution, which affects both the convergence rate of the minimization procedure and the accuracy of the predictions. Consequently, many investigations of deterministic prediction methods are performed under linear wave assumptions (e.g., [1][2][3]). Based on weakly nonlinear wave models, other studies have shown that some nonlinear properties are crucial for the correct wave field representation, especially related to the correction of the dispersion relation (e.g., [4][5][6]) which decides the velocity of the waves. ...
Conference Paper
In view of deterministic ocean wave prediction, we introduce and investigate a new method to reconstruct ocean surfaces based on randomly distributed wave measurements. Instead of looking for the optimal parameters of a wave model through the minimization of a cost function, our approach directly solves the free surface dynamics — coupled with an interpolation operator — for the quantities of interest (i.e., surface elevation and velocity potential) at grid points that are used to compute the relevant operators. This method allows a high flexibility in terms of desired accuracy and ensures the physical consistency of the solution. Using the linear wave theory and unidirectional wave fields, we validate the applicability of the proposed method. In particular, we show that our grid-based method is able to reach similar accuracy than the wave-model parameterization method at a reasonable cost.
... Depuis quelques années, une des applications envisagées pour ces radars est l'obtention de grands jeux de données (spatio-temporelles) d'élévation de surface libre autour de leur point d'observation, qui peut être fixe ou en mouvement. De tels systèmes sont aujourd'hui proposés par Ocean-WaveS GmbH™avec WaMoS II (Hilmer et Thornhill, 2015), par NextOcean™ (Naaijen et al., 2018) ou par FutureWaves™ (Kusters et al., 2016). ...
Thesis
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Cette thèse présente une nouvelle méthode pour la prédiction déterministe de houle, capable de décrire précisément l'évolution d'états de mer non-linéaires tout en conservant des temps de calculs raisonnables. Une attention particulière est portée à la qualité de l'information sur la phase des vagues. Contrairement aux approches classiques qui reposent sur des mesures d'élévation de surface libre, les informations sur l'état de mer incident sont ici collectées sous la forme de profils instantanés de vitesse horizontale dus aux vagues, en amont de la zone d'intérêt. Le renseignement de cette information cinématique dans un modèle non-linéaire de propagation reposant sur une approche pseudo-spectrale permet de s'affranchir de l'étape d'assimilation de données habituellement requise dans ce genre de modèle, allégeant le temps de calcul en conservant la qualité de prédiction. Ce travail présente un diagnostic de faisabilité de cette méthode en houle unidirectionnelle. En l'absence d'instrumentation idéale pour la mesure de profils instantanés de vitesse horizontale, une méthode originale est développée pour reconstruire cette information à partir de mesures de profileurs acoustiques de courant à effet Doppler (ADCP), étendant ainsi le champ actuel d'application de ces instruments. Des études numériques de sensibilité évaluent ensuite la qualité de la prédiction obtenue pour diverses configurations de mesures et états de mers. On présente pour finir les essais en bassin conduits à Centrale Nantes, qui constituent la validation expérimentale de la méthode. Les résultats numériques et expérimentaux obtenus font de celle-ci une piste prometteuse.
... A general method to predict sea surfaces is a phase-resolved wave reconstruction via linear Thornhill, 2014, 2015;Kusters et al., 2016;Naaijen and Huijsmans, 2010;Naaijen et al., 2009Naaijen et al., , 2018Ruban, 2016;Van Groesen and Wijaya, 2017;Wijaya et al., 2015) or nonlinear models (Blondel-Couprie et al., 2013;Blondel et al., 2010;Ducrozet et al., 2016;Köllisch et al., 2018;Qi et al., 2018). Linear prediction has the advantage of computational efficiency, but it neglects wave-wave interactions and it is likely not applicable to strongly nonlinear waves. ...
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Extreme waves usually emerge from intensive wave groups. Detection of wave groups from random waves may be a key step in predicting the occurrence of extreme events. A new method to discriminate wave groups in random waves based on the wavelet transform is proposed and investigated. The approach can identify wave groups effectively and efficiently. To test the methods, propagation of random wave trains over constant-spanwise submerged obstacles with a wide range of bottom slopes varying from 1:3 to 1:80 are simulated using a fully nonlinear wave model. Extreme waves satisfying the definition of freak waves are identified close to the top of the obstacles. Steeper slopes increase the probability of freak waves. Moreover, it is found that the non-dimensionalized, maximum of the scaled non-uniformity wavelet power of wave groups can be used as a precursor to predict the occurrence of extreme waves over sloping bottoms. The indicator correlates linearly with the maximum heights of wave groups. Using the simulated data, formulae to predict freak waves for various wave steepness over sloping bottoms are constructed. After testing a large number of cases, it is found that the formulae predict most extreme waves successfully and effectively.
... Due to real-time constraints, i.e., sufficient computational efficiency, existing deterministic wave prediction systems have typically used models based on linear wave theory (LWT) (Hilmer and Thornhill, 2015;Kusters et al., 2016;Naaijen et al., 2018). However, this limits their applicability to sea states with a small characteristic steepness, and further assumes that: (i) bound waves (i.e., harmonic waves that do not obey the dispersion relation) can be neglected, and (ii) the space and time scales of observations and the prediction horizon do not allow time-dependent nonlinear wave-wave interactions (e.g., nonlinear phase shift) to significantly affect wave dynamics. ...
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We assess the capability of fast wave models to deterministically predict nonlinear ocean surface waves from non-uniformly distributed data such as sampled from an optical ocean sensor. Linear and weakly nonlinear prediction algorithms are applied to long-crested irregular waves based on a set of laboratory experiments and corresponding numerical simulations. An array of wave gauges is used for data acquisition, representing the typical spatial sampling an optical sensor (e.g., LIDAR) would make at grazing incidence. Predictions of the weakly nonlinear Improved Choppy Wave Model are compared to those of the Linear Wave Theory with and without a nonlinear dispersion relationship correction. Wave models are first inverted based on gauge data which provides the initial model parameters, then propagated to issue a prediction. We find that the wave prediction accuracy converges with the amount of input data used in the inversion. When waves are propagated in the models, correctly modeling the nonlinear wave phase velocity provides the main improvement in accuracy, while including nonlinear wave shape effects only improves surface elevation representation in the spatio-temporal region where input data are acquired. Surface slope prediction accuracy, however, strongly depends on the appropriate nonlinear wave shape modeling.
... Real-time phase-resolved, ocean waves can be reconstructed over some area surrounding a vessel or structure of interest by fitting a wave model to a large data set of measured surface elevations, acquired for instance with: (i) an X-band radar (Dankert & Rosenthal 2004;Nieto Borge et al. 2004;Hilmer & Thornhill 2014;Qi, Xiao & Yue 2016;Naaijen et al. 2018); or (ii) a light detection and ranging (LIDAR) camera (Belmont et al. 2007;Nouguier et al. 2014). LIDAR cameras operate in the visible light (e.g. ...
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