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The IMO Weather Criterion has proven to be the governing stability criteria regarding minimum GM for e.g. small ferries and large passenger ships. The formulation of the Weather Criterion is based on some empirical relations derived many years ago for vessels not necessary representative for current new buildings with large superstructures. Thus it seems rea-sonable to investigate the possibility of capsizing in beam sea under the joint action of waves and wind using direct time domain simulations. This has already been done in several studies. Here it is combined with the First Order Reliability Method to define possible combined critical wave and wind scenarios leading to capsize and corresponding probability of capsize. The results for a fictitious vessel are compared with Monte Carlo simulation and good agreement is found at a much lesser computational effort. Finally, the results for a large container vessel and a small ferry will be discussed in the light of the current weather criterion.

Fault detection and fault isolation for in-service decision support systems for marine surface vehicles will be presented in this paper. The stochastic wave elevation and the associated ship responses are modeled in the frequency domain. The paper takes as an example fault isolation of a containership on which a decision support system has been installed and it will be demonstrated that all the faults can be isolated. The paper shows how a shipboard decision support system could become highly reliable and comprise built-in supervision of the quality of the sensor signals that are crucial to the quality of decisions given to navigators.

In this paper a new approach for increasing the overall reliability of a monitoring and decision support system will be explained. The focus is on systems used for ship operator guidance with respect to, say, speed and heading. The basic idea is to convert the given system into a fault tolerant system and to improve multi-sensor data fusion for the particular system. Fault isolation is an important part of the fault tolerant design for in-service monitoring and decision support systems for ships. In the paper, a virtual example of fault isolation will be presented. Several possible faults will be simulated and isolated using residuals and the generalized likelihood ratio (GLR) algorithm. It will be demonstrated that the approach can be used to increase accuracy of sea state estimations employing sensor fusion quality test.Copyright © 2010 by ASME

This paper deals with the analysis of recent full-scale strain measurements in the hull of a large container carrier covering several months of operation. The focus is on the real-time prediction accuracy of responses 5-15 seconds ahead of the measurements. Such results are less applicable in the operation of container carriers but are important in e.g. loading/unloading operations at sea or helicopter landings. Three different procedures are discussed: Conditional processes with analytical estimates of the mean values and standard deviations, the autoregressive predictor method and a method based on superposition of sinus-oidal components. The conditional processes do not need offline training and will be applied to measured time series in order to evaluate the accuracy of response predictions within the next 1-30 seconds. The number of measured points and the time distances between them are varied to determine the best solutions. A procedure based on 11 measured points spaced 1 sec, covering the last 10 sec of the instantaneous measured signal seems generally able to give fair predictions up to 5-10 sec ahead of the current time. The full-scale data is provided through the EU FP7 project Tools for Ultra Large Container Ships (TULCS) project no. 234146.

Typically, slow steaming is adopted to lower ship’s resistance, which in turn reduces exhaust gas emissions due to lower propulsion power demands. Fine tuning of the underlying hydrodynamic models is key to the reliable forecasting of fuel consumption. Accurate knowledge of the vessel’s speedthrough- water (STW) is paramount to estimate the actual resistance of the vessel. The paper presents a feasibility study about the use of sensor fusion methods for real-time estimation of STW based on inertial measurements of ship motions and measurement of sea current. By combining a purely kinematic model together with linear Kalman filtering, the paper addresses the challenge of designing an optimal STW estimator by detailing the fundamental design choices. The proposed STW estimator is verified on simulated data and tested with measured data from an in-service container vessel.

The paper deals with sea state estimation from a container carrier (9,400 TEU) en-route. Knowledge of the on-site sea state is fundamental input to any kind of inservice decision support system that evaluates performance of, e.g., accelerations, fuel efficiency, and hull girder strength, related to ship-wave interactions in a seaway. In the paper, sea state estimates are produced by three means: the wave buoy analogy, relying on shipboard response measurements, a wave radar system, and a system providing the instantaneous wave height. The presented results show that for the given data, recorded on five different days of continuous operation, the agreement between the estimating means is reasonable; in terms of both absolute (mean) values and hourly trends of integrated sea state parameters.

In the present study, a Panamax container vessel sailing in the Northern Atlantic is taken as a case study. Deep neural networks are trained on results of cross-spectral analysis of 6-DOF accelerations to predict significant wave height, peak period, and relative wave direction, which were initially derived from measurements of an X-band wave radar. Both an Inception network and a residual network are compared. Overall, it is found that the vector decomposition of the relative wave heading shows superior prediction accuracy in contrast to other methods from state-of-the-art literature. Several possible extensions of the presented methodology are pointed out.

Short-term variability of ship responses is investigated by cross-spectrum analysis. In a steady state condition, it is well known that a certain length of sampled data is required for stable results of the spectral analysis. However, the phase lag between responses, in terms of the phase angle of the cross-spectra, has not been discussed in detail. Using long stationary time series, the transition of amplitudes and relative phase angles of the cross-spectra has been investigated by iterative analyzes with a few seconds of time shifting. In the results, the short-term variability of the relative phase angle was observed. In effect, the variability may compromise the accuracy of the wave buoy analogy.

The wave buoy analogy is a tested means for shipboard sea state estimation. Basically, the estimation principle resembles that of a traditional wave rider buoy which relies, fundamentally, on transfer functions used to relate measured wave-induced responses and the unknown wave excitation. This paper addresses however a newly developed concept of the wave buoy analogy but the approach presented herein is, on the contrary, not relying exclusively on transfer functions. Instead, the method combines a signal-based part, estimating wave frequency, and a model-based part, estimating wave amplitude and phase, where only the model-based part depends on transfer functions whereas the signal-based part relies on the measured vessel response alone. Case studies in terms of hypothetical examples show that the method is capable to reconstruct fully the wave elevation process of a sinusoidal regular wave; which include estimation of the wave’s frequency, amplitude and phase. At this stage, the method is far from being a useful means in practical, real-situation applications but the method provides, indeed, a valuable step towards developing new approaches for shipboard sea state estimation.

The exploration of oil and natural gas in offshore fields has motivated advanced researches about the environmental forces in the oceans. The waves, in particular, have been measured using different techniques, as meteorological buoys, with recent works proposing motion-based estimations procedures using the vessel, or a floating facility, in analogy with the buoys, as a wave sensor. Even though this approach has a number of benefits, the vessels, as dynamic systems, have a cut-off frequency that degrades the estimation of high-frequency waves, which are important for non-linear drift effects predictions. In order to solve this problem, it is proposed the incorporation of wave-probes - gauges used to measure the wave elevation in a point - installed on the hull of the vessel, based on literature suggestions and simple analytical arguments, using the Bayesian statistics as the standing point of a more complete estimation algorithm. In order to incorporate the measurements of the wave-probes, an extended linear model is proposed, showing that only corrections for the vertical motions of the vessel are necessary. The ideal installation positions of the wave-probes are defined using as base the utility Bayesian optimal design of experiments, which is shown to guarantee an upper bound for other optimal criteria, with the \'Elbow Criterion\" defining the optimal number of sensors to be employed. Based on the previous solutions, other proposals are made: a heuristic to solve the optimal sensor placement problem and an optimal prior exploring the probabilistic nature of the algorithm. Finally, all the proposals are tested numerically and experimentally, with a vessel model in a towing tank, concluding that the addition of the wave-probes is able to improve not only the estimation of high-frequency waves, but also the estimation over a large range of frequencies. For unimodal seas with intermediate draft, the addition of just one wave-probe reaches approximately a 37%-55% improvement in the energy parameter estimations - HS and TP; the addition of two or more probes reaches approximately a 62%-65% improvement in the same parameters estimations; the addition of four probes achieved the best cost benefit for mean direction estimation; and the addition of six probes is shown to be the recommendation for the best high-order directional estimation in the entire range of the spectrum. A prospecção de óleo e gás natural em campos offshore tem motivado pesquisas avançadas sobre as forças ambientais em oceanos. As ondas, em particular, têm sido medidas através de diferentes técnicas, como boias meteorológicas, com trabalhos recentes propondo técnicas baseadas em movimento para que os navios, em analogia com as boias, possam ser usados como sensores de onda. Apesar desse método ter uma série de vantagens, os navios, como sistemas dinâmicos, têm uma frequência de corte que dificulta a estimação de ondas de altas frequências, que são importantes para a previsão de efeitos de deriva não-lineares. Para resolver esse problema, sugere-se a adição de wave-probes instalados no costado da embarcação, usando como justificativas sugestões da literatura e simples argumentos analíticos, com estatística Bayesiana como fundamentação para um algoritmo de estimação mais completo. Para que as medidas dos wave-probes possam ser incorporadas, um modelo linear estendido é proposto, mostrando que apenas correções para os movimentos verticais do navio são necessárias. A posição ideal de instalação dos wave-probes é definida usando como base o projeto ótimo de experimentos Bayesianos por utilidade, mostrando que o mesmo garante o limite superior de outros critérios de optimalidade, com o \"critério cotovelo\" definindo o número ótimo de sensores a serem usados. Com base nas soluções anteriores, outras propostas são feitas: uma heurística para resolver o problema de posicionamento ótimo dos sensores e uma priori ótima, explorando a natureza probabilística do algoritmo. Ao final, todas as propostas são testadas numericamente e experimentalmente, utilizando um modelo em escala em um tanque de provas, concluindo que a adição de wave-probes é capaz de melhorar não só a estimação de ondas em alta-frequência, mas também a estimação em uma ampla gama de frequências. Para mares unimodais, com calado intermediário, a adição de apenas um sensor alcançou uma melhoria de aproximadamente 37-55% na estimação dos parâmetros relacionados à energia - HS e TP; a adição de dois ou mais sensores alcançou melhorias de 62-65% na estimação de tais parâmetros; a adição de quatro sensores alcançou o melhor custo benefício para estimação da direção média; e a adição de seis sensores se mostrou ideal para estimação de ordem elevada do espectro direcional de energia.