A major challenge in next-generation industrial applications is to improve numerical analysis by quantifying uncertainties in predictions. In this work we present a formulation of a fully nonlinear and dispersive potential flow water wave model with random inputs for the probabilistic description of the evolution of waves. The model is analyzed using random sampling techniques and nonintrusive methods based on generalized polynomial chaos (PC). These methods allow us to accurately and efficiently estimate the probability distribution of the solution and require only the computation of the solution at different points in the parameter space, allowing for the reuse of existing simulation software. The choice of the applied methods is driven by the number of uncertain input parameters and by the fact that finding the solution of the considered model is computationally intensive. We revisit experimental benchmarks often used for validation of deterministic water wave models. Based on numerical experiments and assumed uncertainties in boundary data, our analysis reveals that some of the known discrepancies from deterministic simulation in comparison with experimental measurements could be partially explained by the variability in the model input. Finally, we present a synthetic experiment studying the variance-based sensitivity of the wave load on an offshore structure to a number of input uncertainties. In the numerical examples presented the PC methods exhibit fast convergence, suggesting that the problem is amenable to analysis using such methods.
The increasing focus on energy efficient operation of vessels can be seen in both legislation and research. This paper focuses attention on the human factor influencing energy efficiency and explores the conditions for improving energy efficiency in working vessels taking situational awareness (SA) theory into consideration.
The study builds on two cases: an offshore supply vessel for the oil & gas industry and an installation vessel for wind turbines. The study used qualitative methods based on 49 interviews with seafarers and onshore employees from the vessels and shipping companies.
The study has identified that the energy efficiency of a ship is mainly influenced by legislation and the praxis formed on board. The results showed that the theory on SA is very a useful tool in explaining the factors affecting the energy efficiency of a vessel and the praxis.
The study has shown that obtaining a more energy efficient operation is complex and depends not only on the officer on board the ship. The improvement of energy efficiency is possible, but there is a need to understand the complexity of the issue and to involve both the crew and the entire system around the ship, and to obtain a shared perspective of energy efficient operation. Furthermore, in order to improve energy efficiency in shipping companies, there is a need to support the seafarers in gaining more skills for operating the ship more energy efficiently; to do this the right way there is a need to create an understanding of the system by the authorities, ship owners and charterers.
The severe slugging flow is always challenging in oil & gas production, especially for the current offshore based production. The slugging flow can cause a lot of potential problems, such as those relevant to production safety, fatigue as well as capability. As one typical phenomenon in multi-phase flow dynamics, the slug can be avoided or eliminated by proper facility design and control of operational conditions. Based on a testing facility which can emulate a pipeline-riser or a gas-lifted production well in a scaled-down manner, this paper experimentally studies the correlations of key operational parameters with severe slugging flows. These correlations are reflected through an obtained stable surface in the parameter space, which is a natural extension of the bifurcation plot. The maximal production opportunity without compromising the stability is also studied. Relevant studies have already showed that the capability, performance and efficiency of anti-slug control can be dramatically improved if these stable surfaces can be experimentally determined beforehand.
An efficient extreme ship response prediction approach in a given short-term sea state is devised in the paper. The present approach employs an active learning reliability method, named as the active learning Kriging + Markov Chain Monte Carlo (AK-MCMC), to predict the exceedance probability of extreme ship response. Apart from that, the Karhunen-Loève (KL) expansion of stochastic ocean wave is adopted to reduce the number of stochastic variables and to expedite the AK-MCMC computations. Weakly and strongly nonlinear vertical bending moments (VBMs) in a container ship, where the former only accounts for the nonlinearities in the hydrostatic and Froude-Krylov forces, while the latter also accounts for the nonlinearities in the radiation and diffraction forces together with slamming and hydroelastic effects, are studied to demonstrate the efficiency and accuracy of the present approach. The nonlinear strip theory is used for time domain VBM computations. Validation and comparison against the crude Monte Carlo Simulation (MCS) and the First Order Reliability Method (FORM) are made. The present approach demonstrates superior efficiency and accuracy compared to FORM. Moreover, methods for estimating the Mean-out-crossing rate of VBM based on reliability indices derived from the present approach are proposed and are validated against long-time numerical simulations.
Fault-tolerance is crucial to maintain safety in offshore operations. The objective of this paper is to show how systematic analysis and design of fault-tolerance is conducted for a complex automation system, exemplified by thruster assisted Position-mooring. Using redundancy as required by classification societies' class notations for offshore position controlled vessels, the paper shows how violations of normal behaviour of main components can be detected and isolated. Using a functional service philosophy, diagnosis procedures are auto-generated based on provable correct graph analysis methods. Functional faults that are only detectable, are rendered isolable through an active isolation approach. Once functional faults are isolated, they are handled by fault accommodation techniques to meet overall control objectives specified by class requirements. The paper illustrates the generic methodology by a system to handle faults in mooring lines, sensors or thrusters. Simulations and model basin experiments are carried out to validate the concept for scenarios with single or multiple faults. The results demonstrate that enhanced availability and safety are obtainable with this design approach. While methods are introduced at a tutorial level, the paper is original by providing a total Position-mooring system design that ensures resilience to any single fault and to selected multiple faults.
We numerically simulate the hydrodynamic response of a floating offshore wind turbine (FOWT) using computational fluid dynamics. The FOWT under consideration is a slack-moored 1:70 scale model of the UMaine VolturnUS-S semi-submersible platform. The test cases under consideration are (i) static equilibrium load cases, (ii) free decay tests, and (iii) two focused wave cases of different wave steepness. The FOWT is modeled using a two-phase Navier-Stokes solver inside the OpenFOAM-v2006 framework. The catenary mooring is computed by dynamically solving the equations of motion for an elastic cable using the MoodyCore solver. The results are shown to be in good agreement with measurements.
In this paper existing guidelines to predict wave overtopping on rubble mound breakwaters and coastal structures are modified and improved with respect to the influence of the roughness and crest width. Data from recently made model tests and existing data are combined to demonstrate the need for modifying these formulations in EurOtop. A new reduction factor γcw for the crest width is established and is an improvement of the method by Besley. The influence of the roughness of the slope normally also includes an influence of the breaker parameter when it is larger than a certain limit (EurOtop suggest ξm-1.0 > 5). The present study shows that the breaker parameter is not the ideal dimensionless parameter describing the influence of the wave period for breakwaters with steep slopes, as for such structures the front slope has much less influence on the overtopping than the wave steepness. Thus slope angle and wave steepness have been uncoupled to describe the influence of the armor roughness on wave overtopping. The improvement in the overtopping prediction compared to EurOtop is significant, specifically for the new data sets that have data outside the range of the calibration data used for influence of roughness in EurOtop. The proposed improved methods enlarge the range of applicability with respect to crest width and wave steepness.
This paper deals with uncertainty modelling of wave-vessel transfer functions used to calculate or predict wave-induced responses of a ship in a seaway. Although transfer functions, in theory, can be calculated to exactly reflect the behaviour of the ship when exposed to waves, uncertainty in input variables, notably speed, draft and relative wave heading, often compromises results. In this study, uncertainty modelling is applied to improve theoretically calculated
transfer functions, so they better fit the corresponding experimental, full-scale ones. Based on a vast amount of full-scale measurements data, it is shown that uncertainty modelling can be successfully used to improve accuracy (and reliability) of theoretical transfer functions.
The completeness and high predictability of hazardous scenarios by hazard identification methods are issues in risk analyses. A way to the improvement is to carry out both an exhaustive - to the extent possible - post-accident and predictive accident analysis. Currently, Natural Language Processing (NLP) allows quick processing of many accident reports. In combination with graphical tools, it is now even possible to automatically output causal diagrammatic models of accidents and visualize them on a multi-scenario accident diagram. A step forward is the application of NLP to support predictive analysis. Predictive accident analysis focuses on identifying deviations from expected or normal conditions, the subsequent events following these deviations, and their interactions leading to an accident. The expected or normal conditions are typically outlined in specifications and procedures. This paper demonstrates how NLP can assist hazard identification and predictive accident analysis during lifting operations on ships and offshore platforms.
Marine cables are primarily designed to support axial loads. The effect of bending stiffness on the cable response is therefore often neglected in numerical analysis. However, in low-tension applications such as umbilical modeling of ROVs or during slack events, the bending forces may affect the slack regime dynamics of the cable. In this paper, we present the implementation of bending stiffness as a rotation-free, nested local Discontinuous Galerkin (DG) method into an existing Lax–Friedrichs-type solver for cable dynamics based on an hp-adaptive DG method. Numerical verification shows exponential convergence of order P and P + 1 for odd and even polynomial orders, respectively. Validation of a swinging cable shows good comparison with experimental data, and the importance of bending stiffness is demonstrated. Snap load events in a deep water tether are compared with field-test data. The bending forces affect the low-tension response for shorter lengths of tether (200–500 m), which results in an increasing snap load magnitude for increasing bending stiffness. It is shown that the nested LDG method works well for computing bending effects in marine cables.