Floating platform offshore operations, such as load transfer from a supply boat and hydrocarbon offloading operations, are often limited by sea conditions. Access to sea condition information from a nearby wave/environmental monitoring buoy is not always available or there may be delays in data transmission. In the absence of reliable and accurate data from a nearby wave buoy, the motions of a floating platform/vessel may be used to estimate the sea conditions. This paper presents a method in using Artificial Neural Network (ANN) models to map the motions of a floating vessel to the wave elevations and to eventually estimate the significant wave heights of the sea the floating vessel is in. The ANN models can be trained using either: (1) measured data in the form of measured vessel motions and data from a nearby wave buoy, or (2) simulated data, i.e. vessel motions computed using numerical simulations. In this paper, demonstration of the ANN method uses simulated data under various sea conditions. The trained ANN models are tested for sea conditions that are not part of the training data, and the ANN predictions are found to be very close to the results calculated using numerical simulations. This is an important step to show that the trained ANN models have learned the presented information and can generalize the knowledge. The methodology presented in this paper may be used to establish an ANN model for estimation of the significant wave heights based on the measured motions of a floating vessel.

This paper presents the methodology of simulating green water events of a spread-moored FPSO platform using a CFD-based Numerical Wave Basin (NWB) with an optimized CFD model. Previous work done on the assessment of the motions of the spread-moored FPSO platform in irregular waves has demonstrated excellent agreement between CFD calculation and model test measurement (Baquet et al., 2019). This study further investigates the CFD methodology to efficiently simulate highly non-linear/breaking waves to extend the NWB applications to include prediction of green water occurrence on FPSO platforms. In this study, a CFD model is developed to enable efficient and direct simulation of green water events identified from global motion analysis of the FPSO platform. Smaller time-steps are used, and mesh refinement is applied at the regions which are potentially subjected to green water to ensure more accurate prediction of the green water elevations could be achieved compared to the global motion analysis model. The computing resources required to run the CFD model are maintained below the practical limits for use during typical FPSO project engineering phase. CFD model simulation results for green water events of the FPSO platform are compared against available model test data and good agreement is observed. This paper demonstrates an application of CFD-based NWB for green water prediction for a spread-moored FPSO platform based on an optimized CFD model which is developed to enable fast simulation of green water events. To evaluate the susceptibility of a FPSO platform to green water, large numbers of green water event simulations will be required to obtain reliable statistical data.

Accurate prediction of roll damping is of utmost importance in estimating the roll motion of ships and other ship-shaped floating structures such as floating, production, storage and offloading (FPSO) and floating liquefied natural gas (FLNG) vessels. Roll damping is non-linear in nature and consists of several components including viscous damping which has been found to be highly dependent on flow separation patterns around the vessel. Industry practice of estimating roll damping by means of model testing and theoretical formulae has not always been reliable in estimating viscous damping due to limitations in both model tests and theoretical formulae. It is the objective of this study to use Computational Fluid Dynamics (CFD) methodology to predict roll damping and roll motion accurately for the concept design of a new-build barge-shaped FPSO vessel with bilge keels. In this study, assessment of roll damping is based on prescribed sinusoidal roll motions applied to the vessel in two-dimensional and three-dimensional CFD simulations. Validation of the CFD prediction is performed using published experimental results. From the CFD simulations, roll damping of a barge-shaped FPSO vessel is found to vary considerably with changes in the bilge corner shapes and bilge keel dimensions. Roll damping increases with bilge keel height and roll amplitude. Results from two-dimensional and three-dimensional simulations show differences which may be attributed to the length of the bilge keels and the bow and stern effect. From this study, the optimum bilge keel configuration for application of this new-build barge-shaped FPSO vessel in moderate environmental conditions is identified in the early design stage.

This paper presents Computational Fluid Dynamics (CFD)-based simulations of the hydrodynamic behaviors of a floating barge in shallow waters on an inclined seabed near shore. The hull hydrodynamic behaviors with respect to water depth are quantified by evaluation of the hydrodynamic coefficients, i.e., added mass, viscous damping coefficients, and current drag coefficients, which are required for the prediction of hull motion responses and mooring loads of the barge. CFD simulations are performed to predict the hull hydrodynamic coefficients with consideration of the actual seabed conditions, including water depth and varying bathymetry. Added mass and viscous damping coefficients are calculated using forced harmonic oscillations, while current drag coefficient is obtained using steady current flow simulation. These hydrodynamic coefficients are calculated for three of the six degrees of freedom (DOFs), i.e., surge, sway, and yaw of the hull. By considering three different nearshore water depths with a flat seabed and two inclined seabeds, the hull added mass, viscous damping, and current drag coefficients are quantified and compared against the coefficients in deepwater conditions. The hydrodynamic coefficients are found to be significantly affected by shallow water depths. Overall trends show exponential increase of added mass and viscous damping coefficients as water depth reduces. There is a further linear increase in the coefficients when the seabed bathymetry changes from flat to inclined, particularly when the water depth to hull draft ratio is less than 4.50. Similarly, current drag coefficients increase with decreasing water depths for flat seabed conditions, while for inclined seabed conditions, they may increase or decrease depending on the directions with respect to the shore and the current heading. This paper demonstrates the efficiency of CFD simulations in predicting a floating barge’s hydrodynamic behaviors in shallow water conditions, including varying nearshore bathymetry and viscous effects. The CFD simulation methodologies presented may be extended for the hydrodynamic behavior assessments of other nearshore floating structures such as Floating Offshore Liquefied Gas Terminals (FLGTs), Floating Storage and Regasification Units (FSRUs), and floating wind turbine structures.