We present a machine learning model for calculation of wave added resistance. The model training is performed using a large set of pre-calculated added resistance curves covering a broad range of ship hulls and operational conditions, i.e. forward speed, draft and relative wave heading. The underlying hydrodynamic model is the classical strip-theory where the wave added resistance is computed according to a modified version of Salvesen’s formulation. It is concluded that the developed data-driven model is able to produce a non-linear mapping between a set of operational conditions as well as the ship’s main particulars to the wave added resistance coefficient.
Current practice for maritime search and rescue (MSAR) adheres to predetermined full-coverage patterns for finding targets. These do not account for key success factors for MSAR missions such as the dynamic location of targets, updates on situational awareness during mission execution, and search vehicle kinematics. Consequently, current practice cannot incorporate realistic MSAR operational conditions into path-finding, increasing the likelihood of mission failure. To address this issue, a novel, flexible path-finding framework is proposed for generating a path while dynamically updating the probability of a target based on the path's trajectories. The solution approach implements the A* algorithm, which can accommodate the dynamics of a vehicle and guarantees the optimality of the final path with respect to the target objective function. Experiments show that a more than 50% improvement in the time needed to guarantee a certain probability of finding a target is exhibited compared to the parallel sweep coverage path-finding approach.
In this article, we develop a deep neural network model to estimate the wave added resistance. The required data to train the model is generated using strip theory calculations over a wide range of hull geometries and operational conditions. The model is efficient as it only requires the ship’s main particulars: length, beam, draft, block coefficient, and slenderness ratio. In addition, we present an application of this model in a vessel performance framework. This will be used for predicting propulsion power and analyzing the degree of biofouling on ships from the company Ultrabulk2. The study shows that the developed deep neural network model produces reliable results in predicting the added wave resistance coefficient in comparison to strip theory calculations. Also, the developed ship propulsion and biofouling analysis display satisfactory output for monitoring hull performance under actual ship operational conditions.
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.