Long-Term Trajectory Prediction for Oil Tankers via Grid-Based Clustering

Vessel trajectory prediction is an important step in route planning, which could help improve the efficiency of maritime transportation. In this article, a high-accuracy long-term trajectory prediction algorithm is proposed for oil tankers. The proposed algorithm extracts a set of waymark points that are representative of the key traveling patterns in an area of interest by applying DBSCAN clustering to historical AIS data. A novel path-finding algorithm is then developed to sequentially identify a subset of waymark points, from which the predicted trajectory to a fixed destination is produced. The proposed algorithm is tested using real data offered by the Danish Maritime Authority. Numerical results demonstrate that the proposed algorithm outperforms state-of-the-art vessel trajectory prediction algorithms and is able to make high-accuracy long-term trajectory predictions

Correlation between machining-induced surface alterations and stress corrosion cracking susceptibility of au stenitic stainless steels

Correlation between machined surface alterations and stress corrosion cracking (SCC) susceptibility of austenitic stainless steels is investigated. The machining-induced residual stress, roughness, micro-hardness and dislocation density were characterized and the surface/subsurface cracks were examined after SCC tests in boiling magnesium chloride solution. The results showed SCC crack initiation and early propagation were highly related to the machining-induced alterations. By establishing a quantitative relationship between the surface characteristics and surface crack density, the comprehensive impact of the residual stress, surface roughness as well as the dislocation density on SCC susceptibility is revealed. The predicted crack density is in good agreement with the measured data. As a result, the SCC susceptibility of the machined surface is described by the surface integrity properties