Time series anomaly detection has always been an important research direction. The early time series anomaly detection methods are mainly statistical methods and machine learning methods. With the powerful functions of deep neural network being continuously mined by researchers, the effect of deep neural network in anomaly detection task has been significantly better than the traditional methods. In view of the continuous development and application of deep neural networks such as transformer and graph neural network (GNN) in time series anomaly detection in recent years, the body of research lacks a comparative evaluation of deep learning methods in recent years. This paper studies various deep neural networks suitable for time series, which are divided into three categories according to anomaly detection methods. The evaluation is conducted on public datasets. By analyzing the evaluation criteria, this paper discusses the performance of each model, as well as the problems and development direction in the field of time series anomaly detection in the future. This study found that in the time series anomaly detection task, transformer is suitable for dealing with long-time series prediction, and studying the graph structure of time series may be the best way to deal with time series anomaly detection in the future

In the field of ocean engineering, the variation of flow field during ship-to-ship (STS) interaction has been a hot topic. Noteworthy, the effect of vortex distribution on flow field characteristic variations during STS interaction remains insufficiently researched. This study modifies the RNG k-$\epsilon$ model using the OpenFOAM platform and verifies its reliability by comparing it with literature data. Subsequently, extended research is conducted to investigate the flow field characteristics of two different ship hull sections under different Reynolds numbers ($Re=68,000$ and $Re=6800$), analyzing velocity components, vortex distribution, and trends in pressure and turbulent kinetic energy fields relative to the vortex field. The research reveals that $Re$ primarily governs changes in upstream and downstream flow fields, while in the gap field, the variation in flow field characteristics is more constrained by geometry and boundary conditions. This research provides a valuable reference for assessing flow field characteristics in STS interactions.