By neglecting the viscosity of fluid and rotation in flow, the theory of potential flow cannot accurately predict the hydrodynamic forces on the structures under significant viscous effects. In this study, the effects of liquid viscosity on the hydrodynamic forces on a horizontal circular cylinder underwater with a large-amplitude forced oscillation were investigated. The study used a two-dimensional two-phase flow wave tank model based on the viscous fluid theory using the OpenFOAM^® package. The numerical calculations were carried out under different types of liquid (i.e., liquid with different viscosities). The liquid viscosity effects are visually shown by comparison of the various frequency components of the hydrodynamic forces on the cylinder, and the magnitude and phase relations of the viscous shear forces and the pressure forces. By analyzing the distribution characteristics of the flow fields around the circular cylinder, the viscous-effect mechanisms are revealed. It is found that the discrepancies of the contributions of viscous shear forces, and the discrepancies of the vortex effects on the phase and magnitude of the pressure forces lead to the obvious differences among the results under different liquid viscosities.

Marine oil spill accidents pose serious threats to marine ecosystems and coastal economic development. X-band marine radar provides an effective means for nearshore oil spill monitoring, yet accurate oil film segmentation remains challenging because of grayscale overlap between oil films and sea clutter, range-dependent echo attenuation, and noise interference. To address these challenges, an unsupervised oil spill detection method integrating HBA-AE ROI extraction with IAEA fine segmentation is proposed. In the coarse detection stage, an autoencoder (AE) maps pixel grayscale intensity, local mean, and local standard deviation into a compact two-dimensional latent space. The AE-derived latent representation is further optimized by HBA to enhance oil film–sea clutter separability, after which K-Means clustering identifies candidate oil spill ROIs. In the subsequent fine segmentation stage, the extracted ROI constrains the search domain, while the Improved Alpha Evolution Algorithm (IAEA) maximizes the three-class Otsu between-class variance to determine the optimal dual thresholds for oil film delineation. Experiments using X-band marine radar data collected during the Dalian 7·16 oil spill accident were conducted against seven representative methods. The proposed framework achieved a Precision of 97.20%, an F1-score of 86.60%, and an IoU of 76.40%, demonstrating superior oil film segmentation accuracy and effective false-alarm suppression under complex sea-clutter conditions. It also maintained a favorable balance between segmentation accuracy and computational efficiency, demonstrating its potential for marine oil spill monitoring under the investigated radar observation conditions.