Shijun Chen

The rotating annular flume has been widely adopted to generate quasi-steady and uniform flow, thus serving for the investigation of sediment motion characteristics. This complex flow structure is significantly associated with the rotational speed ratio. The present study aims to explore the optimal speed ratio based on the OpenFOAM simulation. In this paper, the physical properties of a rotating annular flume with different speed ratios are investigated and analyzed in terms of bottom shear stress, turbulent velocity ratio, cross-sectional secondary flow, and vector field by interFoam, a built-in solver of the open-source CFD program OpenFOAM. The RNG k-epsilon model has been adopted to solve multiphase flow problems. The results demonstrate that the optimal speed ratio differs with the specific evaluation criterion. Given the uniform distribution of bottom shear stress, the turbulence velocity ratio, and the minimum secondary flow as the evaluation criteria, the corresponding optimal speed ratios are determined as 1.2, 1.7, and 1.7, respectively. The conclusion is generally consistent with the results derived by other scholars. Computational fluid dynamics programs have been proven as practical tools for investigating complex hydrodynamic characteristics. The present study shares useful insights into the optimal rotational speed ratio of a rotating annular flume. The OpenFOAM-based numerical model will provide guidance for experimental research using rotating annular flumes.

Waterline usually plays as an important visual cue for the autonomous navigation of marine unmanned surface vehicles (USVs) in specific waters. However, the visual complexity of the inland waterline presents a significant challenge for the development of highly efficient computer vision algorithms tailored for waterline detection in a complicated inland water environment that marine USVs face. This paper attempts to find a solution to guarantee the effectiveness of waterline detection for the USVs with a general digital camera patrolling variable inland waters. To this end, a general deep-learning-based paradigm for inland marine USVs, named DeepWL, is proposed, which consists of two cooperative deep models (termed WLdetectNet and WLgenerateNet, respectively). They afford a continuous waterline image-map estimation from a single video stream captured on board. Experimental results demonstrate the effectiveness and superiority of the proposed approach via qualitative and quantitative assessment on the concerned performances. Moreover, due to its own generality, the proposed approach has the potential to be applied to the waterline detection tasks of other water areas such as coastal waters.