Journal of Waterway, Port, Coastal, and Ocean Engineering

Ship-generated waves are often amplified onshore in confined seaways and are associated with several incidents worldwide. Few tools enable modeling the ship waves’ evolution through complex bathymetry. Here, we assess the skill of XBeach’s ship module for simulating the primary wave generated by a moving pressure head. The model was validated for five ships against field observations at three stations across Lake Saint Pierre, the widest section of the St. Lawrence seaway between Quebec City and Montreal. The study was motivated by reported damages caused by a container ship transiting at 17.6 knots, that is, 20% faster than other ships during extreme flooding. Our model predicted that the ship involved in the incident created drawdown (<20 cm) and runup (<15 cm) that was twice as high as slower ships. However, simulating a wide range of water levels and ship speeds shows that the waves would have been larger at lower water levels due to shoaling. Nonetheless, XBeach could model the evolution of the waves’ drawdown as they propagated over several kilometers from the channel.

The presence of natural mud in the seabed of waterways and ports has an impact on maritime traffic in many parts of the world. Previous research has shown that sailing safely through fluid mud is possible. However, the majority of these studies were experimental and model scale research, omitting many important characteristics of the natural mud to simplify the problem. The research was therefore directed toward computational fluid dynamics (CFD) models to describe the complex properties of the natural mud for nautical applications. Nonetheless, the lack of validation material makes the usage of CFD still uncertain. This work aims to contribute validation cases for numerical solvers through a series of towing tests in muddy environments with a 0.2-m-diameter cylinder. Different fluid combinations are tested, including freshwater, natural mud only, and natural mud with seawater on top. Experimental results in a single interface of natural mud are compared with CFD simulations using three different viscosity laws. Forces calculated using CFD were found to underpredict experimental measurements, indicating that more attention is needed toward describing the natural mud.

An experiment was conducted in a wave flume to measure the trajectories of 20 small objects (gravel and microplastics) in the surf and swash zones on an equilibrium beach and a nourished foreshore beach with erosion and accretion in the swash zone. The trajectory of each of the 20 objects was measured in six tests consisting of 100 runs, with each run lasting 400 s. Gravel particles were mobile within the swash zone only. Large and small microplastics moved onshore from the surf zone and accumulated in the lower swash zone of wave rundown or in the upper swash or berm zone of wave runup. Plastic particles remained on the evolving sand surface of the nourished foreshore. A simple tracking model was developed to predict the measured trajectories of the 20 particles of gravel and microplastics. The calibrated model also predicted limited net displacements of hypothetical sand particles on the equilibrium profile beach. The tracking model may eventually be generalized and become applicable to track and clean up microplastics on beaches and predict the destinations of sand particles placed on eroding beaches.

Significant wave height (SWH) is a fundamental concept in marine-related applications, activities, and renewable wave energy. The sea state is characterized by the SWH, and real-time ocean operations suffer from missing data. Further, expensive deployment and maintenance operations, physical constraints, or both hamper the design of dense SWH buoy networks. In this study, the identification of optimal buoy locations is performed for a specific number of total buoy stations. These optimal locations are then extrapolated to obtain the complete state SWH network data. This extrapolation process is accomplished using the QR decomposition with a column pivoting algorithm, which is executed based on a data-driven approach that utilizes empirical orthogonal function (EOF) analysis. The monitoring network is composed of 15 buoys on the West Coast of the US in the Pacific Ocean. The Nash–Sutcliffe coefficient of efficiency (CE) and mean square error (MSE) diagnostic metrics are utilized for the model performance assessment. Based on the diagnostic metrics, the EOF–QRP model performance is at an acceptable level when two best QR algorithm identified buoys are used. The performance level increases with the total number of stations used. The model performed very well with six buoys’ data according to error metrics. The EOF–QRP model advocated in this study has proved successful when identifying the minimum number of buoys and their locations and provided a general promising framework for optimal station network design.