The purpose of this study is to investigate the effects of three external forces (tidal current, wind, and waves) on the movement of oil spilled during the Hebei Spirit oil spill accident. The diffusion of the spilled oil was simulated by using a random walk (RW) model that tracks the movement caused by advection-diffusion assuming oil as particles. For oil simulation, the wind drift current generated by wind and tidal current fields were computed by using the environmental fluid dynamics code (EFDC) model. Next, the wave fields were simulated by using the simulating waves nearshore (SWAN) model, and the Stokes drift current fields were calculated by applying the equation proposed by Stokes. The computed tidal currents, wind drift currents, and Stokes drift currents were applied as input data to the RW model. Then, oil diffusion distribution for each external force component was investigated and compared with that obtained from satellite images. When the wind drift currents and Stokes drift currents caused by waves were considered, the diffusion distribution of the spilled oil showed good agreement with that obtained from the observation.

Breaking waves generated by wave shoaling in coastal areas have a close relationship with various physical phenomena in coastal regions. Therefore, it is crucial to accurately predict breaker indexes such as breaking wave height and breaking depth when designing coastal structures. Many studies on wave breaking have been carried out, and many experimental data have been documented. Representative studies on wave breaking provide many empirical formulas for the prediction of breaking index, mainly through hydraulic model experiments. However, the existing empirical formulas for breaking index determine the coefficients of the assumed equation through statistical analysis of data under the assumption of a specific equation. This study presents an alternative method to estimate breaker index using representative linear-based supervised machine learning algorithms that show high predictive performance in various research fields related to regression or classification problems. Based on the used machine learning methods, a new simple linear equation for the prediction of breaker index is presented. The newly proposed breaker index formula showed similar predictive performance compared to the existing empirical formula, although it was a simple linear equation.

The hybrid system of wave energy converters (WECs) using coastal structures is an attractive issue in terms of a decrease in construction costs and an improvement of the ability to capture wave energy. Most studies on the utilization of reflected waves from structures, which is one of the hybrid systems, are limited to mathematical analysis based on linear theories. Therefore, this paper presents fundamental experimental results in the presence of a reflection wall simplified as a coastal structure behind a bottom-hinged flap-type WEC under unidirectional regular waves. The behavioral characteristics and the power generation efficiency k_e of the flap were investigated, focusing on wave steepness, initial water depth, and distance from the reflection wall. The results show that the condition of the initial water depth being smaller than the flap height is more effective in terms of avoiding unstable rotating of the flap. The maximum k_e appeared slightly far from the node position of the standing waves because the flap shape and the power take-off (PTO) damping induce the phase difference between the reciprocating behavior of the flap and the period of the standing wave. The results imply that the optimum position of a WEC is dependent on WEC shape, PTO damping, and installation water depth.