To study nonlinear characteristics of freak wave, this research focuses on the spatial evolution process of weakly high-order nonlinear waves propagating over a sloping bottom. We use modified Non-Linear Schrö dinger (NLS) equation considering bathymetry change to conduct a Monte-Carlo simulation, and it gives spatial evolution of kurtosis and skewness of wave surface elevation at different cases. The effect of water depth and initial quasi-resonant interactions on wave evolution have been explored. We also discuss the relationship between kurtosis, skewness and maximum wave height, as well as their performance around critical water depth for four wave resonant interactions. The results indicate that in deep water initial BFI significantly effects kurtosis and occurrence probability of freak wave. From deep to shallow water, transition region around critical water depth makes this effect become complicated.
In this paper, a 3D fully Lagrangian meshfree numerical solver for modeling of hydroelastic Fluid-Structure Interaction (FSI) phenomena is presented. Enhanced version of a projection-based MPS (Moving Particle Semi-implicit) fluid model is coupled with an MPS-based Newtonian structure model, resulting in 3D MPS-MPS coupled FSI solver. The Newtonian structure model is founded on conservation equations of linear and angular momenta corresponding to a linear elastic solid. The fluid-structure coupling is conducted by incorporating the FSA (Fluid-Structure Acceleration-based) scheme, where fluid-structure interface boundary conditions are precisely satisfied. The performance of the presented FSI solver is validated by conducting several benchmark test cases with analytical and experimental reference solutions. The solver is proven to possess notable robustness without use of any artificial stabilizers that often lead to unphysical numerical dissipations.
In this study, the Boussinesq-type model in Kim et al. (2009)1) was applied with the incorporation of the specific mangrove effects to simulate wave propagation and hydrodynamics in mangrove forests. Due to the depth-integrated assumption in Boussinesq-type governing equations, the mangrove effect was parameterized by the Morison-type formula (Morison et al. 1950)2) as an additional force term. The force coefficients were determined based on the experimental findings in Chang et al. (2020)3), who conducted modelscale laboratory experiments using 3D-scanned and printed mangrove trees to reproduce the root structure of a typical mature mangrove tree (Rhizophora species) at a reduced scale. Their proposed formulas were used to estimate the drag and inertia coefficients in the numerical simulation. The vertical variation of the mangrove structure due to the prop roots was also addressed in the computations. A preliminary model validation comparing with the experimental data was presented and discussed. Model tests using different configurations were also provided.
A two-dimensional XBeach model faces difficulty in updating the bathymetry profile under calm conditions. This study investigates the inaccuracy of predicting the undertow, which is a seaward-directed current affecting the suspended sediment load. Progress was made in applying XBeach to the prediction of the observed undertow in the field in the Hasaki coast of Japan. The observations were performed between May 9 and June 2, 2016. To assess the capability of XBeach to reproduce the undertow under low-wave-energy conditions, the datasets observed during the calm situation (after May 24, 2016) are used, and the parameters sensitive to the undertow prediction are adjusted. The results reveal a time-shift problem that occurs at the time-varying undertow velocity. The comparison becomes better when the undertow is forward-shifted for 3 h but remains underestimated. To improve the XBeach default results, a modification is implemented based on the Stokes drift, using the coefficient of water depth. This approach improves the accuracy of undertow prediction in XBeach from bad to reasonable quality for the average of RMAE value (from 0.77 to 0.48), against the observation.
Whitecaps generated by wave breaking on the ocean surface play an important role in the local interaction across the air-sea interface. Whitecap coverage is defined by the area of whitecaps per the unit ocean surface. It has been recognized as one of the most valuable physical quantities for describing the ocean surface fluxes such as the momentum, heat and carbon dioxide, so that the quantitative evaluation of whitecap coverage becomes significant from viewpoints of coastal and ocean engineering. In this study, a progressive high-precision whitecap extraction model is first built by using the algorithm of deep learning. Compared with a traditional whitecap extraction model based on threshold value, the algorithm is found to solve problems caused by illuminance condition and color change on the ocean surface, and effectively extracts fine whitecaps with complicated structures. Further, through comparisons with previous algorithms such as Automatic Whitecap Extraction (AWE), Iterative Between Class Variance (IBCV) and the whitecap extraction based on fixed threshold value, the present algorithm is demonstrated to be more accurate for identifying whitecaps, and it reduces the amount of evaluation load, and can effectively apply for changeable ocean conditions. The new whitecap extraction technology is used to determine whitecap coverage when shooting digital images under complicated sea surface conditions. Due to the progressive characteristics of this algorithm, it has not only a high precision processing effect on images taken by a fixed camera, but also has the potential to analyze accurately images from a non-fixed camera system, such as an observation ship equipped with camera system, unmanned aerial vehicle and so on.
It is important to characterize the transition process of wind-driven water surface to be closely connected to the momentum and gas exchanges across the air-sea interface. In the present study, the transition of the wind-driven surface flow was investigated by means of laboratory experiments, which were carried out using a wind-water tunnel, 17m long, 0.6m wide and 0.8m high. The velocity of the wind-driven surface flow, which is a Lagrangian surface velocity consisting of the Eulerian flow velocity and the Stokes drift velocity, was evaluated by measuring the velocity of float disk rafting on the water surface. According to the experimental results, we examined the critical conditions under which the micro-scale breaking and bubble-mixed breaking waves appear on the water surface. The relation of the surface flow velocity with the friction velocity was found to be changed around u* = 0.3m/s like the relations with the drag coefficient and the Stokes drift velocity at the water surface. Our experimental results also showed the behavior of the wind-driven surface flow velocity to be varied depending on the windsea Reynolds number. The results suggest that the wave breaking controls the wind-driven surface flow.
北西太平洋の歴史的イベントとなったスーパー台風Haiyanを対象に，高解像度大気海洋波浪結合モデルCOAWSTを用いた海面抵抗係数𝐶dと海洋表層混合層に関する数値実験を行った．海面抵抗推算式として風速依存のCharnock式，波形勾配依存のTaylor and Yelland式と高風速の𝐶dの飽和上限値を設定した数値実験，海洋表層混合層として平年及び標準偏差分のばらつきを設定した実験を行った．飽和上限値の有無は海面抵抗係数推算式の違いに比べて，台風に対して大きな影響を及ぼした．飽和上限値無しの波形勾配依存式は最もBest Trackに近い結果を推定し，特に急発達を良く表現していた．海洋混合層の厚さは標準偏差分の差によって台風強度は約5hPa変化した．台風通過による水温構造の変化は，混合層厚さに依らず，表層と混合層底部付近の2層で異なる変動を示した．
Numerical simulation of wind-induced wave in Tokyo Bay under typhoon Faxai (T1915) was carried out and the results are discussed in the paper. Accurate simulation of the wave height distribution in Tokyo bay, especially during the peak period, has been a significant challenge in the past. Majority of the challenge has been posed in finding an accurate wind field to represent the complex gusts under major typhoons, which eventually acts as the energy source for the wave generation. The motivation of the study is to assess the effectiveness of different wind sources of Typhoon Faxai through wave simulation model. In the study GPV wind data released by JMA were used as the main wind source and later wind field was improved through typhoon-bogussing and 4DVar data assimilation. The simulated wave properties (using each of those wind field as the source input) were compared with the wave observation data from locations in Tokyo bay. Results indicate that a significant improvement of peak wave height in Tokyo-bay under typhoon Faxai can be achieved through processing the available wind through 4DVar data assimilation. The results are presented in the paper and improvements and drawbacks of the different wind fields are discussed in the view point of wave simulation.