The goal of this study is to determine the best method to identify the onset of random single and multiple breaking waves over a large domain at the exact time they occur.
#MODÉLISER TERRAIN SURFER 12 FREE#
The model is a computationally efficient, open source code, which solves for the free surface in a numerical wave tank using the High-Order Spectral (HOS) method. This study investigates a wave-breaking onset criteria to be implemented in the non-linear potential flow solver HOS-NWT. Simulation of breaking waves using the high-order spectral method with laboratory experiments: Wave-breaking onset A two-parameter regression involving u(sub *)(exp 2) and a Reynold's number proposed by Toba and his colleagues which relates u(sub *)(exp 2) and peak wave frequency, improves the correlation even more but is less easy to interpret in physical terms. Slightly better standard deviation value and higher correlation coefficient were found by using a Reynolds number as predictor. Substituting U(sub 10)(sup 3.75) (which is the dependence of whitecap cover found by Monahan and coworkers) an equivalent correlation was found to the prediction by u(sub *)(exp 2). Thus, both the level of wave development as determined by inverse wave age, which we may term relative wind effectiveness for wave forcing and the wind forcing on the water surface determine the incidence of wave breaking. Furthermore, for the larger values of u(sub *)(exp 2) the dependence of wave braking and wave age was stronger than at the low end of the values u(sub *)(exp 2) and u(sub *)/Cp. The combination of u(sub *)(exp 2) and u(sub *)/Cp can be understood in physical terms. When combined in a two parameter regression, those two variables gave small standard deviation and had a high correlation coefficient (66 percent). Wave forcing as measured by wind stress (or friction velocity, u(sub *), squared) and by inverse wave age, u(sub *)/Cp where Cp is the phase velocity of the waves at the peak of the frequency spectrum, were found to be good prerictors of percentage of breaking crests. An equivalent percentage of breaking crests were found for spilling and plunging events. These events were correlated with the magnitude of the wave spectrum measured with a resistance wire wave gauge and band pass filtered between 6 and 10 Hz. Video recordings were employed to identify and categorize the breaking events in terms of micro-scale, spilling and plunging breakers. Incidence of wave breaking for pure wind driven waves has been studied on Lake Washington at wind speeds up to 8 m/s. Dependence of Wave-Breaking Statistics on Wind Stress and Wave Development