Sentence examples for mean wave energy from inspiring English sources

The literature recommends the determination of the (Po) point by means of the direction of the mean wave energy flux at the diffraction point.

In a year of median wave intensity, the mean wave energy flux changes from 1.10 kW m−1 at 10 m isobaths to 1.38 kW m−1 at 30 m isobaths.

The angular difference (γd) between the direction of the mean wave energy flux at the diffraction point of the headland and the beach orientation down-coast is also incorporated in the proposed formula.

Throughout the literature, the planform final shape, hereinafter denoted as the Static Equilibrium Beach Orientation (SEBO), is obtained based on the direction of the mean wave energy flux of whole waves impinging on the coast.

This angle represents the difference in degrees between the direction of the mean wave energy flux at the diffraction point (θEF) and the orientation of the dynamic equilibrium beach (θDB), to be utilized in a newly derived DEP formula to predict the shoreline shape in the long term.

The spatial analysis provides information for the entire Black Sea on; the averaged mean wave energy flux over the period 1979 2009, and the decades 1980 1989, 1990 1999, and 2000 2009, seasonal and monthly averages of wave energy flux during 31 years, variability indices for the 1979−2009 period, and variabilities on monthly and seasonality basis based on inter-annual averages during 31 years.

The findings suggest that the periodically occurring wave height modulations at onshore and near coastal areas are mainly caused by the water depth changes by tide, which means that more wave energy can propagate into the coastal area increasing the wave height.

Results in laboratory-scale (order of few meters) simulations show that the wave energy flux, mean flow properties and turbulent kinetic energy agree well with our previous results obtained using a body-fitted grid (BFG).

The nonlinear stiffness system improved the efficiency of wave energy harvesting, increasing mean power, by both pushing up the natural period, and broadening resonance, therefore proving more competitiveness.

In particular, most of the applications of Neural Computation techniques, considered here in a broad sense, focus on the prediction of a variety of wave energy parameters by means of Multilayer Perceptrons and, at a lesser extent, by Support Vector Machines, and Extreme Learning Machines.

Our results demonstrate that in locations where repeated entrainment, transport and boulder breakdown are observed, loose boulder size may not be a reliable means of reconstructing wave energies required to detach, entrain and transport boulders.