The wake characteristics of tidal turbines are significantly influenced by turbulence intensity (TI) and flow velocity in the marine environment. This study employs the Blade Element Momentum (BEM)–CFD method to model two-bladed horizontal tidal turbine wakes, simplifying the turbine geometry while ensuring computational efficiency. The numerical model, validated against experimental data, demonstrates reliable accuracy. Simulations were conducted for background TI levels of 2%, 6%, 10%, 14%, and 18%. Results indicate that wake regions initially expand and then contract, with the contraction point moving closer to the turbine as TI increases. At 2% TI, the wake influence region extends to an axial distance/diameter (X/D) ratio of 20, while at 18% TI, contraction begins at X/D = 4. Low TI results in more extensive low-speed regions, whereas high TI accelerates wake recovery. As TI increases, the wake’s turbulence rapidly blends with the background, leading to a reduction in turbulence increments within the wake. Additionally, an analytical wake model for tidal turbines was developed, incorporating turbulence intensity into the formula. The predicted curve exhibited good agreement with the CFD data. This model enables a quick and efficient prediction of wake velocity changes under varying turbulence intensities.

The wake characteristics of tidal turbines are significantly influenced by turbulence intensity (TI) and flow velocity in the marine environment. This study employs the Blade Element Momentum (BEM) -CFD method to model two-bladed horizontal tidal turbine wakes, simplifying the turbine geometry while ensuring computational efficiency. The numerical model, validated against experimental data, demonstrates reliable accuracy. Simulations were conducted for background TI levels of 2%, 6%, 10%, 14%, and 18%. Results indicate that wake regions initially expand and then contract, with the contraction point moving closer to the turbine as TI increases. At 2% TI, the wake influence region extends to an axial distance/diameter (X/D) ratio of 20, while at 18% TI, contraction begins at X/D = 4. Low TI results in more extensive low-speed regions, whereas high TI accelerates wake recovery. As TI increases, the wake's turbulence rapidly blends with the background, leading to a reduction in turbulence increments within the wake. Additionally, an analytical wake model for tidal turbines was developed, incorporating turbulence intensity into the formula. The predicted curve exhibited good agreement with the CFD data. This model enables a quick and efficient prediction of wake velocity changes under varying turbulence intensities.

Reliable characterization of dependent wind–wave extremes is essential for offshore wind engineering in monsoon- and tropical-cyclone-affected seas. Using a 2002–2020 COAWST hindcast for an offshore site in the northern South China Sea, we analyze 161,400 common-valid hourly samples of 10-m wind speed (U₁₀), significant wave height (H_s), and peak wave period (Tp) and construct 221 temporally consistent monthly extreme pairs. Validation shows that the hindcast captures temporal co-variability but systematically underestimates wind and wave magnitudes, especially severe tropical-cyclone winds and high sea states; long-return-period estimates are therefore interpreted as methodological sensitivity measures rather than design loads. SJ-DPI and upper-tail sensitivity models based on Kernel–Pareto and GEV marginals are compared to quantify extrapolation uncertainty, while the primary uncertainty interpretation avoids relying on potentially unstable far-tail fits from limited monthly extremes. Under a common maximum pseudo-likelihood framework, no copula family is uniformly optimal, and AND-type exceedance is more sensitive to dependence-family choice than OR-type exceedance. Cool- and warm-season distributions differ significantly, but a 1000-split placebo test shows that part of the apparent long-return-period shift is attributable to the smaller-sample SJ-DPI bandwidths rather than seasonality alone. These results highlight the need to jointly consider concurrent sampling, marginal-tail specification, dependence uncertainty, and seasonal heterogeneity.

The distribution and spectral properties of colored dissolved organic matter (CDOM) in the northern Bay of Bengal were investigated in June 2016. Based on in situ data collected from 100 CDOM samples at 25 stations, the distribution characteristics of CDOM in the surface layer differed markedly from those at 30 m, 75 m, and 100 m. The CDOM spectral slope ($S_{350-500}$) exhibited a broad range, varying from 0.0026 to 0.0300 nm⁻¹, and showed a significant negative correlation with the absorption coefficient $a_{\mathrm{CDOM}}\left(443\right)$. Analysis of salinity and temperature profiles revealed no obvious correlation between the distribution of $a_{\mathrm{CDOM}}\left(443\right)$ and these physical parameters. A comparative analysis with satellite-derived wind and current data indicated that elevated $a_{\mathrm{CDOM}}\left(443\right)$ values in the northeastern surface waters were primarily associated with the southwest monsoon. In contrast, $a_{\mathrm{CDOM}}\left(443\right)$ values in the lower mixed layer were mainly influenced by the combined effects of geostrophic and eddy currents.