Preliminary Analysis on Wave Energy Resources in the Sea Adjacent to Shandong Peninsula
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The wave fields in Bohai Sea and Yellow Sea are numerically simulated and the results are validated. According to the numerical simulation of ten-year wave data, the wave energy characteristics in the sea adjacent to Shandong Peninsula are analyzed. The results show that,(a) the offshore wave energy distribution of Shandong Peninsula is uneven, the wave energy near the east peninsula is larger and stable with a annual average wave energy density of mostly more than 2.0 kW/m;(b) the offshore wave energy distribution of Shandong Peninsula is obviously varied with the seasonal changes while the wave energy density is smallest in summer and largest in winter; and(c) the offshore wave energy density of Shandong Peninsula is less than 0.5 kW/m in more than half of coastal time and has a tendency to increase year by year.Keywords:
Peninsula
Shandong peninsula
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The main task of the present research was to analyse wave climate and evaluate energy resources in the Lithuanian territorial waters of the Baltic Sea. Wave and wind parameters were analysed according to long-term measurement site data. Distribution of wave parameters in the Baltic Sea Lithuanian nearshore was evaluated according to wave modelling results. Wave energy resources were estimated for three design years (high, median and low wave intensity). The results indicated that in the coastal area of Lithuania, waves approaching from western directions prevail with mean wave height of 0.9 m. These waves are the highest and have the greatest energy potential. The strongest winds and the highest waves are characteristic for the winter and autumn seasons. In the Baltic Sea Lithuanian nearshore, the mean wave height ranges from 0.68 to 0.98 m, while the estimated mean energy flux reaches from 0.69 to 1.90 kW m−1 during a year of different wave intensity. Distribution of energy fluxes was analysed at different isobaths in the nearshore. Moving away from the coast, both wave height and wave power flux increases significantly when water depth increases from 5 to 20 m. Values of the mentioned parameters tend to change only slightly when the sea is deeper than 20 m. 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 identified differences of wave height and energy along the selected isobaths are insignificant.
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The National Marine Energy Test Site of China is located in the Yellow Sea, north of Chudao Island and about 2 km away from Shandong Province. It is mainly aimed at the deployment and testing of small-scale prototypes of wave energy and tidal current energy converters. In this study, wave energy and tidal current energy resources around the test field were evaluated using ocean models. A spectral wind-wave model and a two-dimensional hydrodynamic model were used to simulate the wave energy and tidal current energy, respectively. The simulation results were validated using the observations, and showed good agreement with the observed wave, surface elevation and tidal current. Further, wave characteristic and wave energy resources were analyzed and the results showed that the spatial distribution of wave field was nearly coincident with an isobath, and gradually decreased from the outer sea to the coastal zones. The 10-year averaged significant wave height in the sea was about 0.6-0.8 m, and wave energy density was 2.0-2.8 kW/m. For the latter, we observed a significant difference with the seasons, the energy density being stronger in winter up to 6.5 kW/m, and weaker in summer with no more than 1.0 kW/m. Evaluation of tidal current energy resources showed a micro-tidal zone, and relatively weak tidal current energy. Tidal current energy density of the test field closed to the island was relatively stronger than that observed in the north, with the maximum tidal energy density of about 125-200 W/m 2 , which could only be used to test small-scale tidal energy converters.
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Abstract. Assessment of wave power potential at different water depths and time is required for identifying a wave power plant location. This study examines the variation in wave power off the central west coast of India at water depths of 30, 9 and 5 m based on waverider buoy measured wave data. The study shows a significant reduction ( ∼ 10 to 27 %) in wave power at 9 m water depth compared to 30 m and the wave power available at 5 m water depth is 20 to 23 % less than that at 9 m. At 9 m depth, the seasonal mean value of the wave power varied from 1.6 kW m−1 in the post-monsoon period (ONDJ) to 15.2 kW m−1 in the Indian summer monsoon (JJAS) period. During the Indian summer monsoon period, the variation of wave power in a day is up to 32 kW m−1. At 9 m water depth, the mean annual wave power is 6 kW m−1 and interannual variations up to 19.3 % are observed during 2009–2014. High wave energy ( > 20 kW m−1) at the study area is essentially from the directional sector 245–270° and also 75 % of the total annual wave energy is from this narrow directional sector, which is advantageous while aligning the wave energy converter.
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Wave simulation was conducted for the period 1976 to 2005 in the South China Sea(SCS) using the wave model, WAVEWATCH-III. Wave characteristics and engineering environment were studied in the region. The wind input data are from the objective reanalysis wind datasets, which assimilate meteorological data from several sources. Comparisons of significant wave heights between simulation and TOPEX/Poseidon altimeter and buoy data show a good agreement in general. By statistical analysis, the wave characteristics, such as significant wave heights, dominant wave directions, and their seasonal variations, were discussed. The largest significant wave heights are found in winter and the smallest in spring. The annual mean dominant wave direction is northeast(NE) along the southwest(SW)-NE axis, east northeast in the northwest(NW) part of SCS, and north northeast in the southeast(SE) part of SCS. The joint distributions of wave heights and wave periods(directions) were studied. The results show a single peak pattern for joint significant wave heights and periods, and a double peak pattern for joint significant wave heights and mean directions. Furthermore, the main wave extreme parameters and directional extreme values, particularly for the 100-year return period, were also investigated. The main extreme values of significant wave heights are larger in the northern part of SCS than in the southern part, with the maximum value occurring to the southeast of Hainan Island. The direction of large directional extreme Hs values is focus in E in the northern and middle sea areas of SCS, while the direction of those is focus in N in the southeast sea areas of SCS.
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Results of the analysis of a long-term data set, including fields of significant wave heights of the surface wave components, and mixed (total) wave field in the Black Sea are presented. The data set was collected on the basis of retrospective calculations using the MIKE 21 SW spectral wave model with the atmospheric forcing based on the ERA-Interim data in the period from 1979 to 2017. A criterion is used to isolate the swell waves from the initial wave data set that takes into account the wave age. We used the experimental data to develop a regression relationship showing that the maximum possible wave height can exceed the significant wave height approximately one and a half times. Analysis of the spatial distribution of wave heights in the Black Sea suggests that a possibility exists that significant wave height of storm waves can be as high as ∼12 m. This result indicates that the actual heights of maximum waves in the Black Sea can reach 18–19 m. Three regions are distinguished on the basis of the wave potential. The times of manifestation of extreme situations in these regions are different: in the southwestern part of the sea, extreme storm situations occur, as a rule, in December–January; in the region south of the Crimea Peninsula this happens in February; in the northeastern part of the sea they occur in November. It was also found that the south-southeastern and eastern parts of the sea are most affected by swell.
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