Abstract:The objective of this work is to analyze the wind and wave energy potential in the proximity of the Greek islands. Thus, by evaluating the synergy between wind and waves, a more comprehensive picture of the renewable energy resources in the target area is provided. In this study, two different data sources are considered. The first data set is provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the ERA-Interim project and covers an 11-year period, while the second data set is Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO) and covers six years of information. Using these data, parameters such as wind speed, significant wave height (SWH) and mean wave period (MWP) are analyzed. The following marine areas are targeted: Ionian Sea, Aegean Sea, Sea of Crete, Libyan Sea and Levantine Sea, near the coastal environment of the Greek islands. Initially, 26 reference points were considered. For a more detailed analysis, the number of reference points was narrowed down to 10 that were considered more relevant. Since in the island environments the resources are in general rather limited, the proposed work provides some outcomes concerning the wind and wave energy potential and the synergy between these two natural resources in the vicinity of the Greek islands. From the analysis performed, it can be noticed that the most energetic wind conditions are encountered west of Cios Island, followed by the regions east of Tinos and northeast of Crete. In these locations, the annual average values of the wind power density (Pwind) are in the range of 286-298.6 W/m 2 . Regarding the wave power density (Pwave), the most energetic locations can be found in the vicinity of Crete, north, south and southeast of the island. There, the wave energy potential is in the range of 2.88-2.99 kW/m.
The use of fuel mixtures of diesel and vegetable oils in diesel engines is a field of research due to the necessity of reducing pollution. Besides the properties required for the normal operation of diesel engines, other aspects that must be investigated are linked to the influence of these mixtures on piston ring–cylinder tribosystem behavior. Methods used for reducing the friction and wear on the engine cylinders, such as special surface machining, lubricant driving piston rings, etc., are well known. If the fuel mixture brings some improvement in this area, such as a reduction of the friction coefficient value, this can be a way to reduce the power lost by friction into the engine cylinders. In this paper, a methodology is presented based on artificial neural networks for analyzing the complex relationship between vegetable oil percentages in fuel mixtures, with the goal of finding an optimal proportion of vegetable oil corresponding to a minimum value of the friction coefficient. Regular methods were used for data acquisition, i.e., a pin-on-disk module mounted on a tribometer, and two types of vegetable oils were studied, namely sunflower and rapeseed oils. The obtained results show that for each type of vegetable oil there is an optimal proportion leading to the best tribological behavior.
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