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.

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/m². 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.

This paper presents an analysis of four clutch disc friction materials (from different manufacturers) used in manual transmissions. Scanning electron microscopy and energy-dispersive X-ray spectroscopy were employed for the microstructural and chemical characterisation of the friction materials. To reveal the tribological properties of the selected clutch discs, three measurements of the friction coefficient between the material and the cast iron disc were conducted. The findings were employed to construct an artificial neural network using Easy NN software (V 14), with the objective of optimising the friction material. The chemical composition of the friction materials was employed as the input data, whereas the minimum, maximum, and average values of the friction coefficient, as well as the temperature generated during friction, were utilised as the output data. To assess the efficacy of the neural network, the correlation between the importance of input data and their sensitivity to output data was examined. It was determined that the model with three hidden layers exhibited a notable correlation between the six most influential chemical elements and their sensitivity. Based on this neural model, the chemical composition of the friction disc materials was optimised using the “Query” mode, aiming to minimise discrepancies in friction coefficients and temperature development.

In MAC (Diesel) engines case, the shape of the piston head has an important influence on burning processes and, consequently on engine performance. Since the piston is subjected to high cyclic stresses during the blasting phase, a fatigue phenomenon occur. The piston design and material must provide a high fatigue resistance and a low weight, leading to low dynamic loading of the crank shaft and piston rod. The paper try to investigate the piston fatigue behavior, for several head shapes and materials, in order to establish an optimal design.

The aim of this paper is to analyze driver's hands coordinates on the steering wheel for an optimal and safe driving experience. A good coordination of the driver's action on the controls is the result of a comfortable position that leads to an optimal reaction while driving. The presented study implies using a thermal imaging camera for analysing palms temperature changes in the contact area with the steering wheel. The resulting data shows that the optimal driving position of drivers' hands is 0° and 180° associating the steering wheel with and trigonometric circle.