The objective of this work is to assess the synergy of the marine resources in the vicinity of several European offshore sites, in order to analyze the viability of the combined wind-wave renewable projects. The reference sites considered for evaluation are located in the vicinity of the European coasts, being already taken into account for various marine projects. As a first step, based on the dataset provided by the European Center for Medium-Range Weather Forecasts for the 10-year interval 2005–2014, it was possible to analyze the joint seasonal distribution of the offshore resources. In the second part of the paper, based on the technical characteristics of various offshore wind turbines and wave energy converters, it was possible to identify the performances of some systems for wind and wave energy conversion. Finally, it can be also highlighted that the results presented in the present work can be considered interesting and useful, since they provide some insights regarding the potential of some operational European sites to support colocated wind-wave projects.

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 prediction of polymer properties, based on its composition, it is a complex problem with no easy method to obtain directly and accurately results. Among the tribological properties, the friction coefficient and wear rate are the most interesting ones. The polymers based on epoxy resin, with clay as filler, show different properties depending on the clay concentration. This paper presents an analysis of the polymer properties variation with its filler concentration. Due to the tribological processes complexity, mechanical and thermal properties must be taken into account. The aim of this study is to find an optimal concentration value, with minimal influence on polymer properties. All value properties will be used in a neural network model in order to optimize and predict the composite properties.

Taking into account that the tribological processes are a combination of many other processes the aim of this paper is building a neural network model based on mechanical and thermal properties for prediction of the tribological behaviour of an Epoxy- Aramidic composite system. The created epoxy based composites with aramidic powders, were tribological tested with diverse parameters in order to obtain follow properties: wear rate and friction coefficient. Bending and compression tests were performed for obtain main mechanical properties. Thermal tests were performed in order to obtain follow properties: specific heat, thermal conductivity and thermal expansions. With all the studied properties was created an Artificial Neural Network (ANN) model. The created ANN model can perform prediction for tribological behaviour of studied composites.

The paper proposes a prediction methodology for the significant wave height (and implicitly the wave power), based on the artificial neural networks. The proposed approach takes as input data the wind speed values recorded for different time periods. The prediction of significant wave height is useful both for assessment of wave energy as also for marine equipment design and navigation. The data used cover the time interval 1999 to 2007 and it was measured on Gloria drilling unit, which operates in the Romanian nearshore of the Black Sea at about 500 meters depth.