The main aim of the presented research was examination of new tribological pairs made of different types of steel. Materials of disc probes were submitted to unusual heat treatment processes and salt bath nitriding. The research is focused on the friction coefficients and mass losses of the material probes. Based on the results it was noticed that the best wear resistant pair was bearing steel 100Cr6 coupled with high speed steel S705. The lowest friction coefficient appeared for the pair bearing steel 100Cr6 and maraging steel C350.

The research presents a review and comparison of different engine constructions. Investigated engines included crankshaft engines, barrel engine, opposed-piston engines and theoretical models to present possible variations of piston motion curves. The work comprises also detailed description of a numerical piston engine model which was created to determine the impact of the cycle parameters including described different piston motion curves on the engine efficiency. Developed model was equipped with Wiebe function to reflect a heat release during combustion event and Woschini’s correlation to simulate heat transfer between the gas and engine components.Various scenarios of selected engine constructions and different working conditions have been simulated and compared. Based on the results it was possible to determine the impact of different piston motion curves on the engine cycle process and present potential efficiency benefits.

The article presents the reason for developing a 0D predictive and diagnostic model for opposed-piston (OP) engines. Firstly, a description of OP engines, together with their most important advantages and challenges are given together with current research work. Secondly, a PAMAR-4 engine characteristic is presented. After that the proposed 0D predictive model is described and compared with the commercially availible software. Test stand with most important sensors and solutions are presented. After that the custom Engine Control Unit software is characterized together with a 0D diagnostic model. Next part discusses specific challenges that still have to be solved. After that the preliminary test bed results are presented and compared to the 0D simulations. Finally, the summary together with possible future improvement of both 0D predictive model and test bed capabilities are given.