Andrzej Sobczyk

Modern transport vehicles and mobile machines have a high advanced functionality and quality of work. However, it is often associated with high power consumption. That is why research focused on reducing vehicle operating costs is so important. The possibilities of reduction of fuel consumption in combustion engines, which are usually the primary source of energy in many vehicles, seem to be exhausted. It came from design constraints of the combustion engine and increasingly stricter emission’s standards. That is way very popular become energy recovery system. Usually, these systems are based on an electric storage battery. Energy is capturing during braking of the vehicle and transferred to the electric motor periodically connected with the drive system. While, in the vehicles driven by hydrostatic system hydraulic energy in a similar way can be captured and stored in the hydro-pneumatic accumulator. This paper presents such solution of energy saving system based on an additional pump and hydro pneumatic accumulator build into vehicle hydrostatic drive system. Mechanical and hydraulic elements of vehicle drive system were modelled using SimulationX software. The standard drive system is comparing with the energy recuperation drive system. Tests have been conducted for assumed working cycle and several operating parameters by the use of SimulationX. Reached than 10% energy saving in one cycle, confirms the correctness of designed structure of the hydraulic energy recovery system.

Shainin’s approach is a specific sequential heuristic aimed at finding and ranking the most important factors which impact the investigated process. The sequential aspect of the approach is simultaneously its strongest and weakest side, because just after detection of the most important factor, the further analysis is stopped without any additional cost. However, such a detection may take place at the end of the whole sequence. This paper tries to answer the question if the dominant factor may be hidden by interactions with other factors.

The well-known statistical tests have been developed on the basis of many additional assumptions, among which the normality of a data source distribution is one of the most important. The outcome of a test is a p-value which may is interpreted as an estimation of a risk for a false negative decision i.e. it is an answer to the question “how much do I risk if I deny?”. This risk estimation is a base for a decision (after comparing with a significance level α): reject or not. This sharp trigger – p-level greater than α or not – ignores the fact that a context is rather smooth and evolves from “may be” through “rather not” to “certainly not”. An alternative option for such assessments is proposed by a fuzzy statistics, particularly by Buckley’s approach. The fuzzy approach introduces a better scale for expressing decision uncertainty. This paper compares three approaches: a classic one based on a normality assumption, Buckley’s theoretical one and a bootstrap-based one