Mathematical modeling of composite materials leads to the solving PDEs with strongly oscillating coefficients. The problem of large number of equations can be solved using homogenization, that replaces heterogeneous material by an ‘equivalent‘ homogeneous one. This approach assumes periodic structure, which is not often true in reality. The first aim of the paper is to compare results obtained by solving the model problem describing the torsion of a bar applied to the random medium and the periodic one, respectively. The second aim is to present four algorithms generating samples of random structures of a two-component fibre composite material similar to the real one. and Obsahuje seznam literatury
Gears are used to transmit power and motion in mechanical, electrical and chemical process industries. Influenced by vibration, torque, temperature, lubrication & specific film thickness, the gear teeth contacts may experience change leading to unexpected failures such as wear, scutting, pitting and micro-pitting on teeth surface. In order to avoid these damages, continuous monitoring is essential using knowledge based systems, Generic capability of artificial neural network (ANN) is exploited to formulate prediction and classification based on heuristic models of condition of lubricating oil in spur gears. Based on the loading conditions such as vibration, temperature and torque, the algorithm predicts film thickness to classify oil conditions as elastohydrodynamic (EHD), mixed wear and severe wear that helps in detecting faults in gear operation. and Obsahuje seznam literatury