The paper presents a method of choosing parameters of a mathematical model for simulation of a working cycle of compression-ignition engine on the basis of experimental measurements. In order to choose the parameters of the model, the Nelder-Mead method has been used. As a result of such an approach, a simplified mathematical model with very good numerical effectiveness can be used for simulation of the working cycle of the engine, while very good compatibility of numerical results and experimental measurements is ensured. Suitable algorithms and results of calculations are presented.

This paper presents a study on applying machine learning algorithms for the classification of a two-phase flow regime and its internal structures. This research results may be used in adjusting optimal control of air pressure and liquid flow rate to pipeline and process vessels. To achieve this goal the model of an artificial neural network was built and trained using measurement data acquired from a 3D electrical capacitance tomography (ECT) measurement system. Because the set of measurement data collected to build the AI model was insufficient, a novel approach dedicated to data augmentation had to be developed. The main goal of the research was to examine the high adaptability of the artificial neural network (ANN) model in the case of emergency state and measurement system errors. Another goal was to test if it could resist unforeseen problems and correctly predict the flow type or detect these failures. It may help to avoid any pernicious damage and finally to compare its accuracy to the fuzzy classifier based on reconstructed tomography images – authors’ previous work.