The article discusses the process of developing and modeling an impeller for an unmanned aircraft of the airplane type. Aerodynamic and strength calculations were carried out, key design parameters were determined, including the number of blades, engine power and choice of material. The developed models were created in the CAD system Compass 3D and manufactured by 3D printing using PETG plastic. Impeller thrust tests were carried out depending on engine speed, which allowed the design to be optimized for maximum efficiency.

Keywords: impeller, unmanned aircraft, aerodynamics, 3D modeling, 3D compass, additive technologies, thrust, testing, APM FEM

The article studies the application of neural networks with long short-term memory (LSTM) for forecasting the precipitation of asphalt-resin-paraffin deposits (ARPD) during oil pumping through main oil pipelines. The authors of the article outline the relevance of the problem of ARPD formation in main oil transportation and consider modern approaches to mathematical modeling of forecasting the precipitation of deposits. The aim of the study was to develop a neural network model that allows constructing a graph of the distribution of ARPD along the length of the model pipeline over time. Taking into account the features of various types of neural networks and the available input data, a corresponding neural network model based on LSTM was developed. The key parameters of the "oil - pipeline - soil" system were determined, which should be taken into account as initial data. The developed model demonstrates a sufficient degree of forecasting accuracy and at the same time has prospects for its improvement. The results obtained can be applied by operators of main oil transportation for more accurate forecasting and determining the most cost-effective period for cleaning the pipeline.

Keywords: recurrent neural network, asphalt-resin-paraffin deposits, neural networks, forecasting, short-term long-term memory networks, oil trunk pipeline, oil transportation