Each aircraft gas turbine engine has design, layout and technological features that affect its operational life. To take into account all the factors that affect the operational life of the engine, a large amount of calculations is required. In this regard, it is important to develop functional models that represent virtual images of the main parts of each engine and contain information about their geometry, loading parameters and characteristics. These models should express the relationship between the many parameters measured and calculated during flight with the calculated values of the cyclic durability of the main engine parts. In recent years, machine learning has been used to solve such problems. The essence of this method is in training in the process of analyzing a variety of solutions under different parameters. This paper presents an approach that allows creating functional models of gas turbine engine elements using numerical modeling of the heat-stressed state and machine learning algorithms.
Numerical modeling hydraulic characteristics of an elbow exhaust duct of a gas turbine engine