Abstract
Nowadays, material science and stress characteristics are crucial in the field of jet engines. There are methods for fatigue life, stress, and temperature prediction; however, the conventional methods are ineffective and time-consuming. The article is devoted to the research in the field of application of the numerical methods in order to develop an innovative methodology for the temperature fields prediction based on the integration of the finite element methods and artificial neural networks, which leads to the creation of the novel methodology for the temperature field prediction. The proposed methodology was applied to the temperature field prediction on the surface blades of the experimental iSTC-21v jet engine turbine. The results confirmed the correctness of the new methodology, which is able to predict temperatures at the specific points on the surface of a turbine blade immediately. Moreover, the proposed methodology is able to predict temperatures at specific points on the turbine blade during the engine runs, even for the multiple operational regimes of the jet engine. Thanks to this new unique methodology, it is possible to increase the reliability and lifetime of turbines and hot parts of any jet engine and to reduce not only the maintenance but also the research and development costs due to the significantly lower time demands. The main advantage is to predict temperature fields much faster in comparison to the methods available today (computational fluid dynamics (CFD), etc.), and the major aim of the proposed article is to predict temperatures using a neural network. Apart from the above-mentioned advantages, the article’s main purpose is devoted to the artificial neural networks, which have been until now used for many applications, but in our case, the neural network was for the first time applied for the temperature field prediction on the turbine blade.
Highlights
This article is an open access articleA pressure turbine of the jet engine is the part where the thermal energy is transformed into mechanical work [1]
As it is reviewed below, there are many other cases that require the determination of the temperature fields of the turbine blades
It is essential to emphasize that the main purpose of the proposed article is to demonstrate methodology and other regimes that can be included in the additional research; the results have shown a high level of accuracy and proved the fact that it is possible to predict temperatures using artificial neural networks (ANN)
Summary
This article is an open access articleA pressure turbine of the jet engine is the part where the thermal energy is transformed into mechanical work [1]. The energy transformation is ensured by increasing the hot gas’s speed between the turbine blades and by their impact on the airfoils of the turbine blades. Numerical simulations of the temperature fields of the turbine blades are required for every element of a gas turbine during the design process of the engine. As it is reviewed below, there are many other cases that require the determination of the temperature fields of the turbine blades. It is essential to know the temperature fields on the surfaces of the turbine blade for the specific regimes of the jet engine operation. Mentioned facts are clearly proving the importance of the turbine temperature field prediction
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