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.

This study provides a novel methodology for the natural frequency estimation of the jet engine turbine blade by using the dimension check. This paper presents a summarization of the ongoing research devoted to the method for the turbine blade natural frequency estimation. The main target of the research presented in the paper is to develop a novel method that can calculate the natural frequency of a particular turbine blade by using the dimensions of investigated turbine blade from a dimension check. This goal is achieved by the combination and interaction of several methods as for instance computed aided design (CAD) finite element modelling (FEM), artificial neural network (ANN) and others. As it is mentioned in the following chapters of the article a unique novel method is developed that can predict natural frequency according to the dimensions. The results confirmed the correctness of the new methodology, which can predict natural frequency by the dimensions of a turbine blade immediately with a relatively high level of accuracy (maximal errors are under 1.5%). Every jet engine manufacturer (GE aviation, Rolls Royce, Prat and Whitney, etc.) has to test jet engine parts for the natural frequencies in order to avoid the resonance at early stage of the manufacturing process in order to mount the blades into the engine. The experimental tests of every single turbine blade are time-consuming, a novel method can predict natural frequency according to the dimensions by using data from dimension check in 0.0051 s. The presented method is under patent pending.

The use of a predictive approach in the aviation industry is an important factor in aircraft maintenance. The main goal of this study was to create a new method for stress prediction during the operation of parts and to apply it on an aircraft torque tube (ATT). The method operates in real time during taxiing, takeoff, and landing using a neural network (NN). The stress calculated by the proposed method can be used in the future to calculate fatigue life and to save maintenance costs related to ATTs. This can play an important role in the evaluation of tests, such as unobserved crack failure. The main contribution of the presented methodology is in the fourth part of this study, where a new method of mechanical-stress prediction using a NN is described. The method essentially replaces finite element methods (FEMs), which require large amounts of time. The new method is much faster than commonly available methods, as the NN predicts the mechanical ATT stress in 0.00046 s, whereas the solution time using FEM is 1716 s for the same load step. In total, 36 regimes were calculated by FEMs in 17 h, 9 min and 36 s, whereas the novel method calculated the ATT stress for 36 regimes in 0.0166 s. The accuracy was also high, with R above 0.99. The main innovation presented in this study is the development of a method that can predict ATT stress in a very short time with a high percentage of accuracy and that can be used for stress and life prediction during the operation of parts. The partial results from the experimental tensile tests are also presented, and they are used for FEM calculations. The FEM results are used as inputs for the stress prediction by the NN.

EPJ Web of Conferences, open-access proceedings in physics and astronomy