## 5 Conclusions  
Prediction of the temperature changes in the material of the jet engine turbine blades, the impact of the temperatures on the stress, deformation and fatigue analyses is undoubtedly an especially important part of the engineering, which helps mainly during the development and monitoring of the selected parts of the jet engine. For the jet engines and also other parts of aircraft structures, monitoring and predictions are particularly crucial because any structural damage can lead to aircraft accidents. The main goal of the performed research was to develop a new faster method for temperature prediction. The fact that the new methodology is faster in comparison with methods like CFD is irrefutable. It can be proved by comparison times for prediction temperature fields using CFD and ANN for one operation regime. In proposed research methodology is applied on an iSTC-21v jet engine, and elapsed time using the CFD method is one h, 42 min and 44.7 s, elapsed time using the new method is 0.0027645 s. From the results presented in the article, it can be concluded that the proposed methodology fulfilled this goal. The new methodology based on the ANN is able to predict temperatures of the turbine blades in the specific point almost in real time; thus, the method can be used for monitoring the temperatures also during the runs in the real operation of the jet engine, forasmuch as the new methodology predicts the temperatures immediately. The results presented in the article proved that the idea is correct. Furthermore, thanks to this methodology, it is possible to predict temperatures for many operation regimes. The novel method offers an incomparable faster prediction of temperatures even in real time, in comparison with the conventionally used CFD method, which needs for the prediction of temperatures for one regime several minutes or even hours according to the complexity of the simulation model and hardware performance. For a better explanation of the proposed methodology and also for the verification purposed, the input data for the predictions, namely temperatures, pressures and rotor speed, were measured in the laboratory condition on the iSTC-21v experimental engine. It is essential to point out the originality of the article, which is the application of the ANN for the temperature prediction and its application on the iSTC-21v jet engine. The methodology was applied for the chosen 24 points at the surface of the turbine blade; however, the proposed methodology can also be applied to other points of the turbine blades. Further improvement of the methodology is also expected if more operational regimes of the jet engine will be examined and added. The performed analyses of statistical errors obtained as a result of the performed calculations showed that the artificial neural network is able to reveal the problems that can occur during the preparation of the CFD model. In our case, one of the chosen nodes in Figure 9 exhibits a statistically significant error with the value of 4.121%, which is probably a consequence of the meshing process, turbulent model or errors resulting from the geometry in this narrow part of the turbine blade. However, the above-mentioned issues do not influence the quality of the proposed methodology, the main goal of which was to apply the neural network for the temperature field prediction. In our case, the methodology was verified on the turbine blade surface, but one of the advantages of the methodology is that it can be applied for the temperature prediction of any other jet engine or its parts. The result of the integration of the CFD model and the ANN approach is that the time demands for the calculations of the temperature fields are significantly reduced, and thus during the optimization process of the jet engines or of the components, it is not necessary to wait for the results of the time-consuming CFD simulations and therefore, the calculation results can be applied in the real time also, for example, during the operation of the jet engine.