Turbine Blade Temperature Field Prediction Using the Numerical Methods

Featured Application The results of the research presented in the proposed article in terms of a novel quick and precise methodology for temperature prediction using numerical methods can be applied to research, development and monitoring engine parts. The methodology can be applied in aviation to increase the reliability and lifetime of turbines and hot parts of any jet engine and to reduce not only maintenance but also research and development costs due to the significantly lower computational time demands. The advantage of this methodology is also that it can be applied to any engine parts without the need for artificial neural network modification; the optimization process must only be performed on the CFD model. 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.

Automated summary

The proposed novel methodology for temperature prediction using numerical methods can enhance the reliability and lifetime of turbines and hot parts of jet engines in aviation, while reducing maintenance and research costs. The method is faster than conventional methods and utilizes CFD model optimization. Additionally, the application of artificial neural networks for temperature prediction on turbine blades is a unique approach in this field.