The development of non-destructive methods for material testing and diagnostics has been, in the last few decades, focused mainly on optical, infrared, thermography, ultrasonic, acoustic or X-ray principles. This article deals with the possibility of adaptation of magnetic sensors for the diagnostics of aircraft structures. The developed sensors are based on the enhanced induction method, allowing contactless diagnostics of the material structure. In the role of the sensing element, amorphous magnetic microwires were used. Thanks to their dimensions, microwires can either be placed on the material surface or be embedded directly into the composite material without structural violations. In the article, the measurement principles of the developed microwire-based tensile stress sensors, together with the experimental measurements with the sensors originally tested in the aircraft wing, are presented.
The presented paper describes methodology for the non-stationary noise analysis of the magnetic sensors' data using the dynamic Allan variance. The methodology was developed for the characterization of clock behavior. In the article the theory is applied for the magnetic sensors noise analysis and verified by the simulations and experiments. Results of the data analysis are graphically presented and statistically evaluated and prove the correctness of the initial hypothesis and confirm suitability of the dynamic Allan variance approach for magnetic sensors with the non-stationary noise behavior.
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.
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