Hydrogen and Corrosion Resistance of Nickel Superalloys for Gas Turbines, Engines Cooled Blades

Balitskii, Alexander; Kvasnytska, Yu. H.; Іvas’kevych, L. М.; Kvasnytska, K. H.; Balitskii, O.A.; Shalevska, Inna; Shynskii, Oleg Y.; Jaworski, Jaroslaw; Dowejko, Jakub

doi:10.3390/en16031154

Cited by 7 publications

(1 citation statement)

References 105 publications

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“…To ensure high strength of the turbine parts, the proper materials with high-performance material properties such as Young's modulus and thermal expansion coefficient have to be used [7]. There are many scientific papers investigating the material properties impact on the operational mechanical characteristics of gas turbines [8,9]. The progress in material science is focused on progressive materials, for example, materials such as superalloys and thermal barrier coatings that are used in gas turbines; these are described in many scientific articles [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Jet Engine Turbine Mechanical Properties Prediction by Using Progressive Numerical Methods

Spodniak,

Hovanec,

Korba

2023

Aerospace

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The propulsion system for an aircraft is one of its most crucial systems; therefore, its reliable work must be ensured during all operational conditions and regimes. Modern materials, techniques and methods are used to ensure this goal; however, there is still room for improvement of this complex system. The proposed manuscript describes a progressive approach for the mechanical properties prediction of the turbine section during jet engine operation using an artificial neural network, and it illustrates its application on a small experimental jet engine. The mechanical properties are predicted based on the measured temperature, pressure and rpm during the jet engine operation, and targets for the artificial neural network are finite element analyses results. The artificial neural network (ANN) is trained using training data from the experimental measurements (temperatures, pressure and rpm) and the results from finite element analyses of the small experimental engine turbine section proposed in the paper. The predicted mechanical stress by ANN achieved high accuracy in comparison to the finite element analyses results, with an error of 1.38% for predicted mechanical stress and correlation coefficients higher than 0.99. Mechanical stress and deformation prediction of the turbine section is a time-consuming process when the finite element method is employed; however, the method with artificial neural network application presented in this paper decreased the solving time significantly. Mechanical structural analyses performed in ANSYS software using finite element modeling take around 30–40 min for one load step. In contrast, the artificial neural network presented in this paper predicts the stress and deformation for one load step in less than 0.00000044 s.

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