Artificial Neural Network Structure Optimisation in the Pareto Approach on the Example of Stress Prediction in the Disk-Drum Structure of an Axial Compressor

Kozakiewicz, Adam; Kieszek, Rafał

doi:10.3390/ma15134451

Cited by 8 publications

(5 citation statements)

References 37 publications

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“…Nowadays, there are works solving the problem of strength calculations of rotor assemblies using artificial neural networks. In the article [7] the process of selecting and optimizing artificial neural networks based on the example of determining the stress distribution in a disk-drum structure compressor stage of an aircraft turbine engine. The presented algorithm allows the determination of Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).…”

Section: Introductionmentioning

confidence: 99%

FSI Application in the Parametric Analysis of Blade-Disc Connection of Turbojet Engine Compressor

Grzejszczak-Pączek,

Kozakiewicz

2023

Preprint

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In the following work, the assessment of the strength properties of the critical area of the rotor stage of the turbine jet engine was undertaken. This is the place of fixing the blade in the rotor disc. An important aspect of this issue are characteristic geometrical dimensions and operating factors e.g., change in rotor speed. In this paper, the assessment of the strength properties of the existing trapezoidal blade-disc connection was undertaken using parametric analysis considering FSI. Virtual models of the compressor rotor stage were developed, including parametric blade-disc connections and discrete models for FEM and CFD analyses. In the parametric analysis of the blade-disc connection, one-way fluid-structure coupling was used. The parametric analysis was aimed at assessing selected geometric parameters in terms of their impact on the maximum reduced stresses in the blade root. It was examined how the adopted value of the friction coefficient influences the maximum reduced stresses in the rotor stage of the compressor.

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Section: Introductionmentioning

confidence: 99%

FSI Application in the Parametric Analysis of Blade-Disc Connection of Turbojet Engine Compressor

Grzejszczak-Pączek,

Kozakiewicz

2023

Preprint

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“…For these functions, FADEC uses many inputs gained from the sensors built into the jet engine that monitor the temperatures, pressures, rpm, altitude, Mach number, physical fan speed, physical core speed, ratio of fuel flow, corrected fan speed, corrected core speed, burner fuel-air ratio, etc. In [21], the authors use 24 monitored inputs for their FADEC study; the result of their research was the remaining life of the turbofan engine. These measured parameters could be used to predict the mechanical properties.…”

Section: Introductionmentioning

confidence: 99%

“…These measured parameters could be used to predict the mechanical properties. There have been several studies on mechanical properties prediction, for example, one of them described the stress prediction of the compressor disc-drum and the process of selecting and optimizing the artificial neural network [21]. In [22], the remaining useful life was predicted by the neural network.…”

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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“…Nowadays, there are works solving the problem of strength calculations of rotor assemblies using artificial neural networks. Kozakiewicz et al [7] presented the use of artificial neural networks to determine the stress distribution in the disk-drum stage of an aircraft turbine engine compressor. The presented algorithm made it possible to determine stress values that could be used for further mass optimization of the entire structure.…”

Section: Introductionmentioning

confidence: 99%

FSI Application in the Parametric Analysis of Blade–Disk Connection of Turbojet Engine Compressor

Grzejszczak-Pączek,

Kozakiewicz

2023

Applied Sciences

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The following paper evaluates the strength properties of the critical area of the turbine rotor stage of a jet engine. This is where the blade is attached to the rotor disk. Important aspects of this issue are characteristic geometrical dimensions and operating factors, e.g., changes in rotor speed. In the article, the strength properties of the rotor stage with a trapezoidal blade–disk connection were analyzed using parametric analysis considering fluid–structure interaction (FSI). Virtual models of the compressor rotor stages were developed, including parametric blade–disk connections and discrete models for FEM (finite element method) and CFD (computational fluid dynamics) analyses. The parametric analysis was aimed at assessing selected geometric parameters in terms of their impact on the maximum stresses in the blade root. The analysis performed shows that by changing the blade’s width and thickness angles at the root, we can influence the value of the maximum stresses in the rotor stage. It was noticed that some combinations of these two parameters may negatively affect the value of the stresses in the rim.

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Artificial Neural Network Structure Optimisation in the Pareto Approach on the Example of Stress Prediction in the Disk-Drum Structure of an Axial Compressor

Cited by 8 publications

References 37 publications

FSI Application in the Parametric Analysis of Blade-Disc Connection of Turbojet Engine Compressor

FSI Application in the Parametric Analysis of Blade-Disc Connection of Turbojet Engine Compressor

Jet Engine Turbine Mechanical Properties Prediction by Using Progressive Numerical Methods

FSI Application in the Parametric Analysis of Blade–Disk Connection of Turbojet Engine Compressor

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