Failure study of an aircraft engine high pressure turbine (HPT) first stage blade

García-Martínez, María; Gordillo, Juan Carlos del Hoyo; González, Ma Pilar Valles; Pastor, A.; Caballero, Beatriz González

doi:10.1016/j.engfailanal.2023.107251

Cited by 14 publications

(3 citation statements)

References 17 publications

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“…Jet engine malfunction and failure can cause disaster. In the study of an aircraft engine high-pressure turbine (HPT) first stage blade, the authors claimed that almost 50% of failures are located in the damage of turbine blades and discs [13]. There are also other studies that have pointed out the design of the aircraft parts, showing that an impropriate design can cause aircraft parts failure [1,[14][15][16]].…”

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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Section: Introductionmentioning

confidence: 99%

Jet Engine Turbine Mechanical Properties Prediction by Using Progressive Numerical Methods

Spodniak,

Hovanec,

Korba

2023

Aerospace

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“…Critical components often operate in harsh environments and are subjected to cyclic stress and strain in specialized applications, such as aerospace engines, offshore platforms, and other similar scenarios, making them susceptible to the formation of micro-cracks and other defects on or near the surface [1][2][3]. If these defects are not detected in a timely manner, they can rapidly propagate and lead to the sudden catastrophic failure of the entire structure [4,5].…”

Section: Introductionmentioning

confidence: 99%

Depth Evaluation of Tiny Defects on or near Surface Based on Convolutional Neural Network

Fei,

Cao,

et al. 2023

Applied Sciences

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This paper proposes a method for the detection and depth assessment of tiny defects in or near surfaces by combining laser ultrasonics with convolutional neural networks (CNNs). The innovation in this study lies in several key aspects. Firstly, a comprehensive analysis of changes in ultrasonic signal characteristics caused by variations in defect depth is conducted in both the time and frequency domains, based on discrete frequency spectra and original A—scan signals. Continuous wavelet transform (CWT) is employed to obtain wavelet time–frequency maps, demonstrating the consistent characteristics of this image with crack depth variations. A crucial innovation in this research involves the targeted design and optimization of the model based on the characteristics of ultrasonic signals and dataset size. This includes aspects such as data preparation, CNN architecture construction, and hyperparameter selection. The model is tested using a random validation set, which effectively demonstrates the CNN model’s validity and high precision. The proposed method enables the recognition and depth assessment of tiny defects on or near surfaces.

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“…Wide-gap brazing (WGB) is usually applied to repair such structures based on isothermal solidification induced by the melting point depressant (MPD) element diffusion. The quantity, morphology, and distribution of porosity defects and eutectic structure with hard brittle characteristics in brazed joints have significant effects on the mechanical properties of these joints [1][2][3]. Eutectic structures are formed due to insufficient diffusion of MPD elements in liquid-phase brazing material during the cooling process [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

First-Principles Study on Si Atom Diffusion Behavior in Ni-Based Superalloys

Sun,

Wang,

et al. 2023

Materials

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The Si atom diffusion behavior in Ni-based superalloys was evaluated based on first-principles calculations. Also, the site occupation of Si atoms as the melting point depressant elements in Cr, Mo, and W atom doped γ-Ni and γ′-Ni3Fe supercells was discussed and Si atom diffusion behaviors between both adjacent octahedral interstices were analyzed. Calculation results indicated that formation enthalpy (∆Hf) was decreased, stability was improved by doping alloying elements Cr, Mo, and W in γ-Ni and γ′-Ni3Fe supercells, Si atoms were more inclined to occupy octahedral interstices and the diffusion energy barrier was increased by increasing the radius of the doped alloy element. Especially, two diffusion paths were available for Si atoms in the γ′-Ni3Fe and Si diffusion energy barrier around the shared Fe atoms between adjacent octahedral interstices and was significantly lower than that around the shared Ni atoms. The increase of interaction strength between the doped M atom/octahedron constituent atom and Si atom increased Si atom diffusion and decreased the diffusion energy barrier. The Si atom diffusion behavior provides a theoretical basis for the phase structure evolution in wide-gap brazed joints.

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Failure study of an aircraft engine high pressure turbine (HPT) first stage blade

Cited by 14 publications

References 17 publications

Jet Engine Turbine Mechanical Properties Prediction by Using Progressive Numerical Methods

Jet Engine Turbine Mechanical Properties Prediction by Using Progressive Numerical Methods

Depth Evaluation of Tiny Defects on or near Surface Based on Convolutional Neural Network

First-Principles Study on Si Atom Diffusion Behavior in Ni-Based Superalloys

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