Characteristics and mechanisms of turboshaft engine axial compressor casing containment

He, Zekan; Guo, Xiaojun; Hu, Xuan; Shan, Xiaoming; Fan, Xuetong; Chen, Chuanyong; Hong, Weirong

doi:10.1016/j.cja.2020.08.050

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…ε, and T during the deformation process independently affect the strain hardening rate (multiplicative terms in flow stress). During the last years, several authors have used Johnson-Cook model to predict the flow stress behavior of metals [35][36][37][38]. Ning and Liang [36] calibrated the model parameters with an analytical method based on a modified chip formation model and experimental data of force and temperature of one orthogonal machining test.…”

Section: Mechanical Model Of Flow Stress Behaviormentioning

confidence: 99%

Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling

Tuninetti,

Forcael,

Valenzuela

et al. 2024

Materials

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The manufacturing processes and design of metal and alloy products can be performed over a wide range of strain rates and temperatures. To design and optimize these processes using computational mechanics tools, the selection and calibration of the constitutive models is critical. In the case of hazardous and explosive impact loads, it is not always possible to test material properties. For this purpose, this paper assesses the efficiency and the accuracy of different architectures of ANNs for the identification of the Johnson–Cook material model parameters. The implemented computational tool of an ANN-based parameter identification strategy provides adequate results in a range of strain rates required for general manufacturing and product design applications. Four ANN architectures are studied to find the most suitable configuration for a reduced amount of experimental data, particularly for cases where high-impact testing is constrained. The different ANN structures are evaluated based on the model’s predictive capability, revealing that the perceptron-based network of 66 inputs and one hidden layer of 30 neurons provides the highest prediction accuracy of the effective flow stress–strain behavior of Ti64 alloy and three virtual materials.

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Section: Mechanical Model Of Flow Stress Behaviormentioning

confidence: 99%

Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling

Tuninetti,

Forcael,

Valenzuela

et al. 2024

Materials

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“…5 Among the simulations using finite elements are the so-called explicit dynamics simulations with large deformations, which can model two or more objects that can strike each other, considering the interaction of high energy levels and strain rates, such as the impact of a car against a wall, the impact between two cars, the detachment of material in a machining process, [6][7][8][9] the deformation and fracture of tensile test pieces, [10][11][12] impact tests, [13][14][15][16][17][18] and others. In the aeronautics industry, such simulations have enabled non-destructive tests to be performed on the new aircraft engine models that have been developed, such as the blade containment test [19][20][21][22][23][24][25] and the bird strike test. [26][27][28] There are different constitutive models to predict the viscoelastic behavior of materials.…”

Section: Introductionmentioning

confidence: 99%

Dynamic numerical prediction of plasticity and damage in a turbofan blade containment test

Sepúlveda

Valle

Pincheira

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part L:

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For the light designs of new aircraft engines, constitutive plasticity and fracture models sensitive to strain rate and temperature are essential for the accurate prediction of deformations and internal stresses of the components during simulations of impact and explosion events. The work described in this article consists of the development and numerical analysis by finite elements of the blade containment test of a commercial aircraft turbofan engine, conducted to evaluate the structural integrity of the casing after being impacted by a detached fan blade. Two simulation models of the test are proposed, in which the resistance behavior of the strain rate-dependent material is described by isotropic laws of strain hardening and Johnson–Cook damage. The strength analysis is based on the numerical field results of equivalent stresses and deformations, along with the internal damage rates of the casing. The first simplified model considers half of the casing impacted by a blade at different speeds and angles of impact. The second model consists of a complete discretization of casing and rotating turbofan, with the initial detached condition of a blade simulated at different rotating speeds of the fan. The material used in this study is the Ti-6Al-4V alloy. The results analysis and advances obtained make it possible to approach an efficient computational tool with more accurate calculations to study a casing redesign with a safe reduction in mass and that fulfills the certification requirements using the blade containment test.

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Failure mechanisms of bolted flanges in aero-engine casings subjected to impact loading

Cao

Zhang

et al. 2021

Chinese Journal of Aeronautics

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Characteristics and mechanisms of turboshaft engine axial compressor casing containment

Cited by 6 publications

References 25 publications

Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling

Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling

Dynamic numerical prediction of plasticity and damage in a turbofan blade containment test

Failure mechanisms of bolted flanges in aero-engine casings subjected to impact loading

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