Probabilistic analyses of structural dynamic response with modified Kriging-based moving extremum framework

Lu, Cheng; Fei, Chengwei; Feng, Yong; Zhao, Ye; Dong, Xiao Wei; Choy, Yat Sze

doi:10.1016/j.engfailanal.2021.105398

Cited by 68 publications

(25 citation statements)

References 36 publications

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“…As the core of the aircraft, aeroengines operate under extreme conditions such as high speeds, high temperatures, and variable loads. eir reliability is the most important factor supporting the performance and survivability of the aircraft [1][2][3]. In recent years, the rapid development of the world aviation industry has put forward new and higher requirements for the reliability and sustainable airworthiness of the aeroengine.…”

Section: Introductionmentioning

confidence: 99%

Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework

Liu

Feng

et al. 2021

Shock and Vibration

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In order to improve the accuracy and calculation efficiency of aeroengine rotor vibration reliability analysis, a time-varying rotor vibration reliability analysis method under the aeroengine operating state is proposed. Aiming at the highly nonlinear and strong coupling of factors affecting the reliability of aeroengine rotor vibration, an intelligent neural network modeling framework (short form-INNMF) is proposed. The proposed method is based on DEA, with QAR information as the analysis data, and four factors including engine working state, fuel/oil working state, aircraft flight state, and external conditions are considered to analyse the rotor vibration reliability. INNMF is based on the artificial neural network (ANN) algorithm through improved particle swarm optimization (PSO) algorithm and Bayesian Regularization (BR) optimization. Through the analysis of the rotor vibration reliability of the B737-800 aircraft during a flight mission from Beijing to Urumqi, the time-varying rotor vibration reliability was obtained, which verified the effectiveness and feasibility of the method. The comparison of INNMF, random forest (RF), and ANN shows that INNMF improves analysis accuracy and calculation efficiency. The proposed method and framework can provide useful references for aeroengine rotor vibration analysis, special treatment, maintenance, and design.

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Section: Introductionmentioning

confidence: 99%

Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework

Liu

Feng

et al. 2021

Shock and Vibration

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“…erefore, it is particularly important and challenging to study the impact damage and impact resistance about ceramic matrix composites [6][7][8]. For the components in the hot end of aeroengines, the most likely impact sources during their services are medium-high speed and low-energy impact.…”

Section: Introductionmentioning

confidence: 99%

Study on Impact Resistance of C/SiC Ceramic Matrix Composites for Thermal Protection of the Aerospace Vehicle

Lin¹,

Liu

2021

Mathematical Problems in Engineering

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This paper evaluates the impact damage tolerance of ceramic matrix composites and contrasts their tensile and compression strengths under impact and nonimpact conditions. The representative C/SiC composites from PIP are examined under the dropped tip system. In this paper, the surface impact test of C/SiC composites is carried out. By testing a series of specimens of various impact energies, the tensile and compression properties after impact are studied. Meanwhile, by comparing the changes of the specimens after impact and nonimpact, the fracture process, fracture modes, and residual strength are investigated. The results indicate that the residual tensile strength after impact reduces greatly, compared with the nonimpact material, by about 25%–45%, while the compression strength reduces slightly. The residual strength of tension and compression illustrates that low-energy impact effect on the structural integrity must be given priority in the thermal protection structure design of astronautics and aeronautics.

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“…However, these methods do not consider the uncertainty and discreteness of natural rock mass and geological conditions. e probabilistic method was commonly used to handle the uncertainty in engineering systems and test data [10][11][12][13][14][15][16]. At the same time, the interaction analysis between rock and support has also made significant progress [17].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Design and Optimization for Tunnels considering Measuring Uncertainties

Jia

Zhao

Zhu

2021

Mathematical Problems in Engineering

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Uncertainty is an essential property of rock mechanics and engineering, which is of great significance to excavation, design, and control of rock engineering. In this study, an innovative framework of the reliability-based design was developed for the rock tunnel under uncertainty. The convergence-confinement method is used to characterize the interaction mechanism between the support structure and surrounding rock mass. Artificial bee colony (ABC) was adopted to solve the optimization problem in the reliability-based design. The probabilistic properties of rock strength and failure envelope were obtained based on the triaxial compression test data using the Bayesian method. The reliability of the tunnel and support structure was evaluated based on the abovementioned probabilistic properties of rock strength using the reliability analysis method. A circular tunnel was used to illustrate the developed framework, and the procedure was presented in detail. The time of rockbolt installed, the thickness of the shotcrete, length of rockbolt, circumferential space, and longitudinal space of rockbolt were determined and met the constraints of reliability index. Results show that the developed framework can consider the uncertainty for support design in the tunnel. It provides a good and promising way to support design considering the uncertainty of test data using the reliability-based design.

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Probabilistic analyses of structural dynamic response with modified Kriging-based moving extremum framework

Cited by 68 publications

References 36 publications

Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework

Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework

Study on Impact Resistance of C/SiC Ceramic Matrix Composites for Thermal Protection of the Aerospace Vehicle

Probabilistic Design and Optimization for Tunnels considering Measuring Uncertainties

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