Uncertain Frequency Responses of Clamp-Pipeline Systems Using an Interval-Based Method

Guo, Xumin; Ma, Hui; Zhang, Xufang; Zhuang, Ye; Fu, Qiang; Z, Liu; Han, Qingkai

doi:10.1109/access.2020.2972396

Cited by 25 publications

(11 citation statements)

References 50 publications

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“…Machine learning can also be used for model order reduction [190,191]; manifold learning and manifold processes for model order reduction should be discussed in detail in our future work. Chaos features can improve the efficiency of reduction models [192][193][194] and the combination method of uncertainty quantification [195,196], and model order reduction will be efficient to study the complex dynamic systems. Another point is the systems with substructures.…”

Section: Other Improved Pod Methods and Related Issuesmentioning

confidence: 99%

A Review of Model Order Reduction Methods for Large‐Scale Structure Systems

Zhang

et al. 2021

Shock and Vibration

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The large-scale structure systems in engineering are complex, high dimensional, and variety of physical mechanism couplings; it will be difficult to analyze the dynamic behaviors of complex systems quickly and optimize system parameters. Model order reduction (MOR) is an efficient way to address those problems and widely applied in the engineering areas. This paper focuses on the model order reduction of high-dimensional complex systems and reviews basic theories, well-posedness, and limitations of common methods of the model order reduction using the following methods: center manifold, Lyapunov–Schmidt (L-S), Galerkin, modal synthesis, and proper orthogonal decomposition (POD) methods. The POD is a powerful and effective model order reduction method, which aims at obtaining the most important components of a high-dimensional complex system by using a few proper orthogonal modes, and it is widely studied and applied by a large number of researchers in the past few decades. In this paper, the POD method is introduced in detail and the main characteristics and the existing problems of this method are also discussed. POD is classified into two categories in terms of the sampling and the parameter robustness, and the research progresses in the recent years are presented to the domestic researchers for the study and application. Finally, the outlooks of model order reduction of high-dimensional complex systems are provided for future work.

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Section: Other Improved Pod Methods and Related Issuesmentioning

confidence: 99%

A Review of Model Order Reduction Methods for Large‐Scale Structure Systems

Zhang

et al. 2021

Shock and Vibration

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“…The stiffness value of the hoop is dispersive, and the stiffness value obtained by static test may be quite different from that of the hoop in real pipeline system [34]. In this study, the corresponding stiffness and damping values can be obtained by inverse identification method [35][36].…”

Section: B the Support Simulation Of The Hoopmentioning

confidence: 97%

Optimization of Hoop Layouts for Reducing Vibration Amplitude of Pipeline System Using the Semi-Analytical Model and Genetic Algorithm

Sun

Gao

2020

IEEE Access

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“…The natural frequency under the tightening torque of 10Nm decreases by 2.1Hz with the increase of the excitation amplitude. This may be caused by the uncertainty of clamp-pipeline system responses [9].…”

Section: Nonlinear Stiffness Characteristicsmentioning

confidence: 99%

“…The results show that the natural frequencies and mode shapes of the reduced model are the same as full FE, but the calculation is faster. Guo et al [9] proposed a nonintrusive multi-dimensional Chebyshev polynomial approximation method (M-CPAM), which is used to study the frequency response of a clamp-pipeline system. The results show that under the same pre-tightening force, the stiffness of a clamp has greater dispersion, and with the increase of tightening torque, the dispersion of the clamppipeline system tends to be concentrated.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Characteristic of Clamp Loosing in Aero-Engine Pipeline System

et al. 2021

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Long service time and excessive vibration of aero-engine will cause failure of clamps, which usually fix pipelines on the outside of an aero-engine. In this paper, the nonlinear vibration characteristics caused by loosening clamps in aero-engine pipeline are determined. According to the real parameters of an aero-engine clamp-pipeline test rig, the clamp is simplified into a nonlinear spring-damping. Thus, a clamppipeline nonlinear model based on cubic stiffness is established. The multi-scale method is used to obtain the analytical solution of the nonlinear model. Based on the real test data, the model parameters of the clamp-pipeline nonlinear system are identified, and then the nonlinear vibration characteristics of the system are obtained. Finally, using the vibration transmissibility method, the influence of the severity of clamp loosening on the nonlinear vibration characteristics of the system is obtained.

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Uncertain Frequency Responses of Clamp-Pipeline Systems Using an Interval-Based Method

Cited by 25 publications

References 50 publications

A Review of Model Order Reduction Methods for Large‐Scale Structure Systems

A Review of Model Order Reduction Methods for Large‐Scale Structure Systems

Optimization of Hoop Layouts for Reducing Vibration Amplitude of Pipeline System Using the Semi-Analytical Model and Genetic Algorithm

Nonlinear Characteristic of Clamp Loosing in Aero-Engine Pipeline System

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