Hybrid uncertainties-based analysis and optimization methods for axial friction force of drive-shaft systems

Feng, Huayuan; Shangguan, Wen‐Bin; Rakheja, Subhash

doi:10.1016/j.jsv.2021.116320

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…Also based on the IGA, Hong et al [279] optimized a rotor system based on many design parameters to ensure there is an adequate margin between working speeds and critical speeds. Feng et al [280] focused on a drive-shaft system with both random and interval hybrid uncertain parameters to optimize the axial friction force on the Fig. 16.…”

Section: Optimization Under Uncertaintymentioning

confidence: 99%

A state-of-the-art review on uncertainty analysis of rotor systems

Sinou

Zhu

et al. 2023

Mechanical Systems and Signal Processing

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Section: Optimization Under Uncertaintymentioning

confidence: 99%

A state-of-the-art review on uncertainty analysis of rotor systems

Sinou

Zhu

et al. 2023

Mechanical Systems and Signal Processing

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“…This approach aims to ensure the robustness of a design that presents uncertainties in its input parameters, seeking the optimal conditions for a model's operating parameters while ensuring that none of its failure criteria are exceeded, thus guaranteeing the predefined reliability for the project. RBDO was used by Feng et al [27] to optimize the lower bound of reliability above 99.9%, considering uncertainties on the axial friction force of drive shaft systems. Hong et al [28] also used RBDO to maximize the efficiency and minimize the weight using an uncertainty model for the stall margin of the NASA stage 37 axial compressor.…”

Section: Introductionmentioning

confidence: 99%

Reliability-Based Design Optimization Applied to a Rotor Supported by Hydrodynamic Bearings

de Castro,

de Paula,

Visnadi

2024

Machines

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Rotating machines are an important part of industrial equipment. It is essential to improve their performance while reducing the manufacturing, operating, and maintenance costs. Ensuring their reliability is also crucial because a machine breakdown can result in significant costs and potential environmental and safety damage. Reliability-based optimization is an approach that aims to find an optimal and robust design that guarantees a machine’s reliability. In this study, we focused on optimizing the shaft diameter and oil temperature of a rotor supported by hydrodynamic bearings. We considered the materials’ elastic moduli, density, and bearing clearance as uncertain parameters. Our goal was to ensure 99% reliability regarding both the vibration amplitude and stability threshold. To model the machine, we used the finite element method and represented the bearings using stiffness and damping coefficients, considering the linear short bearing model. Due to the complexity of the model, we employed surrogate models to solve the reliability-based optimization problem. Our results showed that the optimization problem could be solved successfully using Kriging, polynomial chaos expansion, and polynomial chaos Kriging.

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Surrogate modeling for dynamic analysis of an uncertain notched rotor system and roles of Chebyshev parameters

Zhu

Yang

et al. 2022

Journal of Sound and Vibration

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Hybrid uncertainties-based analysis and optimization methods for axial friction force of drive-shaft systems

Cited by 6 publications

References 31 publications

A state-of-the-art review on uncertainty analysis of rotor systems

A state-of-the-art review on uncertainty analysis of rotor systems

Reliability-Based Design Optimization Applied to a Rotor Supported by Hydrodynamic Bearings

Surrogate modeling for dynamic analysis of an uncertain notched rotor system and roles of Chebyshev parameters

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