Vibration control of a rotor system by shear thickening fluid dampers

Zhao, Qian; Yuan, Jing; Jiang, Huiming; Yao, Hongliang; Wen, Bangchun

doi:10.1016/j.jsv.2020.115883

Cited by 38 publications

(19 citation statements)

References 34 publications

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“…The control of a microturbine is a challenge due to the unbalanced forces, internal self-excitation, external excitation and complex work environment, to name a few. In particular, excessive vibrations in rotating systems cannot be ignored [ 6 ]. In this context, the study of vibrations can improve the control of a microturbine to obtain high efficiency in these systems [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Rodríguez-Abreo

Rodríguez‐Reséndiz

Montoya-Santiyanes

et al. 2021

Sensors

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Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

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Section: Introductionmentioning

confidence: 99%

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Rodríguez-Abreo

Rodríguez‐Reséndiz

Montoya-Santiyanes

et al. 2021

Sensors

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“…A system based on polynomials is proposed to select the surface response model for estimating the amplitude of vibration as a function of speed and temperature. The amplitude is a function of two polynomials of unknown order and coefficients, as shown in Equation (1). Polynomial models have demonstrated advantages in multiple areas of engineering [25,26].…”

Section: Response Surface Model Tuned By a Metaheuristic Algorithmmentioning

confidence: 99%

“…Unbalanced forces, internal and external excitations, complex work environments, and other causes can trigger excessive vibration in rotating systems [1]. The rotor is one of the basic components evaluated in the dynamic analysis of machinery since its misalignment and imbalance can lead to bearing defects [2].…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Algorithm-Based Vibration Response Model for a Gas Microturbine

Montoya-Santiyanes

Rodríguez-Abreo

Rodriguez

et al. 2022

Sensors

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Data acquisition and processing are areas of research in fault diagnosis in rotating machinery, where the rotor is a fundamental component that benefits from dynamic analysis. Several intelligent algorithms have been used to optimize investigations of this nature. However, the Jaya algorithm has only been applied in a few instances. In this study, measurements of the amplitude of vibration in the radial direction in a gas microturbine were analyzed using different rotational frequency and temperature levels. A response surface model was generated using a polynomial tuned by the Jaya metaheuristic algorithm applied to the averages of the measurements, and another on the whole sample, to determine the optimal operating conditions and the effects that temperature produces on vibrations. Several tests with different orders of the polynomial were carried out. The fifth-order polynomial performed better in terms of MSE. The response surfaces were presented fitting the measured points. The roots of the MSE, as a percentage, for the 8-point and 80-point fittings were 3.12% and 10.69%, respectively. The best operating conditions were found at low and high rotational frequencies and at a temperature of 300 ∘C. High temperature conditions produced more variability in the measurements and caused the minimum value of the vibration amplitude to change in terms of rotational frequency. Where it is feasible to undertake experiments with minimal variations, the model that uses only the averages can be used. Future work will examine the use of different error functions which cannot be conveniently implemented in a common second-order model. The proposed method does not require in-depth mathematical analysis or high computational capabilities.

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“…Especially in the experiment of high-speed impact, scholars found that the effect of STF was not ideal under the condition of high-speed impact. In addition, in the field of vibration control, scholars pay more attention to the thickening ratio and initial viscosity of STF [29]. This will determine the compression stroke of the damper and the comfort and practicality of seats, vehicles, and medical devices using the damper [30].…”

Section: Introductionmentioning

confidence: 99%

Rheological characteristics analysis of shear thickening fluids based on response surface methodology

Zhao¹,

Zhang²,

Liu³

et al. 2022

Mater. Res. Express

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This paper analyzed the influence of various components and the external environment on the rheological properties of shear thickening fluid (STF). The single-factor experimental study was carried out with the silica particle size, the mass fraction of silica particles, the continuous phase's average molecular weight, and the temperature as the influencing factors. Obtain the influence of various factors on the initial viscosity, critical shear rate, thickening period and thickening ratio of STF samples. According to the crossover relationship between the shear stress and the intermolecular force when the shear thickening occurs, the STF's critical shear rate criterion is constructed. By introducing the mechanical balance between small molecules, the relationship between the particles' volume fraction and the distance between particles in the suspension system is established. The influencing factors and formulas of the STF's critical shear rate and shear capacity are deduced, which supports the conclusions obtained in the single-factor experiment. Based on the single-factor experiment conclusion, the mass fraction of silica particles, the average molecular weight of the continuous phase and the temperature are used as the influencing factors, and the critical shear rate, shear thickening ratio and maximum viscosity are the responses indicators. According to the Box-Behnken response surface methodology (RSM) design, 15 analysis experiments with three factors and three levels are carried out. Obtain the rheological characteristics of the STF and regression equation between each factor, based on the three-dimensional response surface graph and F-value in ANOVA result. We found that among the interaction effects, the interaction between the continuous phase's average molecular weight and the temperature has the most significant impact on the critical shear rate of the STF. The interaction between the silica particles' mass fraction and the continuous phase's average molecular weight significantly impacts the shear thickening ratio of the STF. The interaction between silica particles' mass fraction and the temperature has the most significant impact on the maximum viscosity of the STF. Finally, according to the response surface methodology experimental results and ideal STF rheological characteristics standard in engineering applications, when the mass fraction of silica particles is 35%, the average molecular weight of the continuous phase is 387, and the temperature is 20 ℃. The critical shear rate of the STF system is 5.84 1/s, the shear thickening ratio is 102.8, and the peak viscosity is 1191.7 Pa∙s, reaching the optimal theoretical value within the value scope.

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Vibration control of a rotor system by shear thickening fluid dampers

Cited by 38 publications

References 34 publications

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Metaheuristic Algorithm-Based Vibration Response Model for a Gas Microturbine

Rheological characteristics analysis of shear thickening fluids based on response surface methodology

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