Temperature-dependent free vibration analysis of functionally graded micro-beams based on the modified couple stress theory

Babaei, Alireza; Noorani, Mohammad-Reza Sayyed; Gholami, Ahmad

doi:10.1007/s00542-017-3285-0

Cited by 40 publications

(31 citation statements)

References 63 publications

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“…It is worth noting that the values obtained by the MCS theory in the present study are always greater than the calculated values based on the classical theory. This important observation is consistent with the results obtained by Babaei et al [45] .…”

Section: Couple Stress Effectsupporting

confidence: 94%

Size-dependent thermoelastic initially stressed micro-beam due to a varying temperature in the light of the modified couple stress theory

Abouelregal

2020

Appl. Math. Mech.-Engl. Ed.

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The bending of the Euler-Bernoulli micro-beam has been extensively modeled based on the modified couple stress (MCS) theory. Although many models have been incorporated into the literature, there is still room for introducing an improved model in this context. In this work, we investigate the thermoelastic vibration of a micro-beam exposed to a varying temperature due to the application of the initial stress employing the MCS theory and generalized thermoelasticity. The MCS theory is used to investigate the material length scale effects. Using the Laplace transform, the temperature, deflection, displacement, flexure moment, and stress field variables of the micro-beam are derived. The effects of the temperature pulse and couple stress on the field distributions of the micro-beam are obtained numerically and graphically introduced. The numerical results indicate that the temperature pulse and couple stress have a significant effect on all field variables.

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Section: Couple Stress Effectsupporting

confidence: 94%

Size-dependent thermoelastic initially stressed micro-beam due to a varying temperature in the light of the modified couple stress theory

Abouelregal

2020

Appl. Math. Mech.-Engl. Ed.

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“…The MEMS inertial device is mainly composed of a basic beam, spring, and mass block. The MEMS inertial device is easily influenced by temperature, rotation speed, attached mass, instant temperature field, material distribution, geometry, and dimension size [ 97 , 98 , 99 , 100 , 101 , 102 ], resulting in structure stress concentration, thermal stress, unstable resonant frequency, and other adverse phenomena. Therefore, in order to better design the MEMS/NEMS device, it is necessary to consider stress release, temperature insensitivity, geometric structure, scale effect, driving/detection mode, appropriate non-classical parameters, and rod model [ 97 , 98 , 99 , 100 , 101 , 102 ].…”

Section: Discussionmentioning

confidence: 99%

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

et al. 2020

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Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

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“…During the last decade, improved computing hardware and software has led to the prosperous application of machine learning (ML) in different areas of oil industry such as seismic data, petrophysical analysis including synthetic log generation or prediction [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], which has shown to be a promising tool to help address their problems in a rigorous, repeatable way. Such methods, by considering various available parameters, can give a better prediction of the missing data than simple linear methods [10,26,27]. ML techniques can be categorized into two main types, known as supervised and unsupervised learning.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning: A Useful Tool in Geomechanical Studies, a Case Study from an Offshore Gas Field

Khatibi

Aghajanpour

2020

Energies

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For a safe drilling operation with the of minimum borehole instability challenges, building a mechanical earth model (MEM) has proven to be extremely valuable. However, the natural complexity of reservoirs along with the lack of reliable information leads to a poor prediction of geomechanical parameters. Shear wave velocity has many applications, such as in petrophysical and geophysical as well as geomechanical studies. However, occasionally, wells lack shear wave velocity (especially in old wells), and estimating this parameter using other well logs is the optimum solution. Generally, available empirical relationships are being used, while they can only describe similar formations and their validation needs calibration. In this study, machine learning approaches for shear sonic log prediction were used. The results were then compared with each other and the empirical Greenberg–Castagna method. Results showed that the artificial neural network has the highest accuracy of the predictions over the single and multiple linear regression models. This improvement is more highlighted in hydrocarbon-bearing intervals, which is considered as a limitation of the empirical or any linear method. In the next step, rock elastic properties and in-situ stresses were calculated. Afterwards, in-situ stresses were predicted and coupled with a failure criterion to yield safe mud weight windows for wells in the field. Predicted drilling events matched quite well with the observed drilling reports.

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Temperature-dependent free vibration analysis of functionally graded micro-beams based on the modified couple stress theory

Cited by 40 publications

References 63 publications

Size-dependent thermoelastic initially stressed micro-beam due to a varying temperature in the light of the modified couple stress theory

Size-dependent thermoelastic initially stressed micro-beam due to a varying temperature in the light of the modified couple stress theory

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

Machine Learning: A Useful Tool in Geomechanical Studies, a Case Study from an Offshore Gas Field

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