An intelligent computer method for vibration responses of the spinning multi-layer symmetric nanosystem using multi-physics modeling

Guo J. Baharvand A. Tazeddinova D. Habibi M. Safarpour H. Roco-Videla A. Selmi A.
December 2022 Springer Science and Business Media Deutschland GmbH

Engineering with Computers
2022 #38 4217 - 4238 pp.

This article is the first attempt to employ deep learning to estimate the frequency performance of the rotating multi-layer nanodisks. The optimum values of the parameters involved in the mechanism of the fully connected neural network are determined through the momentum-based optimizer. The strength of the method applied in this survey comes from the high accuracy besides lower epochs needed to train the multi-layered network. It should be mentioned that the current nanostructure is modeled as a nanodisk on the viscoelastic substrate. Due to rotation, the centrifugal and Coriolis effects are considered. Hamilton’s principle and generalized differential quadrature method (GDQM) are presented for obtaining and solving the governing equations of the high-speed rotating nanodisk on a viscoelastic substrate. The outcomes show that the number of layers viscoelastic foundation, angular velocity speed, angle of ply, nonlocal, and length-scale parameters have a considerable impact on the amplitude and vibration behavior of a laminated rotating cantilevered nanodisk. As an applicable result in related industries, in the initial value of radius ratio, damping of the foundation does not have any effect on the dynamics of the system, but when the outer radius is bigger enough, the effect of damping parameter on the frequency of the laminated nanostructure will be bold sharply.

Adaptive learning-rate optimization , Deep-learning , Dynamic stability , Laminated cantilevered nanodisk , Rotation , Viscoelastic foundation

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Huawei School of Technology, Weifang University of Science and Technology, Shandong, Weifang, 262700, China
Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Shandong, Weifang, 262700, China
Department of Physics, Lorestan University, Khorramabad, Iran
South Ural State University, Chelyabinsk, Russian Federation
Zhangir Khan West Kazakhstan Agrarian Technical University, Uralsk, Kazakhstan
Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam
Faculty of Electrical–Electronic Engineering, Duy Tan University, Da Nang, 550000, Viet Nam
Center of Excellence in Design, Robotics, and Automation, Department of Mechanical Engineering, Sharif University of Technology, Azadi Avenue, P.O. Box 11365-9567, Tehran, Iran
Faculty of Engineering, Department of Mechanics, Imam Khomeini International University, Qazvin, Iran
Facultad de Ciencias de la Salud, Programa Magister en Ciencias Químico-Biológicas, Universidad Bernardo O’Higgins, Santiago, Chile
Departamento de Ingeniería Civil, Facultad de Ingeniería, Universidad Católica de la Santísima Concepción, Concepción, Chile
Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia
Civil Engineering Laboratory, Ecole Nationale d’Ingénieurs deTunis (ENIT), Le belvedere, B.P. 37, Tunis, 1002, Tunisia

Huawei School of Technology
Shandong Provincial University Laboratory for Protected Horticulture
Department of Physics
South Ural State University
Zhangir Khan West Kazakhstan Agrarian Technical University
Institute of Research and Development
Faculty of Electrical–Electronic Engineering
Center of Excellence in Design
Faculty of Engineering
Facultad de Ciencias de la Salud
Departamento de Ingeniería Civil
Department of Civil Engineering
Civil Engineering Laboratory

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