Machine Learning Approach to Nonlinear Fluid-Induced Vibration of Pronged Nanotubes in a Thermal–Magnetic Environment

Yinusa, Ahmed and Amokun, Ridwan and Eke, John and Sobamowo, Gbeminiyi and Oguntala, George and Ehinmowo, Adegboyega and Salami, Faruq and Osigwe, Oluwatosin and Adelaja, Adekunle and Ojolo, Sunday and Usman, Mohammed (2025) Machine Learning Approach to Nonlinear Fluid-Induced Vibration of Pronged Nanotubes in a Thermal–Magnetic Environment. Vibration, 8 (3). p. 35. ISSN 2571-631X

Preview

Text vibration-08-00035.pdf - Published Version Available under License Creative Commons Attribution. Download (9MB)

Abstract

Exploring the dynamics of nonlinear nanofluidic flow-induced vibrations, this work focuses on single-walled branched carbon nanotubes (SWCNTs) operating in a thermal–magnetic environment. Carbon nanotubes (CNTs), renowned for their exceptional strength, conductivity, and flexibility, are modeled using Euler–Bernoulli beam theory alongside Eringen’s nonlocal elasticity to capture nanoscale effects for varying downstream angles. The intricate interactions between nanofluids and SWCNTs are analyzed using the Differential Transform Method (DTM) and validated through ANSYS simulations, where modal analysis reveals the vibrational characteristics of various geometries. To enhance predictive accuracy and system stability, machine learning algorithms, including XGBoost, CATBoost, Random Forest, and Artificial Neural Networks, are employed, offering a robust comparison for optimizing vibrational and thermo-magnetic performance. Key parameters such as nanotube geometry, magnetic flux density, and fluid flow dynamics are identified as critical to minimizing vibrational noise and improving structural stability. These insights advance applications in energy harvesting, biomedical devices like artificial muscles and nanosensors, and nanoscale fluid control systems. Overall, the study demonstrates the significant advantages of integrating machine learning with physics-based simulations for next-generation nanotechnology solutions.

Item Type:	Article
Identification Number:	10.3390/vibration8030035
Dates:	Date Event 13 May 2025 Accepted 27 June 2025 Published Online
Uncontrolled Keywords:	branched SWCNT, data wrangling, extreme gradient boosting, random forest, category boosting, artificial neural network, differential transform method
Subjects:	CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-01 - engineering (non-specific)
Divisions:	Architecture, Built Environment, Computing and Engineering > Engineering
Depositing User:	Gemma Tonks
Date Deposited:	11 Aug 2026 15:36
Last Modified:	11 Aug 2026 15:36
URI:	https://www.open-access.bcu.ac.uk/id/eprint/17154

Actions (login required)

View Item