Constitutive models of magnetorheological fluids having temperature-dependent prediction parameter


Bahiuddin, I. and Imaduddin, Fitrian and Shapiai, I. and , Ubaidillah and Mazlan, S. A. and Choi, Seung Bok (2018) Constitutive models of magnetorheological fluids having temperature-dependent prediction parameter. Smart Materials and Structures, 27 (9). ISSN 0964-1726; eISSN: 1361-665X

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This work presents constitutive models of magnetorheological (MR) fluids, which can predict the shear and dynamic yield stress depending on temperature. Two existing models, the Herschel–Bulkley rheological and power law model, which are frequently used in MR fluid research, are adopted and modified to take the temperature into account. A new constitutive model of MR fluids is developed using the extreme learning machine (ELM) method. In this development, among many machine learning approaches, a simple and efficient learning algorithm for a single hidden layer feed-forward neural network (SLFN) is adopted and applied to the rheological model of MR fluids. The temperature, shear rate, and magnetic field are treated as inputs, and the shear stress is taken as an output. After formulating the models associated with experimental coefficients, the two most important properties of MR fluids; the shear and yield stress are predicted and compared with the measured values. The prediction accuracy for the field-dependent rheological properties of MR fluids in several different temperatures is evaluated and compared. It is shown that the ELM model developed in this work provides the best accuracy, followed by two other modified constitutive equations.

Item Type: Article
Uncontrolled Keywords: magnetorheological fluids
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK452-454.4 Electric apparatus and materials. Electric circuits. Electric networks
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 27 Oct 2020 19:58
Last Modified: 27 Oct 2020 19:58


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