Abstract
Magnetorheological fluid (MRF) models are important tools in the design of the material based-valve in a damper. Although Bingham Plastic and polynomial models are quite widely employed, these data-driven models have disadvantages in term of the accuracy and narrow applicable operating conditions, including magnetic field and shear rate. Therefore, this paper aims to utilize an extreme learning machine (ELM) based-shear stress model to design an magnetorheological (MR) valve. Firstly, an MRF model of MRF 132DG is built using rotational rheometer test results. Secondly, the model is employed to model a meandering MR valve drop pressure utilizing the known design parameters and a finite element magnetic method (FEMM) results. The comparison of the steady state pressure between the simulation and experimental results (in literature) has shown a good agreement in term of the pattern and accuracy with error of less than 3%. In summary, ELM has shown its potential to model MRF behavior while employing it to an MR device.
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References
de Vicente J, Klingenberg DJ, Hidalgo-Alvarez R (2011) Magnetorheological fluids: a review. Soft Matter 7:3701. https://doi.org/10.1039/c0sm01221a
El Wahed A, Balkhoyor L (2017) Characteristics of magnetorheological fluids under single and mixed modes. Proc Inst Mech Eng Part C J Mech Eng Sci 231:3798–3809. https://doi.org/10.1177/0954406216653621
Imaduddin F, Mazlan SA, Zamzuri H (2013) A design and modelling review of rotary magnetorheological damper. Mater Des 51:575–591. https://doi.org/10.1016/j.matdes.2013.04.042
Ghaffari A, Hashemabadi SH, Ashtiani M (2015) A review on the simulation and modeling of magnetorheological fluids. J Intell Mater Syst Struct 26:881–904. https://doi.org/10.1177/1045389X14546650
Imaduddin F, Amri Mazlan S, Azizi Abdul Rahman M, Zamzuri H, Ubaidillah, Ichwan B (2014) A high performance magnetorheological valve with a meandering flow path. Smart Mater Struct 23:65017. https://doi.org/10.1088/0964-1726/23/6/065017
Bahiuddin I, Mazlan SA, Imaduddin F, Ubaidillah, Ichwan B (2016) Magnetorheological valve based actuator for improvement of passively controlled turbocharger system. In: AIP conference proceedings. https://doi.org/10.1063/1.4943431
Wang DH, Ai HX, Liao WH (2009) A magnetorheological valve with both annular and radial fluid flow resistance gaps. Smart Mater Struct 18:115001. https://doi.org/10.1088/0964-1726/18/11/115001
Li WH, Wang XY, Zhang XZ, Zhou Y (2009) Development and analysis of a variable stiffness damper using an MR bladder. Smart Mater Struct 18:74007. https://doi.org/10.1088/0964-1726/18/7/074007
Bahiuddin I, Mazlan SA, Shapiai MI, Choi S-B, Imaduddin F, Rahman MAA, Ariff MHM (2018) A new constitutive model of a magneto-rheological fluid actuator using an extreme learning machine method. Sens Actuat A Phys 281:209–221. https://doi.org/10.1016/j.sna.2018.09.010
Bahiuddin I, Wahab NAA, Shapiai MI, Mazlan SA, Mohamad N, Imaduddin F, Ubaidillah (2019) Prediction of field-dependent rheological properties of magnetorheological grease using extreme learning machine method. J Intell Mater Syst Struct (in press). https://doi.org/10.1177/1045389X19844007
Bahiuddin I, Mazlan SA, Shapiai I, Imaduddin F, Ubaidillah, Choi S-B (2018) Constitutive models of magnetorheological fluids having temperature-dependent prediction parameter. Smart Mater Struct 27:95001. https://doi.org/10.1088/1361-665X/aac237
Rabbani Y, Shirvani M, Hashemabadi SH, Keshavarz M (2017) Application of artificial neural networks and support vector regression modeling in prediction of magnetorheological fluid rheometery. Colloids Surf A Physicochem Eng Asp 520:268–278. https://doi.org/10.1016/j.colsurfa.2017.01.081
Bahiuddin I, Mazlan SA, Shapiai MI, Imaduddin F, Ubaidillah (2017) Study of extreme learning machine activation functions for magnetorheological fluid modelling in medical devices application. In: 2017 International conference on robotics, automation and sciences (ICORAS). IEEE, pp 1–5. https://doi.org/10.1109/ICORAS.2017.8308053
Jung ID, Kim M, Park SJ (2016) A comprehensive viscosity model for micro magnetic particle dispersed in silicone oil. J Magn Magn Mater 404:40–44. https://doi.org/10.1016/j.jmmm.2015.12.024
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: Theory and applications. Neurocomputing. 70:489–501. https://doi.org/10.1016/j.neucom.2005.12.126
Bahiuddin I, Mazlan SA, Shapiai MI, Imaduddin F, Ubaidillah, Choi S-B (2019) A new platform for the prediction of field-dependent yield stress and plastic viscosity of magnetorheological fluids using particle swarm optimization. Appl Soft Comput 76:615–628. https://doi.org/10.1016/j.asoc.2018.12.038
Acknowledgements
The authors gratefully acknowledge the financial support of the Ministry of Education in Malaysia and Universiti Teknologi Malaysia under grant vote no: PDRU (04E02).
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Bahiuddin, I. et al. (2020). Extreme Learning Machine Based-Shear Stress Model of Magnetorheological Fluid for a Valve Design. In: Sabino, U., Imaduddin, F., Prabowo, A. (eds) Proceedings of the 6th International Conference and Exhibition on Sustainable Energy and Advanced Materials. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-4481-1_27
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