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Geometric optimisation of double ended magnetorheological fluid damper

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Abstract

The main focus of this paper is to optimally design the Magnetorheological Fluid Damper using modern optimization algorithms such as Genetic Algorithm (GA), JAYA and GWO (Grey Wolf Optimizer). The objective function of the problem is to maximize the damping force of the damper. To calculate the damping force for different values of the design parameter Computational Fluid Dynamics (CFD) analysis has been used. The fluid properties to be implemented in the CFD analysis are evaluated through rheological experimentation under different magnetic flux. The design variables in the problem are the length of fluid exposed to the magnetic, Yield Shear stress, and fluid flow gap. The objective function has been formulated by Statistical modeling of the damper wherein a set of Design of Experiments. Obtained set of experiments are statistically (i.e. Analysis of Variance, ANOVA) analyzed to confirm the suitability of the design variable values in the optimization techniques. This equation along with a set of design variables constraints are used in the optimization techniques to find the optimal values of dimensions of the damper to achieve the maximum damping force. From the results it has been observed that the JAYA and GWO technique offered maximum damping force compared to the GA.

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Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.

References

  1. Rabinow, J.: The magnetic fluid clutch. Inst. Electr. Eng. Trans. 67, 1308–1315 (1948)

    Article  Google Scholar 

  2. Gao, F., Liu, Y.N., Liao, W.H.: optimal design of a magnetorheological damper used in smart prosthetic knees. Smart. Mater. Struct. 26, 035034 (2017)

    Article  Google Scholar 

  3. Wang, D., Zi, B., Qian, S., Qian, J.: Steady-state heat-flow coupling field of a high power magnetorheological fluid clutch utilizing liquid cooling. J. Flu. Eng. 139 (2017).

  4. Batterbee, D.C., Sims, N.D,: Magnetorheological platform dampers for mountain bikes. In SPIE Smart. Struct. Mater. Nondestruct. Eval. Health. Monit. 7290, 72900B–11, (2009).

  5. Ding, Y., Zhang, L., Zhu, H.T., Li, Z.X.: A new magnetorheological damper for seismic control. Smart. Mater. Struct 22, 115003 (2013)

    Article  Google Scholar 

  6. Jolly, M.R., Bender, J., Carlson, D.J.: Properties and applications of commercial magnetorheological fluids. J. Intell. Mater. Syst. Struct 1, 5–13 (1999)

    Article  Google Scholar 

  7. Chen, J., Liao, W.H.: A leg exoskeleton utilizing a magnetorheological actuator. In Proceedings of ROBIO’06. In: IEEE Int. Conf. Robot. Biom. 824– 829. Kunming, China. (2006).

  8. Chrzan M. J., Carlson, J.D.: MR fluid sponge devices and their use in vibration control of washing machines. In: Proceedings of the SPIE Conf. Smart. Struct. Mater.: Damping and Isolation. 4331, 370. (2001).

  9. Alexandridis, A.A.: The MagneRide System. In Proceedings of the US Vehicle Dynamics, Expo, Novi, MI (US). (2007).

  10. Nguyen, Q.H., Choi, S.B.: Optimal design of a vehicle magnetorheological damper considering the damping force and dynamic range. Smart. Mater. Struct. 18, 015013 (2008)

    Article  Google Scholar 

  11. Parlak, Z., Engin, T., Ari, V., Sahin, I., Calli, I.: Geometrical optimisation of vehicle shock dampers with magnetorheological fluid. Int. J. Vehi. Des. 54(4), 371–392 (2010)

    Article  Google Scholar 

  12. Priyandoko, G., Baharom, M.Z.: PSO-optimized adaptive neuro-fuzzy system for magneto-rheological damper modelling. Int. J. App. Electr. Mech. 41, 301–312 (2013)

    Google Scholar 

  13. Gurubasavaraju, T.M., Kumar, H., Arun, M.: Optimization of monotube magnetorheological damper under shear mode. J. Braz. Soc. Mech. Sci. Eng. 39, 2225–2240 (2017)

    Article  Google Scholar 

  14. Hu, G., Xie, Z., Li, W.: Optimal design of a double coil magnetorheological fluid damper with various piston profiles. Worl. Cong. Struct. Multid. Opti. 2–7 (2015)

  15. Gurubasavaraju, T.M., Kumar, H.: Arun, M,: Evaluation of optimal parameters of MR fluids for damper application using particle swarm and response surface optimization. J. Braz. Soc. Mech. Sci. Eng 39(9), 3683–3694 (2017)

    Article  Google Scholar 

  16. Liu, G., Fei, G.A.O., Liao, W.H.: Shape optimization of magnetorheological damper piston based on parametric curve for damping force augmentation. Smart. Mater. Struct. 31 (2021)

  17. Nguyen, Q.H., Choi, S.B., Woo, J.K.: Optimal design of magnetorheological fluid-based dampers for front-loaded washing machines. Proc. Inst. Mech. Eng. C 228, 294–306 (2014)

    Article  Google Scholar 

  18. Tharehalli Mata, G., Mokenapalli, V., Krishna, H.: Performance analysis of MR damper based semi-active suspension system using optimally tuned controllers. Proc. Inst. Mech. Eng. D: J. Automob. Eng. 235, 2871–2884 (2021)

    Article  Google Scholar 

  19. Tharehallimata, G., Mokenapalli, V.: Transverse dynamic analysis of semi-active quarter car model controlled with an optimal conventional controller. Int. J. Veh. Perform. 6, 310–326 (2020)

    Article  Google Scholar 

  20. Badri, Y., Syam, T., Sassi, S., Hussein, M., Renno, J.: Ghani, S,: Investigating the characteristics of a magnetorheological fluid damper through CFD modeling. Mater. Res. Exp. 8, 055701 (2021)

    Article  Google Scholar 

  21. Elsaady, W., Oyadiji, S.O., Nasser, A.: A one-way coupled numerical magnetic field and CFD simulation of viscoplastic compressible fluids in MR dampers. Int. J. Mech. Sci. 167, 105265 (2020)

    Article  Google Scholar 

  22. Elsaady, W., Oyadiji, S.O., Nasser, A.: Study of failure symptoms of a single-tube MR damper using an FEA-CFD approach. J. Intell. Mater. Syst. Struct. 32, 1391–1419 (2021)

    Article  Google Scholar 

  23. Pei, P., Peng, Y.: Constitutive modeling of magnetorheological fluids: a review. J. Magn. Magn. Mater 550, 169076 (2022)

    Article  Google Scholar 

  24. Parlak, Z., Şahin, İ, Parlak, N.: One-way coupled numerical model utilizing Viscoelastic Maxwell model for MR damper. J. Intell. Mater. Syst. Struct. (2022). https://doi.org/10.1088/1361-665X/aa5494

    Article  Google Scholar 

  25. Schott, F., Chamoret, D., Baron, T., Salmon, S., Meyer, Y.: Performance measure and tool for benchmarking metaheuristic optimization algorithms. Journal of Applied and Computational Mechanics 7(3), 1803–1813 (2021). https://doi.org/10.22055/jacm.2021.37664.3060

    Article  Google Scholar 

  26. Bagherkhani, A., Mohebbi, M.: Optimal design of MR Dampers by considering design criteria and dampers distribution effect. Iranian J. Sci. Technol. Trans. Civil Eng., 1–17. (2022)

  27. Franulović, M., Marković, K., Trajkovski, A.: Calibration of material models for the human cervical spine ligament behaviour using a genetic algorithm. Facta Universitatis Series: Mech. Eng. 19(4), 751–765 (2021)

    Article  Google Scholar 

  28. Jiang, M., Rui, X., Yang, F., Zhu, W., Zhang, Y.: Multi-objective optimization design for a magnetorheological damper. J. Intell. Mater. Syst. Struct. 33(1), 33–45 (2022)

    Article  Google Scholar 

  29. TharehalliMata, G., Krishna, H., Keshav, M.: Characterization of magneto-rheological fluid having elongated ferrous particles and its implementation in MR damper for three-wheeler passenger vehicle. Proc. Inst. Mech. Eng. D: J. Automob. Eng. (2022). https://doi.org/10.1177/09544070221078

    Article  Google Scholar 

  30. Parlak, Z., Engin, T., Çallı, İ: Optimal design of MR damper via finite element analyses of fluid dynamic and magnetic field. Mecha. 22, 890–903 (2012)

    Google Scholar 

  31. St, L., Wold, S.: Analysis of variance (ANOVA). Chemom. Intell. Lab. Syst. 6, 259–272 (1989)

    Article  Google Scholar 

  32. Rao, R.: Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Indus. Eng. Comput. 7, 19–34 (2016)

    Google Scholar 

  33. Zitar, R.A., Al-Betar, M.A., Awadallah, M.A., Doush, I.A., Assaleh, K.: An intensive and comprehensive overview of JAYA algorithm, its versions and applications. Arch. Comput. Methods Eng. 1–30 (2021).

  34. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

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Tharehallimata, G., Narasimhamu, K.L. Geometric optimisation of double ended magnetorheological fluid damper. Int J Interact Des Manuf 17, 1339–1349 (2023). https://doi.org/10.1007/s12008-022-01127-1

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