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Advances in Modeling and Control of Magnetorheological Elastomers for Engineering Applications.

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Název: Advances in Modeling and Control of Magnetorheological Elastomers for Engineering Applications.
Autoři: Dhiman, Nishant Kumar, Salodkar, Sandeep M., Gagandeep, Susheel, Chanderkant
Zdroj: Archives of Computational Methods in Engineering; Apr2024, Vol. 31 Issue 3, p1823-1865, 43p
Abstrakt: Magnetorheological elastomers (MREs), as smart materials, exhibit notable changes in their mechanical properties in response to an external magnetic field, which makes them ideal candidates for vibration-damping applications across engineering applications. This article delves into the complexities of MRE behavior, discussing in detail the various modeling approaches developed to capture this behavior and the control algorithms designed to exploit MRE properties for real-world applications optimally. These methods include microscopic models that consider the magnetizable particles with potential as magnetic poles, enabling a more in-depth view of localized magnetic interaction. Additionally, continuum-based models are discussed, offering insight into the interplay of local magnetic and mechanical fields within MREs; however, at the macroscopic scale, models that capture the dynamic behavior of MREs are reviewed. Macroscopic models are critical for designing control strategies in MRE-based systems, as they can represent how the material responds under varying magnetic fields and mechanical loads. The paper also introduces the reader to non-parametric models, primarily machine learning approaches. These data-driven methods provide an alternative for understanding and predicting MRE behavior without the necessity for explicit mathematical models. A significant part of the review addresses the difficulties encountered in control design due to the inherent non-linearity of MRE-based systems. These challenges often stem from the complex magnetic-mechanical coupling and hysteresis inherent in MREs, which render traditional linear control techniques ineffective. The article reviews conventional methods while suggesting alternative strategies for real-time adaptability, a critical requirement in applications like adaptive vibration control. [ABSTRACT FROM AUTHOR]
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Abstrakt:Magnetorheological elastomers (MREs), as smart materials, exhibit notable changes in their mechanical properties in response to an external magnetic field, which makes them ideal candidates for vibration-damping applications across engineering applications. This article delves into the complexities of MRE behavior, discussing in detail the various modeling approaches developed to capture this behavior and the control algorithms designed to exploit MRE properties for real-world applications optimally. These methods include microscopic models that consider the magnetizable particles with potential as magnetic poles, enabling a more in-depth view of localized magnetic interaction. Additionally, continuum-based models are discussed, offering insight into the interplay of local magnetic and mechanical fields within MREs; however, at the macroscopic scale, models that capture the dynamic behavior of MREs are reviewed. Macroscopic models are critical for designing control strategies in MRE-based systems, as they can represent how the material responds under varying magnetic fields and mechanical loads. The paper also introduces the reader to non-parametric models, primarily machine learning approaches. These data-driven methods provide an alternative for understanding and predicting MRE behavior without the necessity for explicit mathematical models. A significant part of the review addresses the difficulties encountered in control design due to the inherent non-linearity of MRE-based systems. These challenges often stem from the complex magnetic-mechanical coupling and hysteresis inherent in MREs, which render traditional linear control techniques ineffective. The article reviews conventional methods while suggesting alternative strategies for real-time adaptability, a critical requirement in applications like adaptive vibration control. [ABSTRACT FROM AUTHOR]
ISSN:11343060
DOI:10.1007/s11831-023-10031-0