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Purpose: We aim to provide a systematic methodology for the optimal design of MRD for improved damping capacity and dynamical adjustability in performing its damping function. Methods: A modified Bingham model is employed to model and simulate the MRD considering the MR fluid’s compressibility. The parameters that describe the structure of MRD and the property of the fluid are systematically examined for their contributions to the damping capacity and dynamically adjustability. A response surface method is employed to optimize the damping force and dynamically adjustable coefficient for a more practical setting related to the parameters. Results: The simulation system effectively shows the hysteretic characteristics of MRDs and shows our common sense understanding that the damping gap width and yoke diameter have significant effects on the damping characteristics of MRD. By taking a typical MRD device setup, optimal design shows an increase of the damping force by 33% and an increase of the dynamically adjustable coefficient by 17%. It is also shown that the methodology is applicable to other types of MDR devices. Conclusion: The compressibility of MR fluid is one of the main reasons for the hysteretic characteristics of MRD. The proposed simulation and optimization methods can effectively improve the MRD’s damping performance in the design stage.

As a new type of intelligent damper, the magnetorheological damper has been widely used in robot, automobile NVH, and intelligent structure. However, for the intelligent response control from the structural excitation, it is the challenge to realize the intelligent control of the magnetorheological damping system. In this paper, the prediction-control mechanism of the magnetorheological damping system is modeled by a data-driven method, such as neural network and classification algorithm. The NARX (Nonlinear autoregressive with external input) neural network is used to predict the desired damping force required for the structural system in the forward direction, and the decision tree classification algorithm is used to reversely-control the desired current of the magnetorheological damping system in instant response to the structural system’s damping force requirement. The analysis results show that the prediction-control method is feasible to realize the intelligent control of the damper based on the state data of the damped system, which provides a new idea for the intelligent control of the magnetorheological damper system.

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