Magnetorheological elastomer (MRE) is one of the smart materials whose stiffness and damping ratios can be controlled by applying a magnetic field. Several studies have been conducted on the utilization of MREs as base isolators in semi-active systems. A base isolator using a MRE utilizes an electromagnetic system to generate a magnetic field for the MRE. Generally, single-layered electromagnetic systems are used in MRE-based isolators. The single-layered electromagnetic system forms an inter-space between the MRE and the electromagnetic system to consider the deformation of the MRE. A larger inter-space is observed when the strain of the MRE is increased, which causes limitations owing to the excessive volume and electric power of the base isolator. Therefore, a electromagnetic system is proposed to overcome these limitations, in which the single-layered electromagnetic system is divided into several layers and behaves according to the deformation of the MRE. To validate the superiority of the proposed electromagnetic system, numerical analyses were conducted, using ANSYS Electronics, to compare its magnetic flux density, resistance, size, and volume. Based on the results of these analyses, dynamic characteristic tests were performed to compare the MR effects in each system.

Computer experiments are widely used to evaluate the performance and reliability of engineering systems with the lowest possible time and cost. Sometimes, a high-fidelity model is required to ensure predictive accuracy; this becomes computationally intensive when many computational analyses are required (for example, inverse analysis or uncertainty analysis). In this context, a surrogate model can play a valuable role in addressing computational issues. Surrogate models are fast approximations of high-fidelity models. One efficient way for surrogate modeling is the sequential sampling (SS) method. The SS method sequentially adds samples to refine the surrogate model. This paper proposes a multiple-update-infill sampling method using a minimum energy design to improve the global quality of the surrogate model. The minimum energy design was recently developed for global optimization to find multiple optima. The proposed method was evaluated with other multiple-update-infill sampling methods in terms of convergence, accuracy, sampling efficiency, and computational cost.

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