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When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.
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Magnetorheological (MR) elastomers (MREs) with controllable stiffness and damping properties offer significant potential for fail-safe semi-active and active control of vibration in many engineering applications. While the properties of MREs in the shear mode have been widely characterized, the compression mode properties of the MREs have been addressed in fewer studies. Unlike the shear mode, the compression mode characterization necessitates compensation for the magnetic force developed by the electromagnet in order to extract viscoelastic force from the measured force. In this dissertation research, a systematic methodology and a mathematical model are developed for compensating for the magnetic force for extracting the viscoelastic component of the force attributed to compression mode viscoelasticity of the MRE. For this purpose, an optimal design of a UI-shaped electromagnet was realized with minimum mass and magnetic flux density up to 1 Tesla. An experiment was designed to apply magnetic field in the direction of mechanical loading and to facilitate measurement of magneto-mechanical force. Results revealed peak errors in equivalent stiffness and damping constants of the MRE in the orders of 90% and 163%, respectively, in the absence of magnetic force compensation. The proposed compensation methodology provided a framework for accurate compression mode characterizations of the MREs as functions of the volume fraction and anisotropy of iron particles, and shape factor apart from the magnetic field intensity, pre-strain and strain amplitude and strain rate excitations.
MREs typically experience large static pre-strain in many applications in order to support the machine/structure weight, which can alter the distances among the magnetizable particles and thus the MR effect. The dynamic compression mode properties of isotropic and anisotropic MREs with 30% volume fraction of iron-particles and nominal shape factor (SF) of 0.56 were experimentally characterized under broad ranges of strain amplitude (2.5–20%) superimposed on a large static pre-strain of 21%, excitation frequency (0.1–50 Hz) and magnetic flux density (0–750 mT). Subsequently, the experiments were designed to evaluate the effects of the shape factor (SF), pre-strain and particle volume fraction (PVF) on compression mode properties of the isotropic and anisotropic MREs under broad ranges of excitations and magnetic flux density. The measured data were analyzed to evaluate compression mode properties in terms of stress-strain characteristics, relative MR effect, equivalent stiffness, equivalent damping, and elastic and loss moduli as functions of the design factors (pre-strain, PVF, SF and anisotropy) and operating factors (strain amplitude, strain rate and the magnetic field intensity). The results invariably, revealed hysteretic stress-strain characteristics with strongly nonlinear and coupled dependence on the various design and operating factors. Among the design factors considered, the PVF revealed greatest effects on the stiffening and dampening behaviors of the MREs. Increasing the SF and PVF generally resulted in substantially higher MR effect in view of the elastic modulus of the MREs. The relative MR effect, however, decreased with increase in the pre-strain.
The influences of various design and operating factors on the compression mode properties of are thoroughly analyzed and discussed, which would serve as design guidance for the MREs in different engineering applications. Phenomenological models with only a few unknown parameters were further formulated to predict the SF-, pre-strain-, and PVF-dependent compression elastic and loss moduli of both types of MREs. The effectiveness of the models is demonstrated by comparing the model-predicted properties with the measured data over broad ranges of design and operating conditions.