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This paper presents the design, analysis, testing and modeling of an electrorheological (ER) fluid damper developed for vibration and seismic protection of civil structures. The damper consists of a main cylinder and a piston rod that pushes an ER fluid through a stationary annular duct. The behavior of the damper can be approximated with Hagen - Poiseuille flow theory. The basic equations that describe the fluid flow across an annular duct are derived. Experimental results on the damper response with and without the presence of electric field are presented. As the rate of deformation increases, viscous stresses prevail over field-induced yield stresses and a smaller fraction of the total damper force can be controlled. Simple physically motivated phenomenological models are considered to approximate the damper response with and without the presence of electric field. Subsequently, the performance of a multilayer neural network constructed and trained by an efficient algorithm known as the Dependence Identification Algorithm is examined to predict the response of the electrorheological damper. A combination of a simple phenomenological model and a neural network is then proposed as a practical tool to approximate the nonlinear and velocity-dependent damper response.
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Makris et al. (1996) studied this question.
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