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In an automotive suspension, a shock absorber plays a significant role to enhance the vehicle performances, particularly ride comfort and road holding. Because of its important influences on the overall vehicle performances, the understanding of its physical characteristics is essential. Thus, this paper develops a mathematical model of twin-tube shock absorber that is widely used in modern production cars. The model is derived based on a rational polynomial formulation. This formulation generally represents the flow behaviors of fluid across a restriction. Further, simulation results are compared to those obtained from experiments to determine the model accuracy. The result comparison illustrates that the model is able to describe the behavior of shock absorber with slight discrepancies.

‘Three kinds of new simultaneous optimum design methods of plural dynamic absorbers are proposed. These methods allow the optimum tuning in many natural modes of multiple degrees of freedom structures or a continuous bodies simultaneously to effectively suppress vibration. Changes of natural modes and natural frequencies of the main structure due to added mass effect of dynamic absorbers can be taken into account in the design. Validity and usefulness of the proposed methods are verified by both a computer simulation and by experiments.

The method was established to identify the dynamic stiffness of the engine mount using modal parameters acquired from experimental modal analysis. Vibration tests were conducted using actual large outboard motor the BF225 (165 kW), and the dynamic stiffness of the mounts was identified. The results show that this method can identify the engine mount dynamic stiffness more adequately than the conventional method, even when the engine mounts are subjected to loads corresponding to thrust force or even in the case that the stiffness of the parts supporting an outboard motor is low.

In this paper, a new method which directly identifies characteristic matrices (the mass, damping and stiffness matrices) of the mechanical structure using measured forces input and responses data is proposed. This algorithm is based upon the Maximum Likelihood Estimation, so that the accuracy of identified matrices is stable to experimental errors (random errors). After a theoretical formulation is performed, two examples are provided to illustrate and validate this algorithm. One is analytical example which identifies analytically generated data with random noises, and the other experimentarly identified engine/mount system of automobiles.