• Smart Structures and Systems
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• v.23 no.6
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• pp.537-551
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• 2019

In this paper, a method for analyzing the damping performance of stay cables incorporating magnetorheological (MR) dampers in the passive control mode is developed taking into account the cable sag and inclination, the damper coefficient, stiffness and mass, and the stiffness of damper support. Both numerical and asymptotic solutions are obtained from complex modal analysis. With the asymptotic solution, analytical formulas that evaluate the equivalent damping ratio of the sagged cable-damper system in consideration of all the above parameters are derived. The main thrust of the present study is to develop an general design formula and a universal curve for the optimal design of MR dampers for adjustable passive control of sagged cables. Two sag-affecting coefficients are derived to reflect the effects of cable sag on the maximum attainable damping ratio and the optimal damper coefficient. For the cable configurations commonly used in cable-stayed bridges, the sag-affecting coefficients are directly expressed in terms of the sag-extensibility parameter to facilitate the control design. A case study on adjustable passive vibration control of the longest cable (536 m) on Stonecutters Bridge is carried out to demonstrate the influence of the sag for the damper design, and to figure out the necessity of adjustability of damper coefficients for achieving maximum damping ratio for different vibration modes.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.2
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• pp.30-37
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• 2012

Pneumatic Active Engine Mount(PAEM) with open-loop control system has been developed to reduce the transmission of the idle-shake vibration induced by engine effectively and economically. A solenoid valve installed between PAEM and vacuum tank is on-off switched by the Pulse Width Modulate(PWM) control signal to decrease the dynamic stiffness of the engine mount. This paper presents the methodology to identify the optimal values of control parameters of a PAEM, i.e, turn-on timing and duty ratio of PWM signal for 6 different idle driving conditions. A scanning algorithm was first applied to the vehicle test to obtain the approximate optimal control parameters minimizing the vibration at front seat rail and at steering wheel. Then the PAEM system identification was fulfilled to find accurate optimal control parameters by using multi-layer neural networks of Levenberg-Marquardt algorithm with vehicle test data.

• Tribology and Lubricants
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• v.36 no.6
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• pp.332-341
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• 2020

High-speed turbomachinery implements gas foil bearings (GFBs) due to their distinctive advantages, such as high efficiency, lesser part count, and lower weight. This paper provides the test results of the static structural stiffnesses and loss factors of bump-type foil thrust bearings with increasing preload and bearing deflection. The focus of the current work is to experimentally quantify variability in structural stiffnesses and loss factors among the four test thrust bearings with identical design values and material of the bump and top foil geometries using the same (open-source) fabrication method. A simple test setup, using a rigidly mounted non-rotating shaft and thrust disk, measures the bearing bump deflections with increasing static loads on the test bearing. The inner and outer diameters of the test bearings are 41 mm and 81 mm, respectively. The loss factor, best-representing energy dissipation in the test bearings, is estimated from the area inside the local hysteresis loop of the load versus the bearing deflection curve. The measurements show that structural stiffnesses and loss factors of the test bearings significantly rely on applied preloads and bearing deflections. Local structural stiffnesses of the test bearings increase with applied preloads but decrease with bearing deflections. Changes of loss factors are less sensitive to applied preloads and bearing deflections compared to those of structural stiffnesses. Up to 35% variability in static load structural stiffnesses is found between bearings, while up to 30% variability in loss factors is found between bearings.