• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.10a
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• pp.28-28
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• 2005

• Wind and Structures
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• v.7 no.6
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• pp.405-420
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• 2004

In common approaches, bridge dynamics under wind action is analyzed by modeling the interaction between fluid and structure by means of transient wind loads acting over the structure itself. Amid various possible manners to describe such types of loads, a representation based on families of 'indicial functions' is adopted here. The aim is to investigate its flexibility to capture the main features of wind-bridge interaction. A set of coefficients is involved in indicial functions. The values that one may attribute to them suffer uncertainties coming from experimental errors affecting data. Here, the sensitivity of a 2-DOF schematic model to the variations of these coefficients is investigated at fixed values of dynamic derivatives and for various types of indicial functions. It is shown how parameter variations influence phase portraits.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2371-2373
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• 2000

This paper presents a new vector control scheme for induction motor. An exact knowledge of the rotor flux position is essential for a high-performance vector control. The position of the rotor flux is measured in the direct scheme and estimated in the indirect schemes. Since the estimation of the flux position requires a priori knowledge of the induction motor parameters, the indirect schemes are machine parameter dependent. The rotor and stator resistance among the parameters change with temperature. Variations in the parameters of induction machine cause deterioration of both the steady state and dynamic operation of the induction motor drive. Several methods have presented to minimize the consequences of parameter sensitivity in indirect scheme. In this paper, new estimation scheme of rotor flux position is presented to eliminate sensitivity due to variation in the resistance. The simulation is executed to verify the proposed vector control performance and to compare its performance with that of indirect vector control.

• Journal of Power Electronics
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• v.19 no.4
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• pp.1054-1067
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• 2019

This paper systematically investigates the influence of device parameters spread on the current distribution of paralleled silicon carbide (SiC) MOSFETs. First, a variation coefficient is introduced and used as the evaluating norm for the parameters spread. Then a sample of 30 SiC MOSFET devices from the same batch of a well-known company is selected and tested under the same conditions as those on datasheet. It is found that there is big difference among parameters spread. Furthermore, comprehensive theoretical and simulation analyses are carried out to study the sensitivity of the current imbalance to variations of the device parameters. Based on the concept of the control variable method, the influence of each device parameter on the steady-state and transient current distributions of paralleled SiC MOSFETs are verified separately by experiments. Finally, some screening suggestions of devices or chips before parallel-connection are provided in terms of different applications and different driver configurations.

• Steel and Composite Structures
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• v.18 no.3
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• pp.547-563
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• 2015

Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage in concrete) from the elastic moments and elastic mid-span deflections (neglecting cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.