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RISS 인기검색어
- 검색키워드
- 저자 : Hung V. Dang (검색결과 2 건)
- 무료
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A multi-core compound droplet passing through a diffuser channel
Dang T. Bui,Hung V. Vu,Quang D. Nguyen,Truong V. Vu 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.11
This study‘s aim is to improve the understanding of the dynamical behavior of a multi-core compound droplet traveling in an axisymmetric channel consisting of a diffuser element. The compound droplet typically consisting of two inner droplets distributed one after another is initially located at a certain distance from the entrance of the channel. A front-tracking method is used to handle the movement and deformation of the droplet. The numerical simulation results show that the compound droplet is stretched in the channel, and it takes a certain time,“the transit time”, to pass through the diffuser. The compound droplet has the largest deformation in the diffuser region and tends to return to its nearly original shape after leaving the diffuser. The deformation and transit time of the compound droplet are affected by some typical parameters, such as the capillary number and the diffuser angle. For small capillary numbers, the leading inner droplet takes a shorter transit time than the rear one does. The transit time also increases with an increase in the diffuser angle and the number of inner droplets enclosed in the compound droplet.
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Connection stiffness reduction analysis in steel bridge via deep CNN and modal experimental data
Hung V. Dang,Mohsin Raza,H. Tran-Ngoc,T. Bui-Tien,Huan X. Nguyen 국제구조공학회 2021 Structural Engineering and Mechanics, An Int'l Jou Vol.77 No.4
This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.
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