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- 저자 : Jinxing Yang (검색결과 2 건)
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Multibody dynamics analysis of a silent chain drive timing system
Jinxing Yang,Zengming Feng,Huanhuan Gao,Tianrui Wang,Kai Xu 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.4
Multibody dynamics analysis of a silent chain drive timing system can quickly detect system defects in the product design stage and shorten the development cycle. At present, most of the research on chain drives focuses on roller chains. A 3D silent chain drive timing system model considering the fluctuation of crankshaft sprocket speed and camshaft sprocket torque was established in this paper based on recursive algorithm and Hertz contact theory. The accuracy of the model was verified by comparing the experimental and simulation results of the chain tension. The simulation results show that the chain tension fluctuation with time is obviously affected by the camshaft torque fluctuation, and the maximum chain tension ranges from 233 N to 1202 N, which is less than the safe rotation fatigue limit 1600 N. When the crankshaft sprocket speed is 6000 r/min, the maximum contact force between the chain and crankshaft sprocket and slack guide is 751 N and 588 N, respectively. The maximum chain fluctuation caused by meshing impact between chain and sprocket and polygon effect is 0.52 mm and 0.48 mm, respectively. The system transmission error ranges from 0.14° to 0.38°, which is less than 1° of the maximum allowable transmission error.
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DHEM: a deep heat energy method for steady-state heat conduction problems
Huanhuan Gao,Wenjie Zuo,Zengming Feng,Jinxing Yang,Tingting Li,Ping Hu 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.11
Based on the deep energy method recently brought forward to handle linearelastic or hyper-elastic finite deformation problems in solid mechanics, in this paper, we propose a deep heat energy method (DHEM) which is specially tailored to deal with structural steady-state heat conduction problems with the help of deep learning techniques. In our work, the deep neural networks are utilized to construct the admissible temperature fields; secondly, the potential energy functional in the heat conduction process which works as the loss function of the deep neural networks is calculated by numerical integration techniques; finally, the parameters of the network including weights and bias, are optimized by the quasi-Newton method to yield the minimal of the potential energy functional which indicates the heat conduction has entered a steady state. Numerical examples with a diversity of materials, including the isotropic and homogeneous material, the orthotropic material, the non-homogeneous materials and temperature dependent materials, are carried out to illustrate the validity and capacity of DHEM in both linear uncoupled and thermal-material coupling heat conduction problems.
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