PDF
Full
Outline
Addressing the challenge of accurately solving unstable stick-slip vibration problems in non-smooth dynamics, this paper proposes a solution algorithm based on Physics-informed Neural Networks (PINN). Firstly, the classical stick-slip vibration problem is dynamically modeled using the linear complementarity theory under unilateral constraints. Then, the linear complementarity relationship is designed as a loss function to guide the training of the neural network, constructing a PINN algorithm for solving multi-point friction-induced stick-slip vibration problems. The accurate simulation of complex responses of multiple sliders'stick-slip vibrations in frictional systems is conducted. By comparing the numerical results with the Switching Model method that includes event detection and the traditional Time-Stepping method without event detection, the accuracy of the PINN algorithm is verified. The proposed PINN algorithm transforms the traditional optimization problem calculation into network training of the machine learning algorithm, making it suitable for stick-slip vibration analysis with multiple contact points. This method achieves accurate nonsmooth state transitions and provides a convenient and easy-to-use new approach for the accurate simulation of complex nonlinear vibration responses in multi-degree-of-freedom frictional systems.
PDF
65
27
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
