Shengyuan Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Shengyuan Liu has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
Yaojun Ge
Professor, State Key Lab of Disaster Reduction in Civil Engineering, Tongji Univ., Shanghai 200092, China; Professor, Key Laboratory of Transport Industry of Wind Resistant Technology for Bridge Structures, Tongji Univ., Shanghai 200092, China
3 shared publicationsLin Zhao
Professor, State Key Lab of Disaster Reduction in Civil Engineering, Tongji Univ., Shanghai 200092, China; Professor, Key Laboratory of Transport Industry of Wind Resistant Technology for Bridge Structures, Tongji Univ., Shanghai 200092, China
2 shared publicationsChuanxin Hu
1 shared publicationGenshen Fang
Assistant Professor, State Key Lab of Disaster Reduction in Civil Engineering, Tongji Univ., Shanghai 200092, China; Assistant Professor, Key Laboratory of Transport Industry of Wind Resistant Technology for Bridge Structures, Tongji Univ., Shanghai 200092, China
1 shared publication- Genshen Fang
State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China
1 shared publication - Lin Zhao
Guangxi Laboratory of Whole Life Safety for Land-Sea Corridor Engineering, Guangxi University 1 , Daxuedong Rd. 100, Nanning
1 shared publication
Latest Publications
- 1 February 2024
- Published by American Society of Civil Engineers (ASCE) in Journal of Engineering Mechanics
- Vol. 150 (2)
Instantaneous Power Balance Algorithm for Flutter Critical Wind Speed Prediction of Bridge Girders
- 1 February 2024
- Published by American Society of Civil Engineers (ASCE) in Journal of Engineering Mechanics
- Vol. 150 (2)
Instantaneous Power Balance Algorithm for Flutter Critical Wind Speed Prediction of Bridge Girders
- 1 February 2023
- Published by AIP Publishing in Physics of Fluids
- Vol. 35 (2)
Data-based autonomously discovering method for nonlinear aerodynamic force of quasi-flat plate
Expression of nonlinear aerodynamic phenomena and calculation of nonlinear aeroelastic response require accurate and concise aeroelastic force function. In this paper, a group sparse regression method is used to reveal the nonlinear mapping aerodynamics relationship between motion and force from data. The aeroelastic force function discovered by this method balances modeling accuracy and simplicity. A quasi-flat plate in coupled vertical-torsional harmonic motion is employed as experimental object in this work. Aerodynamic motion-force dataset is collected by forced motion test in wind tunnel, including 484 cases. The sparse regression analytic result show that $\alpha\dot{\alpha}, \alpha^3, \dot{\alpha}^3$ ($\alpha$ is torsional displacement) can represent the non-linearity in aerodynamic for all cases, even wind speed, amplitude, amplitude ratio, frequency ratio and angle of attack are in different combinations.- 1 February 2023
- Published by AIP Publishing in Physics of Fluids
- Vol. 35 (2)
Data-based autonomously discovering method for nonlinear aerodynamic force of quasi-flat plate
Expression of nonlinear aerodynamic phenomena and calculation of nonlinear aeroelastic response require accurate and concise aeroelastic force function. In this paper, a group sparse regression method is used to reveal the nonlinear mapping aerodynamics relationship between motion and force from data. The aeroelastic force function discovered by this method balances modeling accuracy and simplicity. A quasi-flat plate in coupled vertical-torsional harmonic motion is employed as experimental object in this work. Aerodynamic motion-force dataset is collected by forced motion test in wind tunnel, including 484 cases. The sparse regression analytic result show that $\alpha\dot{\alpha}, \alpha^3, \dot{\alpha}^3$ ($\alpha$ is torsional displacement) can represent the non-linearity in aerodynamic for all cases, even wind speed, amplitude, amplitude ratio, frequency ratio and angle of attack are in different combinations.