Pengyu Lai
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Pengyu Lai has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Hui Xu
Shanghai Jiao Tong University
3 shared publications Rui Wang
2 shared publications- Wei Zhang
Marine Design and Research Institute of China
2 shared publications - Haoqi Fei
School of Aeronautics and Astronautics, Shanghai Jiao Tong University , Shanghai 200240
1 shared publication Jing Wang
1 shared publicationYi Liu
Science and Technology on Water Jet Propulsion Laboratory, Marine Design and Research Institute of China, Shanghai 200011, China
1 shared publication
Latest Publications
- 1 May 2024
- Published by AIP Publishing in Physics of Fluids
- Vol. 36 (5)
On the extreme wall shear stress events in a turbulent pipe flow
This study investigates the extreme wall shear stress events in a turbulent pipe flow by direct numerical simulation at a frictional Reynolds number Reτ≈500. A two-step conditional averaging scheme is implemented to identify the locations of extreme events and construct their spatial structures. Combined with the joint probability density functions of shear stresses, further evidence is provided for the argument that extreme positive events occur below an intense sweep event (Q4), and the formation of the backflow events is predominantly aided by an identifiable oblique vortex. Moreover, the conditional probability distribution of shear stress for varying thresholds used to define extreme events reveals that, when the threshold is above or below the mean, the probability distributions of the extreme positive events or the backflow events generally follow an exponential relationship, suggesting the extreme wall shear stress events are a threshold-independent process. Finally, the conditional space–time proper orthogonal decomposition is performed to extract the dominant modes and characterize the evolution of the extreme events from inception to dissipation, which exhibits morphological features of real flow structures. It is found that the observation of uθ modes can provide a basic representation of the entire variation process and the extreme values return to normal levels in a very short time.
- 1 May 2024
- Published by AIP Publishing in Physics of Fluids
- Vol. 36 (5)
On the extreme wall shear stress events in a turbulent pipe flow
This study investigates the extreme wall shear stress events in a turbulent pipe flow by direct numerical simulation at a frictional Reynolds number Reτ≈500. A two-step conditional averaging scheme is implemented to identify the locations of extreme events and construct their spatial structures. Combined with the joint probability density functions of shear stresses, further evidence is provided for the argument that extreme positive events occur below an intense sweep event (Q4), and the formation of the backflow events is predominantly aided by an identifiable oblique vortex. Moreover, the conditional probability distribution of shear stress for varying thresholds used to define extreme events reveals that, when the threshold is above or below the mean, the probability distributions of the extreme positive events or the backflow events generally follow an exponential relationship, suggesting the extreme wall shear stress events are a threshold-independent process. Finally, the conditional space–time proper orthogonal decomposition is performed to extract the dominant modes and characterize the evolution of the extreme events from inception to dissipation, which exhibits morphological features of real flow structures. It is found that the observation of uθ modes can provide a basic representation of the entire variation process and the extreme values return to normal levels in a very short time.
- 1 March 2023
- Published by AIP Publishing in Physics of Fluids
- Vol. 35 (3) , 037111
Intelligent controller for unmanned surface vehicles by deep reinforcement learning
With the development of the applications of unmanned surface vehicles (USVs), USV automation technologies are attracting increasing attention. In the industry, through the subtask division, it is generally believed that course-keeping is a critical basic sub-system in a series of complex automation systems and affects USV automation performance to a great extent. By course-keeping, we mean USV adjusts its angle to the desired angle and keeps it. In recent decades, course-keeping has been mainly achieved through classical first principles technologies, such as proportion–integral–differential (PID) controllers, leading to extremely laborious parameter tuning, especially in changeable wave environments. With the emergence and extensive application of data-driven technologies, deep reinforcement learning is conspicuous in sequential decision-making tasks, but it introduces a lack of explainability and physical meaning. To take full advantage of the data-driven and first principles paradigm and easily extend to the industry, in this paper, we propose an intelligent adaptive PID controller enhanced by proximal policy optimization (PPO) to achieve USV high-level automation. We then further verify its performance in path-following tasks compared with the PID controller. The results demonstrate that the proposed controller inherits the merits of explainability from PID and excellent sequential decision making from PPO and possesses excellent disturbance rejection performance when facing the disturbance of a changeable wave environment.
- 1 March 2023
- Published by AIP Publishing in Physics of Fluids
- Vol. 35 (3) , 037111
Intelligent controller for unmanned surface vehicles by deep reinforcement learning
With the development of the applications of unmanned surface vehicles (USVs), USV automation technologies are attracting increasing attention. In the industry, through the subtask division, it is generally believed that course-keeping is a critical basic sub-system in a series of complex automation systems and affects USV automation performance to a great extent. By course-keeping, we mean USV adjusts its angle to the desired angle and keeps it. In recent decades, course-keeping has been mainly achieved through classical first principles technologies, such as proportion–integral–differential (PID) controllers, leading to extremely laborious parameter tuning, especially in changeable wave environments. With the emergence and extensive application of data-driven technologies, deep reinforcement learning is conspicuous in sequential decision-making tasks, but it introduces a lack of explainability and physical meaning. To take full advantage of the data-driven and first principles paradigm and easily extend to the industry, in this paper, we propose an intelligent adaptive PID controller enhanced by proximal policy optimization (PPO) to achieve USV high-level automation. We then further verify its performance in path-following tasks compared with the PID controller. The results demonstrate that the proposed controller inherits the merits of explainability from PID and excellent sequential decision making from PPO and possesses excellent disturbance rejection performance when facing the disturbance of a changeable wave environment.
- 1 October 2021
- Published by AIP Publishing in Physics of Fluids
- Vol. 33 (10) , 107110
Parameter optimization of open-loop control of a circular cylinder by simplified reinforcement learning
Open-loop control is commonly considered an efficient approach in flow control, in which the search for control parameters with excellent performance is mostly carried out by grid search, leading to an extremely tedious process of parameter optimization. With extensive applications of machine learning, reinforcement learning (RL) has emerged as a powerful tool to achieve optimal strategies, which constructively leads to the result that parameter optimization can be performed by RL. In this paper, we provide the concept of simplified RL formally and show the corresponding properties. In order to implement simplified RL for flow control, a high-order numerical approach is coupled with simplified RL to develop a new framework for parameter optimization and determination. In order to validate the performance of the framework, flows past a rotary oscillating circular cylinder at low Reynolds number Re = 200 (defined as , where is the free-stream velocity and ν is the kinematic viscosity) are investigated by varying the parameters of rotary amplitude and frequency individually or simultaneously. By numerical investigations, a satisfactory drag reduction effect is achieved, which demonstrates the capability of the framework to perform parameter optimization in terms of open-loop control.- 1 October 2021
- Published by AIP Publishing in Physics of Fluids
- Vol. 33 (10) , 107110
Parameter optimization of open-loop control of a circular cylinder by simplified reinforcement learning
Open-loop control is commonly considered an efficient approach in flow control, in which the search for control parameters with excellent performance is mostly carried out by grid search, leading to an extremely tedious process of parameter optimization. With extensive applications of machine learning, reinforcement learning (RL) has emerged as a powerful tool to achieve optimal strategies, which constructively leads to the result that parameter optimization can be performed by RL. In this paper, we provide the concept of simplified RL formally and show the corresponding properties. In order to implement simplified RL for flow control, a high-order numerical approach is coupled with simplified RL to develop a new framework for parameter optimization and determination. In order to validate the performance of the framework, flows past a rotary oscillating circular cylinder at low Reynolds number Re = 200 (defined as , where is the free-stream velocity and ν is the kinematic viscosity) are investigated by varying the parameters of rotary amplitude and frequency individually or simultaneously. By numerical investigations, a satisfactory drag reduction effect is achieved, which demonstrates the capability of the framework to perform parameter optimization in terms of open-loop control.