arXiv subjects

Hua-Lin Wu

Publications and source records attributed to Hua-Lin Wu.

4 recordsLinked to original sources

arXiv2026

Learning to traverse convective flows at moderate to high Rayleigh numbers

We study the navigation of a self-propelled inertial particle in two-dimensional Rayleigh-B\'enard convection at Prandtl number $Pr=0.71$ and cell aspect ratio $\Gamma=4$ for Rayleigh numbers $Ra$ ranging from $10^7$ to $10^{11}$. A reinforcement-learning (RL) controller selects the propulsive acceleration, subject to an upper bound $\mathcal{A}_{\max}$, to achieve a prescribed horizontal displacement. We find that the success rate increases abruptly with $\mathcal{A}_{\max}$ at moderate $Ra$, whereas at higher $Ra$ the transition becomes more gradual and shifts to larger $\mathcal{A}_{\max}$. Moreover, although the completion time increases with $Ra$, the propulsion energy required for successful traversal decreases. Proper orthogonal decomposition indicates that these performance differences are associated with reorganisation of the carrier flow. At moderate $Ra$, the dominant large-scale circulation partitions the domain through persistent transport barriers, requiring a finite thrust surplus to cross them; at higher $Ra$, energy is distributed across many modes, the barriers fragment and transient plume-assisted pathways emerge. Compared with a constant-heading baseline, the learned policy aligns with local currents and consumes significantly less energy. Lagrangian coherent structure analysis further suggests that the RL agent tends to cross repelling barriers and surf along attracting pathways. Finally, by mapping these behaviours onto the local Eulerian flow topology using Voronoi tessellation and the $Q$-criterion, we distil an interpretable, physics-based heuristic strategy that retains robust navigability. These results connect turbulent-flow organisation with autonomous navigation under bounded actuation.

physics.flu-dyn↗

arXiv2025

Super-resolution reconstruction of turbulent flows from a single Lagrangian trajectory

We studied the reconstruction of turbulent flow fields from trajectory data recorded by actively migrating Lagrangian agents. We propose a deep-learning model, track-to-flow (T2F), which employs a vision transformer as the encoder to capture the spatiotemporal features of a single agent trajectory, and a convolutional neural network as the decoder to reconstruct the flow field. To enhance the physical consistency of the T2F model, we further incorporate a physics-informed loss function inspired by the framework of physics-informed neural network (PINN), yielding a variant model referred to as T2F+PINN. We first evaluate both models in a laminar cylinder wake flow at a Reynolds number of $Re = 800$ as a proof of concept. The results show that the T2F model achieves velocity reconstruction accuracy comparable to that of existing flow reconstruction methods, while the T2F+PINN model reduces the normalised error in vorticity reconstruction relative to the T2F model. We then apply the models in a turbulent Rayleigh-B\'enard convection at a Rayleigh number of $Ra = 10^8$ and a Prandtl number of $Pr = 0.71$. The results show that the T2F model accurately reconstructs both the velocity and temperature fields, whereas the T2F+PINN model further improves the reconstruction accuracy of gradient-related physical quantities, such as temperature gradients, vorticity and the Q value, with a maximum improvement of approximately 60 % compared to the T2F model. Overall, the T2F model is better suited for reconstructing primitive flow variables, while the T2F+PINN model provides advantages in reconstructing gradient-related quantities. Our models open a promising avenue for accurate flow reconstruction from a single Lagrangian trajectory.

physics.flu-dyn↗

arXiv2023

Long-distance migration with minimal energy consumption in a thermal turbulent environment

We adopt the reinforcement learning algorithm to train the self-propelling agent migrating long-distance in a thermal turbulent environment. We choose the Rayleigh-Bénard turbulent convection cell with an aspect ratio ($Γ$, which is defined as the ratio between cell length and cell height) of 2 as the training environment. Our results showed that, compared to a naive agent that moves straight from the origin to the destination, the smart agent can learn to utilize the carrier flow currents to save propelling energy. We then apply the optimal policy obtained from the $Γ=2$ cell and test the smart agent migrating in convection cells with $Γ$ up to 32. In a larger $Γ$ cell, the dominant flow modes of horizontally stacked rolls are less stable, and the energy contained in higher-order flow modes increases. We found that the optimized policy can be successfully extended to convection cells with a larger $Γ$. In addition, the ratio of propelling energy consumed by the smart agent to that of the naive agent decreases with the increase of $Γ$, indicating more propelling energy can be saved by the smart agent in a larger $Γ$ cell. We also evaluate the optimized policy when the agents are being released from the randomly chosen origin, which aims to test the robustness of the learning framework, and possible solutions to improve the success rate are suggested. This work has implications for long-distance migration problems, such as unmanned aerial vehicles patrolling in a turbulent convective environment, where planning energy-efficient trajectories can be beneficial to increase their endurance.

physics.flu-dyn↗

arXiv2022

Migration of self-propelling agent in a turbulent environment with minimal energy consumption

We present a numerical study of training a self-propelling agent to migrate in the unsteady flow environment. We control the agent to utilize the background flow structure by adopting the reinforcement learning algorithm to minimize energy consumption. We considered the agent migrating in two types of flows: one is simple periodical double-gyre flow as a proof-of-concept example, while the other is complex turbulent Rayleigh-Bénard convection as a paradigm for migrating in the convective atmosphere or the ocean. The results show that the smart agent in both flows can learn to migrate from one position to another while utilizing background flow currents as much as possible to minimize the energy consumption, which is evident by comparing the smart agent with a naive agent that moves straight from the origin to the destination. In addition, we found that compared to the double-gyre flow, the flow field in the turbulent Rayleigh-Bénard convection exhibits more substantial fluctuations, and the training agent is more likely to explore different migration strategies; thus, the training process is more difficult to converge. Nevertheless, we can still identify an energy-efficient trajectory that corresponds to the strategy with the highest reward received by the agent. These results have important implications for many migration problems such as unmanned aerial vehicles flying in a turbulent convective environment, where planning energy-efficient trajectories are often involved.

physics.flu-dyn↗