arXiv subjects

Wang Jia

Publications and source records attributed to Wang Jia.

8 recordsLinked to original sources

arXiv2025

Vortex shedding suppression in elliptical cylinder via reinforcement learning

Flow control of bluff bodies plays a critical role in engineering applications. In this study, deep reinforcement learning (DRL) is employed to develop flow control strategies for the flow past an elliptical cylinder confined between two walls. The primary objective is to investigate the feasibility of achieving multi-objective flow control for an elliptical cylinder with varying aspect ratios ($Ar$), while maintaining low control energy input. DRL training results demonstrate that for an elliptical cylinder with larger $Ar$, the control strategy effectively reduces drag, minimizes lift fluctuations, and completely suppresses vortex shedding, all while maintaining low external energy consumption. Conversely, decreasing the $Ar$ compromises the effectiveness of multi-objective control, even when greater energy input is applied. Through detailed physical analysis, the coupling effect between the blockage ratio ($β$) and $Ar$ is identified as a limiting factor for vortex shedding suppression and wake stabilization. At lower values of $β$, the control strategy successfully achieves multi-objective optimization for elliptical cylinders across the entire range of $Ar$. Although balancing energy efficiency and control performance remains challenging for highly slender cylinders, the proposed DRL strategy still achieves effective vortex shedding suppression. This work highlights the potential of DRL-based control strategies to effectively stabilize wake flows around slender bluff bodies, with an explicit emphasis on maintaining energy efficiency.

physics.flu-dyn↗

arXiv2025

Strategies for energy-efficient flow control leveraging deep reinforcement learning

This study investigates active flow control in two-dimensional flows at a Reynolds number of 100 using Deep Reinforcement Learning (DRL). We utilize DRL to develop flow control strategies that enhance energy efficiency and minimize energy consumption, thereby addressing the limitations of traditional methods. We find that the optimal jet placement for both square and circular cylinders is at the main flow separation point, achieving the best balance between energy efficiency and control effectiveness. For the circular cylinder, positioning the jet at approximately 105° from the stagnation point requires only 1% of the inlet flow rate and achieves an 8% reduction in drag, with energy consumption one-third of that at other positions. For the square cylinder, placing the jet near the rear corner requires only 2% of the inlet flow rate, achieving a maximum drag reduction of 14.4%, whereas energy consumption near the front corner is 27 times higher, resulting in only 12% drag reduction. In multi-action control, the convergence speed and stability are lower compared to single-action control, but activating multiple jets significantly reduces initial energy consumption and improves energy efficiency. Physically, the interaction of the synthetic jet with the flow generates new vortices that modify the local flow structure, significantly enhancing the cylinder's aerodynamic performance. Our control strategy achieves a superior balance between energy efficiency and control performance compared to previous studies, underscoring its significant potential to advance sustainable and effective flow control.

physics.flu-dyn↗

arXiv2025

SILM: A Subjective Intent Based Low-Latency Framework for Multiple Traffic Participants Joint Trajectory Prediction

Trajectory prediction is a fundamental technology for advanced autonomous driving systems and represents one of the most challenging problems in the field of cognitive intelligence. Accurately predicting the future trajectories of each traffic participant is a prerequisite for building high safety and high reliability decision-making, planning, and control capabilities in autonomous driving. However, existing methods often focus solely on the motion of other traffic participants without considering the underlying intent behind that motion, which increases the uncertainty in trajectory prediction. Autonomous vehicles operate in real-time environments, meaning that trajectory prediction algorithms must be able to process data and generate predictions in real-time. While many existing methods achieve high accuracy, they often struggle to effectively handle heterogeneous traffic scenarios. In this paper, we propose a Subjective Intent-based Low-latency framework for Multiple traffic participants joint trajectory prediction. Our method explicitly incorporates the subjective intent of traffic participants based on their key points, and predicts the future trajectories jointly without map, which ensures promising performance while significantly reducing the prediction latency. Additionally, we introduce a novel dataset designed specifically for trajectory prediction. Related code and dataset will be available soon.

cs.RO↗

arXiv2024

Effect of Synthetic Jets Actuator Parameters on Deep Reinforcement Learning-Based Flow Control Performance in a Square Cylinder

We conduct an active flow control (AFC) study on the mass flow rate of synthetic jets on the upper and lower surfaces of a square cylinder using a deep reinforcement learning (DRL) algorithm, with a focus on investigating the influence of the position and width of the synthetic jets on the flow control performance. At Reynolds numbers ($Re$) of 100 and 500, it is found that our proposed method significantly reduced the lift and drag coefficients of the square cylinder, and completely suppressed vortex shedding in the wake. In particular, at $Re=100$, placing the synthetic jets near the tail corner was beneficial for reducing drag, with a maximum drag reduction rate of 14.4%. When $Re=500$, positioning the synthetic jets near the leading edge corner resulted in a maximum optimal drag reduction effect of 65.5%. This indicates that locating the synthetic jet at the main flow separation point can achieve optimal control. Furthermore, we notice that when the synthetic jets are positioned near the tail corner, vortex shedding can be completely suppressed. Additionally, a narrower width of the synthetic jets can enhance flow instability and increase the cost of flow control.

physics.flu-dyn↗

arXiv2024

Optimal Parallelization Strategies for Active Flow Control in Deep Reinforcement Learning-Based Computational Fluid Dynamics

Deep Reinforcement Learning (DRL) has emerged as a promising approach for handling highly dynamic and nonlinear Active Flow Control (AFC) problems. However, the computational cost associated with training DRL models presents a significant performance bottleneck. To address this challenge and enable efficient scaling on high-performance computing architectures, this study focuses on optimizing DRL-based algorithms in parallel settings. We validate an existing state-of-the-art DRL framework used for AFC problems and discuss its efficiency bottlenecks. Subsequently, by deconstructing the overall framework and conducting extensive scalability benchmarks for individual components, we investigate various hybrid parallelization configurations and propose efficient parallelization strategies. Moreover, we refine input/output (I/O) operations in multi-environment DRL training to tackle critical overhead associated with data movement. Finally, we demonstrate the optimized framework for a typical AFC problem where near-linear scaling can be obtained for the overall framework. We achieve a significant boost in parallel efficiency from around 49% to approximately 78%, and the training process is accelerated by approximately 47 times using 60 central processing unit (CPU) cores. These findings are expected to provide valuable insights for further advancements in DRL-based AFC studies. Consequently, it continues to be a prominent and actively studied problem of significant interest.

cs.LG↗

arXiv2024

Robust and Adaptive Deep Reinforcement Learning for Enhancing Flow Control around a Square Cylinder with Varying Reynolds Numbers

The present study applies a Deep Reinforcement Learning (DRL) algorithm to Active Flow Control (AFC) of a two-dimensional flow around a confined square cylinder. Specifically, the Soft Actor-Critic (SAC) algorithm is employed to modulate the flow of a pair of synthetic jets placed on the upper and lower surfaces of the confined squared cylinder in flow configurations characterized by $Re$ of 100, 200, 300, and 400. The investigation starts with an analysis of the baseline flow in the absence of active control. It is observed that at $Re$ = 100 and $Re$ = 200, the vortex shedding exhibits mono-frequency characteristics. Conversely, at $Re$ = 300 and $Re$ = 400, the vortex shedding is dominated by multiple frequencies, which is indicative of more complex flow features. With the application of the SAC algorithm, we demonstrate the capability of DRL-based control in effectively suppressing vortex shedding, while significantly diminishing drag and fluctuations in lift. Quantitatively, the data-driven active control strategy results in a drag reduction of approximately 14.4%, 26.4%, 38.9%, and 47.0% for $Re$ = 100, 200, 300, and 400, respectively. To understand the underlying control mechanism, we also present detailed flow field comparisons, which showcase the adaptability of DRL in devising distinct control strategies tailored to the dynamic conditions at varying $Re$. These findings substantiate the proficiency of DRL in controlling chaotic, multi-frequency dominated vortex shedding phenomena, underscoring the robustness of DRL in complex AFC problems.

physics.flu-dyn↗

arXiv2024

Deep reinforcement learning-based active flow control of an elliptical cylinder: transitioning from an elliptical cylinder to a circular cylinder and a flat plate

We study the adaptability of deep reinforcement learning (DRL)-based active flow control (AFC) technology for bluff body flows with complex geometries. It is extended from a cylinder with an aspect ratio $Ar = 1$ to a flat elliptical cylinder with $Ar=2$, slender elliptical cylinders with $Ar$ less than 1, and a flat plate with $Ar=0$. We utilize the Proximal Policy Optimization (PPO) algorithm to precisely control the mass flow rates of synthetic jets located on the upper and lower surfaces of a cylinder to achieve reduction in drag, minimization of lift, and suppression of vortex shedding. Our research findings indicate that, for elliptical cylinders with $Ar$ between 1.75 and 0.75, the reduction in drag coefficient ranges from 0.9% to 15.7%, and the reduction in lift coefficient ranges from 95.2% to 99.7%. The DRL-based control strategy not only significantly reduces lift and drag, but also completely suppresses vortex shedding while using less than 1% of external excitation energy, demonstrating its efficiency and energy-saving capabilities. Additionally, for $Ar$ from 0.5 to 0, the reduction in drag coefficient ranges from 26.9% to 43.6%, and the reduction in lift coefficient from 50.2% to 68.0%. This reflects the control strategy's significant reduction in both drag and lift coefficients, while also alleviating vortex shedding. The interaction and nonlinear development of vortices in the wake of elliptical cylinders lead to complex flow instability, and DRL-based AFC technology shows adaptability and potential in addressing flow control problems for this type of bluff body flow.

physics.flu-dyn↗

arXiv2020

The monochromatic X-rays facilities at NIM

Space scientific exploration is becoming the main battlefield for mankind to explore the universe. Countries around the world have successively launched various space exploration satellites. Accurate calibration on the ground is a key factor for space science satellites to obtain observational results. In order to provide calibration for various satellite-borne detectors, several monochromatic X-rays facilities has been built at National Institute of Metrology, P.R. China (NIM). These facilities are mainly based on grating diffraction and Bragg diffraction, the energy range of produced monochromatic X-rays is (0.218-301) keV. The facilities have a good performance on energy stability, monochromaticity and flux stability. Monochromaticity of all facilities is better than 3.0%, the stability of energy is better than 1.0% over 8 hours, and the stability of flux is better than 2.0% over 8 hours. The calibration experiments of satellite-borne detectors, such as energy linearity, energy resolution, detection efficiency and temperature response can be carried out on the facilities. So far we have completed the calibration of two satellites, and there are still three satellites in progress. This work will contribute to the development of X-ray astronomy, and contribute to the development of Chinese space science.

astro-ph.IM↗