arXiv subjects

Mohammed Daqaq

Publications and source records attributed to Mohammed Daqaq.

4 recordsLinked to original sources

arXiv2026

Deep Reinforcement Learning Discovers a Novel Control Algorithm for Mitigating Flow-Induced Vibrations in Underactuated Tandem Cylinders

This study presents the first experimental implementation of deep reinforcement learning (DRL) for the active real-time suppression of flow-induced vibrations in simultaneously vibrating tandem cylinders using rotary actuation, considering fully actuated and underactuated configurations. In the fully actuated case, where both cylinders are independently controlled, the DRL agent discovers a high-frequency, phase-locked bang-bang control strategy that suppresses the vibrations of both cylinders by more than 95\%. Analysis of the training dynamics reveals a physically interpretable learning process in which the agent first identifies the optimal phase relationship between the actuators before refining the actuation frequency. In the underactuated configuration, where only the upstream cylinder is actuated, equally weighted rewards produce ineffective control, suppressing vibrations only in the actuated cylinder. Introducing asymmetric reward weighting enables the DRL agent to discover a low-frequency lock-on strategy that achieves 70\% and 90\% vibration suppression in the upstream and downstream cylinders, respectively. For staggered arrangements with lateral offset, conventional training fails to converge, requiring a curriculum learning approach. The resulting two-stage curriculum identifies a statically biased bi-harmonic rotational control signal capable of suppressing vibrations in both cylinders. The success of the underactuated control strategy highlights its potential to reduce energy consumption and hardware complexity in multi-body flow control systems.

physics.flu-dyn↗

arXiv2025

Deep Reinforcement Learning in Action: Real-Time Control of Vortex-Induced Vibrations

This study showcases an experimental deployment of deep reinforcement learning (DRL) for active flow control (AFC) of vortex-induced vibrations (VIV) in a circular cylinder at a high Reynolds number (Re = 3000) using rotary actuation. Departing from prior work that relied on low-Reynolds-number numerical simulations, this research demonstrates real-time control in a challenging experimental setting, successfully addressing practical constraints such as actuator delay. When the learning algorithm is provided with state feedback alone (displacement and velocity of the oscillating cylinder), the DRL agent learns a low-frequency rotary control strategy that achieves up to 80% vibration suppression which leverages the traditional lock-on phenomenon. While this level of suppression is significant, it remains below the performance achieved using high-frequency rotary actuation. The reduction in performance is attributed to actuation delays and can be mitigated by augmenting the learning algorithm with past control actions. This enables the agent to learn a high-frequency rotary control strategy that effectively modifies vortex shedding and achieves over 95% vibration attenuation. These results demonstrate the adaptability of DRL for AFC in real-world experiments and its ability to overcome instrumental limitations such as actuation lag.

cs.LG↗

arXiv2025

Casting Computational Fluid Mechanics into a Convex Quadratic Optimization Framework

We employ the principle of minimum pressure gradient to transform problems in unsteady computational fluid dynamics (CFD) into a convex optimization framework subject to linear constraints. This formulation permits solving, for the first time, CFD problems efficiently using well-established quadratic programming tools or using the well-known Karush-Kuhn-Tucker (KKT) condition. The proposed approach is demonstrated using three benchmark examples. In particular, it is shown through comparison with traditional CFD tools that the proposed framework is capable of predicting the flow field in a lid-driven cavity, in a uniform pipe (Poiseuille flow), and that past a backward facing step. The results highlight the potential of the method as a simple, robust, and potentially transformative alternative to traditional CFD approaches.

physics.flu-dyn↗

arXiv2024

The Principle of Minimum Pressure Gradient: An Alternative Basis for Physics-Informed Learning of Incompressible Fluid Mechanics

Recent advances in the application of physics-informed learning into the field of fluid mechanics have been predominantly grounded in the Newtonian framework, primarly leveraging Navier-Stokes Equation or one of its various derivative to train a neural network. Here, we propose an alternative approach based on variational methods. The proposed approach uses the principle of minimum pressure gradient combined with the continuity constraint to train a neural network and predict the flow field in incompressible fluids. We describe the underlying principles of the proposed approach, then use a demonstrative example to illustrate its implementation and show that it reduces the computational time per training epoch when compared to the conventional approach.

physics.flu-dyn↗