🔬 Category

physics.flu-dyn

967 papers

Fluid dynamics explores how liquids and gases move, shaping everything from weather patterns to the flow of blood through our veins. This field bridges the gap between abstract mathematical equations and the tangible forces that drive our physical world, offering insights into turbulence, aerodynamics, and fluid behavior in complex environments.

On Gist.Science, we process every new preprint in this category directly from arXiv to make cutting-edge research accessible to everyone. Each paper is transformed into a clear, plain-language overview alongside a detailed technical summary, ensuring both students and experts can grasp the latest findings without getting lost in dense jargon.

Below, you will find the most recent studies in fluid dynamics, curated and explained for a broader audience.

Deep reinforcement learning with spatial and temporal awareness for active boundary control of buoyancy-driven convection

This paper proposes a deep reinforcement learning framework that overcomes the degenerate actuation issues of prior methods by integrating convolutional networks, recurrent memory, off-policy training, and action-smoothness constraints, successfully achieving significant heat transfer reduction in Rayleigh–Bénard convection and adaptive mixing enhancement in double-diffusive convection without requiring full-field data augmentation.

Giorgio Maria Cavallazzi, Miguel Pérez Cuadrado, Alfredo Pinelli2026-06-05🔬 physics

Drag reduction or reward hacking? Recurrent multi-agent reinforcement learning that earns its reward

This paper identifies and rectifies three specific flaws in multi-agent reinforcement learning for drag reduction in wall turbulence—credit assignment loss, memoryless policies, and misaligned rewards—by implementing a differentiable projection, recurrent policies, and a true power-based reward, ultimately achieving a genuine 17% energy saving that avoids the pitfalls of reward hacking.

Giorgio Maria Cavallazzi, Miguel Pérez-Cuadrado, Alfredo Pinelli2026-06-05🔬 physics

A high-order Fourier Continuation (FC)-based spectral incompressible Smoothed Particle Hydrodynamics (ISPH) scheme for general boundary conditions in wall-bounded domains

This paper introduces a high-order Fourier Continuation (FC)-based spectral incompressible Smoothed Particle Hydrodynamics (ISPH) scheme that extends the method to wall-bounded domains with general boundary conditions, enabling high-order convergence and accurate simulation of complex vortex dynamics through frequency-space discretization on a periodic extension of the domain.

Meixuan Lin, Georgios Fourtakas, Benedict D. Rogers2026-06-05🔬 physics

Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks

This study demonstrates that while Physics-Informed Neural Networks (PINNs) can reconstruct wall shear stress from passive scalar data only when near-wall measurements are available, a differentiable physics framework based on PDE-constrained optimization successfully recovers accurate wall shear stress across diverse measurement scenarios in both canonical and patient-specific cardiovascular flows.

Mahmoud Elhadidy, Siva Viknesh, Roshan M. D'Souza, Amirhossein Arzani2026-06-05🔬 physics

Flow-priority optimization of additively manufactured variable-TPMS lattice heat exchanger based on macroscopic analysis

This study proposes a macroscopic modeling and optimization framework based on Darcy–Forchheimer theory to design variable-TPMS lattice heat exchangers with non-uniform channel widths, which experimental validation confirms achieve a 28.7% performance improvement over uniform lattice configurations.

Kazutaka Yanagihara, Jun Iwasaki, Kiyoto Saso, Taichi Yamashita, Shomu Murakoshi, Akihiro Takezawa2026-06-04🔬 physics

Hydrodynamic Behavior of Non-spherical Particles in Confined Vertical Flows: A Resolved CFD-DEM Study

This study employs resolved CFD-DEM simulations to demonstrate that non-spherical polymetallic nodules experience significantly enhanced drag and reduced terminal velocities compared to volume-equivalent spheres due to shape-induced wake asymmetry, while revealing how particle size and confinement govern distinct drag variance behaviors during vertical hydraulic transport.

Amiya Prakash Das, Shakti Swaroop Choudhury, Sujith Reddy Jaggannagari, Amudha Krishnan, Gopkumar Kuttikrishnan, Balaji Ramakrishnan, Ratna Kumar Annabattula2026-06-04🔬 physics.app-ph

Turbulence teaches equivariance to neural networks

This paper demonstrates that the rotational nature of turbulence inherently teaches neural networks equivariance through implicit data augmentation, and that explicitly enforcing this symmetry as an architectural inductive bias significantly improves generalization across different flow conditions while reducing model complexity.

Ryley McConkey, Julia Balla, Jeremiah Bailey, Ali Backour, Elyssa Hofgard, Tommi Jaakkola, Abigail Bodner, Tess Smidt2026-06-04🔬 physics

Ceci n'est pas une Couche de Mélange: The Meaning of Resolved Turbulent Radiative Mixing

This paper argues that the apparent resolution independence of total cooling in Turbulent Radiative Mixing Layer simulations is an unphysical artifact of cancelling numerical errors, and establishes that accurately resolving the phase structure and observable properties requires capturing the "turbulent Field length" where turbulent diffusion timescales match cooling timescales.

Lachlan Lancaster, Rajsekhar Mohapatra, Drummond B. Fielding, Greg L. Bryan2026-06-04🔭 astro-ph

The Origin of Da Scaling: Suppressed Cooling in Fast-Cooling Mixing Layers

This paper explains the transition in radiative cooling scaling from E˙cool∝Da1/2\dot{E}_{\rm cool} \propto {\rm Da}^{1/2} to E˙cool∝Da1/4\dot{E}_{\rm cool} \propto {\rm Da}^{1/4} in fast-cooling turbulent mixing layers as a result of ram pressure from inflowing gas suppressing the turbulent folding and fractal structure of the interface.

Lachlan Lancaster, Drummond B. Fielding, Rajsekhar Mohapatra, Greg L. Bryan2026-06-04🔭 astro-ph