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.

Experimental study of turbulent thermal diffusion of inertial particles in a convective turbulence forced by oscillating grids

This study experimentally demonstrates that turbulent thermal diffusion in convective turbulence drives inertial particles (10 μm) to form large-scale clusters near mean temperature minima with an effective drift velocity 1.5 to 2.5 times greater than that of non-inertial particles (0.7 μm), confirming theoretical predictions regarding the dependence of this drift on Stokes and Reynolds numbers.

E. Elmakies, O. Shildkrot, N. Kleeorin, A. Levy, I. Rogachevskii2026-05-26🔬 physics

On the Two-Dimensional Structure and Asymmetries of Ionic Liquid Electrospray Plumes

This study presents the first fully two-dimensional time-of-flight mass spectrometry survey of an ionic liquid electrospray plume, revealing significant spatial compositional asymmetries and a ring-shaped monomer distribution that challenge the assumption of uniformity, thereby demonstrating that whole-plume surveys are essential for accurately assessing propulsive efficiency and explaining previously "missing mass" in electrospray propulsion.

Zach Ulibarri, Giuliana Hofheins, Sophia Gessman, Elaine Petro2026-05-26🔬 physics.app-ph

A contaminant-concentration-dependent surface tension does not explain the absence of solutal Marangoni flow in evaporating droplets

This study demonstrates through combined experiments and modeling that the absence of predicted Marangoni flows in evaporating droplets is not caused by standard contaminant-concentration-dependent surface tension effects, but rather indicates that Marangoni stresses are effectively suppressed altogether, with observed flows being driven entirely by natural convection.

Javier Martínez-Puig, Théophile Gaichies, Javier Rodríguez-Rodríguez2026-05-26🔬 physics

Geometry, elasticity, and activity in the transport of self-propelled filaments in turbulence

This study reveals that the transport of elastic active filaments in two-dimensional turbulence is governed by propulsion geometry, where fixed-direction propulsion enables superdiffusive motion by overcoming vortex trapping, whereas conformationally coupled propulsion remains diffusive due to dominant trapping, with elasticity and activity cooperatively shaping filament conformations to influence this competition.

Kunal Kumar, Aliv Sahoo, Rahul Kumar Singh, Samriddhi Sankar Ray2026-05-26🔬 physics

Rheotaxis of microswimmers in colloid-laden channel flow

Using multi-particle collision dynamics simulations, this study reveals that while channel flow alone induces wall-oscillatory behavior in microswimmers, the presence of colloidal particles significantly alters their rheotactic trajectories and reduces their downstream velocity, with distinct differences observed between pusher, puller, and neutral swimmer types.

Margam Ramprasad, Shubhadeep Mandal, Pallab Sinha Mahapatra2026-05-26🔬 cond-mat

Fractal-based variable drag model for porous-media tree representations

This paper proposes a fractal-based variable drag model that assigns cell-wise drag coefficients dependent on effective branching order and Reynolds number to porous-media tree representations, thereby improving the robustness and accuracy of urban micrometeorological simulations across varying grid resolutions and inflow conditions compared to conventional constant-drag approaches.

Takumi Tokiwa, Yuwei Yin, Ryo Onishi2026-05-26🔬 physics

Improving turbulence control through explainable deep learning

This paper demonstrates that integrating explainable deep learning with deep reinforcement learning enables the identification of key turbulence-sustaining structures, resulting in a control strategy that achieves superior drag reduction and net-energy savings compared to direct drag-minimization approaches while maintaining effectiveness across varying Reynolds numbers and geometries.

Miguel Beneitez, Andres Cremades, Luca Guastoni, Ricardo Vinuesa2026-05-25🔬 physics