Physical Review Fluids
Granular collapse on a rough slope
Author(s): Haozhe Geng, Wen-Li Chen, Hui Li, and Donglai Gao
Gravity-driven collapses such as landslides, avalanches, and mudslides have garnered increasing attention, and understanding their complex dynamics is a key concern for risk assessment. In this paper, we design a laboratory experiment to investigate the dry granular column collapse from a rough slop…
[Phys. Rev. Fluids 10, 063801] Published Mon Jun 23, 2025
Nonlinear evolution and higher harmonics in extreme water waves based on higher order Peregrine solutions of the nonlinear Schrödinger equation
Author(s): Junnan Cui, Qunbin Chen, Jingsong He, Liu Yang, and Xingya Feng
This study studies the generation of extreme waves in a physical wave flume and in a numerical wave tank based on the higher order Peregrine solutions to the Schrodinger equation. Higher harmonics of the wave elevations during modulation and demodulation are extracted and analyzed. Through spectral analysis, the nonlinear energy transfer characteristics of Peregrine solutions are identified.
[Phys. Rev. Fluids 10, 064802] Published Mon Jun 23, 2025
Influences of streamwise driving forces on turbulent statistics in direct numerical simulations of compressible turbulent channel flows
Author(s): Xuke Zhu, Yubin Song, Peng Zhang, Xiaoshuo Yang, Yongchao Ji, and Zhenhua Xia
Despite decades of research on compressible turbulent channel flows (CTCFs), studies inconsistently employ either spatially uniform or density-weighted body forces. We conduct direct numerical simulations of CTCFs with symmetric (cold) and asymmetric (cold/quasi-adiabatic) thermal walls to systematically assess the impact of these two forcing strategies on turbulence statistics. While differences are minimal in symmetric cases, strong compressibility or large wall temperature differences lead to notable discrepancies, highlighting the importance of force selection in high-Mach or thermally asymmetric flows.
[Phys. Rev. Fluids 10, 064616] Published Fri Jun 20, 2025
Inverse reinforcement learning for objective discovery in collective behavior of artificial swimmers
Author(s): Daniel Wälchli, Pascal Weber, Michail Chatzimanolakis, Robert Katzschmann, and Petros Koumoutsakos
This paper introduces inverse reinforcement learning to discover objectives in fish schooling. The methodology is not specific to fish schools and applicable across other natural systems. It provides a new path to bioinspired optimization by analyzing data to infer goals rather than a-priori specifying them.
[Phys. Rev. Fluids 10, 064901] Published Wed Jun 18, 2025
Universal energy cascade in homogeneous binary fluid turbulence: A direct comparison of different exact relations
Author(s): Nandita Pan and Supratik Banerjee
Below critical temperature, turbulence prevents the spontaneous phase separation of binary mixtures, resulting in a phase arrested state of emulsion. The current study explores if a Kolmogorov-like energy cascade exists in fully developed binary fluid turbulence. Using exact relations and direct numerical simulations with up to 10243 grid points, we show that the combined kinetic and interfacial energy exhibits a cascade with a constant transfer rate across the inertial scales. In addition, the cascade rates computed from the three exact laws in divergence, alternative and correlator forms show excellent agreement, thus confirming the equivalence between the three formulations.
[Phys. Rev. Fluids 10, 064615] Published Tue Jun 17, 2025