physics.flu-dyn 篇论文

流体力学是研究流体如何流动、变形以及与其他物质相互作用的迷人领域。从日常的气流到浩瀚的星系演化，这一学科无处不在。在本分类中，我们聚焦于该领域的核心动态，用通俗的语言解读那些看似复杂的物理现象，让非专业读者也能领略流体世界的奇妙逻辑。

Gist.Science 每日从 arXiv 收录并处理所有流体力学相关的新预印本。我们不仅提供详尽的技术摘要，更提炼出通俗易懂的通俗解读，确保每一位访客都能无障碍地获取前沿科学成果。

以下是该领域最新的预印论文列表，涵盖了从基础理论到工程应用的最新发现。

Using Physics Informed Neural Network (PINN) and Neural Network (NN) to Improve a $k-ω$ Turbulence Model

本文提出了一种结合物理信息神经网络（PINN）与神经网络（NN）改进的 $k-\omega$ 湍流模型（k-omega-PINN-NN），通过修正湍流扩散项并优化模型系数，显著提升了该模型在通道流、平板边界层及周期山丘流动中对湍流动能及速度分布的预测精度，并展示了利用符号回归将模型转化为可嵌入商业 CFD 代码的 Python 形式。

Lars Davidson2026-02-26🔬 physics

Non-reciprocal Binary-fluid Turbulence

该论文通过建立二维非互易 Cahn-Hilliard-Navier-Stokes 模型，首次揭示了非互易二元流体湍流中伴随非互易通量产生的新型逆级联能量谱（ $E(k)\sim k^{-5/3}$ ）及其随雷诺数增加而抑制通量的特性，并系统对比了其与经典二维流体湍流在谱、通量及拓扑结构等方面的异同。

Biswajit Maji, Nadia Bihari Padhan, Axel Voigt, Rahul Pandit2026-02-26🔬 cond-mat

Conservation laws, fluxes, and symmetries: lessons from a perturbative approach for self-organized turbulence

本文通过微扰理论框架，深入探讨了由两个守恒量驱动的自组织湍流现象，揭示了二维纳维 - 斯托克斯方程与大尺度准地转方程的普适特性，并将其成功推广至旋转三维湍流及可变罗斯比变形半径的浅水准地转模型中，阐明了不同尺度下大尺度结构（如凝结态）的形成机制与对称性破缺规律。

Anna Frishman, Sébastien Gomé, Anton Svirsky2026-02-26🔬 physics

Physics Constrained Neural Collision Operators for Variable Hard Sphere Surrogates and Ab Initio Angle Prediction in Direct Simulation Monte Carlo

本文提出了一种物理约束的神经网络碰撞算子框架，通过引入随机层恢复热涨落并严格守恒动量与能量，不仅实现了从一维到二维流动的零样本泛化，还构建了针对 Jäger 势的*ab initio*散射预测模型，从而在保持高保真度的同时显著降低了直接模拟蒙特卡洛（DSMC）在稀薄气体动力学中的计算成本。

Ehsan Roohi, Ahmad Shoja-Sani, Stefan Stefanov2026-02-26🔬 physics

Chapman-Enskog expansion for chirally colliding disks

本文研究了具有手性碰撞的二维硬盘流体，证明了尽管微观上破坏了时间反演对称性，系统仍满足 H 定理并弛豫至平衡态，且通过查普曼 - 恩斯科格展开和分子动力学模拟获得了剪切粘度、奇异性粘度和热导率的解析表达式。

Ruben Lier, Paweł Matus2026-02-26🔬 cond-mat

A CFD-Based Investigation of Local Luminal Curvature as a Primary Determinant of Hemodynamic Environments in Cerebral Aneurysms

该研究基于 76 例患者特异性脑动脉瘤的 CFD 模拟，证实了局部管腔曲率（鞍形与球形区域）是决定壁面剪切应力等血流动力学特征的关键因素，从而为识别动脉瘤破裂风险区域及制定精准介入治疗方案提供了新的几何学依据。

Marcella P. A. Dallavanzi, José L. Gasche, Iago L. Oliveira2026-02-26🔬 physics

physics.flu-dyn

Using Physics Informed Neural Network (PINN) and Neural Network (NN) to Improve a $k-ω$ Turbulence Model

Non-reciprocal Binary-fluid Turbulence

Conservation laws, fluxes, and symmetries: lessons from a perturbative approach for self-organized turbulence

Physics Constrained Neural Collision Operators for Variable Hard Sphere Surrogates and Ab Initio Angle Prediction in Direct Simulation Monte Carlo

Chapman-Enskog expansion for chirally colliding disks

A CFD-Based Investigation of Local Luminal Curvature as a Primary Determinant of Hemodynamic Environments in Cerebral Aneurysms

Unstable magnetic reconnection self-generates turbulence

Sorting prolate and oblate spheroids with a diatomic gas in a magnetic field

Out-of-time-ordered correlators for turbulent fields: a quantum-classical correspondence

Frequency-Dependent Magnetic modulation of deposition morphology

physics.flu-dyn

Using Physics Informed Neural Network (PINN) and Neural Network (NN) to Improve a k−ωk-ωk−ω Turbulence Model

Non-reciprocal Binary-fluid Turbulence

Conservation laws, fluxes, and symmetries: lessons from a perturbative approach for self-organized turbulence

Physics Constrained Neural Collision Operators for Variable Hard Sphere Surrogates and Ab Initio Angle Prediction in Direct Simulation Monte Carlo

Chapman-Enskog expansion for chirally colliding disks

A CFD-Based Investigation of Local Luminal Curvature as a Primary Determinant of Hemodynamic Environments in Cerebral Aneurysms

Unstable magnetic reconnection self-generates turbulence

Sorting prolate and oblate spheroids with a diatomic gas in a magnetic field

Out-of-time-ordered correlators for turbulent fields: a quantum-classical correspondence

Frequency-Dependent Magnetic modulation of deposition morphology

Using Physics Informed Neural Network (PINN) and Neural Network (NN) to Improve a $k-ω$ Turbulence Model