130 papers
This paper demonstrates that wave-like spatial statistics in walking-droplet quantum analogs can be generically explained by the low-dimensional nonlinear dynamics of inertial active particles with internal degrees of freedom, rather than requiring nonlocal wave effects.
This paper demonstrates that neglecting heat flux in relativistic Couette-type flows leads to qualitatively incorrect profiles due to the "inertia of heat," a phenomenon where heat flux contributes to momentum density and necessitates energy transport across boundaries to balance viscous heating.
This paper proposes a molecular kinetic model for Lennard-Jones fluids that accurately reproduces equilibrium properties and reveals significant deviations from the classical Hertz-Knudsen relation under non-equilibrium evaporation conditions, thereby advancing the development of efficient computational frameworks for real fluid flows.
This paper provides a learner-focused, methodical exposition on deriving the General Lagrangian Mean (GLM) and pseudo-Lagrangian equations for inviscid, incompressible, homogeneous fluids to describe the interactions between mean flows and waves.
This study combines theoretical analysis and numerical simulations to demonstrate that a stationary triangular strain field induces resonant instability in a Batchelor vortex, where the introduction of axial flow reduces critical layer damping to activate additional unstable mode pairs and ultimately shifts the dominant instability from a specific mode combination to one involving the first branches of both azimuthal wavenumbers.
This paper extends the Theory of Porous Media to model non-isothermal material injection into porous structures, specifically for percutaneous vertebroplasty, by incorporating local thermal non-equilibrium conditions and demonstrating thermodynamic consistency through numerical simulations.
This paper proposes a trajectory-driven global optimization framework, inspired by CMA-ES, that enables unified, high-fidelity structural-hydrodynamic modeling of underwater underactuated and soft robotic systems by simultaneously identifying coupled internal and external parameters, achieving accurate real-to-sim consistency across diverse mechanisms without manual retuning.
This paper establishes a hydrodynamic framework for force dipoles in compressible membranes with odd viscosity by deriving an exact Green tensor that reveals distinct dynamical regimes and chiral observables, such as transverse drift and relative motion, arising from the interplay of shear, compressional, and odd-viscous modes.
This paper establishes the global existence and uniqueness of generalized solutions for the three-dimensional inviscid quasi-geostrophic equation on a cylindrical domain with a multiply connected cross-section under specific Neumann and no-flux boundary conditions, assuming the initial potential vorticity is essentially bounded.
This paper addresses the pressure-load inaccuracies in existing immersed interface methods for C0 triangulated surfaces by proposing a pressure-robust formulation that utilizes reconstructed continuous surface normal vectors—via L2 projection or inverse centroid-distance weighting—to significantly reduce numerical leakage.
This paper demonstrates that the macroscopic behavior of non-equilibrium two-phase flow in porous media can be successfully predicted by mapping droplet distributions onto an equilibrium spin-glass model derived via machine learning and the maximum entropy principle, revealing that the transition to a glassy flow regime with hysteresis and non-linear dynamics coincides with the spin-glass phase transition.
This paper demonstrates that spatial symmetry acts as a fundamental physical optimality principle for efficient locomotion in viscous fluids, explaining the prevalence of symmetric swimming gaits in nature through a hydrodynamic duality that proves symmetric strokes yield optimal speeds and efficiencies unattainable by generic non-symmetric strokes.
This paper presents a novel high-resolution experimental platform using 3D Lagrangian particle tracking and advanced geometric filtering to reliably measure the sub-Kolmogorov clustering and collision dynamics of inertial micro-droplets in air turbulence, overcoming previous limitations caused by spurious artifacts.
This study employs RANS simulations to demonstrate that increasing the inlet Reynolds number in an isothermal swirl combustor significantly intensifies axial velocities and recirculation zones while maintaining a stable inner recirculation zone location, suggesting robust flame anchoring under varying inertial conditions.
This paper introduces Manifold-Adapted Sparse RBF-SINDy, an unsupervised framework that recovers the geometric skeleton of turbulent wall flow dynamics from wall measurements alone by correcting structural biases in library construction through arc-length resampling and Mahalanobis metric clustering, thereby enabling the discovery of distinct dynamical states and the reconstruction of the system's invariant measure.
This study demonstrates that aerofoil shape optimisation for curvilinear blade kinematics is effective only in high-solidity configurations where light dynamic stall allows geometric modifications to suppress leading-edge vortex separation, whereas deep dynamic stall renders such optimisation ineffective due to dominant flow separation.
This paper demonstrates that the transition between weak- and strong-field dynamo regimes in spherical shells is characterized by a breaking of equatorial symmetry, which is triggered by the sudden growth of the magnetic field and subsequently sustains the strong-field branch.
This paper demonstrates that a shape-changing microswimmer, guided by reinforcement learning to adapt its aspect ratio based on local flow signals, can robustly maximize its displacement in turbulent environments, outperforming fixed-shape strategies and revealing a physically interpretable control paradigm for navigation in complex flows.
This paper proposes a Meta-PINNs framework that integrates meta-learning to enhance physics-informed neural networks, demonstrating significantly improved convergence, generalization, and accuracy with reduced computational costs for predicting turbomachinery flows under varying operating conditions compared to standard methods.
This paper presents a reduced mathematical model based on center manifold theory for open cavity flow, which successfully captures key dynamics such as unstable quasi-periodic edge states and limit-cycle switching by explaining the stability exchange through cross-coupling terms in amplitude equations.