130 papers
This paper extends the Statistical State Dynamics framework to shallow water magnetohydrodynamic turbulence, demonstrating how the interplay of Reynolds and Maxwell stresses leads to the formation and equilibration of zonal jet-toroidal field structures that explain both steady phenomena like solar super-rotation and time-dependent events such as the solar cycle.
This study employs global linear stability analysis and simulations to reveal that the stability of rising oblate bubble pairs is primarily governed by inclination-induced rotational feedback and lift rather than deformation, while also identifying distinct short-range and long-range coupling mechanisms for different instability modes and a new oscillatory mode driven by unsteady recirculation.
This paper argues that for steady, fully developed incompressible Navier-Stokes flows, the condition where the total mechanical energy gradient is perpendicular to the streamline causes the viscous term to vanish and the solution to lose -regularity, thereby creating a weak singularity where the equations degenerate into the Euler equations.
This study combines experiments and simulations to reveal that premixed NH3/H2/air flames are stabilized behind bluff bodies through a coupled feedback mechanism where preferential hydrogen diffusion creates a localized diffusion flame at the root, enhancing radical production and anchoring, while thermal expansion significantly alters the flow field to sustain a robust, carbon-free combustion regime.
This paper presents a general numerical framework that combines weighted interpolation with a conservative flux mapping algorithm to accurately reconstruct interfaces and transfer data between partitioned multiphysics domains, achieving high geometric and flux conservation accuracy across various applications.
This study improves the hydrodynamic modeling of free-swimming *Chlamydomonas reinhardtii* by demonstrating that a modified three-sphere model incorporating differential drag on flagellar spheres successfully reproduces experimental flow characteristics that the standard model fails to capture.
This paper establishes a low-dimensional dynamical framework for two-dimensional vertical falling films to identify and characterize exact coherent states—such as travelling waves, relative periodic orbits, and equilibria—that underpin the system's chaotic interfacial dynamics.
This paper introduces FDTO, a memory-efficient and accurate optimization-based solver that combines finite-difference time-stepping with body-fitted curvilinear grids to overcome the conditioning and efficiency limitations of existing methods like PINNs and ODIL for solving incompressible flow problems.
This paper presents a novel theoretical and numerical framework to demonstrate that non-rotating quasi-2D viscous flows over topographies settle into steady states where large-scale vortices occupy topographic valleys, a behavior distinct from rotating systems and significant for understanding slow planetary flows.
This paper presents a robust semi-analytical pseudo-spectral method utilizing mapped associated Legendre polynomials and an advanced exponential time differencing scheme to efficiently and accurately simulate rotating, stratified flows in unbounded cylindrical domains by overcoming the numerical stiffness typically caused by strong shear and fast wave forces.
This paper presents a "Unified Blockage Model" that analytically predicts the performance of rotors in confined flows across arbitrary thrust coefficients and misalignment angles, successfully bridging the gap between existing simplified theories and complex fluid dynamics validated by simulations and experimental data.
This paper introduces Extracted Mode Tracking (EMT), an unsupervised machine learning framework that resolves gravity-capillary wave evolution in vessels with unknown boundary conditions by directly extracting wave modes from spatio-temporal data, thereby enabling quantitative analysis of nonlinear dynamics without requiring prior theoretical modeling.
This paper demonstrates that the Boltzmann-Curtiss formulation, which incorporates both rotational and translational degrees of freedom, significantly improves the accuracy of modeling shock structures and nonequilibrium flow profiles for gases like argon and nitrogen compared to traditional Navier-Stokes equations and Maxwell-Boltzmann-based theories.
This study presents a machine learning framework for predicting steady-state flow through porous media, demonstrating that the Fourier Neural Operator (FNO) outperforms convolutional autoencoders and U-Nets by achieving high accuracy, significant computational speedups over traditional CFD, and mesh-invariant properties ideal for topology optimization.
This paper investigates the nonlinear evolution of the Sun's most prominent high-latitude inertial mode () on a differentially rotating sphere, demonstrating through direct numerical simulations that it undergoes a supercritical Hopf bifurcation where Reynolds stresses smooth the differential rotation to saturate the instability at amplitudes comparable to solar observations.
This paper presents a theoretical framework demonstrating that wall friction significantly alters the quasistatic mechanical response of confined poroelastic media by introducing hysteresis, slip fronts, and energy dissipation mechanisms that depend on whether the system is driven by piston loading or fluid pressure.
This paper investigates the nonlinear breakup dynamics of an active chiral fluid strip, demonstrating through analytical slender body theory and numerical simulations that its thickness vanishes as a power law in finite time, with results showing strong agreement with experimental observations.
This paper demonstrates that a reinforcement learning controller, trained on a data-driven reduced-order model (DManD) combining POD, autoencoders, and neural ODEs, successfully stabilizes high-Rayleigh-number Rayleigh-Bénard convection and reduces heat transfer by 16–23% when deployed in direct numerical simulations.
This paper introduces the SHallow REcurrent Decoder (SHRED), a novel deep learning architecture that successfully reconstructs full-state subsurface turbulent flow fields from sparse surface height measurements or video footage, enabling robust inference up to two integral length scales deep using as few as three sensors.
This paper presents a first-principles simulation method that incorporates both droplet and bath deformation to accurately predict non-coalescing droplet rebounds at low Weber numbers, a capability validated by new experimental data.