Searcharxiv⌕ Search

arXiv subjects

Isaac J. G. Lewis

Publications and source records attributed to Isaac J. G. Lewis.

2 recordsLinked to original sources

Exact coherent states underlying chaotic falling-film dynamics

Dynamical-systems approaches to spatiotemporal chaos have been developed primarily for single-phase flows, where the system state is defined by bulk velocity fields. Extending these ideas to two-phase flows remains challenging because the dynamics are intrinsically coupled to the evolution of a deforming interface. Here, we address this challenge for a two-dimensional vertical falling film by formulating the dynamics in terms of the interface evolution. Starting from the Navier--Stokes equations, we recover a classical long-wave interface evolution equation, originally derived by Topper & Kawahara (1978). Using this formulation, we perform an extensive parametric study to construct a regime map in the space of domain size and dispersion parameter. The resulting map reveals a rich range of interfacial behaviors, including travelling waves, bursting travelling waves, and fully chaotic regimes. In the chaotic falling film regime, we exploit the dissipative nature of the governing equation, which suggests that the long-time dynamics evolve onto an inertial manifold. Using a data-driven approach, we parameterize this inertial manifold and estimate its intrinsic dimension, suggesting approximately linear growth with domain size. We then construct low-dimensional models in manifold coordinates to facilitate the search for exact coherent states of the full system. Using this approach, we identify travelling waves, relative periodic orbits and equilibria embedded within the chaotic attractor. Chaotic trajectories repeatedly approach the neighbourhoods of these invariant solutions, indicating that the recurrent interfacial patterns observed in the dynamics correspond to visits to these coherent states. To the best of our knowledge, this constitutes the first identification of exact coherent structures embedded in chaotic falling-film dynamics.

physics.flu-dyn↗

Data-driven modeling of a settling sphere in a quiescent medium

We develop data-driven models to predict the dynamics of a freely settling sphere in a quiescent Newtonian fluid using experimentally obtained trajectories. Particle tracking velocimetry was used to obtain a comprehensive dataset of settling motions, which we use to train neural networks that model the spatial evolution of a spherical particle without explicitly resolving the surrounding fluid dynamics. We employ deterministic neural ordinary differential equations (NODEs) and stochastic neural stochastic differential equations (NSDEs) to reconstruct the sphere's trajectory and capture key statistical features of the settling process. The models are evaluated based on short- and long-time dynamics, including ensemble-averaged velocity evolution, settling time distributions, and probability density functions of the final settling positions. We also examine the correlation between lateral displacement and streamwise velocity and assess the impact of dataset size on predictive accuracy. While NODEs excel in trajectory reconstruction and generalization across different initial conditions, NSDEs effectively capture statistical trends in the long-time behavior but are more sensitive to data availability. Acceleration profiles computed via second-order finite difference schemes confirm that both approaches accurately capture long-time dynamics, though short-time transients pose challenges.

physics.flu-dyn↗