Data-Driven Particle Method for Multiscale Multiphase Flows
Please login to view abstract download link
Bridging across multiple length scales and timescales introduces emergent behaviors such as nonlocality, dissipation, and stochasticity. Data-driven particle dynamics is a thermodynamically-consistent framework that uses metriplectic brackets to adhere to conservation laws and bridges across scales. [1–3] These metriplectic brackets are trained on particle trajectories in order to learn a mesh-free nonlocal model. We extend this framework to multiphase systems and for advection-diffusion problems by intro- ducing mass fraction as a new state variable, in addition to positions, velocities, and entropies. We use the model to learn and infer the dynamics of a smoothed dissipative particle dynamics for ideal mixtures, finite-volume Raleigh-Taylor dataset, and a LAMMPS-generated molecular dynamics trajectory of a binary fluid. [4–6] This work introduces a new data-driven model that can be trained on various kinds of coarse-grained data.
