Ensemble Data Assimilation for Particle-based Methods

TL;DR

This paper introduces two novel particle-based data assimilation methods using the Ensemble Kalman Filter, addressing the gap in applying these techniques to Lagrangian simulations, and demonstrates their effectiveness in 1D and 2D flow problems.

Contribution

It proposes two new methodologies for particle-based data assimilation, including an Eulerian transformation and a purely Lagrangian scheme, expanding the application scope of ensemble data assimilation.

Findings

Methods are effective in 1D advection-diffusion models.

Methods are feasible for complex 2D flow problems.

Performance benchmarks show competitive results against grid-based filters.

Abstract

This study presents a novel approach to applying data assimilation techniques for particle-based simulations using the Ensemble Kalman Filter. While data assimilation methods have been effectively applied to Eulerian simulations, their application in Lagrangian solution discretizations has not been properly explored. We introduce two specific methodologies to address this gap. The first methodology employs an intermediary Eulerian transformation that combines a projection with a remeshing process. The second is a purely Lagrangian scheme designed for situations where remeshing is not appropriate. The second is a purely Lagrangian scheme that is applicable when remeshing is not adapted. These methods are evaluated using a one-dimensional advection-diffusion model with periodic boundaries. Performance benchmarks for the one-dimensional scenario are conducted against a grid-based assimilation filter Subsequently, assimilation schemes are applied to a non-linear two-dimensional incompressible flow problem, solved via the Vortex-In-Cell method. The results demonstrate the feasibility of applying these methods in more complex scenarios, highlighting their effectiveness in both the one-dimensional and two-dimensional contexts.