Uncertain Transport in Unsteady Flows

TL;DR

This paper introduces a novel method to identify barriers and enhancers to stochastic transport in unsteady flows using the diffusion barrier strength (DBS), enabling efficient analysis of uncertainty in real-world flow data.

Contribution

The paper develops the diffusion barrier strength (DBS) metric, which detects stochastic transport barriers without expensive simulations, and provides a visualization method for flow uncertainty.

Findings

DBS effectively identifies transport barriers and enhancers.

Method reduces computational complexity compared to Monte Carlo simulations.

Application demonstrated on real-world flow data.

Abstract

We study uncertainty in the dynamics of time-dependent flows by identifying barriers and enhancers to stochastic transport. This topological segmentation is closely related to the theory of Lagrangian coherent structures and is based on a recently introduced quantity, the diffusion barrier strength (DBS). The DBS is defined similar to the finite-time Lyapunov exponent (FTLE), but incorporates diffusion during flow integration. Height ridges of the DBS indicate stochastic transport barriers and enhancers, i.e. material surfaces that are minimally or maximally diffusive. To apply these concepts to real-world data, we represent uncertainty in a flow by a stochastic differential equation that consists of a deterministic and a stochastic component modeled by a Gaussian. With this formulation we identify barriers and enhancers to stochastic transport, without performing expensive Monte Carlo simulation and with a computational complexity comparable to FTLE. In addition, we propose a complementary visualization to convey the absolute scale of uncertainties in the Lagrangian frame of reference. This enables us to study uncertainty in real-world datasets, for example due to small deviations, data reduction, or estimated from multiple ensemble runs.