Predicting flow reversals in chaotic natural convection using data assimilation

TL;DR

This paper investigates predicting flow reversals in chaotic natural convection by comparing a Lorenz-like model with CFD simulations, using data assimilation to improve forecasts despite model errors and limited observations.

Contribution

It introduces a data assimilation approach to forecast flow reversals in a simplified chaotic convection model, accounting for model errors and limited data, and provides new insights into the fluid dynamics during reversals.

Findings

Flow reversals can be forecasted using data assimilation techniques.

Short assimilation windows help infer dynamics not captured by the model.

Chaotic flow reversal timing and states can be predicted with analysis data.

Abstract

A simplified model of natural convection, similar to the Lorenz (1963) system, is compared to computational fluid dynamics simulations in order to test data assimilation methods and better understand the dynamics of convection. The thermosyphon is represented by a long time flow simulation, which serves as a reference "truth". Forecasts are then made using the Lorenz-like model and synchronized to noisy and limited observations of the truth using data assimilation. The resulting analysis is observed to infer dynamics absent from the model when using short assimilation windows. Furthermore, chaotic flow reversal occurrence and residency times in each rotational state are forecast using analysis data. Flow reversals have been successfully forecast in the related Lorenz system, as part of a perfect model experiment, but never in the presence of significant model error or unobserved variables. Finally, we provide new details concerning the fluid dynamical processes present in the thermosyphon during these flow reversals.