Predicting Flow Reversals in a Computational Fluid Dynamics Simulated Thermosyphon using Data Assimilation

TL;DR

This paper develops and verifies data assimilation methods to predict flow reversals in a chaotic thermal convection loop, using CFD simulations and reduced order models as testbeds for algorithm development.

Contribution

It introduces four distinct data assimilation methods applied to a CFD model and a reduced order model for predicting flow reversals in a thermosyphon system.

Findings

Successful prediction of flow reversals using DA methods

Verification of four distinct DA techniques

Enhanced understanding of flow dynamics in thermal convection

Abstract

A thermal convection loop is a circular chamber filled with water, heated on the bottom half and cooled on the top half. With sufficiently large forcing of heat, the direction of fluid flow in the loop oscillates chaotically, forming an analog to the Earth's weather. As is the case for state-of-the-art weather models, we only observe the statistics over a small region of state space, making prediction difficult. To overcome this challenge, data assimilation methods, and specifically ensemble methods, use the computational model itself to estimate the uncertainty of the model to optimally combine these observations into an initial condition for predicting the future state. First, we build and verify four distinct DA methods. Then, a computational fluid dynamics simulation of the loop and a reduced order model are both used by these DA methods to predict flow reversals. The results contribute to a testbed for algorithm development.