Reconstructing Rayleigh-B\'enard flows out of temperature-only measurements using nudging

TL;DR

This paper demonstrates that nudging can effectively reconstruct turbulent Rayleigh-Bénard flows using only temperature measurements, revealing insights into flow dynamics and control from limited data.

Contribution

It introduces a novel application of nudging to reconstruct full flow fields from temperature-only data in turbulent convection systems.

Findings

Successful reconstruction of flow fields at various turbulence levels.

Effective prediction of heat transfer properties like Nusselt number.

Insights into velocity-temperature correlation and flow control possibilities.

Abstract

Nudging is a data assimilation technique that has proved to be capable of reconstructing several highly turbulent flows from a set of partial spatiotemporal measurements. In this study we apply the nudging protocol on the temperature field in a Rayleigh-B\'enard Convection system at varying levels of turbulence. We assess the global, as well as scale by scale, success in reconstructing the flow and the transition to full synchronization while varying both the quantity and quality of the information provided by the sparse measurements either on the Eulerian or Lagrangian domain. We asses the statistical reproduction of the dynamic behaviour of the system by studying the spectra of the nudged fields as well as the correct prediction of the heat transfer properties as measured by the Nusselt number. Further, we analyze the results in terms of the complexity of the solutions at various Rayleigh numbers and discuss the more general problem of predicting all state variables of a system given partial or full measurements of only one subset of the fields, in particular temperature. This study sheds new light on the correlation between velocity and temperature in thermally driven flows and on the possibility to control them by acting on the temperature only.