Inferring flow parameters and turbulent configuration with physics-informed data-assimilation and spectral nudging

TL;DR

This paper investigates how spectral nudging can be used in physics-informed data assimilation to infer flow parameters and reconstruct large-scale turbulent properties from sparse measurements, with applications in meteorology and astrophysics.

Contribution

It demonstrates the novel use of spectral nudging for inferring unknown flow parameters and reconstructing turbulence features without detailed forcing information.

Findings

Successfully inferred rotation rate and shear mechanism in turbulent flows.

Reconstructed energy-containing scales in rotating turbulence from sparse data.

Highlighted potential of nudging for broader physics-informed data assimilation applications.

Abstract

Inferring physical parameters of turbulent flows by assimilation of data measurements is an open challenge with key applications in meteorology, climate modeling and astrophysics. Up to now, spectral nudging was applied for empirical data-assimilation as a mean to improve deterministic and statistical predictability in the presence of a restricted set of field measurements only. Here, we explore under which conditions a nudging protocol can be used for two novel objectives: to unravel the value of the physical flow parameters and to reconstruct large-scale turbulent properties starting from a sparse set of information in space and in time. First, we apply nudging to quantitatively infer the unknown rotation rate and the shear mechanism for turbulent flows. Second, we show that a suitable spectral nudging is able to reconstruct the energy containing scales in rotating turbulence by using a blind set-up, i.e. without any input about the external forcing mechanisms acting on the flow. Finally, we discuss the broad potentialities of nudging to other key applications for physics-informed data-assimilation in environmental or applied flow configurations.