The memory of Rayleigh-Taylor turbulence

TL;DR

This paper develops a Bayesian inference framework using DNS data and physics-informed neural networks to estimate initial conditions of Rayleigh-Taylor turbulence from turbulent measurements, revealing flow memory effects.

Contribution

It introduces a novel method combining PINNs and Bayesian inference to infer initial conditions of Rayleigh-Taylor turbulence from turbulent quantities.

Findings

Identifies which turbulent quantities best predict flow evolution.

Quantifies the influence of initial parameters on turbulence growth.

Proposes a new strategy for modeling transition to turbulence.

Abstract

In this work, we consider the problem of inferring the initial conditions of a Rayleigh-Taylor mixing zone by measuring the 0D turbulent quantities at an unspecified time. To this aim, we have generated a comprehensive dataset through direct numerical simulations (DNS), focusing on miscible fluids with slight density contrasts. The initial interface deformations in these simulations are characterized by an annular spectrum which is parametrized by four non dimensional numbers. %In order to study the sensitivity of 0D turbulent quantities to the initial interface perturbation distributions, we build a surrogate model for the simulations using a physics-informed neural network (PINN). This allows us to compute the Sobol indices for the turbulent quantities, disentangling the effects of the initial parameters on the growth of the mixing layer. Within a Bayesian framework, we use a Markov chain Monte-Carlo method to determine the posterior distributions of initial conditions given various state variables. %This sheds light on the inertial or diffusive trajectories along with how the initial conditions are progressively forgotten during transition to turbulence. Moreover, it identifies which turbulent quantities are better predictors for the dynamics of Rayleigh-Taylor mixing zones by more effectively retaining the memory of the flow. By inferring the initial conditions and forward propagating its maximum a posteriori (MAP) estimate, we propose a strategy to model the Rayleigh-Taylor transition to turbulence.