Data assimilation for aerothermal mean flow reconstruction using aero-optical observations: a synthetic investigation

Overview & Synopsis

In this study, we investigate a Data Assimilation approach for the numerical reconstruction of turbulent aerothermal flows, and more particularly of their average density fields. The proposed approach relies on an ensemble Bayesian inference method providing a derivativefree optimization framework suitable for solving inverse problems and quantifying model uncertainties. The originality of this work lies in the use of non-intrusive and dense measurements such as those obtained by the optical technique referred to as Background Oriented Schlieren (BOS). Such a reconstruction problem is usually tackled with a purely aero-optical modeling as in tomographic BOS. The present approach introduces additional physical constraints by leveraging Reynolds Averaged Navier-Stokes flow models. To validate the relevance of this approach, the developed framework is applied as a simple calibration tool on numerical experiments involving a hot jet impinging on a flat plate, providing approximate inference for uncertain closure coefficients parametrizing a k − ω (SST) turbulence model.