Bayesian-Based Method with Metamodels for Rebuilding Freestream Conditions in Atmospheric Entry Flows

Abstract

The paper investigates a new methodology to rebuild freestream conditions for the trajectory of a reentry vehicle from measurements of stagnation-point pressure and heat flux. Uncertainties due to measurements and model parameters are taken into account and a Bayesian setting supplied with metamodels is used to solve the associated stochastic inverse problem.

About the uncertainty quantification of turbulence and cavitation models in cavitating flows simulations

“…Note that measurements of the pressure are not used in the inverse problem, because of the behaviour of the cavitation model for simulating pressure signals (see Section 4.1). For details concerning the MCMC algorithm, refer to [38].…”

Forward and backward uncertainty quantification with active subspaces: Application to hypersonic flows around a cylinder

“…The motivation of this paper is to assess the interest in using heat flux measurements, which are available in recent space missions, for rebuilding freestream conditions, requiring the set-up of multiple numerical tools for tackling several issues which are analyzed and cured here. In a recent work, Tryoen et al [9] proposed a novel approach which was exploiting wall pressure and heat flux data, and applied it to the entry trajectory of the European Experimental Reentry Test-Bed (EXPERT) vehicle. Nevertheless, the authors encountered some difficulties in the training of an adequate surrogate model for the stagnation heat flux, possibly due to a poor mesh convergence for some input conditions explored during the UQ study.…”

“…The main objective of this paper is to assess the resolvability of the problem of freestream calibration starting from stagnation pressure and heat flux data, in the same Bayesian framework as the one introduced by [9]. The posterior distribution, sampled by Markov Chain Monte Carlo accelerated by a surrogate model, trained the lower-dimensional input space constituted by the active variables.…”

“…In the context of calibration of parameters that can not be directly observed, as for instance the freestream data, Bayesian inference [13,14] is a powerful mathematical tool. This setting has received much interest in literature for application in several fields, and has been recently introduced and tested also in application related to flight condition inference in hypersonic scramjet combustor flow [15] and, as already discussed, for atmospheric entry flows [9]. Bayesian calibration offers a rigorous mathematical approach for inferring parameters values from noisy observations and accounting for uncertainties in the forward model describing the system.…”

Surrogate Model With Conservative Error Measure for the Stagnation Heat Flux in Atmospheric Entry Flows

“…In a recent work, Tryoen et al in [15] proposed a strategy for rebuilding freestream conditions starting from stagnation pressure and heat flux measures, which they applied to the entry trajectory of the European Experimental Reentry Test-Bed (EXPERT) vehicle. They used generalized polynomial chaos (PC) combined with a non-intrusive spectral projection to propagate the uncertainties through the forward CFD problem.…”