Robust reconstruction of the catalytic properties of thermal protection materials from sparse high-enthalpy facility experimental data

Sanson, Francois; Panerai, Francesco; Magin, Thierry; Congedo, Pietro Marco

doi:10.1016/j.expthermflusci.2018.03.028

Cited by 7 publications

(6 citation statements)

References 44 publications

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“…As it was explained in previous works [10] and in this work (Sec. 3), one fundamental uncertainty in the way of rebuilding the TPS catalytic behavior is the fact that the boundary layer edge conditions cannot be estimated accurately if the auxiliary material behavior is not a priori well-known, which is the present case.…”

Section: Influence Of the Auxiliary Testing Materialssupporting

confidence: 62%

“…The statistics associated with these distributions are gathered in Table 2. The differences between these results and the outcomes of [10] are clear when looking at the values in the table and the shapes of the distribution functions obtained. A reduction of almost 20% of the standard deviation and 40% of the Coefficient of Variation (CV) for the catalytic parameter of the reference material is observed.…”

Section: Discussion On the Posterior Distributionmentioning

confidence: 77%

“…A reduction of almost 20% of the standard deviation and 40% of the Coefficient of Variation (CV) for the catalytic parameter of the reference material is observed. There is no reporting of the posterior statistics for γ TPS in [10]. The capability of learning γ TPS from experimental data is lost without any particular treatment of the nuisance parameters in the formulation of the inference problem.…”

Section: Discussion On the Posterior Distributionmentioning

confidence: 99%

“…This last issue is detrimental to learning the parameters of interest. In [10] non-informative priors were used for all the nuisance parameters. This approach only approximates the posterior of Q including the nuisance parameters, and the influence of the (unknown) prior densities of these parameters is unclear.…”

Section: Bayesian Formulation Of the Inference Problemmentioning

confidence: 99%

“…The inversion problem for the catalytic properties was first proposed in [10]. In it, Sanson et al point out that the parameter for the reference material is not always perfectly known, and the testing conditions are subjected to uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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A surrogate-based optimal likelihood function for the Bayesian calibration of catalytic recombination in atmospheric entry protection materials

del Val,

Maître,

Chazot

et al. 2020

Preprint

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This work deals with the inference of catalytic recombination parameters from plasma wind tunnel experiments for reusable thermal protection materials. One of the critical factors affecting the performance of such materials is the contribution to the heat flux of the exothermic recombination reactions at the vehicle surface. The main objective of this work is to develop a dedicated Bayesian framework that allows us to compare uncertain measurements with model predictions which depend on the catalytic parameter values. Our framework accounts for uncertainties involved in the model definition and incorporates all measured variables with their respective uncertainties. The physical model used for the estimation consists of a 1D boundary layer solver along the stagnation line. The chemical production term included in the surface mass balance depends on the catalytic recombination efficiency. As not all the different quantities needed to simulate a reacting boundary layer can be measured or known (such as the flow enthalpy at the inlet boundary), we propose an optimization procedure built on the construction of the likelihood function to determine their most likely values based on the available experimental data. This procedure avoids the need to introduce any a priori estimates on the nuisance quantities, namely, the boundary layer edge enthalpy, wall temperatures, static and dynamic pressures, which would entail the use of very wide priors. Furthermore, we substitute the optimal likelihood of the experimental measurements with a surrogate model to make the inference procedure both faster and more robust. We show that the resulting Bayesian formulation yields meaningful and accurate posterior probability distributions of the catalytic parameters with a reduction of more than 20% of the standard deviation with respect to previous works. We also study the implications of an extension of the experimental procedure on the improvement of the quality of the inference.

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Section: Influence Of the Auxiliary Testing Materialssupporting

confidence: 62%

Section: Discussion On the Posterior Distributionmentioning

confidence: 77%

Section: Discussion On the Posterior Distributionmentioning

confidence: 99%

Section: Bayesian Formulation Of the Inference Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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