Inference of nitridation reaction efficiencies of graphite in nitrogen plasma flows using a Bayesian formulation

Anabel Del Val, Pietro M. Congedo, Thierry E. Magin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work, we seek to infer nitrogen ablation model parameters from different experimental measurements. First, the dependencies of the proposed stagnation line forward model are studied to establish the influence of the different parameters on the simulated counterparts of the available observations. Results show that recession rate and CN local density predictions in the boundary layer are mostly affected by the efficiency of the nitridation reactions at the gas-surface interface for the experimental conditions considered. In turn, it is expected that the available measured counterparts provide enough information to calibrate nitridation parameters. We then carry out different stochastic model calibrations and compare them on the basis of the experimental data considered. When used independently, measured recession rates and CN densities each give information about nitridation reactions. As they are both part of the same experiments, one model should be capable of explaining both observations at once. Checking for this consistency with the available data has the potential of improving the characterization of nitridation mechanisms and signaling issues with the model and/or the experiments. It is identified that the results obtained when using a particular measurement of local CN densities present inconsistencies when compared to the results obtained using the measured recession rate for the same experimental condition and under the same chosen physical model. The uncertainties of the experimental data are then included as free parameters in the inference framework with the results showing great departure of the calibrated standard deviation for the problematic condition from the rest of the dataset, suggesting that the measurement should be repeated in the future.

Original languageEnglish (US)
Title of host publicationAIAA AVIATION 2022 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624106354
DOIs
StatePublished - 2022
Externally publishedYes
EventAIAA AVIATION 2022 Forum - Chicago, United States
Duration: Jun 27 2022Jul 1 2022

Publication series

NameAIAA AVIATION 2022 Forum

Conference

ConferenceAIAA AVIATION 2022 Forum
Country/TerritoryUnited States
CityChicago
Period6/27/227/1/22

Bibliographical note

Publisher Copyright:
© 2022, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

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