Calibration and uncertainty quantification of ViSTA ablator material database using Bayesian inference

Przemyslaw Rostkowski, Simone Venturi, Marco Panesi, Ali Omidy, Haoyue Weng, Alexandre Martin

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


The current design of NASA's Multi-Purpose Crew vehicle uses the latest iteration of AVCOAT, an ablating thermal protection material. However, restrictions placed on the export of experimental data concerning its performance make collaborative efforts aimed at improving existing heat-shield design tools difficult to establish. The material model dubbed VISTA provides an alternative open-source platform upon which the physics of ablation can be thoroughly investigated. In this paper, calibration of a material model through Bayesian inference is demonstrated with VISTA and open-source Apollo-era AVCOAT flight data. A sensitivity analysis is first carried out using Pearson correlation coefficients and method of Sobol where the results of both approaches are compared. The calibration methodology used in this paper is then demonstrated first with the use of manufactured data. Following, the parameters of VISTA are calibrated using material temperature data recorded during the Apollo 4 test flight and uncertainties due to parametric, modeling, and data inaccuracy sources are simultaneously quantified. Uncertainty quantification of model output in this work is done by forward propagating quantified uncertainties onto model output where a large reduction in total uncertainty is observed.

Original languageEnglish
Pages (from-to)356-369
Number of pages14
JournalJournal of Thermophysics and Heat Transfer
Issue number2
StatePublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 American Institute of Aeronautics and Astronautics Inc. All rights reserved.

ASJC Scopus subject areas

  • Condensed Matter Physics


Dive into the research topics of 'Calibration and uncertainty quantification of ViSTA ablator material database using Bayesian inference'. Together they form a unique fingerprint.

Cite this