A Data-Driven Approach For Real-Time Estimation of Material Uncertainty

Rui Fu, Sujit Sinha, Christopher T. Barrow, John F. Maddox, Jesse B. Hoagg, Alexandre Martin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Modern Thermal Protection Systems (TPS) used for planetary exploration missions often utilize lightweight porous materials as their outer isolating layer. Due to its stochastic nature, such material exhibits a high level of material variability in various properties. Therefore, it is of paramount importance to accurately estimate the uncertainty margin and to understand how material response is affected. In this study, a well-established data-driven algorithm is used to estimate the FiberForm conductivity by using real-time experimental data. By combining the real-time experimental data and a high-fidelity simulation model, the inherent material property is obtained via the proposed method-the retrospective adaptation algorithm. The results also indicate that this methodology is of great potential and can be applied to a wide range of engineering problems, including parameter evaluation, and experimental data processing.

Original languageEnglish
Title of host publicationAIAA AVIATION 2022 Forum
DOIs
StatePublished - 2022
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.

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Aerospace Engineering

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