A supervised learning model to predict length-scale dependent permeability of porous carbon composites

Luis Chacon, Vijay B. Mohan Ramu, Savio J. Poovathingal

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

1 Scopus citations


The computation of permeability for porous carbon composites used as heat shield materials is an intensive and a tedious process. Surrogate modeling of physical simulations offer a significantly cheaper alternative to computing permeability. The main objective of this work is to develop a supervised learning model which approximates the physical simulations involving a single gaseous species through a porous material and capture the length-scale dependency of the material’s permeability. This length-scale dependency can be integrated in material response solvers to better simulate the physics of gas transport inside the material instead of assuming a constant value for the property. This work focuses on the development of an analytical function which relates the permeability of the porous material with the thermodynamic conditions and length-scale of the microstructure. The analytical function is realized using support vector regression (SVR) which is found to be a robust technique in order to capture the complex relationship between temperature, average pressure, and length-scale of the microstructure. The predicted values are found to have a maximum relative error of about 20 percent with the majority of the relative errors being less than 7 percent. The analytical function is validated against a range of inputs beyond the scope of trained values to justify the use of the developed supervised learning model. The capability of the developed model to capture the length-scale dependency on permeability is emphasized by noting the difference in predicted permeability and it’s accuracy for data points at either edges of the training domain.

Original languageEnglish
Title of host publicationAIAA AVIATION 2022 Forum
StatePublished - 2022
EventAIAA AVIATION 2022 Forum - Chicago, United States
Duration: Jun 27 2022Jul 1 2022

Publication series

NameAIAA AVIATION 2022 Forum


ConferenceAIAA AVIATION 2022 Forum
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This work was supported by a Space Technology Research Institutes grant from NASA’s Space Technology Research Grants Program under grant number 80NSSC21K1117. We would also like to thank the University of Kentucky Center for Computational Sciences and Information Technology Services Research Computing for their support and use of the Lipscomb Compute Cluster and associated research computing resources.

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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