Machine learning and data-driven characterization framework for porous materials: Permeability prediction and channeling defect detection-Reference-Cited by-同舟云学术

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Machine learning and data-driven characterization framework for porous materials: Permeability prediction and channeling defect detection

Published:2021-09 Issue: Volume:420 Page:130069
ISSN:1385-8947
Container-title:Chemical Engineering Journal
language:en
Short-container-title:Chemical Engineering Journal

Author:

Yasuda Tomoki,Ookawara Shinichi,Yoshikawa Shiro,Matsumoto Hideyuki

Funder

Ministry of Education, Culture, Sports, Science and Technology

Tokyo Institute of Technology

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,Environmental Chemistry,General Chemistry

Reference52 articles.

1. Lattice Boltzmann modeling of transport phenomena in fuel cells and flow batteries;Xu;Acta Mech. Sin.,2017

2. Hierarchical porous ceramics with 3D reticular architecture and efficient flow-through filtration towards high-temperature particulate matter capture;Liu;Chemical Engineering Journal,2019

3. Preparation and characterization of monodisperse, micrometer-sized, hierarchically porous carbon spheres as catalyst support;Cheng;Chemical Engineering Journal,2014

4. K–V–Ca catalysts supported on porous alumina ceramic substrate for soot combustion: Preparation and characterization;Sui;Chemical Engineering Journal,2009

5. Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques;Bostanabad;Progress in Materials Science,2018

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