An artificial intelligence approach for modeling and prediction of water diffusion inside a carbon nanotube

Samad Ahadian, Yoshiyuki Kawazoe

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

Modeling of water flow in carbon nanotubes is still a challenge for the classic models of fluid dynamics. In this investigation, an adaptive-network-based fuzzy inference system (ANFIS) is presented to solve this problem. The proposed ANFIS approach can construct an input-output mapping based on both human knowledge in the form of fuzzy if-then rules and stipulated input-output data pairs. Good performance of the designed ANFIS ensures its capability as a promising tool for modeling and prediction of fluid flow at nanoscale where the continuum models of fluid dynamics tend to break down.

Original languageEnglish
Pages (from-to)1054-1058
Number of pages5
JournalNanoscale Research Letters
Volume4
Issue number9
DOIs
Publication statusPublished - 2009

Keywords

  • Artificial intelligence
  • Carbon nanotube
  • Modeling and prediction
  • Water diffusion

ASJC Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics

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