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An Artificial Intelligence Approach for Modeling and Prediction of Water Diffusion Inside a Carbon Nanotube

Samad Ahadian* and Yoshiyuki Kawazoe

Author affiliations

Institute for Materials Research (IMR), Tohoku University, Sendai, 980-8577, Japan

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Citation and License

Nanoscale Research Letters 2009, 4:1054-1058  doi:10.1007/s11671-009-9361-3

Published: 4 June 2009


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.

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