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Prediction and analysis of flow behavior of a polymer melt through nanochannels using artificial neural network and statistical methods

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Abstract

A new methodology, namely, artificial neural network (ANN) approach was proposed for modeling and predicting flow behavior of the polyethylene melt through nanochannels of nanoporous alumina templates. Wetting length of the nanochannels was determined to be a function of time, temperature, diameter of nanochannels, and surface properties of the inner wall of the nanochannels. An ANN was designed to forecast the relationship between the length of wetting as output parameter and other aforementioned parameters as input variables. It was demonstrated that the ANN method is capable of modeling this phenomenon with high accuracy. The designed ANN was then employed to obtain the wetting length of the nanochannels for those cases, which were not reported by the wetting experiments. The results were then analyzed statistically to identify the effect of each independent variable, namely, time, temperature, diameter of nanochannels, and surface properties of the inner wall of nanochannels as well as their combinations on the wetting length of the nanochannels. Interesting results were attained and discussed.

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Acknowledgments

S. Ahadian greatly appreciates fruitful discussions with Dr. Jie Kong. The authors sincerely appreciate the staff of the Center for Computational Materials Science of the Institute for Materials Research (IMR), Tohoku University, for its continuous support of the supercomputing facilities. This study was supported (in part) by the Japan Society for the Promotion of Science (JSPS).

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Authors and Affiliations

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

    Samad Ahadian, Hiroshi Mizuseki & Yoshiyuki Kawazoe

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  1. Samad Ahadian
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  2. Hiroshi Mizuseki
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  3. Yoshiyuki Kawazoe
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Correspondence to Samad Ahadian.

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Ahadian, S., Mizuseki, H. & Kawazoe, Y. Prediction and analysis of flow behavior of a polymer melt through nanochannels using artificial neural network and statistical methods. Microfluid Nanofluid 9, 319–328 (2010). https://doi.org/10.1007/s10404-009-0549-8

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