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Research Papers

Multiphysics-Based Statistical Model for Investigating the Mechanics of Carbon Nanotubes Membranes for Proton-Exchange Membrane Fuel Cell Applications

[+] Author and Article Information
V. Vijayaraghavan

School of Mechanical and
Manufacturing Engineering,
The University of New South Wales,
Kensington,
Sydney, NSW 2033, Australia

A. Garg

Intelligent Manufacturing Key Laboratory of
Ministry of Education,
Shantou University,
Shantou, China
e-mail: akhil@stu.edu.cn

Liang Gao

State Key Laboratory for Digital Manufacturing
Equipment and Technology,
Huazhong University of
Science and Technology,
1037 Luoyu Road,
Wuhan 430074, China

1Corresponding author.

Manuscript received September 21, 2018; final manuscript received January 6, 2019; published online February 19, 2019. Assoc. Editor: Soumik Banerjee.

J. Electrochem. En. Conv. Stor. 16(3), 031005 (Feb 19, 2019) (11 pages) Paper No: JEECS-18-1100; doi: 10.1115/1.4042554 History: Received September 21, 2018; Revised January 06, 2019

The filter membrane made up of carbon nanostructure is one of the important components in proton exchange membrane fuel cell (PEMFC). The membrane while under operating conditions of a PEMFC is subjected to various dynamical loads due to the imposition of several input operating factors of the PEMFC. Hence, it is important to estimate optimal process parameters, which can maximize the strength of the membrane. Current studies in PEMFC focus on adsorption and transport-related properties of PEMFC membrane, without adequately investigating the mechanical strength of the membrane. This study proposes a multiphysics model of the membrane, which is used to extract the mechanical properties of the membrane by systematically varying various input factors of PEMFC. The extracted data are then fed into a neural search machine learning cluster to obtain optimal design parameters for maximizing the strength of the membrane. It is expected that the findings from this study will provide critical design data for manufacturing PEMFC membranes with high strength and durability.

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Figures

Grahic Jump Location
Fig. 1

Working principle of PEMFC

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Fig. 2

Multiphysics-based neural search modeling of PEMFC membrane. Multiphysics model is used to obtain the data for mechanical strength by varying each of the identified input parameters. The data from multiphysics model are fed into machine learning cluster to optimize the parameters, which result in maximum mechanical strength of the CNT.

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Fig. 3

The carbon atoms at the either end of the SWCNT as enclosed inside the rectangle are subjected to an outward displacement to simulate tensile loading in the SWCNT

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Fig. 4

Force–strain plot of a (10,10) CNT of length 100 Å with hydrogen concentration of 8.34 wt % at 300 K. The force increases almost linearly with the applied strain for strain values until 0.2 that marks the region of elastic loading. Further application of tensile strain results in plastic deformation of the CNT marked by the nonlinear variation of force with applied strains for strain values exceeding 0.2.

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Fig. 5

Snap shots of CNT with hydrogen storage concentration of 4.3 wt % undergoing tensile loading at 300 K. (a) The CNT is first equilibrated to remove residual stresses before application of tensile loading, (b) extension of CNT at either ends increases the internal energy of carbon atoms, and (c) further extension results in fracture of CNT leading to release of encapsulated hydrogen atoms.

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Fig. 6

Statistical analysis of the ANS model. The data plots predicted by ANS and the multiscale MD model can be represented by a linear equation with an accuracy of 0.978.

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Fig. 7

Performance analysis of ANS model based on actual physics of PEMFC membrane. It can be seen that the maximum tensile force of CNT increases with diameter of the CNT while it decreases with increasing defects, temperature, and hydrogen concentration.

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Fig. 8

Three-dimensional interaction and contour plots of the effects of two factors on the mechanical strength of PEMFC membrane: (a) force versus (diameter and concentration), (b) force versus (temperature and concentration), (c) force versus (diameter and temperature), (d) force versus (diameter and defect), and (e) force versus (temperature and defect)

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Fig. 9

Percentage contribution of input factor to mechanical strength of CNT membrane. The diameter of the CNT has highest influence on the mechanical strength of CNT followed by the hydrogen concentration, temperature, and defects.

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