[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.1163/156855109X428790

Failure Pressure Prediction of Composite Cylinders for Hydrogen Storage Using Thermo-mechanical Analysis and Neural Network

Hu, J. (Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology)
Sundararaman, S. (Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology)
Menta, V.G.K. (Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology)
Chandrashekhara, K. (Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology)
Chernicoff, William (US Department of Transportation)

Publication Information

Advanced Composite Materials / v.18, no.3, 2009 , pp. 233-249 More about this Journal

Abstract

Safe installation and operation of high-pressure composite cylinders for hydrogen storage are of primary concern. It is unavoidable for the cylinders to experience temperature variation and significant thermal input during service. The maximum failure pressure that the cylinder can sustain is affected due to the dependence of composite material properties on temperature and complexity of cylinder design. Most of the analysis reported for high-pressure composite cylinders is based on simplifying assumptions and does not account for complexities like thermo-mechanical behavior and temperature dependent material properties. In the present work, a comprehensive finite element simulation tool for the design of hydrogen storage cylinder system is developed. The structural response of the cylinder is analyzed using laminated shell theory accounting for transverse shear deformation and geometric nonlinearity. A composite failure model is used to evaluate the failure pressure under various thermo-mechanical loadings. A back-propagation neural network (NNk) model is developed to predict the maximum failure pressure using the analysis results. The failure pressures predicted from NNk model are compared with those from test cases. The developed NNk model is capable of predicting the failure pressure for any given loading condition.

Keywords

Composite cylinder; hydrogen storage; finite element analysis; neural network;

Citations & Related Records

Times Cited By Web Of Science : 1 (Related Records In Web of Science)

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