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Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17

C. H. Dagli
C. H. Dagli
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ASME Press
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Composite high-pressure cylinders have potential application for hydrogen storage in automotive and transportation systems. Safe installation and operation of these cylinders is of primary concern. A neural network model has been developed for predicting the failure of composite storage cylinders subjected to thermo-mechanical loading. A backpropagation Neural Network model is developed to predict composite cylinder failure. The inputs of the neural network model are the laminate thickness, winding angle, and temperatures. The output of the model is the failure pressure. The finite element model of the cylinder is based on laminated shell theory accounting for transverse shear deformation and geometric nonlinearity. A composite failure model is used to evaluate the failure under various thermo-mechanical loadings. The neural network is trained using failure results of simulation under different thermal loadings and lay-up. The developed neural network model is found to be quite successful in determining the failure of hydrogen storage cylinders.

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