Journal
JOURNAL OF MARINE SCIENCE AND ENGINEERING
Volume 8, Issue 2, Pages -Publisher
MDPI
DOI: 10.3390/jmse8020125
Keywords
aquaculture net cage; drag force; net cage volume; generalized additive model; Backpropagation neural network
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In Japan, the marine aquaculture net cage has an important role in farming pacific bluefin tuna farming in oceans, and the design of the net cage needs to ensure robustness against hostile oceanic conditions. Accordingly, this study focuses on the drag forces and the cage volume of the net cage, and on their variations induced by different design parameters (netting solidity ratio, netting height, and bottom weight). A series of parametric studies on drag force and deformation of the net cage was conducted using a numerical simulation model. Accordingly, the contribution of each parameter to the drag and volume was analyzed using a generalized additive model. The results indicate that the bottom weight had the highest contribution to the holding ratio of the cage volume, whereas the netting height had the highest contribution to the drag coefficient of the net cage. Finally, a fast prediction model was created by a backpropagation (BP) neural network model and was examined for the accurate prediction of the objective variables.
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