Dynamic Simulation of Pelagic Longline Retrieval
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To improve fishing gear efficiency, it is important to understand the interactions among sea current, fishing vessel, line hauler, and catches during pelagic longline gear retrieval. In this study, fishing gear configuration parameters, operational parameters, and 3D ocean current data were collected from Indian Ocean. Dynamic models of pelagic longline gear retrieval were built using the lumped mass method and solved using the Euler-Trapezoidal method. From the results, the pulling force of line hauler exerted on the gear was 2800–3600 N. There were no significant differences (P > 0.05) between the time of the hook retrieval measured at sea and that obtained from the simulation. The absolute values of the movement velocity at representative nodes along the X, Y, and Z axes were 0.01–25.5 m s−1. These results suggest that the dynamic model of longline fishing gear retrieval can be used to analyze the interactions among sea current, fishing vessel, line hauler, longline gear, and catches, and to acquire the basic data for optimizing the design of the line hauler. Moreover, the model can serve as a reference to study the hydrodynamic performance of other fishing gears during the hauling process.
Key wordspelagic longline retrieval dynamic simulation visualization
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The project is funded by the National High Technology Research and Development Program of China (No. 2012 AA092302), the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20113104110004), and Shanghai Municipal Education Commission Innovation Project (No. 12ZZ168). We thank Mr. Daochang Zheng, Fei Lin and the crews of longliner ‘Xinshiji No. 85’ of Zhejiang Ocean Family Co., Ltd. for their support. Gratitude also goes to Professor Yong Chen at University of Maine for reviewing the manuscript. Moreover, we thank the anonymous referees for their valuable comments and suggestions.
- Boggs, C. H., 1992. Depth, capture time, and hooked longevity of longline caught pelagic fish: Timing bites of fish with chips. Fishery Bulletin, 90: 642–658.Google Scholar
- Cao, D. M., 2011. The dynamic simulation of tuna longline. Master thesis. Shanghai Ocean University, Shanghai (in Chinese with English abstract).Google Scholar
- Jiang, L. B., Xu, L. X., and Huang, J. L., 2005. Relationship between vertical distribution of bigeye tuna (Thunnus obesus) and water temperature in Indian Ocean. Journal of Shanghai Fisheries University, 14 (3): 333–336 (in Chinese with English abstract).Google Scholar
- Johansen, V., 2007. Modelling of flexible slender systems for real–time simulation and control applications. PhD thesis. Norwegian University of Science and Technology, Trondheim.Google Scholar
- Mizuno, K., Okazaki, M., and Miyabe, N., 1998. Fluctuation of longline shortening rate and its effect on underwater longline shape. Bulletin of the National Research Institute of Far Seas Fisheries, 35: 155–164.Google Scholar
- Mizuno, K., Okazaki, M., Nakano, H., and Okamura, H., 1999. Estimation of underwater shape of tuna longline with microbathythermographs. Inter–American Tropical Tuna Commission Special Report, 10.Google Scholar
- Nakano, H., Okazaki M., and Okamoto, H., 1997. Analysis of catch depth by species for tuna longline fishery based on catch by branch lines. Bulletin of the National Research Institute of Far Seas Fisheries, 34: 43–62.Google Scholar
- Shen, Z. B., 2016. The numerical simulation of tuna longline operation. Master thesis. Shanghai Ocean University, Shanghai (in Chinese with English abstract).Google Scholar
- Song, L. M., 2008. Habitat environment integration index of bigeye tuna (Thunnus obesus) in the Indian Ocean–Based on longline survey data. PhD thesis. Shanghai Ocean University, Shanghai (in Chinese with English abstract).Google Scholar
- Song, L. M., 2015. Environmental Biology of Fishes and Gear Performance in the Pelagic Tuna Longline Fishery. Science Press, Beijing, 40–42.Google Scholar
- Song, L. M., and Gao, P. F., 2006. Captured depth, watertemperature and salinity of bigeye tuna (Thunnus obesus) longlining in Maldives waters. Journal of Fishery Sciences of China, 30 (3): 335–340 (in Chinese with English abstract).Google Scholar
- Song, L. M., Chen, X. J., and Xu, L. X., 2004. Relationship between bigeye tuna vertical distribution and the temperature, salinity in the central Atlantic Ocean. Journal of Fishery Sciences of China, 11 (6): 561–566 (in Chinese with English abstract).Google Scholar
- Song, L. M., Zhang, Z., Yuan, J. T., and Li, Y. W., 2011a. Numeric modeling of the pelagic longline based on the finite element analysis. Oceanologia et Limnologia Sinica, 42 (2): 256–261 (in Chinese with English abstract).Google Scholar
- Suzuki, Z., Warashina, Y., and Kishida, M., 1977. The comparison of catches by regular and deep tuna longline gears in the western and central equatorial Pacific. Bulletin of the Far Seas Fisheries Research Laboratory, 15: 51–89.Google Scholar
- Wan, R., Cui, J. H, Song, X. F., Tang, Y. L., Zhao, F. F., and Huang, L. Y., 2005. A numerical model for predicting the fishing operation status of tuna longline. Journal of Fisheries of China, 29 (2): 238–245 (in Chinese with English abstract).Google Scholar
- Wu, Y. W., and Wu, Y. S., 2005. The application of catenary and parabola theories in tuna longline fishery. Marine Fisheries, 27 (1): 1–9.Google Scholar
- Zhang, X. F., Cao, D. M., Song, L. M., Zou, X. R., Chen, X. J., Xu, L. X., Zhang, M., Zhang, J., and Zhou, Y. Q., 2012. Application of whole–implicit algorithm and virtual neural lattice in pelagic longline modeling. International Conference on Fuzzy Systems & Knowledge Discovery (FSKD 2012), May 29–31,2012, Chongqing, China, 9, 2616–2619.Google Scholar
- Zhou, Y. Q., Xu, L. X., and He, Q. Y., 2001. The Dynamics of Fishing Gear. China Agricultural Press, Beijing, 161pp (in Chinese).Google Scholar