Dynamic simulation of tuna longline gear using numerical methods

Lee, Jihoon; Lee, Chun‐Woo; Jin, Bong

doi:10.1111/j.1444-2906.2005.01095.x

Cited by 21 publications

(20 citation statements)

References 12 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, by coupling longline depth monitoring and vertical current profiles, Mizuno et al (1998) clearly showed variations of the sag ratio parameter during a single longline set. Recently, longline shape simulations using numerical methods have also shown sag ratio variations and the shoaling of the mainline subjected to current effects (Lee et al, 2005;Miyamoto et al, 2006). Sag ratio is one of the key parameters defining mainline shape and depth and can be monitored by deploying GPS buoys Okamura et al, 1998;Miyamoto et al, 2006).…”

Section: Bsmentioning

confidence: 98%

Effects of the gear deployment strategy and current shear on pelagic longline shoaling

et al. 2009

View full text Add to dashboard Cite

Section: Bsmentioning

confidence: 98%

Effects of the gear deployment strategy and current shear on pelagic longline shoaling

et al. 2009

View full text Add to dashboard Cite

“…According to their data, the depth of hook at the center when the number of hooks per basket is 10 was found to be 178. (Lee et al, 2005b). However, the CCM has a disadvantage in that it provides an equilibrium shape; thus, reflecting the physical properties of the longline gear is difficult, and the influence of the current cannot be considered.…”

Section: Comparison Of Hook Depth According To the Computation Methodsmentioning

confidence: 99%

“…1). The detailed description of the mathematical model is described in a previous article (Lee et al, 2005b); therefore, we briefly describe the mathematical model below. The equation of motion, according to Lee et al (2005a), can be described as follows:…”

Section: Methodsmentioning

confidence: 99%

“…Mass-spring model (MSM, Lee et al, 2005b) For the modeling of longline gear, we described the mathematical model by dividing the line element at regular intervals, placing the mass point at the center of the divided elements, and connecting these mass points to the elastic lines (Fig. 1).…”

Section: Methodsmentioning

confidence: 99%

“…Most of the previous interpretations are considered to be catenary curves (Irvine, 1981;Kim, 1999). Recently, a comparison between flume tank experiment and static methods and a dynamic method involving the use of the mass-spring model was conducted (Lee et al, 2005b;Song et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of tuna longline hook depth for improved performance in Fiji

Bainves¹,

Lee²,

Park³

2017

J. Korean Soc. Fish. Technol.

View full text Add to dashboard Cite

In pelagic longline, deploying the gear such that the depth of the hook is the same as that of the target fish is important to improve the fishing performance and selectivity. In this study, the depth of the tuna longline hook was estimated using the mass-spring model, catenary curve method, and secretariat of the pacific commission Pythagorean method in order to improve the performance of the longline gear in Fiji. The former two methods were estimated to be relatively accurate, and the latter showed a large error. Further, the mass-spring model accounted for the influence of tidal current in the ocean, which was found to be appropriate for use in field trials.

show abstract

Which factors strongly influence the sinking speed of a demersal longline?

Lee

Karlsen

et al. 2013

Aquatic Conservation

View full text Add to dashboard Cite

Sinking speed is one of the main factors affecting the performance of longlines. Demersal and pelagic longlines need to sink quickly, to prevent bycatch of marine animals and bait loss to seabirds. The incidental catch or bycatch of seabirds by longlining is a problem from the environmental conservation point of view. Thus, it is necessary to develop techniques to reduce the incidental catch or bycatch by longlining. This study focuses on the sinking speed of longlines. To determine the significant factors that may influence the sinking speed, the study considers the various parameters for longlining such as bait properties, rope materials, rope thickness, anchor weights, shooting speeds, shooting methods, and the ratio between the depth and mainline length. The results of the numerical analysis were verified by comparing them with observations from field experiments. The results suggest that the sinking speed can be increased and the bycatch during the sinking process can be reduced when baits have elliptical shapes with a small projected area relative to the sinking orientation, as well as when high‐density and thicker materials are used. Heavier anchors also help to increase the sinking speed of the anchor part, excluding other parts such as the middle part of a mainline. If the water is shallow compared with the line's length, the anchor's weight has a smaller influence on the speed of sinking in the middle of the line. In this case, the factors that influence the sinking speed of a longline 10, 30, and 50 m from the stern are rope material, rope thickness, and shooting speed. These results may be used to design techniques for bycatch prevention. Based on these results, accounting for these important factors when designing a longline is expected to increase the fishing efficiency. Copyright © 2013 John Wiley & Sons, Ltd.

show abstract

Dynamic simulation of tuna longline gear using numerical methods

Cited by 21 publications

References 12 publications

Effects of the gear deployment strategy and current shear on pelagic longline shoaling

Effects of the gear deployment strategy and current shear on pelagic longline shoaling

Estimation of tuna longline hook depth for improved performance in Fiji

Which factors strongly influence the sinking speed of a demersal longline?

Contact Info

Product

Resources

About