基于最小势能原理的延绳钓渔具作业状态数值模拟

Numeric modeling of a pelagic longline based on minimum potential energy principle

基于有限元理论及最小势能原理,以2008年9月到2009年1月海上实测的188枚钓钩的深度、24个站点不同深度的三维海流数据、渔具参数和作业参数为基础,建立并验证了延绳钓三维最小势能模型。结果表明:(1)建立的延绳钓最小势能模型可以计算得出任何三维分层海流作用下延绳钓的三维形状和钓钩的深度,大部分钓钩的实测深度与数学模型数值深度之间的差别不大,其平均差值为12.03 m,差值范围为0.02～40.36 m(方差S2=100.30,标准差S=10.01,n=188),通过成对双样本均值分析,实测深度与数值深度无显著性差异(P>0.05);(2)延绳钓渔具的干线在海水中稳定后并不是呈平滑的悬链线,而是波浪形的曲线;(3)圆柱体轴线与流向垂直时的阻力系数(CN90)取值对于数值模拟的结果有一定的影响,CN90值的选取与研究对象的雷诺数有关。延绳钓最小势能数值模型能够有效模拟并预测延绳钓在不同深度的三维海流作用下的形状、钓钩深度并达到可视化。

Fishing parameters（such as the shooting speed of mainline,vessel speed,time interval between two hooks,numbers of hooks between two floats） can be adjusted to deploy the hooks to water layers that are preferred by target species,such as tuna.As a result,the catch rate of the target species can be increased and the catch of bycatch species（e.g.,loggerhead turtles,Caretta caretta;blue sharks,Prionace glauca;silky sharks,Car-charhinus falciformis） can be reduced.Together,these actions improve fishing efficiency and help maintain bio-logical diversity.To better understand the relationship between these factors and the fishing depth of longline gear,we developed a numeric model of the behavior of a pelagic longline.We conducted surveys on board Chinese large scale tuna longliners in the Indian Ocean between September 2008 and January 2009.During the surveys,the vessels targeted bigeye tuna（Thunnus obesus）but also caught yellowfin tuna（Thunnus albacares）,swordfish（Xiphias gladius）,albacore（Thunnus alalunga） and billfishes（Istiophoridae）.The hook depths（188 hooks） were measured using temperature depth recorders（TDRs） and the three dimensional current was measured at a range of depths at 24 sites using an acoustic doppler current profiler（ADCP）.We developed a three-dimensional numerical longline model（3DNLM） using finite element analysis and the minimum potential energy principle method.We used Matrix Laboratory（MATLAB） software to program and conduct the numerical calculations.The three di-mensional current data were assigned to seven,50 m depth intervals（e.g.,0–50,50–100,or 300–350 m）.The co-ordinates of all the nodes of the longline（including the float lines,mainline,and branch lines） were calculated by inputting three-dimensional current profile data,fishing gear parameters（the diameter of the mainline and branch line,the total weight of the branch line and the bait in the water,the density of the mainline and branch line,the elastic modulus of the mainline,the length of the branch line,and the length of the float rope）,operating parame-ters（vessel speed,line shooter speed,and the time interval between two hooks） into the numerical model.The model then outputs the shape of the longline under water and the depth of each hook.We verified the model output using experimental data.The model was able to accurately depict the three-dimensional shape and hook depths of the pelagic longline.There was no significant difference between the hook depth measured by TDR and the model estimate of hook depth（P=0.220.05）.The average difference between two methods was 12.03 m（range： 0.02–40.36 m,S2=100.30,S=10.01,n=188）.The underwater shape of the main line was represented by a wave-shaped curve.The shape was related to the force of the branch line.This load was concentrated at the re-spective node of the main line and made the depth of this node deeper.The main line between two nodes may have floated somewhat because of lift generated by sea currents,especially upwelling currents.The model estimates of the three-dimensional shape and the hook depths were influenced by the value of the drag coefficient（CN90）.CN90 was defined as the drag coefficient associated with water flow plumb to the cylinder.The value of the drag coeffi-cient（CN90） was determined based on the Reynolds number（Re） of the study object.