CN-122021390-A - Ocean scale drifting track prediction method of artificial fish collecting device

CN122021390ACN 122021390 ACN122021390 ACN 122021390ACN-122021390-A

Abstract

The invention relates to the field of marine fishery facilities. A ocean scale drifting track prediction method for an artificial fish collecting device is characterized by comprising the steps of obtaining and arranging water flow velocity data collected by a purse net operation fleet in an actual operation sea area, manufacturing a physical model of the drifting type artificial fish collecting device according to a certain proportion, measuring hydrodynamic characteristics of the physical model under a controlled environment, carrying out numerical simulation on the hydrodynamic characteristics of the device by using a computational fluid mechanics method, fitting a drifting velocity function, simulating drifting behaviors of the device under different water flow velocity conditions by adopting a device model with a certain proportion in the computational fluid mechanics simulation, obtaining motion response and drifting velocity data of the device under various incoming flow velocities through a plurality of groups of simulation tests, carrying out quantitative analysis and function fitting on the influence of the underwater vertical structure length of the device on the drifting velocity, and carrying out Lagrange track simulation prediction on the fish collecting device in an ocean scale ocean environment field by using the obtained drifting velocity function.

Inventors

ZHANG TONGZHENG
FAN GUANGQI
ZHOU CHENG
WEN SHUO
SANG HAORAN

Assignees

上海海洋大学

Dates

Publication Date: 20260512
Application Date: 20251222

Claims (10)

1. The ocean scale drifting track prediction method of the artificial fish collecting device is characterized by comprising the following steps of: S1, acquiring and arranging water flow velocity data acquired by a purse seine operation fleet in an actual operation sea area; s2, manufacturing a physical model of the drifting type artificial fish collecting device according to a certain proportion, and measuring hydrodynamic characteristics of the physical model in a controlled environment; s3, carrying out numerical simulation on hydrodynamic characteristics of the artificial fish gathering device by using a computational fluid dynamics method; S4, fitting a drift velocity function, namely in computational fluid dynamics simulation, simulating drift behaviors of the device under different water flow velocities by adopting a certain proportion of artificial fish collecting device model, obtaining motion response and drift velocity data of the artificial fish collecting device under various incoming flow velocities through a plurality of groups of simulation tests, and quantitatively analyzing and fitting functions aiming at the influence of the underwater vertical structure length of the artificial fish collecting device on the drift velocity; s5, performing Lagrange locus simulation prediction on the artificial fish gathering device in the ocean scale ocean environment field by using the obtained drift velocity function; In step S4, the influence of the length of the underwater vertical structure of the artificial fish collecting device on the drift velocity is quantitatively analyzed, the length of the underwater structure of the artificial fish collecting device is gradually shortened under the same incoming flow velocity condition, and under each underwater structure length, the stable drift velocity of the artificial fish collecting device is recorded, so that a series of data of the drift velocity changing along with the length of the underwater structure is obtained, curve fitting is performed, a function of the drift velocity changing along with the length of the structure is obtained, and the influence of different structures on the drift performance is quantified.
2. The ocean scale drift trajectory prediction method of the artificial fish gathering device according to claim 1, wherein in the step S5, the drift velocity function obtained in the step S4 is firstly embedded into a Lagrange particle trajectory model frame, ocean current data of a sea area to be predicted are obtained according to the sea area of the artificial fish gathering device, HYCOM data with proper time resolution and spatial resolution are selected as environmental input fields according to prediction requirements, the artificial fish gathering device is put into simulation by setting an initial state of the artificial fish gathering device, the initial state comprises an initial position and an initial drift moment, the artificial fish gathering device is used as Lagrange tracer particles, the next position of the artificial fish gathering device is calculated according to the environmental field at the current position of the device, namely, the ocean current velocity provided by the HYCOM at the initial position is read, the drift velocity function is substituted into the real-time motion direction and velocity, the position of the artificial fish gathering device is pushed through numerical integration, and the simulation and the real-time prediction of the position of the artificial fish gathering device along with time evolution are repeated.
3. The ocean scale drifting trajectory prediction method of the artificial fish gathering device according to claim 1 or 2, wherein the physical model of the drifting artificial fish gathering device comprises a raft body, ropes and sinkers, wherein the raft body is positioned on the water surface, provides buoyancy for the device and forms a shadow area with a certain area on the water surface, the ropes hang downwards from the bottom of the raft body and penetrate through a plurality of water layers, the sinkers are fixed at the tail ends of the ropes, and the ropes are enabled to keep sufficient vertical extension in the water through self gravity, so that winding and curling are avoided, and meanwhile, the hydrodynamic resistance of the whole device is increased.
4. The ocean scale drift trajectory prediction method of the artificial fish gathering apparatus according to claim 1 or 2, wherein the water flow velocity data obtained in the step S1 includes surface layer and typical depth layer water flow velocity data for creating an original data set including time, position and flow velocity information.
5. The ocean scale drift trajectory prediction method of an artificial fish gathering device according to claim 4, wherein the quality control and pretreatment of the data of the original data set comprise removing abnormal values, unifying time coordinates and space coordinates, sorting according to the operation sea area and water depth, obtaining frequency distribution, average value, standard deviation and typical working condition interval of the water flow velocity of the target sea area through statistical analysis, selecting a plurality of representative water flow velocity gears according to the statistical result, taking the typical flow velocity values as the basis of inflow boundary conditions in the physical model simulation test in steps S2-S4, and guaranteeing that the water flow working conditions adopted in the physical model test and the numerical simulation can truly reflect the actual operation environment.
6. The ocean scale drifting trajectory prediction method of the artificial fish gathering device according to claim 1 or 2, wherein in the step S2, pure water flow test is carried out by adopting a water tank or a water tank, a model of the artificial fish gathering device is placed in the center of the water tank or the water tank, hydrodynamic resistance of the model under different water flow rates in a constant current state is measured, drifting speed and posture of the model under different flow rates are recorded, a dynamometer, a flowmeter or a camera tracking system is arranged on the model, and data of stress and drifting speed of the model changing with time are obtained.
7. A ocean scale drifting track prediction method of an artificial fish collecting device according to claim 5 is characterized in that in the simulation process of the step S3, a three-dimensional model of the artificial fish collecting device is firstly built according to a set scale and comprises a floating raft body and an underwater accessory structure, a calculation domain is built by proper computational fluid mechanics simulation software, a flow field is meshed, working conditions corresponding to tests are set in the simulation, a plurality of representative values are respectively taken according to incoming flow speeds, the incoming flow direction has positive incident flow and a certain yaw angle to simulate the change of the ocean flow direction relative to the artificial fish collecting device, a turbulence model used for solving is selected according to a Reynolds number range, a boundary condition is set to be inlet steady flow and outlet free outflow, the water surface is simplified to be a free surface boundary, the surface of the artificial fish collecting device is a non-sliding fixed wall condition, a distribution flow field around the artificial fish collecting device and fluid dynamic force acting on the artificial fish collecting device are obtained through steady or non-steady solving, reliability of the model is verified through comparison with a physical test result, finally the artificial fish collecting device is simulated at high accuracy, and the model is designed according to the Reynolds number 1, and the relationship between the artificial flow field and the artificial flow collecting device and the artificial flow model is not required to be equal to the integral stress coefficient or the stress coefficient.
8. A prediction system based on the ocean scale drift trajectory prediction method of the artificial fish gathering device according to claim 1 is characterized by comprising a physical model of the artificial fish gathering device, a water flow test water tank or water pool, a water flow velocity data set containing time, position and flow velocity information, and a simulation module for simulating the drift trajectory of the artificial fish gathering device, so as to predict the ocean scale drift trajectory of the artificial fish gathering device.
9. A storage medium storing a program which when executed by a processor implements the method of any one of claims 1 to 7.
10. A physical model of a drifting type artificial fish collecting device is characterized by comprising a raft body, ropes and sinkers, wherein the raft body is positioned on the water surface, provides buoyancy for the device, forms a shadow area with a certain area on the water surface, the ropes hang downwards from the bottom of the raft body and penetrate through a plurality of water layers, the sinkers are fixed at the tail ends of the ropes, the ropes are enabled to keep sufficient vertical extension in the water through self gravity, winding and curling are avoided, and hydrodynamic resistance of the whole device is increased.

Description

Ocean scale drifting track prediction method of artificial fish collecting device Technical Field The invention relates to the fields of ocean science, facility fishery engineering and fishery resource management, in particular to a ocean scale drifting track prediction method of an artificial fish collecting device. Background Drifting artificial fish gathering device (FISH AGGREGATING DEVICE, FAD) is widely used in tropical tuna purse seiner industry to lure and gather tuna, and the fishing yield is improved. FAD is typically composed of a buoyant raft body (e.g., bamboo raft, foam buoys, etc.) and depending ancillary structures such as ropes, mesh, etc. suspended therebelow for increasing drag in the water to slow down the drift velocity and enhance the fish gathering effect. The fishermen installs satellite communication location buoy and sonar fish detection equipment on FAD, can acquire FAD's position and the quantity of gathering the shoal below in real time. These data are used not only to guide fishing operations on fishing vessels, but also to track and study the drifting behavior of FAD by researchers and to evaluate their fishery ecological impact. Recent statistics show that the number of FADs deployed annually in the western, middle pacific ocean only is up to 30,000-40,000. Because FAD can drift with ocean current in the ocean for months or even years, the coverage distance can reach thousands of kilometers, and a considerable proportion of FAD loses control signals during the period or is abandoned at sea, and finally becomes ocean floating garbage, which causes serious harm to the ecological environment. To mitigate the negative impact of FAD loss, some areas have begun to explore the establishment of FAD drift monitoring and recovery mechanisms, but to achieve efficient recovery, the key is to accurately predict future drift trajectories of FAD. Therefore, in scientific research and fishery management, there is an urgent need for a method capable of performing drift trajectory prediction on FAD based on marine environmental data, for predicting the drift path, drift loss direction and possible landing sites of FAD, thereby assisting fishery resource management and marine environmental decision. Disclosure of Invention The invention aims to solve the technical problem of providing a ocean scale drifting track prediction method of an artificial fish collecting device, and solving the technical problem that FAD in the prior art loses control signals or is abandoned on the sea to finally become ocean floating garbage, so that serious harm is caused to ecological environment. Technical proposal The ocean scale drifting track prediction method of the artificial fish collecting device comprises the following steps: S1, acquiring and arranging water flow velocity data acquired by a purse seine operation fleet in an actual operation sea area; s2, manufacturing a physical model of the drifting type artificial fish collecting device according to a certain proportion, and measuring hydrodynamic characteristics of the physical model in a controlled environment; s3, carrying out numerical simulation on hydrodynamic characteristics of the artificial fish gathering device by using a computational fluid dynamics method; S4, fitting a drift velocity function, namely in computational fluid dynamics simulation, simulating drift behaviors of the device under different water flow velocities by adopting a certain proportion of artificial fish collecting device model, obtaining motion response and drift velocity data of the artificial fish collecting device under various incoming flow velocities through a plurality of groups of simulation tests, and quantitatively analyzing and fitting functions aiming at the influence of the underwater vertical structure length of the artificial fish collecting device on the drift velocity; s5, performing Lagrange locus simulation prediction on the artificial fish gathering device in the ocean scale ocean environment field by using the obtained drift velocity function; In step S4, the influence of the length of the underwater vertical structure of the artificial fish collecting device on the drift velocity is quantitatively analyzed, the length of the underwater structure of the artificial fish collecting device is gradually shortened under the same incoming flow velocity condition, and under each underwater structure length, the stable drift velocity of the artificial fish collecting device is recorded, so that a series of data of the drift velocity changing along with the length of the underwater structure is obtained, curve fitting is performed by adopting a polynomial fitting method, a function of the drift velocity changing along with the length of the structure is obtained, and the influence of different structures on the drift performance is quantified. Further, in step S5, the drift velocity function obtained in step S4 is first embedded into the lagrangian particle trac