Aquaculture pond effluent numerical simulation method and system

Abstract

The application discloses a fish culture pond sewage discharge numerical simulation method and system, and relates to the technical field of aquaculture engineering.The simulation scene is upgraded from water flow unidirectional driving particles to fish and water flow jointly driving particles, so that the change degree of different culture densities and different fish sizes on the flow field structure is accurately quantified, and the particles are tracked in the dynamic flow field, so that the influence of high-frequency pulsating flow induced by fish movement on the suspension and transport of particles is captured, real-time full-dynamic coupling of fish school biological movement and the flow field-particle transport is realized, and finally the transport and removal rules of particles in the actual high-density culture environment under the condition of fish are quantified through motion trajectory analysis, so that the sewage discharge efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of aquaculture engineering technology, and in particular to a numerical simulation method, system, equipment and medium for wastewater discharge from fish farming ponds. Background Technology

[0002] In recirculating aquaculture systems, the efficient removal of solid particles such as uneaten feed and feces is a key bottleneck to ensure stable water quality and healthy fish growth. Studies have shown that about 30% of the feed enters the water as uneaten feed and feces. If these solid particles are not removed in time, they will not only consume a large amount of dissolved oxygen, but also decompose to produce toxic substances such as ammonia nitrogen, deteriorate water quality, and increase the risk of disease.

[0003] Currently, the main method relies on dual-channel high-flow-rate drainage and high swirling flow velocity to drive particulate matter out, but this method suffers from serious water waste and high operating costs. Therefore, researchers are now designing sewage discharge methods for aquaculture ponds. The core of this method is to rely on empirical formulas and physical model experiments to optimize parameters such as inlet angle, flow velocity, and pond bottom slope in order to create a favorable hydraulic swirling flow in the pond and push particulate matter toward the central drain outlet.

[0004] However, current design optimization methods for aquaculture pond wastewater discharge are typically based on the flow field assumption of empty ponds or stationary fish. In actual high-density aquaculture environments, the schooling and swimming of fish will cause significant and continuous disturbances to the flow field within the pond. This "biological disturbance" will change the original water flow structure, increase turbulence intensity, and change the transport path of particulate matter. Moreover, in the presence of fish, in addition to the time-averaged drag force, particles are also subjected to high-frequency pulsating pressure and periodic velocity pulsations induced by the movement of fish. As a result, the current flow field assumption based on empty ponds or stationary fish cannot simulate the real-time dynamic coupling effect between "fish-water-particles". In other words, it is difficult to quantify the transport and removal patterns of particulate matter in actual high-density aquaculture environments with fish, ultimately resulting in low wastewater discharge efficiency of the optimized scheme obtained from simulation in practical applications. Summary of the Invention

[0005] This invention provides a numerical simulation method and system for wastewater discharge from fish farming ponds, which can solve the problems existing in the prior art.

[0006] This invention provides a numerical simulation method for wastewater discharge from fish farming ponds, comprising the following steps: Collect bio-kinematic characteristics of the target cultured fish, as well as structural parameters of the fish culture pond; Based on the biokinematic characteristics of the target cultured fish, a three-dimensional geometric model of the fish body is established, and a parameterized motion equation is constructed to describe the tail-wagging motion of the fish body. Based on the structural parameters of the fish culture pond, a three-dimensional geometric model of the fluid domain of the culture pond is established, and the three-dimensional geometric model of the fish body is imported into the three-dimensional geometric model of the fluid domain of the culture pond to obtain the fish pond coupling model. The fish pond coupling model is then meshed to construct an initial flow field numerical model. Based on the parameterized motion equations, the coupled dynamic mesh method is used to drive the three-dimensional geometric model of the fish to move in the flow field within the initial flow field numerical model, and the mesh is updated to obtain the dynamic flow field under fish disturbance. The discrete phase model was used to simulate the injection of particulate matter into a dynamic flow field, and the interaction between the particulate matter, the fluid, and the aquaculture pond was set to simulate and iteratively calculate the motion trajectory of the particulate matter in the dynamic flow field. The motion trajectory was then subjected to Lagrange tracking and spatiotemporal analysis to obtain the sewage discharge simulation results.

[0007] Preferably, the construction of the parameterized equations of motion for describing the tail-wagging motion of the fish includes: The fish motion equations are compiled using a custom function (UDF) macro to define the tail fin swing motion of the fish. The fish motion equations adopt the kinematic equations of the chevron family, which are described by a traveling wave function combining a quadratic curve and a sine curve, expressed as follows: y ( x , t )= a ( x sin( kx - ωt ); a ( x )= a 0+ a 1 x + a 2 x 2 ; in: x Coordinates representing the length of the body; y ( x , t )express t time x Longitudinal displacement at the location; a ( x () represents the envelope of the transverse wave amplitude of the fish body; k =2π / λ represents the wave number, and λ represents the volume wave wavelength; ω =2πf, where f represents the caudal fin oscillation frequency; a 0、 a 1 and a 2 represents a constant.

[0008] Preferably, constructing the initial flow field numerical model includes: The fishpond coupling model is divided into a hybrid mesh containing tetrahedrons and hexahedrons, with local refinement in the caudal fin and inlet / outlet regions. The hybrid mesh has 2-3.5 million nodes and an average skewness of <0.5. Material properties and boundary conditions were set for the meshed fishpond coupling model. The material properties included water density and dynamic viscosity, and the boundary conditions included inlet velocity, inlet turbulence intensity, outlet velocity, and outlet turbulence intensity. After the material properties and boundary conditions were set, the initial flow field numerical model was constructed using the RNG k-ε turbulence model.

[0009] Preferably, the transport equations in the RNG k-ε turbulence model include: The equation for turbulent kinetic energy k is expressed as: ; The equation for the turbulent dissipation rate ε is expressed as: ; in: and , These represent the velocity component and the coordinate component, respectively. This represents the turbulent kinetic energy production term k caused by the average velocity gradient; Indicates the effective viscosity; and Let represent the reciprocals of the Prandtl numbers for turbulence, k and ε, respectively. and Represents model constants.

[0010] Preferably, the simulation and iterative calculation of the motion trajectory of particulate matter in the dynamic flow field includes: Using the discrete phase DPM model, solid waste such as uneaten feed and feces are defined as discrete particulate phases; the particle density range is set and the shape factor is non-spherical to ensure consistency with the actual uneaten feed morphology; at the same time, the buoyancy, pressure gradient force, drag force, virtual mass force and Saffman lift between the particulate matter and the fluid and the aquaculture pond are set. The buoyancy effect affects the trajectory of discrete particles in the flow field, so the buoyancy force per unit mass is expressed as: ; In the formula: 𝜌 represents the density of the continuous phase fluid; This represents the density of the discrete phase of the particles; The drag force refers to the resistance exerted by a fluid on an object moving within it; for a unit mass of particle, the drag force is... Represented as: ; In the formula: 𝜇 represents the dynamic viscosity of the continuous phase fluid; Indicates the diameter of the particle; Indicates the velocity of a continuous phase fluid; Indicates the velocity of the particles; Indicates the particle Reynolds number; Indicates the drag coefficient; The drag coefficient Represented as: ; The pressure gradient force refers to the force exerted on particles due to spatial changes in pressure within the fluid. Represented as: ; The virtual mass force acting on a unit mass particle is represented as: ; In the formula: Indicates the virtual quality coefficient; When simulating and predicting the trajectory of a particle, the integral of the force balance on the particle is expressed as: ; After force equilibrium is achieved, at each time step, the particle trajectory is tracked based on the Lagrange framework, and the particle feedback to the flow field is updated through bidirectional coupling. The spatiotemporal distribution data of the particles and the number of remaining particles N are iteratively calculated. t And the sewage discharge efficiency η.

[0011] Preferably, the sewage discharge efficiency is expressed as: η=(N0−N t ) / N0×100%; Where: N0 represents the initial total number of injected particles; N t This represents the number of particles remaining in the aquaculture pond at time t during the simulation.

[0012] This invention also provides a numerical simulation system for wastewater discharge from fish farming ponds, comprising: The modeling module is used to collect the bio-kinematic characteristics of the target farmed fish, as well as the structural parameters of the fish farming pond; Based on the biokinematic characteristics of the target cultured fish, a three-dimensional geometric model of the fish body is established, and a parameterized motion equation is constructed to describe the tail-wagging motion of the fish body. Based on the structural parameters of the fish culture pond, a three-dimensional geometric model of the fluid domain of the culture pond is established, and the three-dimensional geometric model of the fish body is imported into the three-dimensional geometric model of the fluid domain of the culture pond to obtain the fish pond coupling model. The fish pond coupling model is then meshed to construct an initial flow field numerical model. The simulation module is used to drive the three-dimensional geometric model of the fish to move in the flow field within the initial flow field numerical model based on the parameterized motion equations and the coupled dynamic mesh method, and update the mesh to obtain the dynamic flow field under fish disturbance. The discrete phase model was used to simulate the injection of particulate matter into a dynamic flow field, and the interaction between the particulate matter, the fluid, and the aquaculture pond was set to simulate and iteratively calculate the motion trajectory of the particulate matter in the dynamic flow field. The motion trajectory was then subjected to Lagrange tracking and spatiotemporal analysis to obtain the sewage discharge simulation results.

[0013] This invention also provides an electronic device, including a memory and a processor; The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the steps of the numerical simulation method for sewage discharge from a fish farming pond as described above.

[0014] This invention also provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the steps of a numerical simulation method for wastewater discharge from a fish farming pond as described above.

[0015] This invention provides a numerical simulation method and system for wastewater discharge from fish farming ponds. Compared with existing technologies, its advantages are as follows: This invention couples a parametric motion model based on fish kinematics to a three-dimensional geometric model of the fluid domain in aquaculture ponds using a dynamic mesh method. The coupled dynamic mesh method drives the movement of the three-dimensional geometric model of the fish within the flow field, updating the dynamic flow field to realistically simulate the disturbance of the fluid caused by the wagging of the fish's tail fins. This accurately characterizes the dynamic flow field structure within the aquaculture pond under fish-containing conditions. Furthermore, based on this precise characterization of the dynamic flow field structure, discrete phase particles are injected into the dynamic flow field, and the interactions between particles, fluid, and the aquaculture pond are comprehensively considered to accurately track and analyze the trajectory of each particle within the dynamic flow field. Thus, this invention upgrades the simulation scenario from one-way water flow driving particles to a scenario where fish and water flow jointly drive particles, accurately quantifying the degree of change in the flow field structure caused by different stocking densities and fish sizes. By tracking particles in the dynamic flow field, the influence of high-frequency pulsating flow induced by fish movement on particle suspension and transport is captured, achieving real-time, fully dynamic coupling between fish biological movement and flow field-particle transport. Finally, motion trajectory analysis quantifies the transport and removal patterns of particles in actual high-density aquaculture environments under fish-containing conditions, improving wastewater discharge efficiency. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall process of a numerical simulation method for wastewater discharge from a fish farming pond, provided in an embodiment of the present invention. Figure 2This is a schematic diagram of a circular aquaculture pond model provided in an embodiment of the present invention; Figure 3 A schematic diagram of a three-dimensional model of a biomimetic fish provided in an embodiment of the present invention; Figure 4 A schematic diagram of the fluctuation curve of the trevally pattern provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the mesh generation of the numerical model provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the DPM model in Fluent provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the deposition of particles in the pool over time, provided as an embodiment of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0018] like Figure 1 The figure shows a numerical simulation method for wastewater discharge from a fish farming pond provided by an embodiment of the present invention, which includes the following steps: Step 1: 3D modeling of the fish body.

[0019] Based on the biological characteristics of the target farmed fish (such as body size, caudal fin aspect ratio, and swimming posture), a refined fish body geometric model with both rigid and flexible features is constructed using 3D modeling software such as SolidWorks. The model must retain the streamlined shape, caudal fin swing joints, and surface texture of the real fish body to ensure the accuracy of subsequent fluid-structure interaction calculations.

[0020] Step 2: Numerical modeling of the aquaculture pond.

[0021] Based on the actual structural parameters of the aquaculture pond (pond type, size, water depth, location and number of inlet and outlet), a complete three-dimensional fluid domain model is established in the CAD / CAE platform; the inlet and outlet pipes, pond wall roughness and internal components are realistically reproduced to provide geometric basis for subsequent boundary condition setting.

[0022] Step 3: Model import.

[0023] The three-dimensional fish model obtained in step 1 is dynamically assembled and implanted into the fluid domain of the aquaculture pond in step 2 to complete the construction of the fish-pond coupled spatial model. Boolean operations are used to ensure that there is no interference gap between the fish and the water, and a shared topology interface is automatically generated to facilitate the continuous transition of the subsequent mesh.

[0024] Step 4: Grid generation.

[0025] The fish-pond coupling model is subjected to unstructured tetrahedral / hexahedral hybrid mesh generation. Local meshing is implemented in the boundary layer of the fish surface, the tail fin swinging area, the inlet and outlet, and the high shear zone at the bottom of the pond to ensure the near-wall analytical requirements of the fish body with y+≈1.

[0026] Step 5: Material and boundary condition settings.

[0027] Define the material properties of the computational domain as follows: water density ρ = 998.2 kg / m³, dynamic viscosity μ = 1.03 × 10⁻³ Pa·s; set the boundary conditions as follows: no slip on the wall, inlet velocity and turbulence intensity, outlet velocity and turbulence intensity; adopt the RNG k-ε turbulence model and give the corresponding model constants.

[0028] The transport equations of the RNG k-ε turbulence model include: Turbulent kinetic energy (k) equation: .

[0029] Equation for turbulent dissipation rate (ε): .

[0030] in: and , These are the velocity components and the coordinate components, respectively. This is the turbulent kinetic energy production term caused by the average velocity gradient; Effective viscosity; and The reciprocals of the Prandtl numbers for turbulence, k and ε, respectively, and α k = 1.393, α ε = 1.393, and These are model constants.

[0031] Step 6: Definition of fish movement.

[0032] Based on the tail fin undulation patterns of trevally fish, a C language user-defined function (UDF) macro file was written to compile the traveling wave motion equations. y ( x , t )= a ( x sin( kx - ωt ), where the amplitude envelope a ( x )= a 0+ a 1 x + a 2 x 2 During the calculation process, the tail fin is driven to swing in real time, realizing the flexible deformation of the fish body and the dynamic interaction between the fish body and the water.

[0033] Step 7: Start dynamic mesh technology.

[0034] The dynamic mesh module is activated, and a coupling strategy of spring smoothing method and local mesh reconstruction method is adopted: the spring constant factor is set to 0.1, the maximum element skewness threshold is 0.7, and the reconstruction iteration is 1; the dynamic layer technology is used to ensure that the mesh quality always meets the computational stability requirements when the tail fin swings greatly.

[0035] Step 8: Flow field solution calculation.

[0036] A pressure-based implicit solver was selected, and the SIMPLE algorithm was used to achieve pressure-velocity coupling. Pressure, momentum, turbulent kinetic energy, and turbulent dissipation rate were all discretized using a second-order upwind scheme. The pressure sub-relaxation factor was set to 0.3, momentum to 0.7, and turbulent kinetic energy and dissipation rate to 0.8. Iterative calculations were performed until the residuals of all variables decreased to 1 × 10⁻. 4 The following is how a stable dynamic flow field is obtained.

[0037] Step 9: Activate the Discrete Phase Model (DPM).

[0038] After the flow field has stabilized sufficiently, the DPM model is activated, defining solid waste such as uneaten bait and feces as discrete particulate phases; the particle density range is set, and the shape factor is set to non-spherical to ensure consistency with the actual morphology of uneaten bait.

[0039] Step 10: Particle stress, properties and injection method settings.

[0040] The simulation considers buoyancy, pressure gradient force, drag force, virtual mass force, and Saffman lift; the Rosin-Rammler distribution function is used to describe the particle size distribution; a circular jet source is created on the water surface, and the particle injection rate is dynamically adjusted according to the measured feed amount to ensure that the simulation scenario matches the real feeding process.

[0041] The force characterization equations for the injected particles include: The buoyancy also affects the trajectory of particles in the flow field. The buoyancy force per unit mass can be expressed as: .

[0042] In the formula: ρ is the density of the continuous phase fluid, kg / m³. 3 ; Density of the particulate discrete phase, kg / m³ 3 .

[0043] Dragging force is the resistance exerted by a fluid on an object moving within it; for a unit mass of particles, the drag force is ( ). ) is represented as: .

[0044] In the formula: 𝜇 is the dynamic viscosity of the continuous phase fluid, Pa∙s; Let be the diameter of the particle, in meters (m). The velocity of the continuous phase fluid is expressed in m / s. The velocity of the particle is in m / s; The particle Reynolds number, This is the drag coefficient.

[0045] drag coefficient The empirical formula is expressed as: .

[0046] Pressure gradient force refers to the force exerted on particles due to spatial changes in pressure within a fluid. This can be represented as: .

[0047] The virtual mass force on a unit mass particle can be expressed as: .

[0048] In the formula: This is a virtual quality coefficient with a value of 0.5.

[0049] To predict the trajectory of a particle, the force balance on the particle is integrated; the force balance equation can be expressed in the following form: .

[0050] Step 11: Set DPM boundary conditions.

[0051] A particle-fluid interaction model is defined, and "escape" and "capture" boundaries are set for the wall and the outlet, respectively, to accurately calculate the sewage discharge efficiency.

[0052] Step 12: Dynamic coupling solution.

[0053] Within each time step, the particle trajectory is tracked based on the Lagrange framework; the particle feedback to the flow field is updated through bidirectional coupling, and the calculation is iterated until the total simulation duration is 120 s; the spatiotemporal distribution data of the particles and the number of remaining particles N are output every 10 s. t And the sewage discharge efficiency η.

[0054] Step 13: Post-processing of results.

[0055] Post-processing tools such as ParaView and Ensight are used to generate particle motion trajectory animations and bottom sediment maps; the sewage discharge efficiency η=(N0−N) is calculated and output. t ) / N0×100%, which is the optimal structure of the aquaculture pond.

[0056] More specifically: Step 1: Establish a numerical model.

[0057] A 3D model of the circular aquaculture pond was constructed in the Geometry module of ANSYS, as follows: Figure 2 As shown. The circular culture pond has a diameter (D) of 1 m and a height (i.e., water depth, H) of 0.2 m. The outlet at the center of the bottom is a drain with an inner diameter of 0.02 m, and the bottom has no slope. The water inlet system is driven by a dual-pipe jet system with a diameter of 0.02 m. The inlet pipes are vertically arranged on opposite curved walls, allowing water to enter tangentially. Each inlet pipe has nine evenly distributed inlet holes with a diameter of 0.004 m, and the distance from the inlet holes to the pond wall is 0.01 m. A three-dimensional model of the fish was created using Solidworks software, based on the *Sebastes schlegelii* as the prototype. Figure 3 As shown, during the modeling process, all parts of the fish body except the tail fin were simplified. The simplified three-dimensional bionic fish has a length of 0.1 m, a maximum height of 0.04 m, and a maximum width of 0.01 m.

[0058] Step 2: Establish a model of fish movement in the aquaculture pond.

[0059] The 3D model of the fish is imported into Geometry and then coupled with the 3D model of the breeding pond for modeling. The movement and deformation of the body are mainly achieved by defining the changes in the positions of mesh nodes through a UDF macro file. The UDF macro file in this invention is compiled based on the kinematic equations of the trevally swimming pattern. In the UDF macro file, DEFINE_GRID_MOTION is used to control the flexible movement of the bionic fish; the surface mesh is traversed through begin_f_loop(f,tf), and all nodes of the surface mesh are traversed through f_node_loop(f,tf,n); the 3D coordinates of the surface mesh nodes of the bionic fish at the current time are read, and then the coordinates of the surface mesh nodes of the bionic fish at the next time are calculated based on the given motion model. Specifically, the kinematic equations based on the trevally swimming pattern are as follows:

[0060] y ( x , t )= a ( x sin( kx - ωt ).

[0061] a ( x )= a 0+ a 1 x + a 2 x 2 .

[0062] in: x Coordinates representing the length of the body; y ( x , t )express t time x Longitudinal displacement at the location; a ( x () represents the envelope of the transverse wave amplitude of the fish body; k =2π / λ represents the wave number, and λ represents the volume wave wavelength; ω =2πf, where f represents the caudal fin oscillation frequency. In this invention, the values ​​of each constant are taken as a0=0.02, a1=-0.008, a2=0.16, and λ=0.95; in this invention, when f=1, the traveling wave function curve of the trevally pattern is as follows: Figure 4 As shown.

[0063] Step 3: Grid generation.

[0064] This invention employs a tetrahedral mesh generation method, such as... Figure 5As shown; the local refinement method is selected to refine the mesh of the fish body, inlet and outlet surfaces, especially the mesh at the tail fin.

[0065] Step 4: Solver setup and calculation.

[0066] After importing the mesh file into Fluent, first check the mesh to avoid negative volume. In setup, set the parameters and boundary conditions, and related parameters. The boundary conditions are shown in Table 1. Set Time to steady state, select the RNG kε model turbulent model, define the water material and define the inlet velocity and hydraulic diameter, set the residuals, set the detection surface initialization, set the number of iteration steps, automatically save the number of steps, and observe whether the residual curve converges to the set residual value in each step of the calculation until the model converges.

[0067] Table 1 Boundary Condition Settings Step 5: Open the PDM model in the flow field and set the particle forces, properties and injection method.

[0068] The forces acting on the particles were set as buoyancy, pressure gradient force, drag force, and virtual mass force; the Rosin-Rammler distribution function was used to describe the particle size distribution; a circular jet source was created on the water surface, and the particle injection rate was dynamically adjusted according to the measured feed amount to ensure that the simulation scenario matched the real feeding process. 1000 uniformly distributed discrete phase particles were injected into the water surface (i.e., a plane with Z = 0.2 m) at one time, with an injection surface radius of 0.49 m and an initial particle velocity of 0; the particle material properties were set based on the properties of a general-purpose sedimentary juvenile fish feed measured in the laboratory, i.e., an average density of approximately 1100 kg / m³. 3 The average particle size is approximately 0.0027 μm, the shape factor is approximately 0.85, and the particle material is a homogeneous solid; the DPM model in Fluent is as follows. Figure 6 As shown.

[0069] Step 6: Set DPM boundary conditions and define the particle-fluid interaction model.

[0070] "Escape" and "capture" boundaries were set for the wall and the outlet respectively to accurately calculate the sewage discharge efficiency. The model adopts unsteady particle tracking, and the trajectory of the particles is tracked as the fluid flow time changes. The particle tracking time step is set to 0.01 s, the maximum tracking time step is 12000, and the number of tracking time steps per time is 1000, that is, the particle tracking data is read once every 10 s, and the tracking is carried out for a total of 120 s.

[0071] Step 7: Post-processing of results.

[0072] Post-processing tools such as ParaView and Ensight are used to generate particle motion trajectory animations and bottom sediment maps. Figure 7 (This is a sedimentation map showing the accumulation of particles in the pool over time); calculate and output the wastewater discharge efficiency η=(N0−N t ) / N0×100%.

[0073] This invention introduces fish movement as an active variable into the simulation framework. Through parameterized trevally model motion equations and dynamic mesh technology, it realistically reproduces the real-time disturbance of the surrounding flow field caused by the swaying of the fish's tail fin, revealing the intrinsic mechanism of hydraulic efficiency reduction caused by "bioturbulence." This provides theoretical support for designing robust aquaculture pond structures that can "counter" or "adapt" to bioturbulence. Furthermore, this invention constructs a complete particle force model including drag force, pressure gradient force, virtual mass force, and buoyancy. By tracking particles in a dynamic flow field, this invention can capture particle-turbulence interactions that traditional methods cannot simulate. This results in simulated particle trajectories and deposition distribution patterns that highly match actual aquaculture scenarios, achieving a leap in simulation accuracy from "qualitative trend" to "quantitative characterization."

[0074] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A numerical simulation method for wastewater discharge from fish farming ponds, characterized in that, Includes the following steps: Collect bio-kinematic characteristics of the target cultured fish, as well as structural parameters of the fish culture pond; Based on the biokinematic characteristics of the target cultured fish, a three-dimensional geometric model of the fish body is established, and a parameterized motion equation is constructed to describe the tail-wagging motion of the fish body. Based on the structural parameters of the fish culture pond, a three-dimensional geometric model of the fluid domain of the culture pond is established, and the three-dimensional geometric model of the fish body is imported into the three-dimensional geometric model of the fluid domain of the culture pond to obtain the fish pond coupling model. The fish pond coupling model is then meshed to construct an initial flow field numerical model. Based on the parameterized motion equations, the coupled dynamic mesh method is used to drive the three-dimensional geometric model of the fish to move in the flow field within the initial flow field numerical model, and the mesh is updated to obtain the dynamic flow field under fish disturbance. The discrete phase model was used to simulate the injection of particulate matter into a dynamic flow field, and the interaction between the particulate matter, the fluid, and the aquaculture pond was set to simulate and iteratively calculate the motion trajectory of the particulate matter in the dynamic flow field. The motion trajectory was then subjected to Lagrange tracking and spatiotemporal analysis to obtain the sewage discharge simulation results.

2. The numerical simulation method for wastewater discharge from fish farming ponds according to claim 1, characterized in that, The construction of parameterized motion equations to describe the tail-wagging motion of fish includes: The fish motion equations are compiled using a custom function (UDF) macro to define the tail fin swing motion of the fish. The fish motion equations adopt the kinematic equations of the chevron family, which are described by a traveling wave function combining a quadratic curve and a sine curve, expressed as follows: y ( x , t )= a ( x )sin( kx - ωt ); a ( x )= a 0+ a 1 x + a 2 x 2 ； in: x Coordinates representing the length of the body; y ( x , t )express t time x Longitudinal displacement at the location; a ( x () represents the envelope of the transverse wave amplitude of the fish body; k =2π / λ represents the wave number, and λ represents the volume wave wavelength; ω =2πf, where f represents the caudal fin oscillation frequency; a 0、 a 1 and a 2 represents a constant.

3. The numerical simulation method for wastewater discharge from fish farming ponds according to claim 1, characterized in that, The construction of the initial flow field numerical model includes: The fishpond coupling model is divided into a hybrid mesh containing tetrahedrons and hexahedrons, with local refinement in the caudal fin and inlet / outlet regions. The hybrid mesh has 2-3.5 million nodes and an average skewness of <0.

5. Material properties and boundary conditions were set for the meshed fishpond coupling model. The material properties included water density and dynamic viscosity, and the boundary conditions included inlet velocity, inlet turbulence intensity, outlet velocity, and outlet turbulence intensity. After the material properties and boundary conditions were set, the initial flow field numerical model was constructed using the RNG k-ε turbulence model.

4. The numerical simulation method for wastewater discharge from fish farming ponds according to claim 3, characterized in that, The transport equations in the RNG k-ε turbulence model include: The equation for turbulent kinetic energy k is expressed as: ； The equation for the turbulent dissipation rate ε is expressed as: ； in: and , These represent the velocity component and the coordinate component, respectively. This represents the turbulent kinetic energy production term k caused by the average velocity gradient; Indicates the effective viscosity; and Let represent the reciprocals of the Prandtl numbers for turbulence, k and ε, respectively. and Represents model constants.

5. The numerical simulation method for wastewater discharge from fish farming ponds according to claim 1, characterized in that, The simulation and iterative calculation of the motion trajectory of particulate matter in the dynamic flow field includes: Using the discrete phase DPM model, solid waste such as uneaten feed and feces are defined as discrete particulate phases; the particle density range is set and the shape factor is non-spherical to ensure consistency with the actual uneaten feed morphology; at the same time, the buoyancy, pressure gradient force, drag force, virtual mass force and Saffman lift between the particulate matter and the fluid and the aquaculture pond are set. The buoyancy effect affects the trajectory of discrete particles in the flow field, so the buoyancy force per unit mass is expressed as: ； In the formula: 𝜌 represents the density of the continuous phase fluid; This represents the density of the discrete phase of the particles; The drag force refers to the resistance exerted by a fluid on an object moving within it; for a unit mass of particle, the drag force is... Represented as: ； In the formula: 𝜇 represents the dynamic viscosity of the continuous phase fluid; Indicates the diameter of the particle; Indicates the velocity of a continuous phase fluid; Indicates the velocity of the particles; Indicates the particle Reynolds number; Indicates the drag coefficient; The drag coefficient Represented as: ； The pressure gradient force refers to the force exerted on particles due to spatial changes in pressure within the fluid. Represented as: ； The virtual mass force acting on a unit mass particle is represented as: ； In the formula: Indicates the virtual quality coefficient; When simulating and predicting the trajectory of a particle, the integral over the force balance on the particle is expressed as: ； After force equilibrium is achieved, at each time step, the particle trajectory is tracked based on the Lagrange framework, and the particle feedback to the flow field is updated through bidirectional coupling. The spatiotemporal distribution data of the particles and the number of remaining particles N are iteratively calculated. t And the sewage discharge efficiency η.

6. The numerical simulation method for wastewater discharge from a fish farming pond according to claim 5, characterized in that, The sewage discharge efficiency is expressed as: η=(N0−N t ) / N0×100%; Where: N0 represents the initial total number of injected particles; N t This represents the number of particles remaining in the aquaculture pond at time t during the simulation.

7. A numerical simulation system for wastewater discharge from fish farming ponds, characterized in that, include: The modeling module is used to collect the bio-kinematic characteristics of the target farmed fish, as well as the structural parameters of the fish farming pond; Based on the biokinematic characteristics of the target cultured fish, a three-dimensional geometric model of the fish body is established, and a parameterized motion equation is constructed to describe the tail-wagging motion of the fish body. Based on the structural parameters of the fish culture pond, a three-dimensional geometric model of the fluid domain of the culture pond is established, and the three-dimensional geometric model of the fish body is imported into the three-dimensional geometric model of the fluid domain of the culture pond to obtain the fish pond coupling model. The fish pond coupling model is then meshed to construct an initial flow field numerical model. The simulation module is used to drive the three-dimensional geometric model of the fish to move in the flow field within the initial flow field numerical model based on the parameterized motion equations and the coupled dynamic mesh method, and update the mesh to obtain the dynamic flow field under fish disturbance. The discrete phase model was used to simulate the injection of particulate matter into a dynamic flow field, and the interaction between the particulate matter, the fluid, and the aquaculture pond was set to simulate and iteratively calculate the motion trajectory of the particulate matter in the dynamic flow field. The motion trajectory was then subjected to Lagrange tracking and spatiotemporal analysis to obtain the sewage discharge simulation results.

8. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the steps of the numerical simulation method for sewage discharge from a fish farming pond as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Used to store a computer program, which, when executed by a processor, implements the steps of a numerical simulation method for wastewater discharge from a fish farming pond as described in any one of claims 1 to 6.

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