Deep sea hydrothermal plume substance diffusion model construction method based on particulate matter concentration boundary recognition

Abstract

The invention relates to the technical field of deep-sea hydrothermal plume detection and modeling, and particularly discloses a method for constructing a deep-sea hydrothermal plume substance diffusion model based on particulate matter concentration boundary recognition. The method comprises the following steps: acquiring mass concentration three-dimensional distribution data of hydrothermal plume particles under the influence of underlying cross flow; a feature height formula is obtained through parameter analysis; constructing a Gaussian diffusion model considering upper boundary constraint, wherein an upper boundary is determined by the height of the neutral buoyancy layer; and using a particle swarm optimization algorithm to invert and solve horizontal and vertical diffusion coefficients in the Gaussian diffusion model based on a CFD simulation result. According to the method, the defect that the height of the neutral buoyancy layer cannot be accurately determined when the plume central axis is bent due to underlying cross flow in a traditional method is overcome, the material diffusion model construction method which is clear in physical significance and higher in precision is provided, and reliable theoretical support is provided for autonomous detection and traceability of the deep sea hydrothermal liquid nozzle.

Description

Technical Field

[0001] This invention relates to the field of marine exploration and numerical simulation technology, and in particular to a method for constructing a deep-sea hydrothermal plume material diffusion model based on particulate matter concentration boundary identification. Background Technology

[0002] Deep-sea hydrothermal plumes serve as conduits for the transport of matter and energy between submarine hydrothermal vents and the vast marine environment. Studying their diffusion characteristics is crucial for locating active hydrothermal vents, assessing mineral resource potential, and understanding deep-sea ecosystems. Hydrothermal particulate matter (such as Fe and Mn sulfides) within the plumes is an important detection indicator, and its spatial distribution patterns in the ocean form the basis for constructing material diffusion models.

[0003] Currently, the main methods for constructing hydrothermal plume material diffusion models are empirical models and numerical simulations. Among them, Gaussian diffusion models are widely used in environmental fluid dynamics and source tracing algorithms due to their simplicity and computational efficiency. However, traditional Gaussian models typically assume that the plume develops symmetrically along its central axis, and the determination of its characteristic heights (such as the height of the neutral buoyancy layer) depends on the measurement of physical quantities (such as potential temperature and turbidity) at the plume's central axis. In actual marine environments with bottom crossflows, the central axis of the hydrothermal plume will be significantly bent, making it impossible to accurately identify and locate the neutral buoyancy layer. This severely affects the accuracy of determining characteristic heights using traditional methods based on the central axis, ultimately leading to distorted diffusion model predictions.

[0004] To address the aforementioned issues, computational fluid dynamics (CFD) numerical simulation methods have been used to study the complex flow fields of hydrothermal plumes. While CFD can provide detailed three-dimensional flow field information, its computational demands are enormous, making it difficult to directly apply to real-time requirements of autonomous underwater vehicle (AUV) exploration missions. Therefore, how to combine high-precision CFD simulation results with efficient analytical models (such as Gaussian models) to construct an accurate yet lightweight mass diffusion model has become a pressing technical challenge. Currently, there is a lack of methods to effectively utilize CFD simulation data to accurately determine the characteristic height of plumes in low-level crossflow environments and, based on this, construct a high-precision diffusion model. Summary of the Invention

[0005] The purpose of this invention is to address the problem in the prior art where, under the influence of bottom crossflow, the bending of the central axis of the hydrothermal plume makes it impossible to accurately determine the height of the neutral buoyancy layer, thus affecting the prediction accuracy of the material diffusion model. This invention provides a method for constructing a deep-sea hydrothermal plume material diffusion model based on the identification of particulate matter concentration boundaries.

[0006] To achieve the above objectives, this invention provides a method for constructing a deep-sea hydrothermal plume material diffusion model based on particulate matter concentration boundary identification, comprising the following steps: S1. Obtain three-dimensional distribution data of the mass concentration of hydrothermal particles in the hydrothermal plume. The three-dimensional distribution data includes the spatial coordinates of the hydrothermal plume in the flow field and the mass concentration of hydrothermal particles corresponding to the coordinates. S2. Determine the feature height based on the obtained three-dimensional distribution data of the mass concentration of hydrothermal particles in the hydrothermal plume: The characteristic height includes the neutral buoyancy layer height and the maximum rise height; The physical mechanism is as follows: at the maximum ascent height, the vertical momentum of the particles ejected from the nozzle becomes zero, and the particles accumulate here, forming a concentration peak; in the neutral buoyancy layer, the plume carries particles over long distances, and the particles stay at this height for the longest time, thus forming a concentration peak; the maximum particle concentration at different downwind distances all occur near the neutral buoyancy layer, which is in high agreement with the results obtained by the traditional density difference method.

[0007] The specific criteria for determining the height of the features include: based on the MTT (Morton-Taylor-Turner) theoretical model, introducing... To quantify the influence of the bottom crossflow on the hydrothermal plume, the height of the neutral buoyancy layer was obtained. and maximum ascent height The calculation formula; in, It is the crossflow velocity at the bottom layer; and The coefficients to be determined are obtained by fitting the parameters using the three-dimensional distribution data of hydrothermal particulate matter mass concentration obtained in step S1, and then using the coefficients to calculate the height of the neutral buoyancy layer and the maximum rise height. S3. Construct a Gaussian diffusion model considering upper boundary constraints. In the Gaussian diffusion model, the virtual source method is used to satisfy the physical constraint conditions of the upper boundary on the concentration of plume particles. This method originates from the mirror method. The virtual source method involves setting up an identical "virtual" emission source at a position symmetrical to the real emission source about the upper boundary. Based on the premise that settlement occurs only at the upper boundary, introducing a virtual source yields a Gaussian diffusion model considering the upper boundary constraint: in, n =1 represents the number of virtual sources. For the location The mass concentration of hydrothermal particulate matter; Source emission rate of particulate matter; The bottom crossflow velocity; and The distance of the eruption source on the y-axis; For effective source high , Take the nozzle height above the ground The equivalent centerline height of the plume after it has been initially lifted by power and buoyancy and begins to undergo far-field Gaussian diffusion; The mixing layer thickness is the maximum height from which particles can be fully mixed in the vertical direction, equivalent to the maximum upward height. ,Right now = This definition will As a physical parameter describing the upper boundary of diffusion in the Gaussian model, combined with the "virtual source method", it can accurately simulate the diffusion behavior of plumes that is suppressed in the vertical direction; and These are the diffusion coefficients in the horizontal and vertical directions, respectively, and are determined by the following formula: , For diffusion parameters, This represents the distance along the crossflow direction.

[0008] S4. Based on the three-dimensional distribution data of mass concentration obtained in step S1, the diffusion coefficient is solved using the particle swarm optimization algorithm.

[0009] Preferably, S1 specifically includes the following steps: A numerical simulation model of a deep-sea hydrothermal plume, including bottom crossflow, was established using computational fluid dynamics software. The hydrothermal vent is set as a velocity inlet, with an ejection velocity of 1-1.5 m / s and an ejection temperature of 270-370℃; The calculation domain is configured with a non-slip wall at the bottom and a pressure outlet at the top. A velocity inlet is set on one side of the computational domain to simulate the underlying crossflow, with a crossflow velocity range of 0.01-0.03 m / s.

[0010] Using realizable k- Multiphase flow coupled simulations were performed using turbulence and discrete phase (DPM) models to simulate the mixing process of hydrothermal fluids with surrounding seawater, with particle sizes ranging from 5 to 500 μm. The transport process of hydrothermal particles was analyzed to obtain the mass concentration distribution data of hydrothermal particles in three-dimensional space.

[0011] Preferably, the establishment of a numerical simulation model of a deep-sea hydrothermal plume including bottom crossflow further includes: setting the simulation parameters as follows.

[0012] Preferably, in step S2, the parameter fitting of the three-dimensional distribution data of hydrothermal particulate matter mass concentration obtained in step S1 specifically includes: From the three-dimensional distribution data, the spatial locations with the highest mass concentration of hydrothermal particles at several different horizontal distances from the hydrothermal vent are extracted. Based on the heights corresponding to these spatial locations, a standard value of the neutral buoyancy layer height is fitted. From the three-dimensional distribution data, extract the spatial location with the highest mass concentration of hydrothermal particles at several different vertical distances from the hydrothermal vent in the seawater area far from the seabed. The height corresponding to this location is the standard value of the maximum rise height. Based on the standard value of the neutral buoyancy layer height, parameter fitting is performed on the undetermined coefficients in the calculation formula of the neutral buoyancy layer height; Based on the standard value of the maximum ascent height, parameter fitting is performed on the undetermined coefficients in the formula for calculating the maximum ascent height.

[0013] Preferably, in step S2, In the MTT theoretical model, the maximum ascent height The calculation formula and the height of the neutral buoyancy layer Subject to background buoyancy frequency and source buoyancy flux The impact, and its calculation formulas are as follows: and ,in and These are coefficients to be determined.

[0014] The MTT theoretical model was one of the first to propose a one-dimensional steady-state solution to describe the buoyancy plume transport and mixing process, and obtained the relationship of characteristic height.

[0015] Preferably, in step S2, the height of the neutral buoyancy layer is obtained. and maximum ascent height The calculation formulas are as follows: in, All are undetermined coefficients.

[0016] Preferably, the values ​​of the undetermined coefficients are as follows: ; =1.85, =2.51; The height of the neutral buoyancy layer and maximum ascent height In the calculation formula, in The depth density function of the current sea area Midsea water density relative to seawater depth The rate of change of seawater depth The definition is as follows: vertical coordinates Upward is positive, that is, the sea surface. ,down ; Source buoyancy flux, subject to nozzle mass flow rate Influence; and The calculation formula is as follows: in, The density of the hydrothermal fluid within the range of the hydrothermal eruption height. The density at the bottom of the seawater. Take the acceleration due to gravity as 9.81 m / s². 2 .

[0017] The ejection velocity of the hydrothermal plume from the nozzle; Where is the nozzle radius; Preferably, step S4 further includes: Using the hydrothermal particulate matter mass concentration data obtained in step S1 as the true value and the calculation results of the Gaussian diffusion model in step S3 as the predicted value, the following fitness function is constructed: and The first The concentration values ​​obtained in step S1 and the concentration values ​​predicted by the Gaussian model in step S3 are used to determine the optimal diffusion coefficient by iteratively searching to minimize the fitness function. and ; When the initial population size of the particle swarm optimization algorithm is 1000 and the maximum number of iterations is 100, the inertia weight is 0.8, and the self-learning factor and the swarm learning factor are both 0.5.

[0018] Preferably, in step S1, the computational domain of the simulation model is a cuboid with dimensions of 1000 m × 500 m × 500 m. The mesh is drawn using the Triangles method, and local mesh refinement is implemented in the near-field region of the nozzle. The refinement area is defined as 50r × 50r × 50r, where r is the nozzle diameter. One-tenth of the nozzle radius is selected as the minimum mesh size, and the mesh expansion rate is set to 1.02. The nozzle radius is also set. Nozzle height .

[0019] Preferably, step S4 further includes: substituting the solved diffusion coefficient into the Gaussian diffusion model considering the upper boundary constraint, and using the Gaussian diffusion model considering the upper boundary constraint to predict the hydrothermal particulate matter mass concentration of the hydrothermal plume in an ocean environment with bottom crossflow.

[0020] The beneficial effects of this invention are as follows: 1. Innovatively solves the problem of characteristic height identification: This invention proposes to determine the height of the neutral buoyancy layer by identifying the abrupt boundary of hydrothermal particle mass concentration in the vertical direction. This method completely avoids dependence on the plume central axis and fundamentally solves the problem of traditional methods failing when the central axis is bent due to the crossflow at the bottom layer, significantly improving the accuracy and robustness of characteristic height determination.

[0021] 2. A high-precision model with clear physical meaning was constructed: The constructed Gaussian diffusion model explicitly introduces the effective source height determined by the height of the neutral buoyancy layer. As the upper boundary, it more realistically reflects the physical process of plumes being constrained by buoyancy in stratified oceans, making the model predictions more consistent with actual physical phenomena.

[0022] 3. Accurate calibration of model parameters was achieved: The diffusion coefficient was solved by inverting high-precision CFD simulation data using the particle swarm optimization algorithm, which ensured the physical rationality and numerical accuracy of the model parameters and avoided subjective errors caused by empirical values.

[0023] 4. Balancing accuracy and efficiency: This method utilizes CFD simulation to obtain key physical information and then constructs a lightweight analytical model, which ensures the accuracy of the model while significantly reducing computational costs. It is very suitable for integration into the autonomous detection and path planning system of AUVs, providing strong technical support for achieving efficient and accurate deep-sea hydrothermal vent tracing. Attached Figure Description

[0024] Figure 1 Overview of the Longqi hydrothermal vent area; (a) Distribution of hydrothermal vents and CTD stations; (b) Turbidity variation at CTD stations; (c) Potential temperature-potential density curves of the Longqi hydrothermal vent area; Figure 2 A schematic diagram of the computational domain and mesh generation for a numerical simulation model of a hydrothermal plume; Figure 3 These are the temperature-density function and temperature-specific heat capacity function of the hydrothermal fluid; Figure 4 This is a linear simplification of the temperature-density function of hydrothermal fluids; Figure 5A comparison of the maximum hydrothermal particle concentration and the height of the neutral buoyancy layer at different diffusion distances; Figure 6 This is a comparison chart of plume characteristic height results; blue circles indicate the fitting results of the formula in the example, and red lines indicate that the numerical simulation and formula calculation are in agreement; Figure 7 A flowchart of the particle swarm optimization algorithm; Figure 8 The iterative process of the particle swarm optimization algorithm and the values ​​of C1-C4 in the current embodiment at u=0.01m / s; Figure 9 This is a comparison chart of CFD simulation results and Gaussian diffusion model prediction results. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0026] Example This embodiment takes the Longqi hydrothermal vent area (37°47'S, 49°39'E) in the southwestern Indian Ocean as the research object, such as... Figure 1 As shown, Figure 1 In the diagram, 'a' represents the distribution of hydrothermal vents and CTD stations in the Longqi hydrothermal area. The implementation example primarily uses data from CTD05-2 for simulation and verification. For example... Figure 1 In point b, a significant turbidity anomaly was detected at a water depth of 2550m-2650m in the Longqi hydrothermal field, suggesting that the neutral buoyancy layer height of the hydrothermal plume in the Longqi field is between 150m and 250m. Figure 1 As can be seen from 'c', the potential temperature and potential density of the background environment are basically linearly related, which can be used as the basis for setting the initial simulation conditions in this embodiment. The specific implementation of the construction method of this invention includes the following steps: Step 1: Obtain three-dimensional distribution data of the mass concentration of hydrothermal particles in the hydrothermal plume. like Figure 2 As shown, a cuboid computational domain with dimensions of 1000m (x-direction) × 500m (y-direction) × 500m (z-direction) was established. An unstructured triangular mesh was generated using ICEM CFD software, resulting in approximately 9.27 million mesh elements across the entire domain. Local mesh refinement was applied within a 50-times-the-nozzle diameter area centered on the nozzle. Boundary conditions were set as follows: the plane at x=0 was designated as the velocity inlet, simulating the underlying crossflow, with the velocity profile as follows. ,in Let H be the crossflow velocity at the bottom layer (0.01 m / s in this embodiment), and H be the boundary layer thickness. A no-slip wall is set at the bottom of the computational domain (z=0); a pressure outlet is set at the top of the computational domain (z=500 m); and the hydrothermal vent is set as a velocity inlet with an eruption velocity of 1.5 m / s and an eruption temperature of 370℃. During the hydrothermal fluid eruption process, its physical properties are mainly affected by temperature. During the plume's ascent, seawater salinity and pressure can be approximated as constant. Therefore, seawater density and specific heat capacity can be considered as functions of temperature at a fixed salinity (34.98 PSU) and pressure (27 MPa). Predicting the seawater physical properties of the Longqi hydrothermal vent area yields the changes in density and specific heat capacity with temperature, such as... Figure 3 As shown in the figure. Since deep-sea temperatures are generally below 4℃, linear processing of seawater below 4℃ simplifies the model, yielding a constant background buoyancy frequency N and reducing computational load. Similarly, simplification is applied to other temperature ranges, and the results are shown in the figure. Figure 4 As shown.

[0027] Ansys Fluent software was used, and realizable k- was selected. Turbulence model and discrete phase model (DPM), simulating a particle size of 5 m, 10 m, 50 m, 100 m and 500 The simulation process involved the transport of hydrothermal particles. The total simulation duration was 25,000 seconds, with a time step of 0.5 seconds, until the flow field reached a quasi-steady state. Finally, the mass concentration distribution data of the hydrothermal particles in three-dimensional space were obtained.

[0028] Table 1. Particle size distribution and some simulation environment settings

[0029] Step 2: Determine the characteristic height based on particulate matter concentration distribution and verify the accuracy of the formula. like Figure 5 As shown, curves illustrating the variation of the maximum hydrothermal particulate mass concentration along the horizontal (x-direction) at different x-positions are extracted from CFD simulation results. The height corresponding to the concentration peak is consistent with the theoretically predicted neutral buoyancy layer position, clearly indicating the initiation interface for large-scale horizontal diffusion of particulate matter. Therefore, this study defines the neutral buoyancy layer height as the peak position of hydrothermal particulate mass concentration in the vertical direction under the influence of crossflow.

[0030] The height of the neutral buoyancy layer in this example is determined using the method described above. The maximum rising height is recorded as the point where the vertical velocity of the particle is 0. And this example source buoyancy flux and background buoyancy frequency Substituting into the formula for comparison yields... , In this case, the neutral buoyancy layer formula has an accuracy of 1%, and the maximum rise height formula has an accuracy of 3.7%. Figure 6 This is a comparison chart of plume characteristic height results under different embodiments.

[0031] Step 3: Construct a Gaussian diffusion model considering upper boundary constraints Constructing a three-dimensional Gaussian diffusion model: Based on the results of step two, take As the height of the neutral buoyancy layer, and the effective source height Hybrid layer height .

[0032] diffusion coefficient in the model , C1-C4 are parameters to be determined.

[0033] Step 4: Use the particle swarm optimization algorithm to solve for C1-C4 as undetermined parameters, and then obtain the diffusion coefficient. like Figure 7 As shown, the particle swarm optimization algorithm is used to solve for the parameters. First, 1000 data points are randomly selected from the CFD simulation domain, and their coordinates are obtained. and the corresponding hydrothermal particulate matter mass concentration Then, these coordinates are substituted into the Gaussian model from step three to calculate the predicted concentration. A fitness function as described in the invention is constructed. The PSO algorithm parameters are set as follows: population size 100, dimension 4 (optimizing C1-C4), maximum number of iterations 100, particle position constraints [0, 10], particle velocity constraints [-0.5, 0.5], inertia weight 0.8, and initial learning factors a1 (self-learning factor) and a2 (group learning factor) both 0.5. The optimization results of the PSO algorithm show that the fitness function converges rapidly during the iteration process, eventually converging to 10. -2 -10 -3 The magnitude is negligible compared to the overall particulate matter mass concentration of discrete units within the computational domain (its error is less than 1%). The diffusion parameters C1-C4 are obtained, as follows: Figure 8 As shown. The diffusion coefficient was calculated. and A deep-sea hydrothermal plume material diffusion model under these conditions was obtained.

[0034] Model Validation like Figure 9 As shown, the prediction results of the calibrated Gaussian diffusion model are compared with the CFD simulation results. The results show that in the plume core region (|y|<200 m), the concentration distribution trends of the two models are highly consistent, with a correlation coefficient R0. 2 The mean relative error is >0.85; in the far-field region (x>400 m), the mean relative error is less than 15%. This result verifies the effectiveness and accuracy of the model constructed in this invention.

[0035] In summary, this invention provides a novel method for accurately constructing a deep-sea hydrothermal plume material diffusion model under the influence of bottom crossflow, which has significant theoretical value and broad application prospects.

Claims

1. A method for constructing a deep-sea hydrothermal plume material diffusion model based on particulate matter concentration boundary identification, characterized in that, Includes the following steps: S1. Obtain three-dimensional distribution data of the mass concentration of hydrothermal particles in the hydrothermal plume. The three-dimensional distribution data includes the spatial coordinates of the hydrothermal plume in the flow field and the mass concentration of hydrothermal particles corresponding to the coordinates. S2. Determine the feature height based on the obtained three-dimensional distribution data of the mass concentration of hydrothermal particles in the hydrothermal plume: The characteristic height includes the neutral buoyancy layer height and the maximum rise height; The specific characteristics for determining height include: based on the MTT theoretical model, introducing... To quantify the influence of the bottom crossflow on the hydrothermal plume, the height of the neutral buoyancy layer was obtained. and maximum ascent height The calculation formula; in, It is the crossflow velocity at the bottom layer; and The coefficients to be determined are obtained by fitting the parameters using the three-dimensional distribution data of hydrothermal particulate matter mass concentration obtained in step S1, and then using the coefficients to calculate the height of the neutral buoyancy layer and the maximum rise height. S3. Construct a Gaussian diffusion model that considers the upper boundary constraints: In the Gaussian diffusion model, the virtual source method is used to satisfy the physical constraint conditions of the upper boundary on the concentration of plume particles. Based on the fact that settlement occurs only at the upper boundary, a virtual source is introduced, resulting in a Gaussian diffusion model considering the upper boundary constraint: in, n =1 represents the number of virtual sources. For the location The mass concentration of hydrothermal particulate matter; Source emission rate of particulate matter; The bottom crossflow velocity; and The distance of the eruption source on the y-axis; For effective source high , Take the nozzle height above the ground The equivalent centerline height of the plume after it has been initially lifted by power and buoyancy and begins to undergo far-field Gaussian diffusion; The mixing layer thickness is the maximum height from which particles can be fully mixed in the vertical direction, equivalent to the maximum upward height. ,Right now = , and These are the diffusion coefficients in the horizontal and vertical directions, respectively, and are determined by the following formula: ， For diffusion parameters, This represents the distance along the crossflow direction; S4. Based on the three-dimensional distribution data of mass concentration obtained in step S1, the diffusion coefficient is solved using the particle swarm optimization algorithm.

2. The method for constructing a deep-sea hydrothermal plume mass diffusion model based on particulate matter concentration boundary identification as described in claim 1, characterized in that, S1 specifically includes the following steps: A numerical simulation model of a deep-sea hydrothermal plume, including bottom crossflow, was established using computational fluid dynamics software. The hydrothermal vent is set as a velocity inlet, with an ejection velocity of 1-1.5 m / s and an ejection temperature of 270-370℃; The calculation domain is configured with a non-slip wall at the bottom and a pressure outlet at the top. A velocity inlet was set on one side of the computational domain to simulate the underlying crossflow, with a crossflow velocity range of 0.01–0.03 m / s; Using realizable k- Multiphase flow coupled simulations were performed using turbulence and discrete phase (DPM) models to simulate the mixing process of hydrothermal fluids with surrounding seawater, with particle sizes ranging from 5 to 500 μm. The transport process of hydrothermal particles was analyzed to obtain the mass concentration distribution data of hydrothermal particles in three-dimensional space.

3. The method for constructing a deep-sea hydrothermal plume mass diffusion model based on particulate matter concentration boundary identification as described in claim 2, characterized in that, The establishment of a numerical simulation model for deep-sea hydrothermal plumes that includes bottom crossflows also includes: Configure the simulation using the parameters above.

4. The method for constructing a deep-sea hydrothermal plume mass diffusion model based on particulate matter concentration boundary identification as described in claim 1, characterized in that, In step S2, the parameter fitting of the three-dimensional distribution data of hydrothermal particulate matter mass concentration obtained in step S1 specifically includes: From the three-dimensional distribution data, the spatial locations with the highest mass concentration of hydrothermal particles at several different horizontal distances from the hydrothermal vent are extracted. Based on the heights corresponding to these spatial locations, a standard value of the neutral buoyancy layer height is fitted. From the three-dimensional distribution data, extract the spatial location with the highest mass concentration of hydrothermal particles at several different vertical distances from the hydrothermal vent within the seawater area. The height corresponding to this location is the standard value of the maximum rise height. Based on the standard value of the neutral buoyancy layer height, parameter fitting is performed on the undetermined coefficients in the calculation formula of the neutral buoyancy layer height; Based on the standard value of the maximum ascent height, parameter fitting is performed on the undetermined coefficients in the formula for calculating the maximum ascent height.

5. The method for constructing a deep-sea hydrothermal plume mass diffusion model based on particulate matter concentration boundary identification as described in claim 4, characterized in that, In step S2, In the MTT theoretical model, the maximum ascent height The calculation formula and the height of the neutral buoyancy layer Subject to background buoyancy frequency and source buoyancy flux The impact, and its calculation formulas are as follows: and ,in and These are coefficients to be determined.

6. The method for constructing a deep-sea hydrothermal plume mass diffusion model based on particulate matter concentration boundary identification as described in claim 5, characterized in that, In step S2, the height of the neutral buoyancy layer is obtained. and maximum ascent height The calculation formulas are as follows: in, All are undetermined coefficients.

7. The method for constructing a deep-sea hydrothermal plume mass diffusion model based on particulate matter concentration boundary identification as described in claim 6, characterized in that, The values ​​of the undetermined coefficients are as follows: ； =1.85， =2.51; The height of the neutral buoyancy layer and maximum ascent height In the calculation formula, in The depth density function of the current sea area Midsea water density relative to seawater depth The rate of change of seawater depth The definition is as follows: vertical coordinates Upward is positive, that is, the sea surface. ,down ; Source buoyancy flux, subject to nozzle mass flow rate Influence; and The calculation formula is as follows: in, The density of the hydrothermal fluid within the range of the hydrothermal eruption height. The density at the bottom of the seawater. Take the acceleration due to gravity as 9.81 m / s². 2 ; The ejection velocity of the hydrothermal plume from the nozzle; Where is the nozzle radius.

8. The method for constructing a deep-sea hydrothermal plume material diffusion model based on particulate matter concentration boundary identification as described in claim 1, characterized in that, Step S4 also includes: Using the hydrothermal particulate matter mass concentration data obtained in step S1 as the true value and the calculation results of the Gaussian diffusion model in step S3 as the predicted value, the following fitness function is constructed: and The first The concentration values ​​obtained in step S1 and the concentration values ​​predicted by the Gaussian model in step S3 are used to determine the optimal diffusion coefficient by iteratively searching to minimize the fitness function. and ; When the initial population size of the particle swarm optimization algorithm is 1000 and the maximum number of iterations is 100, the inertia weight is 0.8, and the self-learning factor and the swarm learning factor are both 0.

5.

9. The method for constructing a deep-sea hydrothermal plume mass diffusion model based on particulate matter concentration boundary identification as described in claim 2, characterized in that, In step S1, the computational domain of the simulation model is a cuboid with dimensions of 1000 m × 500 m × 500 m. The mesh is drawn using the Triangles method, and local mesh refinement is implemented in the near-field region of the nozzle. The refinement area is defined as 50r × 50r × 50r, where r is the nozzle diameter. One-tenth of the nozzle radius is selected as the minimum mesh size, and the mesh expansion rate is set to 1.

02. The nozzle radius is also set. Nozzle height .

10. The method for constructing a deep-sea hydrothermal plume mass diffusion model based on particulate matter concentration boundary identification as described in claim 1, characterized in that, Step S4 further includes: substituting the solved diffusion coefficient into the Gaussian diffusion model considering the upper boundary constraint, and using the Gaussian diffusion model considering the upper boundary constraint to predict the hydrothermal particulate matter mass concentration of hydrothermal plumes in an ocean environment with bottom crossflow.

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