Simulation optimization method and system for gas-liquid two-phase flow field of fixed dredging module pipeline

Abstract

This invention proposes a simulation optimization method and system for gas-liquid two-phase flow fields in fixed dredging module pipelines, belonging to the field of simulation optimization technology for gas-liquid two-phase flow fields in dredging. Addressing the problems in existing technologies where the determination of aeration parameters largely relies on empirical values, leading to unstable dredging effects, high energy consumption, and a lack of dynamic control over aeration parameters under different flow conditions, this invention conducts multi-condition numerical simulations using a gas-liquid two-phase flow model. With the objectives of maximizing the disturbance range and minimizing unit energy consumption, multi-dimensional disturbance indices are extracted and combined with unit energy consumption indices to construct a multi-objective optimization evaluation function. A comprehensive performance score is calculated using a combination of Pareto front screening and congestion distance scoring. Closed-loop iterative correction is performed through physical model verification experiments, achieving dynamic adjustment of aeration parameters based on real-time flow velocity monitoring. This invention significantly improves the energy efficiency ratio, accuracy, and reliability of aeration parameters in dredging operations, while reducing reliance on manual operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of simulation and optimization technology for gas-liquid two-phase flow fields in dredging, and particularly to a method and system for simulation and optimization of gas-liquid two-phase flow fields in fixed dredging module pipelines. Background Technology

[0002] Aeration-based dredging technology, as an emerging and environmentally friendly dredging method, has shown broad application prospects in the field of water environment management in recent years. This technology injects compressed air into the water body, utilizing the shear force generated during the rise of the air bubbles to disturb the bottom sediment, causing the sediment to resuspend and be transported by natural water flow, thereby achieving the purpose of dredging. Compared with traditional mechanical dredging, aeration-based dredging does not require ship anchoring operations and does not interfere with waterway traffic; it has lower energy consumption, significantly reducing operating costs; and it does not involve the addition of chemical agents, minimizing the impact on the aquatic ecological environment, thus meeting the requirements of green and low-carbon development. With its high efficiency, environmental friendliness, and economic advantages, aeration-based dredging technology has broad market application prospects in port and waterway maintenance, reservoir dredging, and urban river management.

[0003] However, existing aeration-based dredging technologies still have many limitations in practical applications. First, the determination of aeration parameters lacks scientific basis, currently relying heavily on empirical values, leading to unstable dredging effects and high energy consumption. Studies show that parameters such as air supply volume, air supply pressure, and aeration orifice diameter have complex coupling relationships with environmental factors such as water flow velocity and water depth; single fixed parameters are difficult to adapt to dredging needs under different working conditions. Second, existing technologies lack the ability to dynamically adjust aeration parameters under different water flow conditions. When the water flow velocity in the target area changes, the diffusion range of the bubble plume and the intensity of water disturbance change accordingly, resulting in a decrease in dredging efficiency. Furthermore, the pipeline structure design of aeration equipment is often not systematically optimized; the matching between structural parameters such as nozzle arrangement and pipeline layout and aeration parameters is insufficient, hindering further improvement in dredging effectiveness.

[0004] Therefore, determining the optimal combination of aeration parameters for different water flow conditions and achieving refined and intelligent control of the aeration-type sludge removal system has become an urgent technical problem to be solved. Summary of the Invention

[0005] To address these issues, this invention provides a simulation optimization method and system for the gas-liquid two-phase flow field of a fixed dredging module pipeline. This method overcomes the problems in existing technologies where the determination of aeration parameters relies heavily on empirical values, leading to unstable dredging effects, high energy consumption, difficulty in adapting to different dredging needs under different working conditions with a single fixed parameter, lack of dynamic control capability for aeration parameters under different water flow conditions, and insufficient matching between the pipeline structure design of the aeration equipment and the aeration parameters.

[0006] To achieve the above objectives, this invention provides a simulation optimization method for the gas-liquid two-phase flow field of a fixed dredging module pipeline, comprising:

[0007] S1. Establish a three-dimensional geometric model based on the pipeline structure of the fixed dredging module, and divide the flow field region into grids to generate a water area grid for numerical calculation.

[0008] S2, collect measured hydrological data of the target dredging area to determine the water flow velocity of each water grid;

[0009] S3. In the fluid dynamics simulation software, select the gas-liquid two-phase flow model, set the gas phase as compressible air and the liquid phase as incompressible water, and define the flow velocity of the water as the boundary condition.

[0010] S4 sets multiple aeration parameter combinations for different water flow velocities, including gas flow rate and inlet pressure, and performs numerical simulation of gas-liquid two-phase flow field to obtain bubble plume morphology, gas phase volume fraction distribution and turbulent kinetic energy field data under different aeration parameter combinations.

[0011] S5, with the goal of maximizing the disturbance range and minimizing the unit energy consumption, determines the optimal combination of aeration parameters for different water flow velocities and constructs a mapping relationship model between water flow velocity and the optimal combination of aeration parameters.

[0012] S6. Select several typical velocity grids, and based on the corresponding optimal aeration parameter combination, build a physical model in a wave tank to carry out verification experiments, measure the bubble plume morphology and sediment suspension effect, and compare the experimental results with the results of numerical simulation of gas-liquid two-phase flow field to verify the accuracy of the mapping relationship.

[0013] S7, based on the verified mapping relationship, outputs a table of optimal aeration parameter combinations for different water flow velocities, which is used to guide the intelligent operation of stationary dredging modules in practice.

[0014] Furthermore, for different water flow velocities, multiple sets of aeration parameter combinations, including gas flow rate and inlet pressure, are set to perform numerical simulations of the gas-liquid two-phase flow field, including:

[0015] S41, Select a water flow velocity value to be analyzed, and under the current water flow velocity conditions, set a series of discrete gas flow rates and corresponding inlet pressure values ​​to form multiple sets of aeration parameter combinations to be calculated.

[0016] S42, Substitute the multiple sets of aeration parameter combinations into the fluid dynamics simulation software after setting boundary conditions, and perform unsteady or steady gas-liquid two-phase flow field numerical simulation calculations respectively.

[0017] S43 sets a unified simulation calculation time and convergence residual standard to ensure that the flow field calculation under each working condition reaches a stable or statistically converged state.

[0018] S44. After the simulation calculation is completed, extract and record the bubble plume morphology characteristics, spatial distribution data of gas phase volume fraction in the flow field, and distribution and intensity data of turbulent kinetic energy corresponding to each set of aeration parameter combinations from the calculation results.

[0019] Furthermore, the process of determining the optimal aeration parameter combination corresponding to different water flow velocities, with the goal of maximizing the disturbance range and minimizing unit energy consumption, and constructing a mapping relationship model between water flow velocity and the optimal aeration parameter combination, includes:

[0020] S51, for each set of aeration parameter combinations under each water flow velocity, extract indicators for quantifying the disturbance range based on the bubble plume morphology, gas phase volume fraction distribution, and turbulent kinetic energy field data obtained in S4. At the same time, calculate the unit energy consumption of each set of aeration parameter combinations based on gas flow rate and inlet pressure. The unit energy consumption is expressed as the ratio of input power to effective disturbance range. The indicators include the lateral diffusion width of the bubble plume, the volume of the region where the gas phase volume fraction is greater than a preset threshold, and the spatial range where the turbulent kinetic energy exceeds the background value.

[0021] S52, establish a multi-objective optimization evaluation function, perform positive normalization on the disturbance range index and negative normalization on the unit energy consumption index, and calculate the comprehensive performance score of each aeration parameter combination by screening through Pareto optimal frontier.

[0022] S53. Compare the comprehensive performance scores of all aeration parameter combinations under each water flow rate, determine the combination with the highest score as the optimal aeration parameter combination under that water flow rate, and record the corresponding gas flow rate and inlet pressure value.

[0023] S54 uses a sample dataset consisting of multiple different water flow velocities and their corresponding optimal gas flow rate and inlet pressure values. Through neural network mathematical modeling, a mapping relationship model is constructed with water flow velocity as input and optimal gas flow rate and optimal inlet pressure as output.

[0024] Furthermore, the disturbance range index is positively normalized, the unit energy consumption index is negatively normalized, and the comprehensive performance score of each aeration parameter combination is calculated using the Pareto optimal front screening method, including:

[0025] S521, for all aeration parameter combinations under each water flow velocity, the lateral diffusion width of the bubble plume, the volume of the region where the gas phase volume fraction is greater than the preset threshold, and the spatial range where the turbulent kinetic energy exceeds the background value are positively normalized to obtain the normalized disturbance range score; at the same time, the unit energy consumption index is negatively normalized to obtain the normalized energy consumption score.

[0026] S522 uses both the perturbation range score and the energy consumption score as optimization objectives. It employs a fast non-dominated sorting algorithm to perform Pareto front layering for all aeration parameter combinations, obtaining the Pareto front layer number of each combination. The first front layer is the non-dominated solution set.

[0027] S523. For each combination of aeration parameters, a base score is assigned based on the number of Pareto front layers to which it belongs. The higher the number of front layers, the higher the base score. At the same time, the crowding distance between the combination in the same front layer and the adjacent solution in the same layer is calculated and an additional score is assigned. The larger the crowding distance, the higher the additional score.

[0028] S524 adds the base score and the additional score of each combination to obtain the overall performance score of that combination.

[0029] Furthermore, in S2, after collecting measured hydrological data of the target dredging area, the process also includes a step of classifying the water flow velocity. Specifically, the continuous water flow velocity is divided into several discrete velocity intervals, and a representative value is assigned to each velocity interval for subsequent simulation calculations.

[0030] Furthermore, the verification experiment conducted by building a physical model in the wave tank includes:

[0031] The prototype silt of the target dredging area is laid at the bottom of the wave tank, and the aeration device is installed according to the fixed pipeline structure in S1.

[0032] Adjust the water flow generating device in the water tank to make the water flow velocity reach the water flow velocity value of the selected typical flow velocity grid;

[0033] The aeration device is turned on according to the optimal aeration parameter combination obtained in S5. The bubble plume morphology is recorded by a high-speed camera, and the suspended sediment concentration and diffusion range are monitored by a turbidimeter.

[0034] The rising height, diffusion width, and suspended sediment concentration distribution of the bubble plume measured by the physical model experiment were compared with the results of the gas-liquid two-phase flow field numerical simulation obtained by S4 to verify the accuracy of the mapping relationship.

[0035] Furthermore, if the deviation between the physical model test results and the numerical simulation results of the gas-liquid two-phase flow field is greater than or equal to the preset deviation threshold, the gas-liquid two-phase flow model parameters set in S3 are corrected according to the test data, and S4 to S6 are re-executed until the deviation between the physical model test results and the numerical simulation results of the gas-liquid two-phase flow field is less than the preset deviation threshold.

[0036] Furthermore, the intelligent operation involves real-time monitoring of the actual water flow velocity in the target water area, automatically matching and adjusting the corresponding aeration parameters according to the optimal aeration parameter combination table, thereby achieving dynamic adjustment of the aeration volume.

[0037] This invention also provides a simulation and optimization system for the gas-liquid two-phase flow field of a fixed dredging module pipeline. The system is used to implement the simulation and optimization method for the gas-liquid two-phase flow field of a fixed dredging module pipeline as described in any one of the claims. The system includes:

[0038] The 3D modeling module is used to create a 3D geometric model based on the pipeline structure of the fixed dredging module, and to divide the flow field region into meshes to generate a water area mesh for numerical calculation.

[0039] The velocity assignment module, connected to the three-dimensional modeling module, is used to collect measured hydrological data of the target dredging area and determine the water velocity of each water grid.

[0040] The boundary setting module, connected to the velocity assignment module, is used to select a gas-liquid two-phase flow model in the fluid dynamics simulation software, set the gas phase as compressible air and the liquid phase as incompressible water, and define the water velocity as the boundary condition.

[0041] The simulation calculation module, connected to the boundary setting module, is used to set multiple sets of aeration parameter combinations for different water flow velocities, including gas flow rate and inlet pressure, to perform numerical simulation of the gas-liquid two-phase flow field and obtain bubble plume morphology, gas phase volume fraction distribution and turbulent kinetic energy field data under different aeration parameter combinations.

[0042] The multi-objective optimization module, connected to the simulation calculation module, is used to determine the optimal combination of aeration parameters for different water flow velocities with the objectives of maximizing the disturbance range and minimizing unit energy consumption, and to construct a mapping relationship model between water flow velocity and the optimal combination of aeration parameters.

[0043] The physical verification module, connected to the multi-objective optimization module, is used to select several typical velocity grids, build a physical model in a wave tank based on the obtained optimal aeration parameter combination, conduct verification experiments, measure the bubble plume morphology and sediment suspension effect, and compare the experimental results with the results of numerical simulation of the gas-liquid two-phase flow field to verify the accuracy of the mapping relationship.

[0044] The parameter output module, connected to the physical verification module, is used to output an optimal aeration parameter combination table for different water flow velocities based on the verified mapping relationship, which is used to guide the intelligent operation of the fixed dredging module in practice.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] Firstly, this invention establishes a multi-objective optimization evaluation function with the goal of maximizing the disturbance range and minimizing the unit energy consumption. It normalizes multi-dimensional disturbance indices such as the lateral diffusion width of the bubble plume, the volume of the gas phase region, and the volume of the turbulent region with the unit energy consumption index. It then calculates the comprehensive performance score by combining Pareto front screening and crowding distance scoring. Finally, it uses a neural network to construct a nonlinear mapping relationship model between water flow velocity and optimal aeration parameters. This solves the technical problems in the prior art, such as the dependence of aeration parameters on empirical values, the difficulty in objectively balancing multiple objectives, and the inability of discrete optimization results to meet the requirements of real-time continuous control. It significantly improves the scientificity and precision of the determination of optimal parameters, as well as the intelligent response capability of the control system to arbitrary flow velocity conditions.

[0047] Secondly, this invention solves the technical problems of the inability to verify the rationality of the simulation model parameter assumptions and the inability of the model to absorb experimental feedback for self-improvement. It significantly improves the restoration accuracy of the simulation model and the engineering reliability of the mapping relationship model. By laying prototype sediment in the wave tank and setting aeration pipes consistent with the actual situation, selecting typical flow velocity points to carry out physical model verification tests, quantitatively comparing the bubble plume morphology and sediment suspension effect measured in the test with the numerical simulation results, and setting a deviation threshold, the gas-liquid two-phase flow model parameters are corrected based on the experimental data when the deviation exceeds the standard, and the simulation optimization is re-executed until the deviation meets the requirements.

[0048] Thirdly, this invention provides a complete technical solution for dynamically adjusting aeration parameters by integrating a three-dimensional geometric model, hydrological data classification and processing, gas-liquid two-phase flow numerical simulation, multi-objective optimization modeling, physical verification and iterative correction, and finally outputting an optimal parameter combination table for the entire flow velocity range. This solution utilizes real-time flow velocity monitoring, PLC automatic table lookup matching, and closed-loop PID control to solve the technical problems of existing aeration dredging technologies, such as difficulty in scientifically matching parameters with environmental conditions, lack of dynamic control capabilities, unstable dredging effects, and high energy consumption. It achieves a shift from experience-driven to data-driven dredging operations, significantly improving the dynamic adaptability of the dredging system to complex water flow environments, overall energy efficiency ratio, and consistency of operational results, while reducing reliance on manual operation and maintenance. Attached Figure Description

[0049] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0050] Figure 1 A flowchart illustrating the method for simulating and optimizing the gas-liquid two-phase flow field of a fixed dredging module pipeline according to an embodiment of the present invention;

[0051] Figure 2 The structural block diagram of the gas-liquid two-phase flow field simulation optimization system for the fixed dredging module pipeline provided in the embodiment of the present invention. Detailed Implementation

[0052] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0053] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0054] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0055] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0056] Example 1

[0057] like Figure 1 As shown, this invention provides a simulation optimization method for the gas-liquid two-phase flow field of a fixed dredging module pipeline, including:

[0058] S1. Establish a three-dimensional geometric model based on the pipeline structure of the fixed dredging module, and divide the flow field region into grids to generate a water area grid for numerical calculation; S2. Collect measured hydrological data of the target dredging area and determine the water flow velocity of each water area grid; In S2, after collecting the measured hydrological data of the target dredging area, the step of classifying the water flow velocity is also included, specifically dividing the continuous water flow velocity into several discrete flow velocity intervals and assigning a representative value to each flow velocity interval for subsequent simulation calculations.

[0059] In one possible implementation, taking a fixed dredging module as an example, its core component is a horizontally arranged aeration pipe with a length of 5m and a diameter of 200mm. Two rows of aeration holes with a diameter of 5mm are evenly distributed on the pipe, with a hole spacing of 50mm and an included angle of 90 degrees between the two rows of holes. A 1:1 scale geometric model is created using 3D modeling software such as SolidWorks, SpaceClaim, or ANSYS DesignModeler.

[0060] To improve the quality of subsequent mesh generation and computational efficiency, the model is simplified as necessary. For example, minor chamfers, threads, and flange connections that have little impact on flow are ignored. Simultaneously, to simulate the operation of the pipeline underwater, a rectangular water computational domain containing the pipeline is established. The dimensions of this computational domain are: length (flow direction) = twice the pipeline length (10m); width = 15 times the pipeline diameter (3m); and height = water depth (2m in this embodiment). The pipeline is located at the bottom center of the computational domain.

[0061] Meshing software, such as ANSYS Meshing, Fluent Meshing, or Pointwise, was used to mesh the water region of the above geometric model. For the main flow region far from the pipeline, structured hexahedral meshes or unstructured tetrahedral meshes were used, with a base mesh size of 50 mm. Near the aeration holes, around the pipeline, and in areas where bubble plumes might rise, local mesh refinement was required due to the large velocity and phase fraction gradients. A cylindrical refinement zone centered on the pipeline and extending upwards to the water surface was defined, with the mesh size within this zone refined to 10 mm. The final generated water region mesh number was approximately 5 to 8 million. The mesh quality was checked to ensure that the distortion was less than 0.85, the orthogonality was greater than 0.15, and the aspect ratio was less than 100 to meet the computational requirements of the subsequent CFD solver.

[0062] In one possible implementation, an acoustic Doppler current profiler is used to continuously observe the target area of ​​a dredging project in a river estuary or reservoir, collecting flow velocity, direction, and depth data over at least one complete tidal cycle. This embodiment focuses on the flow velocity component along the axis of the dredging module. The acquisition frequency is set to record every 10 minutes, resulting in a time-series flow velocity data set.

[0063] The collected hydrological data is continuously changing; therefore, it is necessary to discretize and classify the continuous measured flow velocity data to reduce the number of simulated operating conditions while covering the main flow conditions in actual operation. Specifically, statistical analysis is performed on the collected axial flow velocity time series data to obtain the range of flow velocity variation, for example, from -0.3 m / s to +0.8 m / s. Negative values ​​represent the opposite direction to the set direction and can be handled by adjusting the direction of the boundary conditions. This embodiment takes positive flow velocity as an example.

[0064] Based on the probability density distribution of the flow velocity and the sensitivity range of engineering interest, continuous flow velocities are divided into several discrete velocity ranges. For example, the velocity range [0 m / s, 0.8 m / s] can be divided into the following 5 ranges:

[0065] Interval 1 ([0 m / s, 0.1 m / s)) represents still water or very slow flow;

[0066] Interval 2, [0.1 m / s, 0.3 m / s), low flow velocity;

[0067] Interval 3, [0.3 m / s, 0.5 m / s), medium to low flow velocity;

[0068] Interval 4, [0.5 m / s, 0.7 m / s), medium to high flow velocity;

[0069] Interval 5, [0.7 m / s, 0.8 m / s], high flow velocity.

[0070] For each velocity range, a velocity value that represents the overall flow characteristics of that range is selected for subsequent simulation calculations. The representative value can be the median or a weighted average of the range, such as 0.05 m / s, 0.2 m / s, 0.4 m / s, 0.6 m / s, and 0.75 m / s.

[0071] This invention solves the technical problems of insufficient discretization accuracy of the computational domain, inaccurate near-wall flow analysis, and waste of computational resources caused by excessive number of grids when directly simulating complex physical spaces by using 3D modeling software to establish an accurate 3D geometric model based on the actual pipeline structure of the fixed dredging module, and by using a combination of techniques such as global structured grid division of the computational domain, local densification near the aeration holes, and wall boundary layer grid setting. This significantly improves the geometric reproduction accuracy of the numerical simulation and the convergence and computational accuracy of the flow field solution, while reducing the risk of numerical dissipation and computational divergence caused by poor grid quality.

[0072] This invention solves the technical problem that measured hydrological data, being continuous and time-varying, cannot be directly used as boundary conditions for a finite number of numerical simulations by employing an acoustic Doppler current profiler to collect long-term continuous hydrological data in the target dredging area, and by statistically analyzing the collected continuously changing flow velocity time series, dividing it into several discrete flow velocity intervals, and assigning representative flow velocity values ​​to each interval. This significantly improves the coverage and representativeness of the simulation conditions for the actual complex water flow environment, while reducing the redundancy and overall computational cost of simulation calculations caused by the need to simulate an infinite number of flow velocity conditions.

[0073] S3. In the fluid dynamics simulation software, select the gas-liquid two-phase flow model, set the gas phase as compressible air and the liquid phase as incompressible water, and define the flow velocity of the water as the boundary condition.

[0074] S4, for different water flow velocities, sets multiple combinations of aeration parameters, including gas flow rate and inlet pressure, to perform numerical simulations of the gas-liquid two-phase flow field, obtaining bubble plume morphology, gas phase volume fraction distribution, and turbulent kinetic energy field data under different aeration parameter combinations, including:

[0075] S41, Select a water flow velocity value to be analyzed, and under the current water flow velocity conditions, set a series of discrete gas flow rates and corresponding inlet pressure values ​​to form multiple sets of aeration parameter combinations to be calculated.

[0076] S42, Substitute the multiple sets of aeration parameter combinations into the fluid dynamics simulation software after setting boundary conditions, and perform unsteady or steady gas-liquid two-phase flow field numerical simulation calculations respectively.

[0077] S43 sets a unified simulation calculation time and convergence residual standard to ensure that the flow field calculation under each working condition reaches a stable or statistically converged state.

[0078] S44. After the simulation calculation is completed, extract and record the bubble plume morphology characteristics, spatial distribution data of gas phase volume fraction in the flow field, and distribution and intensity data of turbulent kinetic energy corresponding to each set of aeration parameter combinations from the calculation results.

[0079] In one possible implementation, ANSYS Fluent 2021 R2 is selected as the fluid dynamics simulation software, and a pressure-based transient solver is chosen to capture the dynamic evolution of the bubble plume. Gravitational acceleration is set to the negative Y-axis direction with a magnitude of 9.81 m / s². 2 The Euler-Euler multiphase flow model was selected, which treats both the gas and liquid phases as continuous, interconnected media, making it suitable for simulating the motion of bubbles in liquids. The interphase momentum exchange model was enabled, including drag, lift, and virtual mass force. The Grace model was selected for drag, with the lift coefficient set to 0.5 and the virtual mass force coefficient set to 0.5. To accurately simulate the turbulent mixing effect caused by rising bubbles, the RNG turbulence model was selected, and the multiphase flow turbulence interaction option was enabled to consider the enhancement or inhibition of liquid phase turbulence by bubbles.

[0080] The gas phase is set to air. Since changes in gas pressure during aeration may cause density changes, the gas phase is set as a compressible ideal gas, and the density is dynamically calculated with local pressure. Viscosity is set to a constant. The liquid phase is set to water. Considering that water can be regarded as an incompressible fluid in practical engineering, the density is set to a constant of 998.2 kg / m³. 3 The viscosity was set to a constant. The bubble diameter was preset to 3 mm based on experience and dynamically adjusted in subsequent simulations using a swarm equilibrium model according to local turbulence conditions.

[0081] Taking 0.4 m / s, one of the five representative flow velocity values ​​defined in S2, as an example, as the current flow velocity value of the water body to be analyzed, a series of aeration parameter combinations are designed under this flow velocity condition. The parameter combinations cover two variables: gas flow rate and inlet pressure, which are correlated through the flow-pressure characteristic curve of the aeration orifice. In this embodiment, the gas flow rate is set from 1 m... 3 / h to 10m 3 / h, step size 1m 3 / h, a total of 10 discrete values. Based on the resistance characteristics of the pipeline system, the required inlet pressure at the aeration port corresponding to each gas flow rate is calculated. For example, combination 1: gas flow rate 1m³ / h. 3 / h, inlet pressure 5kPa, combination 2: gas flow rate 2m 3 The flow rate is 0.4 m / s, and the inlet pressure is 12 kPa. Therefore, for a flow rate of 0.4 m / s, 10 sets of aeration parameter combinations are generated for calculation. This step will be repeated for the other four representative flow rate values, resulting in a total of 50 calculation conditions.

[0082] The 10 sets of aeration parameter combinations were sequentially substituted into the Fluent software with pre-set boundary conditions. For each set of conditions, the gas phase pressure value in the inlet boundary conditions was set, and transient calculations were initiated. Based on the Coulomb number requirement, the time step was set to 0.001 seconds to ensure calculation stability. The maximum number of iterations within each time step was set to 20.

[0083] To ensure the flow field reaches statistical stability, meaning the bubble plume morphology no longer changes significantly and the turbulent kinetic energy statistics tend to stabilize, the physical computation time for each operating condition is set to 30 seconds. During the calculation, the gas phase volume fraction and the average velocity at the outlet are monitored throughout the computational domain. When the monitoring curves show stable fluctuations, the flow field can be considered to have reached statistical convergence. The calculations are performed on a high-performance workstation with 48 cores, and the computation time for a single operating condition is approximately 8–12 hours.

[0084] After calculation, horizontal sections are created at different heights directly above the pipeline, such as 0.5m, 1.0m, and 1.5m. Contour maps of the gas volume fraction on these sections are then plotted using post-processing software. The maximum lateral span of the region with a gas volume fraction greater than 1% is defined as the plume width at that height, and its average value over time is recorded. A longitudinal profile is created perpendicular to the pipeline, and the maximum height that the bubble cloud can reach is observed; this is the plume's rising height. The three-dimensional distribution field data of the gas volume fraction and the distribution data of the liquid phase turbulent kinetic energy within the entire computational domain are exported. Monitoring lines or surfaces are created at different heights directly above the pipeline, and the maximum value of the turbulent kinetic energy and its lateral distribution curve are extracted. The magnitude of the turbulent kinetic energy directly reflects the disturbance and suspension capacity of the water flow on sediment.

[0085] This invention addresses the technical challenges of traditional single-phase or simplified two-phase flow models failing to accurately describe the momentum exchange between bubbles and water, the expansion effect during bubble ascent, and the bubble-induced turbulence enhancement mechanism. By employing an Euler-Euler multiphase flow model and enabling interaction mechanisms such as interphase drag, lift, and virtual mass force, and by setting the gas phase as a compressible ideal gas and the liquid phase as an incompressible fluid, and by using an RNG turbulence model suitable for gas-liquid two-phase flow and enabling multiphase turbulence interaction options, this invention solves the problem that traditional single-phase flow models or simplified two-phase flow models cannot accurately describe the momentum exchange between bubbles and water, the expansion effect during bubble ascent, and the bubble-induced turbulence enhancement mechanism. This significantly improves the physical fidelity and simulation confidence of numerical simulations of real gas-liquid two-phase flow behavior. Furthermore, by setting a series of parameters for each representative water volume velocity value that encompass changes in gas flow rate and inlet pressure, this invention further enhances the simulation's effectiveness. Discretized aeration parameter combinations were listed, and these combinations were sequentially substituted into a fluid dynamics simulation software with pre-set boundary conditions for systematic transient numerical calculations. Simultaneously, a unified calculation time and convergence residual standard were set to ensure the comparability of calculation results for each combination. This approach solved the technical problems that a single aeration parameter could not cover the diverse operational requirements of real-world conditions, and that calculation results under different parameter combinations could not be directly compared and analyzed due to differences in calculation time or convergence degree. This significantly improved the systematic nature and data comparability of parameter research. Furthermore, it constructed a comprehensive and quality-controlled flow field characteristic database for subsequent multi-objective optimization analysis, reducing the unreliability of optimization results caused by insufficient or inconsistent data samples.

[0086] S5, aiming to maximize the disturbance range and minimize unit energy consumption, determines the optimal combination of aeration parameters for different water flow velocities, and constructs a mapping model between water flow velocity and the optimal combination of aeration parameters, including:

[0087] S51, for each set of aeration parameter combinations under each water flow velocity, extract indicators for quantifying the disturbance range based on the bubble plume morphology, gas phase volume fraction distribution, and turbulent kinetic energy field data obtained in S4. At the same time, calculate the unit energy consumption of each set of aeration parameter combinations based on gas flow rate and inlet pressure. The unit energy consumption is expressed as the ratio of input power to effective disturbance range. The indicators include the lateral diffusion width of the bubble plume, the volume of the region where the gas phase volume fraction is greater than a preset threshold, and the spatial range where the turbulent kinetic energy exceeds the background value.

[0088] S52, establish a multi-objective optimization evaluation function, perform positive normalization on the disturbance range index and negative normalization on the unit energy consumption index, and calculate the comprehensive performance score of each aeration parameter combination by screening through Pareto optimal frontier.

[0089] S53. Compare the comprehensive performance scores of all aeration parameter combinations under each water flow rate, determine the combination with the highest score as the optimal aeration parameter combination under that water flow rate, and record the corresponding gas flow rate and inlet pressure value.

[0090] S54 uses a sample dataset consisting of multiple different water flow velocities and their corresponding optimal gas flow rate and inlet pressure values. Through neural network mathematical modeling, a mapping relationship model is constructed with water flow velocity as input and optimal gas flow rate and optimal inlet pressure as output.

[0091] The disturbance range index was positively normalized, and the unit energy consumption index was negatively normalized. Then, using the Pareto optimal front screening method, the comprehensive performance score for each combination of aeration parameters was calculated, including:

[0092] S521, for all aeration parameter combinations under each water flow velocity, the lateral diffusion width of the bubble plume, the volume of the region where the gas phase volume fraction is greater than the preset threshold, and the spatial range where the turbulent kinetic energy exceeds the background value are positively normalized to obtain the normalized disturbance range score; at the same time, the unit energy consumption index is negatively normalized to obtain the normalized energy consumption score.

[0093] S522 uses both the perturbation range score and the energy consumption score as optimization objectives. It employs a fast non-dominated sorting algorithm to perform Pareto front layering for all aeration parameter combinations, obtaining the Pareto front layer number of each combination. The first front layer is the non-dominated solution set.

[0094] S523. For each combination of aeration parameters, a base score is assigned based on the number of Pareto front layers to which it belongs. The higher the number of front layers, the higher the base score. At the same time, the crowding distance between the combination in the same front layer and the adjacent solution in the same layer is calculated and an additional score is assigned. The larger the crowding distance, the higher the additional score.

[0095] S524 adds the base score and the additional score of each combination to obtain the overall performance score of that combination.

[0096] In one possible implementation, the gas flow rate is 5 m³ / s at a velocity of 0.4 m / s. 3 Taking the / h combination as an example, import the CFD result file exported in step S44 into the post-processing software. On the YZ plane perpendicular to the pipeline, where Z represents the height and Y represents the lateral direction, create a horizontal monitoring line at a height Z = 1.0m from the bottom surface. Extract the gas phase volume fraction distribution curve on this monitoring line. Set a preset threshold of 1%, meaning that areas with a gas phase volume fraction exceeding 1% are considered to have been effectively disturbed. Measure the lateral distance between the two endpoints on the curve where the volume fraction is greater than 1%, and record this as the lateral diffusion width of the bubble plume. The measured lateral diffusion width of the bubble plume under this combination is 1.2m.

[0097] By integrating the entire computational domain, the volume of the region with a gas phase volume fraction greater than the preset threshold was calculated to be 0.85 m³. 3The background turbulent kinetic energy field under non-aeration conditions (i.e., pure water flow) was calculated to obtain the average background turbulent kinetic energy. Then, a disturbance threshold was set to twice the average background turbulent kinetic energy. The entire computational domain was integrated to calculate the volume of the spatial region where the liquid-phase turbulent kinetic energy exceeds the disturbance threshold. The calculation showed that the spatial range where the turbulent kinetic energy exceeds the background value under this combination is 1.2 m. 3 Based on the gas flow rate and corresponding inlet pressure, the input power was calculated to be 48.65W. The effective disturbance range was defined as the combination of the three disturbance range indices mentioned above. In this embodiment, the geometric mean was used for preliminary synthesis, and the unit energy consumption was calculated to be 45.47W. The above calculation was repeated for the remaining 9 combinations at a flow rate of 0.4m / s to obtain the original data matrix. The disturbance range index was positively normalized using the extreme value normalization formula, and the unit energy consumption index was negatively normalized using the negative normalization formula.

[0098] Using three perturbation range scores and one energy consumption score as four optimization objectives, a fast non-dominated ranking is performed on all 10 combinations. Specifically, if all four normalized indices of a combination A are not inferior to those of combination B, and at least one index is superior to that of combination B, then A is said to dominate B. Combinations not dominated by any other combination constitute the first Pareto front. The higher the front layer, the higher the base score. This embodiment uses linear scoring: the base score for the first front layer is 100 points, the second front layer is 80 points, the third front layer is 60 points, and so on. Assuming combination 5 is located in the first front layer, its base score is 100 points. For combinations located in the same front layer, their crowding distance needs to be calculated. The crowding distance reflects how tightly the combination is surrounded by other adjacent solutions in the target space. The larger the distance, the sparser the solutions in the region where the combination is located, the better its uniqueness, and the more worthwhile it is to retain. This is done at a flow rate of 0.4 m / s and a gas flow rate of 5 m³ / s. 3 Taking the combination of / h as an example, in the same frontier layer, two adjacent combinations are used, such as a gas flow rate of 4m³ / h. 3 / h combination and gas flow rate 6m 3 The congestion distance is obtained by normalizing and summing the distances on each normalized target axis for the combination of / h. In this embodiment, the congestion distance is calculated to be 0.85. The congestion distance is multiplied by the additional score weighting coefficient to obtain the additional score. In this embodiment, the additional score weighting coefficient is 20, so the additional score is 17 points. The base score and the additional score are added together to obtain the comprehensive performance score of each combination. The comprehensive performance scores of all 10 aeration parameter combinations at a flow rate of 0.4 m / s are compared, and the combination with the highest score is determined as the optimal aeration parameter combination at that flow rate. The corresponding gas flow rate and inlet pressure values ​​are recorded. The optimization process of S51 to S53 is repeated for the remaining 4 representative flow rate values ​​to obtain the optimal gas flow rate and inlet pressure for each flow rate. Thus, a dataset containing 5 sample points is constructed.

[0099] A backpropagation neural network was used to construct a mapping model. The input layer had one node, representing the water flow velocity, and the output layer had two nodes, representing the optimal gas flow rate and optimal inlet pressure. Two hidden layers were used, with 5 and 4 neurons respectively, and tansig and purelin activation functions were employed. Five sample datasets were randomly divided into training and validation sets for training the neural network. This resulted in a neural network mapping model capable of accurately and in real-time outputting the optimal gas flow rate and optimal inlet pressure based on any input water flow velocity. This model will serve as the core control algorithm and will be embedded into the intelligent control system of the dredging module.

[0100] This invention addresses the technical problem that a single indicator cannot comprehensively reflect the disturbance effect of dredging operations, leading to deviations in optimization direction from actual needs. It extracts the lateral diffusion width, gas phase volume, and turbulent region volume as multidimensional indicators for each set of aeration parameters at each water flow velocity, based on bubble plume morphology, gas phase volume fraction distribution, and turbulent kinetic energy field data. Furthermore, it calculates unit energy consumption based on gas flow rate and inlet pressure as an efficiency evaluation indicator. This significantly improves the comprehensiveness and engineering relevance of the optimization objectives. By establishing a multi-objective optimization evaluation function, the three disturbance range indicators are positively normalized, and the unit energy consumption indicator is negatively normalized. A fast non-dominated sorting algorithm is used to perform Pareto front stratification of parameter combinations, assigning a base score based on the number of front layers and an additional score based on the congestion distance within the same layer. The technique of adding scores to calculate the comprehensive performance score of each combination solves the technical problems of difficulty in objectively weighing multiple optimization objectives when there are mutual constraints, and the inability to distinguish the superiority or inferiority of solutions at the same level by simply relying on the Pareto front. This significantly improves the scientific nature of multi-objective optimization decision-making and the precision of parameter selection. By comparing the comprehensive performance scores of all aeration parameter combinations under each water flow velocity, the combination with the highest score is determined as the optimal aeration parameter combination under that flow velocity. Furthermore, the technique of using neural networks to perform nonlinear mapping modeling of multiple flow velocities and their corresponding optimal parameters solves the technical problem that traditional methods can only obtain the optimal parameters at discrete flow velocity points and cannot provide an instantaneous optimal control strategy for arbitrary real-time flow velocities. This significantly improves the engineering applicability of the optimization results and the intelligent response capability of the control system, while reducing reliance on human experience.

[0101] S6. Select several typical velocity grids, and based on the corresponding optimal aeration parameter combination, build a physical model in a wave tank to carry out verification experiments, measure the bubble plume morphology and sediment suspension effect, and compare the experimental results with the results of numerical simulation of gas-liquid two-phase flow field to verify the accuracy of the mapping relationship.

[0102] A physical model was constructed in a wave flume for verification experiments, including: laying prototype sediment from the target dredging area at the bottom of the wave flume and arranging aeration devices according to the fixed pipeline structure in S1; adjusting the flume flow generator to achieve the water flow velocity value of the selected typical velocity grid; activating the aeration devices according to the optimal aeration parameter combination obtained in S5, recording the bubble plume morphology using a high-speed camera, and monitoring the suspended sediment concentration and diffusion range using a turbidimeter; comparing the bubble plume rise height, diffusion width, and suspended sediment concentration distribution measured by the physical model experiment with the results of the gas-liquid two-phase flow field numerical simulation obtained in S4 to verify the accuracy of the mapping relationship. If the deviation between the physical model experiment results and the gas-liquid two-phase flow field numerical simulation results is greater than or equal to a preset deviation threshold, the gas-liquid two-phase flow model parameters set in S3 are corrected based on the experimental data, and S4 to S6 are re-executed until the deviation between the physical model experiment results and the gas-liquid two-phase flow field numerical simulation results is less than the preset deviation threshold.

[0103] In one possible implementation, to verify the accuracy of the neural network mapping model constructed in step S5, three typical flow velocity points were selected for physical model experiments: 0.2 m / s, 0.4 m / s, and 0.6 m / s. A large wave flume from a key laboratory for port and coastal engineering was used, with dimensions of 50 m long, 1.0 m wide, and 1.5 m deep. The flume was equipped with a high-precision servo motor-driven flow generation system, which could precisely control the constant flow velocity within the flume. Prototype sediment samples were collected from the target dredging area. After screening to remove large particles, a 10 m long and 0.2 m thick sediment layer was laid in the central area at the bottom of the flume. Before laying, the sediment was thoroughly stirred and saturated with water to simulate real riverbed conditions.

[0104] Strictly following the 3D geometric model established in step S1, a 1:1 scale physical aeration pipeline model was constructed. The pipeline was made of transparent acrylic glass to facilitate observation of the water flow within. The pipe diameter was 200mm, the aeration hole diameter was 5mm, the two rows of holes were at a 90-degree angle, and the hole spacing was 50mm. The pipeline was buried in the sediment layer, with the aeration holes facing upwards at an angle, and the top flush with the sediment surface. One end of the pipeline was connected to an air compressor via a flexible hose. A high-precision gas mass flow controller and pressure sensor were installed on the pipeline for precise control and real-time monitoring of aeration parameters.

[0105] The water tank flow generation system was activated, and the average flow velocity within the tank was adjusted to 0.2 m / s, 0.4 m / s, and 0.6 m / s respectively. Each flow velocity was maintained for 10 minutes until the flow field stabilized before the experiment began. Based on the mapping model established in step S5, the aeration parameters were adjusted to the optimal combination of aeration parameters using a gas mass flow controller and pressure regulating valve. Two high-speed cameras were mounted on the side and front of the water tank to capture the dynamic morphology of the bubble plume. The camera field of view covered a range of 1.5m upstream and downstream of the center of the pipe. Turbidimeters were placed at different cross-sections of the water tank, for example, at distances of 0.5m upstream, 0.5m downstream, 1.0m, and 1.5m from the center of the aeration pipe, and at different heights, for example, at distances of 0.1m, 0.3m, and 0.5m from the bottom, to monitor the changes in suspended sediment concentration in real time. The background turbidity value at each measuring point was measured before the experiment, and the turbidity change curve over time was recorded during the experiment. The suspended sediment concentration was calculated using a pre-calibrated turbidity-concentration conversion formula. Each test lasted 30 minutes, with the first 10 minutes being the flow field stabilization period, the middle 10 minutes being the aeration period, and the last 10 minutes being the recovery period. After each test, the sediment was allowed to settle completely before the next test was conducted.

[0106] The data obtained from the physical model experiment were quantitatively compared with the numerical simulation results of the corresponding working condition in step S4. Images of the steady-state phase were extracted from high-speed video footage, and the average rising height of the bubble plume was measured using image processing software. For example, at a flow velocity of 0.4 m / s, the experimentally measured rising height of the bubble plume was 1.15 m. From the numerical simulation results of this working condition in S4, the height of the highest point of the gas phase volume fraction isosurface was extracted, yielding a simulated value of 1.21 m, with a calculated relative deviation of 5.2%. A horizontal profile of the high-speed video image was extracted at the same height, and the lateral width of the bubble cloud was measured. The experimentally measured width was 1.12 m, while the corresponding gas phase volume fraction distribution curve in the numerical simulation showed a width of 1.20 m, with a calculated relative deviation of 7.1%. The stable suspended concentration value recorded by the turbidimeter at a height of 0.1 m above the bottom and 1.0 m downstream was extracted, and the experimentally measured suspended sediment concentration was 0.85 kg / m³. 3 From the numerical simulation results, the solid phase (sediment) concentration values ​​at the same spatial points were extracted. Since the sediment transport model was not coupled in step S3, the comparison here focuses on turbulent kinetic energy distribution—the spatial range of regions with high suspended concentrations in the experiment is compared with that of regions in the simulation where the turbulent kinetic energy exceeds twice the background value. The lateral diffusion width of the high-concentration region observed in the experiment was 0.95 m, while the simulated width of the highly turbulent region was 1.02 m, resulting in a calculation error of 7.4%.

[0107] In this embodiment, the preset deviation threshold is 10%. The deviations of the above three comparison indicators are all less than 10%, indicating that the numerical simulation results are in good agreement with the physical experiment results. This preliminarily verifies that the mapping relationship model constructed by S5 has high accuracy.

[0108] Suppose that under a certain operating condition, the experimentally measured bubble plume rise height is 0.95m, while the numerical simulation result is 1.15m, resulting in a relative deviation of 21%, exceeding the preset threshold of 10%. Model correction and iterative calculations are then required. Analysis suggests that the deviation may stem from an oversimplified bubble diameter model set in step S3. Under high flow rates and high aeration volumes, actual bubbles may experience more severe breakup and coalescence, leading to a reduction in the average bubble diameter, which in turn affects the bubble rise velocity and plume morphology. Returning to step S3, a population equilibrium model is enabled in Fluent software, and bubble breakup and coalescence kernel functions are introduced to allow the bubble diameter to dynamically change according to local turbulent kinetic energy. Simultaneously, the empirical coefficients of the breakup model are corrected based on the experimentally observed bubble size distribution. Using the corrected gas-liquid two-phase flow model, steps S4 through S6 are repeated for all aeration parameter combinations at a flow rate of 0.6m / s, and the above comparative verification process is repeated until the deviations between the experimental and simulation results at all typical flow rates are less than the preset deviation threshold. In this embodiment, after a round of model correction, the deviation under the 0.6 m / s condition was reduced to 6.5%, which meets the requirements.

[0109] This invention addresses the technical problem of not being able to directly verify the rationality of simulation model parameter assumptions and the accuracy of boundary condition settings by quantitatively comparing the bubble plume rise height, diffusion width, and suspended sediment concentration distribution measured by physical model experiments with the numerical simulation results of the gas-liquid two-phase flow field obtained in S4. This significantly improves the objective evaluation ability of the simulation model's confidence level and reduces the risk of subsequent optimization directions deviating from reality due to model errors. By setting a deviation threshold and correcting the gas-liquid two-phase flow model parameters set in S3 based on experimental data when the deviation between the physical model experimental results and the numerical simulation results is greater than or equal to the threshold, and re-executing S4 to S6 until the deviation is less than the preset threshold, this invention solves the technical problem that simulation models become fixed and unchanging once established, unable to absorb experimental feedback for self-optimization and improvement. This significantly improves the simulation model's accuracy in reproducing real physical processes and the robustness of the mapping relationship model, while ensuring that the final output optimal aeration parameter combination table has high reliability verified by experiments.

[0110] S7, based on the verified mapping relationship, outputs a table of optimal aeration parameter combinations for different water flow velocities, which is used to guide the intelligent operation of stationary dredging modules in practice.

[0111] Intelligent operation involves real-time monitoring of the actual water flow velocity in the target water area, automatically matching and adjusting the corresponding aeration parameters according to the optimal aeration parameter combination table, thereby achieving dynamic adjustment of the aeration volume.

[0112] In one possible implementation, based on the neural network mapping model validated by S6, the range of actual water flow velocities that may occur in the target dredging area is continuously calculated. The flow velocity is discretized at intervals of 0.05 m / s, and the corresponding optimal gas flow rate and inlet pressure are calculated by substituting them into the model. This is then integrated into the automatic control system of the fixed dredging module to achieve intelligent dynamic adjustment of aeration parameters. Specifically, a Doppler current meter is installed 5m upstream of the dredging module to monitor the actual water flow velocity in the target area in real time. The current meter collects data at a 1-minute interval and transmits the collected flow velocity signal to the PLC controller, which has a built-in optimal aeration parameter combination table. Upon receiving the real-time flow velocity value, the PLC executes a table lookup program. For example, when the current flow velocity measured by the current meter is 0.38 m / s, the PLC automatically looks up the closest flow velocity value of 0.40 m / s in the table and retrieves the corresponding optimal parameter: gas flow rate 5.0 m³ / s. 3 The inlet pressure is 35 kPa. The PLC sends the target flow rate and pressure values ​​as setpoints to the actuators of the aeration system. The PLC controls the frequency converter to adjust the motor speed of the air compressor, while simultaneously reading the feedback value from the gas mass flow meter, forming a closed-loop PID control to stabilize the actual gas flow rate at 5.0 m³ / h. 3 / h, the PLC adjusts the opening of the electric regulating valve on the pipeline and reads the feedback value of the inlet pressure sensor to stabilize the inlet pressure at 35kPa. The system repeats the above monitoring-matching-adjustment cycle at set time intervals (e.g., 5 minutes). When the water flow velocity changes, the PLC automatically switches the aeration parameters to the optimal combination corresponding to the new flow velocity, realizing adaptive optimization of the entire dredging operation based on water flow conditions.

[0113] This invention solves the problem of traditional dredging operations relying on manual experience to set fixed aeration parameters and failing to adapt to real-time changes in water flow conditions by utilizing real-time flow velocity monitoring, PLC automatic table lookup matching, and closed-loop PID control to dynamically adjust gas flow and inlet pressure. It significantly improves the dynamic adaptability and response speed of dredging operations to complex and variable water environments. Furthermore, by constructing a closed-loop intelligent operation system of real-time monitoring, automatic matching, and dynamic adjustment, the dredging module always operates at its optimal operating point under current water flow conditions. This solves the common technical problems of energy waste at low flow velocities and insufficient disturbance at high flow velocities under fixed-parameter operation modes, significantly improving the overall energy efficiency ratio and consistency of dredging results while reducing manual reliance on on-site operation and maintenance.

[0114] Example 2

[0115] like Figure 2 As shown, the present invention also provides a simulation and optimization system for the gas-liquid two-phase flow field of a fixed dredging module pipeline. The system is used to implement the simulation and optimization method for the gas-liquid two-phase flow field of a fixed dredging module pipeline as described in any one of Embodiment 1. The system includes:

[0116] The 3D modeling module is used to create a 3D geometric model based on the pipeline structure of the fixed dredging module, and to divide the flow field region into meshes to generate a water area mesh for numerical calculation.

[0117] The velocity assignment module, connected to the three-dimensional modeling module, is used to collect measured hydrological data of the target dredging area and determine the water velocity of each water grid.

[0118] The boundary setting module, connected to the velocity assignment module, is used to select a gas-liquid two-phase flow model in the fluid dynamics simulation software, set the gas phase as compressible air and the liquid phase as incompressible water, and define the water velocity as the boundary condition.

[0119] The simulation calculation module, connected to the boundary setting module, is used to set multiple sets of aeration parameter combinations for different water flow velocities, including gas flow rate and inlet pressure, to perform numerical simulation of the gas-liquid two-phase flow field and obtain bubble plume morphology, gas phase volume fraction distribution and turbulent kinetic energy field data under different aeration parameter combinations.

[0120] The multi-objective optimization module, connected to the simulation calculation module, is used to determine the optimal combination of aeration parameters for different water flow velocities with the objectives of maximizing the disturbance range and minimizing unit energy consumption, and to construct a mapping relationship model between water flow velocity and the optimal combination of aeration parameters.

[0121] The physical verification module, connected to the multi-objective optimization module, is used to select several typical velocity grids, build a physical model in a wave tank based on the obtained optimal aeration parameter combination, conduct verification experiments, measure the bubble plume morphology and sediment suspension effect, and compare the experimental results with the results of numerical simulation of the gas-liquid two-phase flow field to verify the accuracy of the mapping relationship.

[0122] The parameter output module, connected to the physical verification module, is used to output an optimal aeration parameter combination table for different water flow velocities based on the verified mapping relationship, which is used to guide the intelligent operation of the fixed dredging module in practice.

[0123] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A simulation optimization method for gas-liquid two-phase flow field in a fixed dredging module pipeline, characterized in that, include: S1. Establish a three-dimensional geometric model based on the pipeline structure of the fixed dredging module, and divide the flow field region into grids to generate a water area grid for numerical calculation. S2, collect measured hydrological data of the target dredging area to determine the water flow velocity of each water grid; S3. In the fluid dynamics simulation software, select the gas-liquid two-phase flow model, set the gas phase as compressible air and the liquid phase as incompressible water, and define the flow velocity of the water as the boundary condition. S4 sets multiple aeration parameter combinations for different water flow velocities, including gas flow rate and inlet pressure, and performs numerical simulation of gas-liquid two-phase flow field to obtain bubble plume morphology, gas phase volume fraction distribution and turbulent kinetic energy field data under different aeration parameter combinations. S5, with the goal of maximizing the disturbance range and minimizing the unit energy consumption, determines the optimal combination of aeration parameters for different water flow velocities and constructs a mapping relationship model between water flow velocity and the optimal combination of aeration parameters. S6. Select several typical velocity grids, and based on the corresponding optimal aeration parameter combination, build a physical model in a wave tank to carry out verification experiments, measure the bubble plume morphology and sediment suspension effect, and compare the experimental results with the results of numerical simulation of gas-liquid two-phase flow field to verify the accuracy of the mapping relationship. S7, based on the verified mapping relationship, outputs the optimal aeration parameter combination table for different water flow velocities, which is used to guide the intelligent operation of the fixed dredging module in practice; The process aims to maximize the disturbance range and minimize unit energy consumption, determining the optimal aeration parameter combination for different water flow velocities, and constructing a mapping model between water flow velocity and the optimal aeration parameter combination, including: S51, for each set of aeration parameter combinations under each water flow velocity, extract indicators for quantifying the disturbance range based on the bubble plume morphology, gas phase volume fraction distribution, and turbulent kinetic energy field data obtained in S4. At the same time, calculate the unit energy consumption of each set of aeration parameter combinations based on the gas flow rate and inlet pressure. The unit energy consumption is expressed as the ratio of input power to effective disturbance range. The indicators include the lateral diffusion width of the bubble plume, the volume of the region where the gas phase volume fraction is greater than a preset threshold, and the spatial range where the turbulent kinetic energy exceeds the background value. S52, establish a multi-objective optimization evaluation function, perform positive normalization on the disturbance range index and negative normalization on the unit energy consumption index, and calculate the comprehensive performance score of each aeration parameter combination by screening through Pareto optimal frontier. S53. Compare the comprehensive performance scores of all aeration parameter combinations under each water flow rate, determine the combination with the highest score as the optimal aeration parameter combination under that water flow rate, and record the corresponding gas flow rate and inlet pressure value. S54. A sample dataset is constructed by taking multiple different water flow velocities and their corresponding optimal gas flow rate and inlet pressure values. A mapping relationship model is built with water flow velocity as input and optimal gas flow rate and optimal inlet pressure as output through neural network mathematical modeling method. The disturbance range index is positively normalized, the unit energy consumption index is negatively normalized, and the comprehensive performance score of each aeration parameter combination is calculated using the Pareto optimal front screening method, including: S521, for all aeration parameter combinations under each water flow velocity, the lateral diffusion width of the bubble plume, the volume of the region where the gas phase volume fraction is greater than the preset threshold, and the spatial range where the turbulent kinetic energy exceeds the background value are positively normalized to obtain the normalized disturbance range score; at the same time, the unit energy consumption index is negatively normalized to obtain the normalized energy consumption score. S522 uses both the perturbation range score and the energy consumption score as optimization objectives. It employs a fast non-dominated sorting algorithm to perform Pareto front layering for all aeration parameter combinations, obtaining the Pareto front layer number of each combination. The first front layer is the non-dominated solution set. S523. For each combination of aeration parameters, a base score is assigned based on the number of Pareto front layers to which it belongs. The higher the number of front layers, the higher the base score. At the same time, the crowding distance between the combination in the same front layer and the adjacent solution in the same layer is calculated and an additional score is assigned. The larger the crowding distance, the higher the additional score. S524 adds the base score and the additional score of each combination to obtain the overall performance score of that combination.

2. The simulation and optimization method for gas-liquid two-phase flow field of a fixed dredging module pipeline according to claim 1, characterized in that, For different water flow velocities, multiple sets of aeration parameter combinations, including gas flow rate and inlet pressure, are set to conduct numerical simulations of the gas-liquid two-phase flow field, including: S41, Select a water flow velocity value to be analyzed, and under the current water flow velocity conditions, set a series of discrete gas flow rates and corresponding inlet pressure values ​​to form multiple sets of aeration parameter combinations to be calculated. S42, Substitute the multiple sets of aeration parameter combinations into the fluid dynamics simulation software after setting the boundary conditions, and perform numerical simulation calculations of unsteady or steady gas-liquid two-phase flow fields respectively. S43 sets a unified simulation calculation time and convergence residual standard to ensure that the flow field calculation under each working condition reaches a stable or statistically converged state. S44. After the simulation calculation is completed, extract and record the bubble plume morphology characteristics, spatial distribution data of gas phase volume fraction in the flow field, and distribution and intensity data of turbulent kinetic energy corresponding to each set of aeration parameter combinations from the calculation results.

3. The simulation and optimization method for gas-liquid two-phase flow field of a fixed dredging module pipeline according to claim 1, characterized in that, In S2, after collecting measured hydrological data of the target dredging area, the step of classifying the water flow velocity is also included. Specifically, the continuous water flow velocity is divided into several discrete flow velocity intervals, and a representative value is assigned to each flow velocity interval for subsequent simulation calculations.

4. The simulation optimization method for gas-liquid two-phase flow field of a fixed dredging module pipeline according to claim 1, characterized in that, The verification experiment, which involves building a physical model in a wave tank, includes: The prototype silt of the target dredging area is laid at the bottom of the wave tank, and the aeration device is installed according to the fixed pipeline structure in S1. Adjust the water flow generating device in the water tank to make the water flow velocity reach the water flow velocity value of the selected typical flow velocity grid; The aeration device is turned on according to the optimal aeration parameter combination obtained in S5. The bubble plume morphology is recorded by a high-speed camera, and the suspended sediment concentration and diffusion range are monitored by a turbidimeter. The rising height, diffusion width, and suspended sediment concentration distribution of the bubble plume measured by the physical model experiment were compared with the results of the gas-liquid two-phase flow field numerical simulation obtained by S4 to verify the accuracy of the mapping relationship.

5. The simulation optimization method for gas-liquid two-phase flow field of a fixed dredging module pipeline according to claim 4, characterized in that, If the deviation between the physical model test results and the numerical simulation results of the gas-liquid two-phase flow field is greater than or equal to the preset deviation threshold, the gas-liquid two-phase flow model parameters set in S3 are corrected according to the test data, and S4 to S6 are re-executed until the deviation between the physical model test results and the numerical simulation results of the gas-liquid two-phase flow field is less than the preset deviation threshold.

6. The simulation optimization method for gas-liquid two-phase flow field of a fixed dredging module pipeline according to claim 1, characterized in that, The intelligent operation involves real-time monitoring of the actual water flow velocity in the target water area, automatically matching and adjusting the corresponding aeration parameters according to the optimal aeration parameter combination table, thereby achieving dynamic adjustment of the aeration volume.

7. A simulation and optimization system for the gas-liquid two-phase flow field of a fixed dredging module pipeline, the system being used to implement the simulation and optimization method for the gas-liquid two-phase flow field of a fixed dredging module pipeline as described in any one of claims 1 to 6, the system comprising: The 3D modeling module is used to create a 3D geometric model based on the pipeline structure of the fixed dredging module, and to divide the flow field region into meshes to generate a water area mesh for numerical calculation. The velocity assignment module, connected to the three-dimensional modeling module, is used to collect measured hydrological data of the target dredging area and determine the water velocity of each water grid. The boundary setting module, connected to the velocity assignment module, is used to select a gas-liquid two-phase flow model in the fluid dynamics simulation software, set the gas phase as compressible air and the liquid phase as incompressible water, and define the water velocity as the boundary condition. The simulation calculation module, connected to the boundary setting module, is used to set multiple sets of aeration parameter combinations for different water flow velocities, including gas flow rate and inlet pressure, to perform numerical simulation of the gas-liquid two-phase flow field and obtain bubble plume morphology, gas phase volume fraction distribution and turbulent kinetic energy field data under different aeration parameter combinations. The multi-objective optimization module, connected to the simulation calculation module, is used to determine the optimal combination of aeration parameters for different water flow velocities with the objectives of maximizing the disturbance range and minimizing unit energy consumption, and to construct a mapping relationship model between water flow velocity and the optimal combination of aeration parameters. The physical verification module, connected to the multi-objective optimization module, is used to select several typical velocity grids, build a physical model in a wave tank based on the obtained optimal aeration parameter combination, conduct verification experiments, measure the bubble plume morphology and sediment suspension effect, and compare the experimental results with the results of numerical simulation of the gas-liquid two-phase flow field to verify the accuracy of the mapping relationship. The parameter output module, connected to the physical verification module, is used to output an optimal aeration parameter combination table for different water flow velocities based on the verified mapping relationship, which is used to guide the intelligent operation of the fixed dredging module in practice.

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