A method, device and electronic device for predicting overflow state of overflow tube

Abstract

The present invention provides a method, device, and electronic device for predicting the overflow state of an overflow tube, belonging to the technical field of dredging engineering. The prediction method comprises: establishing a three-dimensional model of the fluid in a mud tank equipped with an overflow tube; dividing the three-dimensional model into a fluid domain calculation grid; establishing a numerical calculation model of the mud tank fluid to perform flow field calculations and obtain flow field calculation results; and extracting overflow state parameters of the overflow tube based on the flow field calculation results to evaluate the overflow state. The prediction method provided by the present invention uses a numerical simulation method of fluid mechanics to predict the overflow state of the mud tank during the loading process of a trailing suction hopper dredger, obtain overflow state parameters, provide quantitative indicators for overflow state evaluation, and provide guidance for optimizing the size of the overflow tube. This solves the engineering problem of the lack of quantitative indicators for overflow effect evaluation, effectively improves the loading efficiency of the trailing suction hopper dredger, and enhances the benefits of dredging projects.

Description

Technical Field

[0001] The present invention belongs to the technical field of dredging engineering, and in particular relates to a method, a device and an electronic device for predicting the overflow state of an overflow tube. Background Art

[0002] A trailing suction hopper dredger is an engineering vessel equipped with a mud tank system and a drag head. The drag head draws in mud and sand mixtures from the bottom of rivers and lakes and stores them in the mud tank. It is widely used in waterway dredging, port construction, and other water projects. The mud tank, as its core mud storage unit, is equipped with an overflow device (such as an overflow cylinder) to increase the sediment concentration inside the tank by discharging low-concentration mud and water from the surface, thereby improving loading efficiency.

[0003] During dredging, low-density soils like fine silt tend to leak overboard with overflow water due to their slow settling rate, resulting in overflow losses. This not only reduces construction efficiency but also pollutes the water environment through the spread of suspended sediment. Therefore, controlling the overflow state of the overflow canister is crucial during dredging operations.

[0004] As a key component of the overflow system of modern trailing suction hopper vessels, the size of the overflow tube directly affects the thickness of the overflow layer and the degree of sediment loss. If the overflow tube diameter is too large, the overflow velocity will increase, and insufficiently settled sediment will be more easily discharged with the water flow. At the same time, high-speed overflow may trigger air vortexes, and bubbles carrying sediment will diffuse with the wake, exacerbating water turbidity. Conversely, if the overflow tube is too small, the overflow velocity will decrease, resulting in a thicker overflow layer. When the ship is sailing or rocked by wind and waves, mud will easily overflow from the top of the tank to the deck, threatening operational safety and limiting loading capacity. Therefore, the rational design of the overflow tube must balance overflow efficiency, sediment retention rate, and safety.

[0005] At present, the field of dredging construction has not yet established standardized standards for the design of overflow tubes, and the dynamic prediction technology of the overflow layer thickness still has significant bottlenecks. For example, CN115114809A discloses a method and system for calculating the loss amount of loading overflow construction. By building a tidal model, calculating the overflow sediment concentration, and then building an overflow sediment movement model, overflow simulation and loss amount calculation are carried out. CN118519376A discloses a method, device and electronic equipment for overflow control of a bucket-suction dredger. By obtaining a real-time image of the overflow tube and using the image RGB difference, the overflow tube is controlled to descend and rise, thereby shortening the overflow time. The existing technology mostly focuses on the offline simulation of static working conditions, and performs macroscopic calculation or evaluation of the overflow process, which makes it difficult to provide an optimization reference for the control of the overflow process and the design of the overflow tube size.

[0006] Therefore, it is urgent to establish a dynamic prediction method for overflow status and provide a reference for the rational design of overflow tubes to improve the economy and environmental friendliness of dredging projects. Summary of the Invention

[0007] The purpose of the present invention is to provide a prediction method, prediction device and electronic equipment for the overflow state of an overflow tube, which can realize dynamic prediction of the overflow state of the overflow tube based on numerical simulation of fluid mechanics and provide guidance for the design and optimization of the overflow tube.

[0008] In order to achieve the purpose of the invention, the present invention adopts the following technical solutions:

[0009] In a first aspect, the present invention provides a method for predicting the overflow state of an overflow tube, the method comprising the following steps:

[0010] S101: Establishing a three-dimensional model of the fluid in the mud tank provided with an overflow tube;

[0011] S102: Divide the three-dimensional model into a fluid domain calculation grid to obtain a fluid domain grid model;

[0012] S103: establishing a numerical calculation model for the mud tank fluid, performing flow field calculation on the fluid domain grid model, and obtaining flow field calculation results;

[0013] S104: Extracting overflow state parameters of the overflow tube according to the flow field calculation results, and evaluating the overflow state.

[0014] The prediction method provided by the present invention adopts a numerical simulation method of fluid mechanics to predict the overflow state of the mud tank loading process of a trailing suction hopper dredger and obtain overflow state parameters. It can provide quantitative indicators for overflow state evaluation and provide guidance for overflow tube size optimization, solving the engineering problem of lack of quantitative indicators for overflow effect evaluation, and can effectively improve the loading efficiency of the trailing suction hopper dredger and enhance the benefits of dredging projects.

[0015] Preferably, the step of establishing a three-dimensional model of the fluid in the mud tank provided with an overflow tube includes:

[0016] A three-dimensional model of the mud tank is drawn according to its shape, an overflow tube is set in the three-dimensional model of the mud tank, a three-dimensional model of the overflow tube is drawn, and a surface block is divided on the side of one end of the three-dimensional model of the mud tank as an inlet to obtain a three-dimensional model of the mud tank fluid.

[0017] Preferably, dividing the fluid domain into computational grids comprises:

[0018] Divide the main grid of the three-dimensional model of the mud tank fluid;

[0019] The mesh of the overflow tube wall area is encrypted to obtain an encrypted mesh;

[0020] The overflow tube wall area is divided into at least 5 boundary layers.

[0021] Preferably, the size of the main grid is 10-15% of the diameter of the overflow cylinder, for example, it can be 10%, 11%, 12%, 13%, 14% or 15%, but is not limited to the listed values, and other unlisted values ​​within the numerical range are also applicable.

[0022] Preferably, the size of the encrypted grid is 3-5% of the diameter of the overflow cylinder, for example, it can be 3%, 3.5%, 4%, 4.5% or 5%, but is not limited to the listed values, and other unlisted values ​​within the numerical range are also applicable.

[0023] In the present invention, the interlayer growth rate of the boundary layer is 1.2.

[0024] Preferably, the numerical calculation model of the mud tank fluid is established using ANSYS CFX.

[0025] Preferably, the establishing of a numerical calculation model of the mud tank fluid includes:

[0026] The fluid model is set up using a multiphase homogeneous model;

[0027] Set up phase boundary interaction model according to fluid characteristics;

[0028] The standard k-ε turbulence model is used to solve the flow field.

[0029] Preferably, setting the phase boundary interaction model according to fluid characteristics includes:

[0030] According to the D50 particle size of the sediment in the mud tank fluid, the fluid is divided into easy-to-settle soil and difficult-to-settle soil;

[0031] If the soil is easy to precipitate, it is set as a gas-liquid two-phase fluid, and a free surface model based on the VOF method is used between the gas and liquid phases;

[0032] If the soil is difficult to settle, it is set as a gas-liquid-solid three-phase fluid. The free surface model based on the VOF method is used between the gas-liquid phases, and the particle model based on the Euler-Euler method is used between the liquid-solid phases.

[0033] In the present invention, when the D50 particle size of the mud tank fluid is ≥0.5 mm, it is defined as easy-to-sediment soil; when the D50 particle size of the mud tank fluid is <0.5 mm, it is defined as difficult-to-sediment soil.

[0034] Preferably, the parameters of the free surface model include: setting both the gas and liquid phases as continuous fluids, ignoring the velocity difference between the two phases, taking the air density as a reference, and reflecting the buoyancy effect of the liquid phase on the gas phase through the density difference between the two phases;

[0035] The parameters of the particle model include: the solid phase is set as a quasi-continuous fluid composed of dispersed particles, the velocity difference between the two phases is considered, and the drag model adopts the Gidspow model.

[0036] Preferably, performing flow field calculation includes setting boundary conditions.

[0037] The boundary conditions include: setting the upper surface of the mud tank as an open surface; setting the inlet of the mud tank as a flow inlet; and setting the outlet of the overflow tube as a pressure outlet.

[0038] Preferably, the overflow state parameters include:

[0039] At least one of the height of the overflow surface, the thickness of the overflow layer, the distance between the overflow surface and the top of the mud tank, the volume fraction of sediment at the overflow tube outlet, or the volume fraction of air at the overflow tube outlet.

[0040] Preferably, the calculation to obtain the overflow state parameters of the overflow tube includes:

[0041] Select the flow field calculation results;

[0042] According to the selected flow field calculation results, respectively extracting the data of the sediment volume fraction at the overflow tube outlet and / or the air volume fraction at the overflow tube outlet;

[0043] Inserting a gas phase volume fraction isosurface into each selected flow field calculation result, intersecting the gas phase volume fraction isosurface with the mud tank wall to form an intersection line, extracting an average height of the intersection line to obtain data on the height value of the overflow surface, and calculating data on the thickness of the overflow layer and / or the distance between the overflow surface and the top of the mud tank based on the data on the height value of the overflow surface;

[0044] The extracted data are time-averaged to obtain overflow state parameters.

[0045] In the present invention, the gas phase volume fraction isosurface is set according to the gas phase volume fraction being 10%.

[0046] Preferably, the evaluating the overflow state includes:

[0047] Obtain the critical distance between the overflow surface and the top of the mud tank and the critical sediment volume fraction at the overflow tube outlet;

[0048] When the sediment volume fraction at the overflow tube outlet is less than the critical sediment volume fraction at the overflow tube outlet, the distance between the overflow surface and the top of the mud tank is greater than the critical distance between the overflow surface and the top of the mud tank, and the air volume fraction at the overflow tube outlet is greater than 0, it is evaluated as the recommended overflow state;

[0049] When the sediment volume fraction at the overflow tube outlet is ≥ the critical sediment volume fraction at the overflow tube outlet, it is evaluated as overflowing too fast and the overflow loss is large;

[0050] When the distance between the overflow surface and the top of the mud tank is less than or equal to the critical distance between the overflow surface and the top of the mud tank, it is considered as mud tank fluid overflow;

[0051] When the air volume fraction at the overflow tube outlet is 0, it is evaluated that the tube is full and the overflow is too slow, and there is a risk of fluid overflow.

[0052] Preferably, the prediction method further comprises: optimizing the overflow tube size according to the overflow state evaluation.

[0053] Preferably, the optimization of the overflow tube size includes:

[0054] When the overflow state is evaluated as overflowing too fast and / or overflow loss is large, the diameter of the overflow cylinder is reduced;

[0055] When the overflow status is evaluated as overflowing too slowly and / or there is a risk of fluid overflow, the diameter of the overflow cylinder is increased;

[0056] After the overflow cylinder diameter is adjusted, repeat the overflow status evaluation until it reaches the recommended overflow status.

[0057] In a second aspect, the present invention provides a device for predicting the overflow state of an overflow tube, the device comprising:

[0058] A three-dimensional model module is used to draw and establish a three-dimensional model of the fluid in the mud tank with an overflow tube;

[0059] A grid division module, used for dividing the three-dimensional model into a fluid domain calculation grid;

[0060] Flow field calculation module, used to establish numerical calculation models and perform flow field calculations;

[0061] The overflow state evaluation module is used to obtain overflow state parameters based on the flow field calculation results and evaluate the overflow state.

[0062] In a third aspect, the present invention provides an electronic device, comprising:

[0063] at least one processor; and

[0064] a memory communicatively connected to the at least one processor; wherein,

[0065] The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the method for predicting the overflow state of the overflow tube according to the first aspect.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] The prediction method provided by the present invention adopts a numerical simulation method of fluid mechanics to predict the overflow state of the mud tank loading process of a trailing suction hopper dredger and obtain overflow state parameters. It can provide quantitative indicators for overflow state evaluation and provide guidance for overflow tube size optimization, solving the engineering problem of lack of quantitative indicators for overflow effect evaluation, and can effectively improve the loading efficiency of the trailing suction hopper dredger and enhance the benefits of dredging projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a flow chart of the method for predicting the overflow state of the overflow tube provided in Example 1;

[0069] Figure 2 is a schematic diagram of a three-dimensional model of the mud tank fluid provided in Example 1;

[0070] Among them, 1, hatch entrance; 2, upper surface of mud tank; 3, overflow tube; 4, overflow tube inlet; 5, overflow tube outlet;

[0071] Figure 3 is a schematic diagram of overflow state parameters provided in Example 1;

[0072] Figure 4 Schematic diagram of the device for predicting the overflow state of the overflow tube provided in Example 3;

[0073] Figure 5 Schematic diagram of the electronic device structure for implementing the prediction method provided in Example 4. DETAILED DESCRIPTION

[0074] The technical solution of the present invention is further described below by way of specific embodiments. It should be understood by those skilled in the art that the embodiments are merely to help understand the present invention and should not be regarded as specific limitations of the present invention.

[0075] Example 1

[0076] This embodiment provides a Figure 1 The prediction method for the overflow state of the overflow cylinder shown is based on the fluid dynamics software Ansys CFX, and the prediction method includes:

[0077] S101: Establishing a three-dimensional model of the fluid in the mud tank provided with an overflow tube;

[0078] S102: Divide the three-dimensional model into a fluid domain calculation grid to obtain a fluid domain grid model;

[0079] S103: establishing a numerical calculation model for the mud tank fluid, performing flow field calculation on the fluid domain grid model, and obtaining flow field calculation results;

[0080] S104: Extracting overflow state parameters of the overflow tube according to the flow field calculation results, and evaluating the overflow state.

[0081] The specific steps are as follows:

[0082] (1) Establish a three-dimensional model of the fluid in the mud tank with an overflow tube.

[0083] Draw a 3D model of the mud tank according to its shape and size. There is an entrance hatch on one end face of the mud tank. A rectangular surface block is divided at the corresponding position on one end face of the 3D model of the mud tank to serve as the entrance hatch.

[0084] An overflow tube is set in the mud tank. A three-dimensional model of the overflow tube is established in the mud tank. The shape of the overflow tube is cylindrical. In the modeling, the wall thickness of the overflow tube is ignored. The wall of the overflow tube is represented by a curved surface. The three-dimensional model of the mud tank fluid is obtained, as shown in the figure below: Figure 2 shown.

[0085] (2) Divide the three-dimensional model into a fluid domain calculation grid.

[0086] The three-dimensional model of the mud tank fluid is divided into main grids with a grid unit size of 10-15% of the overflow tube diameter D.

[0087] The mesh of the overflow tube wall area is encrypted to obtain an encrypted mesh. The size of the encrypted mesh unit is 3-5% of the overflow tube diameter D.

[0088] At least five boundary layers are divided on the wall of the overflow tube, and the inter-layer growth rate is 1.2.

[0089] (3) Establish a numerical calculation model of the fluid state of the mud tank fluid.

[0090] (3.1) The fluid model adopts the multiphase homogeneous free surface model.

[0091] The free surface model of the multiphase homogeneous model provided by CFX is selected.

[0092] The free surface model is based on the VOF method to track the interface between multiple fluids. Its core is to track the phase interface through the volume fraction equation.

[0093] The VOF method introduces a volume fraction function for each phase of fluid. In each control unit, the sum of the volume fractions of all phases is 1, that is:

[0094]

[0095] For the qth phase fluid, the volume fraction continuity equation is:

[0096]

[0097] Where, α q is the volume fraction of the qth phase fluid in the unit; t is time; v is the velocity component; ρ is the density of the fluid; is the gradient operator.

[0098] The property parameters of the phase can be expressed by the weighted value of the volume fraction, for example, density.

[0099] The density of the mixed fluid can be expressed as:

[0100]

[0101] Where ρ is the density of the fluid; α q is the volume fraction of the qth phase fluid in the unit; ρ q is the density of the qth phase fluid.

[0102] The momentum equation is:

[0103]

[0104] Where P is pressure; v is velocity component; ρ is density of fluid; μ is viscosity of fluid; F surface is the surface tension source phase; F gravity is the gravity source term.

[0105] (3.2) Set up the phase boundary effect calculation model according to the fluid characteristics.

[0106] According to the D50 particle size of the sediment in the mud tank fluid, the mud tank fluid is divided into easy-to-settle soil and difficult-to-settle soil.

[0107] When the sediment D50 particle size is ≥0.5mm, it is considered as easy-to-sediment soil; when the sediment D50 particle size is <0.5mm, it is considered as difficult-to-sediment soil.

[0108] (3.2.1) For easily precipitated soils, the fluid is set as gas-liquid two-phase.

[0109] The fluid pair is set to gas and liquid. The free surface model provided by CFX is used between the gas and liquid phases.

[0110] Both gas and liquid phases are set as continuous fluids.

[0111] The velocity difference between the two phases is ignored, and the relative motion between the gas phase and the liquid phase is not considered in the solution.

[0112] Taking the air density as the reference, the buoyancy effect of the liquid phase on the gas phase is reflected by the density difference between the two phases. Specifically, in the free surface model, the gravity source term is expressed as:

[0113]

[0114] Where, ρ l is the density of the liquid phase; ρ g is the density of the gas phase; V is the volume; g is the acceleration due to gravity.

[0115] (3.2.2) For difficult-to-sediment soils, set it as a gas-liquid-solid three-phase fluid.

[0116] The gas-liquid phases are set as continuous fluids, and the solid phase is set as a quasi-continuous fluid composed of dispersed particles.

[0117] For fluid pairs consisting of a gas and liquid phase, the Free Surface model provided by CFX is used between the two phases. The velocity difference between the two phases is ignored in the calculation. The density difference between the two phases is used as a reference, and the buoyancy effect of the liquid phase on the gas phase is reflected by the density difference between the two phases.

[0118] For fluid pairs consisting of a solid and liquid phase, the particle model provided by CFX is used between the solid and liquid phases. The velocity difference between the two phases is considered in the calculation, and the Gidspow model is used as the drag model.

[0119] The particle model is based on the Euler-Euler method.

[0120] The continuity equation of the particle phase is expressed as:

[0121]

[0122] Where, α k is the volume fraction; ρ k is the density; v k is the velocity component; S k is the mass source term.

[0123] The momentum equation of the particle phase is expressed as:

[0124]

[0125] Where, α k is the volume fraction; ρ k is the density; v k is the velocity component; P is the pressure; τ k is the particle tensor; M k is the momentum exchange term between phases.

[0126] M k Including drag, the drag model applies the Gidspow model.

[0127] The Gidspow model includes the Wen-Yu model and the piecewise formula of the Wen-Yu model.

[0128] The expression of the Gidspow model is as follows:

[0129]

[0130] Where, α s is the volume fraction of the solid phase; α l is the volume fraction of the liquid phase; ρ l is the density of the liquid phase; d s is the particle diameter of the solid phase; v l 、v s are the velocity components of the liquid and solid phases respectively; μ l is the dynamic viscosity of the liquid phase; C D is the drag coefficient of the Wen-Yu model.

[0131] Drag coefficient C D The expression is as follows:

[0132]

[0133] Among them, the particle Reynolds number Re s Defined as:

[0134]

[0135] The Wen-Yu model is based on α l -2.65 Corrected drag force to reflect the weakened interactions between particles at low concentrations.

[0136] (3.3) The standard k-ε turbulence model is used to solve the flow field.

[0137] The basic equation of the standard k-ε turbulence model is:

[0138] The expression of the turbulent kinetic energy K equation is as follows:

[0139]

[0140] The expression of the turbulent dissipation rate ε equation is as follows:

[0141]

[0142] Where ρ is the density; σ k , σ ε are the turbulent Prandtl numbers k and ε respectively; μ is the dynamic viscosity coefficient of the fluid molecules; μ t is the turbulent viscosity coefficient; x i 、x j are the coordinate components respectively; ui is the velocity component; C 1ε 、C 2ε are model constants respectively.

[0143] G is the turbulent kinetic energy generated by the average velocity gradient, and its expression is:

[0144]

[0145] Among them, u i 、u j is the velocity component.

[0146] μ t is the turbulent viscosity coefficient, which can be calculated from the turbulent kinetic energy k and the turbulent dissipation rate ε, and the expression is:

[0147]

[0148] In the standard k-ε model, C 1ε =1.44, C 2ε =1.92, C μ =0.09,σ k =1.0,σ ε =1.3.

[0149] To calculate the residual error below 10 -4 As the convergence criterion for numerical calculations.

[0150] (4) Set boundary conditions, perform flow field calculations, and obtain flow field calculation results.

[0151] The upper surface of the three-dimensional model of the mud tank fluid is set as an open surface. The initial parameters include: the gas phase volume fraction is 100%, the pressure is the standard atmospheric pressure P atm , the flow rate is 0.

[0152] The hatch entrance is set as a flow inlet. For easily precipitated soils, the flow rate is set according to the mortar flow rate for the tank entry condition, with an initial liquid volume fraction of 100%. For difficult-to-precipitate soils, the flow rate is set according to the mortar flow rate for the tank entry condition, with an initial solid volume fraction based on the loading sediment volume concentration for the design condition, with the sum of the liquid and solid volume fractions being 1.

[0153] The overflow tube outlet is set as a pressure outlet and the static pressure condition is set. The initial pressure value is based on the full tank draft of the mud tank h d The converted pressure (ρgh d ) and atmospheric pressure (P atm ), namely ρgh d +P atm .

[0154] The flow field calculation uses a time step of 1 s.

[0155] (5) Process the flow field calculation results to obtain overflow state parameters, such as Figure 3 shown.

[0156] The 10-second data after the flow field state stabilizes are selected.

[0157] During the calculation process, the mass flow rate at the overflow tube outlet is monitored, and the rate of change of the fluctuation amplitude of the outlet mass flow rate value is used as the judgment indicator. When the rate of change is less than 10%, the flow field state is stable.

[0158] In the flow field calculation results per second, isosurfaces with a gas phase volume fraction of 10% are inserted.

[0159] In the flow field results at any second, the isosurface intersects with the surrounding walls of the mud tank model, and the height value between the intersection line and the bottom of the mud tank is extracted. After calculating the average value, the height of the overflow surface at that moment is obtained, which is recorded as Z i .

[0160] The difference between the height of the overflow surface and the height of the overflow tube (Z0) is Z i -Z0, the thickness of the overflow layer at that moment is obtained, recorded as Δz i .

[0161] The difference between the height of the upper surface of the mud tank and the height of the overflow surface is calculated to obtain the distance between the overflow surface and the upper surface of the mud tank at that moment, which is recorded as L i .

[0162] The solid volume fraction at the overflow tube outlet at this moment is denoted as α s,i .

[0163] The gas volume fraction at the overflow tube outlet at this moment is recorded as α g,i .

[0164] The values ​​of the selected 10s data were time-averaged and the corresponding time-averaged values ​​were obtained as the final results, which are: the height Z of the overflow surface, the thickness Δz of the overflow layer, the distance L between the overflow surface and the upper surface of the mud tank, and the sediment volume fraction α at the overflow tube outlet. s , air volume fraction α at the overflow tube outlet g .

[0165] (6) L, α s , α g As an evaluation index of overflow effect, it guides the optimization of overflow tube size.

[0166] α0 is the sediment volume fraction at the critical overflow tube outlet of the trailing suction hopper dredger, which is proposed by the ship user.

[0167] H0 is the distance between the critical overflow surface of the trailing suction hopper dredger and the upper surface of the mud tank, which is set according to the mud tank size, the ship's rolling characteristics and the hydrological conditions of the design and construction waters.

[0168] When α s <α0, keep L>H0 during the loading process, and α g If it is close to 0, it is the recommended overflow state.

[0169] When α s ≥α0, the overflow will be too fast, resulting in insufficient sedimentation of the loaded sediment, and the overflow loss will be too large, so the overflow tube diameter D needs to be reduced.

[0170] When L≤H0, the fluid in the mud tank will overflow during construction, and the overflow tube diameter D needs to be increased.

[0171] When α g =0, the overflow tube is in a full-tube flow state, the overflow may be too slow, the overflow layer thickness continues to rise, and there is a risk of mud tank fluid overflow. The overflow tube diameter D needs to be appropriately increased.

[0172] After adjusting the overflow tube diameter, reassess the overflow status of the mud tank according to steps (1) to (5) until it reaches the recommended overflow status.

[0173] Example 2

[0174] This embodiment provides a method for predicting the overflow state of an overflow tube. The prediction method provided in Example 1 is applied to illustrate the technical solution of the present invention through a specific calculation process.

[0175] The steps of the prediction method are as follows:

[0176] (1) Establish a three-dimensional model of the fluid in the mud tank with an overflow tube.

[0177] Draw a three-dimensional model of the mud tank fluid and overflow tube.

[0178] (2) Divide the three-dimensional model into computational grids.

[0179] The diameter D of the overflow cylinder is 1500mm.

[0180] The main grid is divided for the mud tank fluid, and the size of the main grid is set to 150 mm, which is 10% of the cylinder diameter D.

[0181] The grid within a range of 3000 mm along the wall of the overflow tube is encrypted, and the size of the encrypted grid is 60 mm, which is 4% of the tube diameter D.

[0182] The overflow tube wall is divided into five boundary layers, with a transition rate of 1.2 between layers.

[0183] (3) Establish a computational model for the fluid state of the mud tank fluid.

[0184] (3.1) Select the free surface model of the multiphase homogeneous model provided by CFX.

[0185] (3.2) Set up the phase boundary interaction model.

[0186] In this embodiment, the D50 particle size of the sediment is 0.08 mm, which is difficult to settle. Therefore, it is set as a gas-liquid-solid three-phase fluid.

[0187] The gas-liquid phases are set as continuous fluids, and the solid phase is set as a quasi-continuous fluid composed of dispersed particles.

[0188] For fluid pairs consisting of a gas and liquid phase, the Free Surface model provided by CFX is used. The velocity difference between the two phases is ignored in the calculation, and the air density is used as the reference. The buoyancy effect of the liquid on the gas phase is reflected by the density difference between the two phases.

[0189] For fluid pairs consisting of a solid and liquid phase, the ParticleModel provided by CFX is used. The velocity difference between the two phases is considered in the calculation, and the Gidspow model is used as the drag model.

[0190] (3.3) The standard k-ε turbulence model is used to solve the flow field.

[0191] To calculate the residual error below 10 -4 As the convergence criterion for numerical calculations.

[0192] (4) Set boundary conditions, perform flow field calculations, and obtain flow field calculation results.

[0193] The upper surface of the mud tank fluid model is set as an open surface. The initial parameters include: the gas phase volume fraction is 100%, the pressure is the standard atmospheric pressure P atm , the flow rate is 0.

[0194] The hatch entrance is set as a flow inlet. The flow value is set according to the mortar flow value of the cabin working condition, which is 3m 3 / s, the initial solid phase volume fraction is set according to the loading sediment volume concentration of the design working condition, which is 20%, and the sum of the liquid phase volume fraction and the solid phase volume fraction is 1, that is, the liquid phase volume fraction is 80%.

[0195] The overflow tube outlet is set as a pressure outlet and the static pressure condition is set. The initial pressure value is based on the full tank draft of the mud tank h d The converted pressure (ρghd ) and atmospheric pressure (P atm ), namely ρgh d +P atm .h d The pressure is 83385+P. atm Pa.

[0196] The flow field calculation uses a time step of 1 s.

[0197] (5) Process the flow field calculation results to obtain the overflow state parameters. The 10-second data after the flow field state stabilizes, that is, after the mass flow rate change rate at the overflow tube outlet is less than 10%, is selected.

[0198] In the flow field calculation results per second, isosurfaces with a gas phase volume fraction of 10% are inserted.

[0199] The values ​​of the selected 10s data are time-averaged respectively.

[0200] The overflow status parameters include:

[0201] The height Z of the overflow surface is 8.65 m; the thickness Δz of the overflow layer is 0.65 m; the distance L between the overflow surface and the upper surface of the mud tank is 2.05 m; the volume fraction of sediment at the overflow tube outlet is α s is 8%; the air volume fraction α at the overflow tube outlet g It is 0.65%.

[0202] (6) L, α s , α g As an evaluation index of overflow effect, it guides the optimization of overflow tube size.

[0203] The sediment volume fraction α0 at the critical overflow tube outlet is 6%.

[0204] α s =8%≥6%, breaking through the critical state, the overflow is too fast, resulting in the loading sediment not being fully settled, the overflow loss is too large, and the overflow tube diameter D must be reduced.

[0205] The distance H0 between the upper surface of the critical overflow layer and the upper surface of the mud tank is 1.5m.

[0206] L=2.05m>1.5m, has not broken through the critical state.

[0207] α g It is 0.65%, which is not equal to 0 and has not reached the critical state.

[0208] Therefore, the optimization conclusion is to reduce the overflow tube diameter D.

[0209] After reducing the overflow tube diameter, recalculate the overflow state according to steps (1) to (5) until the overflow state is at the recommended state.

[0210] After iteration, the optimized diameter of the overflow tube is D 1300mm, Z = 8.47m, Δz = 0.47m, L = 2.23m, α s =4%,α g =0.5%, which is the recommended overflow condition.

[0211] Example 3

[0212] This embodiment provides a device for predicting the overflow state of an overflow tube, such as Figure 4 As shown, the prediction device includes:

[0213] 3D model module 110, grid division module 120, flow field calculation module 130 and overflow state evaluation module 140. Among them:

[0214] The three-dimensional model module 110 is used to draw and establish a three-dimensional model of the fluid in the mud tank provided with an overflow tube.

[0215] The grid division module 120 is used to divide the three-dimensional model into a fluid domain calculation grid.

[0216] The flow field calculation module 130 is used to establish a numerical calculation model and perform flow field calculations.

[0217] The overflow state evaluation module 140 is used to obtain overflow state parameters according to the flow field calculation results and evaluate the overflow state.

[0218] In the meshing module 120 , the main mesh of the mud tank fluid model is divided, and the mesh is encrypted in the overflow tube wall area.

[0219] In the flow field calculation module 130, a numerical calculation model is established and flow field calculation is performed, including:

[0220] The multiphase homogeneous model is used to establish the fluid model.

[0221] A phase boundary interaction calculation model was established based on the fluid characteristics. Based on the average sediment particle size, the mud tank fluid was divided into easily settling and difficult-to-settle soils. For easily settling soils, a gas-liquid two-phase flow was established, using a free surface model. For difficult-to-settle soils, a gas-liquid-solid three-phase flow was established, using a free surface model for the gas-liquid phase and a particle model for the liquid-solid phase. The Gidspow model was used as the drag model.

[0222] The turbulence model adopts the standard k-ε turbulence model.

[0223] Set boundary conditions and run the flow field calculation.

[0224] In the overflow state evaluation module 140, the overflow state parameters are obtained, including: extracting the solid phase volume fraction and the gas phase volume fraction at the overflow tube outlet from the flow field calculation results, inserting the gas phase volume fraction isosurface, extracting the overflow surface height, the thickness of the overflow layer, and the distance between the overflow surface and the upper surface of the mud tank, and performing time averaging on the extracted results. The final overflow parameters include: the height Z of the overflow surface, the thickness Δz of the overflow layer, the distance L between the overflow surface and the upper surface of the mud tank, and the sediment volume fraction α at the overflow tube outlet. s , air volume fraction α at the overflow tube outlet g .

[0225] Output overflow state evaluation results based on overflow state parameters and overflow state evaluation critical conditions.

[0226] The device provided in this embodiment can execute the method for predicting the overflow state of the overflow tube provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0227] Example 4

[0228] This embodiment provides an electronic device for implementing a method for predicting overflow status of an overflow tube, such as Figure 5 As shown, the electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0229] like Figure 5 As shown, electronic device 10 includes at least one processor 11 and memory, such as read-only memory (ROM) 12 and random access memory (RAM) 13, communicatively connected to at least one processor 11. The memory stores computer programs executable by the at least one processor. Processor 11 can perform various appropriate actions and processes based on the computer programs stored in ROM 12 or loaded from storage unit 18 into RAM 13. RAM 13 can also store various programs and data required for the operation of electronic device 10. Processor 11, ROM 12, and RAM 13 are interconnected via bus 14. An input / output (I / O) interface 15 is also connected to bus 14.

[0230] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a second storage area, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0231] Processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. Processor 11 executes the various methods and processes described above, such as the method for predicting the overflow state of the overflow bowl.

[0232] In some embodiments, the method for predicting an overflow bowl overflow condition can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the fault diagnosis method for a generator evaporative cooling system described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the method for predicting an overflow bowl overflow condition via any other suitable means (e.g., via firmware).

[0233] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0234] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable target-determining device, so that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0235] In the context of the present application, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0236] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device that has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0237] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0238] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0239] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired information of the technical solution of this application can be achieved. This document is not limited here.

[0240] The applicant declares that the above is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention fall within the scope of protection and disclosure of the present invention.

Claims

1. A method for predicting the overflow state of an overflow tube, characterized in that: The prediction method comprises the following steps: Establish a three-dimensional model of the fluid in the mud tank with an overflow tube; Dividing the three-dimensional model into a fluid domain calculation grid to obtain a fluid domain grid model; Establishing a numerical calculation model of the mud tank fluid and performing flow field calculation on the fluid domain grid model includes: Using ANSYS CFX, the fluid model was set up using a multiphase homogeneous model; Set the phase boundary interaction model based on the fluid characteristics: Based on the D50 particle size of the sediment in the mud tank fluid, the fluid is divided into easily precipitated soil and difficult-to-precipitate soil. If the soil is easily precipitated, it is set as a gas-liquid two-phase fluid, and a free surface model based on the VOF method is used for the gas-liquid phase. If the soil is difficult to settle, it is set as a gas-liquid-solid three-phase fluid, and a free surface model based on the VOF method is used for the gas-liquid phase, and a particle model based on the Euler-Euler method is used for the liquid-solid phase. The standard k-ε turbulence model is used to solve the flow field and obtain the flow field calculation results; Extracting overflow state parameters of the overflow tube according to the flow field calculation results and evaluating the overflow state, including: the overflow state parameters include the height of the overflow surface, the thickness of the overflow layer, the distance between the overflow surface and the top of the mud tank, the sediment volume fraction at the overflow tube outlet, and the air volume fraction at the overflow tube outlet; Selecting flow field calculation results, and extracting data on the sediment volume fraction and / or the air volume fraction at the overflow tube outlet from each selected flow field calculation result; inserting a gas phase volume fraction isosurface, intersecting the gas phase volume fraction isosurface with the mud tank wall to form an intersection line, extracting a height average of the intersection line to obtain data on the height value of the overflow surface, calculating the thickness of the overflow layer and / or the distance between the overflow surface and the top of the mud tank based on the data on the height value of the overflow surface, and performing time averaging processing on the extracted data to obtain overflow state parameters; Obtain the critical distance between the overflow surface and the top of the mud tank and the critical sediment volume fraction at the overflow tube outlet; When the sediment volume fraction at the overflow tube outlet is less than the critical sediment volume fraction at the overflow tube outlet, the distance between the overflow surface and the top of the mud tank is greater than the critical distance between the overflow surface and the top of the mud tank, and the air volume fraction at the overflow tube outlet is greater than 0, it is evaluated as the recommended overflow state; When the sediment volume fraction at the overflow tube outlet is ≥ the critical sediment volume fraction at the overflow tube outlet, the overflow is evaluated as too fast and the overflow loss is large; when the distance between the overflow surface and the top of the mud tank is ≤ the critical distance between the overflow surface and the top of the mud tank, the fluid is evaluated as overflowing from the mud tank; when the air volume fraction at the overflow tube outlet is 0, the tube is evaluated as full flow, the overflow is too slow, and there is a risk of fluid overflow.

2. The prediction method according to claim 1, characterized in that The three-dimensional model of the mud tank fluid provided with the overflow tube is established, comprising: A three-dimensional model of the mud tank is drawn according to its shape, an overflow tube is set in the three-dimensional model of the mud tank, a three-dimensional model of the overflow tube is drawn, and a surface block is divided on the side of one end of the three-dimensional model of the mud tank as an inlet to obtain a three-dimensional model of the mud tank fluid.

3. The prediction method according to claim 1, characterized in that The dividing of the fluid domain into computational grids comprises: Divide the three-dimensional model of the mud tank fluid into a main grid, the size of which is 10-15% of the overflow tube diameter; Encrypting the mesh of the overflow tube wall area to obtain an encrypted mesh, wherein the size of the encrypted mesh is 3-5% of the overflow tube diameter; The overflow tube wall area is divided into at least 5 boundary layers.

4. The prediction method according to claim 1, wherein: The parameters of the free surface model include: setting both the gas and liquid phases as continuous fluids, ignoring the velocity difference between the two phases, taking the air density as the reference, and reflecting the buoyancy effect of the liquid phase on the gas phase through the density difference between the two phases; The parameters of the particle model include: the solid phase is set as a quasi-continuous fluid composed of dispersed particles, the velocity difference between the two phases is considered, and the drag model adopts the Gidspow model.

5. The prediction method according to claim 1, wherein: The prediction method further includes: optimizing the overflow tube size based on the overflow state evaluation; The optimization of overflow tube size includes: When the overflow state is evaluated as overflowing too fast and / or overflow loss is large, the diameter of the overflow cylinder is reduced; When the overflow status is evaluated as overflowing too slowly and / or there is a risk of fluid overflow, the diameter of the overflow cylinder is increased; After the overflow cylinder diameter is adjusted, repeat the overflow status evaluation until it reaches the recommended overflow status.

6. A device for predicting the overflow state of an overflow tube, characterized in that: The prediction device comprises: A three-dimensional model module is used to draw and establish a three-dimensional model of the fluid in the mud tank with an overflow tube; A grid division module, used for dividing the three-dimensional model into a fluid domain calculation grid; Flow field calculation module, used to establish numerical calculation models and perform flow field calculations; Overflow state evaluation module, used to obtain overflow state parameters based on flow field calculation results and evaluate overflow state; The prediction device is used to execute the prediction method according to claim 1.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the method for predicting an overflow state of an overflow bowl according to any one of claims 1 to 5.

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