Slurry shield short screw and rear crusher collaborative deslagging working parameter optimization method based on CFD-DEM coupling modeling

Abstract

The invention discloses a slurry shield short screw and rear crusher collaborative deslagging working parameter optimization method based on CFD-DEM coupling modeling, and relates to the technical field of civil engineering and tunnel engineering.The slurry shield short screw and rear crusher collaborative deslagging working parameter optimization method comprises the steps that rheological behaviors of slurry are simulated through a rheological model; establishing a three-dimensional calculation model, and optimizing short spiral rotating speed setting; based on a coupling technology, establishing a crushing cabin three-dimensional coupling model; performing coupling simulation; the working parameters of the crusher are optimized; obtaining the crushing mass per second of rock slag particles under each group of simulation and the mass flow rate of slag discharged from an outlet of the crushing cabin; and optimizing each decision variable by using a multi-objective particle swarm optimization algorithm, and determining an optimal solution by using an approximation ideal solution. The method can quickly and accurately calculate the optimal combination of the working parameters of the short screw and the working parameters of the crusher at different shield tunneling speeds, alleviates the difficult problem of stagnant discharge of the actual engineering muddy water cabin, and has the advantages of low cost, wide applicability, high practicability and high popularization value.

Description

Technical Field

[0001] The present invention relates to the technical field of civil engineering and tunnel engineering, and in particular to a method for optimizing working parameters of a slurry shield short spiral and a post-crusher for collaborative slag discharge based on CFD-DEM coupling modeling. Background Art

[0002] In recent years, with the rapid advancement of infrastructure construction, slurry shields have been widely used in highway and subway tunnel construction due to their safety and efficiency, particularly in river crossing and water supply projects. When a slurry shield machine excavates hard rock formations, large-particle, high-content rock debris can easily settle at the bottom of the excavation chamber. Over time, this can cause blockage of the chamber and piping system. This can increase slurry pressure in the chamber, leading to face instability and unexpected shield machine shutdowns, threatening construction safety and reducing excavation efficiency.

[0003] Analyzing the "drainage" and "crushing" aspects of shield equipment, the causes of blockage in the excavation chamber of a slurry shield machine in hard rock formations can be summarized as follows: on the one hand, the slurry circulation system's conveying and slag removal performance is insufficient, which is mismatched with the shield machine's excavation capacity. Rock debris cannot be discharged from the slurry chamber in a timely manner, resulting in slag accumulation and blockage in the excavation chamber; on the other hand, the rock crushing capacity of secondary crushing systems such as jaw crushers cannot meet the excavation and slag removal requirements. Insufficiently crushed, oversized rock debris enters the slurry removal system, causing not only blockage in the excavation chamber but also possible blockage of the centrifugal pump of the slurry circulation system. Currently, the use of a large-diameter short screw conveyor coupled with a post-crusher for active rock removal can effectively alleviate the slurry chamber blockage problem and has been applied in actual slurry shield projects. However, there is no reference research on the optimal operating parameters for the coordination of the short screw and post-crusher.

[0004] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention proposes a method for optimizing the working parameters of the coordinated slag discharge of the short spiral of the slurry shield and the rear crusher based on CFD-DEM coupling modeling, which solves the problem of low efficiency of the coordinated application of the active rock discharge and anti-blocking technology of the existing large-diameter short spiral conveyor and the rear crusher proposed in the above background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] The optimization method for the collaborative slag discharge working parameters of the slurry shield short spiral and post-crusher based on CFD-DEM coupling modeling includes:

[0008] Obtain the mass flow rate of rock slag particles and mud mass flow rate in the rock formation, and simulate the rheological behavior of the mud using a rheological model;

[0009] A three-dimensional calculation model was established and used to calculate the slag discharge mass flow rate at the shield short spiral outlet, the short spiral slag discharge efficiency, and the mud mass flow rate. The movement state of the slag particles in the short spiral was analyzed to optimize the short spiral speed setting.

[0010] Based on coupling technology, a three-dimensional coupling model of the crushing chamber is established;

[0011] The slag mass flow rate and mud mass flow rate at the outlet of the short spiral shield machine are used as the inlet slag particle mass flow rate and mud mass flow rate of the crushing chamber three-dimensional coupling model respectively, and the coupling simulation is carried out;

[0012] Based on the coupling simulation results, the crushing mass per second of rock debris particles in the crushing chamber and the discharge mass flow rate of the crushing chamber outlet are analyzed to optimize the crusher operating parameters;

[0013] Design simulation schemes under different decision variables and conduct numerical simulations respectively to obtain the crushing mass of rock slag particles per second and the slag discharge mass flow rate at the crushing chamber outlet under each set of simulations;

[0014] The multi-objective particle swarm optimization algorithm is used to optimize the decision variables, and the optimal solution is determined using the approximate ideal solution method.

[0015] Furthermore, the mass flow rate of rock slag particles and the mass flow rate of mud in the rock formation are obtained, and the rheological behavior of the mud is simulated using a rheological model, including:

[0016] The mud rheological curve is obtained by a coaxial cylinder measurement system, and the mass flow rate of rock slag particles and the mass flow rate of mud in the rock formation are fitted into the rheological model to obtain the parameters of the rheological model;

[0017] Based on the parameters of the rheological model, the rheological model is applied in the fluid simulation software and the flow behavior of the mud is simulated.

[0018] Furthermore, a three-dimensional calculation model was established and used to calculate the slag discharge mass flow rate at the shield short spiral outlet, the short spiral slag discharge efficiency, and the mud mass flow rate. The movement state of the slag particles in the short spiral was analyzed, and the short spiral speed setting was optimized, including:

[0019] Based on the continuity equation, the fluid mass conservation equation is established using time, fluid volume fraction, fluid density and fluid velocity vector to obtain the mass flow rate change of the mud in the short spiral;

[0020] The momentum conservation equation is used to combine the viscous stress tensor, fluid pressure, gravitational acceleration, and the force exerted by all rock debris particles on the fluid to obtain the flow state and velocity distribution of the mud in the short spiral.

[0021] Based on Newton's second law, the translational and rotational equations of the slag particles are established using the mass, velocity, moment of inertia, angular velocity of the slag particles, the forces between the slag particles, the forces between the slag particles and the geometric wall of the shield, and the fluid phase forces. The motion state of the slag particles in the slurry is then determined.

[0022] Based on the fluid phase force, combined with the density, particle size, velocity of the slag particles, effective viscosity of the fluid, Reynolds number and drag coefficient of the slag particles, the motion state of the slag particles in the short spiral is obtained, and the short spiral speed setting is optimized.

[0023] Furthermore, the calculation formula of the short spiral slag removal efficiency is:

[0024] ;

[0025] Where, η represents the short spiral slag removal efficiency; Q n represents the true mass flow rate of the short helix; r represents the speed of the short helix; l represents the pitch of the short helix; D represents the blade diameter of the short helix; and d represents the diameter of the helix axis of the short helix.

[0026] Furthermore, the formula for establishing the translation equation of slag particles is:

[0027] ;

[0028] The formula for establishing the rotation equation of slag particles is:

[0029] ;

[0030] Where m i represents the mass of slag particle i; U p，i represents the velocity of slag particle i; I i represents the inertial motion of slag particle i; ω i represents the angular velocity of slag particle i; F c，ij F represents the contact force between slag particle i and other slag particles; d，ij represents the viscous damping force between slag particle i and other slag particles; T ij represents the torque between slag particle i and other slag particles; F c，iw represents the contact force between the slag particle i and the shield geometric wall; F d，iw represents the viscous damping force between the slag particle i and the shield geometric wall; T iw represents the torque between the slag particle i and the shield geometric wall; F f，i represents the force exerted by the fluid phase on the slag particle i; t represents time.

[0031] Furthermore, the Reynolds number of slag particles is calculated as follows:

[0032] ;

[0033] The drag coefficient of slag particles is calculated as follows:

[0034] ;

[0035] Where Re is the Reynolds number of the slag particles; μ is the fluid dynamic viscosity; ρ is the density; f represents the fluid density; U f represents the fluid velocity vector; U p represents the velocity of slag particles; d p Indicates the particle size of slag particles; C D represents the drag coefficient of the slag particles; a1, a2 and a3 are all constants.

[0036] Furthermore, based on the coupling technology, a three-dimensional coupling model of the crushing chamber is established, including:

[0037] A three-dimensional coupling model of the crushing chamber was established based on CFD-DEM coupling technology to obtain a particle crushing model;

[0038] Based on the particle crushing model, the rock particle crushing process is simulated to obtain several sub-rock particles;

[0039] The particle crushing model is used to simulate the sub-rock particles under the external load until the sub-rock particle size is smaller than the preset size threshold;

[0040] If the impact energy received by the rock particles is less than the critical condition of their fracture damage, damage cracks will be generated inside the rock particles.

[0041] Furthermore, simulation schemes under different decision variables were designed and numerical simulations were performed respectively to obtain the crushing mass of rock slag particles per second and the slag discharge mass flow rate at the crushing chamber outlet under each group of simulations, including:

[0042] Based on the central composite design, the decision variables are coded and mapped horizontally to obtain the actual values corresponding to each decision variable;

[0043] The decision variable combination scheme is constructed using the central composite design to obtain the constructed combination scheme;

[0044] Numerical simulations of various working conditions were carried out based on the structural combination scheme to obtain the crushing mass of rock slag particles per second and the slag discharge mass flow rate at the crushing chamber outlet under each group of simulations.

[0045] Furthermore, the construction combination scheme includes factorial points, axial points and central points.

[0046] Furthermore, the multi-objective particle swarm optimization algorithm is used to optimize each decision variable, and the optimal solution is determined by approximating the ideal solution, including:

[0047] Multi-objective particle swarm optimization is used to optimize each decision variable to obtain the target value of the Pareto optimal set. Based on the target value, a target matrix is constructed for each non-dominated solution to obtain the decision matrix.

[0048] Based on the decision matrix, the target value is standardized to eliminate the dimension effect and obtain the standardized matrix;

[0049] Analyze each target column using a standardized matrix to determine the positive and negative ideal solutions;

[0050] Based on the positive ideal solution and the negative ideal solution, for each solution in the normalized matrix, the Euclidean distance to the positive ideal solution and the negative ideal solution is calculated respectively;

[0051] Use Euclidean distance to calculate the closeness of each solution to obtain the closeness of each solution;

[0052] Sort each solution in descending order based on its closeness, and select the solution with the largest closeness as the optimal solution.

[0053] The beneficial effects of the present invention are as follows: the present invention takes into account the influence of short spiral conveying and crusher crushing on the slag and stone conveying in the mud and water compartment, and can quickly and accurately calculate the optimal combination of short spiral working parameters and crusher working parameters under different shield propulsion speeds, thereby alleviating the problem of sludge discharge in actual engineering mud and water compartments, and has the advantages of low cost, wide applicability, strong practicality, and high value for promotion and popularization. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 Flowchart of a method for optimizing working parameters of a slurry shield short spiral and a post-crusher for collaborative slag discharge based on CFD-DEM coupling modeling according to an embodiment of the present invention;

[0056] Figure 2 is a schematic diagram of a three-dimensional calculation model considering shield tunneling and short spiral conveying and slag removal according to an embodiment of the present invention;

[0057] Figure 3 is a schematic diagram of a slag discharge efficiency change curve according to an embodiment of the present invention;

[0058] Figure 4 is a schematic diagram of the motion state of particles within a short helix according to an embodiment of the present invention;

[0059] Figure 5 is a schematic diagram of a three-dimensional coupled model of a crushing chamber considering short spiral slag discharge and rock slag crushing according to an embodiment of the present invention;

[0060] Figure 6 is a schematic diagram of the HB rheological curve of bentonite slurry according to an embodiment of the present invention;

[0061] Figure 7 4 is a calculation flow chart of the Tavares UFRJ particle breakage model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0063] In the description of the present invention, unless otherwise specified, "plurality" means two or more. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0064] According to an embodiment of the present invention, a method for optimizing the working parameters of a slurry shield short spiral and a post-crusher for collaborative slag discharge based on CFD-DEM coupling modeling is provided.

[0065] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to an embodiment of the present invention, a method for optimizing working parameters of a slurry shield short spiral and a post-crusher for collaborative slag discharge based on CFD-DEM coupling modeling includes:

[0066] S1. Obtain the mass flow rate of rock slag particles and the mass flow rate of mud in the rock formation, and simulate the rheological behavior of the mud using a rheological model (i.e., the HB rheological model);

[0067] Specifically, through the excavation area S, advancement speed V, particle density ρ p , calculate the mass flow rate of particles in the rock formation, the particles are the slag particles; according to the mud fluid density ρ f As well as the volume flow of the slurry inlet pipeline in actual engineering, the mud mass flow rate is calculated and the HB rheological model is used to simulate the mud.

[0068] S2. Establish a three-dimensional calculation model and use it to calculate the slag discharge mass flow rate at the shield short spiral outlet, the short spiral slag discharge efficiency, and the mud mass flow rate, analyze the movement state of the rock slag particles in the short spiral, and optimize the short spiral speed setting;

[0069] Specifically, according to the actual geometric dimensions of the shield machine, based on the CFD-DEM coupling technology, a 1:1 scale three-dimensional calculation model considering shield tunneling and short spiral conveying and slag removal is established. The model schematic diagram is shown as follows: Figure 2 As shown ( Figure 2 (I is the short spiral outlet) and the slag discharge mass flow rate at the shield short spiral outlet, the short spiral slag discharge efficiency η, and the slurry mass flow rate are calculated under different shield advancement speeds V and different short spiral rotation speeds r. The movement state of the particles in the short spiral is observed to see if there is stagnation. With the goal of improving the short spiral slag discharge efficiency and slag discharge mass flow rate, the short spiral rotation speed r setting under different shield tunneling speeds is comprehensively optimized. The slag discharge efficiency change curve is shown in the figure below. Figure 3 As shown ( Figure 3 (II is the spiral inlet), the slag discharge efficiency is the stable value in the slag discharge efficiency curve, and the motion state of the particles (i.e., rock slag particles) in the short spiral is as follows: Figure 4 As shown ( Figure 3 Ⅲ in the middle is the entrance of the crushing chamber or the short spiral outlet Ⅰ).

[0070] S3. Based on coupling technology (i.e., CFD-DEM coupling technology), a three-dimensional coupling model of the crushing chamber is established;

[0071] Specifically, based on CFD-DEM coupling technology, a three-dimensional coupling model of the crushing chamber considering short spiral slag discharge and rock slag crushing is established. The model schematic is shown in the figure below. Figure 5 shown.

[0072] S4. The slag discharge mass flow rate and mud mass flow rate at the shield short spiral outlet are used as the inlet slag particle mass flow rate and mud mass flow rate of the crushing chamber three-dimensional coupling model respectively, and a coupling simulation is performed;

[0073] Specifically, the outlet slag discharge flow rate and mud flow rate obtained at different short spiral speeds are used as the inlet particle mass flow rate and mud mass flow rate of the crushing chamber three-dimensional coupling model, respectively.

[0074] S5. Based on the coupling simulation results, the crushing mass per second of the rock debris particles in the crushing chamber and the discharge mass flow rate of the crushing chamber outlet are analyzed to optimize the crusher operating parameters;

[0075] Specifically, the crushing mass per second of particles in the crushing chamber and the mass flow rate of slag discharge at the crushing chamber outlet were analyzed under different short spiral speed conditions. With the goal of improving the crushing mass per second of particles and the mass flow rate of slag discharge at the crushing chamber outlet, the crusher operating parameters under different short spiral speed conditions, including the swing period T and the maximum swing amplitude θ, were comprehensively optimized.

[0076] S6. Design simulation schemes under different decision variables and perform numerical simulations respectively to obtain the crushing mass of rock slag particles per second and the slag discharge mass flow rate at the crushing chamber outlet under each set of simulations;

[0077] Specifically, the central composite design method was used to design simulation schemes with different decision variables, including short spiral speed, crusher swing period, and maximum swing angle. Each decision variable had 5 levels, for a total of 17 groups of numerical simulation combinations. Numerical simulations were carried out under the parameters of different factors to obtain the crushing mass of particles per second and the slag discharge mass flow rate at the crushing chamber outlet under each group of simulations.

[0078] S7. Use the multi-objective particle swarm optimization algorithm to optimize each decision variable, and use the approximate ideal solution method to determine the optimal solution.

[0079] Specifically, a multi-objective particle swarm optimization algorithm is used to optimize each decision variable with the goal of maximizing the crushing mass of particles per second and the discharge mass flow rate of the crushing chamber outlet. The optimal solution is determined using the approximate ideal solution, and the impact of the weight distribution of each decision variable on the goal is analyzed.

[0080] In this optional embodiment, obtaining the mass flow rate of slag particles and the mass flow rate of mud in the rock formation and simulating the rheological behavior of the mud using a rheological model (i.e., the HB rheological model) includes:

[0081] The mud rheological curve is obtained by a coaxial cylinder measuring system (i.e., coaxial cylinder measuring system CCT-40), and the mass flow rate of rock debris particles and the mass flow rate of mud in the rock formation are fitted into the rheological model to obtain the parameters of the rheological model;

[0082] Based on the parameters of the rheological model, the rheological model is applied in the fluid simulation software (i.e., Fluent fluid simulation software) and the flow behavior of the mud is simulated.

[0083] Specifically, in the HB rheological model, the relationship between the shear stress τ and the shear rate γ is expressed as:

[0084] .

[0085] .

[0086] The mud rheological curve was obtained using the coaxial cylinder measuring system CCT-40. The HB rheological model was used to fit the measured test data, and τ0 was obtained as the yield stress, k as the viscosity coefficient, and n as the power law index. The mud rheological curve is shown in the figure below: Figure 6 As shown in Figure 1, the HB rheological model is used to simulate the mud in the Fluent fluid simulation software. The mud rheological curve is the HB rheological curve of bentonite mud.

[0087] In this optional embodiment, a three-dimensional calculation model is established and used to calculate the slag discharge mass flow rate at the shield short spiral outlet, the short spiral slag discharge efficiency, and the mud mass flow rate, analyze the movement state of the slag particles in the short spiral, and optimize the short spiral speed setting, including:

[0088] Based on the continuity equation, the fluid mass conservation equation is established using time, fluid volume fraction, fluid density and fluid velocity vector to obtain the mass flow rate change of the mud in the short spiral;

[0089] The momentum conservation equation is used to combine the viscous stress tensor, fluid pressure, gravitational acceleration, and the force exerted by all rock debris particles on the fluid to obtain the flow state and velocity distribution of the mud in the short spiral.

[0090] Based on Newton's second law, the translational and rotational equations of the slag particles are established using the mass, velocity, moment of inertia, angular velocity of the slag particles, the forces between the slag particles, the forces between the slag particles and the geometric wall of the shield, and the fluid phase forces. The motion state of the slag particles in the slurry is then determined.

[0091] Based on the fluid phase force, combined with the density, particle size, velocity of the slag particles, effective viscosity of the fluid, Reynolds number and drag coefficient of the slag particles, the motion state of the slag particles in the short spiral is obtained, and the short spiral speed setting is optimized.

[0092] Specifically, the fluid phase is solved by the continuity equation and the momentum conservation equation. The continuity equation is:

[0093] .

[0094] Where t is time, α f is the volume fraction of the fluid, ρ f is the fluid density, U f is the fluid velocity vector.

[0095] The momentum conservation equation is:

[0096] .

[0097] Among them, τ f is the viscous stress tensor; P fis the fluid pressure, g is the acceleration due to gravity, and is taken as 9.81m / s 2 , F pf is the force exerted by all rock debris particles on the fluid.

[0098] Specifically, during the simulation, a single slag particle is affected by other slag particles, mud, and the geometric wall of the shield. Newton's second law of motion is used to describe the movement of the slag particles. For the translation and rotation of the slag particles:

[0099] The formula for establishing the translation equation of slag particles is:

[0100] .

[0101] The formula for establishing the rotation equation of slag particles is:

[0102] .

[0103] Where m i represents the mass of slag particle i; U p，i represents the velocity of slag particle i; I i represents the inertial motion of slag particle i; ω i represents the angular velocity of slag particle i; F c，ij F represents the contact force between slag particle i and other slag particles; d，ij represents the viscous damping force between slag particle i and other slag particles; T ij represents the torque between slag particle i and other slag particles; F c，iw represents the contact force between the slag particle i and the shield geometric wall; F d，iw represents the viscous damping force between the slag particle i and the shield geometric wall; T iw represents the torque between the slag particle i and the shield geometric wall; F f，i represents the force of the fluid phase on the slag particle i; t represents time; j represents slag particle j, i represents slag particle i; F c The contact force represented is the whole.

[0104] Specifically, the interaction force between slag particles and fluid is mainly composed of the buoyancy F B , drag force F D and pressure gradient F P Composition; the resultant force of the fluid phase on the slag particles is:

[0105] .

[0106] Mud buoyancy F B for:

[0107] .

[0108] Mud drag F D for:

[0109] .

[0110] Pressure gradient F P for:

[0111] .

[0112] Where: p is the density of the slag particles, d p is the particle size of the slag particles, U p is the velocity of the slag particles, µ e is the effective viscosity of the fluid, and the Reynolds number of the slag particles Re p and drag coefficient C D The calculation is as follows:

[0113] The calculation formula of the Reynolds number of slag particles is:

[0114] .

[0115] The drag coefficient of slag particles is calculated as follows:

[0116] .

[0117] Where Re is the Reynolds number of the slag particles; μ is the fluid dynamic viscosity; ρ is the density; f represents the fluid density; U f represents the fluid velocity vector; U p represents the velocity of slag particles; d p Indicates the particle size of slag particles; C D represents the drag coefficient of the slag particles; a1, a2, and a3 are all constants, which are given by Morsi and Alexander based on the fluid Reynolds number Re as follows:

[0118] .

[0119] Specifically, the short spiral slag removal efficiency η is defined as follows:

[0120] .

[0121] Where Q n is the actual mass flow rate of the short helix, kg / s, obtained through CFD-DEM simulation monitoring, Q t is the theoretical maximum slag discharge capacity of the short spiral, kg / s, which is calculated by the following formula:

[0122] .

[0123] Where r is the short helix speed, l is the short helix pitch, D and d are the short helix blade diameter and helix shaft diameter, respectively.

[0124] In this optional embodiment, establishing a three-dimensional coupling model of the crushing chamber based on coupling technology includes:

[0125] A three-dimensional coupling model of the crushing chamber was established based on CFD-DEM coupling technology to obtain a particle crushing model;

[0126] Based on the particle crushing model, the rock particle crushing process is simulated to obtain several sub-rock particles;

[0127] The particle crushing model (i.e., Tavares UFRJ particle crushing model) is used to simulate the sub-rock particles under the external load until the sub-rock particle size is smaller than a preset size threshold;

[0128] If the impact energy received by the rock particles is less than the critical condition of their fracture damage, damage cracks will be generated inside the rock particles.

[0129] Specifically, the particle crushing model of the crusher crushing and conveying three-dimensional coupling model considering short spiral slag discharge and rock slag crushing adopts the Tavares UFRJ particle crushing model. This model describes the rock particles once they are subjected to a load greater than their own fracture energy (the particle's own fracture energy E f ) of the impact energy (impact energy E k ), this particle, called the mother particle, will be replaced and split into many smaller particles, called daughter particles (i.e., daughter rock particles). When subjected to external loads, the daughter particles are similarly replaced by smaller particles, and this multi-stage process will stop until the particle size is less than the minimum size d set for the entire simulation. min (i.e., the preset size threshold). If the impact energy received by the particle is less than the critical condition for its own fracture damage, then damage cracks will be generated within the particle, causing its fracture energy to be weakened, making it easier to break in subsequent simulations. The calculation flow chart of the Tavares UFRJ particle breakage model is shown below: Figure 7 shown.

[0130] In this optional embodiment, simulation schemes under different decision variables are designed, and numerical simulations are performed respectively to obtain the crushing mass of rock slag particles per second and the slag discharge mass flow rate at the crushing chamber outlet under each group of simulations, including:

[0131] Based on the central composite design, the decision variables are coded and mapped horizontally to obtain the actual values corresponding to each decision variable;

[0132] The decision variable combination scheme is constructed using the central composite design to obtain the constructed combination scheme;

[0133] Numerical simulations of various working conditions were carried out based on the structural combination scheme to obtain the crushing mass of rock slag particles per second and the slag discharge mass flow rate at the crushing chamber outlet under each group of simulations.

[0134] Specifically, the steps for implementing the central composite design (CCD) include:

[0135] 1) Variable Coding and Level Mapping Decision variables: Short screw speed (x1): 5 levels (-2, -1, 0, 1, 2) corresponding to actual values of 8, 9, 10, 11, and 12 rpm. Crusher swing period (x2): 5 levels (-2, -1, 0, 1, 2) corresponding to actual values of 10, 20, 30, 40, and 50 seconds. Maximum swing angle (x3): 5 levels (-2, -1, 0, 1, 2) corresponding to actual values of 16, 20, 24, 28, and 32 degrees.

[0136] 2) Scheme construction (17 combinations)

[0137] Factorial analysis points (8 groups): Each variable takes the +1 and -1 levels (x1 is +1 and -1, x2 is +1 and -1, x3 is +1 and -1), corresponding to the actual values (9 / 11, 20 / 40, 20 / 28), covering the variable interaction.

[0138] Axial points (6 groups): Each variable takes the levels of +2 and -2, and the rest are 0 (for example, x1 is +2 and -2, x2 is 0, and x3 is 0), corresponding to (8 / 12, 30, 24); the x2 and x3 axial points are processed similarly), expanding the variable range boundaries.

[0139] Center point (3 groups): Each variable takes the 0 level (10, 30, 24), and repeats 3 times to improve the robustness of the model.

[0140] Specifically, the simulation schemes under different decision variables, including short screw speed, crusher swing period, and maximum swing angle, are shown in Table 1 below. Each decision variable is set to 5 levels, as shown in Table 2 below.

[0141] Table 1. Simulation schemes under short spiral speed, crusher swing period, and maximum swing angle

[0142] Table 2. Five levels for each decision variable

[0143] In this alternative embodiment, the construction combination scheme includes factorial points, axial points, and center points.

[0144] In this optional embodiment, optimizing each decision variable using a multi-objective particle swarm optimization algorithm and determining the optimal solution using an approximate ideal solution method include:

[0145] Multi-objective particle swarm optimization is used to optimize each decision variable to obtain the target value of the Pareto optimal set. Based on the target value, a target matrix is constructed for each non-dominated solution to obtain the decision matrix.

[0146] Based on the decision matrix, the target value is standardized to eliminate the dimension effect and obtain the standardized matrix;

[0147] Analyze each target column using a standardized matrix to determine the positive and negative ideal solutions;

[0148] Based on the positive ideal solution and the negative ideal solution, for each solution in the normalized matrix, the Euclidean distance to the positive ideal solution and the negative ideal solution is calculated respectively;

[0149] Use Euclidean distance to calculate the closeness of each solution to obtain the closeness of each solution;

[0150] Sort each solution in descending order based on its closeness, and select the solution with the largest closeness as the optimal solution.

[0151] Specifically, the optimal solution determined using the TOPSIS method includes:

[0152] 1) Build a decision matrix:

[0153] The target values (mass of crushed particles per second and outlet slag mass flow rate, both of which are efficiency indicators) of the Pareto solution set (i.e., Pareto optimal set) obtained by multi-objective particle swarm optimization (MOPSO) (including n non-dominated solutions) are used to construct an n*2 matrix X, where each row corresponds to the target value of a solution.

[0154] 2) Dimensionless processing:

[0155] Normalize the target value to eliminate the dimension effect:

[0156] .

[0157] The normalized matrix Z is obtained, where i represents the solution index (i.e., the i-th non-dominated solution in the Pareto solution set, i=1, 2, …, n, where n is the size of the solution set). j represents the objective function index (here j=1 corresponds to "mass of particles crushed per second" and j=2 corresponds to "mass flow rate of slag discharge at the crushing chamber outlet," for a total of two objectives).

[0158] 3) Determine positive and negative ideal solutions:

[0159] Positive ideal solution Z+: the maximum value of each target column, that is, Z+= (max (z i1), max(z i2 )).

[0160] Negative ideal solution Z-: the minimum value of each target column, that is, Z-= (min (z i1 ), min (z i2 )).

[0161] 4) Calculate Euclidean distance:

[0162] Distance to the positive ideal solution: .

[0163] Distance to the negative ideal solution: .

[0164] 5) Calculate closeness (comprehensive score):

[0165] The larger it is, the closer the solution is to the ideal solution (optimal).

[0166] 6) Sorting and selection:

[0167] C i Sort in descending order, select C i The largest solution is considered the optimal solution. This solution is closest to the ideal objective (maximizing f1 and f2) on the Pareto front, while being far away from the negative ideal objective.

[0168] To sum up, with the help of the above-mentioned technical scheme of the present invention, the present invention takes into account the influence of short spiral conveying and crusher crushing on the slag and stone conveying in the mud and water compartment, and can quickly and accurately calculate the optimal combination of short spiral working parameters and crusher working parameters under different shield propulsion speeds, thereby alleviating the problem of sludge discharge in actual engineering mud and water compartments. It has the advantages of low cost, wide applicability, strong practicality and high value for promotion and popularization.

[0169] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The optimization method of the working parameters of the short spiral and post-crusher coordinated slag discharge of the slurry shield based on CFD-DEM coupling modeling is characterized by: include: Obtain the mass flow rate of rock slag particles and mud mass flow rate in the rock formation, and simulate the rheological behavior of the mud using a rheological model; A three-dimensional calculation model was established and used to calculate the slag discharge mass flow rate at the shield short spiral outlet, the short spiral slag discharge efficiency, and the mud mass flow rate. The movement state of the rock slag particles in the short spiral was analyzed to optimize the short spiral speed setting. Based on coupling technology, a three-dimensional coupling model of the crushing chamber is established; The slag mass flow rate and mud mass flow rate at the outlet of the short spiral shield machine are used as the inlet slag particle mass flow rate and mud mass flow rate of the crushing chamber three-dimensional coupling model respectively, and the coupling simulation is carried out; Based on the coupling simulation results, the crushing mass per second of rock debris particles in the crushing chamber and the discharge mass flow rate of the crushing chamber outlet are analyzed to optimize the crusher operating parameters; Design simulation schemes under different decision variables and conduct numerical simulations respectively to obtain the crushing mass of rock slag particles per second and the slag discharge mass flow rate at the crushing chamber outlet under each set of simulations; The multi-objective particle swarm optimization algorithm is used to optimize the decision variables, and the optimal solution is determined using the approximate ideal solution method.

2. The method for optimizing working parameters of slurry shield short spiral and post-crusher collaborative slag discharge based on CFD-DEM coupling modeling according to claim 1 is characterized in that: The method of obtaining the mass flow rate of slag particles and the mass flow rate of mud in the rock formation and simulating the rheological behavior of the mud using a rheological model includes: The mud rheological curve is obtained by a coaxial cylinder measurement system, and the mass flow rate of rock slag particles and the mass flow rate of mud in the rock formation are fitted into the rheological model to obtain the parameters of the rheological model; Based on the parameters of the rheological model, the rheological model is applied in the fluid simulation software and the flow behavior of the mud is simulated.

3. The method for optimizing working parameters of slurry shield short spiral and post-crusher collaborative slag discharge based on CFD-DEM coupling modeling according to claim 1 is characterized in that: The three-dimensional calculation model is established, and the three-dimensional calculation model is used to calculate the slag discharge mass flow rate at the shield short spiral outlet, the short spiral slag discharge efficiency and the mud mass flow rate, analyze the movement state of the rock slag particles in the short spiral, and optimize the short spiral speed setting, including: By using time, fluid volume fraction, fluid density and fluid velocity vector, the fluid mass conservation equation is established to obtain the mass flow rate change of the mud in the short spiral. Combining the viscous stress tensor, fluid pressure, gravitational acceleration, and the forces exerted by all rock debris particles on the fluid, the flow state and velocity distribution of the mud in the short spiral are obtained. Using the mass, velocity, moment of inertia, angular velocity of the slag particles, the forces between the slag particles, the forces between the slag particles and the geometric wall of the shield, and the fluid phase forces, the translational and rotational equations of the slag particles are established, respectively, to obtain the motion state of the slag particles in the mud. Based on the fluid phase force, combined with the density, particle size, velocity of the slag particles, effective viscosity of the fluid, Reynolds number and drag coefficient of the slag particles, the motion state of the slag particles in the short spiral is obtained, and the short spiral speed setting is optimized.

4. The method for optimizing working parameters of slurry shield short spiral and post-crusher collaborative slag discharge based on CFD-DEM coupling modeling according to claim 1 is characterized in that: The calculation formula of the short spiral slag removal efficiency is: ； Where, η represents the short spiral slag removal efficiency; Q n represents the true mass flow rate of the short helix; r represents the speed of the short helix; l represents the pitch of the short helix; D represents the blade diameter of the short helix; and d represents the diameter of the helix axis of the short helix.

5. The method for optimizing working parameters of slurry shield short spiral and post-crusher collaborative slag discharge based on CFD-DEM coupling modeling according to claim 3 is characterized in that: The formula for establishing the translation equation of slag particles is: ； The formula for establishing the rotation equation of slag particles is: ； Where m i represents the mass of slag particle i; U p，i represents the velocity of slag particle i; I i represents the inertial motion of slag particle i; ω i represents the angular velocity of slag particle i; F c，ij F represents the contact force between slag particle i and other slag particles; d，ij represents the viscous damping force between slag particle i and other slag particles; T ij represents the torque between slag particle i and other slag particles; F c，iw represents the contact force between the slag particle i and the shield geometric wall; F d，iw represents the viscous damping force between the slag particle i and the shield geometric wall; T iw represents the torque between the slag particle i and the shield geometric wall; F f，i represents the force exerted by the fluid phase on the slag particle i; t represents time.

6. The method for optimizing working parameters of slurry shield short spiral and post-crusher collaborative slag discharge based on CFD-DEM coupling modeling according to claim 3 is characterized in that: The calculation formula of the Reynolds number of the slag particles is: ； The drag coefficient calculation formula of the slag particles is: ； Where Re is the Reynolds number of the slag particles; μ is the fluid dynamic viscosity; ρ is the density; f represents the fluid density; U f represents the fluid velocity vector; U p represents the velocity of slag particles; d p Indicates the particle size of slag particles; C D represents the drag coefficient of the slag particles; a1, a2 and a3 are all constants.

7. The method for optimizing working parameters of slurry shield short spiral and post-crusher collaborative slag discharge based on CFD-DEM coupling modeling according to claim 1 is characterized in that: The establishment of a three-dimensional coupling model of the crushing chamber based on coupling technology includes: A three-dimensional coupling model of the crushing chamber was established based on CFD-DEM coupling technology to obtain a particle crushing model; Based on the particle crushing model, the rock particle crushing process is simulated to obtain several sub-rock particles; The particle crushing model is used to simulate the sub-rock particles under the external load until the sub-rock particle size is smaller than the preset size threshold; If the impact energy received by the rock particles is less than the critical condition of their fracture damage, damage cracks will be generated inside the rock particles.

8. The method for optimizing working parameters of slurry shield short spiral and post-crusher collaborative slag discharge based on CFD-DEM coupling modeling according to claim 1 is characterized in that: The simulation schemes under different decision variables are designed and numerical simulations are performed respectively to obtain the crushing mass of rock slag particles per second and the slag discharge mass flow rate at the crushing chamber outlet under each group of simulations, including: Based on the central composite design, the decision variables are coded and mapped horizontally to obtain the actual values corresponding to each decision variable; The decision variable combination scheme is constructed using the central composite design to obtain the constructed combination scheme; Numerical simulations of various working conditions were carried out based on the structural combination scheme to obtain the crushing mass of rock slag particles per second and the slag discharge mass flow rate at the crushing chamber outlet under each group of simulations.

9. The method for optimizing working parameters of slurry shield short spiral and post-crusher collaborative slag discharge based on CFD-DEM coupling modeling according to claim 8 is characterized in that: The structural combination scheme includes factorial points, axial points and center points.

10. The method for optimizing working parameters of slurry shield short spiral and post-crusher collaborative slag discharge based on CFD-DEM coupling modeling according to claim 1, characterized in that: The method of optimizing each decision variable by using a multi-objective particle swarm optimization algorithm and determining the optimal solution by using an approximate ideal solution method includes: Multi-objective particle swarm optimization is used to optimize each decision variable to obtain the target value of the Pareto optimal set. Based on the target value, a target matrix is constructed for each non-dominated solution to obtain the decision matrix. Based on the decision matrix, the target value is standardized to eliminate the dimension effect and obtain the standardized matrix; Analyze each target column using a standardized matrix to determine the positive and negative ideal solutions; Based on the positive ideal solution and the negative ideal solution, for each solution in the normalized matrix, the Euclidean distance to the positive ideal solution and the negative ideal solution is calculated respectively; Use Euclidean distance to calculate the closeness of each solution to obtain the closeness of each solution; Sort each solution in descending order based on its closeness, and select the solution with the largest closeness as the optimal solution.

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