Construction and application method of high-pressure efficient dredge pump simulation calculation model

Abstract

The invention relates to the technical field of high-pressure dredge pump simulation calculation, and discloses a construction and application method of a high-pressure efficient dredge pump simulation calculation model. A model is established based on dynamic fluid-solid coupling parameters (mud pulsating pressure, impeller dynamic stress and the like), soil multiphase rheological parameters (particle phase volume fraction, viscous resistance coefficient and the like) and pipeline transient resistance parameters (transient pressure drop gradient and the like), and factors such as rotating speed-power matching and the like are incorporated to calculate an operation envelope and a critical failure threshold. A multi-physics field coupling analysis system comprising parameter configuration, dynamic simulation (integrating a flow field and a structural vibration solver) and a safety evaluation module is developed, after actually measured data and self-defined parameters are input, dynamic flow velocity distribution, pressure pulsation characteristics and the like are obtained through fluid-solid bidirectional coupling calculation, and an optimal configuration scheme is generated. The system supports database management, multi-rheological model matching and three-dimensional visualization, the simulation precision and the engineering efficiency are improved, and support is provided for performance optimization and safety evaluation of the high-pressure working condition.

Description

Technical Field

[0001] The present invention relates to the technical field of high-pressure mud pump simulation calculation, and specifically to a method for constructing and applying a simulation calculation model of a high-pressure and high-efficiency mud pump. Background Art

[0002] In the modern engineering field, as a basic construction equipment, high-pressure mud pumps are widely used in scenarios such as mine exploitation, tunnel excavation, oil and gas drilling, etc. The pressure range of high-pressure mud pumps is usually between 10 - 60 MPa, and the specific pressure depends on its application industry and working conditions. For example, the pressure range of high-pressure mud pumps commonly used in the oil industry is between 10 - 15 MPa, while industries such as coal mines and tunnel construction may require higher pressures, reaching 20 - 25 MPa or even higher. The performance of high-pressure mud pumps directly affects construction efficiency, safety, and equipment life. With the increase in engineering complexity, higher requirements are put forward for the reliability and energy efficiency of mud pumps under high-pressure, high-load, and multi-condition conditions. However, the traditional design and optimization of mud pumps rely on physical tests and empirical formulas, which have problems such as high cost, long cycle, and insufficient coverage of working conditions, and it is difficult to accurately simulate complex physical phenomena in actual construction, such as fluid-structure interaction, multiphase flow characteristics, and transient cavitation risk, etc.

[0003] In the prior art, mud pump simulation models often neglect the refined description of dynamic fluid-structure interaction effects. For example, only static pressure distribution is considered, and the dynamic load effect of mud pulsation pressure on the impeller structure is not included, resulting in the inability to accurately predict impeller fatigue damage. At the same time, the modeling of soil multiphase rheological characteristics is relatively rough. Usually, the mud is simplified as a single Newtonian fluid, ignoring the influence of parameters such as particle phase volume fraction, viscous resistance coefficient, and thixotropic index on flow characteristics, making the simulation results deviate significantly from the actual working conditions. In addition, the transient resistance analysis of the pipeline system is mostly based on the steady-state flow assumption, without considering the influence of transient pressure drop gradient, local resistance mutation, and cavitation phenomenon on the operation stability of the mud pump, and it is difficult to effectively identify critical failure conditions.

[0004] At the system development level, traditional simulation tools lack the ability of multi-physical field coupling. The flow field and structure field analyses are independent of each other, and it is impossible to achieve real-time data interaction and iterative calculation, resulting in the simulation results being unable to reflect the true physical coupling process. The parameter configuration module has a single function, which is difficult to meet the rapid modeling requirements of diverse soil conditions and pipeline topologies, and the safety assessment system is imperfect, lacking a systematic analysis of the operation envelope and critical failure threshold of the mud pump, and unable to provide comprehensive working condition safety guidance for engineering personnel.

[0005] With the development of computer technology and computational fluid dynamics (CFD) and computational structural mechanics (CSM), multi-physics field coupling simulation has become a key means to solve complex engineering problems. How to construct a high-precision simulation model covering dynamic fluid-structure interaction, multi-phase rheological characteristics, and transient pipeline resistance, and develop an analysis system integrating parameter configuration, dynamic simulation, and safety assessment functions has become the core technical challenge to improve the design and operation and maintenance levels of high-pressure mud pumps. The deficiencies of existing technologies in terms of model integrity, parameter refinement, and system integration urgently require a more comprehensive and efficient simulation calculation method and system to meet the actual engineering needs. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for constructing and applying a simulation calculation model of a high-pressure and high-efficiency mud pump to solve the problems presented in the above background technology.

[0007] To achieve the above purpose, the present invention provides the following technical solution: A method for constructing and applying a simulation calculation model of a high-pressure and high-efficiency mud pump, the method comprising: Establish a simulation calculation model of a high-pressure mud pump based on the dynamic fluid-structure coupling parameters, soil multi-phase rheological parameters, and pipeline transient resistance parameters of the mud pump. The dynamic fluid-structure coupling parameters include mud pulsation pressure and impeller dynamic stress, the soil multi-phase rheological parameters include particle phase volume fraction and viscous resistance coefficient, and the pipeline transient resistance parameters include transient pressure drop gradient and local resistance mutation coefficient; Incorporate the dynamic matching of mud pump speed-power, the stratification effect of mud flow regime, and the risk factors of pipeline transient cavitation into the simulation calculation model, and calculate the operation envelope and critical failure threshold of the mud pump under different construction scenarios through the model; Develop a multi-physics field coupling analysis system for high-pressure mud pumps according to the simulation calculation model. The system includes a parameter configuration module, a dynamic simulation module, and a safety assessment module, wherein the dynamic simulation module integrates a transient flow field solver and a structural vibration solver; Input the measured transient performance data of the mud pump, the custom soil rheological characteristics, and the pipeline topology parameters through the parameter configuration module of the system. The measured transient performance data includes pulsating power spectral density and dynamic head fluctuation amplitude; Perform fluid-structure two-way coupling calculation based on the dynamic simulation module of the system to obtain the dynamic flow velocity distribution, pressure pulsation propagation characteristics, and structural fatigue damage index of the mud pump at different speeds, and generate an optimized configuration plan for high-pressure working conditions.

[0008] Preferably, the dynamic fluid-structure coupling parameters further include the impeller clearance leakage vortex intensity coefficient and the mud non-Newtonian fluid thixotropic index.

[0009] Preferably, the parameter configuration module of the multi-physics field coupling analysis system for high-pressure mud pumps includes: Build a transient performance database of the mud pump, a rheological property database of the soil, and a pipeline topology database, where the transient performance database stores the time-domain signals of pressure pulsations and the frequency-domain energy distributions at different rotational speeds; Match the corresponding impeller geometric parameter set from the database according to the mud pump model, and the geometric parameter set includes the blade wrap angle, the hub ratio, and the outlet setting angle; Generate a three-dimensional mesh model of the mud pump flow channel based on the geometric parameter set and load it into the flow field solver of the dynamic simulation module.

[0010] Preferably, the dynamic simulation module performing fluid-structure interaction calculations includes: Map the pulsating pressure field output by the flow field solver to the impeller surface mesh nodes of the structural vibration solver; Calculate the dynamic stress distribution and modal participation factors of the impeller through the structural vibration solver, and feedback the deformation displacement field to the flow field solver to update the calculation domain; Iteratively calculate until the residuals of the pressure pulsation amplitude and the structural vibration acceleration converge to a preset threshold.

[0011] Preferably, the safety assessment module includes: Extract the peak equivalent stress and the critical pressure for the onset of mud cavitation at the key positions of the impeller according to the dynamic simulation results; Calculate the three-dimensional safety boundary surface of rotational speed - head - power in the mud pump operating envelope and mark the critical failure region; Generate a set of allowable operating condition configurations and corresponding risk level labels based on the safety boundary surface.

[0012] Preferably, the parameter configuration module further includes: Set a mud multiphase rheological model selector, and the selector includes a Bingham fluid model, a power-law fluid model, and a thixotropic fluid model; Automatically match the corresponding rheological model according to the soil gradation parameters and load it into the flow field control equation of the dynamic simulation module.

[0013] Preferably, the high-pressure mud pump multi-physical field coupling analysis system further includes: Design a main control interface, a parameter visualization interface, and a report generation interface, where the main control interface integrates functions such as operating condition configuration, simulation progress monitoring, and result comparison; Synchronously display the three-dimensional dynamic rendering results of the flow field velocity cloud map, pressure contour lines, and structural stress distribution in the parameter visualization interface.

[0014] Preferably, the operating condition configuration function includes: Set the mud pump rotational speed adjustment range, mud concentration gradient, and pipeline topology change sequence; Automatically generate a dynamic performance comparison matrix under different configuration combinations based on the simulation calculation model, and the matrix includes an efficiency-power curve, a pulsation amplitude-frequency spectrum, and predicted fatigue life values.

[0015] Preferably, the calculation of the critical failure threshold includes: Establish a bivariate failure criterion for the S-N curve of the impeller material and the mud cavitation damage accumulation model; Extract the combined damage contribution factors of the dynamic stress spectrum and the pressure pulsation spectrum by the rainflow counting method; When the combined damage contribution factor exceeds the preset safety factor, mark the current working condition as a high-risk state.

[0016] Preferably, the generation of the optimized configuration scheme for high-pressure working conditions includes: According to the set of allowable working conditions output by the safety assessment module, calculate the production-energy consumption ratio and the equipment loss rate under each working condition; Use a multi-objective optimization algorithm to screen the Pareto optimal solution set that meets the constraint conditions, and generate a recommended priority list according to the preset weight.

[0017] Compared with the prior art, the beneficial effects of the present invention are: The simulation calculation model constructed by the present invention comprehensively incorporates dynamic fluid-structure coupling parameters (such as mud pulsation pressure, impeller dynamic stress, leakage vortex intensity coefficient, etc.), soil multi-phase rheological parameters (particle phase volume fraction, viscous resistance coefficient, thixotropy index, etc.), and pipeline transient resistance parameters (transient pressure drop gradient, local resistance mutation coefficient, etc.), breaking through the limitation of the traditional model's incomplete description of the multi-physical field coupling effect. Through refined modeling, it can accurately simulate the dynamic interaction between mud flow and impeller structure, reveal the flow stratification effect under multi-phase flow conditions and the evolution law of pipeline cavitation risk, and provide a more reliable theoretical basis for the performance prediction of mud pumps under different soil and pipeline conditions.

[0018] The multi-physical field coupling analysis system realizes two-way fluid-structure coupling calculation through the dynamic simulation module, deeply integrates the flow field solver and the structure vibration solver, and through the real-time mapping and iterative calculation of the pulsation pressure field and the deformation displacement field, ensures that the simulation results truly reflect the physical coupling process during the operation of the mud pump. This refined calculation can obtain key parameters such as dynamic flow velocity distribution, pressure pulsation propagation characteristics, and structural fatigue damage index. Compared with the traditional method of independently analyzing the flow field or the structure field, it significantly improves the accuracy and credibility of the simulation results, and provides more accurate data support for impeller structure optimization and fatigue life prediction.

[0019] The parameter configuration module realizes the rapid matching of geometric parameters (such as blade wrap angle, hub ratio, etc.) of different types of slurry pumps and the automatic generation of three-dimensional mesh models of flow channels by establishing a transient performance database of slurry pumps, a rheological property database of soil, and a pipeline topology database, and supports the intelligent selection of multiphase rheological models such as Bingham fluid and power-law fluid. This design greatly simplifies the parameter input process, improves the adaptability of the system to diverse construction scenarios. Engineering personnel can quickly construct a simulation model that conforms to the actual working conditions by customizing soil properties and pipeline topology parameters, significantly shortening the pre-modeling time and improving work efficiency.

[0020] Based on the dynamic simulation results, the safety assessment module constructs a three-dimensional safety boundary surface of rotational speed - head - power by extracting parameters such as the peak equivalent stress at key positions of the impeller and the critical pressure of mud cavitation, clearly marks the critical failure area, and generates an allowable operating condition configuration set and risk level labels. This function provides systematic safety guidance for the operation of slurry pumps. Engineering personnel can reasonably select operating conditions according to the risk level, avoid entering high-risk areas, effectively reduce the probability of equipment failure, and improve construction safety. At the same time, through the application of the bivariate failure criterion and the rainflow counting method, a quantitative assessment of the combined damage of dynamic stress and pressure pulsation is achieved, providing a scientific basis for the preventive maintenance of slurry pumps.

[0021] The system integrates functions such as operating condition configuration, simulation progress monitoring, and result comparison through the main control interface, supports flexible adjustment of parameters such as slurry pump rotational speed, mud concentration, and pipeline topology, and automatically generates a dynamic performance comparison matrix under different configuration combinations (including efficiency - power curves, pulsation amplitude - frequency spectra, etc.). The parameter visualization interface synchronously displays the flow field velocity cloud map, pressure contour lines, and structural stress distribution in the form of three-dimensional dynamic rendering, making complex simulation results intuitive and easy to understand. The report generation interface can quickly output an optimized configuration plan, screen the Pareto optimal solution set by combining multi-objective optimization algorithms, and provide the best operating condition recommendations that take into account production, energy consumption, and equipment loss for engineering personnel, significantly improving the comprehensive benefits of slurry pump operation.

[0022] The method and system of the present invention realize the full-process digitization from model construction to simulation analysis, safety assessment, and optimization configuration, getting rid of the high-cost and long-cycle mode that traditionally relies on physical tests. By replacing some physical tests with simulation calculations, the R & D and operation and maintenance costs can be significantly reduced. At the same time, through virtual tests covering a variety of extreme operating conditions, potential risks can be identified in advance, providing strong technical support for the design iteration, construction plan optimization, and intelligent operation and maintenance of high-pressure slurry pumps, and promoting the development of slurry pump simulation technology towards high efficiency, precision, and intelligence. Brief Description of the Drawings

[0023] Figure 1 It is the working principle diagram of the method for constructing and applying a simulation calculation model of a high-pressure and high-efficiency slurry pump described in the present invention; Figure 2 is the working principle diagram of the parameter configuration module; Figure 3 is the flow chart of the fluid-structure two-way coupling calculation; Figure 4 is the working principle diagram of the safety assessment module; Figure 5 is the flow chart of the critical failure threshold calculation. Specific implementation manners

[0024] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0025] Please refer to Figures 1 - 5 , a method for constructing and applying a high-pressure and high-efficiency mud pump simulation calculation model involved in the present invention, and the specific implementation steps are as follows: Establish a simulation calculation model of a high-pressure mud pump based on the dynamic fluid-structure coupling parameters, soil multi-phase rheological parameters, and pipeline transient resistance parameters of the mud pump. Among them, the dynamic fluid-structure coupling parameters include mud pulsation pressure and impeller dynamic stress; the soil multi-phase rheological parameters include particle phase volume fraction and viscous resistance coefficient; the pipeline transient resistance parameters include transient pressure drop gradient and local resistance mutation coefficient. By integrating these parameters, a basic model framework that can reflect the actual working environment of the mud pump is constructed, providing data support for subsequent simulation calculations.

[0026] Incorporate the mud pump speed-power dynamic matching, mud flow state stratification effect, and pipeline transient cavitation risk factors into the simulation calculation model, and calculate the mud pump operation envelope and critical failure threshold under different construction scenarios through the model. Incorporate the key factors that may affect the performance and safety of the mud pump during actual operation into the model to make the model closer to the actual working conditions. The operation envelope and critical failure threshold obtained through calculation provide key reference indicators for the safe and stable operation of the mud pump.

[0027] Develop a multi-physical field coupling analysis system for high-pressure mud pumps according to the simulation calculation model. This system includes a parameter configuration module, a dynamic simulation module, and a safety assessment module, where the dynamic simulation module integrates a transient flow field solver and a structural vibration solver. By developing a dedicated analysis system, the model is transformed into a practical tool to achieve a comprehensive analysis of the multi-physical field coupling problem of the mud pump.

[0028] Input the measured transient performance data of the slurry pump, the custom soil rheological characteristics, and the pipeline topology parameters through the system's parameter configuration module. The measured transient performance data includes the pulsating power spectral density and the amplitude of the dynamic head fluctuation. Ensure that the system can perform accurate calculations based on the actual working conditions and environmental parameters to improve the accuracy and reliability of the simulation results.

[0029] Perform fluid-structure interaction calculations based on the system's dynamic simulation module to obtain the dynamic flow velocity distribution, pressure pulsation propagation characteristics, and structural fatigue damage index of the slurry pump at different rotational speeds, and generate an optimized configuration plan for high-pressure working conditions. Through fluid-structure interaction calculations, deeply analyze the physical characteristics of the slurry pump under different operating conditions to provide a scientific basis for optimizing the operating conditions of the slurry pump.

[0030] The present invention will be further described below in conjunction with Embodiments 1 to 5: Embodiment 1:

[0031] In terms of dynamic fluid-structure coupling parameters, in addition to the above-mentioned slurry pulsating pressure and impeller dynamic stress, the impeller clearance leakage vortex intensity coefficient and the thixotropic index of the slurry non-Newtonian fluid need to be incorporated. The impeller clearance leakage vortex intensity coefficient is a key parameter characterizing the fluid leakage characteristics at the impeller and pump casing clearance. Its numerical value is related to factors such as the impeller outer diameter, pump casing inner diameter, impeller rotational speed, and slurry viscosity. When actually constructing the simulation calculation model, this coefficient needs to be obtained through theoretical analysis of fluid mechanics or experimental measurement. For example, based on the Reynolds-averaged Navier-Stokes (RANS) equation combined with a turbulence model, perform numerical simulation on the clearance flow field, extract data such as the velocity vector and pressure distribution of the leakage vortex, and then calculate the leakage vortex intensity coefficient. This coefficient is used to describe the generation, development, and interference degree of the leakage vortex in the clearance on the mainstream field, and can more accurately reflect the flow loss and energy dissipation at the impeller clearance, thereby affecting the calculation of the overall head and efficiency of the slurry pump.

[0032] The thixotropic index of the slurry non-Newtonian fluid is an important parameter for measuring the thixotropic characteristics of the slurry. The thixotropy of the slurry is manifested as the property that its viscosity decreases with the prolongation of the shear action time and gradually recovers when the shear action stops. For slurries containing colloidal substances such as clay particles, their thixotropic characteristics are significant. The determination of the thixotropic index requires a rheological experiment. For example, use a rotational viscometer to measure the shear stress of the slurry at different shear rates and shear times, draw the shear stress-time curve, and obtain the thixotropic index through curve fitting. After incorporating this parameter into the model, the viscosity change of the slurry caused by different shear histories during stirring and transportation can be accurately characterized in the simulation calculation, thereby affecting the calculation of the flow velocity distribution and pressure loss of the slurry in the flow channel.

[0033] When developing the parameter configuration module of the high-pressure slurry pump multi-physical field coupling analysis system, it is necessary to establish a transient performance database of the slurry pump, a rheological property database of soil, and a pipeline topology database. The construction of the transient performance database of the slurry pump requires collecting the measured data of different types of slurry pumps at various rotational speeds, including the time-domain signal of pressure pulsation and the frequency-domain energy distribution. The time-domain signal of pressure pulsation can be collected by high-frequency pressure sensors installed on the inlet and outlet pipelines of the slurry pump. The sampling frequency needs to meet the Nyquist sampling theorem to ensure that the signal is not distorted. The frequency-domain energy distribution is obtained by performing a fast Fourier transform (FFT) on the time-domain signal, which reflects the proportion of pressure pulsation energy of different frequency components. This database provides rich measured dynamic data for the model, which can be used to verify the accuracy of the simulation calculation results. At the same time, in practical applications, the corresponding performance data can be quickly retrieved according to the slurry pump model, improving the simulation efficiency.

[0034] The rheological property database of soil stores the multi-phase rheological parameters under different soil conditions, including the particle phase volume fraction, viscous resistance coefficient, thixotropic index, etc. The particle phase volume fraction is determined by screening and weighing analysis of soil samples. The viscous resistance coefficient can be calculated based on Darcy's formula through the flow test of slurry in a circular pipe. This database is classified and stored according to soil types (such as clay, silt, sand, etc.), which is convenient for quickly calling relevant parameters according to the soil conditions of the actual construction site. The pipeline topology database records the topology parameters under different pipeline layout forms, such as pipeline length, pipe diameter, number of elbows and their curvature radii, valve types, etc. These parameters are obtained through pipeline design drawings or on-site measurements and are used to construct the geometric model of the pipeline system to simulate the influence of different topological structures on the slurry flow.

[0035] When simulation calculation is required, the corresponding impeller geometric parameter set is matched from the database according to the slurry pump model. This geometric parameter set includes the blade wrap angle, hub ratio, and outlet blade angle. The blade wrap angle is the angle between the inlet edge and the outlet edge of the blade in the circumferential direction of the impeller. Its size directly affects the pushing effect of the blade on the slurry and the energy transfer efficiency. A larger blade wrap angle can increase the action time of the slurry in the impeller and improve the head, but may lead to an increase in flow resistance; a smaller blade wrap angle has the opposite effect. The hub ratio is the ratio of the impeller hub diameter to the impeller outer diameter, which affects the strength of the impeller and the flow state of the fluid in the impeller. The larger the hub ratio, the higher the impeller strength, but the cross-sectional area of the flow channel decreases, which may increase the risk of flow blockage. The outlet blade angle is the angle between the outlet edge of the blade and the circumferential tangent direction of the impeller, which determines the absolute velocity direction and magnitude of the slurry at the impeller outlet and affects the flow rate and power characteristics of the slurry pump.

[0036] Based on the set of impeller geometric parameters obtained through matching, use professional 3D modeling software (such as ANSYS DesignModeler, SolidWorks, etc.) to generate a 3D mesh model of the mud pump flow channel. During the modeling process, it is necessary to accurately describe the geometric shapes of components such as the impeller, pump casing, suction chamber, and discharge chamber to ensure that the flow channel model is consistent with the actual mud pump structure. For complex structures such as the impeller, the method of block modeling can be adopted, where the impeller blades, hub, and rim are modeled separately and then assembled to improve the modeling accuracy and efficiency.

[0037] After generating the 3D geometric model, it is necessary to perform mesh division. The mesh quality directly affects the accuracy of the flow field solution and the computational efficiency. For regions with complex flow in the mud pump flow channel (such as near the impeller blades, gaps), encrypted tetrahedral meshes or hexahedral meshes are used to capture the subtle changes in the flow field; for regions with relatively stable flow (such as the suction chamber, discharge chamber), coarser meshes can be used to reduce the computational amount. After mesh division is completed, it is necessary to perform mesh quality inspection to ensure that indicators such as the mesh distortion rate and aspect ratio meet the requirements of the solver.

[0038] Load the generated 3D mesh model of the mud pump flow channel into the flow field solver of the dynamic simulation module. The flow field solver is based on the theory of computational fluid dynamics (CFD) and uses the finite volume method (FVM) to discretely solve the governing equations. The governing equations include the continuity equation, momentum equation, and energy equation. For non-Newtonian fluids, it is also necessary to select an appropriate constitutive equation according to the rheological properties of the mud, such as the Bingham fluid model, power-law fluid model, or thixotropic fluid model (detailed in subsequent embodiments). During the calculation process, set boundary conditions, such as setting the inlet boundary as a velocity inlet or a mass flow inlet, the outlet boundary as a pressure outlet or a free outflow, and the wall boundary using the no-slip boundary condition, and set the wall function as needed to handle the flow in the near-wall region.

[0039] During the flow field solution process, considering the multiphase flow characteristics of the mud, the mud is regarded as a mixture composed of a continuous phase (liquid phase) and a discrete phase (solid particles), and the Euler-Euler multiphase flow model or the Euler-Lagrange discrete phase model (DPM) is used for simulation. For mud with a high particle phase volume fraction, the Euler-Euler model is more suitable and can solve the momentum equations and continuity equations of each phase; for the case of a low particle phase volume fraction, the Euler-Lagrange model can track the motion trajectories of individual particles and more accurately describe the interaction between particles and fluids.

[0040] Meanwhile, in combination with the impeller clearance leakage vortex intensity coefficient, the leakage flow at the impeller clearance is specifically processed in the flow field solver. The sliding mesh technique or the mixing plane model can be used to simulate the relative motion between the impeller and the pump casing, and the mesh is refined in the clearance area to capture the generation and evolution process of the leakage vortex. The leakage flow rate, leakage velocity distribution, and the interference degree of the leakage vortex on the mainstream field are obtained through calculation, and then the overall performance parameter calculation of the slurry pump is corrected.

[0041] In addition, the thixotropic index of the slurry non-Newtonian fluid participates in the calculation by affecting the viscosity term in the flow field control equation. In the thixotropic fluid model, the viscosity is not only a function of the shear rate but also a function of the shear time, and a viscosity correction term related to time needs to be introduced into the control equation. Through iterative calculation, the viscosity value is gradually updated to reflect the thixotropic characteristics of the slurry caused by the change of shear time during the flow process, making the flow field calculation results closer to the actual working conditions.

[0042] After the settings and preliminary calculations of the flow field solver are completed, the calculation results need to be verified and debugged. By comparing with the measured data in the transient performance database of the slurry pump, the deviations between the calculated values and the measured values of parameters such as the pressure pulsation amplitude, frequency, head, and efficiency are checked. If the deviations exceed the allowable range, parameters such as the mesh division, turbulence model, and boundary conditions are adjusted until the calculation results are in good agreement with the measured data to ensure the accuracy of the flow field calculation.

[0043] Embodiment 2: The process of the dynamic simulation module performing fluid-structure interaction calculation needs to realize the interaction between the flow field and the structure field, and the specific steps are as follows: After the flow field solver completes the calculation of the slurry flow, it outputs the pulsating pressure field data. This pressure field contains the pressure values at each spatial point in the slurry pump flow passage and their variation laws with time, and particular attention is paid to the pressure distribution on the impeller surface because it directly acts on the impeller structure and causes vibration. The flow field solver discretizes the flow passage into grid cells through numerical calculation methods (such as the finite volume method), solves the continuity equation, momentum equation, and energy equation, considering the multiphase flow characteristics of the slurry, non-Newtonian fluid properties, and the centrifugal force and Coriolis force effects brought by the impeller rotation, and finally obtains the pressure values at each grid node.

[0044] The pulsating pressure field output by the flow field solver needs to be mapped to the impeller surface grid nodes of the structural vibration solver. Since the division methods and node distributions of the flow field grid and the structural grid may be different, data mapping technology is required to achieve the transfer of pressure data. Common mapping methods include interpolation methods (such as radial basis function interpolation, trilinear interpolation) or projection methods to ensure that the pressure load is accurately applied to the corresponding positions of the impeller structure model. For example, for the pressure value of a certain node in the flow field grid, its equivalent action point on the surface of the structural grid is determined through interpolation calculation, and the pressure load is distributed to adjacent structural nodes to form a distributed load acting on the impeller surface.

[0045] After completing the pressure field mapping, the structural vibration solver calculates the dynamic stress distribution and modal participation factors of the impeller based on the material properties, geometric shape, and boundary conditions of the impeller. The structural vibration solver uses the finite element method (FEM) to discretize the impeller into finite element units, establish the mass matrix, stiffness matrix, and damping matrix, and construct the dynamic equation:

[0046] where M is the mass matrix, C is the damping matrix, K is the stiffness matrix, u is the displacement vector, is the velocity vector, which is the first derivative of the displacement vector u with respect to time, is the acceleration vector, which is the second derivative of the displacement vector u with respect to time, and F(t) is the pulsating pressure load vector that changes with time. By solving this equation, the displacements, velocities, accelerations, and stress distributions of each node of the impeller are obtained. The dynamic stress distribution reflects the real-time stress state of the impeller under the action of pulsating pressure. High-stress areas usually appear at geometric mutation parts such as the blade roots and impeller hubs, which need to be focused on. The modal participation factor is used to measure the contribution degree of each order of mode in the vibration response of the impeller. Through modal analysis, the natural frequency and vibration mode of the impeller can be determined to avoid resonance with the pressure pulsation frequency.

[0047] After the structural vibration solver calculates the deformation displacement field of the impeller, this displacement field needs to be fed back to the flow field solver to update the calculation domain. The deformation of the impeller will cause changes in the geometric shape of the flow passage. For example, the bending deformation of the blade will cause changes in the cross-sectional area of the flow passage, which will in turn affect the flow characteristics of the slurry. After receiving the deformation displacement data, the flow field solver adjusts the flow field grid through grid deformation technologies (such as spring smoothing method, dynamic layer method, local remeshing method) to update the grid as the impeller deforms. For example, when using the spring smoothing method, the flow field grid is regarded as a particle system connected by springs, and the displacement on the impeller surface is transmitted to the surrounding grid nodes through "springs" to achieve smooth deformation of the entire flow field grid and ensure that the quality of the deformed grid meets the calculation requirements.

[0048] After the update of the flow field calculation domain is completed, the flow field solver recalculates the flow field based on the new geometric boundaries to obtain the updated pulsating pressure field. At this time, the influence of impeller deformation on the flow has been considered in the pressure field, such as the redistribution of flow velocity and the change of pressure gradient caused by the change of flow passage cross-sectional area. Subsequently, the new pressure field is mapped to the structural grid again for the next round of structural vibration calculation, and this iterative process is repeated until the residuals of the pressure pulsation amplitude and the structural vibration acceleration converge to a preset threshold.

[0049] Residual convergence judgment is a key link in the fluid-structure bidirectional coupling calculation, which is used to determine whether the iterative calculation terminates. The residual is defined as the difference between the results of two adjacent iterative calculations. Usually, the root mean square error (RMSE) of the pressure pulsation amplitude and the root mean square error of the structural vibration acceleration are taken as the convergence criteria. The preset threshold is set according to the calculation accuracy requirements. For example, it can be set that the pressure residual is less than 10 -3 Pa, and the acceleration residual is less than 10 -2 m / s 2 . During the iterative process, the change of the residual is monitored in real time. When the residual remains below the threshold for multiple consecutive steps and no longer changes significantly, it is considered that the fluid-structure coupling calculation reaches the convergence state and the iteration stops.

[0050] During the entire fluid-structure bidirectional coupling calculation process, the following technical details need to be noted: Time step synchronization: The same time step needs to be used for the flow field solution and the structural vibration solution to ensure their synchronization in the time domain and avoid the distortion of the coupling results caused by time discretization errors. The selection of the time step needs to comprehensively consider the calculation accuracy and efficiency, and is usually not greater than 1 / 10 of the pressure pulsation period or the structural vibration period.

[0051] Mesh compatibility: The flow field mesh and the structural mesh need to have good geometric compatibility on the impeller surface, that is, the impeller surface boundary of the flow field mesh corresponds one-to-one with the impeller outer surface nodes of the structural mesh or can be accurately matched through a mapping algorithm to ensure the transfer accuracy of the pressure load and displacement data.

[0052] Treatment of nonlinear factors: If the impeller deformation is large or the mud flow shows strong nonlinearity (such as high-concentration particle flow, severe cavitation phenomenon), it is necessary to introduce nonlinear constitutive relations in the calculation, such as the elastic-plastic model of materials, the large deformation geometric nonlinear theory, etc., to more realistically simulate the actual working conditions.

[0053] Computing resource management: The fluid-structure bidirectional coupling calculation involves large-scale data interaction and iterative operations. It is necessary to reasonably allocate computing resources. Parallel computing technologies (such as MPI parallel, GPU acceleration) can be used to improve the computing efficiency and shorten the simulation time.

[0054] Taking a certain type of high-pressure slurry pump as an example, in the fluid-structure interaction calculation, first, the pulsating pressure distribution on the impeller surface is calculated by the flow field solver. Its pressure amplitude fluctuates periodically within the range of 0 - 5 Mpa, and the main frequency is 100 Hz (corresponding to the blade passing frequency at an impeller speed of 6000 r / min). After mapping this pressure field to the structural grid, the maximum equivalent stress at the root of the impeller blade calculated by the structural vibration solver is 200 MPa, and the peak value of the vibration acceleration is 50 m / s 2 , and the participation factor of the first-order mode is 0.78, indicating that the first-order mode is the main contributor to the impeller vibration. After feeding back the impeller deformation displacement (the maximum displacement is 0.1 mm) to the flow field solver, the velocity distribution at the outlet of the flow passage changes, the maximum velocity increases from 15 m / s to 15.5 m / s, and the pressure pulsation amplitude increases by 5% accordingly. After 10 iterative calculations, the pressure residual drops to 8×10 -4 Pa, and the acceleration residual drops to 9×10 -3 m / s 2 , meeting the preset convergence threshold, and the calculation terminates.

[0055] Through the above fluid-structure interaction calculation process, the dynamic interaction simulation between the slurry pump flow field and the structure field can be realized, and the dynamic velocity distribution, pressure pulsation propagation characteristics, and structural fatigue damage index of the slurry pump at different speeds can be accurately obtained. These data provide key bases for the structural strength design, vibration control, and working condition optimization of the slurry pump. For example, the pipeline layout can be optimized by analyzing the pressure pulsation propagation characteristics to reduce vibration noise, and the remaining life of the impeller can be predicted and a maintenance plan can be formulated by evaluating the structural fatigue damage index.

[0056] Example 3: The function realization of the safety assessment module needs to be based on the fluid-structure coupling calculation results output by the dynamic simulation module. By extracting key physical quantities, analyzing boundary conditions, and quantifying risk levels, a safety assessment system for the operation of the slurry pump is constructed.

[0057] Extract the peak value of equivalent stress and the initial critical pressure of mud cavitation at the key positions of the impeller from the dynamic simulation results. The determination of the key positions of the impeller is based on structural mechanics theory and engineering experience, and usually selects the parts prone to stress concentration such as the blade root, the hub transition area, and the impeller outlet edge. In the stress distribution data output by the structural vibration solver, by setting monitoring points or defining regions of interest (ROI), the change curve of the stress value at each position over time is recorded in real time, and the maximum value is extracted as the peak value of equivalent stress. For example, 5 monitoring points are evenly set along the thickness direction of the blade root, and each monitoring point records stress data at each simulation time step. The maximum equivalent stress of each point is determined by traversing all time steps. The extraction of the initial critical pressure of mud cavitation depends on the pressure distribution results of the flow field solver. The occurrence of cavitation is closely related to the local pressure being lower than the vaporization pressure of the mud. In the flow field calculation, first obtain the vaporization pressure value corresponding to the current mud temperature and concentration through the physical property parameter module, and then search for the minimum pressure in the low-pressure prone areas such as the impeller inlet area and the blade back. This minimum value is the evaluation value of the initial critical pressure of cavitation. It should be noted that the initial critical pressure of cavitation is not a fixed value and changes dynamically with the mud composition, temperature, and flow state.

[0058] Construct the three-dimensional safety boundary surface of rotational speed - head - power in the operating envelope of the mud pump. The construction of this surface needs to comprehensively consider various constraint conditions such as the mechanical strength, hydrodynamic characteristics, and energy consumption of the mud pump. The specific steps are as follows: Definition of parameter range: According to the mud pump design manual or measured data, determine the physically feasible intervals of rotational speed (n), head (H), and power (P). For example, the rotational speed range is n min ~n max , the head range is H min ~H max , and the upper limit of power is determined by the rated power of the motor.

[0059] Single-parameter boundary scanning: Uniformly select grid points in a two-dimensional plane (such as the n-H plane), fix two of the parameters, and calculate the critical value of the third parameter through dynamic simulation. For example, fix the rotational speed n0 and the head H0, and gradually increase the power until the equivalent stress of the impeller reaches 90% of the allowable stress of the material or the initial critical pressure of cavitation is lower than the vaporization pressure, and record the power value P crit at this time. This value is the safety boundary power corresponding to this grid point.

[0060] Three-dimensional surface generation: Fit the critical parameter values of all grid points into a continuous surface through interpolation algorithms (such as cubic spline interpolation, radial basis function interpolation). To improve the surface accuracy, the grid points can be encrypted in high-risk areas (such as near the design parameter boundaries) and appropriately thinned in low-risk areas.

[0061] Constraint superposition: In addition to strength and cavitation constraints, other safety constraints such as vibration amplitude and bearing temperature rise need to be considered. Boolean operations are performed on the boundary surfaces corresponding to these constraints to finally obtain a comprehensive three-dimensional safety boundary surface that includes multiple safety constraints.

[0062] In the three-dimensional safety boundary surface, the internal area is the allowable operating condition, and the external area is the prohibited operating condition. To visually display the risk distribution, it is necessary to mark the critical failure area on the surface. The division of the critical failure area is based on the physical mechanisms of different failure modes: Stress failure area: Corresponding to the condition where the equivalent stress of the impeller exceeds the material fatigue limit, usually located in the high-speed and high-head regions, due to the superposition of the impeller centrifugal force and the fluid load resulting in stress concentration.

[0063] Cavitation failure area: Corresponding to the condition where the critical cavitation inception pressure is lower than the vaporization pressure, mostly distributed in the low-speed and high-head regions, because the mud flow velocity decreases, resulting in a decrease in the impeller inlet pressure.

[0064] Vibration failure area: Corresponding to the condition where the structural vibration acceleration exceeds the equipment natural frequency threshold, which may occur in the resonance region where the rotational speed is close to the natural frequency of the impeller.

[0065] By color coding (e.g., red for high risk, orange for medium risk, yellow for low risk) and transparency settings, different types of critical failure areas are distinguished in the three-dimensional visualization interface, facilitating operators to quickly identify dangerous operating conditions.

[0066] Based on the three-dimensional safety boundary surface, the system automatically generates a set of allowable condition configurations and corresponding risk level labels. The set of allowable condition configurations includes all combinations of rotational speed, head, and power that satisfy the safety constraints, and each combination corresponds to an internal point on the surface. The quantitative assessment of the risk level is based on the following indicators: Safety margin: Calculate the shortest geometric distance from the operating condition point to the safety boundary surface. The larger the distance, the higher the safety margin and the lower the risk level. Specifically, it can be achieved through the SignedDistanceFunction. When the function value is positive, it indicates that the operating condition point is within the safe area, and the larger the absolute value, the farther it is from the boundary.

[0067] Multi-physical-field coupling risk weight: Considering the contribution degrees of multiple factors such as stress, cavitation, and vibration to failure comprehensively, weight coefficients are assigned to each physical field (e.g., stress risk weight 0.5, cavitation risk weight 0.3, vibration risk weight 0.2), and the comprehensive risk index is obtained through weighted summation.

[0068] Risk level classification: Normalize the comprehensive risk index to the interval [0, 1], and divide it into three levels: low risk (index < 0.3), medium risk (0.3 ≤ index < 0.7), and high risk (index ≥ 0.7). The risk level label for each operating condition point includes the level name and the corresponding risk description (such as "High risk: Close to the stress failure boundary").

[0069] The safety assessment module also needs to have the functions of operating condition comparison and trend analysis. The operator can select multiple allowable operating condition points, and the system automatically generates a comparison report to show the parameter differences such as stress distribution, cavitation risk, and vibration amplitude under each operating condition to assist in decision-making. In addition, through the storage and analysis of historical data, the system can track the change trend of the safety boundary surface of the mud pump during long-term operation. For example, the stress boundary shrinks due to impeller wear, or the cavitation boundary expands due to changes in mud properties, so as to give early warnings of equipment performance degradation.

[0070] At the technical implementation level, the safety assessment module needs to achieve data interconnection with the dynamic simulation module and the parameter configuration module. Obtain the fluid-structure interaction calculation results from the dynamic simulation module, and obtain the basic data such as mud pump model, mud properties, and pipeline topology from the parameter configuration module. Parallel computing technology is used during the calculation process to accelerate the three-dimensional surface fitting and risk assessment to ensure the output of results within a reasonable time. The visualization interface is developed based on graphics libraries such as OpenGL or VTK, supporting the rotation, scaling of three-dimensional models and the dynamic display of parameter cloud maps to improve the user experience.

[0071] Example 4: As the basic data input interface of the multi-physical field coupling analysis system for high-pressure mud pumps, the parameter configuration module needs to achieve the structured management and dynamic call of the mud pump performance parameters, soil rheological properties, and pipeline topology structure, and at the same time integrate the intelligent matching function of multi-physical field models.

[0072] The parameter configuration module includes a mud multi-phase rheological model selector, which presets the Bingham fluid model, power-law fluid model, and thixotropic fluid model, corresponding to the mud rheological properties under different soil conditions. The Bingham fluid model is applicable to mud with an obvious yield stress, such as high-concentration clay mud, and its constitutive equation is:

[0073] Among them, τ is the shear stress, τ y is the yield stress, μ p is the plastic viscosity, and γ is the shear rate. The power-law fluid model is applicable to non-Newtonian fluids with shear thinning or shear thickening, such as silty mud containing fine particles, and its expression is:

[0074] Among them, K is the consistency coefficient, and n is the rheological index (n < 1 indicates shear thinning, and n > 1 indicates shear thickening). The thixotropic fluid model is used to describe the mud whose viscosity changes with shear time. For example, for the thixotropic mud containing colloidal particles, the time variable needs to be introduced into its constitutive relationship, and the dynamic evolution of viscosity is described through integral form or differential equation.

[0075] To automatically match the corresponding rheological model according to the soil gradation parameters, the mapping rules between the soil gradation and the rheological model need to be established. The soil gradation parameters are obtained through particle size analysis tests, including the percentage of particles smaller than a certain particle size (such as the content of particles smaller than 0.075 mm), the coefficient of uniformity C u and the coefficient of curvature C c and so on. For example, when the content of clay particles (particle size < 0.005 mm) in the soil sample exceeds 30% and the coefficient of uniformity C u < 5, it is determined as cohesive soil, and the Bingham fluid model is automatically called; when the content of silt particles (particle size 0.005 - 0.075 mm) is dominant and the rheological index n < 1, the power-law fluid model is called; if the mud shows gelation after standing and the viscosity drops significantly after shearing, it is determined as a thixotropic fluid, and the thixotropic fluid model is called. During the model matching process, the system processes the parameter boundary conditions through the fuzzy logic algorithm. For example, when the clay particle content is between 25% - 30%, both the Bingham model and the thixotropic model are activated, and the simulation accuracy is improved through the comparison calculation of the two models.

[0076] After the matching is completed, the selected rheological model is loaded into the flow field control equation of the dynamic simulation module. The flow field control equation is based on the continuity equation and the momentum equation. For non-Newtonian fluids, the constitutive equation needs to be substituted into the momentum equation to close the equations. For example, when using the Bingham fluid model, the viscous term in the flow field control equation is determined by the plastic viscosity μ p and when the shear stress is lower than the yield stress τ y , the mud is regarded as a non-flowing rigid body; when using the thixotropic fluid model, the term of viscosity changing with time needs to be introduced into the control equation, and the viscosity value at each time step is updated through iterative calculation.

[0077] In addition, the high-pressure mud pump multi-physics coupling analysis system also includes the design of the human-computer interaction interface, which specifically covers the main control interface, parameter visualization interface and report generation interface. The main control interface integrates the working condition configuration, simulation progress monitoring and result comparison functions. The working condition configuration function allows the operator to set the mud pump speed adjustment range, mud concentration gradient and pipeline topology change sequence. The speed adjustment range is determined according to the rated speed and frequency conversion control capability of the mud pump motor, such as 03000r / min; the mud concentration gradient is set by the upper and lower limit parameters of the particle phase content, such as 10%40%; the pipeline topology change sequence can be defined by selecting preset pipeline models (such as straight pipes, 90° elbows, tees, etc.) and their combinations. Each model corresponds to a specific local resistance mutation coefficient and transient pressure drop gradient parameter.

[0078] Based on the simulation calculation model, the system automatically generates a dynamic performance comparison matrix under different configuration combinations. The matrix contains parameters such as efficiency-power curve, pulsation amplitude-frequency spectrum and fatigue life prediction value. The efficiency-power curve is obtained by calculating the output power and effective power (lift × flow × gravity acceleration × mud density) of the mud pump at different speeds, reflecting the energy conversion efficiency of the mud pump; the pulsation amplitude-frequency spectrum is obtained by Fourier transforming the pressure pulsation time domain signal, showing the pulsation energy distribution of different frequency components; the fatigue life prediction value is based on the rain flow counting method and the material SN curve, and the fatigue damage degree of the impeller is evaluated by accumulating the number of dynamic stress cycles.

[0079] The parameter visualization interface uses three-dimensional dynamic rendering technology to simultaneously display the flow field velocity cloud map, pressure contours and structural stress distribution. The flow field velocity cloud map represents the velocity distribution by drawing velocity vectors or filling color levels on the flow channel section. High velocity areas are usually represented by red, and low velocity areas are represented by blue; pressure contours use lines of different colors to outline the isobaric surface in the flow channel, and the line spacing reflects the pressure gradient; structural stress distribution maps stress cloud maps on the surface of the impeller model, and high stress areas (such as the root of the blade) are highlighted with warm colors (such as red), and low stress areas are displayed with cold colors (such as blue). Operators can interactively rotate and scale the model with the mouse, or select a specific section for cutting to observe the details of the internal physical field distribution.

[0080] The report generation interface supports automatic generation of simulation analysis reports, including operating condition configuration parameters, key physical quantity extraction results, safety assessment conclusions, etc. The report format can be customized as a PDF or Word document, including charts, data tables and text descriptions. For example, the operating condition configuration parameter section lists input parameters such as rotation speed, mud concentration, and pipeline topology; the key physical quantity extraction result section displays data such as the extreme value of dynamic flow velocity distribution, the main frequency of pressure pulsation, and the maximum equivalent stress of the impeller; the safety assessment conclusion section quotes the three-dimensional safety boundary surface analysis results to indicate the risk level of the current operating condition and optimization suggestions.

[0081] In terms of technical implementation, the parameter configuration module realizes the storage and retrieval of the slurry pump transient performance database, soil rheological property database, and pipeline topology database through a database management system (such as MySQL). The database table structure design follows the third normal form to ensure low data redundancy and high query efficiency. For example, the slurry pump transient performance database includes slurry pump model tables, speed tables, pressure pulsation data tables, etc., and realizes fast data retrieval through foreign key association. Data interaction between the dynamic simulation module and the parameter configuration module is achieved through an application programming interface (API) to ensure that parameter modifications can be synchronized to the simulation calculation process in real time.

[0082] To sum up, the parameter configuration module realizes the full-process parameter management of slurry pump simulation calculations through intelligent matching of multiple rheological models, flexible setting of working conditions parameters, visual interface interaction, and automated report generation. This module not only provides accurate input data for dynamic simulation but also reduces the usage threshold for operators through intuitive visualization means and structured reports, enabling the system to adapt to the slurry pump performance analysis requirements under different construction scenarios and improving the efficiency and reliability of multi-physical field coupling analysis of high-pressure slurry pumps.

[0083] Example 5: The calculation of the critical failure threshold needs to comprehensively consider the combined effect of the fatigue damage of the slurry pump structure and mud cavitation, and realizes the quantitative assessment of working condition risks by establishing a bivariate failure criterion and a damage accumulation model. First, establish a bivariate failure criterion for the S-N curve of the impeller material and the mud cavitation damage accumulation model. The S-N curve of the impeller material is obtained through standard fatigue tests, which reflects the fatigue life of the material under different cyclic stress amplitudes and is usually expressed as a linear relationship in logarithmic coordinates. For example:

[0084] where N is the fatigue life (number of cycles), σ is the stress amplitude, and a and b are material constants. The mud cavitation damage accumulation model is based on the evolution law of the cavitation pit depth over time. By analyzing the impact of the micro-jet generated by the collapse of cavitation bubbles on the material surface, a functional relationship between the damage amount and the pressure pulsation amplitude and action time is established. The bivariate failure criterion regards structural fatigue and cavitation damage as independent failure modes, and when the damage amount of any mode exceeds the threshold, the working condition is determined to have failed.

[0085] Extract the combined damage contribution factors of the dynamic stress spectrum and the pressure pulsation spectrum through the rainflow counting method. The rainflow counting method is a statistical method for processing complex load time histories. Its basic principle is to regard the stress-time curve as a series of "rainflows" and extract all closed stress cycles by layer-by-layer peeling. The specific steps are as follows: Pretreatment: Filter the dynamic stress spectrum and pressure pulsation spectrum to remove high-frequency noise and trend terms, and retain the payload cycle components.

[0086] Peak-valley value extraction: Identify the peak points and valley points in the stress-time curve to form a discrete peak-valley sequence.

[0087] Rainflow counting: Starting from each peak-valley point, "flow" downward along the time axis until encountering a valley point smaller than its starting point (corresponding to a stress cycle) or a peak point larger than its starting point (corresponding to a pressure pulsation cycle), and record the amplitude and mean value of each cycle.

[0088] Damage calculation: According to Miner's linear cumulative damage theory, superimpose the damage amounts of each cycle to obtain the combined damage contribution factor D:

[0089] where, n i is the number of stress cycles, N i is the fatigue life at the corresponding stress amplitude; t j is the acting time of the pressure pulsation, T j is the critical time of cavitation damage.

[0090] When the combined damage contribution factor exceeds the preset safety factor, mark the current working condition as a high-risk state. The preset safety factor is determined according to the dredge pump design specifications and engineering experience, usually taking 0.8 - 0.9 to reserve a certain safety margin. For example, if the safety factor is set to 0.85, when D ≥ 0.85, the system triggers a high-risk alarm to prompt the operator to adjust parameters such as rotational speed and head to avoid equipment failure caused by cumulative damage.

[0091] The generation of the optimized configuration plan for high-pressure working conditions is based on the set of allowable working conditions output by the safety assessment module, and realizes the comprehensive balance of production, energy consumption, and equipment loss through a multi-objective optimization algorithm. First, calculate the production-energy consumption ratio and equipment loss rate under each allowable working condition. The production-energy consumption ratio is defined as the mud conveying volume per unit energy consumption, and the calculation formula is:

[0092] where, Q is the mud flow rate, and P is the input power of the dredge pump. This index reflects the energy utilization efficiency of the dredge pump, and the larger the value, the better the economy. The equipment loss rate is obtained by converting the predicted fatigue life value. For example, if the impeller fatigue life under a certain working condition is L hours and the current operating time is t hours, then the loss rate is t / L, which is used to evaluate the wear degree of the equipment.

[0093] The multi-objective optimization algorithm is adopted to screen the Pareto optimal solution set that meets the constraint conditions. The definition of the Pareto optimal solution is as follows: among the two objective functions of the production-energy consumption ratio and the equipment loss rate, there is no other solution that simultaneously dominates all the objectives of this solution. Commonly used algorithms include the Non-dominated Sorting Genetic Algorithm (NSGA-II), the Multi-objective Particle Swarm Optimization Algorithm (MOPSO), etc. Taking NSGA-II as an example, the algorithm flow is as follows: Initialize the population: Randomly generate a certain number of operating conditions in the allowable operating condition set as the initial population.

[0094] Fitness calculation: Calculate the production-energy consumption ratio and the equipment loss rate for each individual, and assign fitness values according to the non-dominated sorting rule.

[0095] Selection, crossover, and mutation: Generate a new generation of population through roulette wheel selection, simulated binary crossover, and polynomial mutation operations.

[0096] Elite retention strategy: Retain the excellent individuals in the parent and offspring generations to ensure the evolutionary direction of the population.

[0097] Termination condition: When the population evolution reaches the preset number of generations or the Pareto front converges, stop the calculation and output the Pareto optimal solution set.

[0098] Generate a recommended priority list according to the preset weights, and the preset weights are determined according to the actual construction requirements. For example, if the construction focus is on energy conservation, assign a weight of 0.6 to the production-energy consumption ratio and a weight of 0.4 to the equipment loss rate; if the equipment maintenance cost is high, adjust the weights to 0.4 and 0.6. Transform the multi-objective optimization problem into a single-objective optimization problem through the linear weighted method, and the calculation formula is:

[0099] where w1 and w2 are weight coefficients, and w1 + w2 = 1. Sort the Pareto optimal solutions according to the f value to generate a recommended priority list, and the operator can select the optimal operating condition configuration according to the list.

[0100] In the technical implementation, the critical failure threshold calculation and the operating condition optimization module need to call high-performance computing resources. Especially, the rain flow counting method and the multi-objective optimization algorithm involve a large amount of data processing and iterative calculations. The system realizes the parallel processing of computing tasks through a distributed computing framework (such as Apache Hadoop) to shorten the computing time. At the same time, to ensure data consistency, asynchronous communication is carried out between the module and the safety assessment module and the dynamic simulation module through a message queue (such as RabbitMQ) to avoid affecting the system response speed due to data interaction delays.

[0101] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0102] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for constructing and applying a simulation calculation model of a high-pressure and high-efficiency mud pump, characterized in that, Including: Establish a simulation calculation model of a high-pressure slurry pump based on the dynamic fluid-structure coupling parameters of the slurry pump, the multi-phase rheological parameters of the soil, and the transient resistance parameters of the pipeline. The dynamic fluid-structure coupling parameters include slurry pulsation pressure and impeller dynamic stress. The multi-phase rheological parameters of the soil include particle phase volume fraction and viscous resistance coefficient. The transient resistance parameters of the pipeline include transient pressure drop gradient and local resistance mutation coefficient. Incorporate the dynamic matching of pump speed-power, the slurry flow stratification effect, and the transient cavitation risk factors into the simulation calculation model, and calculate the pump operation envelope and critical failure threshold under different construction scenarios through the model. Develop a multi-physical field coupling analysis system for high-pressure slurry pumps according to the simulation calculation model. The system includes a parameter configuration module, a dynamic simulation module, and a safety assessment module. The dynamic simulation module integrates a transient flow field solver and a structural vibration solver. Input the measured transient performance data of the slurry pump, the customized soil rheological characteristics, and the pipeline topology parameters through the parameter configuration module of the system. The measured transient performance data includes pulsating power spectral density and dynamic head fluctuation amplitude. Perform fluid-structure two-way coupling calculation based on the dynamic simulation module of the system to obtain the dynamic flow velocity distribution, pressure pulsation propagation characteristics, and structural fatigue damage index of the slurry pump at different speeds, and generate an optimized configuration plan for high-pressure operating conditions.

2. The method for constructing and applying a high-pressure and high-efficiency slurry pump simulation calculation model according to claim 1, wherein The dynamic fluid-structure coupling parameters also include the impeller clearance leakage vortex intensity coefficient and the thixotropic index of the slurry non-Newtonian fluid.

3. The construction and application method of the high-pressure and high-efficiency mud pump simulation calculation model according to claim 2, characterized in that, The parameter configuration module of the multi-physical field coupling analysis system for high-pressure slurry pumps includes: Establish a transient performance database of the slurry pump, a soil rheological characteristics database, and a pipeline topology database. The transient performance database stores the pressure pulsation time-domain signal and frequency-domain energy distribution at different speeds. Match the corresponding impeller geometric parameter set from the database according to the slurry pump model. The geometric parameter set includes blade wrap angle, hub ratio, and outlet setting angle. Generate a three-dimensional grid model of the slurry pump flow channel based on the geometric parameter set and load it into the flow field solver of the dynamic simulation module.

4. The method for constructing and applying a high-pressure and high-efficiency mud pump simulation calculation model according to claim 1, wherein The dynamic simulation module performs fluid-structure two-way coupling calculation including: Map the pulsating pressure field output by the flow field solver to the impeller surface grid nodes of the structural vibration solver. Calculate the impeller dynamic stress distribution and modal participation factor through the structural vibration solver, and feedback the deformation displacement field to the flow field solver to update the calculation domain. Iteratively calculate until the residuals of the pressure pulsation amplitude and the structural vibration acceleration converge to a preset threshold.

5. The method for constructing and applying a high-pressure and high-efficiency mud pump simulation calculation model according to claim 1, wherein The safety assessment module includes: Extract the equivalent stress peak value at the key position of the impeller and the critical pressure for the start of slurry cavitation according to the dynamic simulation results. Calculate the three-dimensional safety boundary surface of speed-head-power in the pump operation envelope and mark the critical failure area. Generate an allowable operating condition configuration set and corresponding risk level labels based on the safety boundary surface.

6. The method for constructing and applying a high-pressure and high-efficiency mud pump simulation calculation model according to claim 5, characterized in that The parameter configuration module also includes: Set a slurry multi-phase rheological model selector, which includes Bingham fluid model, power-law fluid model, and thixotropic fluid model. Automatically match the corresponding rheological model according to the soil gradation parameters and load it into the flow field control equation of the dynamic simulation module.

7. The construction and application method of the high-pressure and high-efficiency slurry pump simulation calculation model according to claim 1, characterized in that, The multi-physical field coupling analysis system of the high-pressure slurry pump further includes: Designing a main control interface, a parameter visualization interface, and a report generation interface, wherein the main control interface integrates functions of working condition configuration, simulation progress monitoring, and result comparison; Synchronously displaying three-dimensional dynamic rendering results of flow field velocity cloud maps, pressure contour lines, and structural stress distributions in the parameter visualization interface.

8. The construction and application method of the high-pressure and high-efficiency slurry pump simulation calculation model according to claim 7, characterized in that The working condition configuration function includes: Setting the adjustment range of the slurry pump speed, the mud concentration gradient, and the pipeline topology change sequence; Automatically generating a dynamic performance comparison matrix under different configuration combinations based on the simulation calculation model, and the matrix includes efficiency-power curves, pulsation amplitude-frequency spectra, and fatigue life prediction values.

9. The method for constructing and applying a high-pressure and high-efficiency mud pump simulation calculation model according to claim 1, wherein, The calculation of the critical failure threshold includes: Establishing a bivariate failure criterion of the S-N curve of the impeller material and the mud cavitation damage accumulation model; Extracting the combined damage contribution factor of the dynamic stress spectrum and the pressure pulsation spectrum by the rainflow counting method; When the combined damage contribution factor exceeds the preset safety factor, marking the current working condition as a high-risk state.

10. The construction and application method of the high-pressure and high-efficiency slurry pump simulation calculation model according to claim 1, characterized in that, The generation of the high-pressure working condition optimization configuration scheme includes: Calculating the production-energy consumption ratio and the equipment loss rate under each working condition according to the allowable working condition set output by the safety assessment module; Using a multi-objective optimization algorithm to screen the Pareto optimal solution set that meets the constraint conditions, and generating a recommended priority list according to the preset weight.

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