Construction and application method of a high-pressure and high-efficiency mud pump simulation calculation model

Abstract

This invention relates to the field of high-pressure mud pump simulation and calculation technology. It discloses a method for constructing and applying a high-pressure, high-efficiency mud pump simulation and calculation model. The model is established based on dynamic fluid-structure coupling parameters (such as mud pulsation pressure and impeller dynamic stress), soil multiphase rheological parameters (such as particle phase content and viscous resistance coefficient), and pipeline transient resistance parameters (such as transient pressure drop gradient). Factors such as speed-power matching are incorporated into the calculation of the operating envelope and critical failure threshold. A multi-physics coupling analysis system has been developed, including parameter configuration, dynamic simulation (integrated flow field and structural vibration solvers), and safety assessment modules. After inputting measured data and custom parameters, the system uses bidirectional fluid-structure coupling calculations to determine dynamic flow velocity distribution, pressure pulsation characteristics, and other characteristics, generating an optimized configuration solution. The system supports database management, multi-rheological model matching, and three-dimensional visualization, improving simulation accuracy and engineering efficiency, and providing support for performance optimization and safety assessment under high-pressure conditions.

Description

Technical Field

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

[0002] In modern engineering, high-pressure mud pumps, as essential construction equipment, are widely used in mining, tunneling, oil and gas drilling, and other scenarios. The pressure range of high-pressure mud pumps typically ranges from 10 to 60 MPa, depending on the industry and operating conditions. For example, the oil industry commonly uses high-pressure mud pumps with a pressure range of 10 to 15 MPa, while industries like coal mining and tunnel construction may require higher pressures, reaching 20 to 25 MPa or even higher. The performance of high-pressure mud pumps directly impacts construction efficiency, safety, and equipment life. As project complexity increases, higher requirements are placed on the reliability and energy efficiency of mud pumps under high pressure, high load, and multiple operating conditions. However, traditional mud pump design and optimization relies on physical testing and empirical formulas, which are subject to high costs, long cycles, and insufficient operating condition coverage. These factors make it difficult to accurately simulate the complex physical phenomena encountered in actual construction, such as fluid-structure interaction, multiphase flow characteristics, and transient cavitation risks.

[0003] In the existing technology, mud pump simulation models often ignore the refined description of dynamic fluid-solid coupling effects. For example, only the static pressure distribution is considered, and the dynamic load effect of the mud pulsating pressure on the impeller structure is not included, resulting in the inability to accurately predict the impeller fatigue damage. At the same time, the modeling of the multiphase rheological properties of soil is relatively rough. The mud is usually simplified into a single Newtonian fluid, and the influence of parameters such as particle phase content, viscous resistance coefficient and thixotropic index on the flow characteristics is ignored, resulting in significant deviations between the simulation results and the actual working conditions. In addition, the transient resistance analysis of the pipeline system is mostly based on the steady-state flow assumption, and does not consider the effects of transient pressure drop gradient, local resistance mutation and cavitation on the operational stability of the mud pump, making it difficult to effectively identify critical failure conditions.

[0004] At the system development level, traditional simulation tools lack multi-physics coupling capabilities. Flow and structural field analysis are independent of each other, preventing real-time data interaction and iterative calculations. Consequently, simulation results fail to reflect the actual physical coupling process. Parameter configuration modules have limited functionality, making it difficult to rapidly model diverse soil conditions and pipeline topologies. Furthermore, an incomplete safety assessment system lacks systematic analysis of the mud pump's operating envelope and critical failure thresholds, preventing comprehensive safety guidance for engineers.

[0005] With the advancement of computer technology, computational fluid dynamics (CFD), and computational structural mechanics (CSM), multi-physics coupled simulation has become a key approach to solving complex engineering problems. Building high-precision simulation models encompassing dynamic fluid-structure interaction, multiphase rheological properties, and transient pipeline resistance, and developing analysis systems that integrate parameter configuration, dynamic simulation, and safety assessment capabilities, have become core technical challenges in improving the design and operation and maintenance of high-pressure mud pumps. Existing technologies lack model integrity, parameter refinement, and system integration, necessitating a more comprehensive and efficient simulation method and system to meet practical engineering needs. Summary of the Invention

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

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for constructing and applying a high-pressure and high-efficiency mud pump simulation calculation model, the method comprising:

[0008] A simulation calculation model of a high-pressure mud pump is established based on the dynamic fluid-solid coupling parameters of the mud pump, the soil multiphase rheological parameters, and the transient resistance parameters of the pipeline. The dynamic fluid-solid coupling parameters include mud pulsation pressure and impeller dynamic stress; the soil multiphase rheological parameters include particle phase content and viscous resistance coefficient; and the transient resistance parameters of the pipeline include transient pressure drop gradient and local resistance mutation coefficient.

[0009] The simulation calculation model incorporates the dynamic matching of mud pump speed and power, mud flow stratification effect, and pipeline transient cavitation risk factors, and uses the model to calculate the mud pump operation envelope and critical failure threshold under different construction scenarios;

[0010] A high-pressure mud pump multi-physics field coupling analysis system is developed based on 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.

[0011] Inputting the measured transient performance data of the mud pump, customized soil rheological characteristics and pipeline topology parameters through the parameter configuration module of the system, wherein the measured transient performance data includes pulsation power spectrum density and dynamic head fluctuation amplitude;

[0012] Based on the dynamic simulation module of the system, fluid-solid bidirectional coupling calculations are performed 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.

[0013] Preferably, the dynamic fluid-solid coupling parameters also include the impeller clearance leakage vortex intensity coefficient and the mud non-Newtonian fluid thixotropy index.

[0014] Preferably, the parameter configuration module of the high-pressure mud pump multi-physics field coupling analysis system includes:

[0015] Establish a mud pump transient performance database, a soil rheological characteristics database, and a pipeline topology database, wherein the transient performance database stores pressure pulsation time domain signals and frequency domain energy distribution at different rotation speeds;

[0016] Matching a corresponding impeller geometric parameter set from the database according to the mud pump model, wherein the geometric parameter set includes a blade wrap angle, a hub ratio, and an outlet placement angle;

[0017] A three-dimensional mesh model of the mud pump flow channel is generated based on the geometric parameter set and loaded into the flow field solver of the dynamic simulation module.

[0018] Preferably, the dynamic simulation module performs fluid-solid bidirectional coupling calculation including:

[0019] Map the pulsating pressure field output by the flow field solver to the impeller surface mesh nodes of the structural vibration solver;

[0020] The dynamic stress distribution and modal participation factor of the impeller are calculated by the structural vibration solver, and the deformation displacement field is fed back to the flow field solver to update the calculation domain;

[0021] The iterative calculation is performed until the residual of the pressure pulsation amplitude and the structural vibration acceleration converges to the preset threshold.

[0022] Preferably, the safety assessment module includes:

[0023] Extract the equivalent stress peak value at the key position of the impeller and the critical pressure for the onset of mud cavitation based on the dynamic simulation results;

[0024] Calculate the three-dimensional safety boundary surface of speed, head and power in the mud pump operation envelope and mark the critical failure area;

[0025] Based on the safety boundary surface, a set of allowable operating condition configurations and corresponding risk level labels are generated.

[0026] Preferably, the parameter configuration module further includes:

[0027] Setting a mud multiphase rheological model selector, wherein the selector includes a Bingham fluid model, a power-law fluid model, and a thixotropic fluid model;

[0028] The corresponding rheological model is automatically matched according to the soil gradation parameters and loaded into the flow field control equation of the dynamic simulation module.

[0029] Preferably, the high-pressure mud pump multi-physics field coupling analysis system further includes:

[0030] Design a main control interface, parameter visualization interface, and report generation interface, where the main control interface integrates working condition configuration, simulation progress monitoring, and result comparison functions;

[0031] The three-dimensional dynamic rendering results of the flow field velocity cloud map, pressure contour lines and structural stress distribution are synchronously displayed in the parameter visualization interface.

[0032] Preferably, the working condition configuration function includes:

[0033] Set the mud pump speed adjustment range, mud concentration gradient and pipeline topology change sequence;

[0034] A dynamic performance comparison matrix under different configuration combinations is automatically generated based on the simulation calculation model, and the matrix includes an efficiency-power curve, a pulsation amplitude-frequency spectrum, and fatigue life prediction values.

[0035] Preferably, the calculation of the critical failure threshold includes:

[0036] Establish a dual-variable failure criterion based on the impeller material SN curve and the mud cavitation damage accumulation model;

[0037] The combined damage contribution factor of dynamic stress spectrum and pressure pulsation spectrum is extracted by rain flow counting method;

[0038] When the combined damage contribution factor exceeds the preset safety factor, the current working condition is marked as a high-risk state.

[0039] Preferably, the generation of the high-pressure working condition optimization configuration scheme includes:

[0040] Based on the set of allowable operating conditions output by the safety assessment module, calculate the output-energy consumption ratio and equipment loss rate under each operating condition;

[0041] A multi-objective optimization algorithm is used to screen the Pareto optimal solution set that meets the constraints, and a recommended priority list is generated according to preset weights.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The simulation model constructed in this paper comprehensively incorporates dynamic fluid-solid coupling parameters (such as mud pulsating pressure, impeller dynamic stress, and leakage vortex intensity coefficient), soil multiphase rheological parameters (particle phase fraction, viscous resistance coefficient, thixotropic index, etc.), and pipeline transient resistance parameters (transient pressure drop gradient, local resistance mutation coefficient, etc.). This overcomes the limitations of traditional models that incompletely describe multi-physics field coupling effects. Through refined modeling, it accurately simulates the dynamic interaction between mud flow and impeller structure, reveals the evolution of flow stratification effects and pipeline cavitation risks under multiphase flow conditions, and provides a more reliable theoretical basis for predicting mud pump performance under different soil and pipeline conditions.

[0044] The multi-physics coupling analysis system implements bidirectional fluid-structure coupling calculations through a dynamic simulation module, deeply integrating the flow field solver with the structural vibration solver. Through real-time mapping and iterative calculations of the pulsating pressure field and deformation displacement field, it ensures that the simulation results truly reflect the physical coupling process during mud pump operation. This refined calculation can obtain key parameters such as dynamic flow velocity distribution, pressure pulsation propagation characteristics, and structural fatigue damage index. Compared with traditional methods that independently analyze flow or structural fields, it significantly improves the accuracy and credibility of simulation results, providing more precise data support for impeller structural optimization and fatigue life prediction.

[0045] By establishing a database for mud pump transient performance, soil rheological properties, and pipeline topology, the parameter configuration module enables rapid matching of different mud pump geometric parameters (such as blade wrap angle and hub ratio) and automatic generation of three-dimensional flow channel mesh models. It also supports the intelligent selection of multiphase rheological models such as Bingham fluids and power-law fluids. This design significantly simplifies the parameter input process and improves the system's adaptability to diverse construction scenarios. By customizing soil properties and pipeline topology parameters, engineers can quickly build simulation models that meet actual working conditions, significantly reducing initial modeling time and improving work efficiency.

[0046] Based on the dynamic simulation results, the safety assessment module extracts parameters such as the equivalent stress peak at key impeller positions and the critical pressure of mud cavitation, constructs a three-dimensional safety boundary surface of speed, head, and power, clearly marks the critical failure area, and generates a set of allowable operating condition configurations and risk level labels. This function provides systematic safety guidance for mud pump operation. Engineering personnel can reasonably select operating conditions based on risk levels, avoid entering high-risk areas, effectively reduce the probability of equipment failure, and improve construction safety. At the same time, through the application of dual-variable failure criteria and rain flow 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 mud pumps.

[0047] The system integrates operating condition configuration, simulation progress monitoring, and result comparison functions through the main control interface. It supports flexible adjustment of parameters such as mud pump speed, mud concentration, and pipeline topology, and automatically generates a dynamic performance comparison matrix (including efficiency-power curves, pulsation amplitude-frequency spectra, etc.) for different configuration combinations. The parameter visualization interface simultaneously displays flow field velocity cloud maps, pressure contours, and structural stress distribution in a three-dimensional dynamic rendering format, making complex simulation results intuitive and easy to understand. The report generation interface can quickly output optimized configuration solutions. In combination with a multi-objective optimization algorithm to screen the Pareto optimal solution set, it provides engineers with optimal operating condition recommendations that balance production, energy consumption, and equipment loss, significantly improving the overall efficiency of mud pump operation.

[0048] The method and system of the present invention digitize the entire process, from model construction to simulation analysis, safety assessment, and optimized configuration, breaking away from the high-cost, long-cycle model of traditional reliance on physical testing. Replacing some physical testing with simulation calculations can significantly reduce R&D and operation and maintenance costs. At the same time, by covering virtual testing under a variety of extreme working 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 mud pumps, and promoting the development of mud pump simulation technology towards efficiency, precision, and intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a working principle diagram of the construction and application method of the high-pressure and high-efficiency mud pump simulation calculation model of the present invention;

[0050] Figure 2 The working principle diagram of the parameter configuration module;

[0051] Figure 3 This is the flow chart of fluid-structure bidirectional coupling calculation;

[0052] Figure 4 This is the working principle diagram of the security assessment module;

[0053] Figure 5 Flowchart for critical failure threshold calculation. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0055] See also Figure 1-Figure 5 The present invention relates to a method for constructing and applying a high-pressure and high-efficiency mud pump simulation calculation model, and the specific implementation steps are as follows:

[0056] A simulation model for a high-pressure dredge pump was established based on the pump's dynamic fluid-structure coupling parameters, soil multiphase rheological parameters, and pipeline transient resistance parameters. Dynamic fluid-structure coupling parameters include mud pulsation pressure and impeller dynamic stress; soil multiphase rheological parameters include particle fraction and viscous resistance coefficient; and pipeline transient resistance parameters include transient pressure drop gradient and local resistance mutation coefficient. By integrating these parameters, a basic model framework was constructed that reflects the actual operating environment of the dredge pump, providing data support for subsequent simulation calculations.

[0057] The simulation model incorporates dynamic pump speed-power matching, mud flow stratification, and transient pipeline cavitation risk factors. The model also calculates the pump's operating envelope and critical failure threshold for different construction scenarios. Key factors that may affect pump performance and safety in actual operation are incorporated into the model, making it more realistic. The calculated operating envelope and critical failure threshold provide key reference indicators for the safe and stable operation of the pump.

[0058] Based on the simulation model, a multi-physics coupling analysis system for high-pressure mud pumps was developed. 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. By developing a dedicated analysis system, the model was transformed into a practical tool, enabling comprehensive analysis of multi-physics coupling issues in mud pumps.

[0059] The system's parameter configuration module inputs measured transient performance data for the mud pump, customized soil rheological properties, and pipeline topology parameters. Measured transient performance data, including pulsation power spectrum density and dynamic head fluctuation amplitude, ensures the system can accurately calculate based on actual operating conditions and environmental parameters, improving the accuracy and reliability of simulation results.

[0060] The system's dynamic simulation module performs fluid-solid bidirectional coupling calculations to obtain the dynamic flow velocity distribution, pressure pulsation propagation characteristics, and structural fatigue damage index of the mud pump at different speeds, generating an optimized configuration plan for high-pressure conditions. Through fluid-solid bidirectional coupling calculations, the physical characteristics of the mud pump under different operating conditions are deeply analyzed, providing a scientific basis for optimizing the mud pump's operating conditions.

[0061] The present invention will be further described below in conjunction with Examples 1 to 5:

[0062] Example 1:

[0063] In terms of dynamic fluid-solid coupling parameters, in addition to the mud pulsation pressure and impeller dynamic stress mentioned above, the impeller gap leakage vortex intensity coefficient and the mud non-Newtonian fluid thixotropy index must also be included. The impeller gap leakage vortex intensity coefficient is a key parameter that characterizes the fluid leakage characteristics at the gap between the impeller and the pump casing. Its numerical value is related to factors such as the impeller outer diameter, the pump casing inner diameter, the impeller speed, and the mud viscosity. When actually constructing a simulation calculation model, this coefficient must be obtained through fluid mechanics theoretical analysis or experimental measurement. For example, based on the Reynolds time-averaged equations (RANS) combined with a turbulence model, the gap flow field is numerically simulated, and the velocity vector, pressure distribution, and other data of the leakage vortex are extracted to calculate the leakage vortex intensity coefficient. This coefficient is used to describe the generation and development of the leakage vortex in the gap and the degree of interference with the mainstream field. It can more accurately reflect the flow loss and energy dissipation at the impeller gap, thereby affecting the overall head and efficiency calculation of the mud pump.

[0064] The thixotropic index of non-Newtonian mud is an important parameter for measuring the thixotropic properties of mud. The thixotropy of mud is characterized by a decrease in viscosity as the shearing time increases and a gradual recovery of viscosity after the shearing stops. For mud containing colloidal substances such as clay particles, its thixotropic properties are significant. The thixotropic index must be determined through rheological tests, such as using a rotational viscometer to measure the shear stress of the mud at different shear rates and shear times, plotting a shear stress-time curve, and obtaining the thixotropic index through curve fitting. By incorporating this parameter into the model, the viscosity changes caused by different shear histories during the mixing and transportation process of the mud can be accurately portrayed in simulation calculations, thereby affecting the flow velocity distribution of the mud in the flow channel and the calculation of pressure loss.

[0065] When developing the parameter configuration module of the multi-physics coupling analysis system for high-pressure mud pumps, it is necessary to establish a mud pump transient performance database, a soil rheological properties database, and a pipeline topology database. The construction of the mud pump transient performance database requires the collection of measured data of different mud pump models at various speeds, including pressure pulsation time domain signals and frequency domain energy distribution. The pressure pulsation time domain signal can be collected by a high-frequency pressure sensor installed on the mud pump inlet and outlet pipes. The sampling frequency must 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, reflecting the proportion of pressure pulsation energy of different frequency components. This database provides the model with a wealth of measured dynamic data, which can be used to verify the accuracy of the simulation calculation results. At the same time, in actual applications, the corresponding performance data can be quickly retrieved according to the mud pump model to improve simulation efficiency.

[0066] The soil rheological properties database stores multiphase rheological parameters under different soil conditions, including particle phase content, viscous resistance coefficient, thixotropic index, etc. The particle phase content is determined by screening and weighing soil samples, and the viscous resistance coefficient can be calculated based on Darcy's formula through mud flow tests in circular pipes. The database is classified and stored by soil type (such as clay, silt, sand, etc.), making it easy to quickly call relevant parameters based on the soil conditions of the actual construction site. The pipeline topology database records the topological parameters of different pipeline layouts, such as pipeline length, pipe diameter, number of elbows and curvature radius, valve type, etc. These parameters are obtained through pipeline design drawings or on-site measurements and are used to construct a geometric model of the pipeline system to simulate the impact of different topological structures on mud flow.

[0067] When simulation calculations are required, the corresponding impeller geometry parameter set is matched from the database based on the dredge pump model. This geometry parameter set includes the blade wrap angle, hub ratio, and outlet angle. The blade wrap angle is the angle between the blade inlet and outlet edges along the impeller circumference. Its magnitude directly affects the blade's propulsion of the mud and its energy transfer efficiency. A larger blade wrap angle increases the mud's interaction time within the impeller and improves lift, but may increase 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's outer diameter, affecting the impeller's strength and the fluid flow within it. A larger hub ratio increases the impeller's strength, but reduces the flow channel cross-sectional area, potentially increasing the risk of flow blockage. The outlet angle is the angle between the blade outlet edge and the tangent line to the impeller circumference. It determines the absolute velocity direction and magnitude of the mud at the impeller outlet, affecting the flow and power characteristics of the dredge pump.

[0068] Based on the matched impeller geometry parameter set, a 3D mesh model of the mud pump flow path is generated using professional 3D modeling software (such as ANSYS Design Modeler and SolidWorks). During the modeling process, the geometric shapes of components such as the impeller, pump casing, suction chamber, and discharge chamber must be accurately described to ensure that the flow path model is consistent with the actual mud pump structure. For complex structures such as the impeller, a block modeling approach can be adopted, where the impeller blades, hub, and rim are modeled separately and then assembled to improve modeling accuracy and efficiency.

[0069] After generating the 3D geometric model, it needs to be meshed. The mesh quality directly impacts the accuracy and computational efficiency of the flow field solution. For areas with complex flow within the mud pump flow path (such as near impeller blades and in gaps), a denser tetrahedral or hexahedral mesh is used to capture subtle variations in the flow field. For areas with relatively stable flow (such as the suction and discharge chambers), a coarser mesh can be used to reduce the computational effort. After meshing is complete, a mesh quality check is performed to ensure that metrics such as the distortion rate and aspect ratio meet the solver's requirements.

[0070] The generated three-dimensional mesh model of the mud pump flow channel is loaded 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 control equations. The control equations include the continuity equation, momentum equation, and energy equation. For non-Newtonian fluids, it is also necessary to select appropriate constitutive equations based on the rheological properties of the mud, such as the Bingham fluid model, power-law fluid model, or thixotropic fluid model (described in detail in subsequent embodiments). During the calculation process, boundary conditions are set, such as setting the inlet boundary to a velocity inlet or a mass flow inlet, setting the outlet boundary to a pressure outlet or a free outflow, and using a no-slip boundary condition for the wall boundary. Wall functions are set as needed to handle the flow in the near-wall area.

[0071] During the flow field solution process, the multiphase flow characteristics of the slurry are considered, and the slurry is treated as a mixture consisting of a continuous phase (liquid phase) and a discrete phase (solid particles). Simulations are performed using the Euler-Euler multiphase flow model or the Euler-Lagrangian discrete phase model (DPM). For slurries with a high particle fraction, the Euler-Euler model is more suitable, solving the momentum and continuity equations for each phase. For slurries with a low particle fraction, the Euler-Lagrangian model tracks the motion of individual particles and more accurately describes the interaction between particles and the fluid.

[0072] At the same time, the leakage flow in the impeller gap is specifically addressed in the flow field solver, taking into account the leakage vortex intensity coefficient in the impeller gap. Sliding mesh technology or a mixing plane model can be used to simulate the relative motion between the impeller and the pump casing. The mesh density in the gap region is refined to capture the generation and evolution of the leakage vortex. By calculating the leakage flow rate and velocity distribution in the gap, as well as the degree of interference of the leakage vortex with the main flow field, the overall performance parameter calculation of the mud pump can be corrected.

[0073] Furthermore, the thixotropic index of non-Newtonian mud fluids contributes to the calculation by influencing the viscosity term in the flow field governing equations. In thixotropic fluid models, viscosity is a function not only of shear rate but also of shear time, necessitating the inclusion of a time-dependent viscosity correction term in the governing equations. Through iterative calculations, the viscosity value is gradually updated to reflect the thixotropic properties of the mud caused by changes in shear time during flow, ensuring that the flow field calculation results more closely reflect actual operating conditions.

[0074] After completing the flow field solver setup and preliminary calculations, the calculated results need to be verified and debugged. By comparing the calculated values ​​with the measured data in the mud pump transient performance database, the deviation between the measured and calculated values ​​of parameters such as pressure pulsation amplitude, frequency, head, and efficiency is checked. If the deviation exceeds the allowable range, the meshing, turbulence model, boundary conditions, and other parameters are adjusted until the calculated results are in good agreement with the measured data, ensuring the accuracy of the flow field calculations.

[0075] Example 2:

[0076] The dynamic simulation module performs fluid-structure bidirectional coupling calculations, which requires the interaction between the flow field and the structural field. The specific steps are as follows:

[0077] After the flow field solver completes the calculation of the mud flow, it outputs pulsating pressure field data. This pressure field includes the pressure values ​​at each spatial point within the mud pump flow channel and how it changes over time. Particular attention is paid to the pressure distribution on the impeller surface, which directly affects the impeller structure and induces vibration. The flow field solver discretizes the flow channel into grid cells using numerical calculation methods (such as the finite volume method) and solves the continuity equation, momentum equation, and energy equation. This solver considers the multiphase flow characteristics of the mud, the non-Newtonian fluid properties, and the centrifugal and Coriolis forces caused by the impeller's rotation, ultimately obtaining the pressure value at each grid node.

[0078] 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 method and node distribution of the flow field grid and the structural grid may be different, the transmission of pressure data must be achieved through data mapping technology. Common mapping methods include interpolation (such as radial basis function interpolation, trilinear interpolation) or projection methods to ensure that the pressure load is accurately applied to the corresponding position of the impeller structure model. For example, for the pressure value of a node in the flow field grid, its equivalent action point on the structural grid surface is determined by interpolation calculation, and the pressure load is distributed to adjacent structural nodes to form a distributed load acting on the impeller surface.

[0079] After completing the pressure field mapping, the structural vibration solver calculates the impeller's dynamic stress distribution and modal participation factors based on the impeller's material properties, geometry, and boundary conditions. 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 equations:

[0080]

[0081] 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-order derivative of the displacement vector u with respect to time, is the acceleration vector, is the second-order 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 displacement, velocity, acceleration and stress distribution 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 sites such as the blade root and impeller hub, and require special attention. The modal participation factor is used to measure the contribution of each order mode to the impeller vibration response. Modal analysis can be used to determine the natural frequency and vibration mode of the impeller to avoid resonance with the pressure pulsation frequency.

[0082] After the structural vibration solver calculates the deformation displacement field of the impeller, it needs to feed this displacement field back to the flow field solver to update the computational domain. Impeller deformation will cause changes in the geometry of the flow channel. For example, blade bending deformation will cause changes in the cross-sectional area of ​​the flow channel, thereby affecting the flow characteristics of the mud. After receiving the deformation displacement data, the flow field solver adjusts the flow field mesh through mesh deformation techniques (such as spring smoothing, dynamic layer method, and local redivision method) so that the mesh is updated as the impeller deforms. For example, when using the spring smoothing method, the flow field mesh is regarded as a system of particles connected by springs. The displacement of the impeller surface is transmitted to the surrounding mesh nodes through the "springs", achieving smooth deformation of the entire flow field mesh and ensuring that the quality of the deformed mesh meets the computational requirements.

[0083] After the flow field computational domain is updated, the flow field solver recalculates the flow field based on the new geometric boundaries, generating an updated pulsating pressure field. This pressure field now accounts for the effects of impeller deformation on the flow, such as velocity redistribution and pressure gradient changes caused by changes in the flow channel cross-sectional area. The new pressure field is then mapped to the structural mesh for the next round of structural vibration calculations. This process is repeated until the residual difference between the pressure pulsation amplitude and the structural vibration acceleration converges to a preset threshold.

[0084] Residual convergence judgment is a key step in fluid-structure bidirectional coupling calculations and is used to determine whether the iterative calculation is terminated. The residual is defined as the difference between two adjacent iterative calculation results. Usually, the root mean square error (RMSE) of the pressure pulsation amplitude and the root mean square error of the structural vibration acceleration are used as convergence criteria. The preset threshold is set according to the calculation accuracy requirements. For example, the pressure residual can be set to less than 10 -3 Pa, acceleration residual is less than 10 -2 m / s 2 During the iteration process, the residual change is monitored in real time. When the residual remains below the threshold for multiple consecutive steps and no longer changes significantly, the fluid-structure interaction calculation is considered to have reached a convergence state and the iteration is stopped.

[0085] During the entire fluid-structure bidirectional coupling calculation process, the following technical details should be noted:

[0086] Time step synchronization: The flow field solution and the structural vibration solution must use the same time step to ensure synchronization in the time domain and avoid distortion of the coupled results due to time discretization errors. The time step selection should take into account both computational accuracy and efficiency and is generally no larger than 1 / 10 of the pressure pulsation period or the structural vibration period.

[0087] Mesh compatibility: The flow field mesh and the structural mesh must have good geometric compatibility on the impeller surface. That is, the impeller surface boundary of the flow field mesh and the impeller outer surface nodes of the structural mesh must correspond one-to-one or be accurately matched through a mapping algorithm to ensure the transmission accuracy of pressure load and displacement data.

[0088] Nonlinear factor processing: If the impeller deformation is large or the mud flow exhibits strong nonlinearity (such as high-concentration particle flow, severe cavitation), it is necessary to introduce nonlinear constitutive relations into the calculation, such as the elastic-plastic model of the material, large deformation geometric nonlinear theory, etc., to more realistically simulate the actual working conditions.

[0089] Computing resource management: Fluid-structure bidirectional coupling calculations involve large-scale data interaction and iterative operations, requiring reasonable allocation of computing resources. Parallel computing technologies (such as MPI parallelization and GPU acceleration) can be used to improve computing efficiency and shorten simulation time.

[0090] Taking a certain model of high-pressure mud pump as an example, in the fluid-structure bidirectional coupling calculation, the flow field solver first calculates the pulsating pressure distribution on the impeller surface. The pressure amplitude fluctuates periodically within the range of 0 to 5 MPa, with a main frequency of 100 Hz (corresponding to the blade pass frequency at an impeller speed of 6000 r / min). After mapping this pressure field to the structural grid, the structural vibration solver calculates the maximum equivalent stress at the impeller blade root to be 200 MPa, and the peak vibration acceleration is 50 m / s. 2 The first-order mode participation factor is 0.78, indicating that the first-order mode is the main contributor to the impeller vibration. After the impeller deformation displacement (maximum displacement is 0.1mm) is fed back to the flow field solver, the velocity distribution at the flow channel outlet changes, the maximum velocity increases from 15m / s to 15.5m / s, and the pressure pulsation amplitude increases by 5%. After 10 iterative calculations, the pressure residual is reduced to 8×10 -4 Pa, the acceleration residual is reduced to 9×10 -3 m / s 2 , the preset convergence threshold is met and the calculation is terminated.

[0091] The aforementioned bidirectional fluid-structure coupling calculation process enables dynamic interactive simulation of the mud pump's flow and structural fields, accurately capturing the dynamic flow velocity distribution, pressure pulsation propagation characteristics, and structural fatigue damage index at different pump speeds. This data provides a key basis for the mud pump's structural strength design, vibration control, and operating condition optimization. For example, analyzing the pressure pulsation propagation characteristics allows for optimized piping layout to reduce vibration and noise, while evaluating the structural fatigue damage index allows for prediction of the impeller's remaining life and the development of maintenance plans.

[0092] Example 3:

[0093] The functional implementation of the safety assessment module needs to be based on the fluid-solid 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 mud pump operation is constructed.

[0094] The equivalent stress peak and the critical pressure for the onset of slurry cavitation are extracted from the dynamic simulation results. The identification of key impeller locations is based on structural mechanics theory and engineering experience, typically selecting locations prone to stress concentration, such as the blade root, the hub transition region, and the impeller outlet edge. Within the stress distribution data output by the structural vibration solver, monitoring points are set or regions of interest (ROIs) are defined to record the stress variation curve at each location over time in real time. The maximum value is extracted as the equivalent stress peak. For example, five monitoring points are evenly spaced along the thickness of the blade root. Each monitoring point records stress data once per simulation time step, and the maximum equivalent stress at each point is determined by traversing all time steps. The extraction of the critical pressure for the onset of slurry cavitation relies on the pressure distribution results from the flow field solver. The occurrence of cavitation is closely related to local pressures below the slurry vaporization pressure. During flow field calculations, the physical parameter module first obtains the vaporization pressure corresponding to the current mud temperature and concentration. The system then searches for the minimum pressure in areas prone to low pressure, such as the impeller inlet and the back of the blades. This minimum serves as the estimated critical pressure for cavitation onset. It's important to note that the critical pressure for cavitation onset is not a fixed value; it adjusts dynamically with changes in mud composition, temperature, and flow conditions.

[0095] Construct a three-dimensional safety boundary surface of speed, head, and power within the mud pump's operating envelope. This surface construction must consider multiple constraints, including the mud pump's mechanical strength, fluid dynamics, and energy consumption. The specific steps are as follows:

[0096] Parameter range definition: According to the mud pump design manual or measured data, determine the physically feasible range of speed (n), head (H), and power (P). For example, the speed range is n min ~n max , lift range is H min ~H max, the upper power limit is determined by the rated power of the motor.

[0097] Single parameter boundary scan: uniformly select grid points in a two-dimensional plane (such as the nH plane), fix two of the parameters, and calculate the critical value of the third parameter through dynamic simulation. For example, with the speed n0 and head H0 fixed, gradually increase the power until the impeller equivalent stress reaches 90% of the material allowable stress or the cavitation onset critical pressure is lower than the vaporization pressure, and record the power value P at this time. crit , which is the safety margin power corresponding to the grid point.

[0098] 3D Surface Generation: Critical parameter values ​​at all grid points are fitted into a continuous surface using an interpolation algorithm (such as cubic spline interpolation or radial basis function interpolation). To improve surface accuracy, the grid points can be denser in high-risk areas (such as near design parameter boundaries) and more sparse in low-risk areas.

[0099] Constraint superposition: In addition to strength and cavitation constraints, other safety constraints such as vibration amplitude and bearing temperature rise must also be considered. Boolean operations are performed on the boundary surfaces corresponding to these constraints to ultimately obtain a comprehensive three-dimensional safety boundary surface that includes multiple safety constraints.

[0100] In the three-dimensional safety boundary surface, the inner area is the permitted operating condition, and the outer 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 mechanism of different failure modes:

[0101] Stress failure area: corresponds to the working condition where the impeller equivalent stress exceeds the material fatigue limit, usually located in the high speed and high head area, where stress concentration occurs due to the superposition of the impeller centrifugal force and the fluid load.

[0102] Cavitation failure area: corresponds to the working condition where the critical pressure of cavitation initiation is lower than the vaporization pressure, mostly distributed in the low speed and high head area, because the reduced mud flow rate causes the impeller inlet pressure to drop.

[0103] Vibration failure area: corresponds to the operating condition where the structural vibration acceleration exceeds the natural frequency threshold of the equipment, which may occur in the resonance area where the speed is close to the natural frequency of the impeller.

[0104] Through color coding (such as red for high risk, orange for medium risk, and yellow for low risk) and transparency settings, different types of critical failure areas can be distinguished in the 3D visualization interface, making it easier for operators to quickly identify dangerous working conditions.

[0105] Based on the three-dimensional safety boundary surface, the system automatically generates a set of allowable operating configurations and corresponding risk level labels. The allowable operating configuration set includes all speed, head, and power combinations that meet the safety constraints, and each combination corresponds to an internal point on the surface. The quantitative assessment of risk level is based on the following indicators:

[0106] Safety Margin: Calculates the shortest geometric distance from the operating point to the safety boundary surface. A larger distance indicates a higher safety margin and a lower risk level. This can be achieved using the Signed Distance Function. A positive value indicates the operating point is within the safety zone, while a larger absolute value indicates it is further away from the boundary.

[0107] Multi-physics coupling risk weight: Comprehensively consider the contribution of multiple factors such as stress, cavitation, and vibration to failure, assign a weight coefficient to each physical field (such as stress risk weight 0.5, cavitation risk weight 0.3, and vibration risk weight 0.2), and obtain a comprehensive risk index through weighted summation.

[0108] Risk Level Classification: The comprehensive risk index is normalized to the range [0, 1] and divided 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 point includes the level name and the corresponding risk description (e.g., "High Risk: Approaching the Stress Failure Boundary").

[0109] The safety assessment module must also include operating condition comparison and trend analysis capabilities. Operators can select multiple permissible operating conditions, and the system automatically generates a comparison report showing the differences in parameters such as stress distribution, cavitation risk, and vibration amplitude under each condition, assisting decision-making. Furthermore, by storing and analyzing historical data, the system can track the changing trends of the safety boundary surface during long-term operation of the mud pump. For example, if the stress boundary shrinks due to impeller wear or the cavitation boundary expands due to changes in mud properties, this can provide early warning of equipment performance degradation.

[0110] At the technical implementation level, the safety assessment module must interconnect data with the dynamic simulation module and the parameter configuration module. The dynamic simulation module obtains fluid-structure interaction calculation results, while the parameter configuration module obtains basic data such as the mud pump model, mud characteristics, and pipeline topology. Parallel computing technology is used during the calculation process to accelerate 3D surface fitting and risk assessment, ensuring that results are output within a reasonable timeframe. The visualization interface, developed using graphics libraries such as OpenGL or VTK, supports 3D model rotation and scaling, as well as dynamic display of parameter cloud charts, enhancing the user experience.

[0111] Example 4:

[0112] As the basic data input interface of the high-pressure mud pump multi-physics field coupling analysis system, the parameter configuration module needs to realize the structured management and dynamic call of mud pump performance parameters, soil rheological characteristics, and pipeline topology, while integrating the intelligent matching function of the multi-physics field model.

[0113] The parameter configuration module includes a mud multiphase rheological model selector, which has preset Bingham fluid model, power law fluid model, and thixotropic fluid model, corresponding to the mud rheological characteristics under different soil conditions. The Bingham fluid model is suitable for muds with obvious yield stress, such as high-concentration clay mud, and its constitutive equation is:

[0114]

[0115] Where 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 shear-thinning or shear-thickening non-Newtonian fluids, such as silt slurry containing fine particles, and its expression is:

[0116]

[0117] Where K is the consistency coefficient and n is the rheological index (n < 1 indicates shear thinning, n > 1 indicates shear thickening). Thixotropic fluid models are used to describe slurries whose viscosity varies with shear time, such as thixotropic slurries containing colloidal particles. Their constitutive relations require the inclusion of a time variable, describing the dynamic evolution of viscosity through integral forms or differential equations.

[0118] In order to automatically match the corresponding rheological model according to the soil gradation parameters, it is necessary to establish the mapping rules between soil gradation and rheological model. The soil gradation parameters are obtained through particle size analysis tests, including the percentage of particles smaller than a certain size (such as the content of particles smaller than 0.075mm), the uniformity coefficient C u and the curvature coefficient C c For example, when the clay content (particle size < 0.005 mm) in the soil sample exceeds 30% and the non-uniformity coefficient C u When the viscosity is <5, it is determined to be clayey soil and the Bingham fluid model is automatically invoked. When the silt content (particle size 0.005-0.075mm) is dominant and the rheological exponent n is <1, the power-law fluid model is invoked. If the mud gels after standing and the viscosity decreases significantly after shearing, it is determined to be a thixotropic fluid and the thixotropic fluid model is invoked. During the model matching process, the system uses fuzzy logic algorithms to handle parameter boundary conditions. For example, when the clay content is between 25% and 30%, both the Bingham model and the thixotropic model are activated simultaneously, improving simulation accuracy through dual-model comparison calculations.

[0119] 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 equation system. For example, when using the Bingham fluid model, the viscosity term in the flow field control equation is replaced by the plastic viscosity μ p Determined, and when the shear stress is lower than the yield stress τ y When the slurry is considered as a non-flowing rigid body, when the thixotropic fluid model is adopted, the viscosity-time-varying term needs to be introduced into the control equation, and the viscosity value of each time step is updated through iterative calculation.

[0120] In addition, the high-pressure mud pump multi-physics coupling analysis system also includes the design of a human-computer interaction interface, specifically covering the main control interface, parameter visualization interface, and report generation interface. The main control interface integrates 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 by the rated speed and frequency conversion control capability of the mud pump motor, for example, 0-3000r / 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.

[0121] Based on the simulation model, the system automatically generates a dynamic performance comparison matrix for different configuration combinations. This matrix includes parameters such as the efficiency-power curve, the pulsation amplitude-frequency spectrum, and fatigue life prediction values. The efficiency-power curve is calculated by calculating the mud pump's output power and effective power (head × flow rate × gravitational acceleration × mud density) at different speeds, reflecting the mud pump's energy conversion efficiency. The pulsation amplitude-frequency spectrum is obtained by Fourier transforming the pressure pulsation time domain signal, showing the distribution of pulsation energy at different frequency components. The fatigue life prediction value is based on the rain flow counting method and the material SN curve, assessing the degree of fatigue damage to the impeller by accumulating the number of dynamic stress cycles.

[0122] The parameter visualization interface uses three-dimensional dynamic rendering technology to simultaneously display flow field velocity cloud maps, pressure contours, and structural stress distribution. The flow field velocity cloud map represents the velocity distribution by drawing velocity vectors or filling color scales on the flow channel cross section. High velocity areas are typically represented by red, and low velocity areas by blue. Pressure contours use lines of different colors to outline the isobaric surfaces within the flow channel, and the spacing between the lines reflects the magnitude of the pressure gradient. Structural stress distribution is achieved by mapping stress cloud maps onto the impeller model surface. High stress areas (such as the blade root) are highlighted with warm colors (such as red), and low stress areas are displayed with cool colors (such as blue). Operators can interactively rotate and zoom the model using the mouse, or select a specific section for cutting to observe the internal physical field distribution details.

[0123] The report generation interface automatically generates simulation analysis reports, including operating configuration parameters, key physical quantity extraction results, and safety assessment conclusions. The report format can be customized as a PDF or Word document, containing charts, data tables, and text descriptions. For example, the operating configuration parameter section lists input parameters such as rotational speed, mud concentration, and pipeline topology; the key physical quantity extraction results section displays data such as the extreme values ​​of the dynamic flow velocity distribution, the main frequency of pressure pulsation, and the maximum equivalent stress of the impeller; and the safety assessment conclusion section cites the results of a three-dimensional safety boundary surface analysis, indicating the risk level of the current operating condition and optimization suggestions.

[0124] Technically, the parameter configuration module uses a database management system (such as MySQL) to store and retrieve the slurry pump transient performance database, the soil rheological properties database, and the pipeline topology database. The database table structure follows the third normal form, ensuring low data redundancy and high query efficiency. For example, the slurry pump transient performance database includes a table of slurry pump models, a speed meter, and a pressure pulsation data table, with foreign key associations enabling rapid data retrieval. Data exchange between the dynamic simulation module and the parameter configuration module is achieved through an application programming interface (API), ensuring that parameter modifications are synchronized to the simulation calculation process in real time.

[0125] In summary, the parameter configuration module enables full-process parameter management for mud pump simulation calculations through intelligent matching of multiple rheological models, flexible setting of operating parameters, interactive visual interfaces, and automated report generation. This module not only provides accurate input data for dynamic simulations but also lowers the barrier to entry for operators through intuitive visualization and structured reporting. This enables the system to adapt to the needs of mud pump performance analysis in various construction scenarios and improves the efficiency and reliability of multi-physics coupling analysis of high-pressure mud pumps.

[0126] Example 5:

[0127] The calculation of the critical failure threshold requires comprehensive consideration of the combined effects of fatigue damage to the mud pump structure and mud cavitation. A quantitative assessment of the operating risk is achieved by establishing a dual-variable failure criterion and a damage accumulation model. First, a dual-variable failure criterion is established, combining the impeller material SN curve and the mud cavitation damage accumulation model. The impeller material SN curve is obtained through standard fatigue testing and reflects the fatigue life of the material under different cyclic stress amplitudes. It is usually expressed as a linear relationship on a logarithmic scale, for example:

[0128]

[0129] Where N is the fatigue life (number of cycles), σ is the stress amplitude, and a and b are material constants. The slurry cavitation damage accumulation model is based on the temporal evolution of cavitation pit depth. By analyzing the impact of microjets generated by the collapse of cavitation bubbles on the material surface, it establishes a functional relationship between the amount of damage and the pressure pulsation amplitude and duration of action. The bivariate failure criterion treats structural fatigue and cavitation damage as independent failure modes. When the damage in either mode exceeds a threshold, the operating condition is deemed to have failed.

[0130] The combined damage contribution factor of the dynamic stress spectrum and pressure pulsation spectrum is extracted using the rainflow counting method. The rainflow counting method is a statistical method for processing complex load-time histories. Its basic principle is to treat the stress-time curve as a series of "rainflows" and extract all closed stress cycles by peeling them off layer by layer. The specific steps are as follows:

[0131] Preprocessing: Filter the dynamic stress spectrum and pressure pulsation spectrum to remove high-frequency noise and trend items and retain the effective load cycle components.

[0132] Peak and valley value extraction: Identify peaks and valleys in the stress-time curve to form a discrete peak-valley sequence.

[0133] Rainflow counting: Starting from each peak-valley point, it "flows" downward along the time axis until it encounters a smaller valley point (corresponding to a stress cycle) or a larger peak point (corresponding to a pressure pulsation cycle) than its starting point, and records the amplitude and mean of each cycle.

[0134] Damage calculation: According to Miner's linear cumulative damage theory, the damage amount of each cycle is superimposed to obtain the joint damage contribution factor D:

[0135]

[0136] Where n i is the number of stress cycles, N i is the fatigue life under the corresponding stress amplitude; t j is the pressure pulsation action time, T j is the critical time for cavitation damage.

[0137] When the combined damage contribution factor exceeds the preset safety factor, the current operating condition is marked as high-risk. The preset safety factor is determined based on mud pump design specifications and engineering experience, typically ranging from 0.8 to 0.9, to provide a safety margin. For example, if the safety factor is set to 0.85, when D ≥ 0.85, the system triggers a high-risk alarm, prompting the operator to adjust parameters such as speed and head to prevent damage accumulation from leading to equipment failure.

[0138] The generation of the high-pressure operating condition optimization configuration plan is based on the set of allowable operating conditions output by the safety assessment module. A comprehensive balance of production, energy consumption, and equipment loss is achieved through a multi-objective optimization algorithm. First, the production-energy consumption ratio and equipment loss rate under each allowable operating condition are calculated. The production-energy consumption ratio is defined as the mud delivery volume per unit energy consumption, and the calculation formula is:

[0139]

[0140] Where Q is the mud flow rate and P is the mud pump input power. This indicator reflects the mud pump's energy efficiency; a larger value indicates better economic efficiency. The equipment wear rate is calculated by converting the fatigue life prediction value. For example, if the impeller fatigue life under certain operating conditions is L hours and the current operating time is t hours, the wear rate is t / L, which is used to assess the degree of equipment wear.

[0141] A multi-objective optimization algorithm is used to select a Pareto-optimal solution set that satisfies the constraints. A Pareto-optimal solution is defined as one in which no other solution simultaneously outperforms all objectives of the output-to-energy ratio and equipment loss rate. Common algorithms include the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective particle swarm optimization algorithm (MOPSO). Taking NSGA-II as an example, the algorithm flow is as follows:

[0142] Initialize the population: Randomly generate a certain number of operating points in the allowed operating condition set as the initial population.

[0143] Fitness calculation: Calculate the output-energy consumption ratio and equipment loss rate for each individual, and assign a fitness value according to the non-dominated sorting rule.

[0144] Selection, crossover, mutation: Generate the next generation of population through roulette wheel selection, simulated binary crossover and polynomial mutation operations.

[0145] Elite retention strategy: retain outstanding individuals from both parent and offspring generations to ensure the direction of population evolution.

[0146] Termination condition: When the population evolution reaches the preset number of generations or the Pareto front converges, the calculation is stopped and the Pareto optimal solution set is output.

[0147] Generate a recommended priority list based on preset weights, which are determined based on actual construction needs. For example, if the construction focus is on energy conservation, assign a weight of 0.6 to the output-to-energy 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. Use the linear weighting method to transform the multi-objective optimization problem into a single-objective optimization problem. The calculation formula is:

[0148]

[0149] Where w1 and w2 are weight coefficients, and w1 + w2 = 1. Pareto optimal solutions are sorted according to the f value to generate a recommended priority list, from which operators can select the optimal operating configuration.

[0150] In its technical implementation, the critical failure threshold calculation and operating condition optimization modules require high-performance computing resources. In particular, the rainflow counting method and multi-objective optimization algorithm involve extensive data processing and iterative calculations. The system utilizes distributed computing frameworks (such as Apache Hadoop) to parallelize computational tasks, reducing computation time. Furthermore, to ensure data consistency, asynchronous communication between the modules and the safety assessment and dynamic simulation modules is implemented via message queues (such as RabbitMQ), minimizing the impact of data exchange delays on system response.

[0151] It should be noted that, in this document, relational terms such as first and second, etc., are used only 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 terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0152] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for constructing and applying a high-pressure and high-efficiency mud pump simulation calculation model, characterized in that: include: A simulation calculation model of a high-pressure mud pump is established based on the dynamic fluid-solid coupling parameters of the mud pump, the soil multiphase rheological parameters, and the transient resistance parameters of the pipeline. The dynamic fluid-solid coupling parameters include mud pulsation pressure and impeller dynamic stress; the soil multiphase rheological parameters include particle phase content and viscous resistance coefficient; and the transient resistance parameters of the pipeline include transient pressure drop gradient and local resistance mutation coefficient. The simulation calculation model incorporates the dynamic matching of mud pump speed and power, mud flow stratification effect, and pipeline transient cavitation risk factors, and uses the model to calculate the mud pump operation envelope and critical failure threshold under different construction scenarios; A high-pressure mud pump multi-physics field coupling analysis system is developed based on 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. The parameter configuration module includes: establishing a mud pump transient performance database, a soil rheological characteristics database, and a pipeline topology database, wherein the transient performance database stores pressure pulsation time domain signals and frequency domain energy distribution at different speeds; matching the corresponding impeller geometric parameter set from the pipeline topology database according to the mud pump model, wherein the geometric parameter set includes blade wrap angle, hub ratio, and outlet placement angle; generating a three-dimensional mesh model of the mud pump flow channel based on the geometric parameter set, and loading it into the flow field solver of the dynamic simulation module; The safety assessment module includes: extracting the equivalent stress peak value and the critical pressure for the onset of mud cavitation at key locations of the impeller based on dynamic simulation results; calculating the three-dimensional safety boundary surface of speed, head, and power in the mud pump operation envelope and marking the critical failure area; generating a set of allowable operating condition configurations and corresponding risk level labels based on the safety boundary surface; Inputting the measured transient performance data of the mud pump, customized soil rheological characteristics and pipeline topology parameters through the parameter configuration module of the system, wherein the measured transient performance data includes pulsation power spectrum density and dynamic head fluctuation amplitude; The dynamic simulation module of the system performs fluid-solid bidirectional coupling calculations to obtain the dynamic flow velocity distribution, pressure pulsation propagation characteristics, and structural fatigue damage index of the mud pump at different speeds, and generates an optimized configuration plan for high-pressure working conditions. The dynamic simulation module performs fluid-solid bidirectional coupling calculations including: Map the pulsating pressure field output by the flow field solver to the impeller surface mesh nodes of the structural vibration solver; The dynamic stress distribution and modal participation factor of the impeller are calculated by the structural vibration solver, and the deformation displacement field is fed back to the flow field solver to update the calculation domain; The iterative calculation is performed until the residual of the pressure pulsation amplitude and the structural vibration acceleration converges to the preset threshold.

2. The method for constructing and applying a high-pressure and high-efficiency mud pump simulation calculation model according to claim 1 is characterized in that: The dynamic fluid-solid coupling parameters also include the impeller clearance leakage vortex intensity coefficient and the mud non-Newtonian fluid thixotropy index.

3. The method for constructing and applying a high-pressure and high-efficiency mud pump simulation calculation model according to claim 1 is characterized in that: The parameter configuration module also includes: Setting a mud multiphase rheological model selector, wherein the selector includes a Bingham fluid model, a power-law fluid model, and a thixotropic fluid model; The corresponding rheological model is automatically matched according to the soil gradation parameters and loaded into the flow field control equation 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 is characterized in that: The high-pressure mud pump multi-physics field coupling analysis system also includes: Design a main control interface, parameter visualization interface, and report generation interface, where the main control interface integrates working condition configuration, simulation progress monitoring, and result comparison functions; The three-dimensional dynamic rendering results of the flow field velocity cloud map, pressure contour lines and structural stress distribution are synchronously displayed in the parameter visualization interface.

5. The method for constructing and applying a high-pressure and high-efficiency mud pump simulation calculation model according to claim 4 is characterized in that: The working condition configuration function includes: Set the mud pump speed adjustment range, mud concentration gradient and pipeline topology change sequence; A dynamic performance comparison matrix under different configuration combinations is automatically generated based on the simulation calculation model, and the matrix includes an efficiency-power curve, a pulsation amplitude-frequency spectrum, and fatigue life prediction values.

6. The method for constructing and applying a high-pressure and high-efficiency mud pump simulation calculation model according to claim 1 is characterized in that: The calculation of the critical failure threshold includes: Establish a dual-variable failure criterion based on the impeller material SN curve and the mud cavitation damage accumulation model; The combined damage contribution factor of dynamic stress spectrum and pressure pulsation spectrum is extracted by rain flow counting method; When the combined damage contribution factor exceeds the preset safety factor, the current working condition is marked as a high-risk state.

7. The method for constructing and applying a high-pressure and high-efficiency mud pump simulation calculation model according to claim 1 is characterized in that: The generation of the high-pressure operating condition optimization configuration scheme includes: Based on the set of allowable operating conditions output by the safety assessment module, calculate the output-energy consumption ratio and equipment loss rate under each operating condition; A multi-objective optimization algorithm is used to screen the Pareto optimal solution set that meets the constraints, and a recommended priority list is generated according to preset weights.

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