Centrifugal pump blade damage suppression design and optimization method based on multi-physics field coupling

Abstract

The invention discloses a centrifugal pump blade damage suppression design and optimization method based on multi-physics field coupling, which comprises the following steps: constructing a fluid grid model of a centrifugal pump and carrying out transient flow field numerical simulation comprising a cavitation model and a dispersed phase model to obtain transient flow field simulation results of a cavitation field and a dispersed phase motion field; according to the transient flow field simulation result of the cavitation field, cavitation collapse wall surface energy is calculated, and a first correlation model of the blade geometric parameters and cavitation collapse energy is established; according to a transient flow field simulation result of the dispersed phase motion field, calculating particle impact wall surface energy, and establishing a second correlation model of the geometric blade parameters and the particle impact wall surface energy; and according to the first correlation model and the second correlation model, the cavitation collapse energy and the particle impact energy are controlled to suppress damage generated by multiple physical fields coupled with cavitation and particle impact, and multi-objective optimization is carried out in a mode of minimizing the cavitation collapse energy and minimizing the particle impact energy, so that optimal blade parameters are determined.

Description

Technical Field

[0001] This application relates to the field of centrifugal pump design and optimization technology, specifically to a centrifugal pump blade damage suppression design and optimization method based on multi-physics coupling. Background Technology

[0002] Centrifugal pumps, as general-purpose fluid transport equipment, are widely used in water conservancy, chemical industry, energy, shipbuilding and other fields. In actual operation, they often face two main damage mechanisms: cavitation erosion and impact erosion. When the local pressure inside the pump is lower than the saturated vapor pressure of the liquid, cavitation occurs, generating cavitation bubbles. These bubbles, carried by the flow, collapse instantaneously when they reach the high-pressure area, generating extremely high micro-jet streams and shock waves, causing repeated impacts on the blade walls, leading to material fatigue and spalling, i.e., cavitation erosion. When the transported medium contains solid particles (such as silt, slag, etc.), the high-speed moving particles will continuously impact the blade surface, causing cutting or impact-type material loss, i.e., impact erosion.

[0003] Current technologies for suppressing these two types of damage mostly employ passive protection methods, such as selecting more durable materials, applying protective coatings, or optimizing operating conditions. These methods generally suffer from high costs, limited protective effects, or narrow applicability. In contrast, addressing the issue at the impeller design level by actively reducing damage energy through optimizing blade geometry represents a more fundamental solution. Optimizing blade structure to control damage energy such as cavitation and particulate erosion can suppress damage at its source. Summary of the Invention

[0004] The purpose of this application is to provide a design and optimization method for suppressing damage to centrifugal pump blades based on multi-physics coupling. By coupling the cavitation model and the discrete phase model, an explicit correlation is established between blade parameters and cavitation collapse energy and particle impact energy. Based on this, multi-objective optimization is performed to effectively suppress blade damage.

[0005] According to a first aspect of the embodiments of this application, a method for designing and optimizing damage suppression of centrifugal pump blades based on multi-physics coupling is provided, including: Obtain the initial constraint range of the centrifugal pump design conditions and blade parameters; Based on the initial constraint range, parametric modeling and mesh generation are performed to obtain the fluid mesh model of the centrifugal pump channel; A transient flow field numerical simulation, including a cavitation model and a discrete phase model, is performed on the fluid mesh model to obtain the transient flow field simulation results of the cavitation field and the transient flow field simulation results of the discrete phase motion field. Based on the transient flow field simulation results of the cavitation field, the cavitation collapse wall energy is calculated, and a first correlation model between the blade geometric parameters and the cavitation collapse energy is established. Based on the transient flow field simulation results of the discrete phase motion field, the particle impact wall energy is calculated, and a second correlation model between the geometric blade parameters and the particle impact wall energy is established. Based on the first and second correlation models, the optimal blade parameters are determined by controlling the cavitation collapse energy and particle impact energy to suppress the damage caused by the multi-physics field coupling of cavitation and particle impact, thereby minimizing the cavitation collapse energy and the particle impact energy and performing multi-objective optimization.

[0006] Optionally, the blade parameters include the blade inlet diameter of the centrifugal pump. outlet diameter Export width Number of leaves Blade inlet placement angle Blade outlet placement angle Leaf wrap angle .

[0007] Optionally, a transient flow field numerical simulation, including a cavitation model and a discrete phase model, is performed on the fluid mesh model to obtain the transient flow field simulation results of the cavitation field and the transient flow field simulation results of the discrete phase motion field, including: Based on the fluid mesh model, a cavitation model is used to conduct transient flow field numerical simulation to obtain the transient flow field simulation results of the cavitation field; By introducing a discrete phase model and defining solid particle properties, the transient flow field simulation results of the discrete phase motion field are obtained by tracking the motion trajectory of the particles in the cavitation field and their collision process with the blade wall through coupled calculation.

[0008] Optionally, based on the transient flow field simulation results of the cavitation field, the cavitation collapse wall energy is calculated, and a first correlation model between the blade geometry parameters and the cavitation collapse energy is established, including: Based on the transient flow field simulation results of the cavitation field, the energy of a single cavitation bubble collapses is extracted and the energy flux data in each cell grid is calculated. The data is integrated within a set time interval to obtain the total instantaneous cavitation bubble collapse energy. Then, by introducing an energy transfer efficiency coefficient for correction, the cumulative cavitation bubble collapse energy acting on the blade wall is calculated. The blade inlet angle, blade outlet angle, and wrap angle were selected as key geometric parameters. Correlation analysis was performed on them with the cumulative cavitation collapse energy, and a first correlation model between the key geometric parameters and the cavitation collapse energy was obtained by fitting.

[0009] Optionally, the expression for the single cavitation collapse energy is as follows; ; in The energy of a single cavitation bubble collapse. To drive the pressure difference, The volume of the cavitation bubble. To drive the local pressure of cavitation collapse, It is the saturated vapor pressure; The expression for the energy flux within the cell grid is as follows: ; in The power of cavitation collapse per unit volume. The volume density is the rate of change of cavitation energy. This represents the volume of the cell containing the cavitation bubble. This represents the power directly resulting from the change in the volume fraction of air bubbles. This represents the volume fraction of air bubbles. This indicates the power resulting from changes in local pressure. The expression for the cumulative cavitation collapse energy is as follows: ; in This refers to the cumulative cavitation collapse energy acting on the wall. The correction factor characterizes the efficiency of energy transfer from cavitation collapse to the wall. Indicates time, The total energy of instantaneous cavitation collapse. The power of cavitation collapse per unit volume. Represents the volume of the fluid domain. For volume.

[0010] Optionally, based on the transient flow field simulation results of the discrete phase motion field, the particle impact wall energy is calculated, and a second correlation model between the blade geometry parameters and the particle impact wall energy is established, including: Based on the transient flow field simulation results of the discrete phase motion field, particle motion data is obtained, all collision events between solid particles and the blade wall are identified and extracted, the normal collision energy and tangential collision energy of each collision are calculated, and then weighted and merged with the preset energy loss coefficient to obtain the particle impact wall energy caused by particle impact. The blade inlet angle, blade outlet angle, and wrap angle were selected as key design parameters. Correlation analysis was performed on them with the particle impact wall energy, and a second correlation model between the key geometric parameters and the particle impact wall energy was obtained by fitting.

[0011] Optionally, the normal collision energy and the tangential collision energy are calculated using the following formula: ; ; in, and These are the normal collision energy and the tangential collision energy, respectively. and These represent the normal and tangential interaction forces between the particle and the wall during a single collision; and These are the normal and tangential velocity components of the particles during the collision, respectively. The duration of the collision; Σ represents the sum of the energy of all collision events within the statistical time. The energy of the particles impacting the wall is calculated using the following formula: ; in, This refers to the energy generated by particles impacting the wall surface. and These are the energy loss coefficients in the normal and tangential directions, respectively, and their values ​​are determined by material properties and collision conditions.

[0012] Optionally, based on the first and second correlation models, by controlling the cavitation collapse energy and particle impact energy to suppress the damage caused by the multi-physics field coupling of cavitation and particle impact, multi-objective optimization is performed to minimize the cavitation collapse energy and the particle impact energy, thereby determining the optimal blade parameters, including: A multi-objective optimization function is constructed with the objectives of minimizing the cavitation collapse energy and minimizing the particle impact energy. Hydraulic efficiency and head are used as constraints. A genetic algorithm is used to automatically find the Pareto optimal solution set within the set parameter constraints by using the correlation model between blade geometry parameters and cavitation collapse energy and the correlation model between blade geometry parameters and particle impact wall energy as surrogate models. From the Pareto optimal solution set, select an optimal combination of blade parameters based on the actual engineering conditions.

[0013] According to a second aspect of the embodiments of this application, a centrifugal pump blade damage suppression design and optimization device based on multi-physics field coupling is provided, comprising: The acquisition module is used to obtain the initial constraint range of the centrifugal pump design conditions and blade parameters; The model creation module is used to perform parametric modeling and mesh generation based on the initial constraint range to obtain the fluid mesh model of the centrifugal pump channel; The numerical simulation module is used to perform transient flow field numerical simulation on the fluid mesh model, including the cavitation model and the discrete phase model, to obtain the transient flow field simulation results of the cavitation field and the transient flow field simulation results of the discrete phase motion field. The first correlation model establishment module is used to calculate the cavitation collapse wall energy based on the transient flow field simulation results of the cavitation field, and to establish a first correlation model between the blade geometric parameters and the cavitation collapse energy. The second correlation model establishment module is used to calculate the particle impact wall energy based on the transient flow field simulation results of the discrete phase motion field, and to establish a second correlation model between the geometric blade parameters and the particle impact wall energy. The multi-objective optimization module is used to perform multi-objective optimization based on the first correlation model and the second correlation model, by controlling the cavitation collapse energy and the particle impact energy to suppress the damage caused by the multi-physics field coupling of cavitation and particle impact, so as to minimize the cavitation collapse energy and the particle impact energy, thereby determining the optimal blade parameters.

[0014] According to a third aspect of the embodiments of this application, an electronic device is provided, comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the method as described in the first aspect.

[0015] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided that stores computer instructions thereon, which, when executed by a processor, implement the steps of the method as described in the first aspect.

[0016] The technical solutions provided by the embodiments of this application may include the following beneficial effects: This application employs a coupled cavitation model and a discrete phase model to conduct transient flow field numerical simulations, establishing correlation models between blade geometric parameters and cavitation collapse energy and particle impact energy, thereby overcoming the technical problem that traditional methods cannot simultaneously quantify the two damage mechanisms of cavitation erosion and erosion, and are difficult to coordinately suppress damage from the design source. This allows for the accurate identification of the core causes of blade damage and the coordinated suppression of cavitation erosion and erosion damage.

[0017] This application adopts a multi-objective optimization strategy that aims to minimize cavitation collapse energy and particle impact energy, combined with hydraulic efficiency and head constraints, and introduces a genetic algorithm for automatic optimization. This overcomes the technical problems of high cost and limited effectiveness of traditional passive protection methods, as well as the risk of sacrificing the core performance of centrifugal pumps in single-objective optimization. In this way, it achieves the optimal blade damage resistance performance while meeting the pump hydraulic performance requirements.

[0018] This application employs a technique of constructing an explicit correlation surrogate model between key geometric parameters of the blade and damage energy, replacing time-consuming full-scale transient simulation. This overcomes the technical problems of traditional optimization methods relying on experience-based trial and error and having low simulation efficiency, thereby significantly improving the efficiency of centrifugal pump blade optimization design and reducing R&D costs.

[0019] This application adopts the technical means of outputting a Pareto optimal solution set instead of a single optimal solution, thereby overcoming the technical problems of poor adaptability and inability to match diverse engineering needs of traditional optimization schemes. This enables the provision of flexible and selectable blade parameter combinations for different application scenarios and improves the engineering practicality of the design scheme.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0022] Figure 1 This is a flowchart illustrating a centrifugal pump blade damage suppression design and optimization method based on multi-physics coupling, according to an exemplary embodiment.

[0023] Figure 2 This is a schematic diagram of a parametric model of a centrifugal pump impeller according to an exemplary embodiment.

[0024] Figure 3 This is a simulated cloud map of the cavitation volume fraction on the blade wall, illustrated according to an exemplary embodiment.

[0025] Figure 4 This is a cloud map of the calculated energy distribution of cavitation collapse on the blade wall, illustrated according to an exemplary embodiment.

[0026] Figure 5 This is a schematic diagram of particle distribution according to an exemplary embodiment.

[0027] Figure 6 This is a schematic diagram illustrating the calculation of particle impact energy according to an exemplary embodiment.

[0028] Figure 7 This is a schematic diagram of multi-objective optimization according to an exemplary embodiment. Detailed Implementation

[0029] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application.

[0030] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used herein are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0031] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0032] Figure 1 The flowchart illustrates a design and optimization method for centrifugal pump blade damage suppression based on multiphysics coupling, according to an exemplary embodiment. Figure 1 As shown, the method includes the following steps: S1: Obtain the initial constraint range of the centrifugal pump design conditions and blade parameters; Specifically, the initial blade parameters are set according to a pre-defined set of basic blade parameters for the centrifugal pump impeller.

[0033] The blade parameters mentioned include the inlet diameter of the centrifugal pump blades. outlet diameter Export width Number of leaves Blade inlet placement angle Blade outlet placement angle Leaf wrap angle The pre-set design flow rate, head, speed, specific speed, and specific work are 1.51. 7.2 2800 ,13,70.6 The main geometric parameters are shown in Table 1, while other parameters are treated as constants.

[0034] Table 1:

[0035] By clearly defining the constraints of operating conditions and the range of blade parameter adaptation, it is possible to ensure from the source that the core performance of the centrifugal pump, such as head, flow rate, and efficiency, meets the preset requirements under the design conditions, and avoid the pump being unable to adapt to the application scenario due to parameter design deviations.

[0036] S2: Based on the initial constraint range, perform parametric modeling and mesh generation to obtain the fluid mesh model of the centrifugal pump channel; Specifically, based on the initial blade parameters, a detailed 3D model of the impeller was created using the 3D modeling software SolidWorks and the parameters in Table 1. In the SolidWorks sketch module, the circular outlines of the impeller hub and rim were drawn to determine... , Dimensions; draw the axial sections of the blade inlet and outlet to determine... , Width. Based on , , Parameters are used to generate the 3D surface of the blades through commands such as "lofting" and "scanning," ensuring that the blade inlet angle, outlet angle, and wrap angle meet the design values. The number of blades is determined according to... The blades are evenly distributed around the impeller circumference. Boolean operations are performed on the blades, hub, and rim to integrate them into a complete 3D solid model of the impeller. The geometric integrity of the model is checked to avoid defects such as gaps and overlaps, ensuring the accuracy of subsequent fluid domain extraction. The model is as follows: Figure 2 As shown.

[0037] The model was imported into computational fluid dynamics preprocessing software for fluid domain extraction and mesh generation. Using a Boolean subtraction operation, the flow domain was extracted from the impeller solid, discarding the impeller solid portion and retaining only the computational fluid domain. Boundary layer meshing was refined in the near-wall region of the blades, with 5-8 boundary layer meshes set. The height of the first mesh layer was controlled to ensure the y+ value was within a reasonable range suitable for the selected turbulence model. For complex flow regions such as the blade inlet / outlet and leading / tailing edges, local mesh refinement was performed to reduce simulation errors related to flow separation and eddies. Boundary conditions, as shown in Table 2, were provided for numerical simulation calculations. Table 2:

[0038] In this step, mesh verification is required, which compares whether the calculated head and efficiency meet the design requirements. If they do not meet the requirements, the mesh is modified until the design requirements are met.

[0039] Parametric modeling strictly adheres to the initial design parameters, resulting in high geometric accuracy and avoiding dimensional errors inherent in manual modeling. This ensures that the flow channel geometry matches the design, providing a precise geometric foundation for subsequent simulations. A "global + local" mesh generation strategy covers the overall flow path while accurately capturing flow details in complex areas such as near-wall surfaces of the blades and inlets / outlets. Boundary layer refinement and y+ value control effectively reduce simulation errors related to hydraulic losses and flow separation, making the numerical simulation results closer to reality.

[0040] S3: Perform transient flow field numerical simulation on the fluid mesh model, including the cavitation model and the discrete phase model, to obtain the transient flow field simulation results of the cavitation field and the transient flow field simulation results of the discrete phase motion field; this step includes the following sub-steps: S31: Based on the fluid grid model, a cavitation model is used to conduct a transient flow field numerical simulation to obtain the transient flow field simulation results of the cavitation field; Specifically, after the mesh meets the requirements and proves that the mesh quality is appropriate and the calculation results are accurate and reliable, the transient flow field numerical simulation and performance verification stage begins, at which point the cavitation field data is extracted first.

[0041] First, in the physical model settings, enable the VOF multiphase flow model and define two phases: the primary phase is liquid water, and the secondary phase is water vapor. The inlet and outlet are set to total pressure inlet and mass flow rate outlet, respectively, with the liquid and gas volume fractions set to 1 and 0, respectively. The wall is set to a no-slip wall, the reference pressure is set to 0, and the gravity magnitude is 9.81. The direction is the negative X-axis; the dynamic-static interface is set to Interface, and the rotation domain calculation uses the sliding mesh method. Then, the Schnerr-Sauer cavitation model and the VOF multiphase flow model are activated, the media are set to water and water vapor, an implicit calculation method is set, the cavitation model is the Schnerr-Sauer cavitation model, and the saturated vapor pressure is set to 3169. Set the saturated vapor pressure to 3169. (corresponding to 25) (water) and default cavitation diameter 2e -6 Turbulence Model Selection The wall surface was reinforced, and the calculation type was set to transient. The boundary conditions remained the same: total pressure at the inlet 6500 Pa and mass flow rate at the outlet 0.43667 kg / s.

[0042] Using 3° of impeller rotation as one time step, the calculated time step is 0.0001785714285. The total computation time is defined as 10 impeller rotations, with a total of 1200 time steps and a maximum of 20 iteration steps. The transient calculation method uses the PISO algorithm, and the governing equations are in the second-order upwind scheme. The initial flow field is the steady-state calculation result of a single-phase flow of clear water. After convergence, transient data of the cavitation field, such as gas volume fraction, pressure and velocity in the cavitation region, are extracted. S32: Introduce a discrete phase model and define solid particle properties. Through coupled calculation, track the motion trajectory of the particles in the cavitation field and their collision process with the blade wall to obtain the transient flow field simulation results of the discrete phase motion field. Specifically, the DPM model is adopted, taking into account the effects of pressure gradient force, drag force, and gravity on particle motion. The particles are set as regular spherical sand grains with a fixed particle size of 0.09 mm. The particle concentration is fixed at 3%, and the particle density is 2650. .

[0043] The main physical properties used in the numerical simulation are: the densities of liquid water and water vapor are 998.2. and 0.5542 The dynamic viscosity is 0.001003. and 1.34×10 -5 .

[0044] Once the flow field stabilizes, a discrete phase model is introduced: transient particle tracking is enabled, solid particle injection is created, Rosin-Rammler diameter distribution is defined, the injection position is set as the inlet boundary, and the erosion / accretion model is checked to record collision data. At the same time, the blade wall is set as a trap boundary condition to track particle impacts.

[0045] After the calculation converges, extract the time-averaged data (head, efficiency, shaft power) for the last 2-3 rotation cycles, and plot the data by changing the flow rate operating point. and External characteristic curves were generated. The simulation results were compared and verified with experimental data, requiring the error at the design operating point to be within 5%. Finally, flow field and DPM analysis were performed to obtain cavitation cloud diagrams, particle trajectory tracking, and wear area prediction based on collision data. These results were then compared and verified with experimental data or empirical formulas to ensure the accuracy of the simulation model.

[0046] This step accurately captures the dynamic processes of impeller rotation, cavitation development, and discrete phase motion. The simulation results closely match the actual operating conditions, enabling a comprehensive evaluation of the centrifugal pump's cavitation performance, discrete phase wear characteristics, and flow stability. This provides precise data support for subsequent anti-cavitation and anti-wear optimization designs. Furthermore, this method allows for flexible switching of discrete phase types, adapting to complex operating conditions such as impurities and multiphase flows, thus enhancing the versatility and practicality of the design method.

[0047] S4: Based on the transient flow field simulation results of the cavitation field, calculate the cavitation collapse wall energy and establish a first correlation model between the blade geometry parameters and the cavitation collapse energy; this step includes the following sub-steps: S41: Based on the transient flow field simulation results of the cavitation field, extract the energy of a single cavitation bubble collapse and calculate the energy flux data in each cell grid. Integrate within a set time interval to obtain the total instantaneous cavitation bubble collapse energy. Then, correct by introducing the energy transfer efficiency coefficient to calculate the cumulative cavitation bubble collapse energy acting on the blade wall. Specifically, from the transient flow field simulation results, the cavitation volume fraction distribution cloud map and wall pressure fluctuation data of one complete operating cycle of the blade wall are extracted to obtain the local pressure driving cavitation collapse. saturated vapor pressure cavitation volume Furthermore, the volume fraction of cavitation can be calculated. The cumulative cavitation collapse energy acting on the wall. Total energy from instantaneous cavitation collapse Integrating over time and introducing an energy transfer efficiency correction factor, we obtain the following: And also related to the energy flux within each computational cell grid. Closely related, and It is also the collapse energy of a single cavitation bubble. The microscopic definition of is given, therefore the subsequent process is as follows:

[0048] cavitation energy Determined by its internal and external pressure difference and its volume, among which The energy of a single cavitation bubble collapse. To drive the pressure difference, The volume of the cavitation bubble. To drive the local pressure of cavitation collapse, This is the saturated vapor pressure. Through the above steps, we can calculate the corresponding... .

[0049] Based on the physical relationship that cavitation energy in a flow field is proportional to its volume, the energy flux within each computational cell grid is used in numerical calculations. To describe cavitation energy, the expression for the energy flux within the cell grid is as follows:

[0050] in The power of cavitation collapse per unit volume. The volume density is the rate of change of cavitation energy. This represents the volume of the cell containing the cavitation bubble. This represents the power directly resulting from the change in the volume fraction of air bubbles. This represents the volume fraction of air bubbles. This represents the power resulting from changes in local pressure. It is obtained through calculation. The corresponding can be calculated .

[0051] The cumulative cavitation collapse energy acting on the wall Ultimately, the total energy can be determined by the instantaneous cavitation collapse. Integrating over time and introducing an energy transfer efficiency correction factor, we obtain:

[0052] in This refers to the cumulative cavitation collapse energy acting on the wall. The correction factor characterizes the efficiency of energy transfer from cavitation collapse to the wall. Indicates time, The total energy of instantaneous cavitation collapse. The power of cavitation collapse per unit volume. Represents the volume of the fluid domain. For volume. The volume calculated in the above process. Substituting into the equation, we can obtain the final calculation result. .

[0053] S42: Select the blade inlet angle, blade outlet angle and wrap angle as key geometric parameters, perform correlation analysis between them and the cumulative cavitation collapse energy, and fit to obtain the first correlation model between the key geometric parameters and the cavitation collapse energy; Specifically, based on the flow data obtained from CFD cavitation simulation, blade geometric parameters and cavitation collapse energy are constructed. The specific steps of the multiple linear regression model are as follows: First, from different blade configurations (corresponding to different inlet angles) Exit Angle Corner In the steady-state cavitation flow simulation results (with equal geometric parameters), the system extracts the wall pressure distribution and cavitation volume fraction under each working condition. and unit energy increment Key physical quantities, and corrected by time integration and energy transfer efficiency coefficient. The corresponding cumulative cavitation collapse energy was calculated. This forms a sample dataset for regression modeling; then, using... As the dependent variable, blade geometric parameters that have a clear physical impact on the cavitation process were selected as independent variables. All variables underwent standardization preprocessing to eliminate dimensional influences, and significant variables were preliminarily screened through correlation analysis. Based on this, a multiple linear regression equation was used. Finally, statistically validated explicit mathematical relationships were obtained.

[0054] This step can accurately quantify the impact of different blade geometric parameters on cavitation collapse energy, providing a clear direction and quantitative basis for blade anti-cavitation optimization. At the same time, standardization and correlation analysis improve the reliability and practicality of the correlation model, which can directly guide the subsequent optimization design of blade geometric parameters and reduce the impact of cavitation on centrifugal pump performance and life.

[0055] S5: Based on the transient flow field simulation results of the discrete phase motion field, calculate the particle impact energy on the wall and establish a second correlation model between the geometric blade parameters and the particle impact energy on the wall; this step includes the following sub-steps: S51: Based on the transient flow field simulation results of the discrete phase motion field, obtain particle motion data, identify and extract all collision events between solid particles and the blade wall, calculate the normal collision energy and tangential collision energy of each collision, and then combine them with the preset energy loss coefficient for weighted merging to obtain the particle impact wall energy caused by particle impact. Specifically, in the transient numerical simulation based on the discrete phase model, detailed information about each collision event between a particle and the blade wall can be recorded by setting the discrete phase output and monitoring options. Specifically, at the instant of the collision, the system automatically acquires the particle's velocity vector. and the unit normal vector of the wall at the point of collision. The magnitude of the collision velocity The magnitude of the velocity vector Give; collision angle It is then defined as a velocity vector. with wall normal The included angle between them is calculated using the following formula:

[0056] Based on what was obtained The value can be further decomposed into normal components. With tangential component This provides the necessary input parameters for subsequent calculations of single-collision energy. All collision-related data, including , Collision location, duration of the collision The corresponding particle identification information is automatically recorded and output to the specified data file during the simulation process.

[0057] In Fluent's DPM settings interface, check "Track Particle Collisions" and "Record Collision Data," and specify the blade wall to be monitored as "CollisionWall." Add the collision model parameters, and after the simulation converges, use the software's "Particle Track" or "Custom Field Functions" function to filter out the event records of "single collision between a particle and the blade wall," and directly read the interaction force perpendicular to the wall direction when the particle collides with the wall. and interaction forces parallel to the wall .

[0058] Based on the transient simulation results of the discrete phase model, the normal collision energy and tangential collision energy are calculated by tracking the collision events between solid particles and the blade wall using the following formula:

[0059]

[0060] in, and These are the normal collision energy and the tangential collision energy, respectively. and These represent the normal and tangential interaction forces between the particle and the wall during a single collision; and These are the normal and tangential velocity components of the particles during the collision, respectively. The duration of the collision; Σ represents the sum of the energy of all collision events within the statistical time. The energy of the particles impacting the wall is calculated using the following formula:

[0061] in, This refers to the energy generated by particles impacting the wall surface. and These are the energy loss coefficients in the normal and tangential directions, respectively, and their values ​​are determined by material properties and collision conditions.

[0062] S52: Select the blade inlet angle, blade outlet angle and wrap angle as key design parameters, perform correlation analysis with the particle impact wall energy, and fit to obtain the second correlation model between the key geometric parameters and the particle impact wall energy; First, let's consider different blade configurations (corresponding to different inlet angles). Exit Angle Corner In the steady-state cavitation flow field and discrete phase simulation results (with equal geometric parameters), the particle collision velocity under each working condition is extracted. Collision angle Collision force Collision time And key physical quantities such as energy loss coefficient, and obtain the corresponding particle impact wall energy through the above energy calculation formula. This forms a sample dataset for regression modeling; then, using... As the dependent variable, blade geometric parameters that have a clear physical impact on cavitation process and particle motion were selected. , , ( ) was used as the independent variable, and all variables were standardized and preprocessed to eliminate the influence of dimensions. Correlation analysis was used to preliminarily screen out those variables that had a significant impact on the overall system. Geometric parameters with significant influence; based on this, a multiple linear regression equation is used to fit the sample data, ultimately obtaining a statistically validated explicit mathematical relationship:

[0063] This step can accurately quantify the impact of different blade geometric parameters on the energy of particle impact on the pump wall, providing a clear direction and quantitative basis for optimizing blade resistance to particle wear. At the same time, standardization and correlation analysis improve the reliability and practicality of the correlation model, which can directly guide the subsequent optimization design of blade geometric parameters and reduce the impact of particle impact on the performance and life of centrifugal pumps.

[0064] S6: Based on the first and second correlation models, the damage caused by the multi-physics field coupling of cavitation and particle impact is suppressed by controlling the cavitation collapse energy and particle impact energy, and multi-objective optimization is performed to minimize the cavitation collapse energy and the particle impact energy, thereby determining the optimal blade parameters; this step includes the following sub-steps: S61: Construct a multi-objective optimization function with the objectives of minimizing the cavitation collapse energy and minimizing the particle impact energy, while taking hydraulic efficiency and head as constraints. Use a genetic algorithm to automatically optimize within the set parameter constraints by using the correlation model between blade geometry parameters and cavitation collapse energy and the correlation model between blade geometry parameters and particle impact wall energy as surrogate models to obtain the Pareto optimal solution set. Specifically, with the objectives of minimizing the cavitation collapse energy and minimizing the particle impact energy, multi-objective optimization is carried out to determine the optimal blade parameters. The specific process is as follows: Place the blade inlet at the angle Exit placement angle Corner Using key geometric parameters as key variables, a bi-objective minimization function is constructed:

[0065] in, This is a vector of decision variables. These are surrogate prediction models for blade parameters established based on S4 and S5, respectively, used to efficiently calculate total cavitation collapse energy and total particle impact energy. Simultaneously, the optimized blade hydraulic efficiency... It shall not be lower than the initial design value or the specified minimum allowable value. ,Right now:

[0066] At the same time, the values ​​of all decision variables must be within the feasible region of the engineering design determined by S1:

[0067]

[0068]

[0069] The non-dominated sorting genetic algorithm (NSGA-II) with an elite retention strategy is used as the solver. The above mathematical model is embedded in the algorithm framework and directly called during the iteration process. A surrogate model and an efficiency estimation model are used to evaluate the fitness of each parameter scheme. Key algorithm parameters, such as population size, maximum number of generations, crossover probability, and mutation probability, are set to balance search breadth and convergence speed. Within the defined parameter constraints, the algorithm is launched, iteratively evolving the population through selection, crossover, and mutation operations, driving the search towards simultaneously reducing... The algorithm uses the Pareto front to advance and outputs the Pareto optimal solution set after convergence.

[0070] S62: Select an optimal combination of blade parameters from the Pareto optimal solution set based on the actual engineering conditions; Specifically, the Pareto optimal solution set contains solutions where each solution represents a blade parameter scheme that achieves a specific trade-off between cavitation erosion suppression and erosion suppression while satisfying all constraints. Visual analysis is typically performed using a Pareto front plot, which... Using the coordinate axis, the trade-offs between different solutions are visually displayed (i.e., improvement in one objective often comes at the cost of degradation in another). Based on the specific operating environment and design priorities of the centrifugal pump, decision rules are formulated to select the final solution from the Pareto solution set: if cavitation resistance is a priority, the Pareto front is selected. The minimum acceptable option; if erosion resistance is the primary concern, then choose... The solution with the minimum value.

[0071] Select a set of optimal blade parameters The data is input into the parametric modeling software SolidWorks, which drives the automatic updating of the 3D geometric model and generates the final optimized 3D digital model of the centrifugal pump blade, thus completing the complete design and optimization closed loop from simulation analysis, proxy model construction to multi-objective automatic optimization.

[0072] This step utilizes a multi-objective optimization algorithm to simultaneously minimize cavitation collapse energy and particle impact energy within constraints, achieving optimal matching of blade geometry parameters. This provides centrifugal pump blades with an optimized solution that balances cavitation resistance and particle wear resistance. Furthermore, through Pareto set visualization analysis, the trade-offs between different optimization objectives can be intuitively displayed. The optimal parameter combination can be selected based on actual engineering needs, directly guiding blade 3D modeling and manufacturing. This effectively reduces the impact of cavitation and particle wear on centrifugal pump performance and lifespan, improving equipment reliability and economy.

[0073] As can be seen from the above embodiments, this application conducts transient flow field numerical simulation by coupling a cavitation model and a discrete phase model, establishing correlation models between blade geometric parameters and cavitation collapse energy and particle impact energy on the wall, respectively. This solves the problems of traditional methods being unable to simultaneously quantify cavitation erosion and erosion damage, having low optimization efficiency, and easily sacrificing hydraulic performance. Based on this, a multi-objective optimization function is constructed with the objectives of minimizing the cavitation collapse energy and minimizing the particle impact energy, constrained by hydraulic efficiency and head. A genetic algorithm combined with a surrogate model is used for efficient optimization, outputting a Pareto optimal solution set. Thus, while ensuring the core performance of the centrifugal pump, it achieves synergistic suppression of coupled cavitation and particle impact damage, significantly improving blade damage resistance and design efficiency. It also provides flexible optimization schemes for different engineering scenarios, achieving the technical effects of accurately identifying damage causes, synergistically suppressing damage, ensuring pump core performance, improving design efficiency, and enhancing the practicality of the solution.

[0074] Corresponding to the aforementioned embodiments of the centrifugal pump blade damage suppression design and optimization method based on multiphysics coupling, this application also provides embodiments of a centrifugal pump blade damage suppression design and optimization device based on multiphysics coupling, the device comprising: The acquisition module is used to obtain the initial constraint range of the centrifugal pump design conditions and blade parameters; The model creation module is used to perform parametric modeling and mesh generation based on the initial constraint range to obtain the fluid mesh model of the centrifugal pump channel; The numerical simulation module is used to perform transient flow field numerical simulation on the fluid mesh model, including the cavitation model and the discrete phase model, to obtain the transient flow field simulation results of the cavitation field and the transient flow field simulation results of the discrete phase motion field. The first correlation model establishment module is used to calculate the cavitation collapse wall energy based on the transient flow field simulation results of the cavitation field, and to establish a first correlation model between the blade geometric parameters and the cavitation collapse energy. The second correlation model establishment module is used to calculate the particle impact wall energy based on the transient flow field simulation results of the discrete phase motion field, and to establish a second correlation model between the geometric blade parameters and the particle impact wall energy. The multi-objective optimization module is used to perform multi-objective optimization based on the first correlation model and the second correlation model, by controlling the cavitation collapse energy and the particle impact energy to suppress the damage caused by the multi-physics field coupling of cavitation and particle impact, so as to minimize the cavitation collapse energy and the particle impact energy, thereby determining the optimal blade parameters.

[0075] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0076] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0077] Accordingly, this application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-described design and optimization method for centrifugal pump blade damage suppression based on multiphysics coupling.

[0078] Accordingly, this application also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the above-described design and optimization method for centrifugal pump blade damage suppression based on multiphysics coupling.

[0079] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only.

[0080] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A design and optimization method for suppressing damage to centrifugal pump blades based on multiphysics coupling, characterized in that, include: Obtain the initial constraint range of the centrifugal pump design conditions and blade parameters; Based on the initial constraint range, parametric modeling and mesh generation are performed to obtain the fluid mesh model of the centrifugal pump channel; A transient flow field numerical simulation, including a cavitation model and a discrete phase model, is performed on the fluid mesh model to obtain the transient flow field simulation results of the cavitation field and the transient flow field simulation results of the discrete phase motion field. Based on the transient flow field simulation results of the cavitation field, the cavitation collapse wall energy is calculated, and a first correlation model between the blade geometric parameters and the cavitation collapse energy is established. Based on the transient flow field simulation results of the discrete phase motion field, the particle impact wall energy is calculated, and a second correlation model between the geometric blade parameters and the particle impact wall energy is established. Based on the first and second correlation models, the optimal blade parameters are determined by controlling the cavitation collapse energy and particle impact energy to suppress the damage caused by the multi-physics field coupling of cavitation and particle impact, thereby minimizing the cavitation collapse energy and the particle impact energy and performing multi-objective optimization.

2. The centrifugal pump blade damage suppression design and optimization method based on multiphysics coupling according to claim 1, characterized in that, The blade parameters include the centrifugal pump's blade inlet diameter, outlet diameter, outlet width, number of blades, blade inlet installation angle, blade outlet installation angle, and blade wrap angle.

3. The centrifugal pump blade damage suppression design and optimization method based on multiphysics coupling according to claim 1, characterized in that, A transient flow field numerical simulation, incorporating both a cavitation model and a discrete phase model, is performed on the fluid mesh model to obtain transient flow field simulation results for both the cavitation field and the discrete phase motion field, including: Based on the fluid mesh model, a cavitation model is used to conduct transient flow field numerical simulation to obtain the transient flow field simulation results of the cavitation field; By introducing a discrete phase model and defining solid particle properties, the transient flow field simulation results of the discrete phase motion field are obtained by tracking the motion trajectory of the particles in the cavitation field and their collision process with the blade wall through coupled calculation.

4. The centrifugal pump blade damage suppression design and optimization method based on multiphysics coupling according to claim 1, characterized in that, Based on the transient flow field simulation results of the cavitation field, the cavitation collapse wall energy is calculated, and a first correlation model between the blade geometry parameters and the cavitation collapse energy is established, including: Based on the transient flow field simulation results of the cavitation field, the energy of a single cavitation bubble collapses is extracted and the energy flux data in each cell grid is calculated. The data is integrated within a set time interval to obtain the total instantaneous cavitation bubble collapse energy. Then, by introducing an energy transfer efficiency coefficient for correction, the cumulative cavitation bubble collapse energy acting on the blade wall is calculated. The blade inlet angle, blade outlet angle, and wrap angle were selected as key geometric parameters. Correlation analysis was performed on them with the cumulative cavitation collapse energy, and a first correlation model between the key geometric parameters and the cavitation collapse energy was obtained by fitting.

5. The centrifugal pump blade damage suppression design and optimization method based on multiphysics coupling according to claim 1, characterized in that, Based on the transient flow field simulation results of the discrete phase motion field, the particle impact wall energy is calculated, and a second correlation model between the blade geometric parameters and the particle impact wall energy is established, including: Based on the transient flow field simulation results of the discrete phase motion field, particle motion data is obtained, all collision events between solid particles and the blade wall are identified and extracted, the normal collision energy and tangential collision energy of each collision are calculated, and then weighted and merged with the preset energy loss coefficient to obtain the particle impact wall energy caused by particle impact. The blade inlet angle, blade outlet angle, and wrap angle were selected as key design parameters. Correlation analysis was performed on them with the particle impact wall energy, and a second correlation model between the key geometric parameters and the particle impact wall energy was obtained by fitting.

6. The centrifugal pump blade damage suppression design and optimization method based on multiphysics coupling according to claim 1, characterized in that, Based on the first and second correlation models, the optimal blade parameters are determined by controlling the cavitation collapse energy and particle impact energy to suppress the damage caused by the multi-physics field coupling of cavitation and particle impact, thereby minimizing the cavitation collapse energy and the particle impact energy through multi-objective optimization, including: A multi-objective optimization function is constructed with the objectives of minimizing the cavitation collapse energy and minimizing the particle impact energy. Hydraulic efficiency and head are used as constraints. A genetic algorithm is used to automatically find the Pareto optimal solution set within the set parameter constraints by using the correlation model between blade geometry parameters and cavitation collapse energy and the correlation model between blade geometry parameters and particle impact wall energy as surrogate models. From the Pareto optimal solution set, select an optimal combination of blade parameters based on the actual engineering conditions.

7. A centrifugal pump blade damage suppression design and optimization device based on multiphysics coupling, characterized in that, include: The acquisition module is used to obtain the initial constraint range of the centrifugal pump design conditions and blade parameters; The model creation module is used to perform parametric modeling and mesh generation based on the initial constraint range to obtain the fluid mesh model of the centrifugal pump channel; The numerical simulation module is used to perform transient flow field numerical simulation on the fluid mesh model, including the cavitation model and the discrete phase model, to obtain the transient flow field simulation results of the cavitation field and the transient flow field simulation results of the discrete phase motion field. The first correlation model establishment module is used to calculate the cavitation collapse wall energy based on the transient flow field simulation results of the cavitation field, and to establish a first correlation model between the blade geometric parameters and the cavitation collapse energy. The second correlation model establishment module is used to calculate the particle impact wall energy based on the transient flow field simulation results of the discrete phase motion field, and to establish a second correlation model between the geometric blade parameters and the particle impact wall energy. The multi-objective optimization module is used to perform multi-objective optimization based on the first correlation model and the second correlation model, by controlling the cavitation collapse energy and the particle impact energy to suppress the damage caused by the multi-physics field coupling of cavitation and particle impact, so as to minimize the cavitation collapse energy and the particle impact energy, thereby determining the optimal blade parameters.

8. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-6.

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