A CFD wear model correction method and system based on high-speed rotating disc test

Abstract

The present application relates to the technical field of pumped storage, in particular to a CFD wear model correction method and system based on high-speed rotating disc test. The method comprises the following steps: screening multiple groups of test particles with different hardness ratios to perform high-speed rotating disc wear test, measuring the surface test wear depth of the test piece, constructing a solid-liquid two-phase flow model of high-speed rotating flow field based on CFD simulation, calculating and simulating the wear depth by using a standard Oka model, fitting the test and simulated wear depth to obtain a particle hardness correction factor, introducing the material yield strength, generating a yield strength correction factor by inversely coupling the material strength influence coefficient through the test data, and integrating the particle hardness correction factor and the yield strength correction factor to reconstruct the standard Oka model. The present application realizes collaborative correction by simultaneously considering the particle hardness and the material yield strength, so as to solve the prediction deviation of different hardness particle working conditions, thereby improving the accuracy of wear depth prediction under high-speed working conditions.

Description

Technical Field

[0001] This invention relates to the field of pumped storage technology, specifically a CFD wear model correction method and system based on high-speed rotating disk testing. Background Technology

[0002] Pumped storage power stations, as core regulating facilities in new power systems, have experienced rapid development globally in recent years. my country, as a leader in this field, has seen a continuous increase in its installed capacity annual growth rate, becoming a core guarantee for power grid peak shaving, frequency regulation, and emergency backup. A pumped storage power station mainly consists of an upper reservoir, a lower reservoir, water pipelines, and a powerhouse. The reversible pump-turbine is the core component of the pumped storage unit, and its stable operation directly affects the amount of hydropower generated. During peak electricity demand, the turbine generates electricity by utilizing potential energy through forward rotation; during off-peak demand, the turbine generates electricity by storing energy through the difference in water level. Pumped storage power stations typically provide rapid regulation when grid load fluctuates significantly, especially providing emergency power during peak demand periods. To respond quickly to changes in grid load, high rotational speeds are required to improve the turbine's start-up and shutdown response speeds. High rotational speeds also improve the efficiency and pumping speed in pump mode, enabling the power station to store energy more rapidly. The working condition and operational stability of a water turbine directly affect the efficiency of a pumped storage unit. However, the presence of silt makes the surfaces of hydraulic machinery more susceptible to cavitation and erosion. The resulting cavitation and silt wear work together to cause material loss from the surfaces of flow components over time, altering flow conditions and characteristics. This leads to a sharp increase in hydraulic losses and unit energy losses, resulting in a significant decrease in both hydraulic and unit efficiency. In severe cases, this can trigger major production safety accidents, causing serious economic and personnel losses. Therefore, studying the wear mechanism of pump-turbines at high speeds and making accurate predictions has become a key focus of pumped storage technology development.

[0003] Computational Fluid Dynamics (CFD) technology, as a core method for wear prediction, has been widely used in the design of pumped storage power station equipment. Mainstream software such as Fluent incorporates various wear models, simulating particle trajectories and calculating erosion rates by coupling a discrete phase model (DPM) with a continuous phase flow field. However, current wear models exhibit a series of failure problems under high-speed pump conditions, urgently requiring theoretical breakthroughs. Currently, commonly used wear models in engineering include the Finnie model, the McLaury model, and the Oka model. The Finnie model's default parameters are only applicable to specific conditions of sand impacting carbon steel; the McLaury model has a narrow applicable speed range, with significant prediction errors for high-speed conditions exceeding 30 m / s; and the Oka model has complex functions, requiring extensive experimental calibration of coefficients. Furthermore, these models do not consider the influence of different particle hardness on the wear rate for silt and only consider the Brinell hardness of the material, neglecting other wear resistance factors. Therefore, the prediction results deviate significantly under conditions of particles with different hardnesses. The wear resistance of different materials set by numerical simulation is only reflected in their hardness and density, and cannot show other wear resistance properties of different materials. Therefore, materials with similar hardness have similar wear depths, which deviates from the experimental results. Summary of the Invention

[0004] The technical problem to be solved by the embodiments of the present invention is to provide a CFD wear model correction method and system based on high-speed rotating disk test, so as to solve the problem of large wear prediction error of water pump turbine at high speed in the prior art.

[0005] This invention discloses a CFD wear model correction method based on high-speed rotating disk testing, comprising: Multiple groups of test particles with different hardness were selected to form test conditions. The test wear depth of the specimen surface under different test conditions was obtained by measuring the high-speed rotating disk flow wear test. Based on CFD simulation, a solid-liquid two-phase flow model was constructed to simulate the motion of particles in a high-speed rotating flow field for each group of tests, and the simulated wear depth on the surface of the specimen was calculated using the standard Oka model. The particle hardness correction factor is obtained by fitting the simulated wear depth and the experimental wear depth on the surface of the specimen. The yield strength of the material is introduced, and the influence coefficient of the material strength is coupled with the test wear depth on the surface of the specimen to obtain the yield strength correction factor. By combining the particle hardness correction factor and yield strength correction factor, the standard Oka model is reconstructed to obtain the modified Oka model. The functional expression of the modified Oka model is as follows:

[0006] In the formula, To correct the wear rate output by the Oka model, This is the yield strength correction factor. For the material's yield strength, This is the influence coefficient on material strength. This represents the percentage of particle hardness. The hardness of the specimen wall. The baseline wear coefficient for the standard Oka model. For impact angle function, For the impact angle, This represents the total mass of particles impacting the surface of the specimen per unit time. This represents the effective area of ​​the specimen surface subjected to particle impact.

[0007] Optionally, the step of screening multiple groups of test particles with different hardnesses and measuring the test wear depth on the specimen surface based on a high-speed rotating disk flow abrasion test includes: Set a particle hardness threshold, and then screen the test particles into multiple test conditions with different hardness ratios based on the set particle hardness threshold. Under identical operating conditions except for the test conditions, the test wear depth of the specimen surface was obtained by non-contact three-dimensional morphology scanning measurement based on the high-speed rotating disk flow wear test of each group of test samples.

[0008] Optionally, the step of constructing a solid-liquid two-phase flow model based on CFD simulation to simulate the motion of each group of test particles in a high-speed rotating flow field, and calculating the simulated wear depth on the specimen surface using the standard Oka model, includes: The solid-liquid two-phase flow model based on the Eulerian-Lagrange method was constructed based on CFD simulation. The velocity inlet boundary conditions and pressure outlet boundary conditions of the solid-liquid two-phase flow model were set, and the MRF rotating model was introduced to simulate the high-speed rotating flow field. The continuous phase of the solid-liquid two-phase flow model is calculated based on the SSTk-ω turbulence model for single-phase steady-state calculation, and the standard Oka model is embedded after the continuous phase flow field calculation converges. The discrete phase of the solid-liquid two-phase flow model is based on the DPM model to track the particle motion trajectory. According to the tracked particle motion trajectory, the embedded standard Oka model is used to discretely calculate the surface wear rate of the specimen under multiple different test conditions to obtain the simulated wear depth of the specimen surface.

[0009] Optionally, based on the tracked particle motion trajectory, the embedded standard Oka model is used to discretely calculate the surface wear of the specimen under multiple different test conditions to obtain the simulated wear depth of the specimen surface, including: The particle motion data is calculated based on the tracked particle trajectory. The function expression for calculating the particle motion data is:

[0010] In the formula, For the mass of the particles, The rate of change of particle velocity over time. The velocity vector of the particle. For the particle motion time, The drag force generated when a body moves relative to a particle. The pressure gradient force in the flow field. This refers to the virtual mass force added during particle acceleration. The force is the turbulent diffusion effect caused by fluid turbulence fluctuations; Based on the particle motion data obtained from the calculation, the average wear rate at each location on the surface of the specimen under each set of test conditions was discretely calculated using the embedded standard Oka model. The simulated wear depth on the surface of the specimen is calculated based on the average wear rate at various locations on the specimen surface.

[0011] Optionally, the step of obtaining the particle hardness correction factor by fitting the simulated wear depth and the experimental wear depth on the specimen surface includes: Obtain the hardness parameters of the test particles and the specimen, and establish a wear depth relationship model based on the hardness parameters of the test particles and the specimen. The test wear depth and simulated wear depth of the specimen surface under each test condition were substituted into the wear depth relationship model for multiple linear regression analysis to obtain a particle hardness correction factor that includes linear correlation parameters of particle hardness ratio and exponential correlation parameters.

[0012] Optionally, the CFD wear model correction method further includes a method for correcting the wear rate output by the standard Oka model using a particle hardness correction factor, comprising: The particle hardness correction factor is used to correct the particle size of the specimen wall hardness. Combined with the average wear rate of the specimen surface calculated using the standard Oka model, the wear rate of the specimen after particle hardness correction is obtained. The functional expression for the particle hardness correction of the specimen wear rate is as follows:

[0013] In the formula, The wear rate of the specimen after particle hardness correction. The hardness of the specimen wall. This is a linear correlation parameter representing the proportion of particle hardness. This is an index-related parameter representing the percentage of particle hardness. The wear rate of the specimen is calculated using the standard Oka model.

[0014] Optionally, the process involves introducing the material yield strength and coupling it with the material strength influence coefficient through the test wear depth on the specimen surface to obtain a yield strength correction factor, including... Based on the wear rate of the specimen after correction for particle hardness, a correlation model between the wear rate and the material yield strength is established by introducing the material yield strength. The functional expression of the correlation model is as follows:

[0015] In the formula, The wear rate is shown at different locations on the surface of the specimen. Based on the established correlation model, the test wear depth of the specimen surface under each test condition is substituted into the correlation model to couple the material strength influence coefficient, and the material strength influence coefficient value is determined. The yield strength correction factor is obtained by correcting the yield strength of the material based on the determined material strength influence coefficient value.

[0016] This invention also discloses a model correction system, employing the aforementioned CFD wear model correction method based on high-speed rotating disk testing. The system includes: The test wear depth acquisition module is used to screen multiple groups of test particles with different hardness to form test conditions, and to obtain the test wear depth of the specimen surface under different test conditions based on the high-speed rotating disk flow wear test measurement. The simulated wear depth acquisition module is used to construct a solid-liquid two-phase flow model based on CFD simulation to simulate the motion of each group of test particles in a high-speed rotating flow field, and to calculate the simulated wear depth on the surface of the specimen using the standard Oka model. The particle hardness correction factor acquisition module is used to obtain the particle hardness correction factor by fitting the simulated wear depth and the experimental wear depth on the surface of the specimen. The yield strength correction factor acquisition module is used to introduce the material yield strength and couple the material strength influence coefficient with the test wear depth on the specimen surface to obtain the yield strength correction factor. The Oka model correction module is used to reconstruct the standard Oka model by combining the particle hardness correction factor and the yield strength correction factor to obtain the corrected Oka model.

[0017] The present invention also discloses a computer-readable storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the above-described CFD wear model correction method based on a high-speed rotating disk test.

[0018] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the above-mentioned CFD wear model correction method based on high-speed rotating disk test.

[0019] Compared with the prior art, the CFD wear model correction method and system based on high-speed rotating disk test provided in this invention have the following advantages: The test wear depth of the specimen surface was obtained by conducting high-speed rotating disk flow wear tests using test particles of different hardness. A solid-liquid two-phase flow model was constructed based on CFD simulation, and the simulated wear depth was calculated using the standard Oka model. A particle hardness correction factor was established through fitting. Furthermore, a yield strength correction factor was established by introducing the material yield strength and coupling it with the material strength influence coefficient. The standard Oka model was reconstructed by combining these two correction factors to obtain a modified Oka model. This effectively addresses the shortcomings of the original model, which did not consider the influence of different particle hardness on the wear rate and did not distinguish between differences in material yield strength. It achieves synergistic correction of particle hardness and material yield strength in wear prediction, thereby improving the accuracy of wear depth prediction under high-speed conditions. Attached Figure Description

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 A schematic block diagram illustrating the steps of a CFD wear model correction method based on a high-speed rotating disk test provided in an embodiment of the present invention. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0022] This invention discloses a CFD wear model correction method based on high-speed rotating disk testing, comprising: S1. Select multiple groups of test particles with different hardness to form test conditions, and measure the test wear depth of the specimen surface under different test conditions based on the high-speed rotating disk flow wear test. S2. Based on CFD simulation, a solid-liquid two-phase flow model was constructed to simulate the motion of each group of test particles in a high-speed rotating flow field, and the simulated wear depth on the surface of the specimen was calculated using the standard Oka model. S3. The particle hardness correction factor is obtained by fitting the simulated wear depth and the test wear depth on the surface of the specimen. S4. Introduce the material yield strength and couple the material strength influence coefficient with the test wear depth on the specimen surface to obtain the yield strength correction factor. S5. By combining the particle hardness correction factor and the yield strength correction factor, the standard Oka model is reconstructed to obtain the modified Oka model. The functional expression of the modified Oka model is as follows:

[0023] In the formula, To correct the wear rate output by the Oka model, This is the yield strength correction factor. For the material's yield strength, This is the influence coefficient on material strength. This represents the percentage of particles with a hardness greater than 4.5 in the sediment. The hardness of the specimen wall. The baseline wear coefficient for the standard Oka model. For impact angle function, For the impact angle, This represents the total mass of particles impacting the surface of the specimen per unit time. This represents the effective area of ​​the specimen surface subjected to particle impact.

[0024] By implementing the above-described CFD wear model correction method, a corrected wear model highly adapted to high-speed conditions is established during the experimental simulation fusion process, significantly improving the accuracy of wear prediction. Specifically, firstly, test particles of different hardness are selected for high-speed rotating disk flow wear tests to directly measure the test wear depth on the specimen surface. Subsequently, a solid-liquid two-phase flow model is constructed based on CFD simulation to simulate the motion trajectory of the test particles in the high-speed flow field, and the simulated wear depth on the specimen surface is calculated using the standard Oka model. The particle hardness correction factor is obtained by fitting and analyzing the test wear depth and the simulated wear depth. This factor can be dynamically adjusted to accurately reflect the actual impact of particles of different hardness on the wear rate, avoiding the prediction bias of the original model for working conditions with particles of different hardness. At the same time, the material yield strength parameter is introduced and coupled with the test wear depth to obtain the material strength influence coefficient, and further the yield strength correction factor is derived. This can effectively solve the problem that the standard model cannot distinguish the difference in yield strength of materials with the same hardness. Thus, the standard Oka model is reconstructed by the synergistic integration of particle hardness correction factor and yield strength correction factor to form a modified Oka model. These factors, as well as key parameters such as impact angle function, total mass of particles impacting the specimen surface per unit time, and effective area of ​​the specimen surface impacted by particles are clearly integrated. This allows the wear rate calculation to simultaneously cover the comprehensive effect of particle hardness and material yield strength, thus showing excellent adaptability in high-speed environments, significantly reducing prediction errors, and realistically simulating the particle impact process, material response, and dynamic behavior of the flow field. It is also compatible with the CFD simulation framework to ensure the direct implantation and operability of the model. The entire process can be integrated without additional hardware support, thus providing a reliable theoretical basis and design guidance for wear prediction of high-speed rotating equipment.

[0025] As mentioned above, the linear correlation parameter for the proportion of particle hardness The linear gain effect of the proportion of high-hardness particles on wear can be directly quantified. Each 10% increase leads to a 0.05 increase in the correction term, significantly exacerbating material loss), while also affecting the exponential correlation parameter of the hardness percentage containing particles ( Adjusting the hardness of the specimen wall The efficiency of suppressing wear rate. That is, when When approaching 100% (dominated by hard particles), the exponent drops to -0.414, meaning that a slight increase in material hardness can significantly suppress wear. When the index approaches 0% (soft particles dominate), it rises to 0.346, significantly weakening the effect of material hardness. This accurately addresses the common failure prediction problem caused by traditional models neglecting the differences in hardness distribution among mixed particles. Therefore, using the CFD wear model correction method of this invention, the maximum error between the experimental wear depth and the simulated wear depth at high flow rates is 18.75%, with an average error of only 14.21%. Furthermore, dynamic functions are preferably implanted through the Fluent UDF (User-Defined Functions) interface, ensuring compatibility with the entire ANSYS Workbench workflow. This eliminates the need for additional hardware. ANSYS Workbench is an integrated platform that allows engineers to use various ANSYS simulation software to analyze product designs.

[0026] Furthermore, multiple groups of test particles with different hardness were screened, and the test wear depth on the surface of the specimen was measured based on a high-speed rotating disk flow wear test, including: Set a particle hardness threshold, and then screen the test particles into multiple test conditions with different hardness ratios based on the set particle hardness threshold. Under identical operating conditions except for the test conditions, the test wear depth of the specimen surface was obtained by non-contact three-dimensional morphology scanning measurement based on the high-speed rotating disk flow wear test of each group of test samples.

[0027] Furthermore, based on CFD simulation, a solid-liquid two-phase flow model was constructed to simulate the motion of each group of experimental particles in a high-speed rotating flow field. The simulated wear depth on the specimen surface was calculated using the standard Oka model, including: A solid-liquid two-phase flow model based on the Eulerian-Lagrange method was constructed based on CFD simulation. Velocity inlet boundary conditions and pressure outlet boundary conditions were set for the solid-liquid two-phase flow model, and an MRF rotating model was introduced to simulate the high-speed rotating flow field. The continuous phase of the solid-liquid two-phase flow model is calculated based on the SSTk-ω turbulence model for single-phase steady-state calculation, and the standard Oka model is embedded after the continuous phase flow field calculation converges. The discrete phase of the solid-liquid two-phase flow model is based on the DPM model to track the particle motion trajectory. According to the tracked particle motion trajectory, the embedded standard Oka model is used to discretize the wear rate of the specimen surface under multiple different test conditions to obtain the simulated wear depth of the specimen surface.

[0028] Furthermore, based on the tracked particle motion trajectory, the embedded standard Oka model was used to discretize the surface wear of the specimens under multiple different test conditions, obtaining the simulated wear depth of the specimen surface, including: The particle motion data is calculated based on the tracked particle trajectory. The function expression for calculating the particle motion data is:

[0029] In the formula, For the mass of the particles, The rate of change of particle velocity over time. The velocity vector of the particle. For the particle motion time, The drag force generated when a body moves relative to a particle. The pressure gradient force in the flow field. This refers to the virtual mass force added during particle acceleration. The force is the turbulent diffusion effect caused by fluid turbulence fluctuations; Based on the particle motion data obtained from the calculation, the wear rate at each location on the surface of the specimen under each set of test conditions was discretely calculated using the embedded standard Oka model. The simulated wear depth of the specimen surface is calculated based on the average wear rate at various locations on the specimen surface.

[0030] Through the implementation of the above-described CFD wear model correction method, test particles of different hardness were first screened and particle hardness thresholds were set to form multiple test conditions with different hardness ratios. While maintaining consistency in other conditions, a high-speed rotating disk flow-around wear test combined with non-contact three-dimensional topography scanning measurement was used to accurately obtain the test wear depth on the surface of each group of specimens. Simultaneously, a solid-liquid two-phase flow model based on the Euler-Lagrange method was constructed using FLUENT software (a widely used computational fluid dynamics simulation software) to describe the solid-liquid erosion wear flow characteristics. Velocity inlet and pressure outlet boundary conditions were set, and an MRF rotating model was introduced to simulate the high-speed rotating flow field. The simulated rotational speed of the disk in the high-speed rotating flow field was preferably 4500 r / min. The continuous phase was calculated using the SSTk-ω turbulence model (Shear-StressTransport k-ω Turbulence Model) for single-phase steady-state operation, and then embedded into the standard Oka model. The discrete phase was tracked using the DPM model (Deformable Parts Model). Particle motion data was calculated based on the particle motion equations composed of drag force, pressure gradient force, virtual mass force, and turbulent diffusion effects. Based on this data, the embedded standard Oka model was applied to discretize the wear depth at various locations on the specimen surface. The wear depths at each location were then integrated to obtain the average wear rate of the specimen surface, ultimately yielding a simulated wear depth that corresponds one-to-one with the experimental conditions. This method, by simultaneously acquiring experimental and simulated wear depths, verifies the fit of the particle hardness correction factor in the standard Oka model, forming a closed-loop verification mechanism between experiment and simulation. This provides a reproducible calibration basis for accurately quantifying the actual impact of particle hardness on wear depth.

[0031] Preferably, the test particles can be divided into three different hardness groups: 100%, 70%, and 50%, based on the percentage of particles with a Mohs hardness greater than 7. A single-factor comparative test of high-speed rotating disk flow wear was conducted under three conditions with particle hardnesses of 100%, 70%, and 50%, while other test conditions remained the same. The test wear depth of the specimens before and after the test was obtained using a three-dimensional topography scanner.

[0032] Furthermore, the simulated wear depth and the experimental wear depth on the specimen surface are fitted to obtain a particle hardness correction factor, including: Obtain the hardness parameters of the test particles and the specimen, and establish a wear depth relationship model based on the hardness parameters of the test particles and the specimen. The test wear depth and simulated wear depth of the specimen surface under each test condition were substituted into the wear depth relationship model for multiple linear regression analysis to obtain the particle hardness correction factor, which includes linear correlation parameters of particle hardness ratio and exponential correlation parameters.

[0033] Furthermore, the CFD wear model correction method also includes a method for correcting the wear rate output by the standard Oka model using a particle hardness correction factor, including: The particle hardness of the specimen wall is corrected for particle size using a particle hardness correction factor. Combined with the average wear rate of the specimen surface calculated using the standard Oka model, the particle hardness-corrected wear rate is obtained. The functional expression for the particle hardness correction of the specimen wear rate is as follows:

[0034] In the formula, The wear rate of the specimen after particle hardness correction. The hardness of the specimen wall. This is a linear correlation parameter representing the proportion of particle hardness. This is an index-related parameter representing the percentage of particle hardness. The wear rate of the specimen is calculated using the standard Oka model.

[0035] Through the implementation of the above-described CFD wear model correction method, the experimental wear depth and simulated wear depth data of the specimen surface are processed simultaneously using multiple linear regression analysis to establish a wear depth relationship model and deduce the particle hardness correction factor. This correction factor consists of a linear correlation parameter containing the proportion of particle hardness. And exponential correlation parameters A common definition is used to achieve a structured correction of the particle hardness parameter to the standard Oka model. A particle hardness correction factor is introduced into the standard Oka model to form a two-parameter coupling mechanism, where the specimen wall hardness... Through exponential correlation parameters Adjusting the nonlinear response of wear depth to hardness, linear correlation parameters The model incorporates the gain effect of particle hardness proportion on wear rate, thus directly addressing the prediction bias in high-speed conditions caused by traditional models neglecting the differences in particle hardness parameters. The Oka model corrected for particle hardness retains the original Oka model structure to ensure compatibility with CFD industrial software, achieving particle attribute embedding and accurate quantification of wear resistance based on the same standard framework, significantly improving the prediction reliability of high-speed wear conditions in pumps and turbines. Specifically, the particle hardness correction factor... , .

[0036] Furthermore, the material yield strength is introduced, and the influence coefficient of material strength is coupled with the test wear depth on the specimen surface to obtain the yield strength correction factor, including... Based on the Oka model modified for particle hardness, a correlation model between wear rate and material yield strength is established by introducing material yield strength. The functional expression of the correlation model is as follows:

[0037] In the formula, The wear rate is shown at different locations on the surface of the specimen. Based on the established correlation model, the test wear depth of the specimen surface under each test condition is substituted into the correlation model to couple the material strength influence coefficient, and the material strength influence coefficient value is determined. The yield strength correction factor is obtained by correcting the yield strength of the material based on the determined material strength influence coefficient value.

[0038] By implementing the above-described CFD wear model correction method, the material yield strength is introduced, and the influence coefficient of material strength is coupled based on the test wear depth to establish a correlation model between wear rate and material yield strength. By substituting the test wear depth of the specimen surface under each test condition into this correlation model, the specific value of the material strength influence coefficient n is determined through inversion fitting, thereby generating the yield strength correction factor. This factor is the first to embed the material's yield strength property into a wear prediction framework, through... The nonlinear suppression effect of quantitative yield strength on wear rate is analyzed while retaining the original particle hardness correction model. Based on the structure, a dual-factor collaborative correction of particle hardness and material yield strength is constructed to solve the common defect of traditional models that ignore the micromechanical properties of materials, resulting in inaccurate wear prediction of materials with the same hardness. This significantly improves the accuracy of wear depth prediction for materials with different yield strengths under high-speed rotation conditions, while maintaining structural compatibility with the standard Oka model.

[0039] This invention also discloses a model correction system, which employs the above-mentioned CFD wear model correction method based on high-speed rotating disk tests. The system includes: The test wear depth acquisition module is used to screen multiple groups of test particles with different hardness to form test conditions, and to obtain the test wear depth of the specimen surface under different test conditions based on the high-speed rotating disk flow wear test measurement. The simulated wear depth acquisition module is used to construct a solid-liquid two-phase flow model based on CFD simulation to simulate the motion of each group of test particles in a high-speed rotating flow field, and to calculate the simulated wear depth on the surface of the specimen using the standard Oka model. The particle hardness correction factor acquisition module is used to obtain the particle hardness correction factor by fitting the simulated wear depth and the experimental wear depth on the surface of the specimen. The yield strength correction factor acquisition module is used to introduce the material yield strength and couple the material strength influence coefficient with the test wear depth on the specimen surface to obtain the yield strength correction factor. The Oka model correction module is used to reconstruct the standard Oka model by combining the particle hardness correction factor and the yield strength correction factor to obtain the corrected Oka model.

[0040] The present invention also discloses a computer-readable storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the above-described CFD wear model correction method based on a high-speed rotating disk test.

[0041] The present invention also discloses a computer-readable storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the above-described CFD wear model correction method based on a high-speed rotating disk test.

[0042] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the above-mentioned CFD wear model correction method based on high-speed rotating disk test.

[0043] This invention is described based on flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to specific embodiments. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the flowcharts and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0044] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0045] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0046] It should be understood that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Those skilled in the art can modify the technical solutions described in the above embodiments, or make equivalent substitutions for some of the technical features; and all such modifications and substitutions should fall within the protection scope of the present invention.

Claims

1. A CFD wear model correction method based on high-speed rotating disk testing, characterized in that, The CFD wear model correction method includes: Multiple groups of test particles with different hardness were selected to form test conditions. The test wear depth of the specimen surface under different test conditions was obtained by measuring the high-speed rotating disk flow wear test. Based on CFD simulation, a solid-liquid two-phase flow model was constructed to simulate the motion of experimental particles in a high-speed rotating flow field, and the simulated wear depth on the surface of the specimen was calculated using the standard Oka model. The particle hardness correction factor is obtained by fitting the simulated wear depth and the experimental wear depth on the surface of the specimen. The yield strength of the material is introduced, and the influence coefficient of the material strength is coupled with the test wear depth on the surface of the specimen to obtain the yield strength correction factor. By combining the particle hardness correction factor and yield strength correction factor, the standard Oka model is reconstructed to obtain the modified Oka model. The functional expression of the modified Oka model is as follows: In the formula, To correct the wear rate output by the Oka model, This is the yield strength correction factor. For the material's yield strength, This is the influence coefficient on material strength. This represents the percentage of particle hardness. The hardness of the specimen wall. The baseline wear coefficient for the standard Oka model. For impact angle function, For the impact angle, This represents the total mass of particles impacting the surface of the specimen per unit time. This represents the effective area of ​​the specimen surface subjected to particle impact.

2. The CFD wear model correction method based on high-speed rotating disk test according to claim 1, characterized in that, The screening of multiple groups of test particles with different hardness, and the measurement of the test wear depth on the surface of the specimen based on a high-speed rotating disk flow abrasion test, includes: Set a particle hardness threshold, and then screen the test particles into multiple test conditions with different hardness ratios based on the set particle hardness threshold. Under identical operating conditions except for the test conditions, the test wear depth of the specimen surface was obtained by non-contact three-dimensional morphology scanning measurement based on the high-speed rotating disk flow wear test of each group of test samples.

3. The CFD wear model correction method based on high-speed rotating disk test according to claim 2, characterized in that, The solid-liquid two-phase flow model was constructed based on CFD simulation to simulate the motion of each group of test particles in a high-speed rotating flow field, and the simulated wear depth on the surface of the specimen was calculated using the standard Oka model, including: The solid-liquid two-phase flow model based on the Eulerian-Lagrange method was constructed based on CFD simulation. The velocity inlet boundary conditions and pressure outlet boundary conditions of the solid-liquid two-phase flow model were set, and the MRF rotating model was introduced to simulate the high-speed rotating flow field. The continuous phase of the solid-liquid two-phase flow model is calculated based on the SSTk-ω turbulence model for single-phase steady-state calculation, and the standard Oka model is embedded after the continuous phase flow field calculation converges. The discrete phase of the solid-liquid two-phase flow model is based on the DPM model to track the particle motion trajectory. According to the tracked particle motion trajectory, the embedded standard Oka model is used to discretely calculate the surface wear rate of the specimen under multiple different test conditions to obtain the simulated wear depth of the specimen surface.

4. The CFD wear model correction method based on high-speed rotating disk test according to claim 3, characterized in that, Based on the tracked particle motion trajectory, the embedded standard Oka model is used to discretize the surface wear of the specimen under multiple sets of different test conditions to obtain the simulated wear depth of the specimen surface, including: The particle motion data is calculated based on the tracked particle trajectory. The function expression for calculating the particle motion data is: In the formula, For the mass of the particles, The rate of change of particle velocity over time. The velocity vector of the particle. For the particle motion time, The drag force generated when a body moves relative to a particle. The pressure gradient force in the flow field. This refers to the virtual mass force added during particle acceleration. The force is the turbulent diffusion effect caused by fluid turbulence fluctuations; Based on the particle motion data obtained from the calculation, the average wear rate at each location on the surface of the specimen under each set of test conditions was discretely calculated using the embedded standard Oka model. The simulated wear depth on the surface of the specimen is calculated based on the average wear rate at various locations on the specimen surface.

5. The CFD wear model correction method based on high-speed rotating disk test according to claim 4, characterized in that, The step of obtaining the particle hardness correction factor by fitting the simulated wear depth and the experimental wear depth on the specimen surface includes: Obtain the hardness parameters of the test particles and the specimen, and establish a wear depth relationship model based on the hardness parameters of the test particles and the specimen. The test wear depth and simulated wear depth of the specimen surface under each test condition were substituted into the wear depth relationship model for multiple linear regression analysis to obtain a particle hardness correction factor that includes linear correlation parameters of particle hardness ratio and exponential correlation parameters.

6. The CFD wear model correction method based on high-speed rotating disk test according to claim 5, characterized in that, The CFD wear model correction method also includes a method for correcting the wear rate output by the standard Oka model using a particle hardness correction factor, including: The particle hardness correction factor is used to correct the particle size of the specimen wall hardness. Combined with the average wear rate of the specimen surface calculated using the standard Oka model, the particle hardness-corrected specimen wear rate is obtained. The functional expression for the particle hardness correction of the specimen wear rate is: In the formula, The wear rate of the specimen after particle hardness correction. The hardness of the specimen wall. This is a linear correlation parameter representing the proportion of particle hardness. This is an index-related parameter representing the percentage of particle hardness. The wear rate of the specimen is calculated using the standard Oka model.

7. The CFD wear model correction method based on high-speed rotating disk test according to claim 6, characterized in that, The process involves introducing the material's yield strength and coupling it with the material strength influence coefficient through the test wear depth on the specimen surface to obtain a yield strength correction factor, including... Based on the wear rate of the specimen after correction for particle hardness, a correlation model between the wear rate and the material yield strength is established by introducing the material yield strength. The functional expression of the correlation model is as follows: In the formula, The wear rate is represented at different locations on the surface of the specimen. Based on the established correlation model, the test wear depth of the specimen surface under each test condition is substituted into the correlation model to couple the material strength influence coefficient, and the material strength influence coefficient value is determined. The yield strength correction factor is obtained by correcting the yield strength of the material based on the determined material strength influence coefficient value.

8. A model correction system, employing the CFD wear model correction method based on high-speed rotating disk testing as described in any one of claims 1-7, characterized in that, The system includes: The test wear depth acquisition module is used to screen multiple groups of test particles with different hardness to form test conditions, and to obtain the test wear depth of the specimen surface under different test conditions based on the high-speed rotating disk flow wear test measurement. The simulated wear depth acquisition module is used to construct a solid-liquid two-phase flow model based on CFD simulation to simulate the motion of each group of test particles in a high-speed rotating flow field, and to calculate the simulated wear depth on the surface of the specimen using the standard Oka model. The particle hardness correction factor acquisition module is used to obtain the particle hardness correction factor by fitting the simulated wear depth and the experimental wear depth on the surface of the specimen. The yield strength correction factor acquisition module is used to introduce the material yield strength and couple the material strength influence coefficient with the test wear depth on the specimen surface to obtain the yield strength correction factor. The Oka model correction module is used to reconstruct the standard Oka model by combining the particle hardness correction factor and the yield strength correction factor to obtain the corrected Oka model.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the CFD wear model correction method based on high-speed rotating disk test as described in any one of claims 1-7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the CFD wear model correction method based on high-speed rotating disk test as described in any one of claims 1-7.

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