Water turbine anti-wear design method and system for multiphase flow simulation control of sand flow separation

Abstract

The invention discloses a water turbine anti-wear design method and system for controlling sand flow separation through multiphase flow simulation, and belongs to the field of hydraulic machinery design and anti-wear. The method comprises the steps that a parameterized geometric model and a water-sand physical property parameter library are established, computational domain grids are divided, boundary conditions are set, a water-sand multiphase flow control equation is established and solved, and the water-sand multiphase flow control equation is obtained. A wear model is implanted to calculate wall surface wear distribution, a sand grain track and a flow field structure are analyzed to identify a wear mechanism, wear-resistant optimization design is carried out based on a sand grain control concept, and design iteration and effect evaluation are carried out. The method can accurately predict the wear distribution of the flow passage component of the water turbine, reduces the wear of a key area by actively guiding a sand flow path, improves the wear resistance and service life of the water turbine, reduces the sand wear from the source, remarkably prolongs the operation cycle of the water turbine while guaranteeing the hydraulic efficiency of the water turbine, and improves the working efficiency of the water turbine. And the method has important engineering practical value and popularization prospect.

Description

Technical Field

[0001] This invention belongs to the field of hydraulic machinery design and wear resistance technology, specifically relating to a wear resistance design method and system for a water turbine with multiphase flow simulation control of sand particle flow separation. Background Technology

[0002] Hydropower turbines, especially mixed-flow and axial-flow turbines operating on rivers with high sediment loads, generally face severe sediment abrasion problems. The high-speed impact and scouring of sediment particles on the surfaces of flow components such as runners and guide vanes leads to material loss, reduced efficiency, deterioration of operational stability, and shortened overhaul cycles, resulting in huge economic losses.

[0003] Existing anti-wear measures mainly focus on two aspects: First, using anti-wear materials or surface coatings, such as high-chromium cast iron and tungsten carbide coatings. This is a passive protection method, which is costly and has limited effectiveness. Second, indirectly reducing wear through hydraulic design optimization. However, traditional design methods mainly focus on energy conversion efficiency and lack precise control over the movement of solid particles. The optimization direction is unclear and relies heavily on experience and experiments, resulting in long cycles, high costs, and limited universality.

[0004] The development of computational fluid dynamics (CFD) technology has provided a powerful tool for studying the internal flow of hydraulic turbines. However, conventional single-phase flow simulations cannot capture the motion characteristics of sediment particles. Although some studies have introduced multiphase flow models, their applications are mostly limited to wear prediction, and a systematic anti-wear design method based on the core idea of ​​actively controlling the flow and separation of sediment particles has not yet been formed. Therefore, there is an urgent need for an innovative design method that can guide sediment particles to avoid critical areas from the perspective of the flow field, thereby reducing wear at its source. Summary of the Invention

[0005] The problem to be solved by this invention is to provide a multiphase flow simulation control method and system for anti-wear design of water turbines. Through numerical simulation, the method can deeply understand and actively control the two-phase flow field of water and sand, aiming to design a water turbine flow channel that can guide the flow of sand particles along a low-wear path, thereby fundamentally reducing the wear of sediment.

[0006] This invention adopts the following technical solution: a method for anti-wear design of a water turbine with multiphase flow simulation control of sand particle flow separation, comprising the following steps:

[0007] S1. Establish a parametric geometric model and a water and sediment physical property parameter library: Construct a three-dimensional parametric geometric model of the entire flow channel of the turbine, including the volute, guide vanes, runner and tailrace, and define the water phase parameters and sediment phase parameters.

[0008] S2. Computational domain mesh generation and boundary condition setting: The computational domain of the parametric geometric model is meshed, and the boundary layer mesh is refined in the guide vane and impeller blade wall areas. The inlet, outlet and wall boundary conditions of the computational domain are set.

[0009] S3. Construct and solve the control equations for multiphase flow of water and sediment: Construct the control equations for two-phase flow of water and sediment based on the Euler-Lagrange framework and solve them numerically. Treat the water phase as a continuous medium and use the Reynolds-averaged Navier-Stokes equations and the SST k-ω turbulence model. Treat the sand phase as a discrete phase and use the discrete phase model to track the particle motion trajectory.

[0010] S4. Implant the wear model and calculate the wall wear distribution: Implant the wear model as a user-defined function into the CFD solver to calculate the cumulative wear rate of each region of the wall and generate a wear cloud map of the surface of the turbine flow components.

[0011] S5. Analyze sand particle trajectory and flow field structure: Identify wear mechanism, analyze sand particle motion trajectory, spatial concentration distribution, and water flow velocity field, pressure field and vorticity field through post-processing, and identify high wear areas by combining wear cloud map and analyze the fluid dynamics root cause.

[0012] S6. Anti-wear optimization design based on sand particle control concept: Modify the parametric geometric model according to the mechanism analysis, with the goal of reducing the particle collision energy and frequency in the high wear area, optimize the impeller blade profile, guide vane airfoil and installation angle, or add non-smooth surface structure and guide groove;

[0013] S7. Design Iteration and Effect Evaluation: Repeat steps S2 to S5 on the optimized parametric geometric model to perform multiphase flow simulation and wear analysis. Compare the maximum wear depth and average wear rate of the key areas before and after optimization, and repeat the iteration until the wear index meets the design requirements.

[0014] As a preferred embodiment, the aqueous phase parameters include density and dynamic viscosity, and the sand phase parameters include particle size distribution, particle density, inflow volume concentration, and shape factor.

[0015] The parametric geometric model supports dynamic adjustment of the geometric dimensions of the volute, guide vanes, impeller, and tailrace pipe, facilitating subsequent optimization design.

[0016] As a preferred embodiment, the meshing adopts unstructured meshing technology, and a boundary layer mesh is set in the near-wall region to ensure that the Y+ value of the wall meets the requirements of the turbulence model;

[0017] The inlet conditions include the incoming flow velocity, turbulence intensity, and sand particle injection conditions. The outlet conditions are set as either a pressure outlet or a free outflow, and the wall conditions use a no-slip boundary.

[0018] As a preferred embodiment, the aqueous phase is used as a continuous medium, and its governing equations are time-averaged Reynolds-averaged Navier-Stokes equations, which are closed using the SST k-ω turbulence model.

[0019] The sand phase is a discrete phase. The discrete phase model is used to track the motion trajectory of a large number of sand particles in the flow field. The forces on the particles include fluid drag force, gravity, buoyancy and pressure gradient force.

[0020] Furthermore, a two-way coupling method was used to calculate the momentum and energy exchange between the water phase and the sand phase.

[0021] As a preferred embodiment, the wear model includes, but is not limited to, the Finnie micro-cutting wear model or the Tabakoff impact wear model. The wear model calculates the cumulative wear rate of each region of the wall based on the velocity, angle and frequency of sand particles colliding with the wall.

[0022] The wear cloud map is generated by post-processing software based on the cumulative wear rate of each region. It displays the wear depth distribution with a color gradient to intuitively identify high wear areas.

[0023] As a preferred option, wear-resistant optimization design is carried out based on the concept of sand particle control. The optimization design variables include:

[0024] The rotor blade profile, by adjusting the blade placement angle, inlet angle and thickness distribution, uses centrifugal force to guide sand particles to the outside of the flow channel or a specific low-wear path;

[0025] The airfoil and installation angle of the fixed or movable guide vanes create a pre-placed vortex upstream, generating a Coriolis force effect to assist in sand particle separation.

[0026] Non-smooth surface structures or flow channels are added to the volute or guide vane area to guide the movement of sand particles in the near-wall area.

[0027] The present invention also provides: a multiphase flow simulation-controlled sand particle flow separation turbine anti-wear system for implementing the aforementioned turbine anti-wear design method, comprising:

[0028] The parametric modeling module is used to establish a parametric geometric model and a library of water and sediment physical properties. It constructs a three-dimensional parametric geometric model of the entire flow channel of the turbine, including the volute, guide vanes, runner, and tailrace, and defines the physical properties of the water phase and the sediment phase.

[0029] The mesh generation and boundary setting module is used to mesh the computational domain of the parametric geometric model, refine the boundary layer mesh in the guide vane and impeller blade wall areas, and set the inlet, outlet and wall boundary conditions of the computational domain.

[0030] The multiphase flow solution module is used to construct and numerically solve the governing equations for water-sediment two-phase flow based on the Euler-Lagrange framework. The water phase is treated as a continuous medium, and the time-averaged Reynolds-averaged Navier-Stokes equations are used as the governing equations for the water phase, with the SST k-ω turbulence model used for closure. The sand phase is treated as a discrete phase, and the particle motion trajectory is tracked through the discrete phase model. The fluid drag force, gravity, buoyancy and pressure gradient force are considered, and the momentum and energy exchange between the water phase and the sand phase is calculated using a two-way coupling method.

[0031] The wear calculation module is used to embed the wear model as a user-defined function into the CFD solver, calculate the cumulative wear rate of each region of the wall based on the velocity, angle and frequency of sand particles colliding with the wall, and generate wear cloud maps of the surface of the turbine flow components.

[0032] The mechanism analysis module is used to analyze the sand particle movement trajectory, spatial concentration distribution, and water flow velocity field, pressure field, and vorticity field through post-processing, and to identify high wear areas and analyze the fluid dynamics root causes by combining wear cloud maps.

[0033] The optimization design module is used to modify the parametric geometric model based on mechanism analysis, with the goal of reducing the energy and frequency of particle collisions in high-wear areas, optimizing the impeller blade profile, guide vane airfoil and installation angle, or adding non-smooth surface structures and guide grooves;

[0034] The iterative evaluation module is used to repeatedly perform multiphase flow simulation and wear analysis on the optimized model, compare the maximum wear depth and average wear rate of key areas before and after optimization, and repeat the iteration until the wear index meets the design requirements.

[0035] The present invention also provides: an electronic device, comprising:

[0036] One or more processors;

[0037] A storage device on which one or more programs are stored;

[0038] When the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned anti-wear design method for water turbines.

[0039] The present invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in the aforementioned anti-wear design method for water turbines.

[0040] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0041] 1. The anti-wear design method for water turbines of the present invention uses numerical simulation to deeply understand and actively control the two-phase flow field of water and sand, which can accurately predict the wear distribution of the flow components of the water turbine, and reduce the wear in key areas by actively guiding the flow path of sand particles, thereby improving the anti-wear performance and service life of the water turbine.

[0042] 2. The anti-wear design method for water turbines of the present invention can guide sand particles to avoid critical areas from the essence of the flow field, thereby reducing the wear of sediment from the source, ensuring the hydraulic efficiency of the water turbine while significantly extending its operating cycle, and has important engineering practical value and promotion prospects. Attached Figure Description

[0043] Figure 1 This is the overall flowchart of the anti-wear design method for water turbines of the present invention;

[0044] Figure 2 This is a wear contour map of the turbine runner blades before optimization in the embodiment.

[0045] Figure 3 This is a wear contour map of the turbine runner blades after optimization in the embodiment;

[0046] Figure 4 This is a schematic diagram comparing the movement trajectories of sand particles within the rotor before and after optimization in the embodiment. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the application will be further described in detail below with reference to the accompanying drawings. The described embodiments are only a part of the embodiments involved in this invention. All non-innovative embodiments based on these embodiments by other researchers in the art are within the protection scope of this invention. Furthermore, the step numbers in the embodiments of this invention are only set for ease of explanation and do not limit the order of the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0048] Example 1

[0049] A method for anti-wear design of hydraulic turbines using multiphase flow simulation to control sand particle flow separation is provided, such as... Figure 1 As shown, the specific steps include the following:

[0050] S1. Establish a parametric geometric model and a water and sediment physical property parameter library. Construct a three-dimensional parametric geometric model of the entire flow channel of the turbine, including the volute, guide vanes, runner and tailrace, and define the physical property parameters of the water phase and the sediment phase.

[0051] In some implementations, establishing a parametric geometric model and a water and sand physical property parameter library is the foundation of the entire wear-resistant design method of this invention. The aim is to construct a three-dimensional parametric model of the entire flow channel of the turbine and define the physical properties of the water phase and sand phase, so as to provide accurate geometric and physical property inputs for subsequent multiphase flow simulation.

[0052] Specifically, in this embodiment, the parametric geometric model is constructed using CAD software (such as SolidWorks, CATIA, etc.) or parametric modeling tools (such as ANSYS DesignModeler), which supports dynamic adjustment of the geometric dimensions of the volute, guide vanes, impeller, and tailrace pipe.

[0053] The model accuracy must meet the requirements of CFD calculations, with detailed processing required for critical components such as the blade leading edge and the tailrace cone. Aqueous phase properties include density (typically 998.2 kg / m³) and dynamic viscosity (typically 0.001003 Pa·s), while sand phase properties include particle size distribution (e.g., median particle size 0.05 mm), particle density (typically 2650 kg / m³), influent volumetric concentration (e.g., 5%), and shape factor (e.g., sphericity 0.7-0.9). All parameters are stored in a database, allowing for quick retrieval and modification.

[0054] This step lays a solid foundation for multiphase flow simulation by establishing an accurate geometric model and a library of physical property parameters, ensuring the reliability and accuracy of the simulation results. It is widely applicable to the anti-wear design of various types of turbines, such as mixed-flow and axial-flow turbines. Through parametric modeling, the system can flexibly adjust the geometric structure, providing convenience for subsequent optimization design.

[0055] Furthermore, S1 includes:

[0056] S11. The aqueous phase parameters include density and dynamic viscosity, and the sand phase parameters include particle size distribution, particle density, inflow volume concentration, and shape factor.

[0057] In some implementations, water phase parameters and sand phase parameters are defined. By accurately setting the physical properties of the two phases, it can be ensured that the simulation results can truly reflect the actual flow and wear characteristics.

[0058] Specifically, in this embodiment, the aqueous phase density is set to 998.2 kg / m³ (room temperature clean water), and the dynamic viscosity is set to 0.001003 Pa·s. Among the grain phase parameters, the particle size distribution is described using a Rosin-Rammler distribution or a log-normal distribution, with the median particle size typically ranging from 0.01 mm to 0.5 mm; the particle density is set to 2650 kg / m³ (quartz sand) or a similar value based on the sediment composition; the inflow volume concentration is set according to the river's sediment content, with a common range of 1% to 10%; the shape factor is used to describe the degree of particle non-spherical shape, typically taking a value of 0.7 to 0.9, where 1.0 represents a perfect sphere.

[0059] This step, by refining the definition of physical property parameters, improves the accuracy of multiphase flow simulation, providing reliable input for subsequent wear calculations and optimization design. It is widely applicable to the wear-resistant design of turbines in rivers with high sediment loads. By accurately setting the physical property parameters, the system can simulate the wear conditions of turbines under different sediment conditions, providing a basis for optimization design.

[0060] S12. The parametric geometric model supports dynamic adjustment of the geometric dimensions of the volute, guide vanes, impeller, and tailrace pipe, which facilitates subsequent optimization design.

[0061] In some implementations, parameterized driving of geometric changes allows designers to quickly modify key dimensions, thereby exploring the impact of different geometric configurations on the sand grain flow path.

[0062] Specifically, in this embodiment, the parametric model uses characteristic parameters (such as the volute wrap angle, guide vane placement angle, and runner blade inlet angle) to control the geometry. For example, the runner blade profile can be defined using B-spline curves or NURBS surfaces, with control point coordinates used as design variables. The system supports parametric scripts (such as Python and JavaScript) or a graphical interface for operation, and the model updates automatically after adjustments. The dynamic adjustment range must cover the turbine design specifications, such as a runner diameter adjustment range of ±10% and a blade angle adjustment range of ±5°.

[0063] This step, through parametric modeling, significantly improves design efficiency, supports rapid iterative optimization, and provides technical assurance for finding the optimal wear-resistant design scheme. It is widely used in hydraulic design and wear-resistant optimization of water turbines. Designers can quickly generate multiple geometric schemes and evaluate their wear-resistant performance by adjusting parameters.

[0064] S2, Computational domain mesh generation and boundary condition setting: The computational domain of the parameterized geometric model is meshed, and the boundary layer mesh is refined in the guide vane and impeller blade wall areas. The inlet, outlet and wall boundary conditions of the computational domain are set.

[0065] In some implementations, a stable and accurate computational environment can be provided for numerical solutions by generating high-quality computational grids and setting reasonable boundary conditions.

[0066] Specifically, in this embodiment, unstructured meshing technology is used for mesh generation, employing tools such as ICEM CFD or ANSYS Meshing to generate a tetrahedral / hexahedral hybrid mesh. Boundary layer refinement is applied to key wall regions such as guide vanes and turbine blades. The height of the first mesh layer is determined based on the Y+ value (typically Y+≈30-100), with 5-10 layers typically used, and a growth ratio of 1.2. The total mesh size is controlled between 1 million and 10 million elements based on the model complexity.

[0067] Boundary condition settings: The inlet is set as a velocity inlet or a mass flow inlet, with given incoming flow velocity (e.g., 10 m / s), turbulence intensity (e.g., 5%), and sand particle incident conditions (e.g., uniform distribution); the outlet is set as a pressure outlet or a free outflow; the wall uses a no-slip boundary, and the sand particle collision model is set as reflection or trap.

[0068] This step, through mesh generation and boundary setting, provides a reliable computational foundation for solving multiphase flow, ensuring the accuracy and stability of the simulation results, and is widely applicable to the simulation of internal flow in water turbines. With fine meshing and reasonable boundary conditions, the system can accurately capture near-wall flow and sand particle movement behavior.

[0069] Furthermore, S2 includes:

[0070] S21. The meshing adopts unstructured meshing technology, and a boundary layer mesh is set in the near-wall region to ensure that the Y+ value of the wall is within a reasonable range.

[0071] In some implementations, ensuring that the wall Y+ value meets the requirements of the turbulence model is a key technical measure to guarantee the accuracy of turbulence simulation, which can accurately analyze the boundary layer flow.

[0072] Specifically, in this embodiment, the unstructured mesh is generated using Delaunay triangulation or the leading edge method, with tetrahedral elements predominating. Local refinement is applied in regions with large flow gradients. The boundary layer mesh uses prism or pyramidal elements. The height of the first layer is calculated based on the Y+ value. For the SST k-ω model, the Y+ value is recommended to be controlled within 1-5 (suitable for low Reynolds number simulations) or 30-100 (suitable for wall function methods). The number of boundary layer layers is typically 5-15, with a growth ratio of 1.1-1.3. Mesh quality indicators include skewness <0.8 and aspect ratio <5:1.

[0073] This step, through refined mesh generation, improves the accuracy of flow simulation and provides reliable data for wear calculation, and is widely used in CFD analysis of hydraulic machinery such as turbines and pumps. With a high-quality mesh, the system can accurately simulate near-wall turbulence and sand particle collision processes.

[0074] S22. The inlet conditions include the incoming flow velocity, turbulence intensity, and sand particle injection conditions. The outlet conditions are set as pressure outlet or free outflow, and the wall conditions adopt a no-slip boundary.

[0075] In some implementations, by setting appropriate boundary conditions, it can be ensured that the simulation results are consistent with the actual operating conditions.

[0076] Specifically, in this embodiment, the inlet flow velocity is set according to the turbine design conditions, such as 10-20 m / s; the turbulence intensity is set to 2%-10% based on the inlet flow conditions; the sand particle incident conditions include the incident velocity (usually the same as the water velocity), the incident position (uniform distribution or a specific area), and the particle size distribution. The outlet conditions are usually set as a pressure outlet, with static pressure referenced to atmospheric pressure; or free outflow, suitable for when the outlet flow is fully developed. The wall conditions adopt a no-slip boundary, and the sand particle collision model is set as elastic reflection, plastic deformation, or trapping based on the material properties.

[0077] This step, through standardized boundary condition settings, improves the reliability and comparability of simulation results, providing accurate input for optimized design. It is widely applicable to flow simulation of equipment such as water turbines, pumps, and valves. With accurate boundary conditions, the system can realistically reflect the flow and wear characteristics during actual operation.

[0078] S3. Construct and solve the multiphase flow control equations for water and sediment. Construct the two-phase flow control equations for water and sediment based on the Euler-Lagrange framework and solve them numerically. The water phase is used as a continuous medium and the Reynolds-averaged Navier-Stokes equations and the SSTk-ω turbulence model are adopted. The sand phase is used as a discrete phase and the particle motion trajectory is tracked through the discrete phase model.

[0079] In some implementations, this step is based on the Eulerian-Lagrange framework, which handles the continuous phase (water) and the discrete phase (sand) separately, and obtains detailed information on the flow field and particle trajectory by numerically solving the governing equations.

[0080] Specifically, in this embodiment, the governing equations for the aqueous phase are Reynolds-averaged Navier-Stokes equations, including mass and momentum conservation equations, and closed using the SST k-ω turbulence model. This model combines the advantages of the k-ε and k-ω models and is suitable for adverse pressure gradient flows and separated flows. The sand phase is tracked using a discrete phase model, solving for particle motion equations, considering fluid drag, gravity, buoyancy, pressure gradient forces, etc. A two-way coupling method is used to calculate the momentum exchange between the aqueous and sand phases (e.g., through the source term). The numerical solution uses the finite volume method, with a discretization scheme such as second-order upwind. The pressure-velocity coupling uses the SIMPLE algorithm, with a convergence criterion of a residual decrease of 3-4 orders of magnitude.

[0081] This step, by constructing and solving the multiphase flow governing equations, provides detailed flow field and particle behavior data, laying the foundation for wear calculation and optimization design. It is widely applicable to multiphase flow analysis of hydraulic machinery such as turbines and pumps. Through high-precision solutions, the system can accurately predict sand particle trajectories and concentration distributions.

[0082] Furthermore, S3 includes:

[0083] S31. The water phase control equation adopts the Reynolds-averaged Navier-Stokes equation after time-averaging, and is closed using the SST k-ω turbulence model.

[0084] In some implementations, reliable flow field data can be obtained by solving the time-averaged Navier-Stokes equations and combining them with the SST k-ω model to accurately predict turbulence characteristics.

[0085] Specifically, in this embodiment, the Reynolds-averaged Navier-Stokes equations include the continuity equation and the momentum equation. After time-averaging, a Reynolds stress term is introduced, and the Boussinesq assumption is used to correlate it with the mean velocity gradient. The SST k-ω model combines the advantages of the standard k-ω model in the near-wall region and the stability of the k-ε model in the far field by solving the transport equations for turbulent kinetic energy k and specific dissipation rate ω. Model constants are set according to standards, such as σ. k1 =1.176, σ ω1 =2.0. The numerical solution uses a coupled solver, the discretization scheme is second-order upwind, and the convergence criterion is residual <1e-4.

[0086] This step provides accurate flow field information through high-precision turbulence simulation, laying the foundation for sand particle trajectory calculation and wear analysis. It is widely used in flow simulation of rotating machinery such as water turbines and pumps. Using the SST k-ω model, the system can accurately capture flow separation and vortex structures.

[0087] S32. The sand phase is tracked by a discrete phase model to track the motion trajectory of a large number of sand particles in the flow field. The forces on the particles include fluid drag force, gravity, buoyancy and pressure gradient force. The momentum and energy exchange between the water phase and the sand phase is calculated by a two-way coupling method.

[0088] In some implementations, by solving the particle motion equations and considering interphase coupling, it can be ensured that the simulation results truly reflect the interaction between the two phases.

[0089] Specifically, in this embodiment, the discrete phase model solves the particle motion equations: m p du p / dt = F D + F G + F B +F P , where F D For the drag force (using the Schiller-Naumann model), F G For gravity, F B For buoyancy, F P The pressure gradient force is used. Particle tracking employs a Lagrangian framework, calculating a large number of particle trajectories (typically 10^5-10^6). Bidirectional coupling is achieved by updating the interphase momentum source term in each iteration step, ensuring that the aqueous and sand phases influence each other. Calculation parameters such as the time step are set according to the flow timescale, typically 1e-4 s to 1e-3 s.

[0090] This step, through precise particle tracking and interphase coupling, provides reliable sand particle motion data, offering crucial input for wear calculations and is widely applicable to two-phase water-sand flow simulations. By employing a discrete phase model and bidirectional coupling, the system can accurately predict sand particle distribution and collision characteristics.

[0091] S4. Implant the wear model and calculate the wall wear distribution. Implant the Finnie micro-cutting wear model or Tabakoff impact wear model as a user-defined function into the CFD solver. Calculate the cumulative wear rate of each region of the wall based on the velocity, angle and frequency of sand particles colliding with the wall, and generate a wear cloud map of the surface of the turbine flow components.

[0092] In some implementations, embedding a wear model and calculating the wall wear distribution transforms the multiphase flow simulation results into wear predictions. By embedding an empirical wear model, the wall wear rate is calculated based on sand particle collision parameters and visualized in the form of a cloud map, which facilitates the identification of high-wear areas.

[0093] Specifically, in this embodiment, the wear model adopts the Finnie micro-cutting model or the Tabakoff impact model, and is embedded into a CFD solver (such as Fluent or CFX) through a user-defined function (UDF). The Finnie model is suitable for cutting-type wear, and the calculation formula is as follows: ;

[0094] in, For θ≤18.5°, for θ>18.5°.

[0095] The Tabakoff model is applicable to impact wear, taking into account the particle rebound effect. Calculations are based on sand particle collision data (velocity, angle, frequency), and the cumulative wear rate is obtained by summation. Wear contour maps are generated using post-processing software (such as CFD-Post), displaying wear depth using color gradients.

[0096] This step, by embedding a wear model, combines flow simulation with wear prediction, providing a quantitative wear assessment and guiding optimized design. It is widely applicable to wear assessment of equipment such as water turbines and pipelines. Wear contour maps allow designers to visually identify wear hotspots.

[0097] Furthermore, S4 includes:

[0098] S41. The wear model is based on the velocity, angle, and frequency of sand particles colliding with the wall surface, and calculates the cumulative wear rate of each region of the wall surface according to the following formula: ;

[0099] in, For wear depth, The mass flow rate of sand particles is calculated using the sand particle flow rate and collision frequency. and For example, for steel, K = 2e-9, n = 2.5; The collision velocity of sand particles. The angle at which the sand grains collide. and Extracted from particle trajectory; The function is related to the collision angle and is selected based on the model (such as the Finnie or Tabakoff function). To calculate the cell area, the calculation is performed on each wall cell, accumulating the contributions of all colliding particles. Parameter values ​​need to be calibrated based on experimental data to ensure prediction accuracy.

[0100] In some implementations, the wear model is based on the velocity, angle, and frequency of sand particles colliding with the wall, and the cumulative wear rate is calculated according to a formula. This is the core technology for achieving quantitative prediction of wear. This step transforms the sand particle collision parameters into wear depth through mathematical modeling, providing quantitative indicators for wear-resistant design.

[0101] This step provides reliable wear predictions through an accurate wear model, supporting targeted optimization design and is widely applicable to wear analysis of hydraulic machinery. By calculating the wear rate, the system can assess the severity of wear in different areas.

[0102] S42. The wear cloud map is generated by post-processing software and displays the wear depth distribution with color gradient, which makes it easy to intuitively identify high wear areas.

[0103] In some implementations, converting numerical calculation results into intuitive graphics can help designers quickly identify areas of high wear.

[0104] Specifically, in this embodiment, the wear contour map is generated using CFD post-processing tools (such as ANSYS CFD-Post and ParaView), mapping the wear depth data onto the geometric surface and representing it with color gradients (e.g., blue indicates low wear, and red indicates high wear). Thresholds can be set for the contour map to highlight areas where wear exceeds the limit. Multi-view comparison is supported, such as comparing contour maps before and after optimization. Output formats include images and animations.

[0105] This step utilizes visualization technology to improve the interpretability of wear data, enhance the efficiency of design decisions, and is widely applicable to engineering design and reporting. Wear cloud maps allow operators to intuitively understand wear distribution, guiding maintenance and optimization.

[0106] S5. Analyze the sand particle trajectory and flow field structure to identify the wear mechanism. Through post-processing, analyze the sand particle motion trajectory, spatial concentration distribution, and water flow velocity field, pressure field, and vorticity field. Combine the wear cloud map to identify high wear areas and analyze the fluid dynamics root cause.

[0107] In some implementations, by synthesizing post-processing and correlating sand particle behavior with flow field characteristics, the fluid dynamic mechanism of high wear formation can be revealed.

[0108] Specifically, in this embodiment, the post-processing analysis includes: a sand particle trajectory map showing the particle movement path, a concentration cloud map showing the spatial distribution of sand particles, and a flow field map showing velocity, pressure, vorticity, etc. Combined with the wear cloud map, high-wear regions (such as the blade leading edge and suction surface) are identified, and the flow field characteristics (such as high-speed regions, low-pressure regions, and vortices) are analyzed to determine how they lead to sand particle aggregation and collisions. For example, vortices may carry sand particles to impact walls, and high-speed flow may increase collision energy. Analysis tools include CFD post-processing software and custom scripts.

[0109] This step, through in-depth data analysis, reveals the flow mechanism of wear, providing a theoretical basis for actively controlling the path of sand particles. It is widely applicable to fault diagnosis of equipment such as water turbines and pumps. Through mechanism analysis, designers can understand the causes of wear and guide optimization.

[0110] S6 is designed for wear resistance based on the concept of sand control. The parametric geometric model is modified according to the mechanism analysis to reduce the energy and frequency of particle collisions in high wear areas. The blade profile, airfoil and installation angle of the impeller are optimized, or non-smooth surface structures and guide grooves are added.

[0111] In some implementations, wear-resistant optimization design is carried out based on the concept of sand control. By adjusting the geometric parameters, sand particles can be guided away from high-wear areas, thereby reducing wear at the source.

[0112] Specifically, in this embodiment, the optimization design includes: optimizing the impeller blade profile, such as adjusting the placement angle, inlet angle, and thickness distribution to use centrifugal force to throw sand particles to the outside of the flow channel; optimizing the guide vanes, such as modifying the airfoil or installation angle to generate pre-swirling flow and utilize Coriolis force to assist separation; and adding non-smooth surfaces (such as pits or ribs) or guide channels to change the near-wall flow and deflect sand particles. The optimization objective is to reduce the collision velocity and frequency in high-wear areas while ensuring hydraulic efficiency. Design variables are adjusted through a parametric model, and the optimization method can employ response surface methodology or a genetic algorithm.

[0113] This step, through geometric optimization, achieves a shift from passive protection to active control, improving the wear resistance of the turbine and making it widely applicable to wear-resistant improvements in turbines. Through active flow control, the system can significantly reduce the risk of wear.

[0114] Furthermore, S6 includes:

[0115] S61. The optimization of the impeller blade profile includes adjusting the blade placement angle, inlet angle and thickness distribution, and using the centrifugal force field to guide sand particles to the outside of the flow channel or a specific low-wear path.

[0116] In some implementations, by modifying the blade geometry to enhance the centrifugal force effect, sand particles can be directed to areas of low wear.

[0117] Specifically, in this embodiment, the blade placement angle is typically adjusted within ±5°, the inlet angle within ±3°, and the thickness distribution is controlled using spline curves. After optimization, the curvature of the blade working surface increases, while the curvature of the suction surface decreases, causing sand particles to move towards the outer side of the flow channel (lower ring side) under centrifugal force. The design must ensure that the hydraulic efficiency does not decrease significantly, such as an efficiency loss of <0.5%. The optimization method combines CFD simulation and experimental verification.

[0118] Furthermore, the wear contour map of the turbine runner blades before optimization in this embodiment is as follows: Figure 2 As shown, the optimized wear contour map of the turbine runner blades is as follows: Figure 3 As shown.

[0119] This step, through targeted geometric modifications, enables the active guidance of sand particles, reducing wear in critical areas, and is widely used in the wear-resistant design of mixed-flow and axial-flow turbines. Through blade optimization, the system can effectively reduce wear on the suction surface.

[0120] S62, the guide vane optimization includes adjusting the airfoil and installation angle of the fixed or movable guide vane, pre-setting a vortex upstream, and generating a Coriolis force effect to assist in sand particle separation.

[0121] In some implementations, a pre-swirling flow is generated through guide vane design, which uses the Coriolis force to push sand particles to the outside of the flow channel, thus reducing the impact on the impeller.

[0122] Specifically, in this embodiment, the guide vane airfoil adopts the NACA series or a custom airfoil, with an installation angle adjustment range of ±5°. The pre-swirl intensity is controlled by the guide vane angle, ensuring the water flow has a tangential velocity component before entering the impeller. The Coriolis force acts on the sand particles, causing them to move towards the pressure surface. Optimization requires balancing the swirl intensity and hydraulic losses to ensure stable efficiency.

[0123] This step, through pre-set swirl, enhances sand control and improves overall wear resistance, making it widely applicable to multi-stage turbines or pumping stations. Through guide vane optimization, the system can achieve sand separation upstream.

[0124] S63. The non-smooth surface structure or guide channel is set in the volute or guide vane area to guide the movement of sand particles in the near-wall area and reduce the direct impact of sand particles on key areas.

[0125] In some implementations, the non-smooth surface structure or flow channel is set in the volute or guide vane region, which is an effective means of changing the near-wall flow to control the movement of sand particles. By modifying the surface, the movement of near-wall sand particles can be guided, and direct impact on critical areas can be avoided.

[0126] Specifically, in this embodiment, the non-smooth surfaces include pits, protrusions, ribs, etc., with dimensions comparable to the sand grain size (e.g., 0.1-1 mm), and are arranged either in a co-current or staggered manner. The guide channels are shallow channel structures, oriented at a certain angle to the mainstream, guiding the sand grains along the channels. These structures alter the boundary layer flow, generating secondary flows or low-pressure zones, thus deflecting the sand grain trajectory. The design must consider fabrication feasibility and flow losses.

[0127] Furthermore, this embodiment compares the trajectory of sand particles within the rotor before and after optimization, such as... Figure 4 As shown, Figure 4 (a) shows the trajectory of the sand grains within the impeller before the improvement. Figure 4(b) in the figure shows the improved trajectory of the sand grains within the impeller.

[0128] This step, through surface structure design, provides additional means of sand control, enhances the flexibility of wear-resistant design, and is widely applicable to the surface treatment of turbine flow components. By using non-smooth surfaces or guide channels, the system can reduce localized wear.

[0129] S7. Design Iteration and Effect Evaluation: Repeat steps S2 to S5 on the optimized model to perform multiphase flow simulation and wear analysis. Compare the maximum wear depth and average wear rate of key areas before and after optimization, and repeat the iteration until the wear index meets the design requirements.

[0130] In some implementations, design iteration and performance evaluation are key steps to ensure that wear resistance optimization achieves the expected goals. This step involves multiple simulation-optimization cycles to progressively improve the design until the wear indicators meet the requirements.

[0131] Specifically, in this embodiment, the iterative process is as follows: based on the initial optimization scheme, the mesh is re-generated, multiphase flow simulation is performed, wear calculation is conducted, and mechanism analysis is performed. Key parameters before and after optimization are compared, such as maximum wear depth (target reduction of more than 50%) and average wear rate (target reduction of more than 30%). Evaluation indicators also include changes in hydraulic efficiency (requiring a loss of <1%). The number of iterations is typically 3-5 times, and automated scripts are used to improve efficiency. The final scheme needs to be verified experimentally.

[0132] This step, through systematic iteration, ensures the reliability and effectiveness of the design, achieves continuous improvement in wear resistance, and is widely applicable to engineering optimization design. Through iterative evaluation, the system can find the optimal wear resistance solution.

[0133] In summary, the multiphase flow simulation control method for sand flow separation in this embodiment can achieve high-precision simulation and active control of the water-sand two-phase flow inside the turbine, improve the scientificity and effectiveness of the wear-resistant design, and provide technical support for the long-life operation of hydraulic machinery.

[0134] Example 2

[0135] A multiphase flow simulation-controlled sand particle flow separation system for hydraulic turbine wear resistance design is provided. Through parametric modeling, mesh generation, multiphase flow solution, wear calculation, mechanism analysis, optimization design, and iterative evaluation, it can accurately predict the wear distribution of turbine flow components and reduce wear in key areas by actively guiding sand particle flow paths, thereby improving the turbine's wear resistance and service life. Following the steps outlined in Example 1, this system can mitigate sediment wear at its source, ensuring turbine hydraulic efficiency while significantly extending its operating cycle, demonstrating significant engineering practical value and promising prospects for widespread application.

[0136] This system mainly includes functional modules such as parametric modeling, mesh generation and boundary setting, multiphase flow solving, wear calculation, mechanism analysis, optimization design, and iterative evaluation.

[0137] Specifically, the parametric modeling module is used to establish a three-dimensional parametric geometric model of the entire flow channel of the turbine, define the physical property parameters of the water phase and the sand phase, support dynamic adjustment of geometric dimensions, and provide a foundation for subsequent simulation and optimization.

[0138] Mesh generation and boundary setting module: Used to mesh the computational domain, employing unstructured meshing technology and refining the boundary layer in the near-wall region, setting inlet, outlet, and wall boundary conditions to ensure the accuracy and stability of the simulation.

[0139] Multiphase flow solution module: Based on the Euler-Lagrange framework, the governing equations for water-sediment two-phase flow are constructed. The Reynolds-averaged Navier-Stokes equations and the SST k-ω turbulence model are used to simulate the water phase. The discrete phase model is used to track the trajectory of sand particles and consider the two-way coupling effect to obtain detailed flow field and particle behavior data.

[0140] Wear calculation module: By embedding Finnie or Tabakoff wear models, the wall wear rate is calculated based on sand particle collision parameters, and wear cloud maps are generated to intuitively display high wear areas.

[0141] Mechanism Analysis Module: Through post-processing analysis of sand particle trajectory, concentration distribution and flow field structure, combined with wear cloud map to identify high wear areas and analyze the root causes of fluid dynamics, providing a basis for optimization design.

[0142] Optimization design module: Based on the mechanism analysis results, modify the parametric geometric model, optimize the impeller blade profile, guide vane airfoil and installation angle, or add non-smooth surface structures and guide grooves to actively control the sand particle path and reduce wear.

[0143] Iterative evaluation module: The optimized model is re-simulated and wear analyzed, the wear indexes before and after optimization are compared, and the iteration is repeated until the design requirements are met, ensuring a balance between wear resistance and hydraulic efficiency.

[0144] In summary, this embodiment constructs a highly efficient and precise anti-wear design system for water turbines through multi-module collaboration. It can actively control the flow path of sand particles, fundamentally reduce wear, and improve the operational reliability and economy of water turbines, thus having broad engineering application prospects.

[0145] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for anti-wear design of a water turbine using multiphase flow simulation control of sand particle flow separation, characterized in that, Includes the following steps: S1. Establish a parametric geometric model and a water and sediment physical property parameter library: Construct a three-dimensional parametric geometric model of the entire flow channel of the turbine, including the volute, guide vanes, runner and tailrace, and define the water phase parameters and sediment phase parameters. S2. Computational domain mesh generation and boundary condition setting: The computational domain of the parametric geometric model is meshed, and the boundary layer mesh is refined in the guide vane and impeller blade wall areas. The inlet, outlet and wall boundary conditions of the computational domain are set. S3. Construct and solve the control equations for multiphase flow of water and sediment: Construct the control equations for two-phase flow of water and sediment based on the Euler-Lagrange framework and solve them numerically. Treat the water phase as a continuous medium and use the Reynolds-averaged Navier-Stokes equations and the SST k-ω turbulence model. Treat the sand phase as a discrete phase and use the discrete phase model to track the particle motion trajectory. S4. Implant the wear model and calculate the wall wear distribution: Implant the wear model as a user-defined function into the CFD solver to calculate the cumulative wear rate of each region of the wall and generate a wear cloud map of the surface of the turbine flow components. S5. Analyze sand particle trajectory and flow field structure: Identify wear mechanism, analyze sand particle motion trajectory, spatial concentration distribution, and water flow velocity field, pressure field and vorticity field through post-processing, and identify high wear areas by combining wear cloud map and analyze the fluid dynamics root cause. S6. Anti-wear optimization design based on sand particle control concept: Modify the parametric geometric model according to the mechanism analysis, with the goal of reducing the particle collision energy and frequency in the high wear area, optimize the impeller blade profile, guide vane airfoil and installation angle, or add non-smooth surface structure and guide groove; S7. Design Iteration and Effect Evaluation: Repeat steps S2 to S5 on the optimized parametric geometric model to perform multiphase flow simulation and wear analysis. Compare the maximum wear depth and average wear rate of the key areas before and after optimization, and repeat the iteration until the wear index meets the design requirements.

2. The anti-wear design method for water turbines according to claim 1, characterized in that, In step S1, the aqueous phase parameters include density and dynamic viscosity; the sand phase parameters include particle size distribution, particle density, inflow volume concentration, and shape factor. The parametric geometric model supports dynamic adjustment of the geometry of the volute, guide vanes, impeller, and tailrace pipe.

3. The anti-wear design method for water turbines according to claim 1, characterized in that, In step S2, the meshing adopts unstructured meshing technology, and a boundary layer mesh is set in the near-wall region so that the Y+ value of the wall meets the requirements of the turbulence model. Set inlet conditions, including: incoming flow velocity, turbulence intensity, and sand particle injection conditions; set outlet conditions as either pressure outlet or free outflow; set wall conditions as no-slip boundary.

4. The anti-wear design method for water turbines according to claim 1, characterized in that, Step S3, which involves constructing and solving the governing equations for multiphase flow of water and sediment, also includes: S31. The water phase control equation adopts the Reynolds-averaged Navier-Stokes equation after time-averaging, and is closed by the SSTk-ω turbulence model; S32. The sand phase is tracked by a discrete phase model to track the motion trajectory of a large number of sand particles in the flow field. The forces on the particles include fluid drag force, gravity, buoyancy and pressure gradient force. S33. The momentum and energy exchange between the water phase and the sand phase is calculated using a two-way coupling method.

5. The anti-wear design method for water turbines according to claim 1, characterized in that, In step S4, the wear model is either the Finnie micro-cutting wear model or the Tabakoff impact wear model. The wear model calculates the cumulative wear rate of each region of the wall based on the velocity, angle and frequency of sand particles colliding with the wall. The wear cloud map is generated by post-processing software based on the cumulative wear rate of each region. It displays the wear depth distribution with a color gradient to intuitively identify high wear areas.

6. The anti-wear design method for water turbines according to claim 5, characterized in that, The cumulative wear rate for each region of the wall is calculated using the following formula: ； Where E is the wear depth. The mass flow rate of sand particles. and For material constants, The collision velocity of sand particles. The angle at which the sand grains collide. A function related to the collision angle. To calculate the area of ​​a unit.

7. The anti-wear design method for water turbines according to claim 1, characterized in that, In step S6, wear-resistant optimization design is carried out based on the concept of sand particle control, including: S61. Optimize the impeller blade profile, including: adjusting the blade placement angle, inlet angle and thickness distribution, and using centrifugal force to guide sand particles to the outside of the flow channel or a specific low-wear path; S62. Optimize the airfoil and installation angle of the guide vane, including: adjusting the airfoil and installation angle of the fixed or movable guide vane, pre-setting the swirl upstream, and generating the Coriolis force effect to assist in sand particle separation; S63. Add non-smooth surface structures and guide channels. Place non-smooth surface structures or guide channels in the volute or guide vane area to guide the movement of sand particles in the near-wall area and reduce the direct impact of sand particles on key areas.

8. A multiphase flow simulation-controlled sand particle flow separation turbine anti-wear system, used in the turbine anti-wear design method according to any one of claims 1 to 7, characterized in that, include: The parametric modeling module is used to establish a parametric geometric model and a library of water and sediment physical properties. It constructs a three-dimensional parametric geometric model of the entire flow channel of the turbine, including the volute, guide vanes, runner, and tailrace, and defines the physical properties of the water phase and the sediment phase. The mesh generation and boundary setting module is used to mesh the computational domain of the parametric geometric model, refine the boundary layer mesh in the guide vane and impeller blade wall areas, and set the inlet, outlet and wall boundary conditions of the computational domain. The multiphase flow solution module is used to construct and numerically solve the governing equations for water-sediment two-phase flow based on the Euler-Lagrange framework. The water phase is treated as a continuous medium, and the time-averaged Reynolds-averaged Navier-Stokes equations are used as the governing equations for the water phase, with the SST k-ω turbulence model used for closure. The sand phase is treated as a discrete phase, and the particle motion trajectory is tracked through the discrete phase model. The fluid drag force, gravity, buoyancy and pressure gradient force are considered, and the momentum and energy exchange between the water phase and the sand phase is calculated using a two-way coupling method. The wear calculation module is used to embed the wear model as a user-defined function into the CFD solver, calculate the cumulative wear rate of each region of the wall based on the velocity, angle and frequency of sand particles colliding with the wall, and generate wear cloud maps of the surface of the turbine flow components. The mechanism analysis module is used to analyze the sand particle movement trajectory, spatial concentration distribution, and water flow velocity field, pressure field, and vorticity field through post-processing, and to identify high wear areas and analyze the fluid dynamics root causes by combining wear cloud maps. The optimization design module is used to modify the parametric geometric model based on mechanism analysis, with the goal of reducing the energy and frequency of particle collisions in high-wear areas, optimizing the impeller blade profile, guide vane airfoil and installation angle, or adding non-smooth surface structures and guide grooves; The iterative evaluation module is used to repeatedly perform multiphase flow simulation and wear analysis on the optimized model, compare the maximum wear depth and average wear rate of key areas before and after optimization, and repeat the iteration until the wear index meets the design requirements.

9. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the multiphase flow simulation control method for sand flow separation in water turbines as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps in the water turbine anti-wear design method for multiphase flow simulation control of sand flow separation as described in any one of claims 1 to 8.

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