Self-adaptive flow simulation method and device for wide-load operation of pump turbine

Abstract

The invention relates to the technical field of digital simulation of hydroelectric power generation equipment, and discloses a pump turbine wide-load operation adaptive flow simulation method and device, and the method comprises the steps: obtaining a target pump turbine wide-load operation condition, and dividing the target pump turbine wide-load operation condition into a plurality of condition feature partitions; turbulence model switching rules are set for the multiple working condition characteristic partitions; acquiring real-time flow characteristics of wide-load operation of the target pump turbine, and based on the real-time flow characteristics, performing grid encryption on key components of the target pump turbine by using a turbulence model switching rule to obtain an encrypted area; performing global grid reconstruction on the encrypted region to obtain an unstructured hybrid grid; and acquiring flow field data corresponding to the unstructured hybrid grid, and determining wide-load operation pressure pulsation of the pump turbine based on the flow field data. According to the method, accurate and rapid prediction of wide-load operation pressure pulsation of the pump turbine is realized.

Description

Technical Field

[0001] This invention relates to the field of digital simulation technology for hydropower equipment, specifically to an adaptive flow simulation method and device for wide-load operation of water pumps and turbines. Background Technology

[0002] As the electricity market becomes more market-oriented, unit dispatching requires greater flexibility and response speed. However, the design of pumped storage units only considers high-load conditions and cannot efficiently cope with rapidly changing electricity demand. Therefore, accurately predicting the flow characteristics of pumped storage units under wide load conditions has become an important research direction.

[0003] When the unit operates under unbalanced conditions, the flow structure is complex, making accurate simulation very difficult. In addition, different flow regimes may be applicable to different turbulence models and numerical methods, and the operating flow regimes of different units under the same operating conditions are also different. Therefore, each unit design requires a long period of commissioning, which is extremely inefficient and has very poor feasibility. It is impossible to accurately assess the pressure pulsation of pump-turbines under wide loads, and thus it is impossible to guarantee the accuracy of predicting the flow characteristics of pumped-storage units under wide loads. Summary of the Invention

[0004] In view of this, the present invention provides an adaptive flow simulation method and apparatus for wide-load operation of water pump turbines to solve the problem of low efficiency and accuracy in evaluating pressure pulsation of water pump turbines under wide-load operation.

[0005] In a first aspect, the present invention provides an adaptive flow simulation method for wide-load operation of a water pump turbine, the method comprising: Obtain the target pump-turbine wide-load operating conditions and divide the target pump-turbine wide-load operating conditions into multiple operating condition feature zones; Set turbulence model switching rules for multiple operating condition characteristic zones; The real-time flow characteristics of the target pump turbine under wide load operation are obtained. Based on the real-time flow characteristics, the key components of the target pump turbine are meshed using turbulence model switching rules to obtain the encrypted region. A global mesh reconstruction is performed on the encrypted region to obtain an unstructured hybrid mesh; Obtain flow field data corresponding to unstructured hybrid grids, and determine the pressure pulsation of water pump turbine under wide load operation based on the flow field data.

[0006] This embodiment provides an adaptive flow simulation method for wide-load operation of a pump-turbine. The method divides the target pump-turbine's wide-load operation into multiple characteristic zones and sets turbulence model switching rules for each zone. These rules are then used to refine the mesh of key components of the target pump-turbine and to reconstruct the global mesh in the refined areas. By integrating an adaptive turbulence model selection mechanism with intelligent mesh reconstruction technology, an automated process from geometric model import to full-load pressure fluctuation prediction is achieved. This solves the problems of reliance on manual debugging and low computational efficiency, providing crucial support for the development of wide-load units. Furthermore, based on the flow field data corresponding to the unstructured hybrid mesh, the method determines the pressure fluctuations of the pump-turbine under wide-load operation, achieving accurate and rapid prediction of these pressure fluctuations.

[0007] In one optional implementation, based on real-time flow characteristics, the key components of the target pump turbine are meshed using turbulence model switching rules to obtain a refined region, including: Initial mesh generation is performed on the key flow-through components of the target water pump turbine to obtain the basic mesh; Based on real-time flow characteristics, the basic grid is refined using turbulence model switching rules to obtain the refined region.

[0008] This embodiment provides an adaptive flow simulation method for wide-load operation of a pump-turbine. It utilizes turbulence model switching rules to design a mapping relationship between triggering conditions and model switching for the instability characteristics of each zone. Encryption is only performed in the regions where the triggering conditions are met (such as guide vane clearance and vortex core region), thereby achieving local encryption of the pump-turbine and avoiding global redundancy.

[0009] In one optional implementation, based on real-time flow characteristics, the base mesh is refined using turbulence model switching rules to obtain a refined region, including: The guide vane clearance vortex intensity and runner outlet Reynolds stress ratio of the target pump turbine are determined based on real-time flow characteristics. The guide vane clearance vortex intensity is compared with a first preset threshold, and the runner outlet Reynolds stress ratio is compared with a second preset threshold. If the guide vane clearance vortex intensity is greater than the first preset threshold, or the runner outlet Reynolds stress ratio is greater than the second preset threshold, then the basic mesh corresponding to the guide vane region is refined using the SAS model, and SST is used... k-ω The model refines the basic mesh corresponding to the wheel channel to obtain the encrypted region.

[0010] In one optional implementation, based on real-time flow characteristics, the base mesh is refined using turbulence model switching rules to obtain a refined region, including: The volume percentage and pressure pulsation amplitude of the runner outlet recirculation zone are determined based on real-time flow characteristics. The volume ratio of the runner outlet recirculation zone is compared with the third preset threshold, and the pressure pulsation amplitude is compared with the fourth preset threshold. If the volume ratio of the runner outlet recirculation zone is greater than the third preset threshold, or the pressure pulsation amplitude is greater than the fourth preset threshold, then the basic grid corresponding to the runner outlet recirculation zone is refined using the DES model to obtain the refined area.

[0011] In one optional implementation, based on real-time flow characteristics, the base mesh is refined using turbulence model switching rules to obtain a refined region, including: The rotational frequency fluctuation of the tailrace vortex and the real-time vortex core capture index are determined based on real-time flow characteristics. The rotation frequency fluctuation of the tailrace vortex band is compared with the fourth preset threshold, and the real-time vortex core capture index is compared with the fifth preset threshold. If the rotation frequency fluctuation of the tailrace vortex band exceeds the fourth preset threshold, or the real-time vortex core capture index exceeds the fifth preset threshold, then the LES model is used to refine the basic mesh of the tailrace cone to obtain the refined region.

[0012] In one optional implementation, a global mesh reconstruction is performed on the encrypted region to obtain an unstructured hybrid mesh, including: Obtain the analytical vorticity and subgrid vorticity corresponding to the target water pump turbine, and calculate the vortex core capture index based on the analytical vorticity and subgrid vorticity; The vortex core capture index is compared with a preset index threshold. If the vortex core capture index is less than the preset index threshold, the vortex core region is recursively subdivided to obtain the adjusted grid region. Based on the adjusted grid region, an unstructured hybrid grid is generated using a parallel reconstruction algorithm.

[0013] This embodiment provides an adaptive flow simulation method for wide-load operation of a pump-turbine. By analyzing vorticity and subgrid vorticity, the vortex core capture index is calculated, and then the vortex core region is recursively subdivided according to the vortex core capture index. This solves the problem of low efficiency due to reliance on manual experience and realizes dynamic parameter optimization and simulation of wide-load operation of pump-turbines, laying the foundation for accurate prediction of pressure pulsation during wide-load operation of pump-turbines.

[0014] Secondly, the present invention provides an adaptive flow simulation device for wide-load operation of a water pump turbine, the device comprising: The segmentation module is used to obtain the wide-load operating conditions of the target water pump turbine and divide the wide-load operating conditions of the target water pump turbine into multiple operating condition feature partitions; The settings module is used to set turbulence model switching rules for multiple operating condition feature zones. The encryption module is used to obtain the real-time flow characteristics of the target pump turbine under wide load operation. Based on the real-time flow characteristics, the key components of the target pump turbine are meshed using the turbulence model switching rules to obtain the encrypted area. The mesh reconstruction module is used to perform global mesh reconstruction on the encrypted area to obtain an unstructured hybrid mesh; The determination module is used to acquire flow field data corresponding to unstructured hybrid grids and determine the pressure pulsation of the water pump turbine under wide load based on the flow field data.

[0015] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the adaptive flow simulation method for wide-load operation of water pumps and turbines described in the first aspect or any corresponding embodiment.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the adaptive flow simulation method for wide-load operation of a water pump turbine as described in the first aspect or any corresponding embodiment.

[0017] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the adaptive flow simulation method for wide-load operation of a water pump turbine as described in the first aspect or any of the corresponding embodiments. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an adaptive flow simulation method for wide-load operation of a water pump turbine according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating another adaptive flow simulation method for wide-load operation of a water pump turbine according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating another adaptive flow simulation method for wide-load operation of a water pump turbine according to an embodiment of the present invention; Figure 4This is a structural block diagram of an adaptive flow simulation device for wide-load operation of a water pump turbine according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] As the electricity market becomes more market-oriented, unit dispatching requires greater flexibility and response speed. However, traditional pumped-storage unit designs only consider high-load conditions and cannot efficiently cope with rapidly changing electricity demands. Considering the complementary operation characteristics of pumped-storage power stations in renewable energy-rich areas with wind power, photovoltaics, and other renewable energy sources, the power generation conditions of pumped-storage units need to break through the traditional operating range of 50% to 100% and expand to below 50% (e.g., 30% to 100%).

[0022] The development of pumped storage units operating under wide load conditions should focus on their pressure pulsation. On the one hand, the flow stability of pumped storage units is usually poor when operating in low load areas. On the other hand, the design point of pumped storage units operating under wide load conditions needs to be shifted towards low flow rates, which may lead to poor flow stability in high load areas. Therefore, it is necessary to evaluate their pressure pulsation throughout the entire operating range.

[0023] However, there is currently no efficient and accurate method or system for predicting the flow characteristics of pumped storage units under wide load conditions. This is because the flow structure is complex when the unit is operating under extreme conditions, making accurate simulation very difficult. In addition, different flow regimes may be applicable to different turbulence models and numerical methods, and the operating flow regimes of different units under the same operating conditions are also different. Therefore, each unit design requires a long period of commissioning, which is extremely inefficient and has very poor feasibility. As a result, at present, the pressure pulsation characteristics of newly developed units can only be evaluated through high-cost model tests.

[0024] This invention provides an adaptive flow simulation method for wide-load operation of pump-turbines. By integrating an adaptive turbulence model selection mechanism and intelligent mesh reconstruction technology, it achieves an automated process from geometric model import to full-condition pressure fluctuation prediction, solving the problems of reliance on manual debugging and low computational efficiency, and providing key support for the development of wide-load units.

[0025] This invention provides an adaptive flow simulation method for wide-load operation of a water pump-turbine. It should be noted that the execution subject of this method can be a water pump-turbine wide-load operation adaptive flow simulation device. This device can be implemented as part or all of an electronic device through software, hardware, or a combination of both. The electronic device can be a server or a terminal. In this embodiment, the server can be a single server or a server cluster composed of multiple servers. The terminal can be a smartphone, personal computer, tablet computer, wearable device, or other intelligent hardware device such as a smart robot. The following method embodiments all use an electronic device as the execution subject for illustration.

[0026] According to an embodiment of the present invention, an embodiment of an adaptive flow simulation method for wide-load operation of a water pump turbine is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] This embodiment provides an adaptive flow simulation method for wide-load operation of a water pump turbine, which can be used in the aforementioned electronic equipment. Figure 1 This is a flowchart of an adaptive flow simulation method for wide-load operation of a water pump turbine according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Obtain the target water pump turbine wide-load operating conditions and divide the target water pump turbine wide-load operating conditions into multiple operating condition feature zones.

[0028] Specifically, the target pump turbine's wide-load operating conditions are divided into three characteristic zones: Zone 1: pumping operation and 70%-100% power generation operation; Zone 2: 50%-70% power generation operation; Zone 3: 30%-50% power generation operation.

[0029] Step S102: Set turbulence model switching rules for multiple operating condition feature zones.

[0030] Specifically, an initial model, triggering conditions, and switching actions are set for each operating condition feature partition.

[0031] The turbulence models include SAS (Scale-Adaptive Simulation), DDES (Detached Eddy Simulation), and LES (Large Eddy Simulation) / RANS (Reynolds-Averaged Navier-Stokes) hybrid models. The construction process of the SAS model includes: based on SST... k-ω The model (a model of shear stress transport turbulent kinetic energy-specific dissipation rate) introduces a vorticity correction term and automatically adjusts the turbulent viscosity by solving the scale-sensing equation. Key parameters are... The coefficient of the violation term in the equation is set to 0.58; the construction process of the DDES model includes: using the Smagorinsky (subgrid viscosity) subgrid model in the LES region, and retaining the SST model in the RANS region. k-ω The model, in which the mixing function achieves regional transition through the eddy viscosity ratio, involves the following steps in constructing the LES / RANS hybrid model: Delayed Separating Eddy Correction (DDES) to prevent mesh-induced separation, and ensuring that the mesh size in the LES region meets the required parameters. (Y is the feature length), and the time step is set to the Courant number. .

[0032] Step S103: Obtain the real-time flow characteristics of the target water pump turbine under wide load operation. Based on the real-time flow characteristics, use the turbulence model switching rules to refine the mesh of the key components of the target water pump turbine to obtain the encrypted region.

[0033] Step S104: Perform global mesh reconstruction on the encrypted region to obtain an unstructured hybrid mesh.

[0034] Step S105: Obtain the flow field data corresponding to the unstructured hybrid grid, and determine the pressure pulsation of the pump turbine under wide load based on the flow field data.

[0035] Specifically, based on the encrypted flow field data (pressure, velocity, vorticity), the dominant frequency of pressure pulsation is extracted using Fast Fourier Transform (FFT) to evaluate the unit's vibration characteristics, thereby generating a database of external characteristic curves containing 200+ operating points.

[0036] This embodiment provides an adaptive flow simulation method for wide-load operation of a pump-turbine. The method divides the target pump-turbine's wide-load operation into multiple characteristic zones and sets turbulence model switching rules for each zone. These rules are then used to refine the mesh of key components of the target pump-turbine and to reconstruct the global mesh in the refined areas. By integrating an adaptive turbulence model selection mechanism with intelligent mesh reconstruction technology, an automated process from geometric model import to full-load pressure fluctuation prediction is achieved. This solves the problems of reliance on manual debugging and low computational efficiency, providing crucial support for the development of wide-load units. Furthermore, based on the flow field data corresponding to the unstructured hybrid mesh, the method determines the pressure fluctuations of the pump-turbine under wide-load operation, achieving accurate and rapid prediction of these pressure fluctuations.

[0037] This embodiment provides an adaptive flow simulation method for wide-load operation of a water pump turbine, which can be used in the aforementioned electronic equipment. Figure 2 This is a flowchart of an adaptive flow simulation method for wide-load operation of a water pump turbine according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain the target pump-turbine wide-load operating conditions and divide these conditions into multiple characteristic zones. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0038] Step S202 involves setting turbulence model switching rules for multiple operating condition characteristic zones. For details, please refer to [link to relevant documentation]. Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0039] Step S203: Obtain the real-time flow characteristics of the target water pump turbine under wide load operation. Based on the real-time flow characteristics, use the turbulence model switching rules to refine the mesh of the key components of the target water pump turbine to obtain the encrypted region.

[0040] Specifically, step S203 includes: Step S2031: Perform initial mesh generation on the key flow components of the target water pump turbine to obtain the basic mesh.

[0041] Specifically, for Zone 1, the first operating condition characteristic zone, initial mesh generation was performed on key flow components such as guide vanes, impeller, and tailrace pipe. The basic mesh size was set to 1 / 200 of the impeller diameter, and the boundary layer mesh was appropriately refined. The height of the first layer mesh was set. ( (Diameter of the rotor outlet) Number of layers ,growth rate .

[0042] Furthermore, the base mesh size of Zone2, the second working condition feature partition, is set to 1 / 200 of the wheel diameter, and the boundary layer mesh is appropriately densified.

[0043] Furthermore, the base mesh size of Zone3, the third working condition feature partition, is set to 1 / 250 of the wheel diameter, and the boundary layer mesh is appropriately densified.

[0044] Step S2032: Based on real-time flow characteristics, the basic grid is refined using turbulence model switching rules to obtain the refined region.

[0045] Specifically, mesh refinement includes: Level 1 refinement: reducing the basic mesh size to 1 / 2 of the initial value, increasing the number of cells by about 8 times; Level 2 refinement: further reducing the size to 1 / 4 (1 / 4 of the initial size) based on Level 1 refinement, increasing the number of cells by about 64 times; Level 3 refinement: reducing the size to 1 / 8 (1 / 8 of the initial size) based on Level 2 refinement, increasing the number of cells by about 512 times.

[0046] Furthermore, the refined mesh data is input into a CFD (Computational Fluid Dynamics) solver for flow separation prediction. Specifically, the initial mesh size (e.g., D / 200) is refined to D / 400 using an octree / hexahedral mesh recursive algorithm. This allows for more accurate capture of complex flow structures such as guide vane clearance vortices and runner outlet separation flows. For instance, when the vortex core capture index (VCI) is less than 0.8, the refined mesh can resolve the velocity gradient changes in the vortex core region, avoiding the numerical dissipation caused by traditional coarse meshes.

[0047] Furthermore, the densified grid provides a high-resolution foundation for subsequent dynamic adjustments. When flow characteristics trigger grid reconstruction (such as VCI < 0.6), the system can perform local densification in the already densified area (such as densifying the turbine outlet area to D / 600) instead of global reconstruction, thereby reducing computational costs while ensuring accuracy. The densified grid is the core data support for flow separation prediction, and its level of refinement directly affects the efficiency and accuracy of dynamic grid adjustment.

[0048] In some optional implementations, step S2032 above includes: Step a1: Determine the guide vane clearance vortex intensity and runner outlet Reynolds stress ratio of the target pump turbine based on real-time flow characteristics.

[0049] Step a2: Compare the guide vane clearance vortex intensity with the first preset threshold, and compare the runner outlet Reynolds stress ratio with the second preset threshold.

[0050] Step a3: If the guide vane clearance vortex intensity is greater than the first preset threshold, or the runner outlet Reynolds stress ratio is greater than the second preset threshold, then the basic mesh corresponding to the guide vane region is refined using the SAS model, and SST is used... k-ω The model refines the basic mesh corresponding to the wheel channel to obtain the encrypted region.

[0051] Specifically, if the ratio of guide vane clearance vorticity intensity to runner outlet Reynolds stress satisfies the trigger condition in the turbulence model switching rule of Zone 1 (characteristic zone 1 of the first operating condition), that is, if the ratio of guide vane clearance vorticity intensity under pumping conditions... ( For reference flow rate, (guide blade chord length), or the Reynolds stress ratio at the turbine outlet during power generation. , ( This indicates that the Reynolds stress tensor is in the circumferential direction ( The normal component in the direction represents the intensity of circumferential turbulent motion. This indicates that the Reynolds stress tensor is in the radial direction ( The normal component in the direction) is then used for the pumping operation, and the initial model SST in Zone 1 of the first operation condition feature partition is divided into two parts. k-ω The model was switched to the SAS model, the guide vane area was refined to level 2, and SST was maintained for power generation conditions. k-ω The model employs a two-level encryption method for the rotary channel.

[0052] In some optional implementations, step S2032 above includes: Step b1: Determine the volume ratio of the runner outlet recirculation zone and the pressure pulsation amplitude based on real-time flow characteristics.

[0053] Step b2: Compare the volume ratio of the runner outlet recirculation zone with the third preset threshold, and compare the pressure pulsation amplitude with the fourth preset threshold.

[0054] Step b3: If the volume ratio of the runner outlet recirculation zone is greater than the third preset threshold, or the pressure pulsation amplitude is greater than the fourth preset threshold, then the basic grid corresponding to the runner outlet recirculation zone is refined using the DES model to obtain the refined area.

[0055] Specifically, if the volume percentage of the runner outlet recirculation zone and the pressure pulsation amplitude meet the trigger conditions in the turbulence model switching rules of Zone 2 (the second operating condition), i.e., if the volume percentage of the runner outlet recirculation zone is >15% or the pressure pulsation amplitude is >15%, then the runner outlet recirculation zone will be affected. (ΔH represents the head corresponding to the pressure difference at that point, i.e., the instantaneous pressure minus the average pressure divided by the product of density and gravitational acceleration; H represents the head in the model test.) Then, the initial model SAS in Zone 2 of the second operating condition is switched to the DES (Delayed Detached Eddy Simulation) model, and the runner outlet recirculation zone is refined to level 2, with the time step changed from 1 / N / 60 adjusted to 1 / N / 60 / 2, N The rotational speed is rpm.

[0056] In some optional implementations, step S2032 above includes: Step c1: Determine the rotation frequency fluctuation of the tailrace vortex and the real-time vortex core capture index based on real-time flow characteristics.

[0057] Step c2: Compare the fluctuation of the tailrace vortex rotation frequency with the fourth preset threshold, and compare the real-time vortex core capture index with the fifth preset threshold.

[0058] Step c3: If the rotation frequency fluctuation of the tailrace vortex band is greater than the fourth preset threshold, or the real-time vortex core capture index is greater than the fifth preset threshold, then the LES model is used to refine the basic mesh of the tailrace cone to obtain the refined region.

[0059] Specifically, if the vortex rotation frequency fluctuation and the real-time vortex core capture index of the tailrace tube meet the triggering conditions in the turbulence model switching rules of the third working condition characteristic zone (Zone3), that is, if the vortex rotation frequency fluctuation of the tailrace tube is >5% or the real-time vortex core capture index is <70% for 5 time steps, then the initial model in the third working condition characteristic zone (Zone3) will be expanded to the entire tailrace tube using the LES / RANS hybrid model based on Delayed Separated Vortex Simulation (DDES), the tailrace cone tube mesh will be refined at level 3, and global mesh reconstruction will be triggered.

[0060] Step S204: Perform global mesh reconstruction on the encrypted region to obtain an unstructured hybrid mesh. For details, please refer to [link to relevant documentation]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0061] Step S205: Obtain the flow field data corresponding to the unstructured hybrid grid, and determine the pressure pulsation of the pump-turbine under wide load operation based on the flow field data. For details, please refer to [link to relevant documentation]. Figure 1 Step S105 of the illustrated embodiment will not be described again here.

[0062] This embodiment provides an adaptive flow simulation method for wide-load operation of a pump-turbine. It utilizes turbulence model switching rules to design a mapping relationship between triggering conditions and model switching for the instability characteristics of each zone. Encryption is only performed in the regions where the triggering conditions are met (such as guide vane clearance and vortex core region), thereby achieving local encryption of the pump-turbine and avoiding global redundancy.

[0063] This embodiment provides an adaptive flow simulation method for wide-load operation of a water pump turbine, which can be used in the aforementioned electronic equipment. Figure 3 This is a flowchart of an adaptive flow simulation method for wide-load operation of a water pump turbine according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: Step S301: Obtain the target pump-turbine wide-load operating conditions and divide these conditions into multiple characteristic zones. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0064] Step S302 involves setting turbulence model switching rules for multiple operating condition characteristic zones. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0065] Step S303: Obtain the real-time flow characteristics of the target pump-turbine under wide load operation. Based on these real-time flow characteristics, refine the mesh of key components of the target pump-turbine using turbulence model switching rules to obtain the refined region. For details, please refer to [link to relevant documentation]. Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0066] Step S304: Perform global mesh reconstruction on the encrypted region to obtain an unstructured hybrid mesh; Specifically, step S304 includes: Step S3041: Obtain the analytical vorticity and subgrid vorticity corresponding to the target water pump turbine, and calculate the vortex core capture index based on the analytical vorticity and subgrid vorticity.

[0067] Specifically, the vorticity field is directly calculated using LES to obtain analytical vorticity. The specific calculation steps include: mesh filtering. 1) Using a box filter, the formula for calculating the filter width is: :

[0068] In the above formula, These represent the local grid scale (cell size / grid spacing) of the computational grid in the x, y, and z coordinate directions in physical space.

[0069] make sure , It refers to the Kolmogorov scale (the size of microclusters formed by the breaking up of fluids through mechanical stirring to induce turbulence).

[0070] 2) Subgrid stress calculation: using the dynamic Smagorinsky model; calculation coefficients Dynamic adjustment is achieved through the Germano identity (a core tool in large eddy simulation used to dynamically adjust the parameters of subgrid models, which enables dynamic updating of model parameters through filtered scaling tests).

[0071] 3) Eddy field reconstruction: eddy tensor is calculated through velocity field gradient; high-precision interpolation scheme (WENO5) is applied to maintain conservation.

[0072] Furthermore, the unresolved vorticity, i.e., subgrid vorticity, is estimated using the dynamic Smagorinsky model. The specific calculation steps include: 1) Coefficient Calculation: Calculate the coefficients using the test filter. :

[0073] in, For Leonard stress, This is the stress term in the Germano identity.

[0074] 2) Vorticity Correction: Subgrid Vorticity The calculation formula is:

[0075] in, For sublattice dissipation rate, It is the sub-grid turbulent kinetic energy.

[0076] 3) Coupled computation: Add the total vorticity field; monitor the resolution via VCI (Vortex - core Capturing Index), and trigger mesh refinement when the proportion of unresolved vorticity is >30%.

[0077] Step S3042: Compare the vortex core capture index with a preset index threshold. If the vortex core capture index is less than the preset index threshold, recursively subdivide the vortex core region to obtain the adjusted grid region.

[0078] Specifically, the global mesh reconstruction is triggered based on the VCI index. When VCI < 75%, the system automatically performs three levels of recursive subdivision of the vortex core region until the resolution requirement is met.

[0079] Furthermore, an octree or hexahedral mesh recursive algorithm is used to subdivide the target region step by step, that is, to perform three levels of recursive subdivision on the vortex core region (each subdivision halves the mesh size), and the adjusted mesh is used to generate an unstructured hybrid mesh through a parallel reconstruction algorithm.

[0080] Step S3043: Based on the adjusted grid region, an unstructured hybrid grid is generated using a parallel reconstruction algorithm.

[0081] Step S305: Obtain the flow field data corresponding to the unstructured hybrid grid, and determine the pressure pulsation of the pump-turbine under wide load operation based on the flow field data. For details, please refer to [link to relevant documentation]. Figure 2 Step S205 of the illustrated embodiment will not be described again here.

[0082] This embodiment provides an adaptive flow simulation method for wide-load operation of a pump-turbine. By analyzing vorticity and subgrid vorticity, the vortex core capture index is calculated, and then the vortex core region is recursively subdivided according to the vortex core capture index. This solves the problem of low efficiency due to reliance on manual experience and realizes dynamic parameter optimization and simulation of wide-load operation of pump-turbines, laying the foundation for accurate prediction of pressure pulsation during wide-load operation of pump-turbines.

[0083] The following specific embodiments illustrate the detailed steps of an adaptive flow simulation method for wide-load operation of a water pump turbine.

[0084] Example 1: Taking a 400MW reversible unit of a pumped storage power station as an example, the specific steps of an adaptive flow simulation method for wide-load operation of a pump-turbine include: 1) Input parameter: Rotor diameter Specific speed Power generation head: 420m (minimum head); Target operating conditions: 35%, 60%, and 85% load power generation conditions, as well as pumping conditions.

[0085] 2) Dynamic optimization process: 35% power generation load: The system detected a VCI of 68% in the tailrace and a vortex belt rotation frequency fluctuation of 6%, triggering a global reconfiguration; after reconfiguration, the grid size increased from 5.2 million to 12.8 million units. ， VCI improved to 82%, and pressure pulsation prediction error <12%.

[0086] Pumping operation: Guide vane clearance vorticity intensity Q When the value reaches 0.63 (threshold 0.6), the SAS model is activated and the interstitial mesh is refined to 0.12 mm, successfully capturing the vortex shedding phenomenon with a frequency error of <9%.

[0087] 3) The comparison data is shown in Table 1 below: Table 1:

[0088] After the above adaptive flow simulation steps for wide-load operation of the pump-turbine, the prediction error of the tailrace vortex frequency was reduced from 31% to 4.2%.

[0089] Example 2: The specific steps of an adaptive flow simulation for wide-load operation of a water pump turbine include: Step 1: Divide the operating range into Zone 1 (70%-100% power generation and pumping conditions), Zone 2 (50%-70% power generation conditions), and Zone 3 (30%-50% power generation conditions).

[0090] Step 2: Dynamic parameter optimization and simulation: 1) Turbulence model switching: Based on real-time flow characteristics, the model is switched.

[0091] Differentiated turbulence model switching rules are set for each partition based on its characteristics: A, Zone 1: Initial model: SST k-ω Initial mesh generation was performed on key flow components such as guide vanes, impeller, and tailrace pipe. The basic mesh size was set to 1 / 200 of the impeller diameter, and the boundary layer mesh was appropriately refined. The height of the first layer mesh was set. ( (Diameter of the rotor outlet) Number of layers ,growth rate .

[0092] Triggering condition: Guide vane clearance vorticity intensity under pumping conditions ( For reference flow rate, (guide blade chord length); Reynolds stress ratio at turbine outlet during power generation operation. .

[0093] Switching actions: Pumping mode: Activate the SAS model and perform level 2 refinement of the guide vane area; Power generation mode: Maintain SST. k-ω The model employs a two-level encryption method for the rotary channel.

[0094] B, Zone 2: Initial model: SAS; the base mesh size is set to 1 / 200 of the wheel diameter, and the boundary layer mesh is appropriately refined.

[0095] Triggering conditions: The volume ratio of the runner outlet recirculation zone is >15% or the pressure pulsation amplitude is greater than 15%. .

[0096] Switching action: Switch to DES model, perform level 2 encryption on the reflow area; time step from 1 / N / 60 adjusted to 1 / N / 60 / 2, N The rotational speed is rpm.

[0097] C, Zone 3: Initial model: LES / RANS hybrid model based on Delayed Separated Eddy Simulation (DDES); the basic mesh size is set to 1 / 250 of the rotor diameter, and the boundary layer mesh is appropriately refined.

[0098] Triggering condition: Fluctuation of tailrace vortex rotation frequency >5% or VCI <70% lasted for 5 time steps.

[0099] Switching action: Expand the LES computation domain to the entire tailrace pipe section, refine the tailrace cone pipe mesh to level 3, and trigger global mesh reconstruction.

[0100] 2) Mesh Reconstruction: Dynamically adjust the encrypted area based on the Vortex Capture Index (VCI).

[0101] Global mesh reconstruction is triggered based on the VCI index (<75%); a three-level recursive subdivision is performed on the vortex core region (the mesh size is halved with each subdivision); the adjusted mesh is used to generate an unstructured hybrid mesh through a parallel reconstruction algorithm.

[0102] Step 3: Input the encrypted flow field data (pressure, velocity, vorticity) into the post-processing module; extract the pressure pulsation frequency through fast Fourier transform; finally generate a database of external characteristic curves containing 200+ operating points.

[0103] The above embodiments have the following advantages: 1) Three-segment dynamic zoning mechanism: For the first time, an adaptive flow simulation system for water turbines is constructed to achieve accurate and rapid prediction of pressure pulsation during wide-load operation of water pumps and water turbines.

[0104] 2) VCI-driven mesh optimization: A method for fusion calculation of analytical vorticity and subgrid vorticity is proposed, and a quantization rule for triggering densification when VCI < 75% is established to solve the problem of traditional methods relying on human experience.

[0105] 3) Cross-condition model switching logic: Design a mapping relationship between triggering conditions and model switching for the flow instability characteristics of each zone (such as the linkage mechanism between VCI and LES domains in Zone3).

[0106] This embodiment also provides an adaptive flow simulation device for wide-load operation of a water pump turbine. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0107] This embodiment provides an adaptive flow simulation device for wide-load operation of a water pump and turbine, such as... Figure 4 As shown, it includes: The partitioning module 401 is used to obtain the wide-load operating conditions of the target water pump turbine and divide the wide-load operating conditions of the target water pump turbine into multiple operating condition feature partitions. The setting module 402 is used to set turbulence model switching rules for multiple operating condition feature partitions respectively; The encryption module 403 is used to obtain the real-time flow characteristics of the target water pump turbine under wide load operation. Based on the real-time flow characteristics, the key components of the target water pump turbine are meshed using the turbulence model switching rules to obtain the encrypted area. Mesh reconstruction module 404 is used to perform global mesh reconstruction on the encrypted area to obtain an unstructured hybrid mesh; The determination module 405 is used to acquire the flow field data corresponding to the unstructured hybrid grid, and to determine the pressure pulsation of the pump turbine under wide load based on the flow field data.

[0108] In some alternative implementations, the encryption module 403 includes: The meshing unit is used to perform initial meshing of the key flow-through components of the target water pump turbine, thus obtaining the basic mesh. The encryption unit is used to encrypt the basic grid based on real-time flow characteristics and using turbulence model switching rules to obtain the encrypted region.

[0109] In some alternative implementations, the encryption unit includes: The first determining sub-unit is used to determine the guide vane clearance vorticity intensity and runner outlet Reynolds stress ratio of the target water pump turbine based on real-time flow characteristics. The first comparison sub-unit is used to compare the guide vane clearance vortex intensity with a first preset threshold and the runner outlet Reynolds stress ratio with a second preset threshold. The first mesh refinement sub-unit is used to refine the basic mesh corresponding to the guide vane region using the SAS model if the guide vane clearance vortex intensity is greater than a first preset threshold, or the runner outlet Reynolds stress ratio is greater than a second preset threshold, and then uses SST. k-ωThe model refines the basic mesh corresponding to the wheel channel to obtain the encrypted region.

[0110] In some alternative implementations, the encryption unit includes: The second determining subunit is used to determine the volume ratio of the runner outlet recirculation zone and the pressure pulsation amplitude based on real-time flow characteristics. The second comparison subunit is used to compare the volume ratio of the runner outlet recirculation zone with the third preset threshold, and to compare the pressure pulsation amplitude with the fourth preset threshold. The second mesh refinement sub-unit is used to refine the basic mesh corresponding to the runner outlet recirculation zone using the DES model if the volume ratio of the runner outlet recirculation zone is greater than the third preset threshold, or the pressure pulsation amplitude is greater than the fourth preset threshold, so as to obtain the refined area.

[0111] In some alternative implementations, the encryption unit includes: The third determining subunit is used to determine the rotational frequency fluctuation of the tailrace vortex band and the real-time vortex core capture index based on real-time flow characteristics. The third comparison subunit is used to compare the rotation frequency fluctuation of the tailrace vortex with the fourth preset threshold, and to compare the real-time vortex core capture index with the fifth preset threshold. The third grid densification sub-unit is used to densify the basic grid of the tailrace cone tube using the LES model if the rotation frequency fluctuation of the tailrace tube vortex band is greater than the fourth preset threshold, or the real-time vortex core capture index is greater than the fifth preset threshold, so as to obtain the densified area.

[0112] In some alternative implementations, the mesh reconstruction module 404 includes: The calculation unit is used to obtain the analytical vorticity and subgrid vorticity corresponding to the target water pump turbine, and calculate the vortex core capture index based on the analytical vorticity and subgrid vorticity; The comparison unit is used to compare the vortex core capture index with a preset index threshold. If the vortex core capture index is less than the preset index threshold, the vortex core region is recursively subdivided to obtain the adjusted grid region. The generation unit is used to generate unstructured hybrid meshes based on the adjusted mesh regions using a parallel reconstruction algorithm.

[0113] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0114] In this embodiment, the adaptive flow simulation device for wide-load operation of a water pump turbine is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0115] This invention also provides a computer device having the above-described features. Figure 4 The diagram shows an adaptive flow simulation device for wide-load operation of a water pump and turbine.

[0116] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 5 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take a processor 10 as an example.

[0117] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPA), or any combination thereof.

[0118] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0119] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0120] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0121] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0122] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0123] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0124] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0125] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An adaptive flow simulation method for wide-load operation of a water pump turbine, characterized in that, The method includes: Obtain the target water pump turbine's wide-load operating conditions, and divide the target water pump turbine's wide-load operating conditions into multiple operating condition feature zones; Set turbulence model switching rules for each of the multiple operating condition feature partitions; The real-time flow characteristics of the target pump turbine under wide load operation are obtained. Based on the real-time flow characteristics, the key components of the target pump turbine are meshed using the turbulence model switching rules to obtain the encrypted region. A global mesh reconstruction is performed on the encrypted region to obtain an unstructured hybrid mesh; Obtain the flow field data corresponding to the unstructured hybrid grid, and determine the pressure pulsation of the water pump turbine under wide load based on the flow field data.

2. The method according to claim 1, characterized in that, Based on the real-time flow characteristics, the key components of the target pump turbine are meshed using the turbulence model switching rules to obtain a refined region, including: Initial mesh generation is performed on the key flow-through components of the target water pump turbine to obtain the basic mesh; Based on the real-time flow characteristics, the basic grid is densified using the turbulence model switching rules to obtain the densified region.

3. The method according to claim 2, characterized in that, Based on the real-time flow characteristics, the basic grid is refined using the turbulence model switching rules to obtain the refined region, including: The guide vane clearance vortex intensity and runner outlet Reynolds stress ratio of the target water pump turbine are determined based on the real-time flow characteristics. The guide vane clearance vortex intensity is compared with a first preset threshold, and the runner outlet Reynolds stress ratio is compared with a second preset threshold; If the guide vane clearance vortex intensity is greater than the first preset threshold, or the runner outlet Reynolds stress ratio is greater than the second preset threshold, then the basic mesh corresponding to the guide vane region is refined using the SAS model, and SST is used... k- ω The model refines the basic mesh corresponding to the wheel channel to obtain the refined region.

4. The method according to claim 2, characterized in that, Based on the real-time flow characteristics, the basic grid is refined using the turbulence model switching rules to obtain the refined region, including: The volume percentage and pressure pulsation amplitude of the runner outlet recirculation zone are determined based on the real-time flow characteristics. The volume ratio of the runner outlet reflux zone is compared with a third preset threshold, and the pressure pulsation amplitude is compared with a fourth preset threshold. If the volume ratio of the runner outlet recirculation zone is greater than the third preset threshold, or the pressure pulsation amplitude is greater than the fourth preset threshold, then the basic grid corresponding to the runner outlet recirculation zone is refined using the DES model to obtain the refined area.

5. The method according to claim 2, characterized in that, Based on the real-time flow characteristics, the basic grid is refined using the turbulence model switching rules to obtain the refined region, including: Based on the real-time flow characteristics, the tailrace vortex rotation frequency fluctuation and the real-time vortex core capture index are determined. The rotation frequency fluctuation of the tailrace vortex band is compared with the fourth preset threshold, and the real-time vortex core capture index is compared with the fifth preset threshold. If the rotation frequency fluctuation of the tailrace vortex band is greater than the fourth preset threshold, or the real-time vortex core capture index is greater than the fifth preset threshold, then the LES model is used to refine the basic mesh of the tailrace cone to obtain the refined region.

6. The method according to claim 1, characterized in that, The step of performing global mesh reconstruction on the encrypted region to obtain an unstructured hybrid mesh includes: Obtain the analytical vorticity and subgrid vorticity corresponding to the target water pump turbine, and calculate the vortex core capture index based on the analytical vorticity and the subgrid vorticity; The vortex core capture index is compared with a preset index threshold. If the vortex core capture index is less than the preset index threshold, the vortex core region is recursively subdivided to obtain the adjusted grid region. Based on the adjusted grid region, the unstructured hybrid grid is generated using a parallel reconstruction algorithm.

7. A wide-load adaptive flow simulation device for water pumps and turbines, characterized in that, The device includes: The segmentation module is used to obtain the wide-load operating conditions of the target water pump turbine and divide the wide-load operating conditions of the target water pump turbine into multiple operating condition feature partitions; The setting module is used to set turbulence model switching rules for each of the multiple operating condition feature partitions; The encryption module is used to acquire the real-time flow characteristics of the target pump turbine under wide load operation. Based on the real-time flow characteristics, the key components of the target pump turbine are meshed using the turbulence model switching rules to obtain the encrypted area. The mesh reconstruction module is used to perform global mesh reconstruction on the encrypted region to obtain an unstructured hybrid mesh; The determination module is used to acquire the flow field data corresponding to the unstructured hybrid grid, and determine the pressure pulsation of the pump turbine under wide load based on the flow field data.

8. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the adaptive flow simulation method for wide-load operation of a water pump turbine as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the adaptive flow simulation method for wide-load operation of a water pump turbine as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the adaptive flow simulation method for wide-load operation of a water pump turbine as described in any one of claims 1 to 6.

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