Simulation method of semi-spiral suction chamber baffle position and flow field correspondence relationship

Abstract

The application discloses a simulation method for the corresponding relationship between the semi-spiral suction chamber baffle tongue position and the flow field, and relates to the technical field of double-suction pump design and numerical simulation. The application establishes the mapping relationship model of the baffle tongue position and the flow field characteristic parameter by constructing the three-dimensional flow channel model of multiple groups of baffle tongue reference position parameters, combining multi-working condition simulation and quantitative analysis, accurately capturing the vortex generation law, turbulent dissipation and pressure fluctuation characteristics of the baffle tongue area, and determining the optimal baffle tongue parameter interval in combination with the flow field uniformity and the energy loss minimization principle, thereby effectively reducing flow loss, reducing vortex intensity and energy dissipation, and improving flow field uniformity, so as to improve pump efficiency, inhibit vibration noise and cavitation phenomenon. The adaptive grid encryption, multiphase flow cavitation model and test verification mechanism are adopted, and the mapping model is corrected in combination with the measured data, so that the optimization result can directly guide the baffle tongue design under different working conditions, the research and development cycle is significantly shortened, and the physical test cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of dual-suction pump design and numerical simulation technology, and in particular to a simulation method for the correspondence between the position of the tongue of the semi-helical suction chamber and the flow field. Background Technology

[0002] Centrifugal pumps, as widely used fluid transport equipment, have their inlet flow field uniformity and stability directly affected by the position of the tongue in the semi-helical suction chamber. In existing technologies, tongue position design often relies on experience or simplified models, which can easily lead to phenomena such as vortices, flow separation, and secondary flows in the flow field. This results in increased energy loss, decreased efficiency, increased vibration and noise, and a higher risk of cavitation, severely impacting the pump's operational reliability and lifespan. Although numerical simulation techniques are used for flow field analysis, the following shortcomings remain:

[0003] In existing technologies, most flow channel models are oversimplified and do not systematically cover the key spatial parameters of the tongue, such as the influence of inclination angle and radial clearance. The extraction of flow field characteristic parameters is one-sided, lacking correlation analysis between local flow and comprehensive performance. Furthermore, the simulation results have a low degree of matching with actual working conditions and have not formed a quantitative mapping relationship between the tongue position and flow field characteristics, making it difficult to guide precise optimization. Summary of the Invention

[0004] The purpose of this invention is to provide a simulation method for the relationship between the position of the tongue of a semi-helical suction chamber and the flow field. By combining numerical simulation and experimental verification, a correlation model between the spatial position of the tongue of the semi-helical suction chamber and the flow field characteristics is established. The influence of different tongue parameters on the flow state is analyzed, providing a parameter design basis for the structural optimization of the semi-helical suction chamber, improving the hydraulic performance of pump products, and solving the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] Simulation methods for the relationship between the position of the septum in the semi-helical intake chamber and the flow field include:

[0007] A three-dimensional flow channel model is constructed. The flow channel structure model is constructed based on the basic structural features of the semi-spiral suction chamber. Based on several sets of different tongue reference position parameters, the corresponding three-dimensional flow channel model of the semi-spiral suction chamber is established. The tongue position parameters are the tongue spatial characteristic parameters that affect the flow field characteristics. Simulation results under different working conditions are obtained based on the three-dimensional flow channel model of the semi-spiral suction chamber.

[0008] Flow field characteristic analysis involves processing simulation results under different operating conditions, extracting flow field characteristic parameters under different operating conditions, analyzing the correspondence between the reference position parameters of the tongue and the flow field characteristic parameters, and plotting the correlation curve between the tongue position and the flow field stability.

[0009] Correspondence generation: Based on the correlation curve between the tongue position and flow field stability, the mapping relationship between the tongue reference position parameters and the flow field characteristic parameters is analyzed, a mapping relationship model is constructed, and the tongue position parameter range within a reasonable range is determined.

[0010] Furthermore, the construction of the three-dimensional flow channel model also includes:

[0011] Mesh generation: The flow channel of the 3D model of the semi-spiral suction chamber is discretized, and the mesh of the key area is refined. The mesh density of the key area is no less than 3 times higher than that of the non-key area.

[0012] Boundary condition setting: Based on actual operating conditions, set the boundary conditions for the inlet and outlet of the suction chamber, and select a turbulence model and cavitation prediction model suitable for rotating machinery flow to simulate the phase change phenomenon that may occur in the flow field.

[0013] Model simulation calculation: For the three-dimensional model of the semi-spiral suction chamber flow channel with different tongue reference position parameters, the flow field state under different working conditions is simulated respectively, and the change data of key parameters in the flow field are recorded.

[0014] Further, model simulation calculations specifically include:

[0015] The initial conditions of the three-dimensional model calculation network of each semi-spiral suction chamber flow channel are determined based on the setting of boundary conditions. Steady and unsteady calculations are performed in sequence to obtain the initial solution of the flow field and capture the transient characteristics of the flow field.

[0016] Combining the turbulence model and cavitation prediction model, in each iteration step, the physical quantity data on the computational grid nodes are acquired, and the time series data of the physical quantity data are determined;

[0017] The convergence status is determined by combining multiple indicators, while monitoring the residual curve, the rate of change of physical quantities at key sections, and the time series of physical quantities at monitoring points.

[0018] When all residuals within multiple consecutive iterations are less than the preset convergence accuracy, and the rate of change of the physical quantity of the key section is less than the preset rate of change, the calculation for this working condition is determined to be converged.

[0019] After the calculation converges, monitoring points are preset in the key areas of the flow field, and the time-averaged physical quantity data of the preset monitoring points are extracted. Data sampling is performed on the target section to obtain the velocity distribution cloud map and key parameters.

[0020] Furthermore, unsteady calculations also include:

[0021] Based on the physical quantity data and time series data of the physical quantity data on the grid nodes, the basic flow field parameters of each key region are obtained, and the grid distribution is adjusted in real time based on the basic flow field parameters.

[0022] When the vortex intensity in the tongue-bar region exceeds the preset threshold, the local mesh refinement mechanism is automatically triggered to perform mesh refinement processing on the target tongue-bar region.

[0023] Furthermore, in the construction of the three-dimensional flow channel model, when constructing the three-dimensional model of the semi-spiral suction chamber flow channel, the flow channel structure model also includes an inlet section, a transition flow channel, and an impeller inlet area, and the flow channel structure of each group of three-dimensional models of the semi-spiral suction chamber flow channel is consistent except for the reference position parameters of the tongue.

[0024] Furthermore, flow field characteristic parameters under different operating conditions are extracted, specifically including:

[0025] Based on the acquired time-averaged physical quantity data, velocity distribution cloud map, and key parameters, a standardized dataset is formed.

[0026] Based on the flow field fundamental parameters of the standardized dataset, the micro-flow characteristics of each key region are determined. Based on the micro-flow characteristics, the intensity and distribution of local flow phenomena are quantified, and local characteristic parameters of the flow field are obtained.

[0027] Construct a comprehensive evaluation index that reflects the overall stability and energy loss of the flow field, form a parameter system that characterizes the comprehensive performance of the flow field, and generate comprehensive characteristic parameters of the flow field;

[0028] The validity of the acquired local flow field feature parameters and comprehensive flow field feature parameters is verified. The verified local flow field feature parameters and comprehensive flow field feature parameters are then integrated to generate a flow field feature parameter dataset.

[0029] The integrated flow field characteristic parameter dataset is classified and organized according to different operating conditions, the corresponding flow field characteristic parameters under each operating condition are identified, and flow field characteristic parameters under different operating conditions are generated.

[0030] Furthermore, the operating conditions include the design flow rate condition, the small flow rate condition (less than the design flow rate), and the large flow rate condition (greater than the design flow rate), and at least five different combinations of tongue reference position parameters are set for each operating condition; the flow field characteristic parameters include velocity distribution, vorticity intensity, pressure fluctuation amplitude, and velocity non-uniformity of the impeller inlet section, wherein the velocity non-uniformity is the ratio of the standard deviation of the velocity at the impeller inlet section to the average velocity, used to characterize the flow field uniformity.

[0031] Furthermore, the generation of correspondences specifically includes:

[0032] Trend analysis is performed on the correlation curves to extract the extreme points, abrupt change points, and smooth sections of the flow field characteristic parameters in the correlation curves, and to determine the critical interval in which the change of the reference position parameter of the tongue has a significant impact on the flow field characteristic parameters.

[0033] Obtain the reference position parameters of the tongue and the flow field characteristic parameters within the critical interval, and establish a mapping relationship model between the reference position parameters of the tongue and the flow field characteristic parameters;

[0034] Based on the mapping relationship model, the coupling effect of different combinations of reference position parameters of the tongue on the flow field characteristic parameters is analyzed, and the target tongue position parameters are determined.

[0035] Based on the principles of flow field uniformity and energy loss minimization, and combined with the mapping relationship model and the influence law of the target tongue position parameters, the range of tongue reference position parameters corresponding to the flow field characteristic parameters is selected to form the optimal correspondence between the tongue position and the flow field characteristics.

[0036] Furthermore, it also includes:

[0037] Physical prototypes were fabricated by selecting 3-5 sets of typical tongue-and-groove position parameters, covering the optimal parameter range and the parameter range corresponding to the critical state in the simulation analysis, and tests were conducted under design flow conditions, small flow conditions and large flow conditions respectively.

[0038] Based on the experimental results, obtain the velocity distribution cloud map of the flow field, collect physical quantity data of key areas of the flow field, measure the pressure fluctuation coefficient and pressure pulsation frequency characteristics of the target section, and extract the characteristic parameters of the measured flow field.

[0039] The consistency of the flow field characteristic parameters in the comparative test results with the flow field characteristic parameters in the simulation results of the three-dimensional model of the semi-spiral suction chamber channel is compared. When the deviation rate between the two exceeds the preset deviation threshold, the weight coefficient of the target tongue position parameter in the mapping relationship model is corrected, and the functional relationship between the tongue reference position parameter and the flow field characteristic parameters is adjusted.

[0040] Furthermore, the typical tongue-and-groove position parameters include the tongue-and-groove reference position parameters with optimal flow field characteristics in the mapping relationship model, the tongue-and-groove reference position parameters corresponding to the critical state of flow field characteristics, and the tongue-and-groove reference position parameters with poor flow field characteristics.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] By constructing a three-dimensional flow channel model with multiple sets of tongue reference position parameters, and combining multi-condition simulation and quantitative analysis, a mapping relationship model between tongue position and flow field characteristic parameters is established. By accurately capturing the vortex generation law, turbulent dissipation and pressure pulsation characteristics in the tongue region, and combining the principles of flow field uniformity and energy loss minimization, the optimal tongue parameter range is determined, effectively reducing flow loss, vortex intensity and energy dissipation, and improving flow field uniformity. This improves pump efficiency, suppresses vibration noise and cavitation phenomena. Adaptive mesh refinement, multiphase flow cavitation model and experimental verification mechanism are adopted, and the mapping model is corrected by combining measured data, so that the optimization results can directly guide the tongue design under different working conditions, significantly shorten the R&D cycle and reduce physical test costs. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the simulation method for the correspondence between the position of the semi-spiral suction chamber septum and the flow field according to the present invention.

[0044] Figure 2 This is a flowchart of the simulation calculation sub-process of the present invention;

[0045] Figure 3 This is a flowchart of the tongue-separating parameter optimization experiment of the present invention;

[0046] Figure 4 Schematic diagram showing the different tilt angles of the semi-spiral inhalation chamber septum of the present invention;

[0047] Figure 5 This is a schematic diagram of the velocity vector distribution of the flow field under different inclination angles in the semi-spiral suction chamber of the present invention. Detailed Implementation

[0048] 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, and 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.

[0049] To address the shortcomings of existing technologies, such as the lack of systematic coverage of the impact of key spatial parameters of the tongue-and-groove joint, the absence of correlation analysis between local flow and overall performance, low matching degree between simulation results and actual operating conditions, and the lack of a quantitative mapping relationship between tongue-and-groove position and flow field characteristics, which hinders precise optimization, please refer to [the relevant documentation / reference]. Figure 1-5 This embodiment provides the following technical solution:

[0050] Simulation methods for the relationship between the position of the septum in the semi-helical intake chamber and the flow field include:

[0051] A three-dimensional flow channel model is constructed based on the basic structural features of the semi-spiral suction chamber, including a complete flow channel structure model containing a tongue, inlet section, and transition channel. Based on several sets of different tongue reference position parameters, such as the relative inclination angle between the tongue and the impeller inlet, the radius of the tongue's arc, the axial distance between the tongue and the inlet section of the suction chamber, and the radial clearance between the tongue and the impeller blades, a corresponding three-dimensional model of the semi-spiral suction chamber flow channel is established. The tongue position parameters are the tongue spatial characteristic parameters that affect the flow field characteristics, ensuring that the model's geometric features cover the possible operating conditions in actual applications. Simulations are performed based on each independent model, and simulation results under different operating conditions are obtained based on the three-dimensional model of the semi-spiral suction chamber flow channel.

[0052] In this embodiment, when constructing the three-dimensional model of the semi-spiral suction chamber flow channel, the flow channel structure model also includes an inlet section, a transition flow channel, and an impeller inlet area, and the flow channel structure of each set of three-dimensional models of the semi-spiral suction chamber flow channel is consistent except for the reference position parameters of the tongue.

[0053] Flow field characteristic analysis involves processing simulation results under different operating conditions and extracting characteristic parameters of the flow field under different operating conditions, including the vortex generation location, development trajectory and dissipation law, turbulent dissipation rate, cavitation volume fraction and pressure fluctuation frequency characteristics in the tongue region, analyzing the correspondence between the tongue reference position parameters and the flow field characteristic parameters, quantifying the influence of tongue parameters on flow separation, secondary flow and other phenomena, and plotting the correlation curve between tongue position and flow field stability by calculating comprehensive parameters such as velocity nonuniformity and pressure fluctuation coefficient at the impeller inlet section.

[0054] In this embodiment, based on the flow field characteristic parameters and the corresponding tongue reference position parameters under different working conditions, the tongue reference position parameters are used as the abscissa and the flow field characteristic parameters are used as the ordinate to plot the correlation curve between the tongue position and the flow field stability under each working condition. The flow field stability is comprehensively characterized by parameters such as velocity nonuniformity and pressure fluctuation amplitude.

[0055] Correspondence generation: Based on the correlation curve between the tongue position and flow field stability, the mapping relationship between the tongue reference position parameters (such as the change in tilt angle) and the flow field characteristic parameters (such as the peak value of vortex intensity and velocity deviation rate) is analyzed. A mapping relationship model is constructed, and based on the principles of flow field uniformity and minimizing energy loss, the range of tongue position parameters that keeps the flow loss and vortex intensity within a reasonable range is determined.

[0056] In this embodiment, a complete flow channel model including the tongue, inlet section, transition channel, and impeller inlet area is constructed. Based on multiple sets of key tongue spatial characteristic parameters, a series of models are established, systematically covering the actual operating conditions and ensuring the comprehensiveness of the analysis. This model can accurately simulate the influence of the tongue position on the flow field of the entire suction process, rather than just local flow. By calculating comprehensive parameters such as velocity nonuniformity and pressure fluctuation coefficient, and plotting the correlation curve between the tongue position and flow field stability, a deep analysis and precise quantification of the influence of the tongue position on the flow field characteristics are achieved. Based on the correlation curve analysis, a mapping relationship model between the tongue position and flow field characteristic parameters is constructed. Based on the principles of flow field uniformity and minimizing energy loss, the optimized tongue position parameter range is determined, directly pointing to the tongue configuration that keeps flow loss and vortex intensity within a reasonable range. This achieves a leap from phenomenon analysis to specific design guidance, significantly improving design efficiency and optimization effect.

[0057] In this embodiment, the construction of the three-dimensional flow channel model further includes:

[0058] Mesh generation: Adaptive meshing technology is used to discretize the flow channel of the 3D model of the semi-spiral suction chamber flow channel. The mesh of key areas such as the vicinity of the tongue and the impeller inlet is refined to ensure that the mesh quality of key areas meets the requirements of numerical calculation accuracy. The mesh density of the key areas is no less than 3 times higher than that of non-key areas.

[0059] Boundary condition setting: Based on actual operating conditions, the boundary conditions of the inlet and outlet of the suction chamber are set, including the inlet boundary adopting a flow inlet to simulate the actual flow rate through the pump, the outlet boundary adopting a free flow outlet to simulate the open or connected state at the end of the flow channel, and the wall setting being a no-slip condition to simulate the interaction between the real fluid and the wall; and selecting a turbulence model (such as the Reynolds stress model) and a cavitation prediction model suitable for rotating machinery flow to simulate the phase transition phenomena that may occur in the flow field;

[0060] Model simulation calculation: For the three-dimensional model of the semi-spiral suction chamber flow channel with different tongue reference position parameters, the flow field state under different working conditions such as design flow rate, small flow rate, and large flow rate is simulated. At the same time, the change data of key parameters in the flow field are recorded, such as the distribution of velocity vector field, the change of pressure gradient, and the spatial distribution of vorticity intensity.

[0061] In this embodiment, the adaptive meshing technology includes a hybrid technique of structured and unstructured meshes. For example, structured meshes are used in regular geometric regions to improve computational efficiency, while unstructured meshes are used in regions containing complex geometric features (such as tongue surfaces and impeller inlet corners) to ensure mesh quality and fit.

[0062] In this embodiment, the turbulence model is the Reynolds stress model or the SSTk-ω model, and the cavitation prediction model is a multiphase flow cavitation model, to adapt to the complex physical processes such as strong rotation, high shear and cavitation that may exist inside the semi-spiral intake chamber.

[0063] In this embodiment, the model simulation calculation specifically includes:

[0064] The initial conditions of the three-dimensional model calculation network of each semi-spiral suction chamber are determined based on the setting of boundary conditions, including setting the fluid medium properties, the initial values ​​of turbulence parameters and the initial distribution of multiphase flow components. A staged iterative strategy is adopted to perform steady and unsteady calculations in sequence to obtain the initial solution of the flow field and capture the transient characteristics of the flow field.

[0065] Combining the turbulence model and cavitation prediction model, in each iteration step, the momentum equation, continuity equation and turbulent transport equation are solved simultaneously to obtain physical quantity data on the computational grid nodes, including velocity vector, pressure value, turbulence parameters (such as turbulent dissipation rate, vorticity intensity, etc.), and the time series data of the physical quantity data are determined.

[0066] The convergence status is determined by combining multiple indicators, while monitoring the residual curve, the rate of change of physical quantities at key sections, and the time series of physical quantities at monitoring points.

[0067] When all residuals within multiple consecutive iterations are less than the preset convergence accuracy, and the rate of change of the physical quantity of the key section is less than the preset rate of change, the calculation for this working condition is determined to be converged.

[0068] After the calculation converges, monitoring points are preset in key regions of the flow field, and time-averaged physical quantity data of the preset monitoring points are extracted, including velocity components, pressure values, and turbulence parameters; data sampling is performed on target sections, such as the impeller inlet section, to obtain velocity distribution contour maps and key parameters, such as... Figure 5 As shown, this provides data support for subsequent flow field characteristic analysis.

[0069] In this embodiment, the unsteady calculation process also includes:

[0070] Based on the physical quantity data and time series data of the physical quantity data on the grid nodes, the basic flow field parameters of each key region are obtained, and the grid distribution is adjusted in real time based on the basic flow field parameters.

[0071] When the vortex intensity in the tongue region exceeds the preset threshold, the local mesh refinement mechanism is automatically triggered to refine the mesh in the target tongue region. The combination of spring smoothing method and mesh reconstruction method is used to ensure that the mesh quality is not lower than 0.3 during deformation, which meets the calculation requirements.

[0072] In this embodiment, adaptive meshing technology is used to specifically refine key areas such as the tongue and impeller inlet, improving the stability and accuracy of complex flow field simulation. This effectively handles drastic flow changes that may occur during the calculation process, ensuring the continuity and accuracy of the simulation. It overcomes the limitations of traditional fixed meshes in complex flow field simulation. Combined with advanced turbulence models and multiphase cavitation models suitable for rotating machinery, the accuracy of the flow field simulation is ensured, especially in accurately capturing complex flow and cavitation phenomena. The combination of meshing technology and advanced physical models enhances the accuracy and reliability of the simulation, enabling a more realistic reflection of the flow field characteristics under actual working conditions. The phased iterative strategy comprehensively acquires steady-state and dynamic flow field information, ensuring the accuracy and comprehensiveness of the calculation results.

[0073] In this embodiment, the flow field characteristic parameters under different operating conditions are extracted, specifically including:

[0074] Based on the acquired time-averaged physical quantity data, velocity distribution cloud map and key parameters, standardization processing is performed. Data smoothing algorithm is used to remove outliers and computational noise. Spatial interpolation method is used to supplement the physical quantity information in sparse regions of the flow field. Time domain alignment processing is performed on the physical quantity time series data obtained from unsteady calculations to ensure that the data sampling frequency and time span are consistent under different working conditions, forming a standardized dataset.

[0075] Based on the flow field parameters of the standardized dataset, the micro-flow characteristics of each key region are determined, including the starting coordinates, geometric shape and spatial spread trajectory of vortex generation. Based on the micro-flow characteristics, the intensity and distribution law of local flow phenomena are quantified, local characteristic parameters of the flow field are obtained, the spatial distribution gradient of turbulent dissipation rate is calculated, high dissipation regions are located, and the main frequency and amplitude characteristics of pressure fluctuations are extracted by Fourier transform in combination with the time series data of physical quantity data, establishing the correlation between local flow phenomena and frequency characteristics.

[0076] A comprehensive evaluation index reflecting the overall stability and energy loss of the flow field is constructed, forming a parameter system characterizing the comprehensive performance of the flow field. Comprehensive characteristic parameters of the flow field are generated. For example, at target sections such as the impeller inlet, based on the velocity distribution data within the processed standardized dataset, the standard deviation and skewness of the velocity vector are calculated to obtain the velocity non-uniformity. Time-domain statistics are performed on the pressure data at the target section to calculate the pressure fluctuation coefficient (such as the deviation rate between pressure extremes and averages). Combined with the volume integral results of the turbulent dissipation rate, the total energy loss coefficient of the flow field is quantified, forming a parameter system characterizing the comprehensive performance of the flow field.

[0077] The validity of the acquired local flow field feature parameters and comprehensive flow field feature parameters is verified. The verified local flow field feature parameters and comprehensive flow field feature parameters are then integrated to generate a flow field feature parameter dataset.

[0078] The integrated flow field characteristic parameter dataset is classified and organized according to different operating conditions, the corresponding flow field characteristic parameters under each operating condition are identified, and flow field characteristic parameters under different operating conditions are generated.

[0079] In this embodiment, the validity verification includes comparing the numerical deviation of the same feature parameter in the standardized dataset under different grid densities, verifying the sensitivity of the same feature parameter to grid resolution, performing consistency checks on repeated calculation results under the same tongue position parameter, calculating the coefficient of variation of the feature parameter, eliminating parameters with insufficient stability, performing correlation analysis on local feature parameters and comprehensive feature parameters in the standardized dataset, and retaining parameters with significant correlation.

[0080] In this embodiment, the operating conditions include the design flow rate condition, the small flow rate condition (less than the design flow rate), and the large flow rate condition (greater than the design flow rate), and at least five different combinations of tongue reference position parameters are set for each operating condition; the flow field characteristic parameters include velocity distribution, vorticity intensity, pressure fluctuation amplitude, and velocity non-uniformity of the impeller inlet section, wherein the velocity non-uniformity is the ratio of the standard deviation of the velocity at the impeller inlet section to the average velocity, which is used to characterize the flow field uniformity.

[0081] In this embodiment, a deep analysis of complex flows from multiple scales and dimensions is conducted to obtain richer and more accurate flow field information. A parameter system including local and comprehensive characteristic parameters is constructed, which can systematically and objectively quantify the comprehensive impact of different tongue positions on flow field uniformity, stability, and energy loss. Multiple tongue reference position parameter combinations are set for each operating condition for simulation. At the same time, specific indicators such as velocity nonuniformity are selected to quantify and evaluate flow field characteristics. The diversity of actual operating conditions is fully considered, and clear quantitative indicators are used to make the simulation results more instructive and able to more accurately guide the optimization design under different operating conditions.

[0082] In this embodiment, the generation of the correspondence specifically includes:

[0083] Trend analysis is performed on the correlation curves to extract the extreme points, abrupt change points, and smooth sections of the flow field characteristic parameters in the correlation curves, and to determine the critical interval in which the change of the reference position parameter of the tongue has a significant impact on the flow field characteristic parameters.

[0084] Obtain the reference position parameters of the tongue and the flow field characteristic parameters within the critical interval. Use data fitting method to establish a mapping relationship model between the reference position parameters of the tongue and the flow field characteristic parameters. Quantify the functional relationship between the reference position parameters of the tongue (such as the relative tilt angle change and the radial clearance adjustment value) and the flow field characteristic parameters (such as the peak value of vorticity intensity and the velocity non-uniformity).

[0085] Based on the mapping relationship model, the coupling effect of different combinations of reference position parameters of the tongue on the flow field characteristic parameters is analyzed, and the target tongue position parameters that play a dominant role in the flow field stability are determined by multi-parameter sensitivity analysis.

[0086] Based on the principles of flow field uniformity and energy loss minimization, and combined with the mapping relationship model and the influence law of the target tongue position parameters, the reference position parameter range of the tongue corresponding to the flow field characteristic parameters (such as velocity nonuniformity less than a preset threshold and vorticity intensity within a reasonable range) is selected to form the optimal correspondence between the tongue position and the flow field characteristics.

[0087] In this embodiment, trend analysis is performed using the correlation curve between the tongue position and flow field stability to extract extreme points, abrupt change points, and flat sections from the curve. This accurately determines the critical range where changes in the tongue reference position parameters significantly affect the flow field characteristic parameters, quickly locating the region most sensitive to the impact of tongue position changes on flow characteristics. This narrows the search range for subsequent optimization, improves efficiency, establishes a mapping relationship, and identifies key influencing factors through sensitivity analysis. It provides precise quantitative relationships and key parameter information, ultimately selecting tongue position parameter ranges that meet preset standards, forming a clear optimization correspondence, and significantly improving the pertinence and effectiveness of the optimization design.

[0088] In this embodiment, it also includes:

[0089] Physical prototypes were fabricated by selecting 3-5 sets of typical tongue-and-groove position parameters, covering the optimal parameter range and the parameter range corresponding to the critical state in the simulation analysis, and tests were conducted under design flow conditions, small flow conditions and large flow conditions respectively.

[0090] Based on the experimental results, particle image velocimetry was used to obtain the velocity distribution cloud map of the flow field. Physical quantity data of key areas of the flow field were collected through testing methods such as pressure sensors. The pressure fluctuation coefficient and pressure pulsation frequency characteristics of target sections such as the impeller inlet were measured. The measured flow field characteristic parameters were extracted, including velocity distribution, vorticity intensity, pressure fluctuation amplitude and velocity non-uniformity of the impeller inlet section.

[0091] The consistency of the flow field characteristic parameters in the comparative test results with the flow field characteristic parameters in the simulation results of the three-dimensional model of the semi-spiral suction chamber was evaluated. When the deviation rate between the two exceeded the preset deviation threshold, the weight coefficient of the target tongue position parameter in the mapping relationship model was corrected, and the functional relationship between the tongue reference position parameter and the flow field characteristic parameters was adjusted so that the deviation rate between the flow field characteristic parameters output by the corrected mapping relationship model and the measured results was within the preset reasonable range, and the optimal correspondence between the tongue position and the flow field characteristics was further optimized.

[0092] In this embodiment, the typical tongue-bar position parameters include the tongue-bar reference position parameters with optimal flow field characteristics in the mapping relationship model, the tongue-bar reference position parameters corresponding to the critical state of flow field characteristics, and the tongue-bar reference position parameters with poor flow field characteristics.

[0093] In this embodiment, as Figure 4 As shown, by changing the inclination angle of the inlet flow channel, the inclination angle is limited to 0° to 60°, and a test inclination angle is set every 10°.

[0094] In this embodiment, the best simulation results for the centrifugal pump under different flow conditions are determined through simulation results. At the same time, the relationship between the inclination angle and the simulation results is summarized, and a new centrifugal pump flow channel design method is summarized. The actual operation of the centrifugal pump under different inclination angles is verified and summarized through experiments, verifying the accuracy of the simulation results. The placement method of the tongue of the semi-spiral suction chamber flow channel is solved, the flow condition of the fluid in the suction flow channel is improved, the flow loss before the fluid enters the impeller flow channel is minimized, and the fluid circulation caused by the construction of the semi-spiral suction chamber is reduced, thus curbing the occurrence of centrifugal pump noise and cavitation, and extending the service life and efficiency of the centrifugal pump.

[0095] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for simulating the correspondence between the position of the diaphragm tongue in a semi-helical suction chamber and the flow field, characterized in that, include: A three-dimensional flow channel model is constructed. The flow channel structure model is constructed based on the basic structural features of the semi-spiral suction chamber. Based on several sets of different tongue reference position parameters, a corresponding three-dimensional model of the semi-spiral suction chamber flow channel is established. The tongue reference position parameters are tongue spatial characteristic parameters that affect the flow field characteristics. Simulation results under different working conditions are obtained based on the three-dimensional model of the semi-spiral suction chamber flow channel. Flow field characteristic analysis involves processing simulation results under different operating conditions, extracting flow field characteristic parameters under different operating conditions, analyzing the correspondence between the reference position parameters of the tongue and the flow field characteristic parameters, and plotting the correlation curve between the tongue position and the flow field stability. Correspondence generation: Based on the correlation curve between the tongue position and flow field stability, the mapping relationship between the tongue reference position parameters and the flow field characteristic parameters is analyzed, a mapping relationship model is constructed, and the tongue position parameter range within a reasonable range is determined; The construction of the three-dimensional flow channel model also includes model simulation calculations, specifically including: The initial conditions of the three-dimensional model calculation network of each semi-spiral suction chamber flow channel are determined based on the setting of boundary conditions. Steady and unsteady calculations are performed in sequence to obtain the initial solution of the flow field and capture the transient characteristics of the flow field. By combining the turbulence model and the cavitation prediction model, in each iteration step, the physical quantity data on the computational grid nodes are obtained, and the time series data of the physical quantity data are determined. The convergence status is determined by combining multiple indicators, while monitoring the residual curve, the rate of change of physical quantities at key sections, and the time series of physical quantities at monitoring points. When all residuals within multiple consecutive iterations are less than the preset convergence accuracy, and the rate of change of the physical quantity of the key section is less than the preset rate of change, the calculation for this working condition is determined to be converged. After the calculation converges, monitoring points are preset in the key areas of the flow field, and the time-averaged physical quantity data of the preset monitoring points are extracted. Data sampling is performed on the target section to obtain the velocity distribution cloud map and key parameters.

2. The simulation method for the correspondence between the position of the semi-helical suction chamber septum and the flow field as described in claim 1, characterized in that, The construction of the three-dimensional flow channel model also includes: Mesh generation: The flow channel of the 3D model of the semi-spiral suction chamber is discretized, and the mesh of the key area is refined. The mesh density of the key area is no less than 3 times higher than that of the non-key area. Boundary condition setting: Based on actual operating conditions, set the boundary conditions for the inlet and outlet of the suction chamber, and select a turbulence model and cavitation prediction model suitable for rotating machinery flow to simulate the phase change phenomenon that may occur in the flow field. Model simulation calculation: For the three-dimensional model of the semi-spiral suction chamber flow channel with different tongue reference position parameters, the flow field state under different working conditions is simulated respectively, and the change data of key parameters in the flow field are recorded.

3. The simulation method for the correspondence between the position of the semi-spiral suction chamber septum and the flow field as described in claim 2, characterized in that, The unsteady calculation process also includes: Based on the physical quantity data and time series data of the physical quantity data on the grid nodes, the basic flow field parameters of each key region are obtained, and the grid distribution is adjusted in real time based on the basic flow field parameters. When the vortex intensity in the tongue-bar region exceeds the preset threshold, the local mesh refinement mechanism is automatically triggered to perform mesh refinement processing on the target tongue-bar region.

4. The simulation method for the correspondence between the position of the septum in the semi-spiral suction chamber and the flow field as described in claim 3, characterized in that, In the construction of the three-dimensional flow channel model, when constructing the three-dimensional model of the semi-spiral suction chamber flow channel, the flow channel structure model also includes the inlet section, the transition flow channel and the impeller inlet area, and the flow channel structure of each group of three-dimensional models of the semi-spiral suction chamber flow channel is consistent except for the reference position parameters of the tongue.

5. The simulation method for the correspondence between the position of the semi-spiral suction chamber septum and the flow field as described in claim 4, characterized in that, Extracting flow field characteristic parameters under different operating conditions, specifically including: Based on the acquired time-averaged physical quantity data, velocity distribution cloud map, and key parameters, a standardized dataset is formed. Based on the flow field fundamental parameters of the standardized dataset, the micro-flow characteristics of each key region are determined. Based on the micro-flow characteristics, the intensity and distribution of local flow phenomena are quantified, and local characteristic parameters of the flow field are obtained. Construct a comprehensive evaluation index that reflects the overall stability and energy loss of the flow field, form a parameter system that characterizes the comprehensive performance of the flow field, and generate comprehensive characteristic parameters of the flow field; The validity of the acquired local flow field feature parameters and comprehensive flow field feature parameters is verified. The verified local flow field feature parameters and comprehensive flow field feature parameters are then integrated to generate a flow field feature parameter dataset. The integrated flow field characteristic parameter dataset is classified and organized according to different operating conditions, the corresponding flow field characteristic parameters under each operating condition are identified, and flow field characteristic parameters under different operating conditions are generated.

6. The simulation method for the correspondence between the position of the semi-spiral suction chamber septum and the flow field as described in claim 5, characterized in that, The operating conditions include the design flow rate condition, the small flow rate condition (less than the design flow rate), and the large flow rate condition (greater than the design flow rate), and at least five different combinations of tongue reference position parameters are set for each operating condition; the flow field characteristic parameters include velocity distribution, vorticity intensity, pressure fluctuation amplitude, and velocity non-uniformity of the impeller inlet section, wherein the velocity non-uniformity is the ratio of the standard deviation of the velocity at the impeller inlet section to the average velocity, which is used to characterize the flow field uniformity.

7. The simulation method for the correspondence between the position of the semi-helical suction chamber septum and the flow field as described in claim 6, characterized in that, The generation of correspondences specifically includes: Trend analysis is performed on the correlation curves to extract the extreme points, abrupt change points, and smooth sections of the flow field characteristic parameters in the correlation curves, and to determine the critical interval in which the change of the reference position parameter of the tongue has a significant impact on the flow field characteristic parameters. Obtain the reference position parameters of the tongue and the flow field characteristic parameters within the critical interval, and establish a mapping relationship model between the reference position parameters of the tongue and the flow field characteristic parameters; Based on the mapping relationship model, the coupling effect of different combinations of reference position parameters of the tongue on the flow field characteristic parameters is analyzed, and the target tongue position parameters are determined. Based on the principles of flow field uniformity and energy loss minimization, and combined with the mapping relationship model and the influence law of the target tongue position parameters, the range of tongue reference position parameters corresponding to the flow field characteristic parameters is selected to form the optimal correspondence between the tongue position and the flow field characteristics.

8. The simulation method for the correspondence between the position of the semi-spiral suction chamber septum and the flow field as described in claim 7, characterized in that, Also includes: Physical prototypes were fabricated by selecting 3-5 sets of typical tongue-and-groove position parameters, covering the optimal parameter range and the parameter range corresponding to the critical state in the simulation analysis, and tests were conducted under design flow conditions, small flow conditions and large flow conditions respectively. Based on the experimental results, a flow field velocity distribution cloud map was obtained, physical quantity data of key areas of the flow field were collected, the pressure fluctuation coefficient and pressure pulsation frequency characteristics of the target section were measured, and the measured flow field characteristic parameters were extracted. The consistency of the flow field characteristic parameters in the comparative test results with the flow field characteristic parameters in the simulation results of the three-dimensional model of the semi-spiral suction chamber channel is compared. When the deviation rate between the two exceeds the preset deviation threshold, the weight coefficient of the target tongue position parameter in the mapping relationship model is corrected, and the functional relationship between the tongue reference position parameter and the flow field characteristic parameters is adjusted.

9. The simulation method for the correspondence between the position of the semi-spiral suction chamber septum and the flow field as described in claim 8, characterized in that, The typical tongue-and-groove position parameters include the tongue-and-groove reference position parameters with optimal flow field characteristics in the mapping relationship model, the tongue-and-groove reference position parameters corresponding to the critical state of flow field characteristics, and the tongue-and-groove reference position parameters with poor flow field characteristics.

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