A performance prediction method for a supercritical carbon dioxide compressor

Abstract

The application discloses a supercritical carbon dioxide compressor performance prediction method and device, electronic equipment and computer readable storage medium, and belongs to the technical field of aerodynamic design. The method comprises the following steps: obtaining input parameters of the compressor, generating a meridian plane calculation grid and constructing an initial flow field; performing internal loop iterative calculation according to a preset velocity gradient equation and a flow boundary condition; when flow convergence and pressure convergence conditions are met, flow field parameters are obtained; when in a blockage working condition, iterative calculation is performed according to a preset pressure gradient equation and a pressure boundary condition; the streamline position of the fluid in the compressor is adjusted based on the flow field parameters, and external loop iterative calculation is performed; and the convergence result is taken as the performance parameter of the compressor. Through the internal and external loop iterative structure, and automatic switching of the control equation and the boundary condition under the blockage working condition, calculation oscillation and iterative divergence are effectively inhibited, and stable and efficient performance prediction in the full working condition range is realized.

Description

Technical Field

[0001] This application belongs to the field of aerodynamic design technology, specifically relating to a method, apparatus, electronic device, and computer-readable storage medium for predicting the performance of a supercritical carbon dioxide compressor. Background Technology

[0002] In the advanced heat-work conversion technology of supercritical carbon dioxide (sCO2) closed Brayton power cycle, the performance of the compressor directly determines the feasibility and efficiency of the entire cycle.

[0003] Currently, the quasi-three-dimensional flow method is one of the mainstream methods for predicting the performance of supercritical carbon dioxide compressors. In the quasi-three-dimensional flow method, existing technologies generally adopt the synchronous iteration of multiple variables such as "flow rate-pressure-streamline" in the iterative strategy. At the same time, for high speed and transonic operating conditions, the flow rate is usually used as the boundary condition and the solution is based on the velocity gradient equation.

[0004] However, in the process of realizing this application, the inventors found that the prior art has at least the following problems: the multivariate synchronous iteration strategy is prone to computational oscillation, resulting in poor stability of the iteration structure. At the same time, when approaching the blockage condition, the boundary conditions fail, which can easily cause iteration divergence and make it difficult to meet the requirements of stable and efficient performance prediction across the entire operating range. Summary of the Invention

[0005] This application aims to provide a method, apparatus, electronic device, and computer-readable storage medium for predicting the performance of a supercritical carbon dioxide compressor, at least solving the problem of poor stability of the iterative structure in the quasi-three-dimensional performance prediction method for compressors.

[0006] In a first aspect, embodiments of this application disclose a method for predicting the performance of a supercritical carbon dioxide compressor, including: The compressor's input parameters are obtained, and a meridional computational grid is generated based on the input parameters to construct an initial flow field. The meridional computational grid is used to characterize the node distribution of the fluid space inside the compressor, and the initial flow field is used to characterize the initial thermodynamic parameters of each node. Based on the preset velocity gradient equation and flow boundary conditions, the meridional computational grid is subjected to internal loop iterative calculation, and when the flow convergence and pressure convergence conditions are met, the flow field parameters are obtained. The flow field parameters reflect the spatial distribution of the fluid flow state inside the compressor. When the impeller of the compressor is determined to be in a blocked condition based on the flow field parameters, the meridional calculation grid is iteratively calculated according to the preset pressure gradient equation and pressure boundary conditions, and the updated flow field parameters are obtained when the pressure convergence condition is met. Based on the flow field parameters, the streamline position of the fluid inside the compressor is adjusted, and the external loop iterative calculation is continued on the meridional computational grid. When the streamline convergence condition is met, the convergence result is used as the performance parameter of the compressor. The streamline position reflects the spatial coordinates of the fluid on the meridional plane.

[0007] Secondly, embodiments of this application also disclose a performance prediction device for a supercritical carbon dioxide compressor, comprising: The data acquisition module is used to acquire the input parameters of the compressor, generate a meridional computational grid based on the input parameters, and construct an initial flow field; the meridional computational grid is used to characterize the node distribution of the fluid space inside the compressor, and the initial flow field is used to characterize the initial thermodynamic parameters of each node; The iterative correction module is used to perform internal loop iterative calculations on the meridional computational grid according to the preset velocity gradient equation and flow boundary conditions, and obtain flow field parameters when the flow convergence and pressure convergence conditions are met. The flow field parameters reflect the spatial distribution of the fluid flow state inside the compressor. The blockage handling module is used to perform iterative calculations on the meridional computational grid according to the preset pressure gradient equation and pressure boundary conditions when it is determined that the impeller of the compressor is in a blockage condition based on the flow field parameters, and to obtain updated flow field parameters when the pressure convergence condition is met. The performance prediction module is used to adjust the streamline position of the fluid inside the compressor based on the flow field parameters, and continue to perform external loop iterative calculations on the meridional computational grid. When the streamline convergence condition is met, the convergence result is used as the performance parameter of the compressor. The streamline position reflects the spatial coordinates of the fluid on the meridional plane.

[0008] Thirdly, embodiments of this application also disclose a computer-readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the method described in the first aspect.

[0009] Fourthly, embodiments of this application also disclose an electronic device, including a processor and a memory, wherein the memory stores a program or instructions executable on the processor, and the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0010] In summary, in this embodiment, by obtaining the compressor's input parameters, a meridional computational grid is generated based on the input parameters, and an initial flow field is constructed. Before the iterative loop, a reasonable computational grid and initial flow field are provided, offering a more reliable structural foundation for subsequent iterations. Based on this, the meridional computational grid is subjected to internal loop iterative calculation according to the preset velocity gradient equation and flow boundary conditions. When the flow convergence and pressure convergence conditions are met, the flow field parameters are obtained. Subsequently, the compressor blockage status is determined based on the flow field parameters. When the compressor impeller is in a blocked condition, the meridional computational grid is iteratively calculated according to the preset pressure gradient equation and pressure boundary conditions. When the pressure convergence condition is met, the updated flow field parameters are obtained. Based on the obtained flow field parameters, the streamline position of the fluid inside the compressor is adjusted, and the external loop iterative calculation of the meridional computational grid continues. When the streamline convergence condition is met, the convergence result is used as the compressor's performance parameters. Therefore, the method based on the embodiments of this application, through the iterative structure of inner and outer loops and the automatic switching of control equations and boundary conditions under congested conditions, effectively suppresses computational oscillations and iterative divergence, and achieves stable and efficient performance prediction across the entire operating range. Attached Figure Description

[0011] In the attached diagram: Figure 1 This is a flowchart illustrating the steps of a performance prediction method for a supercritical carbon dioxide compressor provided in an embodiment of this application. Figure 2 This is a flowchart of another method for predicting the performance of a supercritical carbon dioxide compressor provided in an embodiment of this application; Figure 3 This is a schematic diagram of the velocity vector at the impeller outlet provided in an embodiment of this application; Figure 4 This is a schematic flowchart of the performance prediction method for a supercritical carbon dioxide compressor provided in the embodiments of this application; Figure 5 This is a schematic diagram of a meridional calculation grid provided in an embodiment of this application; Figure 6 This is a block diagram of a performance prediction device for a supercritical carbon dioxide compressor provided in an embodiment of this application; Figure 7 This is a block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, the "and / or" signifies at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects have an "or" relationship.

[0014] The performance prediction method for supercritical carbon dioxide compressors described in this application refers to a compressor using carbon dioxide in a supercritical state as the working fluid. It utilizes the unique physical properties of carbon dioxide above the critical point (304.1 K, 7377 kPa), exhibiting both high density and low viscosity, to achieve a high-efficiency, high-power-density compression process. The power cycle system of this compressor demonstrates excellent heat source adaptability, with its optimal operating range (400-800°C) perfectly matching various heat sources such as solar energy, nuclear energy, fossil fuels, and industrial waste heat. During operation, the working fluid enters the impeller axially, is driven by the high-speed rotating blades to achieve a very high circumferential velocity, and flows out radially under centrifugal force. Mechanical energy is converted into kinetic energy and pressure energy, thereby pressurizing the working fluid. To minimize compression power consumption, the compressor inlet parameters are typically set near the critical point, where the carbon dioxide compressibility factor is extremely low (approximately 0.2), exhibiting extremely high compressibility and significantly reducing compression power consumption. To meet the engineering requirements of rapid and precise design of supercritical carbon dioxide compressors, accurate and efficient performance prediction methods are of great significance for shortening the design cycle, reducing costs, and ensuring stable system operation. Performance prediction mainly involves calculating the internal flow field parameters (such as velocity and pressure) and compressor performance parameters (such as pressure ratio and polytropic efficiency) through numerical simulation methods, thereby evaluating the rationality of the overall compressor design scheme or predicting the operating performance under different operating conditions.

[0015] It should be noted that this application uses a supercritical carbon dioxide compressor as an example, but those skilled in the art will understand that the performance prediction method provided in this application is not limited to supercritical carbon dioxide working fluid. In practical applications, by replacing the real gas property library with the property data of the corresponding working fluid and adjusting the input parameters accordingly, this method can be extended to the performance prediction of compressors with other working fluids.

[0016] like Figure 1 The image shows a performance prediction method for a supercritical carbon dioxide compressor provided in an embodiment of this application.

[0017] The method may include the following steps: Step 101: Obtain the input parameters of the compressor, generate a meridional computational grid based on the input parameters, and construct the initial flow field.

[0018] Among them, the meridional computational grid is used to characterize the node distribution of the fluid space inside the compressor, and the initial flow field is used to characterize the initial thermodynamic parameters of each node.

[0019] In this embodiment of the invention, to discretize the continuous three-dimensional flow space inside the compressor into a form solvable on a computer and to provide reasonable initial values ​​for subsequent iterative calculations, a meridional computational grid is constructed, formed by the interweaving of multiple streamlines and quasi-orthogonal lines. This grid is used to discretize the continuous three-dimensional flow space inside the compressor into a series of computational nodes, on which the flow control equations are solved. The initial flow field is based on the rotor enthalpy conservation principle and the isentropic flow assumption. By assuming no slippage at the impeller outlet and presetting the initial polytropic efficiency, the outlet meridional velocity is iteratively solved until convergence. Then, the full-field meridional velocity distribution is obtained by linear interpolation along the streamlines. Finally, the thermodynamic parameters such as enthalpy, density, temperature, and pressure of each node are directly obtained from a real gas property library. The initial flow field is the set of initial values ​​of the flow parameters at each node of the meridional computational grid, serving as the starting point for subsequent internal circulation iterative calculations. Through the above initialization process, a physically reasonable initial value close to the real solution can be provided for subsequent iterative calculations, thereby effectively reducing the risk of computational divergence and accelerating the convergence speed.

[0020] In a specific example, the input parameters include impeller geometry parameters, inlet parameters, operating parameters, and solution control parameters. The impeller geometry parameters include the impeller meridional profile, blade geometric coordinates, number of blades, and tip clearance. The inlet parameters include total temperature and total pressure. The operating parameters include rotational speed and mass flow rate. The solution control parameters include mesh density and residual convergence conditions. Based on these input parameters, Transfinite Interpolation (TFI) is used. The meridional computational grid is generated using the Interpolation method, with the hub curve, casing curve, inlet edge curve, and outlet edge curve as boundaries. The physical coordinates of each grid node are determined using interpolation formulas in the computational space coordinate system, resulting in a computational grid interwoven with streamlines and quasi-orthogonal lines. After grid generation, an initial flow field is constructed, assuming no slippage at the impeller outlet (i.e., the outlet airflow angle equals the blade geometry angle) and a preset initial polytropic efficiency. The outlet meridional velocity is iteratively solved based on the Euler equation until convergence. Subsequently, the meridional velocity distribution of each node in the meridional computational grid is obtained through linear interpolation along the streamlines. Based on this, combined with the rotor enthalpy conservation and isentropic flow assumptions, thermodynamic parameters such as enthalpy, density, temperature, and pressure at each point in the entire field are obtained directly from a real gas property database, completing the flow field initialization. The entire initialization process does not introduce the ideal gas equation of state; it is entirely based on the actual properties of the working fluid, thus enabling accurate capture of supercritical carbon dioxide at the critical point (304.1 K). It possesses strong nonlinear physical properties, combining high density and low viscosity (7377 kPa and above).

[0021] Step 102: Based on the preset velocity gradient equation and flow boundary conditions, perform internal loop iterative calculations on the meridional computational grid, and obtain the flow field parameters when the flow convergence and pressure convergence conditions are met.

[0022] Among them, the flow field parameters reflect the spatial distribution of the fluid flow state inside the compressor.

[0023] In this embodiment of the invention, in order to obtain accurate flow field parameters while satisfying the physical constraints of mass conservation and to avoid computational oscillations caused by multivariate synchronous iteration, an inner loop iterative calculation is performed on the meridional computational grid. The inner loop includes two sub-loops: flow rate iteration and static pressure correction. First, the flow rate iteration loop is executed. According to the preset velocity gradient equation and flow rate boundary conditions, the velocity gradient equation is solved on the meridional computational grid to obtain the meridional velocity distribution. The meridional velocity distribution is then corrected using the Runge-Kutta method. When the flow rate converges, intermediate flow field parameters are obtained. Intermediate flow field parameters refer to the transitional flow field data where the flow rate has converged but the pressure has not yet converged. Then, the static pressure correction loop is executed. Based on the intermediate flow field parameters obtained by the flow rate iteration, the variable efficiency is calculated using a loss model. The outlet static pressure is iteratively corrected using a relaxation factor until the error is less than the error threshold. After convergence, the outlet entropy is obtained by calling the real physical property parameters. The entropy distribution is given along the streamline, and the overall aerodynamic parameters are updated. The overall aerodynamic parameters include physical quantities such as velocity, pressure, temperature, and density at each node of the meridional computational grid. Through the above-mentioned internal loop iterative calculation, the decoupled iteration of flow and pressure is realized, which effectively avoids the calculation oscillation caused by multi-variable synchronous iteration and significantly improves the stability and convergence efficiency of the iteration process.

[0024] In a specific example, based on the preset velocity gradient equation and flow boundary conditions, an internal loop iterative calculation is performed on the meridional computational grid to solve the velocity gradient control equation established along the quasi-orthogonal line direction on the meridional surface. The Runge-Kutta method is used to integrate along the quasi-orthogonal line direction, progressing from the hub to the casing, to obtain the meridional velocity distribution of each grid node. Then, based on the obtained meridional velocity distribution, the mass flow rate on each quasi-orthogonal line is calculated. The calculated mass flow rate is compared with the design flow rate. If the relative error is greater than a preset threshold, the meridional velocity at the hub is adjusted, and the velocity gradient equation is solved again. Based on the intermediate flow field parameters obtained from the flow iteration, the polytropic efficiency at each streamline position is calculated in combination with the loss model. The outlet static pressure is iteratively corrected by the relaxation factor until convergence. After convergence, the real physical property parameter table is called, and the outlet entropy is obtained by querying the outlet static pressure and outlet enthalpy. Given the entropy distribution law along the streamline direction, the entropy value of each grid point is interpolated. Based on the current enthalpy and entropy values ​​of each grid node, the overall thermodynamic parameters are updated to obtain the converged flow field parameters. Through the above-described inner loop iterative calculation, this embodiment reduces the number of iterations and shortens the calculation time compared to the traditional multivariate synchronous iterative method, and no oscillation or divergence occurs during the entire calculation process.

[0025] Step 103: When the compressor impeller is determined to be in a blocked condition based on the flow field parameters, the meridional calculation grid is iteratively calculated according to the preset pressure gradient equation and pressure boundary conditions, and the updated flow field parameters are obtained when the pressure convergence condition is met.

[0026] In this embodiment of the invention, to ensure stable solution of flow field parameters under clogging conditions and avoid iterative divergence caused by boundary condition failure in traditional methods, the clogging condition is determined by the average relative Mach number of the impeller throat section. Based on the current flow field parameters, the average relative Mach number of the impeller throat section is calculated. If this value is greater than or equal to 1.0, the compressor impeller is determined to be in a clogging condition. Once clogging is determined, the governing equation is switched from the velocity gradient equation to the pressure gradient equation, and the outlet static pressure is used instead of the flow rate as the boundary condition. The meridional computational grid is re-iterated, and the pressure gradient equation is solved along the quasi-orthogonal direction. The iterative calculation continues until the pressure converges, obtaining the updated flow field parameters. Through the above equation switching and boundary condition switching, stable solution of the flow field under clogging conditions is achieved, providing technical support for accurately predicting the pressure ratio and efficiency drop characteristics under clogging conditions.

[0027] In a specific example, based on the current flow field parameters, the average relative Mach number of the impeller throat section is calculated to be greater than 1.0, indicating that the compressor impeller is in a clogging condition. The system automatically switches the control equation from the velocity gradient equation to the pressure gradient equation and the boundary condition from mass flow rate to outlet static pressure. Then, it solves the pressure gradient equation established along the quasi-orthogonal line on the meridional plane. Using the Runge-Kutta method, it calculates the distribution of outlet static pressure along the blade height (from blade root to blade tip), adjusts the static pressure value at the blade root until the average static pressure at the outlet equals the preset value, and obtains the updated flow field parameters. This embodiment effectively overcomes the problems of boundary condition failure and iterative divergence in traditional methods under clogging conditions by switching the control equation and boundary conditions.

[0028] Step 104: Based on the flow field parameters, adjust the streamline position of the fluid inside the compressor, continue to perform external loop iterative calculations on the meridional computational grid, and when the streamline convergence condition is met, use the convergence result as the performance parameter of the compressor.

[0029] The streamline position reflects the spatial coordinates of the fluid on the meridional plane.

[0030] In this embodiment of the invention, to achieve self-consistent solutions for streamlines and flow field parameters based on the internal circulation flow field solution, the streamline positions of the fluid inside the compressor are adjusted based on the current flow field parameters, and an external circulation iterative calculation is performed on the meridional computational grid. Specifically, the streamline positions can be adjusted according to the principle of equal flow division, and a slip model is introduced to correct the airflow angle. After the update is completed, the internal circulation iterative calculation is called again. The principle of equal flow division means dividing the total flow equally according to the number of streamlines, and determining the new position of each streamline on the quasi-orthogonal line through inverse interpolation. The slip model is used to calculate the deviation between the actual airflow angle and the blade geometry angle. In this way, the flow field solution and streamline position adjustment can be coupled with each other, so that the obtained flow field parameters and streamline positions satisfy mass conservation and flow consistency, gradually approximating the real flow state, and finally obtaining accurate and reliable compressor performance parameters.

[0031] In a specific example, during the external circulation iteration based on the current flow field parameters, the external circulation iteration analyzes the flow distribution on each quasi-orthogonal line according to the principle of equal flow distribution. It determines the new position of the streamline through inverse interpolation calculation. At the same time, it calls the Wiesner slip model to calculate the slip coefficient based on parameters such as the number of blades and the outlet radius, and corrects the airflow angle. After the update is completed, the internal circulation iteration calculation is called again. After multiple external circulation iterations, the system detects that the change in the position of the streamline between two adjacent iterations is less than the preset threshold, determines that the streamline has converged, and finally outputs the compressor's overall pressure ratio, polytropic efficiency, and other performance parameters, as well as the meridional aerodynamic parameters, as the performance prediction results.

[0032] In summary, in this embodiment, by obtaining the compressor's input parameters, a meridional computational grid is generated based on the input parameters, and an initial flow field is constructed. Before the iterative loop, a reasonable computational grid and initial flow field are provided, offering a more reliable structural foundation for subsequent iterations. Based on this, the meridional computational grid is subjected to internal loop iterative calculation according to the preset velocity gradient equation and flow boundary conditions. When the flow convergence and pressure convergence conditions are met, the flow field parameters are obtained. Subsequently, the compressor blockage status is determined based on the flow field parameters. When the compressor impeller is in a blocked condition, the meridional computational grid is iteratively calculated according to the preset pressure gradient equation and pressure boundary conditions. When the pressure convergence condition is met, the updated flow field parameters are obtained. Based on the obtained flow field parameters, the streamline position of the fluid inside the compressor is adjusted, and the meridional computational grid is subjected to external loop iterative calculation. When the streamline convergence condition is met, the convergence result is used as the compressor's performance parameters. Therefore, the method based on the embodiments of this application, through the iterative structure of inner and outer loops and the automatic switching of control equations and boundary conditions under congested conditions, effectively suppresses computational oscillations and iterative divergence, and achieves stable and efficient performance prediction across the entire operating range.

[0033] like Figure 2The image shows another method for predicting the performance of a supercritical carbon dioxide compressor provided in this application.

[0034] The method may include the following steps: Step 201: Obtain the input parameters of the compressor, generate a meridional computational grid based on the input parameters, and construct the initial flow field.

[0035] Among them, the meridional computational grid is used to characterize the node distribution of the fluid space inside the compressor, and the initial flow field is used to characterize the initial thermodynamic parameters of each node.

[0036] The method shown in this step has been explained in step 101 and will not be repeated here.

[0037] Optionally, step 201 includes the following sub-steps: Sub-step 2011: Based on the input parameters, a meridional computational grid consisting of streamlines and quasi-orthogonal lines is generated using an overlimit interpolation method.

[0038] In this embodiment of the invention, in order to discretize the continuous meridional flow region inside the compressor into a structured grid that facilitates numerical solution, the Transfinite Interpolation (TFI) method is used to generate the meridional computational grid. This method uses the hub curve, casing curve, inlet edge curve, and outlet edge curve as boundaries, and operates within the computational space... ) and physical space Establishing a bilinear interpolation relationship between them can ensure that the grid lines fit the boundary perfectly and control the orthogonality and smoothness inside the grid.

[0039] Specifically, for the quadrilateral region bounded by the hub curve, casing curve, inlet edge curve, and outlet edge curve, the coordinates of any point within it are determined by the following formula:

[0040] in, To calculate spatial coordinates, the range of values ​​is [0,1]. and The coordinates of the physical space grid points, and The equation of the wheel hub curve. and The curve equation for the casing. and Let be the equation of the curve at the import boundary. and The equation of the curve at the export boundary is... and Equal to the focal coordinates; hub curve The casing curve is obtained by spline interpolation from given discrete points. Similarly, import side and export edge The ends of the hub and the casing are connected respectively.

[0041] In this embodiment of the invention, the meridional plane computational mesh generation is performed according to the following detailed steps: T1 Input the discrete point coordinates of the hub, casing, inlet edge, and outlet edge; T2 Perform spline interpolation on the four boundaries respectively to obtain the continuous boundary curve equations. , , and T3 sets the number of mesh nodes, for example, along... Take m nodes along the direction Take n points for each direction; T4 calculates the spatial coordinates. Substitute these values ​​into the TFI formula above to calculate the corresponding physical space coordinates. T5 outputs the coordinates of all grid nodes for subsequent streamline curvature method calculations, ultimately generating a meridional surface calculation grid composed of streamlines and quasi-orthogonal lines.

[0042] Thus, in this embodiment of the invention, the meridional computational grid generated by the overlimit interpolation method can accurately fit the geometric boundary of the impeller meridional flow channel. The grid lines have good orthogonality and smoothness, providing a stable discrete space for subsequent solution of the velocity gradient equation or pressure gradient equation. The structured grid data generated by this method is continuous and regular, which facilitates numerical integration and interpolation operations along streamlines and quasi-orthogonal lines, laying the foundation for stable convergence of internal and external loop iterative calculations, and further ensuring the accuracy and computational efficiency of compressor performance prediction.

[0043] In a specific example, given the discrete coordinates of the impeller hub (e.g., 20 points from inlet to outlet) and the discrete coordinates of the casing (20 points in total), and the endpoint coordinates of the inlet and outlet edges are known, the number of mesh nodes in the streamline direction is set to m=50, and the number of mesh nodes in the quasi-orthogonal direction is set to n=20. First, spline interpolation is performed on the discrete points of the hub and casing to obtain a continuous boundary curve; then, according to the TFI formula, for each computational space coordinate... ( i =1~50, j =1~20) Calculate the corresponding physical coordinates The final generated meridional computational mesh perfectly fits the impeller geometric boundary, with smooth streamline transitions, quasi-orthogonal lines approximately orthogonal to streamlines, and mesh quality meets the requirements for solving subsequent governing equations.

[0044] Sub-step 2012 calculates the meridional grid based on the meridional plane, assuming no slippage at the impeller outlet and preset initial polytropic efficiency, iteratively solves the outlet meridional velocity, and obtains the initial meridional velocity distribution across the entire field by linear interpolation along the streamline.

[0045] In this embodiment of the invention, in order to quickly obtain the initial velocity values ​​of all nodes of the meridional plane computational grid when only the inlet and outlet boundary conditions are known, a no-slip method with a preset initial polytropic efficiency is adopted. The exit meridional velocity is solved iteratively based on the Euler equation, and then the full-field meridional velocity distribution is obtained by linear interpolation along the streamline.

[0046] Specifically, the inlet density is first obtained by referring to a real gas property library based on the total inlet temperature and total pressure. Then, the inlet meridional velocity is calculated using the mass conservation equation, combined with the inlet area and the design mass flow rate. For example, in the formula: ;in, It is quality flow. It's density. It is the cross-sectional area of ​​the flow path. It is the flow rate; then assume the initial polytropic efficiency. And assumes no slippage at the impeller outlet, i.e., the outlet airflow angle. Equal to the blade geometric angle Initial exit meridional velocity ,in, If the inlet meridional velocity is the inlet velocity, then the circumferential component of the outlet relative velocity can be calculated using the formula: ; It is the blade geometry angle; then the outlet enthalpy is calculated based on the Euler equation. : ,in, It is imported enthalpy. and It is the circumferential speed of import and export. It is the circumferential component of the relative velocity at the inlet; using the isentropic assumption (exit entropy equals inlet entropy), the outlet density is obtained by looking up tables in the real gas property library, and then the new outlet meridional velocity is calculated back based on the law of conservation of mass. Compare the difference between the two meridional speeds. If the relative error between the two values ​​is greater than a set error threshold, the result is considered zero. (For example =10 -4 ), then let Return to iterative calculation; if the error is less than Then the exit meridional velocity converges, and the converged exit meridional velocity is obtained; finally, the meridional velocity is linearly interpolated along the streamline direction (from the inlet to the outlet) to obtain the initial meridional velocity distribution of each node on the meridional surface computational grid.

[0047] In this embodiment of the invention, the method has low computational complexity and is easy to implement. It can quickly generate a physically reasonable initial meridional velocity distribution across the entire field, providing a reliable velocity field basis for the subsequent solution of thermodynamic parameters.

[0048] In a specific example, assuming no slippage at the impeller outlet (i.e., the outlet airflow angle equals the blade geometry angle), and with an initial polytropic efficiency preset to 0.90, and an initial outlet meridional velocity of 80 m / s, the outlet enthalpy is calculated using the Euler equation. The outlet density is then obtained from the supercritical carbon dioxide property library. Finally, the outlet meridional velocity is calculated back based on the design mass flow rate of 5.0 kg / s. After five iterations, the outlet meridional velocity stabilizes at 92.3 m / s, with a relative error less than a preset error threshold (e.g., 10). -4 To determine convergence, 20 nodes are taken at equal intervals from the inlet to the outlet along the streamline, and the meridional velocity of each node in the entire field is calculated by linear interpolation.

[0049] Sub-step 2013, based on the initial meridional velocity distribution across the entire field, combined with the assumptions of rotor enthalpy conservation and isentropic flow, obtains the thermodynamic parameters of each node in the meridional plane computational grid, thus completing the construction of the initial flow field.

[0050] In this embodiment of the invention, to further determine the thermodynamic state of each node based on the obtained initial velocity field, the rotor enthalpy conservation and isentropic flow assumptions are adopted. The thermodynamic parameters such as enthalpy, density, temperature, and pressure at each point in the entire field are obtained directly from a real gas property database to complete the flow field initialization. Specifically, for any node on the meridional plane computational grid, the enthalpy value of that node can be determined by rotor enthalpy conservation. Then, based on the isentropic assumption (entropy remains constant along the streamline and is equal to the inlet entropy), the circumferential velocity component is calculated using the obtained meridional velocity distribution and blade angular distribution, thereby obtaining the node enthalpy value. Using enthalpy and entropy as independent parameters, the static pressure, static temperature, density, and other thermodynamic parameters of that node can be obtained by directly looking up tables in the real gas property database. This calculation is performed sequentially on all grid nodes to complete the initialization of the thermodynamic parameters of the entire flow field. The entire calculation process does not introduce the ideal gas equation of state and is entirely based on looking up tables of actual working fluid properties, providing an accurate initial thermodynamic field for subsequent iterative calculations.

[0051] In a specific example, given an inlet total temperature of 304.3 K (close to the critical point of carbon dioxide, 304.1 K) and a total pressure of 7.7 MPa (slightly higher than the critical pressure of carbon dioxide), the carbon dioxide is in a supercritical state with a density of approximately 644.7 kg / m³ (close to liquid) and a very low viscosity (close to gas). Its properties are extremely sensitive to changes in temperature and pressure. By accessing a real supercritical carbon dioxide property database, the inlet entropy and enthalpy are obtained. For a node within the meridional plane, the enthalpy value at that node is determined by the conservation of rotor enthalpy. Combined with the isentropic assumption (entropy equals inlet entropy), the static pressure, static temperature, and density of that node can be directly obtained by looking up tables using enthalpy and entropy as independent parameters. For example, the table lookup results for a certain node are: static pressure approximately 8.0 MPa, static temperature approximately 345 K, and density approximately 170.9 kg / m³. Since the physical properties of sCO2 change drastically in the near-critical region, the ideal gas assumption would produce a large error. However, this method is based on actual physical properties and can accurately capture the strong nonlinear characteristics of this region. By repeating the above table lookup operation on all grid nodes, the pressure, temperature, density and other distributions of the entire field can be obtained, and the initial flow field can be constructed, providing an accurate and reliable initial flow field for subsequent internal circulation iterations.

[0052] Step 202: Solve the velocity gradient equation to obtain the meridional velocity distribution, and correct the meridional velocity distribution using the Runge-Kutta method. When the flow convergence is satisfied, obtain the intermediate flow field parameters.

[0053] In this embodiment of the invention, in order to obtain a flow field solution that satisfies mass conservation at a fixed streamline position on the meridional computational grid, starting from the initial flow field, a method combining velocity gradient equation and flow rate iteration is adopted. Specifically, on the meridional computational grid, a velocity gradient equation is established and solved along the quasi-orthogonal direction:

[0054] in, It is relative velocity. It is the airflow angle. It is the meridian angle. It is the inclination angle of the quasi-orthogonal line. It is the local radius. It is streamline curvature. These are the blade angular coordinates. It is the length of the quasi-orthogonal line. It is the meridional relative velocity. It is the impeller angular velocity. It is the circumferential relative velocity. It is the rotor enthalpy. It is the circumferential component of absolute velocity. It is the length of the meridional streamline. It is entropy.

[0055] Based on this, calculate the flow rate through each quasi-orthogonal line: ,in, It is the number of leaves. It's density. It is the meridional relative velocity. It is the meridian angle. It is the inclination angle of the quasi-orthogonal line. This refers to the tangential thickness of the blade. Solving the ordinary differential equation for each quasi-orthogonal line yields the distribution of the meridional velocity along that line. To obtain the specific magnitude of the meridional velocity, a boundary value condition needs to be given to the equation. Generally, the magnitude of the hub meridional velocity is given. Using the Runge-Kutta method, according to the law of conservation of mass, the total flow rate through any quasi-orthogonal line should equal the input design flow rate. If the error between the two exceeds a preset threshold, the initial value of the meridional velocity at the hub is adjusted, and the velocity gradient equation is solved again. This process is iterated until the mass flow rate of each quasi-orthogonal line converges to the design flow rate. At this point, intermediate flow field parameters are obtained. These intermediate flow field parameters reflect the velocity field and corresponding thermodynamic parameter distribution that satisfy mass conservation on the initially assumed streamlines, providing an accurate velocity basis for subsequent static pressure correction.

[0056] In a specific example, the design flow rate is 5.0 kg / s. Taking a certain quasi-orthogonal line (q-line) as an example, given an initial meridional velocity of 80 m / s at the hub, the fourth-order Runge-Kutta method is used to advance along the q-line from the hub to the casing. The integration step size is divided into 20 equal parts, and the velocity gradient equation is solved to obtain the meridional velocity distribution at each node. Then, the mass flow rate on the q-line is calculated according to the flow rate formula, and the result is 4.85 kg / s, which deviates from the design flow rate by 3%. Therefore, the hub meridional velocity is adjusted to 83 m / s, the velocity gradient equation is solved again, and the flow rate is calculated. After repeating the iteration 4 times, the mass flow rate stabilizes at 5.0 kg / s, with a relative error of less than 0.01%. The same iteration process is performed on all other q-lines until the flow rate of each quasi-orthogonal line meets the convergence condition. At this point, the intermediate flow field parameters are obtained, laying the foundation for subsequent static pressure correction.

[0057] Step 203: Based on the intermediate flow field parameters, the outlet static pressure is iteratively corrected using the loss model. When the pressure convergence condition is met, the flow field parameters are obtained.

[0058] The flow field parameters include a velocity field and a pressure field. The velocity field reflects the gas velocity distribution at each node inside the compressor and includes three components: axial, radial, and circumferential. The pressure field reflects the gas pressure distribution at each node inside the compressor.

[0059] In this embodiment of the invention, in order to further satisfy energy conservation and pressure balance in the intermediate flow field based on mass conservation, the polytropic efficiency is calculated by combining the loss model, and then the polytropic work is obtained from the enthalpy difference between the inlet and outlet. Then, assuming the outlet static pressure, the new polytropic work is calculated by the polytropic exponential formula. The outlet static pressure is relaxed and corrected until the error is less than the error threshold. After convergence, the physical property parameters are called to obtain the outlet entropy. Given the entropy distribution along the streamline, the aerodynamic parameters of the whole field are updated.

[0060] Specifically, the polytropic efficiency at each streamline position is calculated using a loss model. The total work done by the impeller is calculated based on the enthalpy difference between the inlet and outlet, and the multivariable work determined by the loss model is obtained. : ,in, For export enthalpy, It is the import enthalpy, and the polytropic index is calculated based on the current inlet and outlet cross-sectional parameters. : ,in, and These are the inlet static pressure and the outlet static pressure, respectively. and These are import density and export density, respectively, and the polyvariable work is calculated using the polyvariable index. : ,Compare and Calculate the relative error ;like Greater than a set threshold (e.g.) =10 -4 If so, then the relaxation factor is used. (suggestion =0.1~0.3) Corrected outlet static pressure: Based on the corrected outlet static pressure and the current outlet enthalpy, the outlet density is updated by calling the physical property parameter table, and the polytropic index and polytropic work are recalculated until the error is lower than a given threshold. At this point, the outlet static pressure converges. The outlet entropy is obtained by looking up the table from the converged outlet static pressure and outlet enthalpy. The entropy distribution law is given along the streamline direction (e.g., linear distribution or cubic spline distribution from inlet entropy to outlet entropy), and the entropy value of each grid point is interpolated. Based on the current enthalpy value (determined by the velocity field) and entropy value of each grid point, the thermodynamic parameters such as density, static pressure, and static temperature of the entire field are updated by calling the physical property parameter table, completing the flow field update after this round of static pressure correction. This provides accurate flow field data for subsequent clogging condition judgment and performance parameter output.

[0061] In a specific example, based on intermediate flow field parameters, the polytropic efficiency and polytropic work of the loss model are calculated using a loss model. Simultaneously, the polytropic work of physical properties is calculated based on inlet and outlet parameters. Since there is a certain deviation between the two, considering the drastic changes in physical properties of supercritical carbon dioxide near the critical point, a relaxation factor is used for iterative correction. After several iterations, the outlet static pressure converges to a stable value with an error less than a preset threshold. After convergence, the outlet entropy is obtained by calling a real gas property library from the outlet static pressure and outlet enthalpy. The entropy values ​​of each node are obtained by linear interpolation along the streamline from the inlet entropy. Then, the overall field density, static pressure, static temperature, and other thermodynamic parameters are updated in conjunction with the velocity field. Finally, flow field parameters that satisfy mass and energy conservation are obtained, providing an accurate basis for subsequent blockage judgment and performance output.

[0062] Step 204: When it is determined that the compressor impeller is in a blocked condition based on the flow field parameters, the meridional calculation grid is iteratively calculated according to the preset pressure gradient equation and pressure boundary conditions, and the updated flow field parameters are obtained when the pressure convergence condition is met. The method shown in this step has been explained in step 103 and will not be repeated here.

[0063] Optionally, step 204 includes the following sub-steps: Sub-step 2041: Based on the flow field parameters, calculate the average relative Mach number of the impeller throat section. If the average relative Mach number is greater than or equal to 1.0, it is determined to be a blockage condition.

[0064] In this embodiment of the invention, in order to accurately determine whether the compressor has entered a clogging condition, the average relative Mach number of the impeller throat section is calculated using the current flow field parameters. The throat is the narrowest section in the impeller flow channel, where the gas velocity is the highest and the static pressure is the lowest, making it the bottleneck of the entire impeller flow. If the relative Mach number of the throat is less than 1.0, it is determined that the compressor has not entered a clogging condition, and the velocity gradient equation continues to be used as the control equation, directly proceeding to subsequent streamline adjustment. If the average relative Mach number of the throat is greater than or equal to 1.0, the compressor impeller is determined to have entered a clogging condition. This judgment mechanism has clear physical meaning and is simple to calculate, and can promptly trigger subsequent equation switching and boundary condition switching (switching the velocity gradient equation to the pressure gradient equation and replacing the mass flow boundary condition with the outlet static pressure boundary condition), thereby effectively avoiding the iterative divergence caused by the failure of the flow boundary condition in the traditional method under clogging conditions, and ensuring stable solutions under clogging conditions.

[0065] In a specific example, the relative velocity and local sound velocity at each node of the impeller throat section are extracted, and the relative Mach number at each point is calculated and averaged. The calculation result shows that the average relative Mach number is approximately 1.05, which is greater than 1.0. The system determines that the compressor impeller has entered a clogging condition. At this point, the velocity gradient equation with mass flow rate as the boundary condition is no longer applicable, thus triggering the subsequent equation switching and boundary condition switching process. For supercritical carbon dioxide working fluid, due to its low sound velocity and drastic changes in properties near the critical point, accurately capturing the throat Mach number is particularly crucial. This method accurately calculates the sound velocity and relative velocity using a real gas property library, ensuring the reliability of the judgment.

[0066] Sub-step 2042: Under clogging conditions, the velocity gradient equation is switched to the pressure gradient equation, and the outlet static pressure is used instead of the flow rate as the boundary condition. The meridional computational grid is iteratively calculated, and the updated flow field parameters are obtained when the outlet static pressure convergence condition is met.

[0067] In this embodiment of the invention, in order to stably solve the flow field under clogging conditions and avoid iterative divergence caused by the failure of mass flow rate boundary conditions, the governing equation is switched from the velocity gradient equation to the pressure gradient equation, and the boundary condition is replaced from the design mass flow rate to the given outlet static pressure. After the switch, the pressure gradient equation is solved along the quasi-orthogonal direction using the current flow field parameters as the initial field:

[0068] in, It is relative static pressure. It is the meridional relative velocity. It is the circumferential relative velocity. It is the radial relative velocity. It is the meridian angle. It is the inclination angle of the quasi-orthogonal line. It is the local radius. It is streamline curvature. These are the blade angular coordinates. It is the impeller angular velocity. It is the length of the quasi-orthogonal line. It is the length of the meridional streamline.

[0069] By adjusting the static pressure value to make the average static pressure at the outlet section equal to a given value, and iterating repeatedly until the outlet static pressure distribution converges, this method no longer relies on mass flow rate as a boundary condition, but directly uses outlet static pressure as the driving force. Therefore, it can effectively overcome the dilemma of traditional methods being unable to advance under clogging conditions due to constant flow rate, and achieve stable solution of the flow field in the clogging region, providing support for accurately predicting the pressure ratio and efficiency drop characteristics under clogging conditions.

[0070] In a specific example, because the average relative Mach number at the throat (e.g., 1.05) is greater than 1.0, the system determines that it has entered a clogging condition. Subsequently, it automatically switches the governing equation from the velocity gradient equation to the pressure gradient equation and replaces the boundary condition from the design mass flow rate to the given outlet static pressure (e.g., 8.5 MPa). Using the current flow field as the initial field, the pressure gradient equation is solved along the quasi-orthogonal line direction. By iteratively adjusting the static pressure value, the average outlet static pressure gradually approaches the given value. After about 8 iterations, the deviation between the average outlet static pressure and the given value is less than 0.01%, and convergence is determined. The updated flow field parameters are obtained, which accurately reflect the flow state under clogging conditions and provide a reliable basis for performance evaluation.

[0071] Step 205: Based on the flow field parameters, adjust the streamline position of the fluid inside the compressor, continue to perform external loop iterative calculation on the meridional calculation grid, and when the streamline convergence condition is met, use the convergence result as the performance parameter of the compressor.

[0072] The streamline position reflects the spatial coordinates of the fluid on the meridional plane.

[0073] The method shown in this step has been explained in step 104 and will not be repeated here.

[0074] Optionally, step 205 includes the following sub-steps: Sub-step 2051: Adjust the position of the streamline according to the principle of equal flow distribution.

[0075] In this embodiment of the invention, to achieve self-consistency between flow field parameters and streamline positions, the streamline positions are adjusted using the principle of equal flow distribution. Specifically, based on the current flow field parameters, the flow distribution on each quasi-orthogonal line is calculated, and the total flow is equally divided according to the number of streamlines. Then, the new position of each streamline on the quasi-orthogonal line is determined by inverse interpolation. The lengths of the quasi-orthogonal lines before and after iteration are compared to determine if the error is sufficiently small. If the error exceeds a specified value, the streamlines need to be iteratively calculated. The principle of equal flow distribution ensures that the flow rate between adjacent streamlines is equal, thereby satisfying the reasonable distribution of mass conservation in the spanwise direction. The adjusted streamline positions will serve as the geometric basis for the next round of internal circulation iteration, allowing the streamlines to gradually approximate the actual gas flow path.

[0076] In a specific example, based on the current flow field parameters, the system analyzes the flow distribution along a quasi-orthogonal line: from the hub to the casing, when the cumulative flow reaches one-quarter, half, and three-quarters of the total flow of 5.0 kg / s, the positions of the three internal streamlines are respectively obtained. Through inverse interpolation, the new coordinates of each streamline on the quasi-orthogonal line are obtained (for example, adjusted from the original 20%, 45%, and 70% to 18%, 42%, and 68%). After performing the same operation on all quasi-orthogonal lines, the streamline positions are updated as a whole. Because the density of supercritical carbon dioxide changes drastically near the critical point, the principle of equal flow distribution can adaptively adjust the streamline distribution, ensuring that both high-density and low-density regions have suitable streamline densities, thereby guaranteeing the mass conservation accuracy of subsequent iterative cycles.

[0077] Sub-step 2052: Calculate the slip coefficient based on the slip model, and correct the airflow angle according to the slip coefficient.

[0078] In this embodiment of the invention, in order to correct the deviation of the airflow outlet angle caused by the limited number of blades, a slip model is introduced to calculate the slip coefficient. The airflow angle is then corrected, and the circumferential velocity components are updated. The corrected airflow angle and velocity field are used as the initial field for the new round of internal circulation. The process returns to step 202 for iterative calculation, for example, using the Wiesner model: ,in, The blade geometry angle, It refers to the number of blades. The slip coefficient reflects the degree of deviation between the actual airflow angle and the blade geometry angle. Substituting the slip coefficient into the airflow angle correction formula yields an airflow angle distribution that is closer to the actual flow. The corrected airflow angle is used to update the circumferential velocity component and serves as the boundary condition for the next round of internal circulation iteration, thereby making the coupling of internal and external circulation more accurate.

[0079] In a specific example, the impeller has 15 blades and a blade exit angle of 60°. Using the Wiesner model, the slip coefficient is calculated to be approximately 0.73. Based on the relationship between the slip coefficient and the slip velocity... Obtain the slip velocity The actual airflow angle according to Figure 3 The geometric relationship shown is used for calculation. The corrected airflow angle is substituted into the velocity triangle to update the circumferential velocity component and re-enter the inner circulation iteration. Since supercritical carbon dioxide has high density and low viscosity, the airflow angle correction has a significant impact on efficiency and pressure ratio prediction. Correction by slip model can effectively improve the accuracy of performance prediction.

[0080] in, Figure 3This is a schematic diagram of the velocity vector at the impeller outlet provided in an embodiment of this application. It illustrates the comparison between the airflow velocity triangle under the no-slip assumption and the slip correction condition, intuitively demonstrating the physical process of airflow outlet angle deviation and slip correction caused by the finite number effect of the blades. In the figure, the horizontal vector... Represents the circumferential velocity at the impeller outlet, a vector. Represents absolute velocity, vector Represents relative velocity, vector The circumferential component of absolute velocity, a vector The circumferential component of relative velocity, a vector Indicates meridional velocity / axial velocity. The airflow angle, The blade angle, The diagram clearly illustrates the vector relationship between the circumferential velocity, relative velocity, and absolute velocity at the impeller outlet. It also visually demonstrates the airflow outlet angle deviation caused by the finite number effect of the blades, providing a clear physical basis for the slip model to correct the airflow angle and update the circumferential velocity components.

[0081] Figure 4 This is a flowchart illustrating the performance prediction method for a supercritical carbon dioxide compressor provided in this embodiment of the invention. It shows the overall execution path from generating a computational grid from input parameters and constructing the initial flow field, to iterative solving in both internal and external loops, and finally outputting the performance prediction results. The process takes the compressor's geometric parameters, inlet parameters, operating parameters, and control parameters as input. Through discretization of the meridional flow space, solving the flow field under physical conservation constraints, and adaptive switching of the control equations under clogging conditions, it ultimately outputs performance prediction results such as the overall compressor pressure ratio, polytropic efficiency, and meridional aerodynamic parameters. In practical applications, supercritical carbon dioxide centrifugal compressors face complex challenges such as strong nonlinear properties, oscillations during multivariate synchronous iteration, and failure of boundary conditions under clogging conditions. Traditional quasi-three-dimensional flow methods often employ ideal gas assumptions and fixed velocity gradient equations, making it difficult to maintain stable convergence near the critical region and even more difficult to accurately predict the sharp performance drop characteristics under clogging conditions. This invention utilizes direct lookup of real gas properties, internal circulation iteration decoupled from flow rate and pressure, and an equation switching mechanism based on the relative Mach number at the throat. Starting from the initial flow field, it can automatically identify whether a blockage condition has been entered and switch to solving the pressure gradient equation. This enables stable simulation of the flow field and accurate prediction of performance across the entire operating range, providing reliable technical support for the rapid design and multi-condition evaluation of supercritical carbon dioxide centrifugal compressors.

[0082] S1 Input Parameters: This is the program input section. The input parameters include the compressor's impeller geometry parameters, intake parameters, operating parameters, and solution control parameters, providing basic data for subsequent calculations. S2 generated mesh: Using the overlimit interpolation method, with the hub curve, casing curve, inlet edge curve and outlet edge curve as boundaries, a meridional computational mesh composed of streamlines and quasi-orthogonal lines is generated, discretizing the continuous flow space into a series of computational nodes; S3 Initial Field Calculation: Assuming no slippage at the impeller outlet and a preset initial polytropic efficiency, the outlet meridional velocity is iteratively solved based on the Euler equation until convergence. Then, the full-field meridional velocity distribution is obtained by linear interpolation along the streamline. Combining the rotor enthalpy conservation and isentropic assumptions, the thermodynamic parameters such as enthalpy, density, temperature, and pressure of each node are obtained by looking up tables in the real gas property library to complete the initial flow field construction. S4 Solve for velocity gradient equation parameters: At the current streamline position, establish the velocity gradient equation along the quasi-orthogonal line direction, and calculate the coefficients of each term in the equation based on the current flow field parameters; S5 RK method to correct velocity distribution: The Runge-Kutta method is used to solve the velocity gradient equation from the hub to the casing to obtain the distribution of meridional velocity along the quasi-orthogonal line, thereby obtaining the meridional velocity value of each node; S6 determines whether the flow convergence: Calculate the mass flow rate on each quasi-orthogonal line based on the meridional velocity distribution and compare it with the design mass flow rate. If the relative error is less than the preset threshold, the flow convergence is achieved, and proceed to the next step; otherwise, adjust the initial value of the meridional velocity at the hub and return to S5 for correction. S7 combines loss coefficient to correct static pressure distribution: Based on the intermediate flow field parameters after flow convergence, the polytropic efficiency is calculated by combining the loss model to obtain the polytropic work of the loss model; at the same time, the polytropic work of physical properties is calculated according to the inlet and outlet parameters, the deviation between the two is compared, and the outlet static pressure is iteratively corrected by the relaxation factor. S8 determines whether the pressure converges: Determines whether the deviation of the outlet static pressure before and after correction is less than the preset threshold. If it converges, proceed to the next step; otherwise, return to S7 to continue correction. S9 determines whether there is blockage: Based on the current flow field parameters, calculate the average relative Mach number of the impeller throat section. If the value is greater than or equal to 1.0, it is determined that the blockage condition has been entered and the process switches to S91. Otherwise, the velocity gradient equation and flow boundary conditions are used to continue to apply and the process switches to S10. S91 Solving for pressure gradient equation parameters: Switch the governing equation from the velocity gradient equation to the pressure gradient equation, and switch the boundary condition from mass flow rate to outlet static pressure. Establish the pressure gradient equation along the quasi-orthogonal line direction and calculate various parameters. S92 uses pressure as the boundary condition for iteration: the Runge-Kutta method is used to solve the pressure gradient equation. By adjusting the static pressure value at the blade root, the average static pressure at the outlet section is made equal to the given value. The iteration is repeated until the pressure distribution is stable. S93 Determine if the pressure has converged: Check if the deviation between the outlet average static pressure and the given value is less than the preset threshold. If converged, obtain the updated flow field parameters and proceed to S10. Otherwise, return to S92 to continue iterating. S10 Streamline Position Adjustment: Based on the principle of equal flow distribution, the new position of the streamline is calculated by inverse interpolation according to the flow distribution on each quasi-orthogonal line. At the same time, the slip coefficient is calculated based on the slip model to correct the airflow angle distribution and complete the streamline update. S11 Detect streamline convergence: Determine whether the change in streamline position between two adjacent outer loop iterations is less than a preset threshold. If it is less, the streamline converges and proceeds to S12; otherwise, return to S4 and re-perform the inner loop iteration with the updated streamline position. The flow field solution for S12 is complete: at this point, the flow field parameters and streamline positions are self-consistent, and a convergent flow field solution that simultaneously satisfies mass conservation, energy conservation, and flow consistency is obtained. S13 Output Results: The converged flow field parameters are mass-weighted and averaged to output performance parameters such as overall pressure ratio and polytropic efficiency, as well as the velocity, pressure, and temperature distribution at each node of the meridional plane, which serve as the performance prediction results for the compressor.

[0083] Figure 5 This is a schematic diagram of a meridional computational grid provided in an embodiment of the present invention. It shows a quasi-three-dimensional meridional computational grid generated by an over-limit interpolation method. In the figure, the horizontal axis is the axial coordinate Z, representing the axial distance of the compressor from the inlet to the outlet, and the vertical axis is the radial coordinate R, representing the radial height from the hub to the casing. This meridional computational grid is based on the input hub curve, casing curve, inlet edge curve, and outlet edge curve, and is composed of multiple streamlines and quasi-orthogonal lines interwoven together. The streamlines extend from left (inlet) to right (outlet) along the Z direction, and gradually bend from horizontal to nearly vertical as the radial coordinate R increases, reflecting the actual streamlines of gas flowing in axially, being accelerated by the impeller, and then being ejected radially. The quasi-orthogonal lines are approximately perpendicular to the streamlines, extending from the lower boundary (hub) to the upper boundary (casing), and are used to discretize the flow channel into several computational layers in the spanwise direction. Each intersection of the streamlines and the quasi-orthogonal lines is a computational node, and the velocity gradient equation or pressure gradient equation is solved at these nodes. The meridional computational grid reflects the spatial morphology of the fluid channels inside the compressor and provides a computational basis for subsequent iterative solutions to the flow field.

[0084] In summary, in this embodiment, by obtaining the compressor's input parameters, a meridional computational grid is generated based on the input parameters, and an initial flow field is constructed. Before the iterative loop, a reasonable computational grid and initial flow field are provided, offering a more reliable structural foundation for subsequent iterations. Based on this, the meridional computational grid is subjected to internal loop iterative calculation according to the preset velocity gradient equation and flow boundary conditions. When the flow convergence and pressure convergence conditions are met, the flow field parameters are obtained. Subsequently, the compressor blockage status is determined based on the flow field parameters. When the compressor impeller is in a blocked condition, the meridional computational grid is iteratively calculated according to the preset pressure gradient equation and pressure boundary conditions. When the pressure convergence condition is met, the updated flow field parameters are obtained. Based on the obtained flow field parameters, the streamline position of the fluid inside the compressor is adjusted, and the meridional computational grid is subjected to external loop iterative calculation. When the streamline convergence condition is met, the convergence result is used as the compressor's performance parameters. Therefore, the method based on the embodiments of this application, through the iterative structure of inner and outer loops and the automatic switching of control equations and boundary conditions under congested conditions, effectively suppresses computational oscillations and iterative divergence, and achieves stable and efficient performance prediction across the entire operating range.

[0085] like Figure 6 As shown in the figure, this application provides a performance prediction device for a supercritical carbon dioxide compressor, comprising: The data acquisition module 401 is used to acquire the input parameters of the compressor, generate a meridional computational grid based on the input parameters, and construct an initial flow field; the meridional computational grid is used to characterize the node distribution of the fluid space inside the compressor, and the initial flow field is used to characterize the initial thermodynamic parameters of each node; The iterative correction module 402 is used to perform internal loop iterative calculation on the meridional calculation grid according to the preset velocity gradient equation and flow boundary conditions, and obtain flow field parameters when the flow convergence and pressure convergence conditions are met. The flow field parameters reflect the spatial distribution of the fluid flow state inside the compressor. The blockage handling module 403 is used to perform iterative calculations on the meridional calculation grid according to the preset pressure gradient equation and pressure boundary conditions when it is determined that the impeller of the compressor is in a blockage condition based on the flow field parameters, and to obtain updated flow field parameters when the pressure convergence condition is met. The performance prediction module 404 is used to adjust the streamline position of the fluid inside the compressor based on the flow field parameters, and continue to perform external loop iterative calculations on the meridional calculation grid. When the streamline convergence condition is met, the convergence result is used as the performance parameter of the compressor. The streamline position reflects the spatial coordinates of the fluid on the meridional plane.

[0086] Optionally, the data acquisition module 401 includes: The data acquisition submodule generates a meridional computational grid composed of streamlines and quasi-orthogonal lines based on the input parameters using an overlimit interpolation method. Mesh generation submodule: Based on the meridional plane, the mesh is calculated, assuming no slippage at the impeller outlet and a preset initial polytropic efficiency. The outlet meridional velocity is iteratively solved, and the initial meridional velocity distribution of the whole field is obtained by linear interpolation along the streamline. Initial field construction submodule: Based on the initial meridional velocity distribution across the entire field, and combined with the assumptions of rotor enthalpy conservation and isentropic flow, the thermodynamic parameters of each node of the meridional plane computational grid are obtained, thus completing the construction of the initial flow field.

[0087] Optionally, the iterative correction module 402 includes: Velocity solution submodule: Solve the velocity gradient equation to obtain the meridional velocity distribution, and correct the meridional velocity distribution using the Runge-Kutta method. When the flow convergence is satisfied, the intermediate flow field parameters are obtained. Iterative correction submodule: Based on intermediate flow field parameters, combined with the loss model, the outlet static pressure is iteratively corrected to obtain the flow field parameters when the pressure convergence condition is met.

[0088] Optionally, the congestion handling module 403 includes: The blockage detection submodule calculates the average relative Mach number of the impeller throat section based on the flow field parameters. If the average relative Mach number is greater than or equal to 1.0, it is determined to be a blockage condition. The blockage handling submodule switches the velocity gradient equation to the pressure gradient equation under blockage conditions, and uses the outlet static pressure instead of the flow rate as the boundary condition to perform iterative calculations on the meridional computational grid. When the outlet static pressure convergence condition is met, the updated flow field parameters are obtained.

[0089] Optionally, the performance prediction module 404 includes: The streamline adjustment submodule adjusts the streamline position according to the principle of equal flow distribution; Airflow Angle Correction Submodule: Calculates the slip coefficient based on the slip model and corrects the airflow angle according to the slip coefficient.

[0090] Optionally, the input parameters include impeller geometry parameters, inlet parameters, operating parameters, and solution control parameters; impeller geometry parameters include impeller meridional profile, blade geometric coordinates, number of blades, and tip clearance; inlet parameters include total temperature and total pressure; operating parameters include rotational speed and mass flow rate; and solution control parameters include mesh density and residual convergence conditions.

[0091] In this embodiment of the invention, by flexibly configuring the above-mentioned input parameters, the performance prediction method can be adapted to compressor models with different structures and operating conditions. Among them, the impeller geometric parameters are used to define the boundary shape and computational domain range of the meridional computational grid, the inlet parameters and operating parameters are used to determine the flow boundary conditions and the thermodynamic state of the initial flow field, and the parameters for solving the control equations are used to adjust the speed and accuracy of the iterative calculation. By reasonably setting the grid density and residual convergence conditions in the solution control parameters, a balance can be achieved between computational efficiency and prediction accuracy, meeting the needs of computational iteration at different design stages.

[0092] Optionally, the loss model may include angle of attack loss, friction loss, load loss, tip clearance loss, and wake mixture loss.

[0093] In this embodiment of the invention, by employing an enthalpy-based loss model to calculate the local loss distribution along the spanwise direction, the actual flow loss distribution inside the compressor can be described more accurately. Specifically, angle of attack loss describes the energy loss caused by the airflow deviating from the optimal angle of attack when entering the impeller; friction loss describes the wall friction loss caused by viscosity as the fluid flows along the flow channel; load loss describes the flow loss caused by uneven pressure distribution on the blade surface; tip clearance loss describes the energy dissipation caused by leakage flow between the blade tip and the casing; and wake mixing loss describes the energy loss during the mixing process of the blade wake and the mainstream at the impeller outlet. By combining these five loss models, variable efficiency can be calculated more accurately, thereby improving the accuracy of performance prediction. Furthermore, in practical applications, one or more combinations of loss models can be flexibly selected based on the compressor type and design operating conditions to simplify calculations or improve prediction accuracy.

[0094] In summary, in this embodiment, by obtaining the compressor's input parameters, a meridional computational grid is generated based on the input parameters, and an initial flow field is constructed. Before the iterative loop, a reasonable computational grid and initial flow field are provided, offering a more reliable structural foundation for subsequent iterations. Based on this, the meridional computational grid is subjected to internal loop iterative calculation according to the preset velocity gradient equation and flow boundary conditions. When the flow convergence and pressure convergence conditions are met, the flow field parameters are obtained. Subsequently, the compressor blockage status is determined based on the flow field parameters. When the compressor impeller is in a blocked condition, the meridional computational grid is iteratively calculated according to the preset pressure gradient equation and pressure boundary conditions. When the pressure convergence condition is met, the updated flow field parameters are obtained. Based on the obtained flow field parameters, the streamline position of the fluid inside the compressor is adjusted, and the meridional computational grid is subjected to external loop iterative calculation. When the streamline convergence condition is met, the convergence result is used as the compressor's performance parameters. Therefore, the method based on the embodiments of this application, through the iterative structure of inner and outer loops and the automatic switching of control equations and boundary conditions under congested conditions, effectively suppresses computational oscillations and iterative divergence, and achieves stable and efficient performance prediction across the entire operating range.

[0095] like Figure 7 As shown, this application embodiment provides an electronic device 500, which may include one or more of the following components: a processing component 502, a memory 504, a power supply component 506, a multimedia component 508, an audio component 510, an input / output (I / O) interface 512, a sensor component 514, and a communication component 516.

[0096] Processing component 502 typically controls the overall operation of electronic device 500, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 502 may include one or more processors 520 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 502 may include one or more modules to facilitate interaction between processing component 502 and other components. For example, processing component 502 may include a multimedia module to facilitate interaction between multimedia component 508 and processing component 502.

[0097] Memory 504 is used to store various types of data to support the operation of electronic device 500. Examples of this data include instructions for any application or method operating on electronic device 500, contact data, phonebook data, messages, pictures, multimedia, etc. Memory 504 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0098] Power supply component 506 provides power to various components of electronic device 500. Power supply component 506 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 500.

[0099] Multimedia component 508 includes an interface that provides an output interface between electronic device 500 and user. In some embodiments, the interface may include a liquid crystal display (LCD) and a touch panel (TP). If the interface includes a touch panel, the interface may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may not only sense the boundaries of touch or swipe actions but also detect the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 508 includes a front-facing camera and / or a rear-facing camera. When electronic device 500 is in an operating mode, such as shooting mode or multimedia mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0100] Audio component 510 is used to output and / or input audio signals. For example, audio component 510 includes a microphone (MIC) used to receive external audio signals when electronic device 500 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 504 or transmitted via communication component 516. In some embodiments, audio component 510 also includes a speaker for outputting audio signals.

[0101] Input / output (I / O) interface 512 provides an interface between processing component 502 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0102] Sensor assembly 514 includes one or more sensors for providing state assessments of various aspects of electronic device 500. For example, sensor assembly 514 may detect the on / off state of electronic device 500, the relative positioning of components such as the display and keypad of electronic device 500, changes in position of electronic device 500 or a component of electronic device 500, the presence or absence of user contact with electronic device 500, orientation or acceleration / deceleration of electronic device 500, and temperature changes of electronic device 500. Sensor assembly 514 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 514 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 514 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0103] Communication component 516 facilitates wired or wireless communication between electronic device 500 and other devices. Electronic device 500 can access wireless networks based on communication standards, such as WiFi, carrier networks (such as 2G, 3G, 4G, or 5G), or combinations thereof. In one exemplary embodiment, communication component 516 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 516 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0104] In an exemplary embodiment, the electronic device 500 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to implement the methods provided in the embodiments of this application.

[0105] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 504 including instructions, which can be executed by a processor 520 of an electronic device 500 to perform the above-described method. For example, the non-transitory storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0106] In an exemplary embodiment, the electronic device 500 may also be provided as a server, including a processing component 502, which further includes one or more processors, and memory resources represented by memory 504 for storing instructions, such as applications, that can be executed by the processing component 502. The applications stored in memory 504 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 502 is configured to execute instructions to perform the methods provided in the embodiments of this application.

[0107] Electronic device 500 may also include a power supply component 506 configured to perform power management of electronic device 500, a wired or wireless communication component 516 configured to connect electronic device 500 to a network, and an input / output (I / O) interface 512. Electronic device 500 may operate on an operating system stored in memory 504, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0108] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims below.

[0109] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the claimed technical solutions.

[0110] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0111] It will be readily apparent to those skilled in the art that any combination of the above embodiments is feasible. Therefore, any combination of the above embodiments is an implementation scheme of this application. However, due to space limitations, this specification will not describe them in detail here.

[0112] It should be noted that, unless otherwise expressly stated, the methods provided in the embodiments of this application are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. Based on the above description, the required structure for constructing a system having the solutions of this application is obvious. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0113] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0114] Similarly, it should be understood that, for the purpose of simplification and aiding understanding of one or more aspects of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the application. However, this disclosure should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each of the claimed technical solutions. Rather, as reflected in the claimed technical solutions, the application aspects comprise fewer features than all those in the single embodiment disclosed above. Therefore, the claimed technical solutions following the specific implementation are thus expressly incorporated into that specific implementation, wherein each claimed technical solution is itself a separate embodiment of the application.

[0115] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination of all features disclosed in this application and all processes or units of any method or device so disclosed can be employed. Unless expressly stated otherwise, each feature disclosed in this application may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0116] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the claimed technical solutions, any one of the claimed embodiments can be used in any combination.

[0117] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the methods according to the embodiments of this application. This application can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0118] It should be noted that, for the sake of simplicity, the method embodiments of this application are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential to the embodiments of this application.

[0119] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for predicting the performance of a supercritical carbon dioxide compressor, characterized in that, include: Obtain the compressor's input parameters, generate a meridional computational grid based on the input parameters, and construct an initial flow field; The meridional computational grid is used to characterize the node distribution of the fluid space inside the compressor, and the initial flow field is used to characterize the initial thermodynamic parameters of each node. Based on the preset velocity gradient equation and flow boundary conditions, the meridional computational grid is subjected to internal loop iterative calculation, and when the flow convergence and pressure convergence conditions are met, the flow field parameters are obtained. The flow field parameters reflect the spatial distribution of the fluid flow state inside the compressor. The process of performing an internal loop iterative calculation on the meridional computational grid based on a preset velocity gradient equation and flow boundary conditions, and obtaining flow field parameters when both flow and pressure convergence conditions are met, includes: solving the velocity gradient equation to obtain the meridional velocity distribution, correcting the meridional velocity distribution using the Runge-Kutta method, and obtaining intermediate flow field parameters when flow convergence is met; based on the intermediate flow field parameters, iteratively correcting the outlet static pressure using a loss model, and obtaining the flow field parameters when pressure convergence conditions are met; the flow field parameters include a velocity field and a pressure field, the velocity field reflecting the gas velocity distribution at each node inside the compressor and containing axial, radial, and circumferential components, and the pressure field reflecting the gas pressure distribution at each node inside the compressor; When the impeller of the compressor is determined to be in a blocked condition based on the flow field parameters, the meridional calculation grid is iteratively calculated according to the preset pressure gradient equation and pressure boundary conditions, and the updated flow field parameters are obtained when the pressure convergence condition is met. Specifically, when the compressor impeller is determined to be in a blocked condition based on the flow field parameters, the iterative calculation of the meridional computational grid is performed according to the preset pressure gradient equation and pressure boundary conditions. This includes: calculating the average relative Mach number of the impeller throat section based on the flow field parameters; if the average relative Mach number is greater than or equal to 1.0, it is determined to be a blocked condition; under the blocked condition, the velocity gradient equation is switched to the pressure gradient equation, and the outlet static pressure is used instead of the flow rate as the boundary condition to iteratively calculate the meridional computational grid. When the outlet static pressure convergence condition is met, the updated flow field parameters are obtained. Based on the flow field parameters, the streamline position of the fluid inside the compressor is adjusted, and the external loop iterative calculation is continued on the meridional computational grid. When the streamline convergence condition is met, the convergence result is used as the performance parameter of the compressor. The streamline position reflects the spatial coordinates of the fluid on the meridional plane.

2. The performance prediction method for a supercritical carbon dioxide compressor as described in claim 1, characterized in that, Obtain the compressor's input parameters, generate a meridional computational mesh based on the input parameters, and construct an initial flow field, including: Based on the input parameters, a meridional computational grid composed of streamlines and quasi-orthogonal lines is generated using an overlimit interpolation method. Based on the meridional calculation grid, assuming no slippage at the impeller outlet and a preset initial polytropic efficiency, the outlet meridional velocity is iteratively solved, and the initial meridional velocity distribution across the entire field is obtained by linear interpolation along the streamline. Based on the initial meridional velocity distribution across the entire field, and combined with the assumptions of rotor enthalpy conservation and isentropic flow, the thermodynamic parameters of each node in the meridional surface computational grid are obtained, thus completing the construction of the initial flow field.

3. The performance prediction method for a supercritical carbon dioxide compressor as described in claim 1, characterized in that, Based on the flow field parameters, adjusting the streamline position of the fluid inside the compressor includes: The position of the streamlines is adjusted according to the principle of equal flow distribution; The slip coefficient is calculated based on the slip model, and the airflow angle is corrected according to the slip coefficient. The airflow angle reflects the deviation between the actual fluid flow direction and the impeller blade direction.

4. The performance prediction method for a supercritical carbon dioxide compressor as described in claim 1, characterized in that, The input parameters include impeller geometry parameters, intake parameters, operating parameters, and solution control parameters; The impeller geometric parameters include the impeller meridional profile, blade geometric coordinates, number of blades, and blade tip clearance; the inlet parameters include total temperature and total pressure; the operating parameters include rotational speed and mass flow rate; and the solution control parameters include grid density and residual convergence conditions.

5. The performance prediction method for a supercritical carbon dioxide compressor as described in claim 1, characterized in that, The loss model specifically includes angle of attack loss, friction loss, load loss, tip clearance loss, and wake mixture loss.

6. A performance prediction device for a supercritical carbon dioxide compressor, characterized in that, include: The data acquisition module is used to acquire the compressor's input parameters, generate a meridional calculation grid based on the input parameters, and construct an initial flow field. The meridional computational grid is used to characterize the node distribution of the fluid space inside the compressor, and the initial flow field is used to characterize the initial thermodynamic parameters of each node. The iterative correction module is used to perform internal loop iterative calculations on the meridional computational grid according to the preset velocity gradient equation and flow boundary conditions, and obtain flow field parameters when the flow convergence and pressure convergence conditions are met. The flow field parameters reflect the spatial distribution of the fluid flow state inside the compressor. The step of performing an inner loop iterative calculation on the meridional computational grid based on a preset velocity gradient equation and flow boundary conditions, and obtaining flow field parameters when the flow convergence and pressure convergence conditions are met, includes: Solve the velocity gradient equation to obtain the meridional velocity distribution, and then correct the meridional velocity distribution using the Runge-Kutta method. When the flow convergence is satisfied, obtain the intermediate flow field parameters. Based on the intermediate flow field parameters, the outlet static pressure is iteratively corrected using a loss model. When the pressure convergence condition is met, the flow field parameters are obtained. The flow field parameters include a velocity field and a pressure field. The velocity field reflects the gas velocity distribution at each node inside the compressor and includes three components: axial, radial, and circumferential. The pressure field reflects the gas pressure distribution at each node inside the compressor. The blockage handling module is used to perform iterative calculations on the meridional computational grid according to the preset pressure gradient equation and pressure boundary conditions when it is determined that the impeller of the compressor is in a blockage condition based on the flow field parameters, and to obtain updated flow field parameters when the pressure convergence condition is met. The step of iteratively calculating the meridional computational grid according to a preset pressure gradient equation and pressure boundary conditions when the compressor impeller is determined to be in a blocked condition based on the flow field parameters includes: Based on the flow field parameters, the average relative Mach number of the impeller throat section is calculated. If the average relative Mach number is greater than or equal to 1.0, it is determined to be a blockage condition. Under clogging conditions, the velocity gradient equation is switched to the pressure gradient equation, and the outlet static pressure is used instead of the flow rate as the boundary condition. The meridional computational grid is iteratively calculated, and the updated flow field parameters are obtained when the outlet static pressure convergence condition is met. The performance prediction module is used to adjust the streamline position of the fluid inside the compressor based on the flow field parameters, and continue to perform external loop iterative calculations on the meridional computational grid. When the streamline convergence condition is met, the convergence result is used as the performance parameter of the compressor. The streamline position reflects the spatial coordinates of the fluid on the meridional plane.

7. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the performance prediction method for a supercritical carbon dioxide compressor as described in any one of claims 1 to 5.

8. An electronic device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the performance prediction method for a supercritical carbon dioxide compressor as described in any one of claims 1 to 5.

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