Design method and device of centripetal expansion machine, electronic equipment and storage medium

Abstract

The invention relates to the technical field of centripetal expansion machines, and discloses a centripetal expansion machine design method and device, electronic equipment and a storage medium. The method comprises the following steps: constructing a centripetal expansion machine model-level database; based on expansion machine design parameters input by a user, a target model level with the optimal efficiency is automatically matched from the model level performance database; reading multi-dimensional information curve family data of the target model level; and establishing and executing a performance iterative algorithm, and outputting an expansion machine design report by coupling the multi-dimensional information curve family data of the target model level with the real gas physical property iterative calculation until the isentropic efficiency, the outlet temperature and the output power of the target model level under the design parameters meet iterative convergence conditions. According to the method, iterative calculation of the strict thermodynamic model and the real gas physical property parameters is deeply coupled, closed-loop convergence calculation of the key performance parameters of the expansion machine is achieved in the automatic model selection and performance iteration process, and the design accuracy and reliability are remarkably improved.

Description

Technical Field

[0001] This disclosure relates to the field of centripetal expander technology, and more specifically, to a centripetal expander design method, apparatus, electronic device and storage medium. Background Technology

[0002] Centripetal expanders are characterized by large single-stage enthalpy drop and compact structure, and are widely used in the energy recovery and utilization of natural gas and oilfield gas, with a broad market prospect. Centripetal expanders vary in flow rate, inlet pressure, inlet temperature, and back pressure, and the medium composition also differs, making them individually designed and manufactured products. The current typical design process for expanders involves performing one-dimensional thermodynamic calculations to determine the geometry of the flow path, then shaping the impeller and nozzle blades, followed by three-dimensional flow field CFD analysis and structural optimization.

[0003] The current design process has obvious shortcomings: (1) A lot of CFD analysis is required in the design process, and the scheme is continuously improved based on the CFD results, which makes the expansion machine design cycle too long; (2) Due to the cost of development, it is difficult to conduct prototype testing and verification for each product, which makes the design accuracy poor in many cases, resulting in low efficiency level of the expansion machine during operation. Summary of the Invention

[0004] In view of the above situation, this application provides a centripetal expander design method, apparatus, electronic device and storage medium, which aims to solve the above problems or at least partially solve the above problems.

[0005] In a first aspect, embodiments of this application provide a method for designing a centripetal expander, the method comprising: A centripetal expander model-level database is constructed. The model-level database stores multiple model-level dimensionless characteristic data. The dimensionless characteristic data includes a family of multi-dimensional information curves. Each curve includes multiple data points based on the inlet Mach number. Each data point includes at least one of the following: flow coefficient, total static entropy efficiency, load coefficient, speed ratio, and outlet Mach number. Based on the expander design parameters input by the user, the system automatically matches the target model level with the best efficiency from the model-level performance database. Read the multi-dimensional information curve family data at the target model level; Establish and execute a performance iteration algorithm. By coupling the multi-dimensional information curve family data of the target model level with the real gas properties for iterative calculation, until the isentropic efficiency, outlet temperature and output power of the target model level under the design parameters meet the iterative convergence conditions, and output an expander design report.

[0006] Secondly, embodiments of this application also provide a design apparatus for a centripetal expander, the apparatus comprising: The module is used to build a model-level database for a centripetal expander. The model-level database stores multiple model-level dimensionless characteristic data. The dimensionless characteristic data includes a family of multi-dimensional information curves. Each curve includes multiple data points based on the inlet Mach number. Each data point includes at least one of the following: flow coefficient, total static isentropic efficiency, load coefficient, speed ratio, and outlet Mach number. The matching module is used to automatically match the target model level with the best efficiency from the model-level performance database based on the expander design parameters input by the user. The correction module is used to read the multi-dimensional information curve family data at the target model level and perform model correction; The iteration module is used to establish and execute the performance iteration algorithm. It iteratively calculates the target model level by coupling the multi-dimensional information curve family data with the real gas properties until the target model level's isentropic efficiency, outlet temperature and output power under the design parameters meet the iteration convergence conditions, and outputs the expander design report.

[0007] Thirdly, embodiments of this application also provide an electronic device, including: a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the steps described in the first aspect.

[0008] Fourthly, embodiments of this application also provide a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform the steps described in the first aspect.

[0009] The above-mentioned at least one technical solution adopted in the embodiments of this application can achieve the following beneficial effects: by constructing a model-level database containing experimentally verified data represented in the form of a family of multi-dimensional information curves, and automatically matching and calling the best model-level data according to the input parameters during the design process, the known reliable performance characteristics are effectively utilized, thereby significantly shortening the cycle of repeated CFD analysis and trial and error in the traditional design process; at the same time, the method deeply couples the rigorous thermodynamic model with the iterative calculation of real gas physical parameters, and realizes the closed-loop convergence calculation of the key performance parameters of the expander in the process of automatic selection and performance iteration, which significantly improves the accuracy and reliability of the design, and finally can efficiently generate an optimized centripetal expander design scheme while ensuring design accuracy. Attached Figure Description

[0010] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1The software framework diagram for the selection design of the centripetal expander provided in the embodiments of this application is shown; Figure 2 A flowchart of the centripetal expander design method provided in an embodiment of this application is shown; Figure 3 A flowchart of the performance iteration algorithm provided in an embodiment of this application is shown; Figure 4 The model-level dimensionless curves provided in the embodiments of this application are shown; Figure 5 This application shows a model-level dimensionless curve provided in another embodiment of the present application; Figure 6 This illustrates a model-level dimensionless curve provided in yet another embodiment of this application; Figure 7 A structural diagram of the centripetal expander design device provided in an embodiment of this application is shown; Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to."

[0013] Model-based centripetal expander modular design has the outstanding advantage of using a fully validated model, whose performance is accurate and reliable. Combined with a rigorous thermodynamic calculation model, the performance of the designed centripetal expander can be effectively guaranteed, improving design accuracy. However, this design method also faces significant challenges in development: (1) how to reasonably determine the dimensionless parameters of the model to reflect its "essential" characteristics; (2) for expander design, users usually provide flow rate, inlet pressure, temperature, and outlet back pressure. Research shows that using the outlet flow coefficient to characterize the dimensionless flow rate of the model is more reasonable. In the selection design calculation, it is necessary to interpolate the flow coefficient on the dimensionless curve to obtain the isentropic efficiency and load coefficient, and then perform stage performance iteration calculation. However, the inlet and outlet pressures and temperatures of the intermediate stages of a multi-stage centripetal expander are unknown, which makes it impossible to calculate the outlet flow coefficient. Even if the above design iteration cannot be effectively closed, the selection calculation is difficult to proceed smoothly.

[0014] In addressing the aforementioned challenges, this application proposes a family of multi-dimensional information curves to characterize the essential properties of the expander model; it also develops a performance iteration algorithm coupled with deep coupling of real gas physical parameters, thus solving the design and calculation problem of centripetal expanders based on model-level modeling.

[0015] Figure 1 This diagram illustrates a software framework diagram for the selection and design of a centripetal expander according to an embodiment of this application. The software uses thermodynamic performance calculation as its core module, which includes multiple specialized calculation and processing sub-modules that work together to complete the design task. The specific components are as follows: The core calculation modules include curve interpolation calculation, modeling correction calculation, stage performance iteration calculation, interstage heating calculation, interstage extraction and injection calculation, and adjustable nozzle calculation. These modules are primarily responsible for handling the core calculations and corrections of the expander's aerodynamic and thermodynamic performance.

[0016] Support and functional modules include a physical property parameter calculation module, an automatic selection calculation module, a performance curve graph generation module, a license and security verification module, and a help and prompt function module. These modules provide necessary physical property data support, automated design, result visualization, system security, and operation guidance for selection and design.

[0017] Data storage module: includes an expander model-level database and a user database, which provide the software with verified model-level performance data storage and user data management functions, respectively.

[0018] The entire framework embodies the modular and integrated characteristics of the design software, with clear layers for core computing, functional support, and data management, which together support the rapid and accurate selection and design of the centripetal expander.

[0019] Furthermore, the system hardware for implementing the centripetal expander design consists of a server and several client computers connected to the server via a local area network. The server-side installation includes an expander model-level database, a user database, and server-side programs, responsible for data management, performance calculations for selection, and system maintenance. The client computers are installed on the users' computers and include a client interface and communication programs, responsible for user interface display and data communication processing with the server. The system has the following features: a model database function for easy and rapid input and application of newly developed model-level systems; and a server-based software system with security verification functions to enhance system security. To further improve software security, a dual mechanism based on license and login verification is designed.

[0020] Figure 2 This illustration shows a flowchart of a centripetal expander design method according to an embodiment of this application. Figure 2 It can be seen that the method may include steps S101 to S104: Step S101: Construct a centripetal expander model-level database.

[0021] The model-level database stores multiple model-level dimensionless characteristic data. Dimensionless characteristic data refers to a set of pure numbers obtained by mathematically combining and scaling the actual physical parameters (dimensional quantities) of the expander to eliminate the influence of its specific dimensions, speed, and absolute operating conditions. This set of data does not describe a specific machine, but rather the essential aerodynamic characteristics of a certain type of aerodynamic design model.

[0022] In this embodiment of the application, the dimensionless characteristic data includes a family of multi-dimensional information curves. Each curve includes multiple data points based on the inlet Mach number. Each data point includes at least one of the following: flow coefficient, total static entropy efficiency, load coefficient, speed ratio, and outlet Mach number.

[0023] In addition, the data structure of the multi-dimensional information curve family also includes an information header, which includes at least the model-level name, model-level impeller diameter, impeller type, design point flow coefficient, design point total static entropy efficiency, and design point load coefficient.

[0024] As shown in Table 1 below, an example of model-level TGA1 dimensionless data is presented.

[0025] Table 1

[0026] The dimensionless characteristic data at the model level are obtained by conducting performance tests and / or high-precision CFD analysis on the model in advance, and stored in the database.

[0027] Furthermore, the database also includes a separate database management module for querying, adding, modifying, and maintaining model-level data.

[0028] Step S102: Based on the expander design parameters input by the user, automatically match the target model level with the best efficiency from the model-level performance database.

[0029] The design parameters of the expander include: the composition of the expansion medium, mass flow rate, total inlet pressure, total inlet temperature, and static pressure or speed at the outlet.

[0030] After the user inputs the expander design parameters, the system automatically matches the most efficient target model level from the model-level performance database using an intelligent algorithm. This process begins with a call to a standardized database, where the performance of each model level is stored in a unified family of multi-dimensional information curves, ensuring comparability between different models. The system first calculates the key search indicator—the outlet flow rate coefficient—using real gas property equations, based on input parameters such as mass flow rate, inlet and outlet pressure and temperature, combined with initially assumed rotational speed and outlet temperature. Subsequently, this flow rate coefficient is compared in parallel with the performance curves of all model levels in the database, and two-dimensional interpolation is performed to instantly estimate the isentropic efficiency achievable by each model level under the current assumed operating conditions. Using maximum efficiency as the decision criterion, the system automatically selects the optimal model level and simultaneously loads its complete characteristic curves and modeling correction rules, providing high-quality initial values ​​for subsequent high-precision closed-loop iterative calculations.

[0031] Step S103: Read the multi-dimensional information curve family data at the target model level.

[0032] Step S104: Establish and execute the performance iteration algorithm. By coupling the multi-dimensional information curve family data of the target model level with the real gas properties for iterative calculation, until the isentropic efficiency, outlet temperature and output power of the target model level under the design parameters meet the iterative convergence conditions, and output the expander design report.

[0033] The algorithm uses the efficiency and load factor provided by the automatic selection as initial values. It accurately calculates parameters such as outlet enthalpy, entropy, and temperature under the current assumed operating conditions using real gas property equations, and then back-calculates new outlet flow coefficients and Mach numbers. These updated parameters are then substituted into the model-level curve family for interpolation to obtain a new round of predicted efficiency and load factor values. This process is repeated iteratively. The system continuously compares the theoretical enthalpy drop based on the efficiency definition with the effective enthalpy drop calculated by the actual property equations, and adjusts the assumed values ​​of rotational speed and outlet temperature accordingly. This allows key parameters to gradually approach the true solution in the iterations until the difference between the results of two adjacent iterations is less than the preset convergence tolerance. Once the iteration converges, the algorithm integrates all converged aerodynamic, thermodynamic, and geometric parameters, automatically generating a complete expander design report including performance curves, dimensional parameters, and operating indicators.

[0034] The expander design report includes, but is not limited to, the following: 1. Design Inputs and Basic Conditions: List the original design parameters provided by the user, such as medium composition, mass flow rate, inlet total pressure / temperature, outlet back pressure or speed, etc. 2. Model Level Selection and Key Parameters: The target model level code determined by automatic selection (e.g., TGX1). 3. Core dimensionless design point parameters of this model level, such as design flow coefficient, isentropic efficiency, load coefficient, and key dimensions after modeling, such as impeller diameter, outlet flange diameter, and speed. 4. Final Performance Calculation Results: Thermodynamic Parameters: Inlet and outlet total temperature, total pressure, static temperature, and static pressure of each stage and the entire unit. Aerodynamic Parameters: Isentropic efficiency, polytropic efficiency, effective enthalpy drop, reaction degree, and speed ratio. Power and Flow Rate: Output aerodynamic power, volumetric flow rate of each stage and the entire unit, and Mach number. 5. Performance Curve Graphs: Automatically generated expander performance curves covering a certain operating range, usually with flow rate as the horizontal axis, showing the curves of efficiency, pressure ratio, power, outlet temperature, etc., as a function of flow rate. 6. Operational Limitations and Recommendations: Based on the model-level database and calculations, specify the recommended operating range, surge boundary, and congestion flow limits. 7. Software and Data Version Information: The version of the design software used, the version number of the model-level database, and the calculation timestamp.

[0035] from Figure 2As can be seen from the method shown, this application constructs a model-level database containing experimentally verified data represented by a family of multi-dimensional information curves. During the design process, it automatically matches and calls the best model-level data based on the input parameters, effectively utilizing known reliable performance characteristics. This significantly shortens the cycle of repeated CFD analysis and trial and error in the traditional design process. At the same time, this method deeply couples the rigorous thermodynamic model with the iterative calculation of real gas physical parameters. In the automatic selection and performance iteration process, it achieves closed-loop convergence calculation of the key performance parameters of the expander, significantly improving the accuracy and reliability of the design. Ultimately, it can efficiently generate an optimized centripetal expander design scheme while ensuring design accuracy.

[0036] In some embodiments of this application, the performance iteration algorithm in the above method includes the following steps, such as... Figure 3 As shown: S1. Calculate the outlet Mach number and outlet flow coefficient based on the outlet temperature.

[0037] Specifically, the outlet static pressure is known. mass flow impeller diameter The currently assumed impeller speed n First, calculate the circumferential velocity at the outer edge of the impeller: ,in, Indicates the circumferential velocity of the impeller's outer edge. Indicates the impeller speed. This indicates the impeller diameter.

[0038] Further calculation of the outlet velocity of sound based on outlet static pressure and outlet temperature: (The outlet pressure is then used to calculate the outlet velocity of sound.) and assumed outlet temperature As input, the density under the corresponding state is first solved iteratively. Then use that state point and The speed of sound at that point was calculated using the equations of physical properties. a 2.

[0039] Specifically, the density in the corresponding state is solved iteratively here. The specific process can be found in the following calculation of outlet density based on outlet static pressure and outlet temperature. The process of obtaining accurate state points. After that, the speed of sound is calculated using the partial derivatives of the Helmholtz free energy equation of state. The specific formula is as follows: ,in, , , , , for and Dimensionless variables.

[0040] Further calculation of the exit Mach number based on the impeller outer edge circumferential velocity and exit sound velocity: .

[0041] Further calculation of outlet density based on outlet static pressure and outlet temperature Because the ideal gas law is used, the density cannot be directly obtained from p = T × ρ × R. Newton's iteration method is required. First, the initial density value is calculated using the ideal gas law: ,in R This is the gas constant of the medium. Then, Substitute into the state equation Calculate pressure p ,in, , for The dimensionless variable. The calculated pressure. p With target pressure p 2. Comparison, based on Newton's iterative formula Update the density value ρ until the pressure is calculated. p and p The difference of 2 is less than the set tolerance. At this point... ρ That is, the true export density .

[0042] Further calculate the outlet volumetric flow rate based on outlet density and mass flow rate. : .

[0043] Finally, the outlet flow coefficient is calculated based on the outlet volumetric flow rate, impeller diameter, and impeller outer edge circumferential velocity: .

[0044] S2. Based on the exit Mach number and exit flow coefficient, interpolation is performed in the multi-dimensional information curve family at the target model level to obtain the corresponding isentropic efficiency and load coefficient.

[0045] Specifically, based on the calculated outlet flow coefficient, interpolation is performed on the outlet Mach number-flow coefficient relationship curve of the multi-dimensional information curve family to obtain the outlet Mach number on the corresponding first inlet Mach number curve and the outlet Mach number on the second inlet Mach number curve; based on the outlet Mach number on the two curves, the interpolation coefficient is calculated, and then the efficiency value and load coefficient are obtained by interpolation on the isentropic efficiency-flow coefficient curve and the load coefficient-flow coefficient curve, respectively, according to the outlet flow coefficient; finally, the isentropic efficiency and load coefficient of the current point are calculated.

[0046] For example, with Figures 4-6 Taking the model-level dimensionless curve shown as an example, firstly, based on the calculated outlet flow coefficient... ,exist Figure 6 Interpolation is performed on the two curves shown to obtain the corresponding exit Mach number M. u_out_A and M u_out_B (Point A is on the blue line, and point B is on the orange line). If the Mach number M... u_out If the point is not between two lines, a suitable curve should be re-read from the database.

[0047] Secondly, calculate the interpolation coefficients. Further in Figure 4 Interpolation is performed on the two curves shown to obtain the corresponding isentropic efficiency. and (Point EA is on the blue line, and point EB is on the orange line); In Figure 5 Interpolation was performed on the two curves shown to obtain the corresponding load coefficients. and (The blue line represents point LA, and the orange line represents point LB; finally, the isentropic efficiency is calculated based on the following formula.) and load factor : = + = + .

[0048] S3. Based on the real gas property equations, load factors, and input parameters, iteratively calculate the outlet temperature and total outlet enthalpy until the calculated outlet temperature value converges.

[0049] Given input conditions: load factor Ψ, impeller outer edge circumferential velocity u1, and inlet total enthalpy h obtained from model-level curve interpolation. 01 outlet static pressure p2, outlet flow rate Q m 1. Estimated outlet flow area A2; 2. Assumed outlet temperature T in the current iteration step. 2_old (From the previous step or the initial value).

[0050] Based on T 2_old And p2, calculate the outlet density ρ2 and outlet static enthalpy h2: using Newton's iteration method, according to (p2, T 2_old The exact density ρ2 under this state is calculated, as described in step S2. At the same state point (p2, T) 2_old Under the following conditions, the outlet static enthalpy h2 is calculated using the Helmholtz free energy equation of state: .

[0051] Further calculations were performed on the outlet airflow velocity c2 and the outlet total enthalpy h. 02 Calculate the outlet volumetric flow rate : ; Calculate the outlet airflow velocity : ; Calculate the total enthalpy of exports : = .

[0052] Further utilizing the load factor Ψ, the theoretical total enthalpy h at the outlet is calculated from the perspective of energy conservation. 02_theory According to the definition of load factor: Therefore, based on the current Ψ and u1, the theoretical total enthalpy at the outlet can be derived as: h 02_theory = h 01 -Ψ × .

[0053] Further comparison of the calculated total export enthalpy h 02 With theoretical total enthalpy h 02_theory If h 02 h 02_theory This indicates that based on the current T 2_old The calculated total enthalpy of the gas is too high; the assumed outlet temperature T2 needs to be lowered to allow for more complete gas expansion, thus reducing the enthalpy value. Conversely, if h... 02 <h 02_theory Then the assumed outlet temperature T needs to be increased. 2。 Calculate the new outlet temperature T 2_new .

[0054] Specifically, T in the previous iteration step 2_old Based on h 02 with h 02_theory The comparison results determine a temperature range containing the true solution. , The interval is repeatedly divided into two parts, and the midpoint is taken each time. According to (p2, Calculate the state and obtain h 02 , and the target value h 02_theory The comparison is narrowed down until the difference between the two is less than the set convergence tolerance. At this point... That is, the new outlet temperature T 2_new .

[0055] Next, we calculate the relative change in outlet temperature between the two tests: To determine convergence: if ΔT is less than the preset convergence tolerance, the outlet temperature is considered to have converged, and the loop exits. Otherwise, let T... 2_old = T 2_new A new round of iterations begins.

[0056] S4. Based on the converged parameters, calculate the actual output enthalpy difference and compare it with the isentropic efficiency and the theoretical enthalpy difference derived from the isentropic enthalpy difference.

[0057] Based on import enthalpy and export enthalpy Calculate the actual output enthalpy difference :

[0058] Based on isentropic efficiency and isentropic enthalpy difference Calculate the theoretical enthalpy difference .

[0059] S5. If the difference between the theoretical enthalpy difference and the actual output enthalpy difference does not meet the convergence condition, adjust the assumed value of the impeller speed and iterate again until the convergence condition is met.

[0060] Theoretical enthalpy difference Enthalpy difference from actual output The values ​​are compared. If the difference between the two values ​​is less than the specified iteration tolerance, the impeller speed assumption is adjusted and iterative training is repeated until the values ​​are less than the specified iteration tolerance, at which point the model selection calculation process ends.

[0061] In this embodiment, a performance iteration algorithm is used to achieve closed-loop convergence calculation of the key performance parameters of the expander, which significantly improves the accuracy and reliability of the design.

[0062] In some embodiments of this application, in the above method, for a multi-stage expander, a performance iterative calculation method is used to perform iterative superposition calculations on each stage according to the input inlet and outlet conditions to obtain the overall machine performance.

[0063] Specifically, for a multi-stage centripetal expander, the system uses an iterative performance calculation method to design the entire machine based on the input total inlet and outlet conditions. First, the program assigns initial target outlet pressure values ​​to each stage based on experience or simple estimation, and sets an initial rotational speed for the entire machine. Then, starting from the first stage, the inlet parameters of the entire machine are used as the input for the first stage, with its assigned outlet pressure as the target, to perform a complete single-stage performance iterative calculation, obtaining detailed outlet conditions and performance parameters for that stage. The total outlet pressure and temperature of that stage are then used as the inlet conditions for the next stage. This process is repeated stage by stage until the last stage is completed. After completing one round of calculations for all stages, the program compares the total outlet pressure of the last stage with the user-required final back pressure of the entire machine. If the deviation does not meet the convergence tolerance, the machine rotational speed is intelligently adjusted, and the stage-by-stage calculation is restarted with new parameters. This process is repeated until the outlet pressure of the last stage precisely matches the target back pressure. At this point, all levels are working in coordination under their current optimal conditions. The program then summarizes all results and outputs a final design report containing detailed designs of each level, overall system efficiency, total power, and overall performance curves.

[0064] The embodiments of this application realize the automated and high-precision integrated design of a complex coupled multi-stage expander system through "closed-loop iteration of stepwise decoupling calculation and whole-machine target feedback".

[0065] In some embodiments of this application, in the above method, after completing the calculation of a design point, multi-point iterative calculation is performed near the flow rate of the design point along the direction of increasing flow rate and the direction of decreasing flow rate, and the endpoints of the performance curve are determined by the bisection method to generate a complete performance curve map of the target model under the current operating conditions.

[0066] Specifically, taking the design point as a reference, the program expands in both directions: using the converged design point flow rate Q0 as a reference, it first sets a series of new target flow rate values ​​Q in both directions of increasing and decreasing flow rate, according to a preset step size or logic. i .

[0067] Perform iterative calculations for non-design conditions point by point: for each new target flow Q i The program will maintain the rotational speed n, the model stage (impeller), and the total inlet pressure p. 01 Total temperature T 01 ) remain unchanged, and Q is changed. i As new input conditions, a new set of performance iterative calculations similar to those at the design point is executed. This set of calculations requires rematching the flow coefficient, interpolation efficiency, and iterating over temperature and enthalpy drop to solve for the corresponding outlet pressure, efficiency, and power at that flow rate.

[0068] Endpoint detection and bisection convergence: These are crucial for ensuring the integrity of the curve and preventing overflow. Model-level dimensionless performance curves have clearly defined effective ranges; for example, the flow coefficient has minimum and maximum values, corresponding to surge and blockage boundaries, respectively.

[0069] When the program is calculating in a certain direction, if it detects a certain flow Q t If the next iteration fails to converge, or the calculated flow coefficient exceeds the effective range of the model calibrated in the database, it indicates that the performance endpoint in that direction has been approached. At this point, the program will use a binary search: at the previously successfully calculated flow point Q... t 1 and the current failure point Q t Between, take the midpoint flow Q m Recalculate. Based on the calculation result of point Qm, the program will determine that the actual endpoint is located in [Q]. t 1,Q m [Q] m Q tWithin the specified interval, a binary search is performed within the sub-intervals containing the endpoint. Through several binary search iterations, the program can accurately pinpoint the maximum flow rate achievable by the performance curve in that direction and calculate the performance parameters for that endpoint condition.

[0070] Generating a complete performance graph: After calculating all flow points, the program will organize the data and plot a set of performance curves with flow rate on the x-axis and key parameters such as isentropic efficiency, pressure ratio, outlet temperature, and output power on the y-axis. This graph clearly shows the expander's operating range, high-efficiency zone, and performance boundaries under the current configuration.

[0071] This process extends the single-point design algorithm into a performance prediction tool for the entire operating range. It scans performance through automated multi-point calculations and uses a bisection method to intelligently detect boundaries, ultimately generating a complete and reliable aerodynamic performance curve. This curve is the core basis for unit selection, operation scheduling, and condition analysis, and is also an important component of the output of this design methodology.

[0072] In some embodiments of this application, a centripetal expander design apparatus is provided, which corresponds one-to-one with the centripetal expander design method in the above embodiments. For example... Figure 7 As shown, the centripetal expander design device includes a construction module 101, a matching module 102, a correction module 103, and an iteration module 104.

[0073] The construction module 101 is used to construct a centripetal expander model-level database. The model-level database stores multiple model-level dimensionless characteristic data. The dimensionless characteristic data includes a family of multi-dimensional information curves. Each curve includes multiple data points based on the inlet Mach number. Each data point includes at least one of the following: flow coefficient, total static isentropic efficiency, load coefficient, speed ratio, and outlet Mach number. Matching module 102 is used to automatically match the target model level with the best efficiency from the model-level performance database based on the expander design parameters input by the user; The correction module 103 is used to read the multi-dimensional information curve family data at the target model level and perform model correction; The iteration module 104 is used to establish and execute the performance iteration algorithm. By coupling the multi-dimensional information curve family data of the target model level with the real gas properties for iterative calculation, the algorithm continues until the isentropic efficiency, outlet temperature and output power of the target model level under the design parameters meet the iteration convergence conditions, and then outputs the expander design report.

[0074] In some embodiments of this application, in the above-described apparatus, the dimensionless characteristic data of the model level are obtained by performing performance testing and / or high-precision CFD analysis on the model level in advance, and stored in a database; The database also includes a separate database management module for querying, adding, modifying, and maintaining model-level data.

[0075] In some embodiments of this application, the data structure of the multi-dimensional information curve family in the above-described apparatus further includes an information header, which includes at least the model-level name, model-level impeller diameter, impeller type, design point flow coefficient, design point total static entropy efficiency, and design point load coefficient.

[0076] In some embodiments of this application, the flow coefficient calculation formula for the multi-dimensional information curve family in the above-described apparatus is as follows:

[0077] in, The impeller diameter is... The outer circumferential velocity of the impeller. This is the volumetric flow rate at the impeller outlet.

[0078] In some embodiments of this application, in the above-described apparatus, the performance iteration algorithm includes: Calculate the outlet Mach number and outlet flow coefficient based on the outlet temperature; Based on the exit Mach number and the exit flow coefficient, interpolation is performed in the multi-dimensional information curve family at the target model level to obtain the corresponding isentropic efficiency and load coefficient. Based on the real gas property equation, the load factor and input parameters, the outlet temperature and total outlet enthalpy are calculated iteratively until the calculated outlet temperature value converges. Based on the converged parameters, the actual output enthalpy difference is calculated and compared with the isentropic efficiency and the theoretical enthalpy difference derived from the isentropic enthalpy difference. If the difference between the theoretical enthalpy difference and the actual output enthalpy difference does not meet the convergence condition, the assumed value of the impeller speed is adjusted and the iteration is repeated until the convergence condition is met.

[0079] In some embodiments of this application, in the above-described apparatus, the iteration module 104 is further configured to perform iterative superposition calculations on each stage of a multi-stage expander according to the input inlet and outlet conditions, using the performance iteration calculation method to obtain the overall machine performance.

[0080] In some embodiments of this application, in the above-described apparatus, the iteration module 104 is further configured to perform multi-point iterative calculations near the flow rate of the design point along the direction of increasing flow rate and the direction of decreasing flow rate after completing the calculation of a design point, and use the bisection method to determine the endpoints of the performance curve, thereby generating a complete performance curve map of the target model level under the current operating conditions.

[0081] It should be noted that any of the above-mentioned centripetal expander design devices can be used to implement the aforementioned centripetal expander design method, which will not be elaborated here.

[0082] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Figure 7 As shown, at the hardware level, this electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may include non-volatile memory, such as at least one disk drive. Of course, this electronic device may also include other hardware required for other business operations.

[0083] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0084] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0085] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming a centripetal expander design device at the logical level. The processor executes the program stored in memory and specifically performs the aforementioned methods.

[0086] The processor may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0087] This electronic device can execute the centripetal expander design method provided in several embodiments of this application, and realize a centripetal expander design device. Figure 6 The functions of the embodiments shown are not described in detail here.

[0088] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform the centripetal expander design method provided in several embodiments of this application.

[0089] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0091] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0093] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0094] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0095] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0096] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0097] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A design method for a centripetal expander, characterized in that, The method includes: A centripetal expander model-level database is constructed. The model-level database stores multiple model-level dimensionless characteristic data. The dimensionless characteristic data includes a family of multi-dimensional information curves. Each curve includes multiple data points based on the inlet Mach number. Each data point includes at least one of the following: flow coefficient, total static entropy efficiency, load coefficient, speed ratio, and outlet Mach number. Based on the expander design parameters input by the user, the system automatically matches the target model level with the best efficiency from the model-level performance database. Read the multi-dimensional information curve family data at the target model level; Establish and execute a performance iteration algorithm. By coupling the multi-dimensional information curve family data of the target model level with the real gas properties for iterative calculation, until the isentropic efficiency, outlet temperature and output power of the target model level under the design parameters meet the iterative convergence conditions, and output an expander design report.

2. The method according to claim 1, characterized in that, The dimensionless characteristic data of the model level are obtained by performing performance tests and / or high-precision CFD analysis on the model level in advance, and stored in the database; The database also includes a separate database management module for querying, adding, modifying, and maintaining model-level data.

3. The method according to claim 1, characterized in that, The data structure of the multi-dimensional information curve family also includes an information header, which includes at least the model-level name, model-level impeller diameter, impeller type, design point flow coefficient, design point total static entropy efficiency, and design point load coefficient.

4. The method according to claim 1, characterized in that, The formula for calculating the flow coefficient of the multi-dimensional information curve family is as follows: in, The impeller diameter is... The outer circumferential velocity of the impeller. This is the volumetric flow rate at the impeller outlet.

5. The method according to claim 1, characterized in that, The performance iteration algorithm includes: Calculate the outlet Mach number and outlet flow coefficient based on the outlet temperature; Based on the exit Mach number and the exit flow coefficient, interpolation is performed in the multi-dimensional information curve family at the target model level to obtain the corresponding isentropic efficiency and load coefficient. Based on the real gas property equation, the load factor and input parameters, the outlet temperature and total outlet enthalpy are calculated iteratively until the calculated outlet temperature value converges. Based on the converged parameters, the actual output enthalpy difference is calculated and compared with the isentropic efficiency and the theoretical enthalpy difference derived from the isentropic enthalpy difference. If the difference between the theoretical enthalpy difference and the actual output enthalpy difference does not meet the convergence condition, the assumed value of the impeller speed is adjusted and the iteration is repeated until the convergence condition is met.

6. The method according to claim 1, characterized in that, The method further includes: For a multi-stage expander, the performance is calculated by iteratively superimposing each stage according to the input inlet and outlet conditions to obtain the overall machine performance.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: After completing the calculation at a design point, multi-point iterative calculations are performed near the flow rate at the design point along the directions of increasing and decreasing flow rates. The endpoints of the performance curve are determined using the bisection method, generating a complete performance curve map of the target model under the current operating conditions.

8. A centripetal expander design device, characterized in that, The device includes: The module is used to build a model-level database for a centripetal expander. The model-level database stores multiple model-level dimensionless characteristic data. The dimensionless characteristic data includes a family of multi-dimensional information curves. Each curve includes multiple data points based on the inlet Mach number. Each data point includes at least one of the following: flow coefficient, total static isentropic efficiency, load coefficient, speed ratio, and outlet Mach number. The matching module is used to automatically match the target model level with the best efficiency from the model-level performance database based on the expander design parameters input by the user. The correction module is used to read the multi-dimensional information curve family data at the target model level and perform model correction; The iteration module is used to establish and execute the performance iteration algorithm. It iteratively calculates the target model level by coupling the multi-dimensional information curve family data with the real gas properties until the target model level's isentropic efficiency, outlet temperature and output power under the design parameters meet the iteration convergence conditions, and outputs the expander design report.

9. An electronic device, comprising: processor; as well as A memory is configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the centripetal expander design method as described in any one of claims 1-7.

10. A computer-readable storage medium storing one or more programs that, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the steps of the centripetal expander design method as described in any one of claims 1-7.

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