Compressor digital model modeling method and system
By segmenting compressor frequency and calibrating a reduced set of coefficients for each region, the method addresses long calculation cycles and divergence issues in compressor simulation, achieving accurate and efficient modeling.
Patent Information
- Application Number
- CN202411131661.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-16
- Publication Date
- 2024-12-13
AI Technical Summary
Current compressor simulation models face long calculation cycles, divergence issues, and calculation failures due to excessive coefficients, which are prone to non-convergence when insufficient data is available.
The method involves dividing the compressor operating frequency range into non-overlapping regions, obtaining operating data, and calibrating a reduced set of coefficients (4 mass flow and 4 enthalpy loss coefficients) for each region, ensuring accurate and convergent modeling.
This approach reduces the number of coefficients required, improving simulation accuracy and ensuring full convergence, while maintaining calculation speed.
Smart Images
Figure CN119129132A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of compressor simulation and emulation, and in particular to a compressor digital modeling method and system. Background Art
[0002] At present, simulation technology has been applied to various fields, which is helpful for the research and development of complex systems. In the field of air conditioning research and development, simulation is gradually used to replace manual work. Air conditioning includes four core components, among which the compressor is the main core component. The accuracy of compressor simulation directly determines the accuracy of the air conditioning simulation model. The current problem in the industry for compressor models is that the model calculation cycle is long, it is easy to diverge and not converge, and it is easy to cause calculation failure or simulate wrong results. It is generally believed in the industry that the more coefficients of the simulation model formula, the higher the accuracy, but a large number of experimental results show that this is not the case. The industry often uses the 20-coefficient method or the 30-coefficient method or more coefficient methods of more parameters to simulate the compressor model, but such a model requires a large amount of data to be collected for fitting. If the amount of data is not enough, the coefficients obtained by fitting cannot guarantee correctness, and too many coefficients are the main reasons for calculation failure and non-convergence. That is, although increasing the number of coefficients will improve the accuracy of model simulation to a certain extent, it also increases the risk of non-convergence and calculation failure. Summary of the invention
[0003] The purpose of this application is to provide a compressor digital modeling method and system, which can quickly and accurately obtain a compressor digital model.
[0004] To achieve the above objectives, this application provides the following solutions:
[0005] In a first aspect, the present application provides a compressor digital modeling method, comprising the following steps:
[0006] The compressor operating frequency range is completely divided into several non-overlapping operating frequency regions.
[0007] For any operating frequency region, several compressor operating data of the compressor operating in the operating frequency region are obtained; the compressor operating data includes suction pressure, suction temperature, exhaust pressure, exhaust temperature, compressor cylinder volume and mass flow rate.
[0008] According to a number of compressor working data of the compressor working in the operating frequency region, the coefficient group of the compressor digital model is calibrated to obtain the compressor digital model coefficient group in the operating frequency region; the compressor digital model coefficient group includes 4 mass flow loss coefficients and 4 enthalpy loss coefficients.
[0009] The operating frequency region and the compressor digital model coefficient group of the operating frequency region are bound and stored to complete the modeling of the compressor digital model.
[0010] Optionally, according to a plurality of compressor working data of the compressor working in the operating frequency region, the coefficient group of the compressor digital model is calibrated to obtain the compressor digital model coefficient group in the operating frequency region, which specifically includes the following steps:
[0011] For any compressor working data of the compressor working in the operating frequency region, the compression ratio, the custom dimensionless parameters and the mass flow loss are determined according to the compressor working data.
[0012] According to the compression ratio, custom dimensionless parameters and mass flow loss corresponding to the working data of each compressor, a calibration data group and a verification data group are obtained.
[0013] The compression ratio, the user-defined dimensionless parameter and the mass flow loss in the calibration data group are used to calibrate the coefficient group of the compressor digital model to obtain the coefficient group to be verified.
[0014] Using the verification data set and the coefficient set to be verified, the error value corresponding to the coefficient set to be verified is obtained.
[0015] When the error value corresponding to the coefficient group to be verified is lower than the error threshold, the coefficient group to be verified is used as the compressor digital model coefficient group in the operating frequency region.
[0016] Optionally, the compression ratio, the custom dimensionless parameters and the mass flow loss are determined according to the compressor working data, specifically including the following steps:
[0017] The compression ratio is calculated based on the suction pressure and exhaust pressure.
[0018] Determine the fluid suction density based on the suction pressure and suction temperature.
[0019] Custom dimensionless parameters are calculated based on suction pressure, operating frequency, compressor displacement and fluid suction density; compressor displacement can be determined based on compressor cylinder volume.
[0020] The mass flow loss is calculated based on the operating frequency, compressor displacement, fluid suction density and mass flow rate.
[0021] Optionally, the compression ratio is calculated according to the following formula:
[0022]
[0023] Among them, x p is the compression ratio, p dis is the exhaust pressure, p suc is the suction pressure.
[0024] The custom dimensionless parameter is calculated according to the following formula:
[0025]
[0026] Among them, x n is a custom dimensionless parameter, d isp is the compressor displacement, N is the operating frequency, ρ suc is the fluid suction density.
[0027] The mass flow loss is calculated according to the following formula:
[0028]
[0029] Among them, x m is the mass flow loss, m is the mass flow rate, is the mass flow rate.
[0030] Optionally, the coefficient group of the compressor digital model is calibrated according to the following formula:
[0031]
[0032] Among them, x m is the mass flow loss, {a of ,a nf ,a pf ,a cf ,a oh ,a nh ,a ph ,a ch} is the coefficient group of the compressor digital model, a of ,a nf ,a pf ,a cf is the mass flow loss coefficient, a oh ,a nh ,a ph ,a ch is the enthalpy loss coefficient, x n is a custom dimensionless parameter, x p is the compression ratio.
[0033] Optionally, using the verification data group and the coefficient group to be verified, obtaining the error value corresponding to the coefficient group to be verified specifically includes the following steps:
[0034] The coefficient group to be verified is used as the coefficient group of the compressor digital model, and the simulated mass flow loss value is calculated according to the compression ratio and the customized dimensionless parameters in the verification data group.
[0035] According to the simulated mass flow loss value and the mass flow loss in the verification data group, an error value corresponding to the coefficient group to be verified is determined.
[0036] Optionally, after obtaining the compressor digital model coefficient group in the operating frequency region, before binding and storing the operating frequency region and the compressor digital model coefficient group in the operating frequency region, the following steps are further included:
[0037] Obtain the measured values of suction pressure, suction temperature, exhaust pressure, exhaust temperature and mass flow rate in the operating frequency area.
[0038] The mass flow simulation value is obtained by simulation based on the compressor digital model coefficient group, the measured value of the suction pressure, the measured value of the suction temperature, the measured value of the exhaust pressure and the measured value of the exhaust temperature.
[0039] Determine whether the compressor digital model coefficient group is qualified based on the mass flow simulation value and the mass flow measured value.
[0040] Optionally, after obtaining the compressor digital model coefficient group in the operating frequency region, before binding and storing the operating frequency region and the compressor digital model coefficient group in the operating frequency region, the following steps are further included:
[0041] Obtain the measured values of suction pressure, suction temperature, exhaust pressure, exhaust temperature and mass flow rate in the operating frequency area.
[0042] According to the coefficient group of the compressor digital model, the measured value of the exhaust pressure, the measured value of the exhaust temperature and the measured value of the mass flow rate, the simulated value of the suction pressure and the simulated value of the suction temperature are obtained by simulation.
[0043] Whether the compressor digital model coefficient group is qualified is determined based on the simulated value of suction pressure, the simulated value of suction temperature, the measured value of suction pressure and the measured value of suction temperature.
[0044] Optionally, after obtaining the compressor digital model coefficient group in the operating frequency region, before binding and storing the operating frequency region and the compressor digital model coefficient group in the operating frequency region, the following steps are further included:
[0045] Obtain the measured values of suction pressure, suction temperature, exhaust pressure, exhaust temperature and mass flow rate in the operating frequency area.
[0046] According to the coefficient group of the compressor digital model, the measured value of the suction pressure, the measured value of the suction temperature and the measured value of the mass flow rate, the simulated value of the exhaust pressure and the simulated value of the exhaust temperature are obtained by simulation.
[0047] Determine whether the compressor digital model coefficient group is qualified based on the exhaust pressure simulation value, exhaust temperature simulation value, exhaust pressure actual measurement value and exhaust temperature actual measurement value.
[0048] In a second aspect, the present application provides a compressor digital modeling system, comprising the following modules:
[0049] The operating frequency division module is used to completely divide the compressor operating frequency range into a number of non-overlapping operating frequency regions.
[0050] The working data acquisition module is used to obtain a number of compressor working data of the compressor working in any operating frequency area; the compressor working data includes suction pressure, suction temperature, exhaust pressure, exhaust temperature, compressor cylinder volume and mass flow.
[0051] The coefficient group calibration module is used to calibrate the coefficient group of the compressor digital model according to a number of compressor working data of the compressor working in the operating frequency area, so as to obtain the compressor digital model coefficient group in the operating frequency area; the compressor digital model coefficient group includes 4 mass flow loss coefficients and 4 enthalpy loss coefficients.
[0052] The coefficient group storage module is used to bind and store the operating frequency region and the compressor digital model coefficient group of the operating frequency region to complete the modeling of the compressor digital model.
[0053] According to the specific embodiments provided in this application, this application discloses the following technical effects:
[0054] The present application provides a compressor digital modeling method and system, the method includes: first, completely dividing the compressor operating frequency range into a number of non-overlapping operating frequency regions; second, for any operating frequency region, obtaining a number of compressor operating data of the compressor operating in the operating frequency region; and according to the several compressor operating data of the compressor operating in the operating frequency region, calibrating the coefficient group of the compressor digital model to obtain the compressor digital model coefficient group in the operating frequency region; wherein the compressor digital model coefficient group includes 4 mass flow loss coefficients and 4 enthalpy loss coefficients; finally, binding and storing the operating frequency region and the compressor digital model coefficient group in the operating frequency region to complete the modeling of the compressor digital model. The above scheme of the present application obtains data by segmenting the compressor frequency, so that each operating frequency region can be fitted to obtain a set of coefficients, and compared with the 20 coefficients or 30 coefficients method adopted by the traditional scheme, the number of coefficients is greatly reduced, ensuring that the compressor simulation accuracy and calculation speed are improved, and full convergence can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS
[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0056] Figure 1 A flowchart of a compressor digital modeling method provided in one embodiment of the present application.
[0057] Figure 2 This is a flowchart of step S3 in a compressor digital model building method provided in one embodiment of the present application.
[0058] Figure 3 This is a flowchart of step S31 in a compressor digital model building method provided in one embodiment of the present application.
[0059] Figure 4 A schematic diagram of the functional modules of a compressor digital model building system provided in one embodiment of the present application.
[0060] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0061] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0062] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.
[0063] In an exemplary embodiment, Figure 1 The flowchart shown provides a compressor digital modeling method, comprising the following steps:
[0064] S1. The compressor operating frequency range is completely divided into several non-overlapping operating frequency areas. For example, the compressor operating frequency is 15-120HZ, and each 10HZ segment is divided into 15-25HZ, 25-35HZ, 34-45HZ, 45-55HZ, 55-65HZ, 65-75HZ, 75-85HZ, 85-95HZ, 95-105HZ, and 105-120HZ. Each frequency segment corresponds to a different compressor digital model coefficient group, and the compressor automatically matches the corresponding coefficient group when operating at different frequencies.
[0065] S2. For any operating frequency region, obtain several compressor operating data of the compressor operating in the operating frequency region; the compressor operating data includes suction pressure, suction temperature, exhaust pressure, exhaust temperature, compressor cylinder volume and mass flow rate. For each operating frequency region divided in step S1, a controller specially set for the compressor operating frequency can be used to automatically adjust the compressor operating frequency, so that it can operate at a specific operating frequency, and collect all the working data in this operating frequency region. It not only solves the difficulty of the compressor formula to fully cover the coefficient calibration under the compressor frequency, but also achieves the goal of 100% convergence of calculation.
[0066] S3. Calibrate the coefficient group of the compressor digital model according to a number of compressor working data in the operating frequency region to obtain the compressor digital model coefficient group in the operating frequency region; the compressor digital model coefficient group includes 4 mass flow loss coefficients and 4 enthalpy loss coefficients.
[0067] When calibrating the coefficient group, first input five sets of working data under the operating frequency, use four sets of working data to solve the eight coefficients in the two relational equations, and one set of working data is used to verify the coefficient group under the operating frequency. The verification method is to use the solved eight coefficients to calculate the mass flow loss and enthalpy loss, and compare them with the mass flow loss and enthalpy loss obtained from this set of working data. If the error is very small, these eight coefficients are listed as the mass flow loss coefficient and enthalpy loss coefficient under the operating frequency. Then determine the influence range of the eight coefficients. The coefficient of mass flow loss mainly affects the volumetric efficiency, and the coefficient of the enthalpy loss formula mainly affects the isentropic efficiency.
[0068] Specifically in this embodiment, Figure 2 In the flowchart shown, step S3 includes the following steps:
[0069] S31. For any compressor working data in the operating frequency region, determine the compression ratio, the custom dimensionless parameters and the mass flow loss according to the compressor working data. Figure 3 In the flowchart shown, step S31 specifically includes the following steps:
[0070] S311. Calculate the compression ratio based on the intake pressure and the exhaust pressure. Calculate the compression ratio based on the following formula:
[0071]
[0072] Among them, x p is the compression ratio, p dis is the exhaust pressure, p suc is the suction pressure.
[0073] S312. Determine the fluid suction density according to the suction pressure and the suction temperature.
[0074] S313. Calculate the custom dimensionless parameters based on the suction pressure, operating frequency, compressor displacement and fluid suction density; the compressor displacement can be determined based on the compressor cylinder volume. Calculate the custom dimensionless parameters based on the following formula:
[0075]
[0076] Among them, x n is a custom dimensionless parameter, d isp is the compressor displacement, N is the operating frequency, ρ suc is the fluid suction density.
[0077] S314. Calculate the mass flow loss according to the operating frequency, compressor displacement, fluid suction density and mass flow. Calculate the mass flow loss according to the following formula:
[0078]
[0079] Among them, x m is the mass flow loss, m is the mass flow rate, is the mass flow rate.
[0080] S32. According to the compression ratio, the self-defined dimensionless parameter and the mass flow loss corresponding to the working data of each compressor, a calibration data group and a verification data group are obtained.
[0081] S33, using the compression ratio, the self-defined dimensionless parameter and the mass flow loss in the calibration data group, calibrate the coefficient group of the compressor digital model to obtain the coefficient group to be verified. Specifically, in this embodiment, the coefficient group of the compressor digital model is calibrated according to the following formula:
[0082]
[0083] Among them, x m is the mass flow loss, {a of ,a nf ,a pf ,a cf ,aoh ,a nh ,a ph ,a ch} is the coefficient group of the compressor digital model, a of ,a nf ,a pf ,a cf is the mass flow loss coefficient, a oh ,a nh ,a ph ,a ch is the enthalpy loss coefficient, x n is a custom dimensionless parameter, x p is the compression ratio.
[0084] S34. Using the verification data group and the coefficient group to be verified, obtain the error value corresponding to the coefficient group to be verified.
[0085] Step S34 specifically includes the following steps:
[0086] S341. Using the coefficient group to be verified as the coefficient group of the compressor digital model, and calculating the simulated mass flow loss value according to the compression ratio and the custom dimensionless parameters in the verification data group.
[0087] S342. Determine the error value corresponding to the coefficient group to be verified based on the simulated mass flow loss value and the mass flow loss in the verification data group.
[0088] S35. When the error value corresponding to the coefficient group to be verified is lower than the error threshold, the coefficient group to be verified is used as the compressor digital model coefficient group in the operating frequency region.
[0089] S4. Binding and storing the operating frequency region and the compressor digital model coefficient group of the operating frequency region to complete the modeling of the compressor digital model.
[0090] In another exemplary embodiment of the present application, after obtaining the compressor digital model coefficient group under the operating frequency region, before binding and storing the operating frequency region and the compressor digital model coefficient group under the operating frequency region, the following steps are also included:
[0091] A1. Obtain the measured values of suction pressure, suction temperature, exhaust pressure, exhaust temperature and mass flow rate in the operating frequency region.
[0092] A2. According to the compressor digital model coefficient group, the measured value of the suction pressure, the measured value of the suction temperature, the measured value of the exhaust pressure and the measured value of the exhaust temperature, the mass flow simulation value is obtained by simulation.
[0093] A3. Determine whether the compressor digital model coefficient group is qualified based on the mass flow simulation value and the mass flow measured value. If the error is within ±5%, it is qualified.
[0094] In another exemplary embodiment of the present application, after obtaining the compressor digital model coefficient group under the operating frequency region, before binding and storing the operating frequency region and the compressor digital model coefficient group under the operating frequency region, the following steps are also included:
[0095] B1. Obtain the measured values of suction pressure, suction temperature, exhaust pressure, exhaust temperature and mass flow rate in the operating frequency region.
[0096] B2. According to the compressor digital model coefficient group, the exhaust pressure measured value, the exhaust temperature measured value and the mass flow measured value, the suction pressure simulation value and the suction temperature simulation value are simulated.
[0097] B3. Determine whether the compressor digital model coefficient group is qualified based on the simulated value of suction pressure, simulated value of suction temperature, measured value of suction pressure and measured value of suction temperature. If the error is within ±7%, it is qualified.
[0098] In another exemplary embodiment of the present application, after obtaining the compressor digital model coefficient group under the operating frequency region, before binding and storing the operating frequency region and the compressor digital model coefficient group under the operating frequency region, the following steps are also included:
[0099] C1. Obtain the measured values of suction pressure, suction temperature, exhaust pressure, exhaust temperature and mass flow rate in the operating frequency region.
[0100] C2. According to the compressor digital model coefficient group, the measured value of the suction pressure, the measured value of the suction temperature and the measured value of the mass flow rate, the simulated value of the exhaust pressure and the simulated value of the exhaust temperature are obtained by simulation.
[0101] C3. Determine whether the compressor digital model coefficient group is qualified based on the exhaust pressure simulation value, exhaust temperature simulation value, exhaust pressure actual measured value and exhaust temperature actual measured value. If the error is within ±7%, it is qualified.
[0102] After verification and comparison of experimental data: The compressor frequency segment adopts the 8-coefficient method semi-empirical formula, which does not require the collection of a large number of parameter corrections. Generally, 8 sets of data are sufficient. The verification results are shown in Table 1:
[0103] Table 1 Comparison results of fitting with 48 sets of data and fitting with 8 sets of data
[0104] coefficient 48 sets of data fitting 8 sets of data fitting <![CDATA[a of ]]> 0.8306 0.8894 <![CDATA[a nf ]]> 0.0123 -0.0138 <![CDATA[a pf ]]> 35.6555 76.8004 <![CDATA[a cf ]]> -9.9711 -21.9533 aoh 2.1567 0.6724 <![CDATA[a nh ]]> -1.9576 -0.6236 <![CDATA[a ph ]]> -0.6942 -0.9224 <![CDATA[a ch ]]> -0.2367 -0.0324
[0105] According to the data, the coefficients of fitting 48 sets of data and 8 sets of data are not much different in actual use. And according to Table 2, compared with the traditional 20-parameter or 30-coefficient method, the 8-coefficient method proposed in this application can greatly improve the simulation accuracy.
[0106] Table 2 Simulation comparison results of the 8-coefficient method, 20-coefficient method and 30-coefficient method of this application
[0107]
[0108] The 8 coefficients obtained from the above scheme are used as known parameters to simulate the compressor suction and exhaust pressure temperature and mass flow rate. The cooling and heating capacity output of the air conditioning system is calculated by mass flow rate. The calculation formula is: Q = m·(h 1 -h 2 ), where Q is the heat exchange capacity, which can be either cooling capacity or heating capacity, h 1 is the heat exchanger outlet enthalpy, h 2 is the heat exchanger inlet enthalpy value. m is the mass flow rate of the compressor simulation output. The compressor power output is calculated by simulating the compressor suction and exhaust pressure. The calculation formula is: P = V × ΔH / η, where V is the volume flow rate, ΔH is the compressor pressure drop, that is, the compressor exhaust pressure - suction pressure, and η is the compression efficiency.
[0109] Based on the same inventive concept, the embodiment of the present application also provides a compressor digital modeling system for implementing the compressor digital modeling method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more compressor digital modeling system embodiments provided below can refer to the limitations of the compressor digital modeling method above, and will not be repeated here.
[0110] In an exemplary embodiment, Figure 4 As shown, a compressor digital modeling system is provided, comprising:
[0111] The operating frequency division module is used to completely divide the compressor operating frequency range into a number of non-overlapping operating frequency regions.
[0112] The working data acquisition module is used to obtain a number of compressor working data of the compressor working in any operating frequency area; the compressor working data includes suction pressure, suction temperature, exhaust pressure, exhaust temperature, compressor cylinder volume and mass flow.
[0113] The coefficient group calibration module is used to calibrate the coefficient group of the compressor digital model according to a number of compressor working data of the compressor working in the operating frequency area, so as to obtain the compressor digital model coefficient group in the operating frequency area; the compressor digital model coefficient group includes 4 mass flow loss coefficients and 4 enthalpy loss coefficients.
[0114] The coefficient group storage module is used to bind and store the operating frequency region and the compressor digital model coefficient group of the operating frequency region to complete the modeling of the compressor digital model.
[0115] Some of the above modules may also have sub-units for implementing their functions. Figure 4 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different functions. Figure 4 One or at least two components of the system shown.
[0116] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a compressor digital modeling method is implemented.
[0117] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0118] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.
[0119] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0120] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0121] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0122] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0123] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.
[0124] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0125] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A method for modeling a compressor digital model, It is characterized in that include: Completely divide the compressor operating frequency range into several non-overlapping operating frequency regions; For any operating frequency region, a plurality of compressor operating data of the compressor operating in the operating frequency region are obtained; the compressor operating data includes suction pressure, suction temperature, exhaust pressure, exhaust temperature, compressor cylinder volume and mass flow rate; Calibrate the coefficient group of the compressor digital model according to a plurality of compressor working data of the compressor working in the operating frequency region to obtain the compressor digital model coefficient group in the operating frequency region; the compressor digital model coefficient group includes 4 mass flow loss coefficients and 4 enthalpy loss coefficients; The operating frequency region and the compressor digital model coefficient group of the operating frequency region are bound and stored to complete the modeling of the compressor digital model.
2. The compressor digital modeling method according to claim 1, It is characterized in that According to a plurality of compressor working data of the compressor working in the operating frequency region, the coefficient group of the compressor digital model is calibrated to obtain the compressor digital model coefficient group in the operating frequency region, specifically including: For any compressor working data of the compressor working in the operating frequency region, determine the compression ratio, the custom dimensionless parameter and the mass flow loss according to the compressor working data; According to the compression ratio, the self-defined dimensionless parameter and the mass flow loss corresponding to the working data of each compressor, a calibration data group and a verification data group are obtained; Using the compression ratio, the self-defined dimensionless parameter and the mass flow loss in the calibration data group, calibrate the coefficient group of the compressor digital model to obtain the coefficient group to be verified; Using the verification data group and the coefficient group to be verified, obtaining an error value corresponding to the coefficient group to be verified; When the error value corresponding to the coefficient group to be verified is lower than the error threshold, the coefficient group to be verified is used as the compressor digital model coefficient group in the operating frequency region.
3. The compressor digital modeling method according to claim 2, It is characterized in that The compression ratio, the custom dimensionless parameters and the mass flow loss are determined according to the compressor working data, specifically including: The compression ratio is calculated based on the suction pressure and exhaust pressure; Determine the fluid suction density based on the suction pressure and suction temperature; A custom dimensionless parameter is calculated based on the suction pressure, the operating frequency, the compressor displacement and the fluid suction density; the compressor displacement can be determined based on the compressor cylinder volume; The mass flow loss is calculated based on the operating frequency, compressor displacement, fluid suction density and mass flow rate.
4. The compressor digital modeling method according to claim 3, It is characterized in that The compression ratio is calculated according to the following formula: Among them, x p is the compression ratio, p dis is the exhaust pressure, p suc is the inspiratory pressure; The custom dimensionless parameter is calculated according to the following formula: Among them, x n is a custom dimensionless parameter, d isp is the compressor displacement, N is the operating frequency, ρ suc is the fluid suction density; The mass flow loss is calculated according to the following formula: Among them, x m is the mass flow loss, m is the mass flow rate, is the mass flow rate.
5. The compressor digital modeling method according to claim 2, It is characterized in that The coefficient group of the compressor digital model is calibrated according to the following formula: Among them, x m is the mass flow loss, {a of ,a nf ,a pf ,a cf ,a oh ,a nh ,a ph ,a ch } is the coefficient group of the compressor digital model, a of ,a nf ,a pf ,a cf is the mass flow loss coefficient, a oh ,a nh ,a ph ,a ch is the enthalpy loss coefficient, x n is a custom dimensionless parameter, x p is the compression ratio.
6. The compressor digital modeling method according to claim 2, It is characterized in that Using the verification data group and the coefficient group to be verified, obtaining the error value corresponding to the coefficient group to be verified specifically includes: The coefficient group to be verified is used as the coefficient group of the compressor digital model, and the simulated mass flow loss value is calculated according to the compression ratio and the self-defined dimensionless parameters in the verification data group; An error value corresponding to the coefficient group to be verified is determined based on the simulated mass flow loss value and the mass flow loss in the verification data group.
7. The compressor digital modeling method according to claim 1, It is characterized in that After obtaining the compressor digital model coefficient group in the operating frequency region, and before binding and storing the operating frequency region and the compressor digital model coefficient group in the operating frequency region, the method further includes: Obtaining the actual measured value of the suction pressure, the suction temperature, the exhaust pressure, the exhaust temperature and the mass flow rate in the operating frequency region; The mass flow rate simulation value is obtained by simulation according to the compressor digital model coefficient group, the suction pressure measured value, the suction temperature measured value, the exhaust pressure measured value and the exhaust temperature measured value; Whether the compressor digital model coefficient group is qualified is determined based on the mass flow simulation value and the mass flow actual measurement value.
8. The compressor digital modeling method according to claim 1, It is characterized in that After obtaining the compressor digital model coefficient group in the operating frequency region, and before binding and storing the operating frequency region and the compressor digital model coefficient group in the operating frequency region, the method further includes: Obtaining the actual measured value of the suction pressure, the suction temperature, the exhaust pressure, the exhaust temperature and the mass flow rate in the operating frequency region; According to the compressor digital model coefficient group, the exhaust pressure measured value, the exhaust temperature measured value and the mass flow measured value, the suction pressure simulation value and the suction temperature simulation value are simulated to obtain; Whether the compressor digital model coefficient group is qualified is determined based on the intake pressure simulation value, the intake temperature simulation value, the intake pressure actual measurement value and the intake temperature actual measurement value.
9. The compressor digital modeling method according to claim 1, It is characterized in that After obtaining the compressor digital model coefficient group in the operating frequency region, and before binding and storing the operating frequency region and the compressor digital model coefficient group in the operating frequency region, the method further includes: Obtaining the actual measured value of the suction pressure, the suction temperature, the exhaust pressure, the exhaust temperature and the mass flow rate in the operating frequency region; According to the compressor digital model coefficient group, the suction pressure measured value, the suction temperature measured value and the mass flow measured value, the exhaust pressure simulation value and the exhaust temperature simulation value are simulated to obtain; Whether the compressor digital model coefficient group is qualified is determined based on the exhaust pressure simulation value, the exhaust temperature simulation value, the exhaust pressure actual measurement value and the exhaust temperature actual measurement value.
10. A compressor digital modeling system, It is characterized in that The compressor digital model building system comprises: An operating frequency division module, used to completely divide the compressor operating frequency range into a number of non-overlapping operating frequency regions; A working data acquisition module, for acquiring, for any operating frequency region, a plurality of compressor working data of the compressor operating in the operating frequency region; the compressor working data includes suction pressure, suction temperature, exhaust pressure, exhaust temperature, compressor cylinder volume and mass flow rate; A coefficient group calibration module, used for calibrating the coefficient group of the compressor digital model according to a plurality of compressor working data of the compressor working in the operating frequency region, so as to obtain the compressor digital model coefficient group in the operating frequency region; the compressor digital model coefficient group includes 4 mass flow loss coefficients and 4 enthalpy loss coefficients; The coefficient group storage module is used to bind and store the operating frequency region and the compressor digital model coefficient group of the operating frequency region to complete the modeling of the compressor digital model.