Data calibration method for through-flow performance model of reignition axial flow compressor
By collecting high-precision performance data and correcting the built-in empirical model, a more accurate flow calculation model is formed, which solves the problem of flow performance model calibration in compressor design, significantly improving design accuracy and efficiency.
Patent Information
- Application Number
- CN202411806268.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-10
- Publication Date
- 2025-03-28
AI Technical Summary
In compressor design, it is difficult for the prior art to effectively calibrate the flow performance model, resulting in insufficient accuracy and reliability of flow field prediction and performance prediction.
By collecting high-precision performance data, correcting the built-in empirical model, forming a more accurate flow calculation model, and applying the correction model to perform performance prediction.
It significantly improves the accuracy of the solver prediction, reduces the number of design iterations, speeds up the design cycle, and provides more accurate performance calculation results, especially in complex operating conditions, which can effectively deal with complex flow phenomena such as flow separation and secondary flow.
Smart Images
Figure CN119720549A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the fields of aerospace power, ship power and power generation technology, and in particular to a flow performance model data calibration method for a heavy-burning axial flow compressor. Background Art
[0002] A gas turbine is a rotary machine that converts chemical energy into mechanical energy through combustion. It is widely used in aircraft engines, large ship propulsion, and combined cycle power plant systems. As one of the core components of a gas turbine, the compressor undertakes the important task of compressing air to improve combustion efficiency. The design of a multi-stage axial flow compressor plays a vital role in achieving high-parameter and wide-load development of heavy-duty gas turbines. However, the design process of a multi-stage axial flow compressor often requires multiple iterations and coordination, especially when it comes to precise and reasonable preliminary design methods for two-dimensional flow passages. In addition, in compressor design, flow analysis is a key step, and its model precision and accuracy directly affect the reliability of the design results.
[0003] Considering the complexity of compressor design and the criticality of flow analysis, research on the flow performance model data calibration technology of heavy-duty axial flow compressor is carried out to improve the accuracy and reliability of compressor flow field prediction and performance prediction. Summary of the invention
[0004] The main purpose of the present invention is to provide a method for calibrating flow performance model data of a re-ignition axial flow compressor, which can effectively solve the problems in the background technology.
[0005] To achieve the above purpose, the technical solution adopted by the present invention is:
[0006] A method for calibrating flow performance model data for a re-ignition axial flow compressor comprises the following steps:
[0007] (1) Data collection based on high-precision performance data of axial flow compressors;
[0008] (2) Modification of built-in empirical models based on given data;
[0009] (3) Apply the modified model for performance prediction.
[0010] Preferably, the data collection based on the high-precision turbine performance data in step (1) comprises the following steps:
[0011] Step (1.1), collect high-precision data accumulated over the years during the compressor development process. These data can accurately reflect the actual operating status and performance parameters of the machine, and are the basis for the flow performance model data calibration technology. They usually include experimental data and computational fluid dynamics (CFD) simulation results;
[0012] Step (1.2) organizes and analyzes the collected data to determine its scope of application and accuracy, and selects reliable and valid data for subsequent model modification.
[0013] Preferably, the data used for model correction in step (1.2) are classified and sorted according to the compressor flow layout, specifically: for the rotor component, its outlet total pressure and total temperature are required; for the stator component, its outlet total pressure and airflow angle are required;
[0014] Preferably, the built-in empirical model correction based on given data in step (2) comprises the following steps:
[0015] Step (2.1), using the high-precision performance data collected in step (1), automatically solving and correcting the built-in calculation model according to the high-precision data performance indicators;
[0016] Step (2.2) calculates a new flow calculation result through the calibrated model. The calculation result completely matches the existing test data, and the program obtains the revised model of the two-dimensional model.
[0017] Preferably, the calculation model in step (2.1) mainly refers to the empirical model used in the two-dimensional flow calculation (such as the loss model, the backward angle model, etc.), and the empirical model is corrected by component in turn according to the compressor flow layout, and the loss model and the backward angle model are iterated for convergence until all meet the convergence standard and the calculation is completed. The specific correction scheme is as follows:
[0018] a. For rotor components, correct its loss model to match the rotor outlet total pressure, and correct its lagging angle model to match the rotor outlet total temperature;
[0019] b. For the stator components, correct its loss model to match the rotor outlet total pressure, and correct its lag angle model to match the rotor outlet airflow angle.
[0020] Preferably, when the corrected model is used for performance prediction in step (3) (such as changing the speed, changing the inlet temperature / pressure, adjusting the IGV / EGV / stator angle, changing the working fluid, changing the tip clearance to scale the whole machine, etc.), on the one hand, the program will call the empirical model (this part of the model mainly considers the influence of physical mechanisms, such as friction, secondary flow, mixing, etc.), and on the other hand, it will interpolate the digital model according to the characteristic parameters to obtain the correction value of the empirical model, and finally reach a calculation result with the same accuracy as the given correction data. The empirical model can grasp the main flow and heat transfer physical characteristics, but because the specific formula of the empirical model itself is based on previous test data, the accuracy will vary for different models. After correction with the data model, the corrected model parameters are used for analysis and calculation to obtain the compressor performance prediction results. At this time, the default empirical formula and the test correction compensation data are combined in the calculation, so that the calculation results are more accurate.
[0021] Compared with the prior art, the method for calibrating flow performance model data for a re-ignition axial flow compressor of the present invention has the following beneficial effects:
[0022] First, the present invention can significantly improve the solver prediction accuracy, reduce the number of design iterations, and speed up the design cycle by introducing high-precision data calibration technology in the compressor flow calculation stage. In the two-dimensional flow calculation stage, the data calibration technology can quickly evaluate the design quality in a short time and improve the design efficiency.
[0023] Secondly, the present invention can utilize test data and CFD simulation results to modify traditional loss models, backward angle models, blocking models, etc., to form a more accurate model and provide more accurate performance calculation results.
[0024] Third, the present invention can effectively handle complex flow phenomena such as flow separation and secondary flow under complex working conditions such as high pressure ratio, large flow rate and multiple stages, reduce the errors caused by traditional models, and provide more reliable performance predictions. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] Figure 1 It is a flow chart of the present invention.
[0026] Figure 2 This is the calculation flow chart of the model correction of the present invention.
[0027] Figure 3 This is a flow chart of the performance prediction solution based on data calibration in the present invention. DETAILED DESCRIPTION
[0028] In order to make the objectives, technical means and advantages achieved, and the purposes and effects achieved of the present invention easy to understand, the present invention is further described below in conjunction with specific implementation methods.
[0029] like Figure 1 As shown, a method for calibrating flow performance model data for a re-ignition axial flow compressor involves the following steps:
[0030] (1) Data collection based on high-precision performance data of axial flow compressors includes the following steps:
[0031] Step (1.1), collecting the experimental data and computational fluid dynamics (CFD) simulation results accumulated over the years for the compressor;
[0032] Step (1.2) organizes and analyzes the collected data to determine its scope of application and accuracy, and selects reliable and valid data for subsequent model modification.
[0033] Specifically, the data obtained from existing experiments or simulations are first collected, and the collected data are organized and analyzed based on the compressor flow layout, generally the aerodynamic parameters of the component outlet, among which the rotor components include the total temperature distribution and total pressure distribution, and the stator components include the total pressure distribution and airflow angle distribution.
[0034] Taking the rotor component as an example, the specific calculation process of model correction is as follows: Figure 2 As shown, step (2), the built-in empirical model correction based on given data; the built-in empirical model correction based on given data includes the following steps:
[0035] Step (2.1), using the high-precision performance data collected in step (1), automatically solve and correct the built-in calculation model according to the high-precision data performance indicators. The specific adjustment indicators are as follows:
[0036] a. For rotor components, correct its loss model to match the rotor outlet total pressure, and correct its lagging angle model to match the rotor outlet total temperature;
[0037] b. For the stator components, correct its loss model to match the rotor outlet total pressure, and correct its lag angle model to match the rotor outlet airflow angle.
[0038] Then, based on the built-in calculation model, the solution is automatically solved. Considering the complexity of the compressor flow calculation, in order to improve the solver convergence rate, a two-layer loop is used to calibrate the model. Among them, the inner loop performs model calibration to match the given target performance, without repositioning the streamlines and updating the boundary layer blockage coefficient (that is, calling the simplified analysis solver). After the internal convergence, it jumps to the outer loop, and the outer loop calls the complete analysis solver (repositioning the streamlines and updating the boundary layer blockage coefficient). After that, it determines whether the convergence conditions are met. If not, it enters the inner loop for iteration, and repeats until convergence. Note that the number of external loops must be less than 5 times. When calling the whole machine analysis mode, if boundary layer blockage is encountered, it must be performed in the above-mentioned external loop.
[0039] Step (2.2), calculate the new flow calculation result through the calibrated model. After the calculation result fully matches the existing test data, the program obtains the revised model of the two-dimensional model.
[0040] Taking the rotor component as an example, the performance prediction solution process after data calibration is as follows: Figure 3 As shown; Step (3), by modifying the model to calculate and predict the performance dimensions of other similar models or design structures, as well as the performance after the dimension transformation, the calculated results can still match the actual test data well. Users can make certain changes to the operating conditions as needed, and then perform performance prediction. The specific process is as follows:
[0041] Step (3.1), perform component performance analysis according to the component, and use the default loss and lag angle model to calculate the loss coefficient and lag angle;
[0042] Step (3.2), determining whether the component has a correction model data set, and if so, calculating the interpolation feature quantity of the component according to the operating conditions;
[0043] Step (3.3), using the interpolation feature quantity as a reference, interpolating / extrapolating the existing correction model data set to obtain the corresponding compensation value of the loss and lag angle;
[0044] Step (3.4), superimposing the compensation value with the default model calculation value to obtain the corrected model lag angle or loss parameter;
[0045] Step (3.5), apply the modified model parameters to perform analysis and calculation to obtain the turbine performance prediction results. At this time, the calculation combines the default empirical formula and the test correction and compensation data, and the calculation results are more accurate.
[0046] The data calibration technology for the heavy-duty axial compressor flow performance model described in the present invention has pioneered the field of axial compressor flow performance models for heavy-duty gas turbines. By introducing high-precision data calibration technology in the two-dimensional design stage, the design accuracy is significantly improved, the number of design iterations is reduced, and the design cycle is accelerated. In the two-dimensional flow calculation stage, the data calibration technology can quickly evaluate the design quality in a short time and improve the design efficiency. In the axial compressor flow calculation stage, the data calibration technology can quickly evaluate the design quality in a short time and improve the design efficiency. Based on the collected high-precision performance data of the compressor, the present invention can use test data and CFD simulation results to correct traditional loss models, lag angle models, etc., to form a more accurate model, provide more accurate performance calculation results, correct the empirical model used in the flow performance analysis process, match the target performance index parameters, thereby forming a more accurate model, and use the corrected model to predict the performance of the machine, which can provide more accurate performance calculation results. At the same time, the modified model can also accurately predict the performance of the machine in the form of modeling with variable working conditions and working fluids. Under complex working conditions such as high pressure ratio, large flow rate and multiple stages, the present invention can effectively handle complex flow phenomena such as flow separation and secondary flow, reduce the errors caused by traditional models, and provide more reliable performance predictions. The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will also have various changes, equivalent substitutions, improvements, etc., which should all fall within the scope of the invention to be protected. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.
Claims
1. A method for calibrating flow performance model data for a re-ignition axial flow compressor, characterized in that: The following steps are involved: (1) Data collection based on high-precision performance data of axial flow compressors; (2) Modification of built-in empirical models based on given data; (3) Apply the modified model for performance prediction.
2. The method for calibrating flow performance model data for a heavy-burning axial flow compressor according to claim 1, characterized in that: The data collection based on the high-precision performance data of the turbine in step (1) includes the following steps: Step (1.1), collect high-precision data accumulated over the years during the compressor development process. These data can accurately reflect the actual operating status and performance parameters of the machine, and are the basis for the flow performance model data calibration technology. They usually include experimental data and computational fluid dynamics (CFD) simulation results; Step (1.2) organizes and analyzes the collected data to determine its scope of application and accuracy, and selects reliable and valid data for subsequent model modification.
3. The method for calibrating flow performance model data for a heavy-burning axial flow compressor according to claim 2, characterized in that: The data used for model correction in step (1.2) are classified and sorted according to the compressor flow layout: for the rotor components, their outlet total pressure and total temperature are required; for the stator components, their outlet total pressure and airflow angle are required.
4. The method for calibrating flow performance model data for a heavy-burning axial flow compressor according to claim 3, characterized in that: The built-in empirical model modification based on given data in step (2) includes the following steps: Step (2.1), using the high-precision performance data collected in step (1), automatically solving and correcting the built-in calculation model according to the high-precision data performance indicators; Step (2.2) calculates a new flow calculation result through the calibrated model. The calculation result completely matches the existing test data, and the program obtains a revised model for flow calculation.
5. The method for calibrating flow performance model data for a heavy-burning axial flow compressor according to claim 4, characterized in that: The calculation model in step (2.1) refers to the empirical model used in the two-dimensional flow calculation. The empirical model is modified according to the compressor flow layout, and the loss model and the lagging angle model are iterated for convergence until all meet the convergence criteria. The specific component correction method is as follows: a. For rotor components, correct its loss model to match the rotor outlet total pressure, and correct its lagging angle model to match the rotor outlet total temperature; b. For the stator components, correct its loss model to match the rotor outlet total pressure, and correct its lag angle model to match the rotor outlet airflow angle.
6. The method for calibrating flow performance model data for a re-ignition axial flow compressor according to claim 1, characterized in that: When the corrected model is used for performance prediction in step (3), the program will call the empirical model on the one hand, and interpolate the characteristic parameters in the digital model on the other hand to obtain the corrected value of the empirical model, and finally reach a calculation result with the same accuracy as the given corrected data; after correction with the data model, the corrected model parameters are used for analysis and calculation to obtain the compressor performance prediction result.
7. The method for calibrating flow performance model data for a heavy-burning axial flow compressor according to claim 6, characterized in that: The specific process steps of step (3) are: Step (3.1), perform component performance analysis according to the component, and use the default loss and lag angle model to calculate the loss coefficient and lag angle; Step (3.2), determining whether the component has a correction model data set, and if so, calculating the interpolation feature quantity of the component according to the operating conditions; Step (3.3), using the interpolation feature quantity as a reference, interpolating / extrapolating the existing correction model data set to obtain the corresponding compensation value of the loss and lag angle; Step (3.4), superimposing the compensation value with the default model calculation value to obtain the corrected model lag angle or loss parameter; Step (3.5), apply the modified model parameters to perform analysis and calculation to obtain the turbine performance prediction results. At this time, the calculation combines the default empirical formula and the test correction and compensation data.