CN114091278A - Simulation implementation method for evaluating influence of local manufacturing errors of compressor blade - Google Patents
Simulation implementation method for evaluating influence of local manufacturing errors of compressor blade Download PDFInfo
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Abstract
The invention aims to provide a simulation implementation method for evaluating the influence of local manufacturing errors of blades of a gas compressor, which can evaluate the performance change of the gas compressor caused by the local manufacturing errors of the blades, provides the highest allowable value of the local manufacturing errors of the blades on the premise of meeting the performance requirement, and provides guidance for the formulation of a blade manufacturing process flow considering both performance and economy, thereby reducing the cost of blade production and processing, and is characterized in that: step 1, extracting a blade control section and randomly generating a local error; step 2, generating blade geometric models with errors in batch; step 3, carrying out batch grid division; step 4, calculating the geometric model of the blade with the error in batch according to the set parameters of the original blade simulation calculation; step 5, obtaining and analyzing each performance parameter in batch to obtain the offset and the variation range of each performance parameter; step 6, checking whether the offset can reach but not exceed a critical value; and 7, evaluating the maximum manufacturing error locally allowed by the blade.
Description
Technical Field
The invention belongs to the technical field related to gas turbine manufacturing, and particularly relates to a simulation implementation method for evaluating the influence of local manufacturing errors of compressor blades.
Background
The gas turbine is the thermal-power conversion power equipment with the highest efficiency so far, and is high-end equipment in the fields of power generation, oil and gas transportation, ships and warships and the like. The axial flow compressor is one of three large core parts of the gas turbine and is responsible for pressurizing air and then sending the air into a combustion chamber. To form high pressure air, the compressor requires a large number of complex shaped blades to do work on the air. Because the shape of the blade is complex, the local or integral error of the blade profile is easy to generate during processing, so that the actual blade deviates from the design parameters, thereby affecting the overall performance of the compressor. In the prior art, the machining is generally performed with the minimum tolerance in design parameters in the production process, and the influence of the manufacturing error on the performance can be effectively reduced, but the production cost is multiplied, so that the blade production and machining economy is poor.
Disclosure of Invention
The invention aims to provide a simulation implementation method for evaluating the influence of local manufacturing errors of blades of a gas compressor, which aims to solve the problems in the background art, can evaluate the performance change of the gas compressor caused by the local manufacturing errors of the blades, provides the highest allowable value of the local manufacturing errors of the blades on the premise of meeting the performance requirements, and provides guidance for making a blade manufacturing process flow considering both performance and economy, thereby reducing the cost of blade production and processing.
In order to achieve the above object, the present invention provides the following technical solutions.
A simulation implementation method for evaluating the influence of local manufacturing errors of compressor blades is characterized by comprising the following steps: the method comprises the following steps:
step 1, extracting a blade control section and randomly generating a local error;
step 2, adding local errors to the blade control section, and generating blade geometric models with errors in batches;
step 3, carrying out batch grid division on the geometric blade model with errors;
step 4, using the divided grids, and carrying out batch calculation on the geometric model of the blade with the error according to the setting parameters of the original blade simulation calculation;
step 6, checking whether the offset of each performance parameter can reach but does not exceed the respective critical value, and if so, performing step 7; otherwise, returning to the step 1;
and 7, evaluating the maximum manufacturing error locally allowed by the blade.
Further, the extracting of the blade control section in the step 1 specifically includes: extracting different blade height sections which are two-dimensional base circle blade profiles perpendicular to the radial direction of the blade at different heights of the blade; for a rotor blade, extracting three blade profiles on three sections with the radial height of 10%, 50% and 90% of the blade for adding local errors, and for a stator blade, extracting a section with the radial height of 50% of the blade for adding local errors;
the step 1 of randomly generating the local error specifically includes: setting a sampling space of local errors, wherein samples of the sampling space obey normal distribution, and the probability density function of the sampling space is as follows:
mu is the mean error value, and sigma is the standard deviation; the local error comprises: leading edge profile error, suction surface profile error at the middle part of the blade body, pressure surface profile error at the middle part of the blade body, trailing edge profile error, geometric inlet angle error and geometric outlet angle error; the above-mentioned respective local errors are set as follows: the mean error value of each local error is 0, and the standard deviation is respectively: leading edge profile degree: 0.03 mm; the profile degree of the suction surface at the middle part of the blade body is as follows: 0.05 mm; profile of the pressure surface in the middle of the blade body: 0.05 mm; trailing edge profile degree: 0.05 mm; geometric inlet angle: 1 degree; geometric exit angle: 1 deg.
Further, adding a local error to the blade control section in the step 2, and generating a blade geometric model with an error in batch, specifically: adding randomly generated local errors into sections with different blade heights, then stacking the error sections according to the original radial stacking rule, and generating a blade geometric model with errors in batches;
the regions of different leaf height sections added with randomly generated local errors are: the method includes the steps that the first 15% of chord length of a blade profile is used as an adding region of a front edge profile degree error and a geometric inlet angle error, the second 15% of chord length is used as an adding region of a suction surface profile degree error and a pressure surface profile degree error in the middle of a blade body, and the last 15% of chord length is used as an adding region of a tail edge profile degree error and a geometric outlet angle error;
when randomly generated local errors are added to sections with different leaf heights, a sine function is used as an error coefficient, and the function independent variable range is (0, pi).
Further, the step 3 specifically includes: executing a script file, utilizing Autogrid software, dividing parameters according to original blade grids, carrying out batch grid division on a newly generated blade geometric model with errors, and storing the newly generated blade geometric model to a specific folder;
when the leaf meshes are divided, the leaf body meshes are encrypted.
Further, the step 4 specifically includes: executing a script file, importing the divided grids into CFX software, carrying out batch calculation on the geometric model of the blade with the error according to the setting parameters of the original blade simulation calculation, and storing the calculation result;
the setting parameters include: the turbulence model adopts an SST model, and the transition model adopts gamma-ReθThe model, inlet speed, total inlet temperature, outlet static pressure, dynamic viscosity coefficient and heat conductivity coefficient are changed along with the temperature by adopting a Sutherland formula.
Further, the step 5 specifically includes: step 5.1, reading the calculation result of the step 4 by utilizing a Matlab program and calculating performance parameters: total pressure loss coefficient, isentropic efficiency and surge margin; and 5.2, analyzing the calculation result of the code fitting performance parameters by utilizing Matlab probability statistics to obtain the mean value of each performance parameter and the standard deviation of each performance parameter, wherein the variation of the mean value of each performance parameter and the performance parameter corresponding to the original blade is used as the offset of each performance parameter, and the confidence interval obtained by adding or subtracting 2 times of the standard deviation of each performance parameter to the mean value of each performance parameter is used as the variation range of each performance parameter.
Further, the step 6 specifically includes: step 6.1, setting critical values of various performance parameters: the offset of the total pressure loss coefficient is not more than 2%, the offset of the isentropic efficiency is not more than 0.1%, and the offset of the surge margin is not more than 1%; step 6.2, checking whether the offset of each performance parameter can reach but does not exceed the critical value of each performance parameter, and if so, performing step 7; otherwise, returning to the step 1, resetting the sampling space of the local error, and randomly generating the local error.
Further, the step 7 specifically includes: and taking the currently set local error as the maximum manufacturing error locally allowed by the blade.
Compared with the prior art, the invention provides a simulation implementation method for evaluating the influence of local manufacturing errors of the blades of the compressor, which has the following beneficial effects:
the method comprises the steps of firstly setting a sampling space of the local errors of the blade sections, adding randomly generated local errors into the sections with different blade heights, then stacking the error sections according to an original radial stacking rule, and generating a new geometric model of the blade with errors in batch.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic drawing of an embodiment extraction blade control cross-section;
FIG. 3 is a schematic illustration of a blade control section incorporating a local error according to an embodiment;
FIG. 4 is an error factor of a sinusoidal distribution of an embodiment;
FIG. 5 is a total pressure loss coefficient fit for an example.
Detailed Description
A simulation implementation method for evaluating the influence of local manufacturing errors of compressor blades is characterized by comprising the following steps: the method comprises the following steps:
step 1, as shown in fig. 2, extracting a blade control section and randomly generating a local error.
The control section of the extraction blade specifically comprises the following steps: extracting different blade height sections which are two-dimensional base circle blade profiles perpendicular to the radial direction of the blade at different heights of the blade; for the rotor blade, three profiles are extracted on three sections with a radial height of the blade of 10%, 50% and 90% for adding local errors, and for the stator blade, a section with a radial height of the blade of 50% is extracted for adding local errors.
The randomly generated local error specifically includes: setting a sampling space of local errors, wherein samples of the sampling space obey normal distribution, and the probability density function of the sampling space is as follows:
mu is the mean error value, and sigma is the standard deviation; the local error comprises: leading edge profile error, suction surface profile error at the middle part of the blade body, pressure surface profile error at the middle part of the blade body, trailing edge profile error, geometric inlet angle error and geometric outlet angle error.
In this embodiment, the mean error value of each local error is 0, and the standard deviation is: leading edge profile degree: 0.03 mm; the profile degree of the suction surface at the middle part of the blade body is as follows: 0.05 mm; profile of the pressure surface in the middle of the blade body: 0.05 mm; trailing edge profile degree: 0.05 mm; geometric inlet angle: 1 degree; geometric exit angle: 1 deg. Each of the 200 local errors was randomly generated using Matlab software and stored.
Step 2, as shown in fig. 3, adding a local error to the blade control section, and generating a blade geometric model with errors in batch, specifically: adding randomly generated local errors on sections with different blade heights, then stacking the error sections according to the original radial stacking rule, and generating a blade geometric model with errors in batches.
The regions of different leaf height sections added with randomly generated local errors are: the method is characterized in that the first 15% of chord length of a blade profile is used as a region for adding the profile tolerance of the leading edge and the geometric inlet angle tolerance, the second 15% of chord length is used as a region for adding the profile tolerance of the suction surface and the pressure surface in the middle of a blade body, and the last 15% of chord length is used as a region for adding the profile tolerance of the trailing edge and the geometric outlet angle tolerance.
As shown in fig. 4, when randomly generated local errors are added to the cross sections of different leaf heights, a sine function is used as an error coefficient, and the independent variable range of the sine function is (0, pi); the method is used for ensuring that random errors are applied to the vertexes of the leading edge and the trailing edge and the maximum thickness of the blade body to the maximum extent, and the blade profile line curvature is still continuous after the errors are added.
Step 3, carrying out batch grid division on the geometric blade model with the errors, which specifically comprises the following steps: executing the script file, utilizing the Autogrid software, dividing parameters according to the original blade grid, carrying out batch grid division on the newly generated blade geometric model with the error, and storing the newly generated blade geometric model to a specific folder.
The script file is as follows: python codes called by the AutoGrid are supported, and the error-bearing blade geometric model batch meshing can be realized. The original blade mesh division parameters are as follows: the number of the radial grid points is 120, the change of the geometric shape of the blade is slight after the error is introduced, and the grid of the blade body part is encrypted.
Step 4, using the divided grids to perform batch calculation on the geometric model of the blade with the error according to the setting parameters of the original blade simulation calculation, specifically: and executing the script file, importing the divided grids into CFX software, carrying out batch calculation on the geometric model of the blade with the error according to the setting parameters of the original blade simulation calculation, and storing the calculation result.
The scriptThe file is as follows: and the Python code supporting CFX calling can realize batch calculation of the geometric model of the blade with errors. The setting parameters include: the turbulence model adopts an SST model, and the transition model adopts gamma-ReθThe model has inlet speed of 140.81m/s, total inlet temperature of 318.15K, outlet static pressure of 101325Pa, dynamic viscosity coefficient and heat conductivity coefficient varying with temperature by Sutherland formula.
Step 6, checking whether the offset of each performance parameter can reach but does not exceed the respective critical value, and if so, performing step 7; otherwise, returning to the step 1; the method specifically comprises the following steps: checking whether the offset of the total pressure loss coefficient can reach but is not more than 2%, checking whether the offset of the isentropic efficiency can reach but is not more than 0.1%, and checking whether the offset of the surge margin can reach but is not more than 1%; if yes, performing step 7; otherwise, returning to the step 1, resetting the sampling space of the local error, and randomly generating the local error;
and 7, taking the currently set local error as the maximum manufacturing error allowed by the local part of the blade.
Compared with the prior art, the geometric model of the blade with the errors generated in the step 2 is closer to the actual processing level, the performance change caused by the local errors of the blade is obtained, and the estimation of the maximum manufacturing error allowed by the local errors of the blade can be quickly realized.
In this embodiment, the maximum allowable value of the local manufacturing error of the blade is provided by taking the total pressure loss coefficient variation caused by the error as a performance index, and comparing the machining tolerance of the blade given in the design, as shown in table 1, with respect to the machining tolerance of the blade body given in the design, the profile tolerance of the suction surface and the pressure surface in the middle of the blade body, the profile tolerance of the trailing edge is 0.05mm, the geometric inlet angle tolerance and the geometric outlet angle tolerance are 1 °, and the total pressure loss coefficient variation of the obtained blade is within 2%.
TABLE 1 comparison of the respective Performance indices
The method comprises the steps of firstly setting a sampling space of the local errors of the blade sections, adding randomly generated local errors into the sections with different blade heights, then stacking the error sections according to an original radial stacking rule, generating a new geometric model of the blade with errors in batch, dividing grids by adopting a grid automatic generation technology, then using the grids for simulation calculation and obtaining the offset and the variation range of the performance parameters of the gas compressor, and quickly estimating the maximum allowable manufacturing error of the blade, namely the maximum allowable value of the local manufacturing error of the blade, which is provided on the premise of meeting the performance requirement, so as to provide guidance for the formulation of the manufacturing process flow of the blade with both performance and economy, thereby reducing the cost of the blade production and processing.
Claims (10)
1. A simulation implementation method for evaluating the influence of local manufacturing errors of compressor blades is characterized by comprising the following steps: the method comprises the following steps:
step 1, extracting a blade control section and randomly generating a local error;
step 2, adding local errors to the blade control section, and generating blade geometric models with errors in batches;
step 3, carrying out batch grid division on the geometric blade model with errors;
step 4, using the divided grids, and carrying out batch calculation on the geometric model of the blade with the error according to the setting parameters of the original blade simulation calculation;
step 5, obtaining and analyzing each performance parameter in batch to obtain the offset and the variation range of each performance parameter;
step 6, checking whether the offset of each performance parameter can reach but does not exceed the respective critical value, and if so, performing step 7; otherwise, returning to the step 1;
and 7, evaluating the maximum manufacturing error locally allowed by the blade.
2. The simulation implementation method for evaluating the influence of local manufacturing errors of compressor blades according to claim 1, characterized in that: the extraction of the blade control section in the step 1 specifically comprises the following steps: extracting different blade height sections which are two-dimensional base circle blade profiles perpendicular to the radial direction of the blade at different heights of the blade; for the rotor blade, three profiles are extracted on three sections with a radial height of the blade of 10%, 50% and 90% for adding local errors, and for the stator blade, a section with a radial height of the blade of 50% is extracted for adding local errors.
3. The simulation implementation method for evaluating the influence of local manufacturing errors of compressor blades according to claim 2, characterized in that: the step 1 of randomly generating the local error specifically includes: setting a sampling space of local errors, wherein samples of the sampling space obey normal distribution, and the probability density function of the sampling space is as follows:
mu is the mean error value, and sigma is the standard deviation; the local error comprises: leading edge profile error, suction surface profile error at the middle part of the blade body, pressure surface profile error at the middle part of the blade body, trailing edge profile error, geometric inlet angle error and geometric outlet angle error; and setting the error mean value and the standard deviation of each local error.
4. The simulation implementation method for evaluating the influence of local manufacturing errors of compressor blades according to claim 3, characterized in that: adding local errors to the blade control sections in the step 2, and generating blade geometric models with errors in batches, specifically: adding randomly generated local errors on sections with different blade heights, then stacking the error sections according to the original radial stacking rule, and generating a blade geometric model with errors in batches.
5. The simulation implementation method for evaluating the influence of local manufacturing errors of compressor blades according to claim 4, characterized in that: the regions where randomly generated local errors are added to the different leaf height sections specifically include: the first 15% of the chord length of the blade profile is used as an adding region for the profile tolerance of the front edge and the geometric inlet angle tolerance, the 30% -70% of the chord length is used as an adding region for the profile tolerance of the suction surface and the profile tolerance of the pressure surface in the middle of the blade body, and the last 15% of the chord length is used as an adding region for the profile tolerance of the tail edge and the geometric outlet angle tolerance;
when randomly generated local errors are added to sections with different leaf heights, a sine function is used as an error coefficient, and the independent variable range of the sine function is (0, pi).
6. The simulation implementation method for evaluating the influence of local manufacturing errors of compressor blades according to claim 5, characterized in that: the step 3 specifically comprises the following steps: executing a script file, utilizing Autogrid software, dividing parameters according to original blade grids, carrying out batch grid division on a newly generated blade geometric model with errors, and storing the newly generated blade geometric model to a specific folder; when the leaf meshes are divided, the leaf body meshes are encrypted.
7. The simulation implementation method for evaluating the influence of local manufacturing errors of compressor blades according to claim 6, characterized in that: the step 4 specifically comprises the following steps: executing a script file, importing the divided grids into CFX software, carrying out batch calculation on the geometric model of the blade with the error according to the setting parameters of the original blade simulation calculation, and storing the calculation result; the setting parameters include: the turbulence model adopts an SST model, and the transition model adopts gamma-ReθModel, entry speed, entryThe total temperature of the port, the static pressure of the outlet, the dynamic viscosity coefficient and the heat conductivity coefficient are changed along with the temperature by adopting a Sutherland formula.
8. The simulation implementation method for evaluating the influence of local manufacturing errors of compressor blades according to claim 7, characterized in that: the step 5 specifically comprises the following steps: step 5.1, reading the calculation result of the step 4 by utilizing a Matlab program and calculating performance parameters: total pressure loss coefficient, isentropic efficiency and surge margin; and 5.2, analyzing the calculation result of the code fitting performance parameters by utilizing Matlab probability statistics to obtain the mean value of each performance parameter and the standard deviation of each performance parameter, wherein the variation of the mean value of each performance parameter and the performance parameter corresponding to the original blade is used as the offset of each performance parameter, and the confidence interval obtained by adding or subtracting 2 times of the standard deviation of each performance parameter to the mean value of each performance parameter is used as the variation range of each performance parameter.
9. The simulation implementation method for evaluating the influence of local manufacturing errors of compressor blades according to claim 8, characterized in that: the step 6 specifically comprises the following steps: step 6.1, setting critical values of various performance parameters: the offset of the total pressure loss coefficient is not more than 2%, the offset of the isentropic efficiency is not more than 0.1%, and the offset of the surge margin is not more than 1%; step 6.2, checking whether the offset of each performance parameter can reach but does not exceed the critical value of each performance parameter, and if so, performing step 7; otherwise, returning to the step 1, resetting the sampling space of the local error, and randomly generating the local error; .
10. The simulation implementation method for evaluating the influence of local manufacturing errors of compressor blades according to claim 9, characterized in that: the step 7 specifically comprises the following steps: and taking the currently set local error as the maximum manufacturing error locally allowed by the blade.
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