CN114491984A - Method for extracting thermal fatigue load in aeroengine load spectrum and determining distribution rule - Google Patents
Method for extracting thermal fatigue load in aeroengine load spectrum and determining distribution rule Download PDFInfo
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Abstract
The invention discloses a method for extracting thermal fatigue loads in an aeroengine load spectrum and determining a distribution rule, which comprises the following steps of: preprocessing the real temperature measurement spectrum by using a rain flow filtering program; equally dividing the preprocessed temperature spectrum by taking the temperature number as a reference to obtain a plurality of groups of data units with the same temperature number; screening each group of obtained data units, and extracting thermal fatigue loads; continuously subdividing the data units which do not meet the extraction conditions, and continuously extracting; storing the extracted thermal fatigue load data, continuously extracting the peak and trough values of the storage temperature and the corresponding time, and drawing a thermal fatigue load temperature spectrum according to the data; and (4) counting the characteristic parameters of the thermal fatigue load, and performing fitting analysis on the characteristic parameters to obtain a distribution rule of the thermal fatigue load. The method can be used for rapidly and accurately extracting the thermal fatigue load of the aircraft engine and providing a sufficient and scientific basis for extracting and analyzing the thermal fatigue load of similar complex working components.
Description
Technical Field
The invention belongs to the field of aeroengine load spectrums, and particularly relates to a method for extracting thermal fatigue loads and determining distribution rules of the aeroengine load spectrums.
Background
The aircraft engine is a thermal machine with extremely high complexity, and the scrapping of most aircraft engines is related to the damage of hot end parts of the aircraft engines. In the working process, the hot end component of the aero-engine can work under a high-temperature condition for a long time, and along with the continuous improvement of the performance requirement of the aero-engine, the temperature load of the hot end component of the aero-engine is more complex.
The actually measured temperature spectrum of the aircraft engine is very complex, the characteristic parameters of the thermal fatigue load have strong randomness, and in order to accurately and quickly evaluate the damage caused by the thermal fatigue load, the thermal fatigue load must be extracted, and the research on the distribution rule of the thermal fatigue load is carried out. In order to achieve the above purpose, it is necessary to provide a method for extracting thermal fatigue loads and determining distribution rules in an aircraft engine load spectrum.
Disclosure of Invention
The invention aims to provide a method for extracting and determining a distribution rule of a thermal fatigue load in an aeroengine load spectrum, which is used for converting an actually measured temperature spectrum into the thermal fatigue load spectrum and researching the distribution rule of the thermal fatigue load.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for extracting thermal fatigue loads in an aeroengine load spectrum and determining a distribution rule comprises the following steps:
(1) preprocessing the real temperature spectrum by utilizing a rain flow filtering program to obtain a preprocessed temperature spectrum;
(2) equally dividing the temperature spectrum preprocessed in the step (1) by taking the temperature number as a reference to obtain a plurality of groups of data units with the same temperature number;
(3) setting a judgment standard of the thermal fatigue load, screening each group of data units obtained in the step (2), and extracting the thermal fatigue load;
(4) after the thermal fatigue load is extracted in the step (3), continuously subdividing the data units which do not meet the extraction conditions, setting a new judgment standard, and continuously extracting the data units which meet the new judgment standard;
(5) storing the thermal fatigue load data extracted in the steps (3) and (4), continuously extracting the wave crest and trough value of the storage temperature and the corresponding time, and drawing a thermal fatigue load temperature spectrum according to the data; and (4) counting the characteristic parameters of the thermal fatigue load, and performing fitting analysis on the characteristic parameters to obtain a distribution rule of the thermal fatigue load.
In the step (1), a threshold value in a rain flow filtering program is set to screen out the micro temperature disturbance.
In the step (2), each group of data units includes 100 temperature data.
The step (3) is specifically as follows:
(31) arranging each group of data units obtained in the step (2) according to a time sequence;
(32) calculating the sum, the average value and the standard deviation of the number of the wave crests and the wave troughs of the temperature in each group of data units, and dividing the average value by the standard deviation to obtain the temperature variation coefficient of each group of data units;
(33) judging whether the sum of the number of the wave crest and the wave trough values of each group of data units is more than 10 and the temperature variation coefficient is more than 5% according to the result obtained in the step (32), if the two conditions are met, extracting the data units meeting the conditions into a thermal fatigue load, and storing the temperature and the time corresponding to the temperature; data units that do not satisfy the condition are also stored.
The step (4) is specifically as follows:
(41) after the thermal fatigue load is extracted in the step (3), continuously subdividing the data units which do not meet the conditions at every 10 groups of temperatures to obtain a plurality of data units comprising 10 temperature data;
(42) calculating the sum, the average value and the standard deviation of the number of the wave crests and the wave troughs of the temperature in each group of data units, and dividing the average value by the standard deviation to obtain the temperature variation coefficient of each group of data units;
(43) and (5) judging whether the sum of the number of the wave crest and the wave trough values of each group of data units is more than 2 and the temperature variation coefficient is more than 0.5% according to the result obtained in the step (42), if any one of the two conditions is met, extracting the data units meeting the conditions into a thermal fatigue load, and storing the temperature and the time corresponding to the temperature.
The step (5) is specifically as follows:
(51) integrating the data extracted in the step (3) and the step (4) and storing the data; sequencing the data according to a time sequence, and continuously extracting the peak and trough values of the temperature;
(52) drawing a time-temperature course according to the peak and trough values of the temperature extracted in the step (51) and the corresponding time, and taking the time-temperature course as a thermal fatigue load spectrum;
(53) according to the thermal fatigue load spectrum obtained in the step (52), counting characteristic parameters of the thermal fatigue load, namely the variable range average temperature, the variable range heating and cooling rate and the variable range temperature difference;
(54) and performing fitting analysis on the characteristic parameters, and constructing a fitting curve chart of the variable range average temperature, the variable range heating and cooling rate and the variable range temperature difference to obtain a distribution rule of the thermal fatigue load.
Has the advantages that: the method takes the variable range average temperature, the variable range temperature rise and fall rate and the variable range temperature difference of the thermal fatigue load as the characteristic parameters of the thermal fatigue load; screening out tiny temperature disturbance based on a rain flow filtering program; extracting a judgment standard of the thermal fatigue load, and extracting the thermal fatigue load based on the judgment standard; drawing a thermal fatigue load spectrum according to the extraction result; and determining the distribution rule of the thermal fatigue load through data analysis. Has the following advantages:
(1) according to the method for extracting the thermal fatigue load in the load spectrum, the thermal fatigue load can be extracted and the distribution rule of the thermal fatigue load can be determined only by knowing the specific data of the actual temperature spectrum and depending on the programming software according to the judgment standard. The method is simple and intuitive, has clear steps and strong applicability;
(2) according to the judgment standard of the thermal fatigue load, the thermal fatigue load is extracted according to the dispersion degree of each group of data and the sum of the number of the wave crest and the wave trough values of the temperature, the actual characteristics of the thermal fatigue load are fully considered, and the method has a wide engineering application value;
in conclusion, the method is based on the thermal fatigue load judgment standard, combines the thermal fatigue load extraction program, and is simple and intuitive, clear in steps and strong in applicability. The method can be used for rapidly and accurately extracting the thermal fatigue load of the aircraft engine and providing a sufficient and scientific basis for extracting and analyzing the thermal fatigue load of similar complex working components.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a graph of the actual temperature spectrum of an original aircraft of a civil turboshaft engine;
FIG. 3 is a pre-treatment temperature spectrum after being processed by the rain stream filtering program according to the present embodiment;
FIG. 4 is a comparison graph (partial) of the pre-treatment temperature spectrum and the measured temperature spectrum of the present embodiment;
FIG. 5 is a temperature spectrum of the group 8 data unit in the present embodiment;
FIG. 6 is a temperature spectrum of groups 31-40 and 81-90 after the 8 th group of data units are further subdivided;
FIG. 7 is a thermal fatigue load spectrum extracted in the present embodiment;
FIG. 8 is a comparison graph of the thermal fatigue load spectrum extracted in the present embodiment and the measured temperature spectrum;
FIG. 9 is a graph of the mean variable temperature, the temperature increase/decrease rate, and the temperature difference.
Detailed Description
The invention is further explained below with reference to the drawings.
The invention relates to a method for extracting and determining a distribution rule of thermal fatigue loads in an aircraft engine load spectrum, which takes a variable range average temperature, a variable range temperature rise and fall rate and a variable range temperature difference of the thermal fatigue loads as characteristic parameters of the thermal fatigue loads; screening out tiny temperature disturbance through a rain flow filtering program; extracting a judgment standard of the thermal fatigue load, and extracting the thermal fatigue load based on the judgment standard; drawing a thermal fatigue load spectrum according to the extraction result; and determining the distribution rule of the thermal fatigue load through data analysis. The implementation flow is shown in figure 1.
The present invention is further illustrated by the following specific examples.
Examples
In the embodiment, the practical temperature spectrum of a civil turboshaft engine is taken as an embodiment, and the embodiment analysis of the invention is carried out.
The measured temperature spectrum had 10782 sample points and a sampling frequency of 1HZ, as shown in fig. 2.
(1) Preprocessing the actual temperature spectrum by using a rain flow filtering program, and screening out small temperature disturbance to obtain a preprocessed temperature spectrum; the threshold in the filtering routine of the present embodiment is set to 5 ℃. The pretreatment temperature profile is shown in FIG. 3; a local comparison of the pre-treatment temperature spectrum with the measured temperature spectrum is shown in FIG. 4.
(2) Equally dividing the preprocessed temperature spectrum by taking 100 temperatures as a group of unit data to obtain a plurality of groups of data units with the same temperature quantity;
(3) preprocessing the real temperature spectrum by using a rain flow filtering program, and screening out small temperature disturbance to obtain a preprocessed temperature spectrum; the method specifically comprises the following steps:
(31) arranging each group of unit data obtained in the step (2) according to a time sequence;
(32) calculating the sum, the average value and the standard deviation of the number of the wave crests and the wave troughs of the temperature in each group of data units, and dividing the average value by the standard deviation to obtain the temperature variation coefficient of each group of data units;
(33) judging whether the sum of the number of the wave crest and the wave trough values of each group of data units is more than 10 and the temperature variation coefficient is more than 5% according to the result obtained in the step (32), if the two conditions are met, extracting the data units meeting the conditions into a thermal fatigue load, and storing the temperature and the time corresponding to the temperature; data units that do not satisfy the condition are also stored;
the specific flow of the step (3) and the step (4) is shown below by taking the 8 th group of data units as an example, and the specific data of the 8 th group of data units are shown in table 1;
TABLE 1 set 8 data element
The temperature variation coefficient of the 8 th group of data units is 0.0161, the sum of the peak value and the valley value is 13, which does not meet the judgment standard of the thermal fatigue load, so the data units need to be stored for the next processing. The group 8 data cell temperature spectrum is shown in fig. 5.
(4) After the thermal fatigue load is extracted in the step (3), continuously subdividing the data units which do not meet the extraction condition, and continuously extracting the data units which meet the judgment standard based on the judgment standard; the method specifically comprises the following steps:
(41) subdividing the data units which do not meet the conditions in the step (33) by taking each 10 temperatures as a group of data units to obtain a plurality of data units comprising 10 temperature data;
(42) calculating the sum, the average value and the standard deviation of the number of the wave crests and the wave troughs of the temperature in each group of data units, and dividing the average value by the standard deviation to obtain the temperature variation coefficient of each group of data units;
(43) judging whether the sum of the number of the wave crest and the wave trough values of the temperature in each group of data units is more than 2 and the temperature variation coefficient is more than 0.5% according to the result obtained in the step (42), if any one of the two conditions is met, extracting the data units meeting the conditions into a thermal fatigue load, and storing the temperature and the time corresponding to the temperature;
continuing to process the 8 th group of data units, wherein the processing result is shown in the table 2;
TABLE 2 results of processing of group 8 data units
The thermal fatigue load judgment is performed again on the 10 groups of data units, and according to the judgment standard, except that the groups 1-10, 41-50, 51-60 and 60-70 do not meet the thermal fatigue load judgment standard, all the other groups can be regarded as thermal fatigue loads. The temperature load spectra of the data units in groups 31-40 and groups 81-90 are shown in FIG. 6.
(5) Storing the thermal fatigue load data extracted in the steps (3) and (4), continuously extracting the wave crest and trough value of the storage temperature and the corresponding time, and drawing a thermal fatigue load temperature spectrum according to the data; counting characteristic parameters of the thermal fatigue load, and performing fitting analysis on the characteristic parameters to obtain a distribution rule of the thermal fatigue load; the method specifically comprises the following steps:
(51) integrating the data stored in the step (3) and the step (4) and storing the data; and sorting the data in time sequence; continuously extracting the peak and trough values of the temperature;
(52) drawing a time-temperature course according to the peak and trough values of the temperature extracted in the step (51) and the corresponding time, and taking the time-temperature course as a thermal fatigue load spectrum; the thermal fatigue load spectrum is shown in figure 7, and the comparison graph of the actual temperature measurement spectrum and the thermal fatigue load spectrum is shown in figure 8;
(53) according to the thermal fatigue load spectrum obtained in the step (52), counting characteristic parameters of the thermal fatigue load, namely the variable range average temperature, the variable range temperature rise and fall rate and the variable range temperature difference;
(54) fitting analysis is performed on the characteristic parameters, a fitting curve graph of the variable range average temperature, the variable range temperature rise and fall rate and the variable range temperature difference is constructed, and the distribution rule of the thermal fatigue load is obtained, as shown in fig. 9.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and amendments can be made without departing from the principle of the present invention, and these modifications and amendments should also be considered as the protection scope of the present invention.
Claims (6)
1. A method for extracting and determining a distribution rule of thermal fatigue loads in an aeroengine load spectrum is characterized by comprising the following steps: the method comprises the following steps:
(1) preprocessing the real temperature spectrum by utilizing a rain flow filtering program to obtain a preprocessed temperature spectrum;
(2) equally dividing the temperature spectrum preprocessed in the step (1) by taking the temperature quantity as a reference to obtain a plurality of groups of data units with the same temperature quantity;
(3) setting a judgment standard of the thermal fatigue load, screening each group of data units obtained in the step (2), and extracting the thermal fatigue load;
(4) after the thermal fatigue load is extracted in the step (3), continuously subdividing the data units which do not meet the extraction conditions, setting a new judgment standard, and continuously extracting the data units which meet the new judgment standard;
(5) storing the thermal fatigue load data extracted in the steps (3) and (4), continuously extracting the wave crest and trough value of the storage temperature and the corresponding time, and drawing a thermal fatigue load temperature spectrum according to the data; and (4) counting the characteristic parameters of the thermal fatigue load, and performing fitting analysis on the characteristic parameters to obtain a distribution rule of the thermal fatigue load.
2. The method for extracting thermal fatigue loads and determining distribution rules in an aircraft engine load spectrum according to claim 1, characterized in that: in the step (1), a threshold value in a rain flow filtering program is set to screen out the micro temperature disturbance.
3. The method for extracting thermal fatigue loads and determining distribution rules in an aircraft engine load spectrum according to claim 1, characterized in that: in the step (2), each group of data units includes 100 temperature data.
4. The method for extracting thermal fatigue loads and determining distribution rules in an aircraft engine load spectrum according to claim 1, characterized in that: the step (3) is specifically as follows:
(31) arranging each group of data units obtained in the step (2) according to a time sequence;
(32) calculating the sum, the average value and the standard deviation of the number of the wave crests and the wave troughs of the temperature in each group of data units, and dividing the average value by the standard deviation to obtain the temperature variation coefficient of each group of data units;
(33) judging whether the sum of the number of the wave crest and the wave trough values of each group of data units is more than 10 and the temperature variation coefficient is more than 5% according to the result obtained in the step (32), if the two conditions are met, extracting the data units meeting the conditions into a thermal fatigue load, and storing the temperature and the time corresponding to the temperature; data units that do not satisfy the condition are also stored.
5. The method for extracting thermal fatigue loads and determining distribution rules in an aircraft engine load spectrum according to claim 1, characterized in that: the step (4) is specifically as follows:
(41) after the thermal fatigue load is extracted in the step (3), continuously subdividing the data units which do not meet the conditions at every 10 groups of temperatures to obtain a plurality of data units comprising 10 temperature data;
(42) calculating the sum, the average value and the standard deviation of the number of the wave crests and the wave troughs of the temperature in each group of data units, and dividing the average value by the standard deviation to obtain the temperature variation coefficient of each group of data units;
(43) and (5) judging whether the sum of the number of the wave crest and the wave trough values of each group of data units is more than 2 and the temperature variation coefficient is more than 0.5% according to the result obtained in the step (42), if any one of the two conditions is met, extracting the data units meeting the conditions into a thermal fatigue load, and storing the temperature and the time corresponding to the temperature.
6. The method for extracting thermal fatigue loads and determining distribution rules in an aircraft engine load spectrum according to claim 1, characterized in that: the step (5) is specifically as follows:
(51) integrating the data extracted in the step (3) and the step (4) and storing the data; sequencing the data according to a time sequence, and continuously extracting the peak and trough values of the temperature;
(52) drawing a time-temperature course according to the peak and trough values of the temperature extracted in the step (51) and the corresponding time, and taking the time-temperature course as a thermal fatigue load spectrum;
(53) according to the thermal fatigue load spectrum obtained in the step (52), counting characteristic parameters of the thermal fatigue load, namely the variable range average temperature, the variable range heating and cooling rate and the variable range temperature difference;
(54) and performing fitting analysis on the characteristic parameters, and constructing a fitting curve chart of the variable range average temperature, the variable range heating and cooling rate and the variable range temperature difference to obtain a distribution rule of the thermal fatigue load.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117169709A (en) * | 2023-10-09 | 2023-12-05 | 中国民航大学 | Aviation relay fatigue performance testing method, device and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117169709A (en) * | 2023-10-09 | 2023-12-05 | 中国民航大学 | Aviation relay fatigue performance testing method, device and storage medium |
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