Method and device for processing abnormal aerodynamic noise of model rotor caused by external airflow disturbance

Abstract

The application discloses a processing method and device for removing abnormal aerodynamic noise of a model rotor caused by external airflow disturbance. The method comprises the following steps: step 1, according to the characteristic that the rotor aerodynamic noise of the rotor in a stable state has a main frequency stability, the rotor aerodynamic noise data collected is segmented according to cycles; step 2, the amplitude and frequency of the obtained data of each cycle are analyzed, and the frequency values corresponding to the first three maximum amplitudes in the amplitude spectrum of the data of each cycle are extracted; step 3, the frequency values extracted from all cycles are counted, and the main frequency of the maximum mode is obtained; step 4, the data of the cycle corresponding to the non-maximum mode main frequency is removed, the data of the remaining cycles is subjected to whole-cycle averaging, and the noise data confidence level is identified. The technical scheme provided by the embodiment of the application solves the problem that, in the existing rotor aerodynamic noise test mode, the requirements for the test environment and the test site are relatively high, the test environment and the site limit the existing test bench to carry out related noise tests, and thus the rotor aerodynamic noise test is difficult to be carried out in an environment with external gust disturbance.

Description

Technical Field

[0001] The present invention relates to, but is not limited to, the technical field of rotor noise testing for rotorcraft, and in particular to a method and device for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbance. Background Art

[0002] Rotor aerodynamic noise is the primary source of external noise for rotorcraft. Studying the acoustic field distribution characteristics of rotor aerodynamic noise provides guidance for rotorcraft's ability to pass noise airworthiness certification. Reducing rotor aerodynamic noise is the primary approach to reducing external noise for rotorcraft. Because rotor aerodynamic noise has low frequency, long propagation distance, and slow attenuation, effective measurement provides firsthand experimental data for rotor noise reduction research.

[0003] The rotor aerodynamic noise test research of rotorcraft is currently mainly carried out in an open space without any obstructions in the outdoor field, or in an anechoic chamber and anechoic wind tunnel with a closed and anechoic environment. In other words, the rotor aerodynamic noise test has high requirements for the test environment and test site, but the requirements of the environment and site also limit the relevant noise tests on the existing test benches. Summary of the Invention

[0004] Purpose of the present invention: In order to solve the above-mentioned problems, the embodiments of the present invention provide a method and device for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbances, so as to solve the problem that the existing rotor aerodynamic noise test method has high requirements for the test environment and test site, so that the test environment and site limit the existing test bench from carrying out related noise tests, resulting in the problem that the rotor aerodynamic noise test is difficult to carry out in an environment with external gust interference.

[0005] Technical solution of the present invention: In a third aspect, an embodiment of the present invention provides a method for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbance, comprising:

[0006] Step 1: Based on the characteristic that the rotor aerodynamic noise in a steady state has a main frequency stability, the collected rotor aerodynamic noise data is divided into circles;

[0007] Step 2: Perform amplitude and frequency analysis on the obtained data of each circle, and extract the frequency values ​​corresponding to the first three maximum amplitudes in the amplitude spectrum of each circle of data;

[0008] Step 3: Count the frequency values ​​extracted from all circles to obtain the most common main frequency;

[0009] Step 4: Eliminate the data of the circles corresponding to the non-most common main frequency, average the data of the remaining circles over the entire period, and judge the confidence level of the noise data.

[0010] Optionally, in the above-mentioned method for eliminating abnormal aerodynamic noise of the model rotor caused by external airflow disturbance, step 1 includes:

[0011] Step 11, synchronously collecting noise data and rotor speed pulse data of each measurement point at a sampling frequency St;

[0012] Step 12: Calculate the number of data points per rotor revolution (Np = St / Nr) based on the sampling frequency St and the rotor speed Nr.

[0013] Step 13: Select the noise data of the measurement point farthest from the center line of the model rotor shaft from each noise measurement point, and divide the noise data of M into the measurement points with the rising edge of any pulse of the rotor speed Nr as the starting point. n Circles, each circle has a noise data sequence of Np data points, that is, M n 1-dimensional arrays, each of which has a length of Np.

[0014] Optionally, in the above-mentioned method for eliminating abnormal aerodynamic noise of the model rotor caused by external airflow disturbance, in step 11,

[0015] The collected rotor speed pulse data is a signal that changes from a low level to a high level, remains at a high level for a period of time, and then returns to a low level every time the rotor rotates one circle. The duty cycle of the high level duration period of each circle ranges from 20% to 30%, and St ≥ 1024 × Nr; where Nr is the rotor speed;

[0016] The number of rising edge data points is 4 to 10, and the data acquisition time length T satisfies:

[0017] T = M0 × Nr;

[0018] M0 is the number of turns, and the value range of M0 is 200≥M0≥130.

[0019] Optionally, in the above-mentioned method for eliminating abnormal aerodynamic noise of the model rotor caused by external airflow disturbance, the manner of performing amplitude and frequency analysis on the data of each circle in step 2 includes:

[0020] Step 21, the M obtained by segmentation in step 1 n The 1-dimensional arrays are analyzed for auto-power spectrum respectively, so as to obtain the amplitude 1-dimensional array and frequency 1-dimensional array of the auto-power spectrum of each circle of data;

[0021] Among them, the one-dimensional array of the amplitude of the power spectrum of the first cycle data is marked as [A 1-1 、A 1-2 ...、A 1-Np / 2 ] and the one-dimensional array of frequency values ​​is identified as [F 1-1 、F1-2 …、F 1-Np / 2 ], No. M n The one-dimensional array of the amplitude of the power spectrum of the circle data is marked as [A Mn-1 、A Mn-2 ...、A Mn-Np / 2 ] and the one-dimensional array of frequency values ​​is identified as [F Mn-1 、F Mn-2 …、F Mn-Np / 2 ].

[0022] Optionally, in the above-mentioned method for eliminating abnormal aerodynamic noise of the model rotor caused by external airflow disturbance, the method of extracting the frequency values ​​corresponding to the first three maximum amplitudes in the amplitude spectrum of each circle data in step 2 includes:

[0023] Step 22: extracting the three frequency values ​​corresponding to the first three maximum amplitudes in the one-dimensional array of amplitudes of each circle of data obtained in step 21;

[0024] Among them, the frequency values ​​corresponding to the first three maximum amplitudes in the one-dimensional array of the first circle amplitude are marked as [F max-1-1 ,F max-1-2 ,F max-1-3 ], No. M n The frequency values ​​corresponding to the first three largest amplitudes in the one-dimensional array of circle amplitudes are marked as [F max-Mn-1 ,F max-Mn-2 ,F max-Mn-3 ].

[0025] Optionally, in the above-mentioned method for eliminating abnormal aerodynamic noise of the model rotor caused by external airflow disturbance, step 3 includes:

[0026] According to the sorting of amplitude sizes, for the frequency data of all circles, first extract the maximum mode and number of rows of the frequency corresponding to the first amplitude value, and based on the maximum mode row corresponding to the first amplitude value, extract the maximum mode and number of rows of the frequency corresponding to the second amplitude value, and then based on the maximum mode row corresponding to the second amplitude value, extract the maximum mode and number of rows of the frequency corresponding to the third amplitude value.

[0027] Optionally, in the above-mentioned method for eliminating abnormal aerodynamic noise of the model rotor caused by external airflow disturbance, step 3 includes:

[0028] Step 31, according to the M obtained in step 22 n The frequency values ​​corresponding to the first three maximum amplitudes in each circle generate 4 columns M of the following form n Two-dimensional array M of rows all Identified as:

[0029] [F max-1-1 ,Fmax-1-2 ,F max-1-3 ,1;

[0030] F max-2-1 ,F max-2-2 ,F max-2-3 ,2;

[0031] ...;

[0032] F max-Mn-1 ,F max-Mn-2 ,F max-Mn-3 ,M n ];

[0033] Step 32, from the two-dimensional array M all Select the largest common factor F in the first column max-1 , and the number of rows with the largest commonality is N max-1 ;

[0034] Step 33, from the two-dimensional array M all Extract the first number in each row equal to F max-1 All rows form a new 4-column N max-1 Two-dimensional array M of rows n-max-1 ;

[0035] Step 34, from the two-dimensional array M n-max-1 Select the largest common factor F in the second column max-2 , and the number of rows with the largest commonality is N max-2 ;

[0036] Step 35, from the two-dimensional array M n-max-1 Extract the second number in each row equal to F max-2 All rows form a new 4-column N max-2 Two-dimensional array M of rows n-max-2 ;

[0037] Step 36, from the two-dimensional array M n-max-2 Select the largest common factor F in the third column max-3 , and the number of rows with the largest commonality is N max-3 ;

[0038] Step 37, from the two-dimensional array M n-max-2 Extract the third number in each row equal to F max-3 All rows form a new 4-column N max-3 Two-dimensional array M of rows n-max-3 .

[0039] Optionally, in the above-mentioned method for eliminating abnormal aerodynamic noise of the model rotor caused by external airflow disturbance, step 4 includes the following situations:

[0040] If Nmax-1 <50%M n , then the test data is confirmed to be invalid;

[0041] If 50% M n ≤N max-1 ≤70%M n , then press M n-max-1 The fourth number in each row is used to extract the corresponding circle and average the whole cycle. The averaged data is used for trend analysis and quantitative analysis with low confidence level.

[0042] If N max-1 >70%M n , and N max-2 ≤60%M n , then press M n-max-3 The fourth number in each row is used to extract the corresponding circle and average the whole cycle. The test data of this round is considered valid. The averaged data is used for trend analysis and quantitative analysis with medium confidence level.

[0043] If N max-1 >70%M n , and N max-3 ≤50%M n , then press M n-max-3 The fourth number in each row is used to extract the corresponding circle and average the whole cycle. The test data of this round is considered valid. The averaged data is used for trend analysis and quantitative analysis with medium confidence level.

[0044] If N max-1 >70%M n , and N max-2 >60%M n And N max-3 >50%M n , then press M n-max-3 The fourth number in each row is used to extract the corresponding circle and average the whole cycle. The test data of this round is considered to be completely valid. The averaged data is used for trend analysis and quantitative analysis with high confidence.

[0045] In a second aspect, an embodiment of the present invention provides a device for processing abnormal aerodynamic noise of a model rotor caused by external airflow disturbance, comprising: a data acquisition and segmentation module, an extraction module, a statistics module, and a data processing and identification module;

[0046] The data acquisition and segmentation module is used to segment the collected rotor aerodynamic noise data by circle according to the characteristic that the rotor aerodynamic noise has a main frequency stability in a stable state;

[0047] The extraction module is used to perform amplitude and frequency analysis on the obtained data of each circle and extract the frequency values ​​corresponding to the first three maximum amplitudes in the amplitude spectrum of each circle of data;

[0048] The statistical module counts the frequency values ​​extracted from all circles and obtains the main frequency with the largest number of frequencies;

[0049] The data processing and identification module removes the data of the circles corresponding to the non-most common main frequency, averages the data of the remaining circles over the entire cycle, and identifies the confidence level of the noise data.

[0050] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, comprising: a memory and a processor;

[0051] The memory is configured to store executable instructions;

[0052] The processor is specifically configured to implement any of the above-mentioned methods for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbances when executing the executable instructions stored in the memory.

[0053] Beneficial effects of the present invention: An embodiment of the present invention provides a method for processing abnormal aerodynamic noise of a model rotor for eliminating external airflow disturbances. Based on the requirement of eliminating disturbance signals in a rotor aerodynamic noise test, a method and device for processing abnormal aerodynamic noise of a model rotor according to an embodiment of the present invention are proposed. According to the characteristic that the rotor aerodynamic noise of the rotor in a stable state has main frequency stability, the collected rotor aerodynamic noise data is divided into whole cycles, and then the amplitude and frequency analysis is performed on the obtained data of each circle, and the frequency values ​​corresponding to the top three maximum amplitudes in the amplitude spectrum of each circle of data are extracted. The frequency values ​​extracted from all circles are counted to obtain the main frequency with the largest number of frequencies, and the data of the circles corresponding to the non-largest number of main frequencies are eliminated. Then, the data of the remaining circles are averaged over the whole cycles, and the confidence level of the noise data is judged, thereby effectively eliminating the abnormal aerodynamic noise of the rotor caused by sudden gusts, and thus ensuring that effective rotor rotation noise test data can be extracted from the abnormal aerodynamic noise of the rotor caused by external airflow disturbances in an open environment. By adopting the technical solution provided by the embodiment of the present invention, the process of continuous change of the rotor shaft tilt angle can be accurately simulated in a set of equations, and the dynamic response of the tilt-rotor helicopter in the transition state can be solved therefrom. Specific implementation plan

[0054] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0055] As explained in the above background technology, the rotor aerodynamic noise test method has high requirements for the test environment and test site, which limits the existing test bench from conducting relevant noise tests. As a result, the rotor aerodynamic noise test is difficult to conduct in an environment with external gust interference.

[0056] In this field, patent CN108051659A discloses a method for separating and extracting rotor noise. This patented method first obtains the fundamental frequency of the helicopter's rotor noise, engine noise fundamental frequency, and tail rotor noise fundamental frequency. The noise signal is then Fourier transformed into a frequency domain signal. A high-pass filter is then performed at a selected cutoff frequency to remove ambient noise, engine noise, and tail rotor noise from the frequency domain signal. An inverse Fourier transform is then performed to obtain the sound pressure time domain signal of the rotor noise. The present invention is aimed at removing rotor disturbance noise caused by sudden gusts of wind, and this is performed in the time domain, which is significantly different from the method described in patent CN108051659A. Patent CN202011309542.9 discloses a full-cycle averaging method for rotor vortex interference noise time domain data based on peak search. This method targets vortex interference noise. By intercepting the collected test bench rotor vortex interference noise data for a full cycle, and then statistically analyzing the data lengths of multiple cycles obtained by interception, the full-cycle data with the same data length and the largest proportion are selected for averaging, the required rotor vortex interference noise time domain average data can be obtained. This method does not take into account the disturbance effect of external gusts on normal rotor noise.

[0057] In theory, rotor aerodynamic noise tests in hovering should be dominated by thickness noise and load noise (and usually the noise with the 1x pass frequency component should be the largest). That is, the single-cycle time history curve after full-cycle averaging has a clear single-wave characteristic, and the waveform shapes of measurement points at different azimuths and the same angle should be basically the same except for one azimuth angle difference (the difference between the two azimuth angles). Most waveforms in the data processing results conform to the above rules. However, rotor hover performance tests are usually conducted in a hover test room. During the test, to ensure the smooth flow of rotor airflow, all roller shutters in the hover test room are open. Therefore, it is impossible to guarantee the additional disturbance caused by external gusts on the normal rotor. This disturbance will be reflected in the collected rotor aerodynamic noise data. This disturbance is a kind of interference, and this disturbance signal must be eliminated to obtain true and effective rotor aerodynamic noise data.

[0058] To solve the above problems, an embodiment of the present invention provides a method and device for processing abnormal aerodynamic noise of a model rotor caused by external airflow disturbances. The method and device can accurately simulate the process of continuous change of the rotor shaft tilt angle in a set of equations, and can solve the dynamic response of the tilt-rotor helicopter in the transition state.

[0059] An embodiment of the present invention provides a method for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbances. The method for eliminating abnormal aerodynamic noise of a model rotor includes the following steps:

[0060] Step 1: Based on the characteristic that the rotor aerodynamic noise in a steady state has a main frequency stability, the collected rotor aerodynamic noise data is divided into circles;

[0061] Step 2: Perform amplitude and frequency analysis on the obtained data of each circle, and extract the frequency values ​​corresponding to the first three maximum amplitudes in the amplitude spectrum of each circle of data;

[0062] Step 3: Count the frequency values ​​extracted from all circles to obtain the most common main frequency;

[0063] Step 4: Eliminate the data of the circles corresponding to the non-most common main frequency, average the data of the remaining circles over the entire period, and judge the confidence level of the noise data.

[0064] The method for processing the abnormal aerodynamic noise of the model rotor caused by external airflow disturbances provided in an embodiment of the present invention can effectively eliminate the abnormal aerodynamic noise of the rotor caused by sudden gusts of wind, thereby ensuring that effective rotor rotation noise test data can be extracted from the abnormal aerodynamic noise of the rotor caused by external airflow disturbances in an open environment.

[0065] In one implementation of the embodiment of the present invention, step 1 may include:

[0066] Step 11, synchronously collecting noise data and rotor speed pulse data of each measurement point at a sampling frequency St;

[0067] Step 12: Calculate the number of data points per rotor revolution (Np = St / Nr) based on the sampling frequency St and the rotor speed Nr.

[0068] Step 13: Select the noise data of the measurement point farthest from the center line of the model rotor shaft from each noise measurement point, and divide the noise data of M into the measurement points with the rising edge of any pulse of the rotor speed Nr as the starting point. n Circles, each circle has a noise data sequence of Np data points, that is, M n 1-dimensional arrays, each of which has a length of Np.

[0069] In this step, for example, M n ≥100, in the embodiment of the present invention, M n Take 100 as an example to illustrate, that is, 100 one-dimensional arrays are obtained by segmentation, and the length of the one-dimensional array is Np.

[0070] In one implementation of the embodiment of the present invention, the implementation method of the above step 11 is:

[0071] The collected rotor speed pulse data is a signal that changes from a low level to a high level every time the rotor rotates one circle, the high level lasts for a period of time, and then returns to a low level. For example, it is similar to a TTL signal. The duty cycle of the high level duration period of each circle ranges from 20% to 30%, and St≥1024×Nr (Nr is the rotor speed, unit: revolutions / second).

[0072] In this implementation, for example, if the number of rising edge data points is 4 to 10, the time length T for collecting data satisfies:

[0073] T = M0 × Nr;

[0074] M0 is the number of turns, and the value range of M0 is 200≥M0≥130.

[0075] In one implementation of the embodiment of the present invention, the method of performing amplitude and frequency analysis on each cycle of data in step 2 above may include:

[0076] Step 21, the M obtained by segmentation in step 1 n Perform auto-power spectrum analysis on each of the 1-dimensional arrays (e.g., 100 cycles of noise data) in turn (the sampling rate in the auto-power spectrum analysis is set to St), thereby obtaining a one-dimensional array of amplitude and a one-dimensional array of frequency values ​​of the auto-power spectrum of each cycle of data;

[0077] Among them, the one-dimensional array of the amplitude of the power spectrum of the first cycle data is marked as [A 1-1 、A 1-2 ...、A 1-Np / 2 ] and the one-dimensional array of frequency values ​​is identified as [F 1-1 、F 1-2 …、F 1-Np / 2 ], No. M n The one-dimensional array of the amplitude of the power spectrum of the circle data is marked as [A Mn-1 、A Mn-2 ...、A Mn-Np / 2 ] and the one-dimensional array of frequency values ​​is identified as [F Mn-1 、F Mn-2 …、F Mn-Np / 2 ].

[0078] In this implementation, the one-dimensional array of amplitudes for the 100th cycle is labeled [A 100-1 、A 100-2 ...、A 100-Np / 2 ] and the one-dimensional array of frequency values ​​is identified as [F 100-1 、F 100-2 …、F 100-Np / 2 ].

[0079] In one implementation of the embodiment of the present invention, the method of extracting the frequency values ​​corresponding to the first three maximum amplitudes in the amplitude spectrum of each cycle of data in step 2 above may include:

[0080] Step 22: A one-dimensional array of the amplitude of the power spectrum of each circle of data obtained in step 21 (such as [M 1-1 、M 1-2 …、M 1-(Np / 2) ]), extract the three frequency values ​​corresponding to the first three maximum amplitudes in the one-dimensional array of the circle amplitude;

[0081] Among them, the frequency values ​​corresponding to the first three maximum amplitudes in the one-dimensional array of the first circle amplitude are marked as [F max-1-1 ,F max-1-2 ,F max-1-3 ], No. M n The frequency values ​​corresponding to the first three largest amplitudes in the one-dimensional array of circle amplitudes are marked as [F max-Mn-1 ,F max-Mn-2 ,F max-Mn-3 ].

[0082] In this implementation, for example, the frequency values ​​corresponding to the first three maximum amplitudes in the one-dimensional array of the first cycle amplitude are identified as [F max-1-1 ,F max-1-2 ,F max-1-3 ]、The frequency values ​​corresponding to the first three maximum amplitudes in the one-dimensional array of the 100th amplitude are marked as [F max-100-1 ,F max-100-2 ,F max-100-3 ].

[0083] In one implementation of the embodiment of the present invention, the implementation of step 3 above may include:

[0084] According to the sorting of amplitude sizes, for the frequency data of all circles, first extract the maximum mode and number of rows of the frequency corresponding to the first amplitude value, and based on the maximum mode row corresponding to the first amplitude value, extract the maximum mode and number of rows of the frequency corresponding to the second amplitude value, and then based on the maximum mode row corresponding to the second amplitude value, extract the maximum mode and number of rows of the frequency corresponding to the third amplitude value.

[0085] In one implementation of the embodiment of the present invention, the specific implementation process of the above step 3 may include:

[0086] Step 31, according to the M obtained in step 22 n The frequency values ​​corresponding to the first three maximum amplitudes in each circle generate 4 columns M of the following form n Two-dimensional array M of rows all Identified as:

[0087] [F max-1-1,F max-1-2 ,F max-1-3 ,1;

[0088] F max-2-1 ,F max-2-2 ,F max-2-3 ,2;

[0089] ...;

[0090] F max-Mn-1 ,F max-Mn-2 ,F max-Mn-3 ,M n ];

[0091] Step 32, from the two-dimensional array M all Select the largest common factor F in the first column max-1 , and the number of rows with the largest commonality is N max-1 ;

[0092] Step 33, from the two-dimensional array M all Extract the first number in each row equal to F max-1 All rows form a new 4-column N max-1 Two-dimensional array M of rows n-max-1 .

[0093] In step 33 of this implementation, assuming that the extracted rows are from row 3 to row 98, the two-dimensional array M n-max-1 Identified as:

[0094] [F max-3-1 ,F max-3-2 ,F max-3-3 ,3;

[0095] F max-4-1 ,F max-4-2 ,F max-4-3 ,4;

[0096] ...;

[0097] F max-98-1 ,F max-98-2 ,F max-98-3 ,98];

[0098] Step 34, from the two-dimensional array M n-max-1 Select the largest common factor F in the second column max-2 , and the number of rows with the largest commonality is N max-2 ;

[0099] Step 35, from the two-dimensional array M n-max-1 Extract the second number in each row equal to F max-2 All rows form a new 4-column N max-2 Two-dimensional array M of rows n-max-2 ;

[0100] In step 35 of this implementation, assuming that the extracted rows are from the 4th to the 85th row, the two-dimensional array M n-max-2 Identified as:

[0101] [F max-6-1 ,F max-6-2 ,F max-6-3 ,6;

[0102] F max-7-1 ,F max-7-2 ,F max-7-3 ,7;

[0103] ...;

[0104] F max-90-1 ,F max-90-2 ,F max-90-3 ,90];

[0105] Step 36, from the two-dimensional array M n-max-2 Select the largest common factor F in the third column max-3 , and the number of rows with the largest commonality is N max-3 ;

[0106] Step 37, from the two-dimensional array M n-max-2 Extract the third number in each row equal to F max-3 All rows form a new 4-column N max-3 Two-dimensional array M of rows n-max-3 .

[0107] In step 37 of this implementation, assuming that the extracted rows are from row 3 to row 85, the two-dimensional array M n-max-3 Identified as:

[0108] [F max-8-1 ,F max-8-2 ,F max-8-3 ,8;

[0109] F max-7-1 ,F max-7-2 ,F max-7-3 ,7;

[0110] ...;

[0111] F max-90-1 ,F max-90-2 ,F max-90-3 ,90];

[0112] In one implementation of the embodiment of the present invention, step 4 includes the following situations:

[0113] If N max-1 <50%M n, then the test data is confirmed to be invalid;

[0114] If 50% M n ≤N max-1 ≤70%M n , then press M n-max-1 The fourth number in each row (i.e., the nth circle) is used to extract the corresponding circle and average it over the entire period. The averaged data is used for trend analysis and quantitative analysis with low confidence levels.

[0115] If N max-1 >70%M n , and N max-2 ≤60%M n , then press M n-max-3 The fourth number in each row (i.e., the nth circle) is extracted and averaged over the entire period. The test data for this round are considered valid and can be used for trend analysis and quantitative analysis with a medium confidence level.

[0116] If N max-1 >70%M n , and N max-3 ≤50%M n , then press M n-max-3 The fourth number in each row (i.e., the nth circle) is extracted and the corresponding circle is averaged over the entire period. The test data of this round are considered valid and can be used for trend analysis and quantitative analysis with a medium confidence level.

[0117] If N max-1 >70%M n , and N max-2 >60%M n And N max-3 >50%M n , then press M n-max-3 The fourth number in each row (i.e., the nth circle) is extracted and the corresponding circle is averaged over the entire period. It is considered that the test data of this round is completely valid and can be used for trend analysis and quantitative analysis with high confidence.

[0118] Based on the method for processing abnormal aerodynamic noise of a model rotor caused by external airflow disturbances provided in the above-mentioned embodiment of the present invention, an embodiment of the present invention also provides a device for processing abnormal aerodynamic noise of a model rotor caused by external airflow disturbances, including: a data acquisition and segmentation module, an extraction module, a statistical module, and a data processing and identification module.

[0119] The data collection and segmentation module is used to segment the collected rotor aerodynamic noise data by circle according to the characteristic that the rotor aerodynamic noise has a main frequency stability in a stable state;

[0120] The extraction module is used to perform amplitude and frequency analysis on the obtained data of each circle and extract the frequency values ​​corresponding to the first three maximum amplitudes in the amplitude spectrum of each circle of data;

[0121] The statistical module counts the frequency values ​​extracted from all circles and obtains the main frequency with the largest number of frequencies;

[0122] The data processing and identification module removes the data of the circles corresponding to the non-most common main frequency, averages the data of the remaining circles over the entire cycle, and identifies the confidence level of the noise data.

[0123] Based on the requirement of eliminating disturbance signals in rotor aerodynamic noise testing, embodiments of the present invention provide a method and apparatus for eliminating model rotor abnormal aerodynamic noise caused by external airflow disturbances. Based on the characteristic that rotor aerodynamic noise in a steady state has a stable dominant frequency, the collected rotor aerodynamic noise data is segmented into whole cycles. Then, amplitude and frequency analysis is performed on each cycle of the obtained data. The frequencies corresponding to the top three maximum amplitudes in the amplitude spectrum of each cycle are extracted. The extracted frequencies across all cycles are counted to obtain the most common dominant frequency. The data corresponding to the cycles with non-most common dominant frequencies are eliminated. The remaining cycles are then averaged over the whole cycles, and the confidence level of the noise data is determined. This effectively eliminates the rotor abnormal aerodynamic noise caused by sudden gusts, thereby ensuring that valid rotor rotation noise test data can be extracted from the rotor abnormal aerodynamic noise caused by external airflow disturbances in an open environment. The technical solution provided by the embodiments of the present invention can accurately simulate the continuous change of the rotor shaft tilt angle in a set of equations, and can solve the dynamic response of the tilt-rotor helicopter in the transient state from this equation.

Claims

1. A method for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbance, characterized in that: include: Step 1: Based on the characteristic that the rotor aerodynamic noise in a steady state has a main frequency stability, the collected rotor aerodynamic noise data is divided into circles; Step 2: Perform amplitude and frequency analysis on the obtained data of each circle, and extract the frequency values ​​corresponding to the first three maximum amplitudes in the amplitude spectrum of each circle of data; Step 3: Count the frequency values ​​extracted from all circles to obtain the most common main frequency; Step 4: Eliminate the data of the circles corresponding to the non-most common main frequency, average the data of the remaining circles over the entire period, and judge the confidence level of the noise data.

2. The method for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbance according to claim 1, characterized in that: The step 1 comprises: Step 11, synchronously collecting noise data and rotor speed pulse data of each measurement point at a sampling frequency St; Step 12: Calculate the number of data points per rotor revolution (Np = St / Nr) based on the sampling frequency St and the rotor speed Nr. Step 13: Select the noise data of the measurement point farthest from the center line of the model rotor shaft from each noise measurement point, and divide the noise data of M into the measurement points with the rising edge of any pulse of the rotor speed Nr as the starting point. n Circles, each circle has a noise data sequence of Np data points, that is, M n 1-dimensional arrays, each of which has a length of Np.

3. The method for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbance according to claim 2, characterized in that: In the step 11, The collected rotor speed pulse data is a signal that changes from a low level to a high level, remains at a high level for a period of time, and then returns to a low level every time the rotor rotates one circle. The duty cycle of the high level duration period of each circle ranges from 20% to 30%, and St ≥ 1024 × Nr; where Nr is the rotor speed; The number of rising edge data points is 4 to 10, and the data acquisition time length T satisfies: T = M0 × Nr; M0 is the number of turns, and the value range of M0 is 200≥M0≥130.

4. The method for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbance according to claim 2, characterized in that: The method of performing amplitude and frequency analysis on each cycle of data in step 2 includes: Step 21, the M obtained by segmentation in step 1 n The 1-dimensional arrays are analyzed for auto-power spectrum respectively, so as to obtain the amplitude 1-dimensional array and frequency 1-dimensional array of the auto-power spectrum of each circle of data; Among them, the one-dimensional array of the amplitude of the power spectrum of the first cycle data is marked as [A 1-1 、A 1-2 ...、A 1-Np / 2 ] and the one-dimensional array of frequency values ​​is identified as [F 1-1 、F 1-2 …、F 1-Np / 2 ], No. M n The one-dimensional array of the amplitude of the power spectrum of the circle data is marked as [A Mn-1 、A Mn-2 ...、A Mn-Np / 2 ] and the one-dimensional array of frequency values ​​is identified as [F Mn-1 、F Mn-2 …、F Mn-Np / 2 ].

5. The method for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbance according to claim 4 is characterized in that: The method of extracting the frequency values ​​corresponding to the first three maximum amplitudes in the amplitude spectrum of each circle of data in step 2 includes: Step 22: extracting the three frequency values ​​corresponding to the first three maximum amplitudes in the one-dimensional array of amplitudes of each circle of data obtained in step 21; Among them, the frequency values ​​corresponding to the first three maximum amplitudes in the one-dimensional array of the first circle amplitude are marked as [F max-1-1 ,F max-1-2 ,F max-1-3 ], No. M n The frequency values ​​corresponding to the first three largest amplitudes in the one-dimensional array of circle amplitudes are marked as [F max-Mn-1 ,F max-Mn-2 ,F max-Mn-3 ].

6. The method for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbance according to claim 5, characterized in that: The step 3 comprises: According to the sorting of amplitude sizes, for the frequency data of all circles, first extract the maximum mode and number of rows of the frequency corresponding to the first amplitude value, and based on the maximum mode row corresponding to the first amplitude value, extract the maximum mode and number of rows of the frequency corresponding to the second amplitude value, and then based on the maximum mode row corresponding to the second amplitude value, extract the maximum mode and number of rows of the frequency corresponding to the third amplitude value.

7. The method for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbance according to claim 5, characterized in that: The step 3 comprises: Step 31, according to the M obtained in step 22 n The frequency values ​​corresponding to the first three maximum amplitudes in each circle generate 4 columns M of the following form n Two-dimensional array M of rows all Identified as: [F max-1-1 ,F max-1-2 ,F max-1-3 ,1; F max-2-1 ,F max-2-2 ,F max-2-3 ,2; ……； F max-Mn-1 ,F max-Mn-2 ,F max-Mn-3 ,M n ]; Step 32, from the two-dimensional array M all Select the largest common factor F in the first column max-1 , and the number of rows with the largest common coefficient is N max-1 ; Step 33, from the two-dimensional array M all Extract the first number in each row equal to F max-1 All rows form a new 4-column N max-1 Two-dimensional array M of rows n-max-1 ; Step 34, from the two-dimensional array M n-max-1 Select the largest common factor F in the second column max-2 , and the number of rows with the largest commonality is N max-2 ; Step 35, from the two-dimensional array M n-max-1 Extract the second number in each row equal to F max-2 All rows form a new 4-column N max-2 Two-dimensional array M of rows n-max-2 ; Step 36, from the two-dimensional array M n-max-2 Select the largest common factor F in the third column max-3 , and the number of rows with the largest common coefficient is N max-3 ; Step 37, from the two-dimensional array M n-max-2 Extract the third number in each row equal to F max-3 All rows form a new 4-column N max-3 Two-dimensional array M of rows n-max-3 .

8. The method for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbance according to claim 7, characterized in that: The step 4 includes the following situations: If N max-1 <50%M n , then the test data is confirmed to be invalid; If 50% M n ≤N max-1 ≤70%M n , then press M n-max-1 The fourth number in each row is used to extract the corresponding circle and average the whole cycle. The averaged data is used for trend analysis and quantitative analysis with low confidence level. If N max-1 >70%M n , and N max-2 ≤60%M n , then press M n-max-3 The fourth number in each row is used to extract the corresponding circle and average the whole cycle. The test data of this round is considered valid. The averaged data is used for trend analysis and quantitative analysis with medium confidence level. If N max-1 >70%M n , and N max-3 ≤50%M n , then press M n-max-3 The fourth number in each row is used to extract the corresponding circle and average the whole cycle. The test data of this round is considered valid. The averaged data is used for trend analysis and quantitative analysis with medium confidence level. If N max-1 >70%M n , and N max-2 >60%M n And N max-3 >50%M n , then press M n-max-3 The fourth number in each row is used to extract the corresponding circle and average the whole cycle. The test data of this round is considered to be completely valid. The averaged data is used for trend analysis and quantitative analysis with high confidence.

9. A device for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbance, characterized in that: include: Data acquisition and segmentation module, extraction module, statistics module, data processing and identification module; The data acquisition and segmentation module is used to segment the collected rotor aerodynamic noise data by circle according to the characteristic that the rotor aerodynamic noise has a main frequency stability in a stable state; The extraction module is used to perform amplitude and frequency analysis on the obtained data of each circle and extract the frequency values ​​corresponding to the first three maximum amplitudes in the amplitude spectrum of each circle of data; The statistical module counts the frequency values ​​extracted from all circles and obtains the main frequency with the largest number of frequencies; The data processing and identification module removes the data of the circles corresponding to the non-most common main frequency, averages the data of the remaining circles over the entire cycle, and identifies the confidence level of the noise data.

10. A computer-readable storage medium, characterized in that include: memory and processor; The memory is configured to store executable instructions; The processor is specifically configured to implement the method for eliminating abnormal aerodynamic noise of a model rotor caused by external airflow disturbances according to any one of claims 1 to 8 when executing the executable instructions stored in the memory.