Method for compiling measured severe flight envelope based on extrapolation of overload overshoots

Abstract

The application discloses a kind of based on overload extrapolation of measured severe flight spectrum compilation method, comprising: determining aircraft mission profile;Separate gust overload and maneuver overload in measured overload time history data, and carry out peak and valley value collection;Determine the measured gust / maneuver overload cumulative exceedance curve family and distribution parameter of each mission segment;Calculate the damage coverage of severe spectrum corresponding, determine severe damage index, and obtain exceedance coverage under specified overload according to severe damage index inversion;According to distribution parameter, the exceedance of each mission segment under each level overload is calculated Coverage is P t The exceedance of each level overload under coverage P t Is fitted, and the severe gust / maneuver overload cumulative exceedance curve family under each mission segment is obtained;Compile flight overload spectrum.The gust and maneuver overload dispersion and load characteristics in flight are considered simultaneously in the application, fatigue test time can be effectively reduced, and the load history of fleet can be better reflected.

Description

Technical Field

[0001] This invention relates to the field of flight spectrum compilation technology, and more specifically to a method for compiling measured severe flight spectra based on overload exceedance number extrapolation. Background Technology

[0002] Flight load spectrum refers to the spectrum compiled from the load time history experienced by the aircraft body during flight. The measured flight load spectrum of the aircraft is compiled through specialized testing modifications and flight tests. It is used to determine and verify the design service life of the aircraft and is a prerequisite for determining the fatigue life extension of the aircraft structure.

[0003] Even among aircraft used under the same operational requirements, the load-time histories of different aircraft within a fleet exhibit significant differences, corresponding to the dispersion of load spectra. Durability analysis and testing must be conducted under a defined (unique) load spectrum. Therefore, selecting and compiling a reasonable load spectrum has become crucial for structural durability analysis and assessment. This has led to the development of the "critical load spectrum," which offers advantages such as exposing the aircraft's inherent failure characteristics and reducing testing time. Consequently, the critical load spectrum is increasingly being used in the compilation of measured load spectra.

[0004] Current methods for compiling severe spectrum do not take into account the dispersion and characteristics of flight loads, which not only makes the tests time-consuming but also fails to accurately sense the load history of the aircraft group. Summary of the Invention

[0005] In view of this, the present invention provides a method for compiling a measured severe flight spectrum based on overload overshoot extrapolation, which takes into account the gusts and maneuver load dispersion and load characteristics during flight, can effectively reduce fatigue test time and better reflect the load history of the aircraft group.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for compiling measured severe flight spectra based on overload exceedance number extrapolation includes:

[0008] S1. Determine the aircraft mission profile and the composition, sequence, proportion, and parameters of each mission segment in the mission profile;

[0009] S2. Separate the sudden wind overload and motor overload from the measured overload time history data, and collect peak and valley values;

[0010] S3. Fit the overload time history data after processing S2 to obtain the measured cumulative overload and maneuver overload curve families for each task segment. Then, perform hierarchical discretization on the measured cumulative overload and maneuver overload curve families to obtain the distribution parameters for each task segment.

[0011] S4. The Monte Carlo method is used to simulate the expected family of gust overload exceedance number curves and the family of maneuver overload exceedance number curves. Based on the expected family of gust overload exceedance number curves and the family of maneuver overload exceedance number curves, the expected damage distribution of the aircraft fleet is simulated. Based on the definition of the safety life of the severity spectrum and the Monte Carlo method, the damage coverage corresponding to the severity spectrum is calculated. The severity damage index corresponding to the damage coverage is calculated, and the exceedance number coverage P under the specified overload is obtained by inversion based on the severity damage index. t ;

[0012] S5. Based on the distribution parameters of each task segment, calculate the coverage rate P under each level of overload for each task segment. t The number of exceedances; take the coverage rate as P under each level of overload. t By fitting the exceedance number, we obtain the family of cumulative exceedance number curves for severe gust overload and cumulative exceedance number curves for maneuver overload under each mission segment;

[0013] S6. Compile flight overload spectrum based on the family of cumulative exceedance number curves for severe gust overload and cumulative exceedance number curves for maneuver overload.

[0014] Preferably, in S1, the mission segment parameters include: mission segment altitude, flight speed, aircraft weight, flight altitude, and flight time; the criteria for dividing the mission segment are:

[0015] a) The criterion is that the flap deflection angle changes to 35° at the beginning of task segment 5;

[0016] b) The end point of task segment 5 is determined by the flap deflection angle becoming 0°;

[0017] c) The end point of task segment 2 is determined by the turning point where the height curve flattens out;

[0018] d) The end point of task segment 3 is determined by the inflection point of the descent curve;

[0019] e) The end point of mission segment 4 is determined by the flap deflection angle becoming 35°;

[0020] f) The end point of mission segment 5 is determined by the flap deflection angle becoming 0°.

[0021] Preferably, S2 includes:

[0022] S21. Standardize the measured overload time history data according to the following formula;

[0023] △n y0 =△n yi *Gi / G0

[0024] Where, △n y0 The overload value is the corrected value according to the standard task section specifications; △n yiThe values ​​are measured overload values. Gi is the actual mass of the aircraft, and G0 is the standard mass of the aircraft under the current mission profile and mission segment.

[0025] S22. Separate the motor overload and sudden wind overload in the standardized measured overload time history data according to the preset separation conditions;

[0026] S23. Perform peak and valley detection on the separated measured motor overload time history data and sudden wind overload time history data respectively, filter out the data between the peak and valley values, and retain the peak and valley points.

[0027] Preferably, S3 includes:

[0028] S31. Count the peak and valley values ​​of the sudden wind overload time history data: Take the 0g overload state of each task segment as the benchmark. When the valley value between two peaks or the peak value between two valleys does not exceed the upper or lower deviation of the benchmark of the task segment, only the maximum peak value or the maximum valley value is recorded. Otherwise, both peak values ​​or valley values ​​are recorded.

[0029] S32. Use the rainflow counting method to count the peak and valley values ​​of the maneuver overload time history data that retains peak and valley values.

[0030] S33. The average of the peak count and valley count in the sudden wind overload time history data is taken as the positive overload cumulative exceedance number under sudden wind overload; the peak count and valley count in the motor overload time history data are taken as the positive overload cumulative exceedance number and negative overload cumulative exceedance number, respectively.

[0031] S34. Standardize the cumulative overload exceedance number under sudden wind overload and motor overload according to the following formula;

[0032]

[0033] Among them, t M For the actual flight time; t S N is the standard time for the current task segment; ib N represents the cumulative exceedance number of the i-th level load obtained from the measured flight time; Yi The cumulative exceedance number of the i-th level load over standard time;

[0034] S35. For each task segment, the cumulative overload exceedance data pair (Δn) y N Y ) i By performing a fitting operation, the cumulative overload exceedance number curve for each task segment is obtained; the fitting equation is:

[0035] Δn y =a*lgN Y +b

[0036] wherein a and b are coefficients of the fitting equation;

[0037] S36, discretizing the overload accumulation exceedance curve, and calculating to obtain the exceedance ΔN i =N i -N i-1 to obtain exceedance data pairs (Δn y , ΔN) corresponding to different flight overloads i (i=1, …, n);

[0038] S37, adopting a random variable model, and assuming that the exceedance ΔN corresponding to a specified overload Δn y follows a lognormal distribution;

[0039] S38, solving target distribution parameters μ and σ according to a likelihood function, wherein the solving equation is:

[0040]

[0041]

[0042] wherein n is the number of different overload levels, μ is a logarithmic mean, and σ is a logarithmic standard deviation.

[0043] Preferably, S32 comprises:

[0044] S321, selecting a typical segment starting and ending at the maximum peak and valley values, inputting each peak and valley value according to the sequence of a load spectrum until all data are processed;

[0045] S322, reading in a next peak and valley value, and stopping reading if all data are processed;

[0046] S323, returning to S322 if the number of data points of peak and valley values is less than 3; if the number of data points is greater than or equal to 3, calculating ranges X and Y from the last three read-in peak and valley values; among the three data points, the absolute value of the difference between the first point and the second point is Y, and the absolute value of the difference between the second point and the third point is X;

[0047] S324, comparing the magnitudes of X and Y, returning to S322 if X<Y, and executing S325 if X≥Y;

[0048] S325, recording the range Y as one cycle, deleting the peak and valley values corresponding to Y, and returning to S322.

[0049] Preferably, S4 comprises:

[0050] S41, simulating expected gust overload exceedance curve families and maneuver overload exceedance curve families by adopting a Monte Carlo method, performing discretization, and obtaining gust / maneuver overload versus time sequences (ny,max n y,min ) i ;

[0051] S42, Based on the time series (n) of sudden wind / maneuver overload y,max n y,min ) i Calculate the total overload damage D eq ;

[0052] S43, Assuming total overload damage D eq Following a two-parameter Weibull distribution, the total damage D of each overload level is fitted. eqi and the corresponding empirical frequency value f i The two-parameter Weibull distribution parameters α and β are calculated.

[0053] S44. Calculate the damage coverage rate P corresponding to the severe spectrum based on the average spectrum fleet safety life and the severe spectrum fleet safety life. L Specifically:

[0054] S441: The reliability corresponding to severe damage is obtained using Monte Carlo simulation. Aluminum alloy is selected as the material, the damage coefficient in the Odin transform is set to 4, and the structural dispersion, i.e., the structural life under a specified load spectrum, follows a log-normal distribution with a standard deviation σ. s Taking a value of 0.14, the PSN curve of the aluminum alloy material is obtained. The damage distribution of the load spectrum follows a two-parameter Weibull distribution. Using Monte Carlo simulation, the PSN curve and load spectrum damage are used as input to obtain a sample set of fatigue lives. From this sample set, the life with an empirical frequency function equal to the unreliability level is selected as the safe life Np. This safe life should be equal to the average life under the severe spectrum divided by the structural dispersion factor corresponding to the reliability requirement, as shown in the following formula:

[0055]

[0056] The severity P of severe damage under severe spectrum that meets the reliability requirement is obtained by inverting this formula. L ;

[0057] S45: Calculate the damage coverage P based on the two-parameter Weibull distribution parameters α and β. L Corresponding severe injury index D eqPL Severe injury index D eqPL The calculation formula is:

[0058] D eqPL =exp((ln(-ln(1-P)) L ))+αlnβ) / α)

[0059] Among them, the severe injury index D eqPL To make the proportion of the fleet PL The aircraft has reached the required service life. eq ;

[0060] S46: Based on the severity injury index D eqPL The corresponding transcendence coverage P is obtained through inversion. t .

[0061] Preferably, S41 includes:

[0062] S411. Randomly select a random number μ that follows a standard normal distribution. p ~N(0,1), calculate the i-th level overload Δn yi The corresponding transcendental number ΔN i :

[0063]

[0064] S412, regarding ΔN i Cumulative counting yields the cumulative exceedance number. The fitting equation in S35 is used to fit the data pair (Δn) y ,N) i By fitting the data, the Δn of a single takeoff and landing can be obtained. y Cumulative exceedance curve;

[0065] S413. Repeat S411-S412 for all task segments under this profile to obtain the single-unit expected gust overload exceedance number curve and maneuver overload exceedance number curve represented by this sampling.

[0066] S414. Discretize the obtained family of exceedance number curves for sudden gust overload and the family of exceedance number curves for maneuver overload to obtain the exceedance number data pairs (Δn) corresponding to each level of sudden gust overload. y* ,ΔN) ij Data pairs of overload numbers corresponding to each level of maneuver overload (+Δn) y* ,ΔN) ij 、(-Δn y* ,ΔN) ij , where i represents discrete overload levels and j represents each task segment;

[0067] S415, assign the specified Δn to each task segment of the sudden overload and maneuver overload respectively. y The exceedance numbers are summed to obtain the sudden wind overload and its corresponding total exceedance number data. And the data on maneuver overload and its corresponding total exceedance number. and

[0068] S416. For sudden gust overload and motor overload, sort the positive overload and negative overload of each level according to the magnitude of their absolute values ​​and match them to obtain the time series of sudden gust / motor overload pairs.

[0069] S417. Repeat the above steps to obtain the time series of gusts and maneuver overload under the mission profile.

[0070] S418. If the aircraft has different mission profiles, the above operation should be repeated to obtain the time series of gusts and maneuver overload pairs under all mission profiles.

[0071] Preferably, S42 includes:

[0072] S421, The time series of gust / maneuver overload (n) y,max n y,min ) i The equivalent damage is calculated based on the pulsating cycle, and then linearly accumulated according to Miner's linear cumulative damage theory to obtain the damage spectrum of sudden wind overload and the damage spectrum of maneuver overload; the calculation formula is as follows:

[0073]

[0074] Where, Δn eq Δn is the equivalent load cyclic amplitude; Δn is the load cyclic amplitude; and n is the load cyclic peak value. y,max Subtract the load cycle valley value n y,min Divide by 2; m is the damage index; d eq D represents the equivalent damage from a single cycle, and D represents the damage from the load spectrum.

[0075] S422. Summing the damage spectrum of sudden wind overload and the damage spectrum of motor overload, we obtain the total overload damage D. eq .

[0076] Preferably, S5 includes:

[0077] S51. Calculate the coverage rate P under each level of overload for each task segment. t The transcendental number is calculated using the following formula:

[0078]

[0079] Where, μ i σ i The i-th level overload n y,i The corresponding log-median and standard deviation of the transcendental number, μ Pt To represent the exceedance coverage rate P t The corresponding quantiles;

[0080] S52. Determine the data pairs for each level of overload (Δn) y ΔNPL ) i The curves for the cumulative overload of severe sudden wind and the cumulative overload of severe maneuver are fitted to obtain the curves for the cumulative overload of severe maneuver for each task segment. The curves for the cumulative overload of severe maneuver include the curves for the cumulative overload of severe maneuver positive and the curves for the cumulative overload of severe maneuver negative.

[0081] S53. Perform high-load and low-load cutoff on the cumulative overload curves of severe gust overload and severe maneuver overload for each task segment.

[0082] Preferably, S6 includes:

[0083] S61. Determine the load levels and corresponding cycle counts of positive and negative overloads for each task segment under each task profile, and ensure that the cycle counts of positive and negative overloads are the same for each task segment.

[0084] S62. Based on the flight spectrum of the 4 segments of the flight / mission segment with the highest overload, and assuming that the highest overload in each of the 4 segments of the flight / mission segment is a normal logarithmic extreme distribution, determine the number of times each type of flight occurs.

[0085] S63. Link the overload levels and their corresponding cycle counts of the k-th flight type in the i-th mission segment of the j-th mission profile under a single takeoff and landing, and randomly alternately select peak and valley values ​​to form a severe overload spectrum for a specific flight type in the corresponding mission segment, thereby obtaining the mission segment spectrum of various flight types under each mission segment.

[0086] S64. Sort the mission segment spectrum under each mission profile according to the mission segment order to obtain the mission profile spectrum under a complete flight.

[0087] S65. Sort all mission profile spectra for each complete flight to obtain the final flight overload spectrum.

[0088] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method for compiling a measured severe flight spectrum based on overload overload extrapolation. In the process of compiling the measured severe flight overload spectrum of an aircraft, the dispersion and overload characteristics of gust overload and maneuver overload in flight load are considered at the same time. By reasonably arranging the positions of gust overload and maneuver overload in the flight spectrum, the load history of the aircraft group can be better reflected. At the same time, by back-deriving the coverage of the cumulative overload under a specified overload in the severe spectrum through severe damage, the final compiled severe flight overload spectrum can reduce the fatigue test time, and the dispersion factor used in the life determination only needs to consider the dispersion factor of the structure. Attached Figure Description

[0089] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0090] Figure 1 A flowchart illustrating the measured severe flight spectrum compilation method based on overload exceedance number extrapolation provided by this invention;

[0091] Figure 2 This is a schematic diagram of peak-valley value detection provided by the present invention;

[0092] Figure 3 A schematic diagram of the limitation of peak value counting across the mean provided by the present invention;

[0093] Figure 4 A schematic diagram of the fitting results for the distribution parameter μ provided by this invention;

[0094] Figure 5 A schematic diagram of the fitting results for the distribution parameter σ provided by this invention;

[0095] Figure 6 This invention provides a schematic diagram of cluster damage distribution.

[0096] Figure 7 The severe cumulative exceedance number curves for task segments 1 and 3 of the task profile provided by this invention;

[0097] Figure 8 This is a schematic diagram of the random spectrum of flight type A provided by the present invention. Detailed Implementation

[0098] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0099] like Figure 1 As shown, this embodiment of the invention discloses a method for compiling a measured severe flight spectrum based on overload exceedance number extrapolation, including:

[0100] S1. Determine the proportion and composition of the aircraft mission profile, and determine the composition, sequence, proportion and parameters of the mission segments in each mission profile;

[0101] S2. Separate the sudden wind overload and motor overload from the measured overload time history data, and collect peak and valley values;

[0102] S3. Fit the overload time history data after processing S2 to obtain the measured cumulative overload and maneuver overload curve families for each task segment. Then, perform hierarchical discretization on the measured cumulative overload and maneuver overload curve families to obtain the distribution parameters for each task segment.

[0103] S4. The Monte Carlo method is used to simulate the expected family of gust overload exceedance number curves and the family of maneuver overload exceedance number curves. Based on the expected family of gust overload exceedance number curves and the family of maneuver overload exceedance number curves, the expected damage distribution of the aircraft fleet is simulated. Based on the definition of safe life under the severity spectrum and the Monte Carlo method, the damage coverage corresponding to the severity spectrum is calculated. The severity damage index is determined according to the damage coverage and damage distribution. The exceedance number coverage P under the specified overload is obtained by inversion from the severity damage index. t ;

[0104] S5. Based on the distribution parameters of each task segment, calculate the coverage rate P under each level of overload for each task segment. t The number of exceedances; take the coverage rate as P under each level of overload. t By fitting the exceedance number, we obtain the family of cumulative exceedance number curves for severe gust overload and cumulative exceedance number curves for maneuver overload under each mission segment;

[0105] S6. Compile flight overload spectrum based on the family of cumulative exceedance number curves for severe gust overload and cumulative exceedance number curves for maneuver overload.

[0106] The steps described above will be explained in further detail below.

[0107] In one specific embodiment, S1, determine the task profile:

[0108] The mission profile scale and composition are provided, along with parameters for each mission segment, including: mission segment altitude, flight speed, weight, flight altitude, and flight time. The criteria for segmenting missions are as follows:

[0109] a) The criterion is that the flap deflection angle changes to 35° at the beginning of task segment 5;

[0110] b) The end point of mission segment 5, which is the start point of mission segment 2, is determined by the flap deflection angle becoming 0°.

[0111] c) The turning point where the height curve flattens out at the end point of task segment 2, which is the beginning point of task segment 3, is used as the criterion;

[0112] d) The turning point of the descent curve of the task segment 3 ending point, which is the starting point of the task segment 4, is used as the criterion;

[0113] e) The end point of mission segment 4, which is the start point of mission segment 5, is determined by the flap deflection angle becoming 35°.

[0114] f) The end point of mission segment 5 is determined by the flap deflection angle becoming 0°.

[0115] In one embodiment, S2, measured overload data preprocessing, specifically includes:

[0116] S21, Overload data n y Standardization process:

[0117] The measured overload time history data is standardized according to the following formula;

[0118] △n y0 =△n yi *Gi / G0

[0119] Where, △n y0 The overload value is the corrected value according to the standard task section specifications; △n yi The measured overload value is given by Gi, where Gi is the actual mass of the aircraft and G0 is the standard aircraft mass under the current mission profile and mission segment. The measured center of gravity overload n is then used as the reference value. y Subtracting 1 gives the incremental overload Δn at that moment. y0 Actual mass data is given by subtracting fuel consumption from the aircraft weight. Fuel consumption is calculated as an overall average (the average fuel consumption is calculated from the start of takeoff taxiing to the end of landing impact).

[0120] S22. Separation of flight overload and gust overload:

[0121] The mechanical overload and sudden wind overload in the standardized measured overload time history data are separated according to preset separation conditions; the preset separation conditions are:

[0122] a) If the overload change is less than the preset value and the duration exceeds 2 seconds, it is considered a motor overload; otherwise, it is considered a sudden wind overload.

[0123] b) An overload with an absolute value of 4° or greater on the control surface deflection angle is a gust overload; otherwise, it is a maneuver overload.

[0124] c) For mission segments 5, 1, 2, and 4, no separation of flight and gust is performed. For mission segments 5 and 1, since gust maneuvers are infrequent, the overloads are generally considered to be maneuver overloads, and no further separation of maneuver overload and gust overload is performed. For mission segments 2 and 4, since maneuver maneuvers are infrequent, the overloads are generally considered to be gust overloads, and no further separation of maneuver overload and gust overload is performed.

[0125] S23, Peak-Valuation Value Collection:

[0126] Peak and trough values ​​were detected for the separated measured time history data of motor overload and sudden wind overload, respectively. Data between peak and trough values ​​were filtered out, and the sampling point numbers of peak and trough points were retained.

[0127] Specifically: such as Figure 2 As shown, retain Δn z1 , Δn z3 , Δn z5 , Δn z6 , Δn z7 , Δn z8 , Δn z10 Peak and valley points, remove Δn z2 , Δn z4 , Δn z9 Sampling point.

[0128] If satisfied

[0129] or

[0130] Then the peak value (or valley value) is taken once.

[0131] In one specific embodiment, S3 includes:

[0132] S31, Sudden overload time history count:

[0133] Count the peak and valley values ​​of the wind overload time history data: Take the 0g overload state of each task segment as the baseline. When the valley value between two peak values ​​or the peak value between two valley values ​​does not exceed the upper deviation (positive overload) or lower deviation (negative overload) of the baseline of the task segment, only the maximum peak value (peak value for positive overload) or the maximum valley value (valley value for negative overload) is recorded. Otherwise, both peak values ​​or valley values ​​are recorded.

[0134] This method can be summarized as limiting the number of peak values ​​across the mean, such as Figure 3 As shown, the upper and lower deviations are set at 20% of the maximum peak value; this deviation serves as the limiting condition. In the figure, hollow dots represent counting points, and solid dots represent filtering points.

[0135] S32, Maneuver Overload Time History Count:

[0136] The rainflow counting method is used to count the peak and trough values ​​of the maneuver overload time history data, retaining both peak and trough points; specifically:

[0137] S321. Select a typical segment starting from and ending at the maximum peak and valley values, and input the peak and valley values ​​in the order of the load spectrum until the data is complete.

[0138] S322, Read in the next peak and valley values. If the data is complete, stop reading.

[0139] S323: If the number of peak and valley data points is less than 3, return to S322; if the number of data points is greater than or equal to 3, calculate the ranges X and Y from the last three read-in peak and valley values; among the three data points, the absolute value of the difference between the first point and the second point is Y; the absolute value of the difference between the second point and the third point is X;

[0140] S324: Compare the magnitudes of X and Y, if X<Y, return to S322, if X≥Y, execute S325;

[0141] S325: Record the range Y as one cycle, delete the peak and valley values corresponding to Y, and return to S322.

[0142] S33: Acquisition of cumulative exceedance numbers at all levels of overload:

[0143] The average value of peak count and valley count in gust overload time history data is taken as the positive overload cumulative exceedance number under gust overload; the peak count and valley count in maneuver overload time history data are taken as the positive overload cumulative exceedance number and negative overload cumulative exceedance number under maneuver overload respectively.

[0144] Specifically:

[0145] Select several levels of overload, record the cumulative exceedance number of positive flight overload (gust overload and maneuver overload) and the cumulative exceedance number of negative flight overload for positive peaks and negative valleys respectively, which are denoted as N(+Δn y ) and N(-Δn y ).

[0146] For gust overload, the discrete gust model holds that for gust disturbances with the same intensity (i.e., the same amplitude), the occurrence probability of positive (upward) and negative (downward) gusts is equal, so theoretically the measured positive and negative gust overload cumulative exceedance curves should be symmetrical. However, in actual processes, there is often a certain difference between the positive and negative gust cumulative exceedance curves. In engineering, geometric averaging is used to obtain the average cumulative exceedance number corresponding to a symmetrical gust overload. Thus, the cumulative exceedance curve family of ±Δn y is obtained, as shown in the following formula.

[0147]

[0148] For maneuver overload, it is generally not considered that the occurrence probability of positive (upward) and negative (downward) flight overload with the same intensity is equal, so for maneuver overload, counting shall be performed separately for the positive overload cumulative exceedance number and the negative overload cumulative exceedance number.

[0149] S34: Standardization processing of overload cumulative exceedance number considering flight duration:

[0150] Since the measured load spectrum is based on the mission segment flight time, which differs from the standard time of the mission segment in the mission profile, it is necessary to convert the measured mission segment cumulative overload exceedance data into standard time cumulative exceedance data.

[0151] The cumulative overload exceedance number under sudden wind overload and motor overload is standardized according to the following formula;

[0152]

[0153] Among them, t M For the actual flight time; t S N is the standard time for the current task segment; ib N represents the cumulative exceedance number of the i-th level load obtained from the measured flight time; Yi The cumulative exceedance number of the i-th level load over standard time;

[0154] The cumulative overload exceedance counts for each level of overload are processed according to the above formula to obtain the cumulative overload exceedance count data for the task segment at standard time.

[0155] S35. Fitting the overload cumulative exceedance number curve:

[0156] For each task segment, the cumulative overload exceedance data pair (Δn) y N Y ) i By performing fitting, the cumulative overload exceedance number curves for each task segment are obtained, including the cumulative exceedance number curves for gust overload and maneuver overload (positive / negative); the fitting equation is:

[0157] Δn y =a*lgN Y +b

[0158] Where a and b are the coefficients of the fitted equation;

[0159] S36. Hierarchical discretization of the overload cumulative exceedance number curve:

[0160] Discretize the overload cumulative exceedance curve, and discretize it into (Δn) y (N) i For data pairs (i = 1, ..., n), the transcendental number ΔN is calculated. i =N i -N i-1 Obtain exceedance number data pairs (Δn) corresponding to different flight overloads. y ,ΔN) i (i = 1, ..., n).

[0161] S37. Test of the properties of the cumulative transcendental number distribution:

[0162] Using a random variable model, assuming a specified overload Δn y The corresponding transcendental number ΔN follows a log-normal distribution.

[0163] S38. Estimation of distribution parameters:

[0164] For a specific task segment, when the measured sample size is large, it is recommended to use the maximum likelihood estimation method to estimate the distribution parameters. The likelihood function is shown in the following equation.

[0165]

[0166] In the formula x i =lgΔN i The objective parameters (μ, σ) are solved by finding that the derivatives of the likelihood functions with respect to μ and σ are zero. The solution equation is:

[0167]

[0168]

[0169] Where n is the number of different overload levels, μ is the logarithmic mean, and σ is the logarithmic standard deviation.

[0170] In one embodiment, S4 includes:

[0171] S41. Single-unit mission segment wind overtaking curve and maneuver overtaking curve:

[0172] The Monte Carlo method was used to simulate the expected family of surge overload exceedance number curves and the family of maneuver overload exceedance number curves, and then discretized to obtain the time series (n) of surge / maneuver overload pairs under each level of overload. y,max n y,min ) i Specifically:

[0173] S411. Randomly select a random number μ that follows a standard normal distribution. p ~N(0,1), calculate the i-th level overload Δn yi The corresponding transcendental number ΔN i :

[0174]

[0175] S412, regarding ΔN i Cumulative counting yields the cumulative exceedance number. The fitting equation in S35 is used to fit the data pair (Δn) y ,N) i By fitting the data, the Δn of a single takeoff and landing can be obtained. y Cumulative exceedance curve;

[0176] S413. Repeat S411-S412 for all task segments under this profile to obtain the single-unit expected gust overload exceedance number curve and maneuver overload exceedance number curve represented by this sampling.

[0177] S414. Discretize the obtained family of exceedance number curves for sudden gust overload and the family of exceedance number curves for maneuver overload to obtain the exceedance number data pairs (Δn) corresponding to each level of sudden gust overload. y* ,ΔN) ij Data pairs of overload numbers corresponding to each level of maneuver overload (+Δn) y* ,ΔN) ij 、(-Δn y* ,ΔN) ij , where i represents discrete overload levels and j represents each task segment;

[0178] S415, assign the specified Δn to each task segment of the sudden overload and maneuver overload respectively. y The exceedance numbers are summed to obtain the sudden wind overload and its corresponding total exceedance number data. And the data on maneuver overload and its corresponding total exceedance number. and

[0179] S416. For sudden gust overload and motor overload, sort the positive overload and negative overload of each level according to the magnitude of their absolute values ​​and match them to obtain the time series of sudden gust / motor overload pairs.

[0180] S417. Repeat the above steps to obtain the time series of gusts and maneuver overload under the mission profile.

[0181] S418. If the aircraft has different mission profiles, the above operation should be repeated to obtain the time series of gusts and maneuver overload pairs under all mission profiles.

[0182] S42, Based on the time series (n) of sudden wind / maneuver overload y,max n y,min ) i Calculate the total overload damage D eq Specifically:

[0183] S421, The time series of gust / maneuver overload (n) y,max n y,min ) i The equivalent damage is calculated based on the pulsating cycle, and the equivalent damage of each cycle is calculated. Then, according to Miner's linear cumulative damage theory, all single-cycle damages are linearly accumulated to obtain the damage spectrum of sudden wind overload and the damage spectrum of maneuver overload. The calculation formula is as follows:

[0184]

[0185] Where, Δn eq Δn is the equivalent load cyclic amplitude; Δn is the load cyclic amplitude; and n is the load cyclic peak value. y,max Subtract the load cycle valley value n y,min Divide by 2; m is the damage index, i.e., the slope of the material / structure fatigue SN curve when R=0. For aluminum alloys, it can be approximated as 4; d eq D represents the equivalent damage from a single cycle, and D represents the damage from the load spectrum.

[0186] S422. Summing the damage spectrum of sudden wind overload and the damage spectrum of motor overload, we obtain the total overload damage D. eq .

[0187] S43. Group Damage Statistical Analysis:

[0188] Assuming total overload damage D eq Following a two-parameter Weibull distribution, the distribution parameters are calculated using the rank statistics method, as shown in the following formula, to fit the total damage D of each overload level. eqi and the corresponding empirical frequency value f i The two-parameter Weibull distribution parameters α and β are calculated.

[0189] ln(-ln(1-f i ))=αlnD eqi -αlnβ

[0190] S44. Calculate the damage coverage rate P corresponding to the severe spectrum based on the average spectrum fleet safety life and the severe spectrum fleet safety life. L Specifically:

[0191] S441: The reliability corresponding to severe damage is obtained using Monte Carlo simulation. Aluminum alloy is selected as the material, the damage coefficient in the Odin transform is set to 4, and the structural dispersion, i.e., the structural life under a specified load spectrum, follows a log-normal distribution with a standard deviation σ. s Taking a value of 0.14, the PSN curve of the aluminum alloy material was obtained, and the damage distribution of the load spectrum followed a two-parameter Weibull distribution. Using Monte Carlo simulation, the PSN curve and load spectrum damage were used as input to obtain a sample set of fatigue life. From this, the life with an empirical frequency function equal to the unreliability was selected as the safe life N. p The safe life should be equal to the average life under the severity spectrum divided by the structural dispersion factor corresponding to the reliability requirement, as shown in the following formula:

[0192]

[0193] The severity P of severe damage under severe spectrum that meets the reliability requirement is obtained by inverting this formula. L .

[0194] S45. Calculate the severity index D based on the Weil distribution parameters α and β. eqPL And inversely determine the corresponding severe damage index D eqPL of And the corresponding exceedance coverage P t ; P represents the coverage rate of the number of exceedances. t The corresponding quantile, the severity index D eqPL The calculation formula is:

[0195] D eqPL =exp((ln(-ln(1-P)) L ))+αlnβ) / α)

[0196] Among them, the severe injury index D eqPL To ensure that the proportion of the fleet is P L The aircraft has reached the required service life. eq .

[0197] In one embodiment, S5, determining the severe cumulative exceedance curve specifically includes:

[0198] S51. Calculate the coverage rate P under each level of overload for each task segment. t The transcendental number is calculated using the following formula:

[0199]

[0200] Where, μ i σ i The i-th level overload n y,i The corresponding log-median and standard deviation of the transcendental number;

[0201] S52. Determine the data pairs for each level of overload (Δn) y ΔN PL ) i The curves for the cumulative overload of severe sudden wind and the cumulative overload of severe maneuver are fitted to obtain the curves for the cumulative overload of severe maneuver for each task segment. The curves for the cumulative overload of severe maneuver include the curves for the cumulative overload of severe maneuver positive and the curves for the cumulative overload of severe maneuver negative.

[0202] S53. For the cumulative overload and severe maneuver overload curves of each task segment, high-load and low-load truncation are performed, specifically including:

[0203] (1) High load interception

[0204] High loads in the overload spectrum have two effects: firstly, they increase fatigue damage, so it is necessary to appropriately supplement the spectrum with high loads based on the overload spectrum conditions; secondly, the coupling effect of more high loads and low loads increases lifespan. Therefore, it is necessary to determine the level and number of high loads in the overload spectrum, usually using a high load occurring once per 1000 flights in this mission segment as the cutoff value.

[0205] (2) Low-load cutoff

[0206] The spectrum typically contains numerous minor overload cycles, which need to be removed or converted to a certain level of overload for equivalent damage. Generally, the fatigue limit of critical components is determined based on the aforementioned analysis, and the overload value corresponding to 70%–80% of the fatigue limit is removed.

[0207] In one specific embodiment, S6 includes:

[0208] S61. Determination of overload levels based on task profile:

[0209] Determine the load levels and corresponding cycle counts for positive and negative overloads in each task segment under each task profile, ensuring that the cycle counts for positive and negative overloads are the same for each task segment; specifically including:

[0210] (1) Determination of load levels:

[0211] The overload of each mission segment is discrete into 5 levels. The number of overloads after discrete processing is an integer in a program block. Each mission segment has at least one flight. The representative value of each overload level (i.e., equivalent overload) is determined by adjusting for damage to discrete segments.

[0212] (2) Determination of load parameters for each stage of the 5×5 spectrum

[0213] Based on the cumulative exceedance number curve of severe overload in the task segment, the equivalent load spectrum is calculated. Equivalent load calculation: Assume that the equivalent load for a discrete segment requiring equivalent load calculation is Δn. yd The equivalent load cycle number is N eq If we replace the discrete segment of the curve with m straight lines, the linear equation of the i-th segment is:

[0214] Δg=a i lgN+b i

[0215] In the formula, a i b i Let be a constant for the i-th segment of the load spectrum curve, and N represent the transcendence number.

[0216] The equivalent load is:

[0217]

[0218] In the formula:

[0219]

[0220] Where S is the slope parameter of the SN curve of the material, and S = 2.0 for aluminum alloy.

[0221] Equivalent load cycle number

[0222]

[0223] A total of 5 equivalent loads Δn were obtained. ydi (i = 1, 2, 3, 4, 5) and the corresponding number of loops N eqi (i = 1, 2, 3, 4, 5). The same applies to negative overload.

[0224] It is important to note that for each task segment of the maneuver overload, the total number of cycles for positive and negative overloads within that task segment must be equal. This ensures that positive and negative overloads are paired without generating unnecessary peaks or troughs. The Δn value, which represents the minimum absolute value of the negative maneuver overload, can be modified. yd , such that the corresponding N eq Changes were made to accommodate both positive and negative overloads. equal.

[0225] S62. Determination of Flight Type:

[0226] Based on the flight spectrum of the four flight / mission segments with the highest overload, and assuming that the highest overload in each of the four flight / mission segments follows a normal logarithmic extreme distribution, the number of occurrences of each type of flight is determined.

[0227] In this embodiment, the load spectrum of the flight mission profile 1 is compiled according to 5 different flight types. The principle for determining the typical flight type of mission profile 1 is: based on the flight spectrum of the flight segment with the highest load (mission segment 4), and assuming that the highest load in each flight segment (mission segment 4) is a normal logarithmic extreme value distribution, the frequency of each type of flight is determined (y is determined). i Based on the assumption that the flight spectrum shapes of various flight types are similar, the incremental overload spectrum of various flight types is compiled for 1000 flights (determining B). ij As shown in Table 1.

[0228] Table 1 5×5 spectrum

[0229]

[0230]

[0231] In the table, y1+y2+y3+y4+y5 should equal the number of times this profile appears in 1000 flights. Compile the 5×5 spectrum for all mission segments under all profiles using the method described above. Furthermore, y1, y2, y3, y4, and y5 should be the same for the 5×5 spectrum of each mission segment under each mission profile; for the 5×5 spectrum of positive / negative overload maneuvers under each mission segment, it should maintain... same.

[0232] S63, Task Segment Compilation:

[0233] By linking the various levels of G-forces and their corresponding cycle counts under a single takeoff and landing for the k-th flight type in the i-th mission segment of the j-th mission profile, and randomly pairing peak and valley values, a severe G-force spectrum for a specific flight type in the corresponding mission segment is formed, resulting in the mission segment spectrum for various flight types under each mission segment. Specifically:

[0234] Based on the data in the compiled 5×5 spectrum, the overload levels and their corresponding frequencies under the k-th flight type of the i-th mission segment in the j-th profile are linked together. Peak and trough values ​​are randomly selected and paired to form the load spectrum for the specific flight type of the mission segment, denoted as (Δn). ydn ,-ΔΔn ydm ) h The task segment spectrum (j×i×k segments in total) is represented as the following load pair sequence.

[0235] f i,j,k =(Δn) ydn ,-Δn ydm ) h

[0236] In the formula, Δn ydn This represents the nth (n = 1, 2, 3, 4, 5) level of positive overload in this task segment, signifying the peak overload; -Δn ydm The m-th (m = 1, 2, 3, 4, 5) level of negative overload in this mission segment represents the valley overload (for mission segment 3, positive and negative overloads originate from gust / maneuvering overloads; for mission segments 2 and 4, positive and negative overloads originate from sudden gusts; for mission segments 1 and 5, positive and negative overloads originate from maneuvering overloads). The total number of overload pairs for each mission segment of each flight type should be equal to the total number of cycles for each flight in Table 1 above. Following this method, the spectrum (load pair sequence) for each flight type under all mission segments is compiled.

[0237] S64. Task Profile Compilation:

[0238] Based on the sequence of mission segments under each mission profile, the mission segment spectrum under that mission profile is sorted to obtain the mission profile spectrum for a complete flight; specifically:

[0239] Let F be the load spectrum of the k-th flight type on the j-th profile. j,k F j,k This can be represented as a load pair sequence f i,j,k Sort the tasks according to the order i (i = 1, 2, 3... m) under this profile, i.e., f 1,j,k ,f 2,j,k ,f 3,j,k ,f 4,j,k ...f m,j,k This yields the mission profile spectrum for a complete flight. The same method is used to compile mission profile spectra for all flight types under all mission profiles. All mission profile spectra are then represented in vector form.

[0240] B = [F] 1,1 …F 1,K F 2,1 … F 2,K F 3,1 … F 3,K ... F L,1 …F L,K ]

[0241] Mission Profile 1 contains five mission segments, each with five flight types. Therefore, five mission profile spectra need to be compiled for Mission Profile 1, representing the five flight types respectively, denoted as A1, B1, C1, D1, and E1. When compiling the A1 spectra, the spectra of flight type A under each mission segment are arranged, i.e., f... 1,j,k ,f 2,j,k ,f 3,j,k The A1 spectrum can then be obtained, and the same applies to other types of task profile spectra.

[0242] S65. Final Flight Overload Spectrum Compilation:

[0243] The final flight overload spectrum is obtained by sorting all mission profile spectra from each complete flight.

[0244] Specifically: Let y represent the occurrence of the j-th profile (j = 1, 2...L) and the k-th flight type (k = 1, 2...K) in 1000 flights. j,k Next. All the y j,k Expressed in vector form as

[0245] A = [y 1,1 … y 1,K y 2,1 … y 2,K y 3,1… y 3,K ... y L,1 …y L,K (16)

[0246] In the formula, A is a vector representing the frequency of occurrence of various task profile spectra.

[0247] Vectors A and B have a one-to-one correspondence, indicating how many times the task profile spectrum appears in this 1000-times block spectrum. The total spectrum can then be represented as the task profile spectrum sequence G.

[0248]

[0249] By reordering the mission profile spectrum for each complete flight in G, the final flight-continued-flight spectrum can be obtained. The specific method is as follows: First, number all mission profiles in G sequentially using a natural number sequence. Then, combine these numbers with a random integer sequence "x" respectively. is "Corresponding to this, press x" is Sort by numerical value (e.g., the corresponding x) is If it is 5, then the corresponding task profile spectrum is placed in the 5th position. This gives us the final load spectrum.

[0250] The random number sequence is obtained using a pseudo-random method based on the congruence multiplication method, and reasonable random results are given by adjusting the random parameters.

[0251] Multiplication by congruence:

[0252] y i+1 =λy i (mod2 k )

[0253] x i =y i / 2 m

[0254] First, "y" is formed. i "A sequence of numbers, then a random sequence of numbers "x i Calculate x i The total number of columns arranged in ascending order x is This yields a random integer sequence "x" is ".

[0255] The initial value y1 is an odd number; the coefficient λ = 8t - 3, where t is any natural number, and k is determined according to the required random period "n", such that 2 k-2 ≥n; m takes the value 2. By changing the values ​​of y1, t, and k, different random number sequences can be obtained.

[0256] The invention will be further illustrated below with specific examples.

[0257] S1. Determine the task profile

[0258] Take a typical mission profile of a certain type of aircraft as mission profile 1. The data is compiled in units of 1000 takeoffs and landings.

[0259] Table 2 Task Profile 1: Task Segments

[0260] Task Segment 1 1.9 Task Segment 2 2.1 Task Segment 3 10.1 Task Segment 4 2.3 Task Segment 5 2.2

[0261] S2. Preprocessing of measured overload data:

[0262] The actual flight data of a certain aircraft were measured in 10 takeoffs and landings. The flight data provided includes time, flight altitude, Y-axis overload at the center of gravity, left / right engine remaining fuel, flap deflection angle, and elevator deflection angle.

[0263] The measured overload data were standardized according to method S21 to obtain the standard quality of each task segment as shown in Table 3.

[0264] Table 3 Standard Quality for Each Task Segment

[0265] Task Segment 1 42500 Task Segment 2 42000 Task Segment 3 40000 Task Segment 4 35000 Task Segment 5 34500

[0266] Peak and valley value collection:

[0267] Before performing the counting statistics, peak and valley values ​​of the statistical parameters are detected according to S23 to obtain flight overload peak and valley value data pairs (Δn). y峰 ,Δn y谷 ) i .

[0268] S3, Statistics of the family of overload cumulative exceedance curves:

[0269] With Δn y =Starting at 0.05g, with intervals of 0.05g. Overload time history counting was performed on the standardized overload time history to obtain flight overload peak-valley data pairs (Δn). y峰 ,Δn y谷 ) i .

[0270] The cumulative exceedance counts were performed for both positive peak and negative trough overload values, taking flight duration into account, and the cumulative exceedance counts for each level of overload were standardized. Distribution parameters for each level of overload exceedance were estimated, and the results are as follows: Figure 4-5 As shown.

[0271] S4. The expected damage distribution of the simulated aircraft cluster, the damage distribution of mission profile 1 is as follows: Figure 6 As shown in Table 4, the parameters of the two-parameter Weibull distribution are as follows: Table 4 Two-parameter Weibull distribution parameters

[0272]

[0273] In this example, P is 99.9%, σ s Take 0.1. P L =91.1%.

[0274] The severe damage value is 1.36287. Damage inversion yields a corresponding up = 1.2943.

[0275] S5, the severe cumulative exceedance curves of task segments 1 and 3 are as follows: Figure 7 As shown.

[0276] The high-load and low-load cutoff values ​​for the cumulative overload exceeding number curves of severe gust overload and severe maneuver overload for each task segment are shown in Tables 5 and 6.

[0277] Table 5 High-load interception values ​​for Task Segment 3

[0278] Task Segment 3 0.98

[0279] Table 6 Low-load deletion values ​​for Task Segment 3

[0280] Task Segment 3 0.17

[0281] S6. Overload spectrum plotting

[0282] Based on the compilation principles, the number of flight types in mission profile 1 of the severe flight spectrum is given in Table 7. The flight spectrum of mission segment 2 under mission profile 1 is given in Tables 8-10.

[0283] Table 7. Number of Flight Types in Typical Flight Profiles (1000 Flights)

[0284]

[0285] Table 8 Task Profile 1 Task Segment 3 Gust Spectrum

[0286]

[0287] Table 9. Mission Profile 1, Mission Segment 3, Positive Maneuver Overload Spectrum (+)

[0288]

[0289] Table 10 Mission Profile 1, Mission Segment 3, Negative Maneuver Overload Spectrum (-)

[0290]

[0291]

[0292] Based on the above principles, a fatigue load spectrum (load time series) was compiled. The random spectrum for flight type A is shown below. Figure 8 .

[0293] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0294] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for compiling measured severe flight spectra based on overload exceedance number extrapolation, characterized in that, include: S1. Determine the aircraft mission profile and the composition, sequence, proportion, and parameters of each mission segment in the mission profile; S2. Separate the sudden wind overload and motor overload from the measured overload time history data, and collect peak and valley values; S3. Fit the overload time history data after processing S2 to obtain the measured cumulative overload and maneuver overload curve families for each task segment. Then, perform hierarchical discretization on the measured cumulative overload and maneuver overload curve families to obtain the distribution parameters for each task segment. S4. Simulate the expected family of overload and overload curves for sudden gusts and maneuvering overloads using the Monte Carlo method. Based on these family of curves, simulate the expected damage distribution of the aircraft fleet. Calculate the damage coverage corresponding to the severity spectrum based on the definition of safe life of the severity spectrum and the Monte Carlo method. Calculate the severity damage index corresponding to the damage coverage and, based on the severity damage index, obtain the overload coverage P under the specified overload. t ; S5. Based on the distribution parameters of each task segment, calculate the coverage rate P under each level of overload for each task segment. t The number of exceedances; take the coverage rate as P under each level of overload. t By fitting the exceedance number, we obtain the family of cumulative exceedance number curves for severe gust overload and cumulative exceedance number curves for maneuver overload under each mission segment; S6. Compile flight overload spectrum based on the family of cumulative exceedance number curves for severe gust overload and cumulative exceedance number curves for maneuver overload; S4 includes: S41. The expected family of surge overload exceedance number curves and family of maneuver overload exceedance number curves are simulated using the Montto Carlo method, and then discretized to obtain the time series (n) of surge / maneuver overload pairs under each level of overload. y,max n y,min ) i ; S42, Based on the time series (n) of sudden wind / maneuver overload y,max n y,min ) i Calculate the total overload damage D eq ; S43, Assuming total overload damage D eq Following a two-parameter Weibull distribution, the total damage D of each overload level is fitted. eqi and the corresponding empirical frequency value f i The two-parameter Weibull distribution parameters α and β are calculated. S44. Calculate the damage coverage rate P corresponding to the severe spectrum based on the average spectrum fleet safety life and the severe spectrum fleet safety life. L Specifically: S45: Calculate the damage coverage P based on the Weil distribution parameters α and β. L The corresponding severe injury index D eqPL Severe injury index D eqPL The calculation formula is: D eqPL =exp((ln(-ln(1-P L ))+ αlnβ) / α) Among them, the severe injury index D eqPL To make the proportion of the fleet P L The aircraft has reached the required service life. eq ; S46: Based on the severity injury index D eqPL The corresponding transcendence coverage P is obtained through inversion. t .

2. The method for compiling the measured severe flight spectrum based on overload exceedance number extrapolation according to claim 1, characterized in that, In S1, mission segment parameters include: mission segment altitude, flight speed, aircraft weight, flight altitude, and flight time; the criteria for dividing mission segments are: a) The criterion is that the flap deflection angle changes to 35° at the beginning of task segment 5; b) The end point of task segment 5 is determined by the flap deflection angle becoming 0°; c) The end point of task segment 2 is determined by the inflection point where the height curve flattens out; d) The end point of task segment 3 is determined by the inflection point of the descent curve; e) The end point of mission segment 4 is determined by the flap deflection angle becoming 35°; f) The end point of mission segment 5 is determined by the flap deflection angle becoming 0°.

3. The method for compiling the measured severe flight spectrum based on overload exceedance number extrapolation according to claim 1, characterized in that, S2 include: S21. Standardize the measured overload time history data according to the following formula; △n y0 =△n yi *Gi / G0 Where, △n y0 The overload value is the corrected value according to the standard task section specifications; △n yi The values ​​are measured overload values. Gi is the actual mass of the aircraft, and G0 is the standard mass of the aircraft under the current mission profile and mission segment. S22. Separate the motor overload and sudden wind overload in the standardized measured overload time history data according to the preset separation conditions; S23. Detect the peak and valley values ​​of the separated measured motor overload time history data and sudden wind overload time history data, respectively, filter out the data between the peak and valley values, and retain the peak and valley values.

4. The method for compiling the measured severe flight spectrum based on overload exceedance number extrapolation according to claim 3, characterized in that, S3 include: S31. Count the peak and valley values ​​of the sudden wind overload time history data: Take the 0g overload state of each task segment as the benchmark. When the valley value between two peaks or the peak value between two valleys does not exceed the upper or lower deviation of the benchmark of the task segment, only the maximum peak value or the maximum valley value is recorded. Otherwise, both peak values ​​or valley values ​​are recorded. S32. Use the rainflow counting method to count the peak and valley values ​​of the maneuver overload time history data that retains peak and valley values. S33. The average of the peak count and valley count in the sudden wind overload time history data is taken as the positive overload cumulative exceedance number under sudden wind overload; the peak count and valley count in the motor overload time history data are taken as the positive overload cumulative exceedance number and negative overload cumulative exceedance number, respectively. S34. Standardize the cumulative overload exceedance number under sudden wind overload and motor overload according to the following formula; Among them, t M For the actual flight time; t S N is the standard time for the current task segment; ib N represents the cumulative exceedance number of the i-th level load obtained from the measured flight time. Yi The cumulative exceedance number of the i-th level load over standard time; S35. For each task segment, the cumulative overload exceedance data pair (Δn) y , N Y ) i By performing a fitting operation, the cumulative overload exceedance number curve for each task segment is obtained; the fitting equation is: Where a and b are the coefficients of the fitted equation; S36. Discretize the overload cumulative exceedance number curve and calculate the exceedance number ΔN. i =N i -N i-1 Obtain exceedance number data pairs (Δn) corresponding to different flight overloads. y ,ΔN) i (i=1, ..., n); S37. Using a random variable model, assume a specified overload Δn. y The corresponding transcendental number ΔN follows a log-normal distribution; S38. Solve for the target distribution parameters μ and y using the likelihood function. The equation can be solved as follows: =0 Where n is the number of different overload levels, and μ is the logarithmic mean; is the logarithmic standard deviation.

5. The method for compiling measured severe flight spectra based on overload exceedance number extrapolation according to claim 4, characterized in that, S32 includes: S321. Select a typical segment starting from and ending at the maximum peak and valley values, and input the peak and valley values ​​in the order of the load spectrum until the data is complete. S322, Read in the next peak and valley values. If the data is complete, stop reading. S323: If the number of peak and valley data points is less than 3, return to S322; if the number of data points is greater than or equal to 3, calculate the ranges X and Y from the last three read-in peak and valley values; among the three data points, the absolute value of the difference between the first point and the second point is Y; the absolute value of the difference between the second point and the third point is X; S324: Compare the magnitudes of X and Y, if X<Y, return to S322, if X≥Y, execute S325; S325: Record the range Y as one cycle, delete the corresponding peak and valley values of Y, and return to S322.

6. The method for compiling the measured severe flight spectrum based on overload exceedance number extrapolation according to claim 5, characterized in that, S41 comprises: S411. Randomly select a random number u that follows a standard normal distribution. p ~N(0,1), calculate the i-th level overload Δn yi The corresponding transcendental number ΔN i : ΔN P,i = S412, regarding ΔN i Accumulated counting yields the cumulative transcendence number N. i = For data pairs (Δn) y , ) i By fitting the data, the Δn of a single takeoff and landing can be obtained. y Cumulative exceedance curve; S413: Repeat S411-S412 for all mission segments to obtain the single-machine expected gust overload exceedance curve and maneuver overload exceedance curve represented by this sampling; S414. Discretize the obtained family of overload exceedance number curves for sudden gusts and the family of overload exceedance number curves for maneuver, and obtain the exceedance number data pairs corresponding to each level of sudden gust exceedance. ) ij Data on overload corresponding to each level of maneuver overload (+) ) ij 、(- ) ij , where i represents discrete overload levels and j represents each task segment; S415, specify the tasks for each segment of the gust overload and maneuver overload respectively. The exceedance numbers are summed to obtain the data pairs of sudden wind overload and its corresponding total exceedance number. ) i And the data pairs of maneuver overload and its corresponding total exceedance number ( ) i and( ) i ; S416. For sudden gust load and maneuver load, sort the positive and negative load levels according to their absolute values ​​and match them to obtain the time series of sudden gust / maneuver load pairs (n). y,max n y,min ) i n y,max n represents the peak value of the load cycle. y,min This represents the load cycle valley value.

7. The method for compiling the measured severe flight spectrum based on overload exceedance number extrapolation according to claim 5, characterized in that, S42 comprises: S421, Based on the time series (n) of sudden wind / maneuver overload y,max n y,min ) i Calculate the equivalent damage for a single cycle and accumulate it to obtain the damage spectrum of sudden wind overload and the damage spectrum of maneuver overload; the calculation formula is: in, This is the equivalent load cyclic amplitude; n is the load cycle amplitude, and n is the load cycle peak value. y,max Subtract the load cycle valley value n y,min Divide by 2; m is the damage index; D represents the equivalent damage from a single cycle, and D represents the damage from the load spectrum. S422. Summing the damage spectrum of sudden wind overload and the damage spectrum of motor overload, we obtain the total overload damage D. eq .

8. The method for compiling the measured severe flight spectrum based on overload exceedance number extrapolation according to claim 5, characterized in that, S5 comprises: S51. Calculate the coverage rate P under each level of overload for each task segment. t The transcendental number is calculated using the following formula: ΔN Pt,i = in, The i-th level overload n y,i The corresponding log-median and standard deviation of the transcendental number, To represent the exceedance coverage rate P t The corresponding quantiles; S52. Determine the data pairs for each level of overload (Δn) y ΔN PL ) i The curves for the cumulative overload of severe sudden wind and the cumulative overload of severe maneuver are fitted to obtain the curves for the cumulative overload of severe maneuver for each task segment. The curves for the cumulative overload of severe maneuver include the curves for the cumulative overload of severe maneuver positive and the curves for the cumulative overload of severe maneuver negative. S53: Perform high-load interception and low-load truncation on the severe gust overload cumulative exceedance curve and severe maneuver overload cumulative exceedance curve of each mission segment.

9. The method for compiling the measured severe flight spectrum based on overload exceedance number extrapolation according to claim 1, characterized in that, S6 comprises: S61: Determine the load levels and corresponding number of cycles of positive overload and negative overload for each mission segment under each mission profile, wherein the number of cycles of positive overload and negative overload under each mission segment is the same; S62: Based on the 4-segment flight spectrum of flight / mission segment with the highest overload, assume that the highest overload in each 4-segment flight / mission segment follows a log-normal extreme value distribution, and determine the occurrence times of various types of flights; S63: Associate each level of overload and corresponding number of cycles per single take-off and landing for the k-th flight type of the i-th mission segment of the j-th mission profile, randomly and alternately select peak values and valley values for random pairwise arrangement, form a severe overload spectrum for the specific flight type of the corresponding mission segment, and obtain mission segment spectrums of various flight types under each mission segment; S64: Sort the mission segment spectrums under each mission profile according to the mission segment sequence under each mission profile, to obtain the mission profile spectrum under one complete flight; S65: Sort all mission profile spectrums under each complete flight, to obtain the final flight overload spectrum.

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