Severity spectrum compilation method based on severe damage extrapolation overload exceedance curve

Abstract

The present invention discloses a method for compiling a severity spectrum based on a severe damage extrapolated overload exceedance curve, comprising: S1. clarifying an aircraft mission profile; S2. preparing load data; S3. statistically processing the load data to obtain a family of overload cumulative exceedance curves for each mission segment; S4. obtaining a severe gust overload cumulative exceedance curve: using a Monte Carlo method to simulate the gust load time history of a fleet mission segment, and then calculating the damage distribution of the fleet mission segment to obtain the fleet reliability P. L The damage is taken as the severe damage of the mission segment; then based on the inversion of the severe damage of the mission segment, the coverage rate P of the exceedance number under the specified overload of the corresponding mission segment is obtained. t ; Extrapolate the coverage rate at each level of overload to P t The cumulative exceedance number is obtained by fitting the severe overload cumulative exceedance number curve; S5. Determine the 5-level load and 5×5 spectrum by the load spectrum equivalent criterion, and compile the severe gust overload spectrum; the present invention can reduce the time of fatigue testing, and the dispersion coefficient used in life determination only needs to consider the dispersion coefficient of the structure.

Description

Technical Field

[0001] The present invention relates to the technical field of flight load spectra, and more particularly to a method for compiling a severity spectrum based on a severe damage extrapolated overload exceedance curve. Background Art

[0002] A flight load spectrum is a spectrum compiled from the time history of loads experienced by an aircraft during flight. This spectrum is compiled through specialized test modifications and actual flight test measurements. It is used to determine and verify the aircraft's design service life and is a prerequisite for determining the life extension of aircraft structural fatigue. Gust loads contribute significantly to damage in flight, making the compilation of aircraft's measured gust spectra crucial for extending the life of both military and civilian aircraft. The aviation industry has accumulated a wealth of measurement data, resulting in the development of gust spectrum compilation methods based on gust velocity exceedance curves, including the European "TWIST" method and Boeing's "5×5" spectrum method. However, these methods generally only compile average gust intensity spectra and fail to reflect the dispersion of gusts.

[0003] Even for aircraft operating under the same requirements, the gust load-time histories of different aircraft within a fleet can vary significantly, corresponding to the dispersion of the load spectrum. Durability analysis and testing must be conducted under a defined (unique) load spectrum. Therefore, selecting and compiling a reasonable load spectrum is crucial for structural durability analysis and assessment. To this end, the concept of "severity spectrum" has been proposed. Severity spectrums have the advantages of revealing the failure characteristics of the aircraft itself and reducing testing time. Consequently, they are increasingly used in the compilation of measured load spectra.

[0004] Therefore, how to propose a method for compiling aircraft severe gust measured spectra while fully considering gust dispersion while ensuring safety and economy is an urgent problem that technicians in this field need to solve. Summary of the Invention

[0005] In view of this, the present invention provides a severe spectrum compilation method based on the severe damage extrapolation overload exceedance curve

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The method for compiling a severity spectrum based on the severe damage extrapolated overload exceedance curve includes the following steps:

[0008] S1. Define the aircraft mission profile: According to the aircraft's operating requirements, provide the composition and proportion of the mission profile, and for each mission profile, define the mission profile parameters;

[0009] S2. Payload data preparation: Preprocess the measured payload data obtained based on the mission profile to obtain payload data;

[0010] S3. Statistical processing of load data: Obtain the family of measured gust overload cumulative exceedance curves for the mission segment, and estimate the exceedance distribution parameters under the specified overload using the maximum likelihood estimation function;

[0011] S4. Obtain the severe gust overload cumulative exceedance curve:

[0012] S41. Randomly extract random numbers that obey the standard normal distribution and use them as the coverage of the exceedance number to obtain the exceedance number data pairs (±Δn y* , ΔN) i ; And sort the positive overloads and negative overloads of gust overloads by their absolute values, and match them to obtain the time series of gust overload (n y,max , n y,min ) i ; Where i is the overload level;

[0013] S42. According to the gust overload, the time series (n y,max , n y,min ) i Calculate the equivalent damage of a single cycle and accumulate the load spectrum damage D of the mission segment eq ; where D eqi is the load spectrum damage corresponding to the i-th sample data;

[0014] S43. Damage D according to the load spectrum of the mission segment eq Obey the lognormal distribution, fitting D eqi And the corresponding empirical frequency value f i Calculate the lognormal distribution parameters: log mean μ and log standard deviation σ; where D eqi is the load spectrum damage corresponding to the i-th sample data;

[0015] S44. Obtain the load spectrum reliability P of each task segment L and the transcendental coverage ratio P t For each level of load in each task segment, according to the distribution function and distribution parameters in S3, the extrapolated coverage rate is P t The transcendental number of

[0016]

[0017] Among them, μ i , σ i are respectively the i-th level overload n y,i the logarithmic median and standard deviation of the corresponding transcendental numbers;

[0018] In the obtained (Δn y , ΔN Pt ) iAfter the data are matched, the cumulative exceedance curve of severe gust overload corresponding to each mission segment is obtained by fitting;

[0019] S5. Determine the load level 5 using the load spectrum equivalence criterion and compile a severe gust overload spectrum using the square root of 5×5: Compile a severe mission segment spectrum for typical mission segments in the mission profile; construct a severe mission profile spectrum from the mission segments; and compile a severe flight-continuation-flight spectrum with a takeoff and landing cycle by randomly sorting the severe mission profile spectrum in proportion; where a is preset based on the total life of the aircraft.

[0020] The safety life of the fleet determined by the severe spectrum and the average spectrum is the same. The difference is that the dispersion coefficient of the former only considers the dispersion of the structure, while the dispersion coefficient of the latter considers both the dispersion of the load and the dispersion of the structure. In S44, the load spectrum reliability P L The calculation method is:

[0021] The corresponding fleet safety life under the average spectrum is:

[0022]

[0023] Where σ0 is the standard deviation of the fleet life; P is the fleet life reliability requirement; μ0 is the logarithmic mean life of the fleet; μ P is the normal distribution bias coefficient for the specified reliability P;

[0024] The corresponding safe life under the severe spectrum is:

[0025]

[0026] Among them, P L is the load spectrum reliability corresponding to the severe spectrum, Ps is the reliability corresponding to the structural dispersion

[0027] The reliability P is specified L , the normal distribution bias coefficient of Ps;

[0028] σ L is the load standard deviation, σ s is the structural standard deviation;

[0029] because

[0030] N P =N P，S

[0031] Obtain the load spectrum reliability P corresponding to the determined severity spectrum L :

[0032]

[0033] In this embodiment, P is set to 99.9%, σ0 is set to 0.15, and σ s Take 0.1, σ L Equal to the load spectrum D of the task segment eq The logarithmic standard deviation of , in other embodiments, can be replaced with other values ​​with reference to the definition of the parameter.

[0034] Calculate the transcendental coverage P t The method is:

[0035] Load spectrum reliability P L Corresponding severe injury D eqpl for:

[0036]

[0037] The current severe damage D is obtained by inversion eqpl The corresponding u pt And the corresponding transcendental coverage P t , so that the overload cycle damage under the exceedance coverage is equal to the current severe damage D eqpl , where u pt corresponds to the transcendental coverage P t Quantile of the standard normal distribution.

[0038] Preferably, the specific content of S1 includes:

[0039] Determine the aircraft's mission profile, mission profile ratio, and mission profile composition, and provide mission profile parameters, where the mission profile parameters include: mission segment, mission segment altitude, speed, weight, flight distance, and flight time; and divide the measured data mission segments according to the following principles:

[0040] a) The criterion for starting the climb is when the flap angle reaches 0°;

[0041] b) The end point of climb / beginning of level flight is the turning point where the altitude curve turns level;

[0042] c) The end point of level flight / start point of descent is determined by the turning point of the descending altitude curve;

[0043] d) The descent end point is determined by the flap angle becoming 35°.

[0044] Preferably, the specific contents of preprocessing the measured load data in S2 include:

[0045] (1) Overload data n y Standardization of

[0046] △n y0 =△n yi *Gi / G0

[0047] Where, △n y0 is the overload value after correction according to the standard mission section; △n yi is the measured overload value, G i is the actual mass of the aircraft corresponding to the measured overload value, G0 is the standard aircraft mass of this mission segment of the flight profile;

[0048] (2) Separation of gusts and maneuvering overloads

[0049] The maneuvering and gust loads contained in the measured load data are separated, and the interpretation conditions of the maneuvering and gust load spectra in airborne flight are:

[0050] a) If the overload changes slowly and lasts for more than 2 seconds, it is considered a maneuver; otherwise, it is considered a gust;

[0051] b) An overload with an absolute value of the rudder deflection angle equal to or greater than 4° is considered a maneuvering overload, otherwise it is considered a gust overload;

[0052] c) For the climb and descent missions, since there are fewer maneuvers, the loads are approximately considered to be gust loads, and the separation of gust and maneuver is no longer performed;

[0053] (3) Peak and valley value collection

[0054] During the counting process, all peaks and valleys are detected and expressed in sequence as Δn zi , filter out the data between the peak and valley values, while retaining the corresponding sampling point numbers, if:

[0055]

[0056] The peak or valley value is taken once.

[0057] Preferably, S3 also includes:

[0058] (1) Overload time history counting adopts the limited span average peak counting method;

[0059] Taking the 1g load state of each mission segment as the benchmark, when the valley value between two peaks or the peak value between two valleys does not exceed the deviation of the baseline of the mission segment:

[0060] For positive overload, the deviation is the upper deviation, and only the maximum peak value is recorded;

[0061] For negative overload, the deviation is the lower deviation, and only the minimum valley value is recorded;

[0062] The absolute values ​​of the upper deviation and the lower deviation are both 20% of the maximum peak value;

[0063] (2) Obtain the cumulative exceedance number of each level of load;

[0064] The peak value obtained by the restricted span average peak counting method is greater than 0 as a positive peak value, and the valley value is less than 0 as a negative valley value; select several load levels, record the positive gust overload cumulative exceedance number and the negative gust overload cumulative exceedance number for the positive peak value and the negative valley value, respectively, and record them as N(+Δn y ) and N(-Δn y );

[0065] Obtain ±Δn y The family of cumulative transcendental curves of :

[0066]

[0067] (3) Normalization of the cumulative overload exceedance considering the flight duration;

[0068] The flight time corresponding to the measured data is t M , the standard time of the task segment is t S , the cumulative exceedance number N of the i-th level load obtained during the measured time ib , then N ib The standardization rules are:

[0069]

[0070] where N i is the cumulative exceedance number of the i-th level load at the standard time;

[0071] The cumulative exceedance of each level of load is standardized to obtain the cumulative exceedance data of the standard time mission segment (Δn y ,N) i , and fitting is performed to obtain the overload cumulative transcendental number curve family of this task segment. The fitting equation is as follows:

[0072] Δn y =a*lgN+b

[0073] The overload-cumulative exceedance curve is discretized into (Δn y ,N) i (i=1,...,n) data pairs, calculate the exceedance number ΔN from the cumulative exceedance number N, and obtain the exceedance number data pairs (Δn y ,ΔN) i , where the transcendental number ΔN i The calculation method is

[0074] ΔN i =N i -N i-1

[0075] ΔN i Represents a transcendental number.

[0076] Preferably, the distribution parameter estimation method in S3 is:

[0077] The maximum likelihood estimation method is used to estimate the distribution parameters; the likelihood function is:

[0078]

[0079] Where x i =lgΔN i ;

[0080] According to the likelihood function, the derivatives of μ and σ are zero to solve the target parameters (μ, σ)

[0081]

[0082]

[0083] Preferably, the specific content of S41 includes:

[0084] 1) Randomly draw a random number u that follows a standard normal distribution p ,u p Refers to the quantile of the standard normal distribution corresponding to the coverage rate P, the i-th level gust overload Δn yi The corresponding coverage is the transcendental number ΔN of P P,i for:

[0085]

[0086] 2) Obtain the exceedance data pairs (±Δn y* , ΔN) i , where i is the level of overload;

[0087] 3) For gust overload, sort the positive overloads and negative overloads at all levels by their absolute values ​​and match them to obtain the gust overload time series (n y,max , n y,min ) i .

[0088] Preferably, the specific content of S42 includes:

[0089] According to the compiled gust overload time series (n y,max , n y,min ) i , calculate the equivalent damage of a single cycle, and accumulate the load spectrum damage D eq , the specific method is:

[0090]

[0091] Where m is the damage index, i.e. the slope of the material / structure fatigue SN curve under stress ratio R = 0, d eq Refers to damage to a single cycle.

[0092] Preferably, S4 further includes: S45. performing high-load interception and low-load interception on the severe cumulative exceedance curve of the task segment;

[0093] (1) High load interception

[0094] Determine the level and number of high loads in the severe gust spectrum, and select the high load at the preset frequency as the cutoff value;

[0095] (2) Low load cutoff

[0096] The small loads circulating in the severe gust spectrum are deleted or converted into a certain level of load with equal damage.

[0097] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method for compiling a severe spectrum based on a severe damage extrapolated overload exceedance curve, wherein a method for determining the severe damage reliability PL of a cluster is first disclosed, and the Monte Carlo method is used to simulate the gust load-time distribution of the cluster, and then the damage distribution of the cluster is calculated, and the damage of the cluster whose reliability is PL is taken as severe damage, and then the coverage rate Pt of the exceedance under the corresponding specified overload is obtained based on the inversion of the damage, and the cumulative exceedance rate of Pt under each level of overload is extrapolated, and the severe overload cumulative exceedance curve is obtained by fitting, and then the severe gust spectrum is compiled through the curve. This method of compiling severe spectra can reduce the time of fatigue testing, and the dispersion coefficient used in life determination only needs to consider the dispersion coefficient of the structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0099] Figure 1 A schematic flow chart of a method for compiling a severe spectrum based on a severe damage extrapolated overload exceedance curve provided by the present invention;

[0100] Figure 2 A schematic diagram of peak-valley value detection in the severe spectrum compilation method based on the severe damage extrapolated overload exceedance curve provided by the present invention;

[0101] Figure 3 A schematic diagram of limiting span-average peak counts in the severe spectrum compilation method based on the severe damage extrapolated overload exceedance curve provided by the present invention;

[0102] Figure 4 A schematic diagram of representing a full score as a sequence of task profile spectra provided by the present invention;

[0103] Figure 5 A schematic diagram of μ fitting results in the estimation of the exceedance number distribution parameters under a specified overload in the severe spectrum compilation method based on the severe damage extrapolated overload exceedance number curve provided by the present invention;

[0104] Figure 6 A schematic diagram of the σ fitting results in the estimation of the exceedance number distribution parameters under a specified overload in the severe spectrum compilation method based on the severe damage extrapolated overload exceedance number curve provided by the present invention;

[0105] Figure 7 A schematic diagram of the damage distribution of a cluster of machines in the severe spectrum compilation method based on the severe damage extrapolated overload exceedance curve provided by the present invention;

[0106] Figure 8 A schematic diagram of a severe gust overload cumulative exceedance curve in the severe spectrum compilation method based on the severe damage extrapolated overload exceedance curve provided by the present invention;

[0107] Figure 9 This is the random spectrum of flight type A in the severe spectrum compilation method based on the severe damage extrapolated overload exceedance number curve provided by the present invention. DETAILED DESCRIPTION

[0108] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0109] The embodiment of the present invention discloses a method for compiling a severe spectrum based on a severe damage extrapolated overload exceedance curve, such as Figure 1 As shown, the following steps are included:

[0110] S1. Define the aircraft mission profile: According to the aircraft's operating requirements, provide the composition and proportion of the mission profile, and for each mission profile, define the mission profile parameters;

[0111] S2. Payload data preparation: Preprocess the measured payload data obtained based on the mission profile to obtain payload data;

[0112] S3. Statistical processing of load data: Estimate distribution parameters based on the maximum likelihood estimation function to obtain a family of cumulative exceedance curves of measured gust overloads based on the mission segment;

[0113] S4. Obtain the severe gust overload cumulative exceedance curve:

[0114] S41. Randomly extract random numbers that obey the standard normal distribution and use them as the coverage of the exceedance number to obtain the exceedance number data pairs (±Δn y* , ΔN) i ; And sort the positive overloads and negative overloads of gust overloads by their absolute values, and match them to obtain the time series of gust overload (n y,max , n y,min ) i ; Where i is the overload level;

[0115] S42. According to the gust overload, the time series (n y,max , n y,min ) i Calculate the equivalent damage of a single cycle and accumulate the load spectrum damage D of the mission segment eq ;

[0116] S43. Damage D according to the load spectrum of the mission segment eq Obey the lognormal distribution, fitting D eqi And the corresponding empirical frequency value f i The lognormal distribution parameters are calculated: logarithmic mean μ, logarithmic standard deviation σ; where D eqi is the load spectrum damage corresponding to the i-th sample data;

[0117] S44. Obtain load spectrum reliability P L and the transcendental coverage ratio P t For each level of load in each task segment, according to the distribution function and distribution parameters in S3, the extrapolated coverage rate is P t The transcendental number of

[0118]

[0119] Among them, μ i , σ i are respectively the i-th level overload n y,i the logarithmic median and standard deviation of the corresponding transcendental numbers;

[0120] In the obtained (Δn y , ΔN Pt ) i After the data are matched, the cumulative exceedance curve of severe gust overload corresponding to each mission segment is obtained by fitting;

[0121] S5. Determine the load level 5 using the load spectrum equivalent criterion, and compile a severe gust overload spectrum using the square root of 5×5: Compile a severe mission segment spectrum for typical mission segments in the mission profile; construct a severe mission profile spectrum from the mission segments; and compile a severe flight-continuation-flight spectrum with a takeoff and landing cycle by randomly sorting the severe mission profile spectrum in proportion; where a is preset based on the total life of the aircraft; in this embodiment, a is set to;

[0122] In S44, the load spectrum reliability P L The calculation method is:

[0123] The corresponding fleet safety life under the average spectrum is:

[0124]

[0125] Where σ0 is the standard deviation of fleet life; in this example,

[0126]

[0127] σ L is the load standard deviation, σ s is the structural standard deviation; P is the reliability requirement of the fleet life;

[0128] μ0 is the logarithmic mean life span of the fleet; μ P is the normal distribution bias coefficient for the specified reliability P;

[0129] The corresponding safe life under the severe spectrum is:

[0130]

[0131] Among them, P L is the load spectrum reliability corresponding to the severe spectrum, Ps is the reliability corresponding to the structural dispersion

[0132] The reliability P is specified L , the normal distribution bias coefficient of Ps;

[0133] σ L is the load standard deviation, σ s is the structural standard deviation;

[0134] because

[0135] N P =N P，S

[0136] Obtain the load spectrum reliability P corresponding to the determined severity spectrum L :

[0137]

[0138] Calculate the transcendental coverage P t The method is:

[0139] Load spectrum reliability P L Corresponding severe injury D eqpl for:

[0140]

[0141] The current severe damage D is obtained by inversion eqpl The corresponding u pt And the corresponding transcendental coverage P t , so that the overload cycle damage under the exceedance coverage is equal to the current severe damage D eqpl , where u pt corresponds to the transcendental coverage P t Quantile of the standard normal distribution.

[0142] It should be noted that:

[0143] The severity spectrum should meet the requirements of GJB 67.6A-2008 "Military Aircraft Strength Specification: Repeated Loads, Durability and Damage Tolerance" on the durability severity spectrum. According to the standard, the durability severity spectrum should reflect 90% of the fleet's usage. However, the 90% reliability has no theoretical support and is more derived from engineering experience without any supporting mathematical basis. This paper proposes a method to determine the severe damage reliability P of a fleet. L The Monte Carlo method is used to simulate the gust overload-time distribution of the cluster, and then the damage distribution of the cluster is calculated. The reliability of the cluster is taken as P L The damage is regarded as severe damage. Then, based on the inversion of the damage, the coverage ratio P of the exceedance number under the corresponding specified overload is obtained. t The coverage rate under each level of overload is extrapolated to P t The core of the durability severe gust spectrum compilation lies in how to determine the severe gust overload cumulative exceedance curve based on the mission segment.

[0144] S43 assumes damage D eq Obeying the lognormal distribution, the rank statistics method is used to calculate the distribution parameters, as shown in the following formula, fitting D eqi And the corresponding empirical frequency value f i The lognormal distribution parameters are calculated: log mean μ and log standard deviation σ.

[0145] lgD eqi =μ i +u p σ i

[0146] In order to further implement the above technical solutions, the specific contents of S1 include:

[0147] Determine the aircraft's mission profile, mission profile ratio, and mission profile composition, and provide mission profile parameters, where the mission profile parameters include: mission segment, mission segment altitude, speed, weight, flight distance, and flight time; and divide the measured data mission segments according to the following principles:

[0148] a) The criterion for starting the climb is when the flap angle reaches 0°;

[0149] b) The end point of climb / beginning of level flight is the turning point where the altitude curve turns level;

[0150] c) The end point of level flight / start point of descent is determined by the turning point of the descending altitude curve;

[0151] d) The descent end point is determined by the flap angle becoming 35°.

[0152] It should be noted that:

[0153] The overload spectrum for the main mission segment of the medium-altitude flight profile is compiled using a "5×5" spectrum. First, the overload spectrum is discretized into five levels, and then the discrete spectrum is compiled into load spectra for five typical flight types with varying degrees of severity. There are five typical flight types in total.

[0154] In order to further implement the above technical solution, the specific contents of preprocessing the measured load data in S2 include:

[0155] (1) Overload data n y Standardization of

[0156] △n y0 =△n yi *Gi / G0

[0157] Where, △n y0 is the overload value after correction according to the standard mission section; △n yi is the measured overload value, G i is the actual mass of the aircraft corresponding to the measured overload value, G0 is the standard aircraft mass of the mission segment of the flight profile; the measured center of gravity overload n y Subtract 1 to get the incremental overload Δn at that moment y .

[0158] It should be noted that:

[0159] The actual mass data of the load is calculated by subtracting the fuel consumption from the aircraft weight. The fuel consumption is obtained by total average calculation (the time for calculating the average fuel consumption is from the start of takeoff taxiing to the end of landing impact).

[0160] (2) Separation of gusts and maneuvering overloads

[0161] The maneuvering and gust loads contained in the measured load data are separated, and the interpretation conditions of the maneuvering and gust load spectra in airborne flight are:

[0162] a) If the overload changes slowly and lasts for more than 2 seconds, it is considered a maneuver; otherwise, it is considered a gust;

[0163] b) An overload with an absolute value of the rudder deflection angle equal to or greater than 4° is considered a maneuvering overload, otherwise it is considered a gust overload;

[0164] c) For the climb and descent missions, since there are fewer maneuvers, the loads are approximately considered to be gust loads, and the separation of gust and maneuver is no longer performed;

[0165] (3) Peak and valley value collection

[0166] During the counting process, all peaks and valleys are detected and expressed in sequence as Δn zi , filter out the data between the peak and valley values, while retaining the corresponding sampling point numbers, if:

[0167]

[0168] The peak or valley value is taken once.

[0169] In this example, if 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.

[0170] To further implement the above technical solution, S3 also includes:

[0171] (1) Overload time history counting adopts the limited span average peak counting method;

[0172] The restricted span peak counting method is a counting method formed by adding certain restriction conditions on the basis of the peak method. Figure 3 The specific processing requirements are as follows: Taking the 1g load 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 deviation of the baseline of the task segment:

[0173] For positive overload, the deviation is the upper deviation, and only the maximum peak value is recorded;

[0174] For negative overload, the deviation is the lower deviation, and only the minimum valley value is recorded;

[0175] The absolute values ​​of the upper deviation and the lower deviation are both 20% of the maximum peak value;

[0176] (2) Obtain the cumulative exceedance number of each level of load;

[0177] The peak value obtained by the restricted span average peak counting method is greater than 0 as a positive peak value, and the valley value is less than 0 as a negative valley value; select several load levels, record the positive gust overload cumulative exceedance number and the negative gust overload cumulative exceedance number for the positive peak value and the negative valley value, respectively, and record them as N(+Δn y ) and N(-Δn y );

[0178] The discrete gust model assumes that for gust disturbances of the same intensity (i.e., the same amplitude), the probability of occurrence of positive (upward) and negative (downward) gust disturbances is equal. Therefore, theoretically, the measured positive and negative gust overload cumulative exceedance curves should be symmetrical. However, in practice, there are often certain differences between the positive and negative gust cumulative exceedance curves. In engineering, the geometric mean is used to obtain a symmetrical average of the cumulative exceedance corresponding to the gust overload. Obtain ±Δn y The cumulative transcendental number curve family of :

[0179]

[0180] (3) Normalization of the cumulative overload exceedance considering the flight duration;

[0181] Since the load spectrum measured is the mission segment flight time and the mission segment standard time in the mission profile, it is necessary to convert the measured mission segment overload cumulative exceedance data into the cumulative exceedance data of the standard time. The flight time corresponding to the measured data is t M , the standard time of the task segment is t S , the cumulative exceedance number N of the i-th level load obtained during the measured time ib , then N ib The standardization rules are:

[0182]

[0183] where N i is the cumulative exceedance number of the i-th level load at the standard time;

[0184] The cumulative exceedance of each level of load is standardized to obtain the cumulative exceedance data of the standard time mission segment (Δn y ,N) i , and fitting is performed to obtain the overload cumulative transcendental number curve family of this task segment. The fitting equation is as follows:

[0185] Δn y =a*lgN+b

[0186] The overload-cumulative exceedance curve is discretized into (Δn y ,N) i (i=1,...,n) data pairs, calculate the exceedance number ΔN from the cumulative exceedance number N, and obtain the exceedance number data pairs (Δn y ,ΔN) i , where the transcendental number ΔN i The calculation method is

[0187] ΔN i =N i -N i-1

[0188] ΔN i Represents a transcendental number.

[0189] It should be noted that:

[0190] It also includes a test of the distribution characteristics of transcendental numbers: a random variable model is usually used, assuming that the transcendental number ΔN corresponding to the specified Δny obeys a log-normal distribution.

[0191] In order to further implement the above technical solution, the distribution parameter estimation method in S3 is:

[0192] The maximum likelihood estimation method is used to estimate the distribution parameters; the likelihood function is:

[0193]

[0194] Where x i =lgΔN i ;

[0195] According to the likelihood function, the derivatives of μ and σ are zero to solve the target parameters (μ, σ)

[0196]

[0197]

[0198] In order to further implement the above technical solution, the specific contents of S41 include:

[0199] 1) Randomly draw a random number u that follows a standard normal distribution p ,,u p Refers to the quantile of the standard normal distribution corresponding to the coverage rate P, the i-th level gust overload Δn yi The corresponding coverage is the transcendental number ΔN of P P,i for:

[0200]

[0201] 2) Obtain the exceedance data pairs (±Δn y* , ΔN) i , where i is the level of overload; the discretization process is: calculate the transcendental number ΔN i =N i -N i-1 , obtain the exceedance number data pairs corresponding to different gust overloads (Δn y , ΔN) i (i=1,…,n).

[0202] 3) For gust overload, sort the positive overloads and negative overloads at all levels by their absolute values ​​and match them to obtain the gust overload time series (n y,max , n y,min ) i ;

[0203] In order to further implement the above technical solution, the Δn of the task segment obtained by fitting in 2) y The specific method of transcendental curve is:

[0204] For each task segment, the overload cumulative exceedance data pair (Δn y ,N) i Perform fitting, the fitting equation is:

[0205] Δn y =a*lgN+b

[0206] The overload cumulative exceedance curve of each task segment is calculated.

[0207] In order to further implement the above technical solution, the specific contents of S42 include:

[0208] According to the compiled gust overload time series (n y,max , n y,min ) i , calculate the equivalent damage of a single cycle, and accumulate the load spectrum damage D eq , the specific method is:

[0209]

[0210] Where m is the damage index, i.e. the slope of the material / structure fatigue SN curve under stress ratio R = 0, d eq Refers to the damage of a single cycle. For aluminum alloy materials, R can be approximately taken as 4.

[0211] It should be noted that:

[0212] The load damage is calculated using the Odin transform + linear cumulative damage method. In this embodiment, the overload spectrum is taken as an example.

[0213] In order to further implement the above technical solution, S4 also includes: S45. performing high-load interception and low-load interception on the severe cumulative exceedance curve of the task segment;

[0214] (1) High load interception

[0215] Determine the level and number of high loads in the severe gust spectrum, and select the high load at the preset frequency as the cutoff value;

[0216] (2) Low load cutoff

[0217] The small loads circulating in the severe gust spectrum are deleted or converted into a certain level of load with equal damage.

[0218] It should be noted that:

[0219] High load interception: High loads in the load spectrum have two effects: on the one hand, they increase fatigue damage, so it is necessary to appropriately add high loads to the spectrum according to the load spectrum; on the other hand, the coupling effect of more high loads and low loads will increase the life. Therefore, it is necessary to determine the level and number of high loads in the spectrum; usually, the high load that appears once in 1000 flights in this mission segment is used as the interception value;

[0220] Underload truncation: The spectrum often contains a large number of small load cycles, which need to be deleted or converted to a certain load level. The fatigue limit of the critical parts is usually determined based on the above analysis, and the overload value corresponding to 70% to 80% of the fatigue limit is truncation.

[0221] The following will further explain the specific process of determining the level 5 load based on the load spectrum equivalent criterion and compiling the severe gust overload spectrum using the square root of 5×5:

[0222] 1. Determination of loads at all levels

[0223] (1) Determination of load levels based on mission profile

[0224] Each mission segment of the mid-altitude flight is discretized into five levels. The number of discrete loads in a program block is an integer, and each mission segment must fly at least once. The representative value of each load level (i.e., equivalent load) is determined by converting the damage of the discrete segment.

[0225] (2) Determination of load parameters at each level of the 5×5 spectrum

[0226] Based on the cumulative exceedance curve of severe gust overload in the mission segment, the load spectrum equivalent calculation is performed using the method in the "Civil Aircraft Structural Durability and Damage Tolerance Design Manual Volume 1". Calculation of equivalent load: Assume that the equivalent load of a discrete segment to be calculated is Δn yd, the equivalent load cycle number is N eq , m straight lines are used to replace the curve of the discrete segment, and the linear equation of the i-th segment is:

[0227] Δg=a i lgN+b i

[0228] Where a i 、b i is the constant of the load spectrum curve in the i-th section.

[0229] Then the equivalent load is:

[0230]

[0231] Where:

[0232]

[0233] S is the slope parameter of the material SN curve, and for aluminum alloy, S=2.0.

[0234] Equivalent load cycles

[0235]

[0236] 2. Determination of flight type

[0237] The load spectra of the medium altitude flight mission profile are compiled according to five different flight types. The principle for determining the typical flight type of medium altitude flight is: taking the gust spectrum of the flight (glide) segment with the highest load as the benchmark, and assuming that the highest load in each flight (glide) segment is a normal logarithmic extreme value distribution, determine the number of occurrences of each type of flight (determine y i ); Based on the assumption that the gust spectra of various flight types are similar in shape, the gust incremental overload spectra of various types of flights under 1000 flights are compiled (to determine B ij ); as shown in Table 1.

[0238] Table 1 5×5 spectrum

[0239]

[0240] In the table, y1+y2+y3+y4+y5 should equal the number of times the profile appears in 1000 flights. Use the above method to compile 5×5 spectra for all mission segments under all profiles. For each mission segment under each mission profile, y1, y2, y3, y4, and y5 are identical in the 5×5 spectra.

[0241] 3. Compilation of severe gust overload spectrum

[0242] (1) Compile task segment spectrum

[0243] According to the data in the 5×5 spectrum compiled in 4.4.2, the loads of each level and their corresponding frequencies under the single takeoff and landing of the kth flight type in the i-th mission segment of the j-th profile are linked together, and the peaks and valleys are randomly selected alternately (i.e., the peaks and valleys are random) and randomly arranged in pairs to form the load spectrum of the specific flight type of the mission segment, which is recorded as (Δn ydn , -Δn ydm ) h , and the task segment spectrum (a total of j×i×k) is expressed as the following load pair sequence

[0244]

[0245] Where Δn ydn is the nth (n=1, 2, 3, 4, 5) level load of the task segment, representing the peak load; -Δn ydm The mth (m = 1, 2, 3, 4, 5) load for this mission segment represents the valley load. The total number of load pairs for each mission segment of each flight type should be equal to the total number of cycles per flight in the table above. Using this method, a spectrum (load pair sequence) is compiled for each flight type under all mission segments.

[0246] (2) Compilation of mission profiles

[0247] The load spectrum of the kth flight type of the jth profile is F j,k . F j,k It can be expressed as a load pair sequence f i,j,k Sort by the order of the task segments i (i = 1, 2, 3 ... m) under the section, i.e. f 1,j,k ,f 2,j,k ,f 3,j,k ,f 4,j,k ...f m,j,k In this way, the mission profile spectrum of a complete flight is obtained. According to the above method, the mission profile spectrum of various flight types under all mission profiles is compiled. All types of mission profile spectra are expressed in vector form as

[0248] 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 ]

[0249] For example, there are three mission segments under the hollow profile, and each mission segment has five flight types. Therefore, for the hollow profile, a total of five mission profile spectra need to be compiled, representing five flight types. They are denoted as A1, B1, C1, D1, and E1. When compiling the A1 spectrum, the spectra of flight type A under each mission segment are arranged, that is, f 1,j,k ,f 2,j,k ,f 3,j,k , we can get the A1 spectrum, and the same is true for other types of mission profile spectra.

[0250] (3) Compilation of fly-continue-fly charts

[0251] Record the number of times y appears in the jth profile (j=1,2...L) and the kth flight type (k=1,2...K) under 1000 flights. j,k times. j,k Expressed in vector form as

[0252] 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 ]

[0253] Where A is the vector of the number of times each mission profile appears.

[0254] The A vector and the B vector are in a one-to-one correspondence, indicating how many times the task profile spectrum appears in this 1000-time block spectrum. The total spectrum can be expressed as a task profile spectrum sequence G, such as Figure 4 shown.

[0255] By reordering the mission profile spectrum of each complete flight in G, the final flight-continuation-flight spectrum can be obtained.

[0256] The specific method is as follows: first, all the mission profiles of G are numbered in sequence with natural numbers, and then the numbers are compared with the random integer sequence "x is "Correspondingly, press x is Sort by the numerical size (for example, the corresponding x is is 5, then the corresponding mission profile spectrum is placed at the 5th position) thus obtaining the final load spectrum.

[0257] (4) Random sorting method

[0258] The random sequence "x" of randomly alternately selecting peaks, valleys and total load spectrum G mentioned above is iThe selection of " is realized by the pseudo-random method of multiplication congruential method, and reasonable random results are given by adjusting the random parameters.

[0259] Multiplication by congruence method:

[0260] y i+1 =λy i (mod2 k ) (1)

[0261] x i =y i / 2 m (2)

[0262] First, form "y i "Sequence, and then the random sequence "x" is obtained by formula (2) i Calculate x i In the order of the total number column from smallest to largest, x is , so we get the random integer sequence "x is ”.

[0263] Regarding the parameter values ​​in equations (1) and (2), the initial value y1 is an odd number; the coefficient λ = 8t-3, t is an arbitrary natural number, and k is determined according to the random period "n" required, so that 2 k-2 ≥n; m is 2. By changing the size of y1, t, k, different random number sequences can be obtained.

[0264] The method of compiling the average gust spectrum will be further explained below:

[0265] 1. Determination of the average cumulative exceedance curve

[0266] For each load level of each mission segment, the exceedance number for 50% coverage is calculated, that is,

[0267]

[0268] In the obtained (Δn y , ΔN 50 ) i After the data is matched, ΔN 50i The average gust overload cumulative exceedance curve corresponding to each task segment is obtained by accumulation.

[0269] 2. Compilation of average gust spectrum

[0270] According to the above method, the average gust spectrum is compiled.

[0271] The present invention will be further described below based on examples:

[0272] 1. Mission Profile

[0273] A certain type of aircraft has a common mission profile, which is medium-altitude flight. The unit is organized into 1,000 takeoffs and landings.

[0274] Table 2 Task sections of the hollow section

[0275] Task segment Time (min) Climb 2.1 Flying in the air 10.1 Downward 2.3

[0276] 2. Load spectrum measured data

[0277] The flight data of 10 takeoffs and landings of the X-plane were measured. The flight data provided included time, flight altitude, y-axis load at the center of gravity, remaining fuel in the left and right engines, flap angle, and elevator angle.

[0278] 3. Preprocessing of measured load data

[0279] (1) Overload data n y Standardization of

[0280] The standard aircraft mass data for the mission phase is shown in the table below. The actual payload mass is calculated by subtracting fuel consumption from the aircraft weight. Fuel consumption is calculated as the average fuel consumption (the average fuel consumption is calculated from the start of takeoff taxi to the end of landing impact). The Y-axis overload data at the aircraft's center of gravity is normalized.

[0281] Table 3 Standard quality of each task segment

[0282] Task segment Total mass (kg) Climb 22500 Flying in the air 20000 Downward 14500

[0283] (2) Peak and valley value collection

[0284] Before counting statistics, perform peak-valley value detection on statistical parameters according to the method in Section 4.2.3 to obtain the gust overload peak-valley value data pairs (Δn y峰 , Δn y谷 ) i .

[0285] 4. Statistical analysis of the family of curves of severe cumulative exceedances

[0286] (1) Overload time history counting

[0287] With Δn y =0.05g, and the interval is 0.05g. The standardized overload time history is counted to obtain the gust overload peak-valley value data pair (Δn y峰 , Δn y谷 ) i , where the peak values ​​greater than are positive overloads, and the valley values ​​are negative overloads.

[0288] (2) Normalization of overload transcendence considering time

[0289] The cumulative overload exceedances are counted for both positive peak and negative valley overloads. The cumulative overload exceedances at each level are standardized, taking into account flight duration. The standard mission duration is shown in Table 2.

[0290] (3) Overload transcendental curve family

[0291] The overload-cumulative exceedance numbers of each task segment are fitted to obtain the overload exceedance number curve family of each task segment.

[0292] (4) Statistical parameters of overload exceedance in each task segment

[0293] The distribution parameters of the overload exceedance numbers at each level were estimated, and the results are as follows: Figure 5-6 shown.

[0294] 5. Compilation of severe gust spectrum

[0295] (1) Damage distribution of aircraft fleet

[0296] The damage distribution of the climbing mission segment is as follows: Figure 7 shown

[0297] The lognormal distribution parameters are shown in the following table

[0298] Table 4 Lognormal distribution parameters

[0299]

[0300] (2) Determination of severity of severity spectrum

[0301] In this embodiment, for the climbing mission segment, P is set to 99.9%, σ0 is set to 0.15, and σ s Take 0.1, σ L The standard deviation of the logarithm of the damage was 0.11. L =91.1%.

[0302] The severe damage value is 4.6878.

[0303] (3) Exceedance number coverage P t Sure

[0304] The corresponding up is obtained by damage inversion = 1.196

[0305] (4) Determination of severe cumulative exceedance curve

[0306] The severe cumulative exceedance curve of the climbing mission segment is as follows: Figure 8 shown.

[0307] (5) High load interception and low load interception

[0308] 1) High load interception

[0309] The high load cutoff values ​​for each task segment are shown in the table below.

[0310] Table 5 High load cutoff values ​​for each task segment

[0311] Task segment High load cutoff value / g mid-altitude climb 0.86 Medium-altitude level flight 0.98 Hollow Slide 0.99

[0312] 2) Low load cutoff

[0313] The low load deletion values ​​for each task segment are shown in the table below.

[0314] Table 6 Low load deletion values ​​for each task segment

[0315] Task segment Low load deletion value / g mid-altitude climb 0.16 Medium-altitude level flight 0.17 Hollow Slide 0.22

[0316] (6) Load spectrum compilation

[0317] 5×5 score compilation

[0318] According to the compilation principles, the flight type numbers of the severe gust spectrum and the medium-altitude flight profile are given respectively. The flight type numbers of the severe gust spectrum are shown in Table 7, and the gust spectrum of the climb mission segment under the medium-altitude flight profile is shown in Table 8.

[0319] Table 7 Number of flight types for typical flight profiles (1000 flights)

[0320]

[0321] Table 8 Mid-altitude flight climb gust spectrum

[0322]

[0323] Random arrangement of fatigue load spectra

[0324] According to the above principles, fatigue load spectrum (load time series) is compiled. The random spectrum of flight type A is as follows: Figure 9 shown.

[0325] The innovation of the present invention lies in that, when the load spectrum damage dispersion obeys the lognormal distribution and the structural dispersion obeys the lognormal distribution, a method for determining the severity of the severe spectrum is given, and the load time history of each task segment is determined by Monte Carlo simulation. Note that for this task segment load time history extraction method, the coverage rate corresponding to each level of load is not the same. Then, according to the severe damage of the task segment, the coverage rate Pt of the exceedance number under the specified overload of the corresponding task segment is inverted, and then the severe overload cumulative exceedance number curve is obtained.

[0326] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0327] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one 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 present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A severe spectrum compilation method based on severe damage extrapolated overload exceedance curve is characterized by: The following steps are involved: S1. Define the aircraft mission profile: According to the aircraft's operating requirements, provide the composition and proportion of the mission profile, and for each mission profile, define the mission profile parameters; S2. Payload data preparation: Preprocess the measured payload data obtained based on the mission profile to obtain payload data; S3. Statistical processing of load data: Obtain a family of measured gust overload cumulative exceedance curves for the mission segment, and estimate the distribution parameters of the exceedance under the specified overload using the maximum likelihood estimation function; S4. Obtain the severe gust overload cumulative exceedance curve: S41. Randomly extract random numbers that obey the standard normal distribution and use them as the coverage of the exceedance number to obtain the exceedance number data pairs (±Δn y* ,ΔN) i ; And sort the positive overloads and negative overloads of gust overloads by their absolute values, and match them to obtain the time series of gust overload (n y,max , n y,min ) i ; Where i is the overload level; S42. According to the gust overload, the time series (n y,max , n y,min ) i The load spectrum damage D of the mission segment is calculated eq ; S43. Damage D according to load spectrum eq Obey the lognormal distribution, fitting D eqi And the corresponding empirical frequency value f i Calculate the lognormal distribution parameters: log mean μ and log standard deviation σ; where D eqi is the load spectrum damage corresponding to the i-th sample data; S44. Obtain load spectrum reliability P L and the transcendental coverage ratio P t For each level of load in each task segment, according to the distribution function and distribution parameters in S3, the extrapolated coverage rate is P t The transcendental number of Among them, μ i , σ i are respectively the i-th level overload n y,i the logarithmic median and standard deviation of the corresponding transcendental numbers; In the obtained (Δn y , ΔN Pt ) i After the data are matched, the cumulative exceedance curve of severe gust overload corresponding to each mission segment is obtained by fitting; S5. Determine the load level 5 using the load spectrum equivalence criterion and compile a severe gust overload spectrum using the square root of 5×5: Compile a severe mission segment spectrum for typical mission segments in the mission profile; construct a severe mission profile spectrum from the mission segments; and compile a severe flight-continuation-flight spectrum with a takeoff and landing cycle by randomly sorting the severe mission profile spectrum in proportion; where a is preset based on the total life of the aircraft. In S44, the load spectrum reliability P L The calculation method is: The corresponding fleet safety life under the average spectrum is: Where σ0 is the standard deviation of fleet life. In this example, σ L is the load standard deviation, σ s is the structural standard deviation; P is the reliability requirement of the fleet life; μ0 is the logarithmic mean life of the fleet; μ P is the normal distribution bias coefficient for the specified reliability P; The corresponding safe life under the severe spectrum is: Among them, P L is the load spectrum reliability corresponding to the severity spectrum, Ps is the reliability corresponding to the structural dispersion; The reliability P is specified L , the normal distribution bias coefficient of Ps; σ L is the load standard deviation, σ s is the structural standard deviation; because N P =N P,s Obtain the load spectrum reliability P corresponding to the determined severity spectrum L : Calculate the transcendental coverage P t The method is: Load spectrum reliability P L Corresponding severe injury D eqpl for: The current severe damage D is obtained by inversion eqpl The corresponding u pt And the corresponding transcendental coverage P t , so that the overload cycle damage under the exceedance coverage is equal to the current severe damage D eqpl , where u pt corresponds to the transcendental coverage P t Quantile of the standard normal distribution.

2. The method for compiling a severity spectrum based on a severe damage extrapolated overload transcendence curve according to claim 1, characterized in that: The specific contents of S1 include: Determine the aircraft's mission profile, mission profile ratio, and mission profile composition, and provide mission profile parameters, where the mission profile parameters include: mission segment, mission segment altitude, speed, weight, flight distance, and flight time; and divide the measured data mission segments according to the following principles: a) The criterion for starting the climb is when the flap angle reaches 0°; b) The end point of climb / beginning of level flight is the turning point where the altitude curve turns level; c) The end point of level flight / start point of descent is determined by the turning point of the descending altitude curve; d) The descent end point is determined by the flap angle becoming 35°.

3. The method for compiling a severity spectrum based on a severe damage extrapolated overload transcendence curve according to claim 1, characterized in that: The specific contents of preprocessing the measured load data in S2 include: (1) Overload data n y Standardization of △n y0 =△n yi *Gi / G0 Where, △n y0 is the overload value after correction according to the standard mission section; △n yi is the measured overload value, G i is the actual mass of the aircraft corresponding to the measured overload value, G0 is the standard aircraft mass of this mission segment of the flight profile; (2) Separation of gusts and maneuvering overloads The maneuvering and gust loads contained in the measured load data are separated, and the interpretation conditions of the maneuvering and gust load spectra in airborne flight are: a) If the overload changes slowly and lasts for more than 2 seconds, it is considered a maneuver; otherwise, it is considered a gust; b) An overload with an absolute value of the rudder deflection angle equal to or greater than 4° is considered a maneuvering overload, otherwise it is considered a gust overload; c) For the climb and descent missions, since there are fewer maneuvers, the loads are approximately considered to be gust loads, and the separation of gust and maneuver is no longer performed; (3) Peak and valley value collection During the counting process, all peaks and valleys are detected and expressed in sequence as Δn zi , filter out the data between the peak and valley values, while retaining the corresponding sampling point numbers, if: or Then take the peak or valley value once.

4. The method for compiling a severity spectrum based on a severe damage extrapolated overload transcendence curve according to claim 1, characterized in that: S3 also includes: (1) Overload time history counting adopts the limited span average peak counting method; Taking the 1g load state of each mission segment as the benchmark, when the valley value between two peaks or the peak value between two valleys does not exceed the deviation of the baseline of the mission segment: For positive overload, the deviation is the upper deviation, and only the maximum peak value is recorded; For negative overload, the deviation is the lower deviation, and only the minimum valley value is recorded; The absolute values ​​of the upper deviation and the lower deviation are both 20% of the maximum peak value; (2) Obtain the cumulative exceedance number of each level of load; The peak value obtained by the restricted span average peak counting method is greater than 0 as a positive peak value, and the valley value is less than 0 as a negative valley value; select several load levels, record the positive gust overload cumulative exceedance number and the negative gust overload cumulative exceedance number for the positive peak value and the negative valley value, respectively, and record them as N(+Δn y ) and N(-Δn y ); Obtain ±Δn y The cumulative transcendental number of : (3) Normalization of the cumulative overload exceedance considering the flight duration; The flight time corresponding to the measured data is t M , the standard time of the task segment is t S , the cumulative exceedance number N of the i-th level load obtained during the measured time ib , then N ib The standardization rules are: where N i is the cumulative exceedance number of the i-th level load at the standard time; The cumulative exceedance of each level of load is standardized to obtain the cumulative exceedance data of the standard time mission segment (Δn y ,N) i , and fitting is performed to obtain the overload cumulative transcendental number curve family of this task segment. The fitting equation is as follows: Δn y =a*lgN+b The overload-cumulative exceedance curve is discretized into (Δn y ,N) i (i=1,...,n) data pairs, calculate the exceedance number ΔN from the cumulative exceedance number N, and obtain the exceedance number data pairs (Δn y ,ΔN) i , where the transcendental number ΔN i The calculation method is ΔN i =N i -N i-1 ΔN i Represents a transcendental number.

5. The method for compiling a severity spectrum based on a severe damage extrapolated overload transcendence curve according to claim 1, characterized in that: The distribution parameter estimation method in S3 is: The maximum likelihood estimation method is used to estimate the distribution parameters; the likelihood function is: Where x i =lgΔN i ; According to the likelihood function, the derivatives of μ and σ are zero to solve the target parameters (μ, σ) 6. The method for compiling a severity spectrum based on a severe damage extrapolated overload transcendence curve according to claim 1, characterized in that: The specific contents of S41 include: 1) Randomly draw a random number u that follows a standard normal distribution p ,u p Refers to the quantile of the standard normal distribution corresponding to the coverage rate P, the i-th level gust overload Δn yi The corresponding coverage is the transcendental number ΔN of P P,i for: 2) Obtain the exceedance data pairs (±Δn y* ,ΔN) i , where i is the level of overload; 3) For gust overload, sort the positive overloads and negative overloads at all levels by their absolute values ​​and match them to obtain the gust overload time series (n y,max , n y,min ) i .

7. The method for compiling a severity spectrum based on a severe damage extrapolated overload transcendence curve according to claim 6, characterized in that: 2) The Δn of the task segment is obtained by fitting y The specific method of transcendental curve is: For each task segment, the overload cumulative exceedance data pair (Δn y ,N) i Perform fitting, the fitting equation is: Δn y =a*lgN+b The overload cumulative exceedance curve of each task segment is calculated.

8. The method for compiling a severity spectrum based on a severe damage extrapolated overload transcendence curve according to claim 1, characterized in that: The specific contents of S42 include: According to the compiled gust overload time series (n y,max , n y,min ) i , calculate the equivalent damage of a single cycle, and accumulate the load spectrum damage D eq , the specific method is: Where m is the damage index, i.e. the slope of the material / structure fatigue SN curve under stress ratio R = 0, d eq Refers to damage to a single cycle.

9. The method for compiling a severity spectrum based on a severe damage extrapolated overload transcendence curve according to claim 1, characterized in that: S4 also includes: S45. performing high-load interception and low-load interception on the severe cumulative exceedance curve of the mission segment; (1) High load interception Determine the level and number of high loads in the severe gust spectrum, and select the high load at the preset frequency as the cutoff value; (2) Low load cutoff The small loads circulating in the severe gust spectrum are deleted or converted into a certain level of load with equal damage.

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