A method for compiling severe gust spectra based on the fusion of historical data and measured data

Abstract

This invention discloses a method for compiling a severe gust spectrum based on the fusion of historical and measured data, comprising: determining the aircraft mission profile; acquiring measured load data and separating it into measured gust overload data; obtaining the measured gust overload cumulative exceedance number curve based on the measured gust overload data for each mission segment; acquiring historical gust velocity cumulative exceedance number curves and converting them into historical gust overload cumulative exceedance number curves; merging the measured gust overload cumulative exceedance number curves and historical gust overload cumulative exceedance number curves into a family of gust overload cumulative exceedance number curves; calculating the severe gust overload cumulative exceedance number curve for each mission segment; calculating the equivalent load based on the severe gust overload cumulative exceedance number curve for each mission segment; and compiling the severe gust spectrum. This invention, when limited by measured gust data, references and combines historical gust load data. Using this method to compile a severe spectrum for fatigue testing can reduce test time.

Description

Technical Field

[0001] The present invention relates to the technical field of gust spectrum compilation, and more specifically to a method for compiling a severe gust spectrum based on the fusion of historical data and measured data. Background Art

[0002] The flight load spectrum refers to a spectrum compiled from the time history of the load experienced by the aircraft body during flight. The measured flight load spectrum of the aircraft is compiled through special test modifications and flight test measurements. It is used to determine and verify the design service life of the aircraft and is a prerequisite for the fatigue determination and life extension of the aircraft structure. Gust loads account for a high proportion of damage in flight loads. The compilation of the measured gust spectrum of the aircraft is of great significance for the life extension of military / civil aircraft. The aviation industry has accumulated a large amount of measurement data and formed a gust spectrum compilation method based on the gust speed exceedance number curve, including the European 'TWIST' method and Boeing's '5×5' spectrum method. However, these methods are generally only used to compile the average intensity spectrum of gusts and cannot reflect the dispersion of gusts.

[0003] Even if the aircraft are used according to the same requirements, the load-time history of different aircraft in the fleet is significantly different, corresponding to the dispersion of the load spectrum, and durability analysis and testing must be carried out under a certain (unique) load spectrum. Therefore, how to select and compile a reasonable load spectrum has become the key to structural durability analysis and assessment. For this purpose, the "severity spectrum" is proposed. The severe spectrum has the advantages of being able to expose the failure characteristics of the aircraft itself and reducing the test time. Therefore, the severe spectrum is increasingly used in the compilation of today's measured spectra.

[0004] In the process of compiling the measured severe spectrum, there is still a situation where the number of gusts is small and the measured data almost does not contain gusts. Therefore, it is necessary to propose a method for compiling the aircraft severe gust measured spectrum that fully considers the dispersion of the measured gusts and past gust loads when there are few measured gust data. Summary of the invention

[0005] In view of this, the present invention provides a method for compiling a severe gust spectrum based on the fusion of historical data and measured data. The severe spectrum compiled by this method 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.

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

[0007] The severe gust spectrum compilation method based on the fusion of historical data and measured data includes:

[0008] S1: Determine the aircraft mission profile and determine the mission segment composition, sequence, proportion and parameters of the mission profile;

[0009] S2: Acquire measured load data, perform standardization on the measured load data, and separate the measured gust overload data;

[0010] S3: Based on the measured gust overload data, a measured gust overload cumulative exceedance curve based on the mission segment is obtained;

[0011] S4: Obtain the historical gust speed cumulative exceedance curve and convert it into the historical gust overload cumulative exceedance curve corresponding to the aircraft type, and merge the measured gust overload cumulative exceedance curve and the historical gust overload cumulative exceedance curve into a gust overload cumulative exceedance curve family;

[0012] S5: Perform hierarchical discretization on the gust overload cumulative exceedance number curve family to obtain the exceedance number of the preset coverage rate under each level of load in each mission segment; take the exceedance number of the preset coverage rate under each level of load for fitting to obtain the severe gust overload cumulative exceedance number curve under each mission segment;

[0013] S6: Calculate the equivalent load based on the severe gust overload cumulative exceedance curve under each mission segment and compile the severe gust spectrum.

[0014] Preferably, in SI, the mission segment parameters include mission segment altitude, flight speed, flight distance, flight time and aircraft weight; the mission segment division criteria are:

[0015] a) The criterion for starting the climb is when the flap angle becomes 0°;

[0016] b) The end point of the climb shall be determined by the turning point where the altitude curve flattens;

[0017] c) The end point of level flight shall be determined by the turning point of the descending altitude curve;

[0018] d) The end point of the descent is determined by the flap angle becoming 35°.

[0019] Preferably, S2 comprises:

[0020] S21: Obtain measured load data, and perform standardization on the measured load data according to the following formula:

[0021] △n y0 =△n yi *Gi / G0

[0022] Among them, △n y0 is the overload value corrected according to the standard mission section; △n yi is the measured overload value, Gi is the real mass of the aircraft, and G0 is the standard aircraft mass under the current mission profile and the current mission segment;

[0023] S22: Separate the maneuvering overload and gust overload in the measured load data after standardization according to a preset separation condition; the preset separation condition is:

[0024] a) When the overload change is less than the preset value and lasts for more than 2s, it is a maneuvering overload, otherwise it is a gust overload; b) When the overload with the absolute value of the rudder deflection angle equal to or greater than 4° is a gust overload, otherwise it is a maneuvering overload; c) For the climbing and descending mission segments, all loads are gust overloads;

[0025] S23: Detect the peak and valley values ​​of the separated measured gust overload data, filter out the data between the peak and valley values, and retain the peak points and valley points.

[0026] Preferably, S3 includes:

[0027] S31: Count the peak values ​​and valley values ​​of the measured gust overload data: take the 1g overload state of each task segment as the benchmark, and when the valley value between two peak values ​​or the peak value between two valley values ​​does not exceed the upper deviation or lower deviation of the benchmark of the task segment, record the maximum peak value or maximum valley value;

[0028] S32: taking the measured gust overload data corresponding to the maximum peak value and the maximum valley value as the positive gust overload cumulative exceedance number and the negative gust overload cumulative exceedance number, respectively, and averaging the positive gust overload cumulative exceedance number and the negative gust overload cumulative exceedance number to obtain the gust overload cumulative exceedance number data based on the task segment;

[0029] S33: Standardize the gust overload cumulative exceedance data:

[0030]

[0031] Among them, t M is the measured flight time; t S is the standard time of the current task segment; N ib is the cumulative exceedance number of the i-th level load obtained during the measured flight time; N i is the cumulative exceedance number data of gust overload of the i-th level load at standard time;

[0032] S34: The standardized gust overload cumulative exceedance data pair (Δn y ,N Y ) i The fitting equation is:

[0033] Δn y =a*lgN Y +b

[0034] Among them, a and b are the coefficients of the fitting equation, N Y It is the cumulative exceedance number of gust overload.

[0035] Preferably, S4 specifically includes:

[0036] S41: Obtain the historical gust speed cumulative exceedance curve, and calculate the corresponding historical gust overload cumulative exceedance curve through the load response algorithm:

[0037]

[0038] Where: Δn is the gust load coefficient increment; R is the vertical gust response parameter of the wing and fuselage; U de is the equivalent gust speed; ρ0 is the air density at sea level; C Nα is the slope of the lift line; V e is the equivalent airspeed; G / S is the wing loading; K W is the discrete gust mitigation factor; μ g is the aircraft mass parameter; g is the acceleration of gravity; C is the average geometric chord length of the wing; ρ h is the air density at the altitude;

[0039] S42: Combining the historical gust overload cumulative exceedance number curve and the measured gust overload cumulative exceedance number curve under the corresponding task segment according to a certain weight to obtain a gust overload cumulative exceedance number curve family.

[0040] Preferably, S5 includes:

[0041] S51: Discretize the gust overload cumulative exceedance curve family, and discretize it into (Δn y , N) i (i=1,…,n) data pairs, and obtain the exceedance number ΔN corresponding to different gust overloads i =N i -N i-1 , obtain the exceedance number data pairs corresponding to different gust overloads (Δn y , ΔN) i (i=1, ..., n);

[0042] S52: Using a random variable model, assuming a specified overload Δn y The corresponding transcendental number ΔN obeys the log-normal distribution. The target distribution parameters μ and σ are solved according to the likelihood function, and the solution equation is:

[0043]

[0044]

[0045] S53: Calculate the exceedance number of the preset coverage rate under each level of load in each task segment according to the target distribution parameter:

[0046]

[0047] 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 number; ΔN 90,i is the exceedance number for the preset coverage rate of 90%; μ 90 is the quantile with 90% coverage of the standard normal distribution;

[0048] S54: fitting the exceedance number of the preset coverage rate under each level of load in each mission section, and the severe gust overload cumulative exceedance number curve under each mission section;

[0049] S55: Perform high-load interception and low-load elimination on the severe gust overload cumulative exceedance curve under each mission section.

[0050] Preferably, S6 includes:

[0051] S61: Calculate equivalent load based on the severe gust overload cumulative exceedance curve under each mission segment;

[0052] S62: Taking the flight / glide flight spectrum with the highest load as the benchmark, 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;

[0053] S63, linking the loads of each level and their corresponding frequencies in a single takeoff and landing of the kth flight type in the i-th mission segment of the j-th mission profile, randomly selecting peak values ​​and valley values ​​alternately for random pair arrangement, forming a load spectrum of a specific flight type in the corresponding mission segment, and obtaining mission segment spectra of various flight types in each mission segment;

[0054] S64, sorting the mission segment spectrum under each mission profile according to the mission segment sequence under the mission profile, to obtain a mission profile spectrum under a complete flight;

[0055] S65. Sort all mission profile spectra of each complete flight to obtain the final severe gust spectrum.

[0056] Preferably, the specific calculation process of the equivalent load is:

[0057] Assume that the equivalent load of the 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:

[0058] Ag=a i lgN+b i

[0059] In the formula, a i , b i is the constant of the load spectrum curve of the i-th section;

[0060] The equivalent load is:

[0061]

[0062] Where:

[0063]

[0064] S is the slope parameter of the material SN curve, n is the number of cycles of equivalent load, Δg i ,Δg i+1 It refers to the minimum Δg and maximum Δg of the i-th curve;

[0065] Equivalent load cycles:

[0066]

[0067] It can be seen from the above technical solution that, compared with the prior art, the present invention discloses a method for compiling a severe gust spectrum based on the fusion of historical data and measured data. When the measured gust data is small, the measured gust characteristics and the dispersion of past gust data are fully considered. By merging the measured gust overload cumulative exceedance number curve and the historical gust overload cumulative exceedance number curve into a gust overload cumulative exceedance number curve family, the severe gust overload cumulative exceedance number curve under each mission segment is obtained, which 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

[0068] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0069] Figure 1 The attached figure is a flow chart of the method for compiling severe gust spectra based on the fusion of historical data and measured data.

[0070] Figure 2 The attached figure is a schematic diagram of peak and valley value detection.

[0071] Figure 3 The attached figure is a schematic diagram of limiting the span average peak count.

[0072] Figure 4 The attached figure is a curve chart of the historical gust speed cumulative exceedance number.

[0073] Figure 5 The attached figure is a curve of historical gust overload cumulative exceedance numbers.

[0074] Figure 6 The attached figure is a schematic diagram of the fitting results of the distribution parameter μ.

[0075] Figure 7 The attached figure is a schematic diagram of the fitting results of the distribution parameter σ.

[0076] Figure 8 The attached figure is a schematic diagram of the severe cumulative exceedance number curve.

[0077] Fig. 9 The attached figure is a schematic diagram of the random spectrum of flight type A. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0079] The embodiment of the present invention discloses a method for compiling a severe gust spectrum based on the fusion of historical data and measured data, such as Figure 1 As shown, including:

[0080] S1: Determine the aircraft mission profile and determine the mission segment composition, sequence, proportion and parameters of the mission profile;

[0081] S2: Acquire measured load data, perform standardization on the measured load data, and separate the measured gust overload data;

[0082] S3: Based on the measured gust overload data, a measured gust overload cumulative exceedance curve based on the mission segment is obtained;

[0083] S3: Obtain the historical gust speed cumulative exceedance curve and convert it into the historical gust overload cumulative exceedance curve corresponding to the aircraft type;

[0084] S4: Combine the measured gust overload cumulative exceedance curve and the historical gust overload cumulative exceedance curve into a gust overload cumulative exceedance curve family;

[0085] S5: Perform hierarchical discretization on the gust overload cumulative exceedance number curve family to obtain the exceedance number of the preset coverage rate under each level of load in each mission segment; take the exceedance number of the preset coverage rate under each level of load for fitting to obtain the severe gust overload cumulative exceedance number curve under each mission segment;

[0086] S6: Calculate the equivalent load based on the severe gust overload cumulative exceedance curve under each mission segment and compile the severe gust spectrum.

[0087] The above steps are further explained below.

[0088] In a specific embodiment, S1 includes: determining a mission profile, including a mission profile ratio and a mission profile, determining a mission segment composition, sequence, ratio and parameters of the mission profile, wherein the parameters include mission segment altitude, flight speed, flight distance, flight time and weight; the division criteria of the mission segment are:

[0089] a) The criterion for starting the climb is when the flap angle becomes 0°;

[0090] b) The end point of climb (i.e. the start point of level flight) is determined by the turning point where the altitude curve turns flat;

[0091] c) The end point of level flight (i.e. the start point of descent) is determined by the turning point of the descending altitude curve;

[0092] d) The end point of the descent is determined by the flap angle becoming 35°.

[0093] In a specific embodiment, S2 includes:

[0094] S21: Obtain measured load data n y , and the measured load data is standardized according to the following formula:

[0095] △n y0 =△n yi *Gi / G0 (1)

[0096] Among them, △n y0 is the overload value corrected according to the standard mission section; △n yi is the measured overload value. The real mass data of the load is calculated by subtracting the fuel consumption from the aircraft weight. The fuel consumption is calculated by the total average (the time for calculating the average fuel consumption is from the start of takeoff taxiing to the end of landing impact). Gi is the real mass of the aircraft, and G0 is the standard aircraft mass under the current mission profile and the current mission segment. The measured overload n y You need to subtract 1 first, and then standardize to get △n y0 .

[0097] S22: Separation of gusts and maneuvering overloads

[0098] The measured load data includes maneuvering and gust loads, which need to be separated. The interpretation of maneuvering and gust load spectra in air flight is determined by comprehensive consideration of the following three conditions:

[0099] The maneuvering overload and gust overload in the measured load data after standardization are separated according to the preset separation conditions; the preset separation conditions are:

[0100] a) When the overload change is less than the preset value and lasts for more than 2s, it is considered as maneuvering overload, otherwise it is considered as gust overload; b) When the overload with the absolute value of the rudder deflection angle is equal to or greater than 4°, it is considered as gust overload, otherwise it is considered as maneuvering overload; c) For the climbing and descending mission segments, since there are fewer maneuvering actions, all loads are considered as gust overloads, and the separation of gust and maneuvering is no longer performed;

[0101] S23: Peak and valley value collection:

[0102] The so-called peak-valley detection is to detect all peak values ​​and valley values ​​in the counting process, filter out the data between the peak values ​​and valley values, and retain the corresponding sampling point numbers. 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.

[0103] If satisfied

[0104]

[0105] Then take the peak value (or valley value) once.

[0106] In a specific embodiment, the statistical analysis of the S3 gust overload cumulative exceedance curve family specifically includes:

[0107] S31: Count the peak values ​​and valley values ​​of the measured gust overload data: Count the peak values ​​and valley values ​​of the measured gust overload data: Take the 1g overload state of each task segment as the benchmark, and when the valley value between two peak values ​​or the peak value between two valley values ​​does not exceed the upper deviation or lower deviation of the benchmark of the task segment, record the maximum peak value (peak value for positive overload) or maximum valley value (valley value for negative overload);

[0108] The method can be generalized to limit the cross-average peak count, such as Figure 3As shown, the upper and lower deviations are taken as 20% of the maximum peak value, and this deviation is the limiting condition. This method is a counting method formed by adding certain limiting conditions on the basis of the peak method. In the figure, the hollow points are counting points, and the solid points are filtering points.

[0109] S32: Obtain the cumulative exceedance data of gust overload at each level of load: Select several levels of load, 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 );

[0110] 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 number curves should be symmetrical. However, in practice, there are often certain differences between the positive and negative gust cumulative exceedance number curves. In engineering, the geometric mean is used to obtain a symmetrical gust overload corresponding cumulative exceedance number. In this way, ±Δn is obtained. y The cumulative transcendental number curve family of is shown in formula (3):

[0111]

[0112] S33: Standardize the gust overload cumulative exceedance data:

[0113] Since the load spectrum is measured during the mission flight time and there is a difference between the mission standard time in the mission profile, it is necessary to convert the measured mission gust overload cumulative exceedance data into the cumulative exceedance data of the standard time:

[0114]

[0115] Among them, t M is the measured flight time; t S is the standard time of the current task segment; N ib is the cumulative exceedance number of the i-th level load obtained during the measured flight time; N Yi is the cumulative exceedance number data of gust overload of the i-th level load at standard time;

[0116] The gust overload cumulative exceedance numbers of each level of load are replaced according to the above formula to obtain the gust overload cumulative exceedance number data of the mission section of standard time.

[0117] S34: The standardized gust overload cumulative exceedance data pair (Δn y ,N Y ) iThe fitting equation is:

[0118] Δn y =a*lgN Y +b (5)

[0119] In a specific embodiment, S4 specifically includes:

[0120] S41: Obtain the historical gust speed cumulative exceedance curve, and calculate the corresponding historical gust overload cumulative exceedance curve through the load response algorithm:

[0121]

[0122] Where: Δn is the gust load coefficient increment; R is the vertical gust response parameter of the wing and fuselage (s / m); U de is the equivalent gust speed (m / s) (abbreviated as U de or gust speed); ρ0 is the air density at sea level (kg / s); C Nα is the lift line slope (1 / rad); V e is the equivalent airspeed (m / s); G / S is the wing load (N / m2); K W is the discrete gust mitigation factor; μ g is the aircraft mass parameter; g is the acceleration due to gravity (m / s2); is the average geometric chord length of the wing (m); ρ h is the air density at that height (kg / m3).

[0123] S42: Combining the historical gust overload cumulative exceedance number curve and the measured gust overload cumulative exceedance number curve under the corresponding task segment according to a certain weight to obtain a gust overload cumulative exceedance number curve family.

[0124] In a specific embodiment, S5 specifically includes:

[0125] S51: Discretize the gust overload cumulative exceedance curve family: Discretize into (Δn y , N) i (i=1,…,n) data pairs, 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).

[0126] Transcendental number distribution characteristics test:

[0127] Usually a random variable model is used, assuming that Δn is specified yThe corresponding transcendental number ΔN obeys the log-normal distribution.

[0128] Distribution parameter estimation:

[0129] For a specific task segment, when the measured sample is large, it is recommended to use the maximum likelihood estimation method to estimate the distribution parameters. The likelihood function is shown as follows:

[0130]

[0131] Where x i =lgΔN i . According to the likelihood function, the derivatives of μ and v are zero to solve the target parameters (μ, σ):

[0132]

[0133]

[0134] Where n is the number of different overload levels, μ is the logarithmic mean, and σ is the logarithmic standard deviation;

[0135] S52: Based on the loads at each level of each task segment, calculate the exceedance number of the preset coverage rate at each level of loads in each task segment:

[0136]

[0137] Among them, μ i , σ i are respectively the i-th level overload n y,i The corresponding logarithmic median and standard deviation of the exceedance number, the severity spectrum should reflect the severe usage of 90% of the aircraft in the fleet, and the corresponding overload-cumulative exceedance number curve is the cumulative exceedance number curve corresponding to 90% reliability.

[0138] S53: After fitting the exceedance number of the preset coverage rate under each level of load in each task segment, high load interception and low load interception are performed:

[0139] (1) High load interception

[0140] The cutoff value is the high load that occurs once in 1000 flights in this mission segment;

[0141] (2) Low load cutoff

[0142] The spectrum usually contains a large number of small load cycles, which need to be deleted or converted to a certain level of load. The fatigue limit of the key parts is usually determined based on the above analysis, and the overload value corresponding to 70% to 80% of the fatigue limit is truncated.

[0143] In a specific embodiment, S6 specifically includes:

[0144] S61: Calculate the equivalent load based on the cumulative exceedance curve of severe gust overload under each mission segment, determine it as level 5 load, and compile a 5×5 spectrum based on the mission segment: The specific calculation process of the equivalent load is:

[0145] Assume that the equivalent load of the 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:

[0146] Δg=a i lgN+b i (11)

[0147] In the formula, a i , b i is the constant of the load spectrum curve of the i-th section;

[0148] The equivalent load is:

[0149]

[0150] Where:

[0151]

[0152] S is the slope parameter of the material SN curve;

[0153] Equivalent load cycles:

[0154]

[0155] S62: Determine the flight type: The load spectra of the medium-altitude flight mission profile are compiled according to 5 different flight types. The principle for determining the typical flight type of medium-altitude flight is: take the flight / glide segment flight spectrum with the highest load as the benchmark, assume that the highest load in each flight / glide segment is a normal logarithmic extreme value distribution, and determine the number of occurrences of each type of flight;

[0156] The following is an 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.

[0157] Table 1 5×5 spectrum

[0158]

[0159] In the table, y1+y2+y3+y4+y5 should be equal to the number of times the profile appears in 1000 flights. The 5×5 spectra of various mission segments under all profiles are compiled according to the above method. And the y1, y2, y3, y4, and y5 of the 5×5 spectra of each mission segment under each mission profile are the same.

[0160] S63: According to the data in the compiled 5×5 spectrum, 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 jth profile are linked, and the peaks and valleys are randomly selected alternately (i.e., the peaks and valleys are random respectively) 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 express the task segment spectrum (a total of j×i×k) as the following load pair sequence:

[0161] f i，j，k =(Δn ydn , -Δl ydm )h (15)

[0162] In the formula, Δn ydn is the nth (n=1, 2, 3, 4, 5) level load of the task segment, representing the peak load; -Δn ydm is the mth (m=1,2,3,4,5) level load of the mission segment, representing the valley load, and h represents the load cycle peak-valley data pair. The total number of load pairs for each type of flight mission segment should be equal to the total number of cycles per flight in the above table. According to this method, the mission segment spectra (load pair sequences) of various flight types under all mission segments are compiled.

[0163] S64: 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 profile, that is, 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

[0164] B=[F 1，1 … F 1,K F 2，1 … F2, K F 3，1 …F 3，K …… FL，1 … F L，K ] (16)

[0165] 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 recorded as A1, B1, C1, D1, and E1. When compiling the A1 spectrum, arrange the spectra of flight type A under each mission segment, that is, f 1,j,k ,f 2,j,k ,f 3,j,k , we can get the A1 spectrum, and the same goes for other types of mission profile spectra.

[0166] S65: Sort all mission profile spectra for each complete flight to obtain the final severe gust spectrum.

[0167] Record the occurrence of y 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

[0168] 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 ] (17)

[0169] Where A is the vector of the number of occurrences of various mission profiles.

[0170] 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 the task profile spectrum sequence G

[0171]

[0172] The final flight-continuation-flight spectrum can be obtained by reordering the mission profile spectrum of each complete flight in G. 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 respectively 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.

[0173] The specific random sorting method is:

[0174] Randomly select the peak value, valley value and total load spectrum G in a random sequence "x i The selection of " is realized by the pseudo-random method of square median method, and reasonable random results are given by adjusting random parameters.

[0175] Square middle method:

[0176]

[0177]

[0178] First, we use (19) to form “x i "Then the random number sequence "u i Calculate u i In the order of the total number column from small to large u is , thus we get the random integer sequence "u is ”.

[0179] Regarding the parameter values ​​in equations (19) and (20), different random number sequences can be obtained by changing the size of s and x0.

[0180] The present invention will be further described below with reference to specific examples.

[0181] S1: Determine the mission profile:

[0182] The typical mission profile of a certain type of aircraft is medium-altitude flight, and the compilation is based on 1,000 takeoffs and landings.

[0183] Table 2 Task sections of hollow sections

[0184]

[0185] S2: Obtain measured load data:

[0186] A total of 10 takeoff and landing flight parameter data of an aircraft were measured. The flight parameter data provided included time, flight altitude, y-axis overload at the center of gravity, remaining fuel in the left / right engine, flap deflection angle, and elevator deflection angle.

[0187] Standardize the measured load data:

[0188] The standard aircraft mass data for the mission segment is shown in Table 3 below. The real mass data of the load is calculated by subtracting the fuel consumption from the aircraft weight. The fuel consumption is calculated based on the total average (the average fuel consumption is calculated from the start of takeoff taxiing to the end of landing impact). The aircraft center of gravity y-axis overload data is standardized according to S21.

[0189] Table 3 Standard quality of each task segment

[0190] Task segment Total mass (kg) Climb 42000 Flying in the air 40000 Downward 35000

[0191] Peak-valley value collection: According to S23, the peak-valley value detection of statistical parameters is performed to obtain the peak-valley value data pair of gust overload (Δn y峰 , Δn y谷 ) i .

[0192] S3: Statistical analysis of the gust overload cumulative exceedance curve family

[0193] With Δn y =0.05g, and the interval is 0.05g. S31 is used to count the standardized overload time history, and the gust overload peak-to-valley value data pair (Δn y峰 , Δn y谷 ) i , where the peak values ​​are all positive overloads and the valley values ​​are all negative overloads.

[0194] The cumulative exceedance of gust overloads is counted for positive peak overloads and negative valley overloads. Considering the flight duration, the cumulative exceedance of gust overloads at all levels is standardized. The standard time of the mission segment is shown in Table 2.

[0195] S4: The historical gust speed cumulative exceedance curves of 6 types of aircraft were collected and converted into historical gust overload cumulative exceedance curves.

[0196] For the level flight mission segment of the medium-altitude mission segment, the altitude is about 6,000 meters, corresponding to the airspace of about 9,500ft-19,500ft altitude. The gust speed cumulative exceedance curve and gust overload cumulative exceedance curve are as follows: Figure 4 and Figure 5 shown.

[0197] The curve family is statistically analyzed in the ratio of measured to historical data equal to 10 to 6, that is, for all mission segments, the gust overload exceedance number curves of 10 takeoffs and landings measured and the gust overload cumulative exceedance number curves of 6 models under the corresponding height of the mission segment in historical documents are combined to form a gust overload cumulative exceedance number curve family.

[0198] S5: Based on the S51 method, the distribution parameters of the overload exceedance number at each level are estimated, with Δn y =0.05g, and the fitting is performed with an interval of 0.05g. The results of the mid-altitude level flight mission are as follows Figure 6 and Figure 7 shown.

[0199] The severe cumulative exceedance curve is drawn using the S52 method. The severe cumulative exceedance curve for the mid-altitude climb mission is shown in Figure 8 .

[0200] High load interception and low load interception

[0201] (1) High load interception

[0202] The high load cutoff values ​​for each task segment are shown in Table 4 below.

[0203] Table 4 High load cutoff values ​​for each task segment

[0204] Task segment High load cutoff value / g Medium altitude flight 0.98

[0205] (2) Low load cutoff

[0206] The low load deletion values ​​for each task segment are shown in Table 5 below.

[0207] Table 5 Low load deletion values ​​for each task segment

[0208] Task segment Low load deletion value / g Medium altitude flight 0.17

[0209] S6: Load spectrum compilation:

[0210] 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 6, and the gust spectrum of the climb mission segment under the medium-altitude flight profile is shown in Table 7.

[0211] Table 6 Number of flight types for typical flight profiles (1000 flights)

[0212]

[0213] Table 7 Medium altitude flight climb gust spectrum

[0214]

[0215] According to the above principles, fatigue load spectrum (load time series) is compiled. The random spectrum of medium altitude flight climb mission segment A flight type is shown in Fig. 9 .

[0216] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0217] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A severe gust spectrum compilation method based on the fusion of historical data and measured data is characterized by: include: S1: Determine the aircraft mission profile and determine the mission segment composition, sequence, proportion and parameters of the mission profile; S2: Acquire measured load data, perform standardization on the measured load data, and separate the measured gust overload data; S3: Based on the measured gust overload data, a measured gust overload cumulative exceedance curve based on the mission segment is obtained; S4: Obtain the historical gust speed cumulative exceedance curve and convert it into the historical gust overload cumulative exceedance curve corresponding to the aircraft type, and merge the measured gust overload cumulative exceedance curve and the historical gust overload cumulative exceedance curve into a gust overload cumulative exceedance curve family; specifically including: S41: Obtain the historical gust speed cumulative exceedance curve, and calculate the corresponding historical gust overload cumulative exceedance curve through the load response algorithm: Where: Δn is the gust load coefficient increment; R is the vertical gust response parameter of the wing and fuselage; U de is the equivalent gust speed; ρ0 is the air density at sea level; C Nα is the slope of the lift line; V e is the equivalent airspeed; G / S is the wing loading; K W is the discrete gust mitigation factor; μ g is the aircraft mass parameter; g is the acceleration due to gravity; is the average geometric chord length of the wing; ρ h is the air density at the altitude; S42: combining the historical gust overload cumulative exceedance number curve and the measured gust overload cumulative exceedance number curve under the corresponding task section according to a certain weight to obtain a gust overload cumulative exceedance number curve family; S5: Perform hierarchical discretization on the gust overload cumulative exceedance number curve family to obtain the exceedance number of the preset coverage rate under each level of load in each mission segment; take the exceedance number of the preset coverage rate under each level of load for fitting to obtain the severe gust overload cumulative exceedance number curve under each mission segment; S6: Calculate the equivalent load based on the severe gust overload cumulative exceedance curve under each mission segment and compile the severe gust spectrum.

2. The method for compiling severe gust spectrum based on the fusion of historical data and measured data according to claim 1 is characterized in that: In S1, the mission segment parameters include mission segment altitude, flight speed, flight distance, flight time and aircraft weight; the mission segment division criteria are: a) The criterion for starting the climb is when the flap angle becomes 0°; b) The end point of the climb shall be determined by the turning point where the altitude curve flattens; c) The end point of level flight shall be determined by the turning point of the descending altitude curve; d) The end point of the descent is determined by the flap angle becoming 35°.

3. The method for compiling a severe gust spectrum based on the fusion of historical data and measured data according to claim 1 is characterized in that S2 include: S21: Obtain measured load data, and perform standardization on the measured load data according to the following formula: △n y0 =△n yi *Gi / G0 Among them, △n y0 is the overload value corrected according to the standard mission section; △n yi is the measured overload value, Gi is the real mass of the aircraft, and G0 is the standard aircraft mass under the current mission profile and the current mission segment; S22: Separate the maneuvering overload and gust overload in the measured load data after standardization according to a preset separation condition; the preset separation condition is: a) When the overload change is less than the preset value and lasts for more than 2s, it is a maneuvering overload, otherwise it is a gust overload; b) When the overload with the absolute value of the rudder deflection angle equal to or greater than 4° is a gust overload, otherwise it is a maneuvering overload; c) For the climbing and descending mission segments, all loads are gust overloads; S23: Perform peak and valley value detection on the separated measured gust overload data, filter out data between the peak and valley values, and retain the peak points and valley points.

4. The method for compiling a severe gust spectrum based on the fusion of historical data and measured data according to claim 3 is characterized in that S3 include: S31: Count the peak values ​​and valley values ​​of the measured gust overload data: take the 1g overload state of each task segment as the benchmark, and when the valley value between two peak values ​​or the peak value between two valley values ​​does not exceed the upper deviation or lower deviation of the benchmark of the task segment, record the maximum peak value or maximum valley value; S32: taking the measured gust overload data corresponding to the maximum peak value and the maximum valley value as the positive gust overload cumulative exceedance number and the negative gust overload cumulative exceedance number, respectively, and averaging the positive gust overload cumulative exceedance number and the negative gust overload cumulative exceedance number to obtain the gust overload cumulative exceedance number data based on the task segment; S33: Standardize the gust overload cumulative exceedance data: Among them, t M is the measured flight time; t S is the standard time of the current task segment; N ib is the cumulative exceedance number of the i-th level load obtained during the measured flight time; N Yi is the cumulative exceedance number data of gust overload of the i-th level load at the standard time; S34: The standardized gust overload cumulative exceedance data pair (Δn y ,N Y ) i The fitting equation is: Δn y =a*lgN Y +b Among them, a and b are the coefficients of the fitting equation, N Y It is the cumulative exceedance number of gust overload.

5. The method for compiling a severe gust spectrum based on the fusion of historical data and measured data according to claim 1, characterized in that S5 include: S51: Discretize the gust overload cumulative exceedance curve family into (Δn y , N) i (i=1,…,n) data pairs, and obtain the exceedance number ΔN corresponding to different gust overloads i =N i -N i-1 , and the exceedance data corresponding to different gust overloads (Δn y , ΔN) i (i=1, ..., n); S52: Using a random variable model, assuming a specified overload Δn y The corresponding transcendental number ΔN obeys the log-normal distribution. The target distribution parameters μ and σ are solved according to the likelihood function, and the solution equation is: Where n is the number of different overload levels; S53: Calculate the exceedance number of the preset coverage rate under each level of load in each task segment according to the target distribution parameter: 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 number; ΔN 90,i is the exceedance number for the preset coverage rate of 90%; μ 90 is the quantile with 90% coverage of the standard normal distribution; S54: fitting the exceedance number of the preset coverage rate under each level of load in each mission section, and the severe gust overload cumulative exceedance number curve under each mission section; S55: Perform high-load interception and low-load elimination on the severe gust overload cumulative exceedance curve under each mission section.

6. The method for compiling a severe gust spectrum based on the fusion of historical data and measured data according to claim 1, characterized in that S6 include: S61: Calculate equivalent load based on the severe gust overload cumulative exceedance curve under each mission segment; S62: Taking the flight / glide flight spectrum with the highest load as the benchmark, 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; S63, linking the loads of each level and their corresponding frequencies in a single takeoff and landing of the kth flight type in the i-th mission segment of the j-th mission profile, randomly selecting peak values ​​and valley values ​​alternately for random pair arrangement, forming a load spectrum of a specific flight type in the corresponding mission segment, and obtaining mission segment spectra of various flight types in each mission segment; S64, sorting the mission segment spectrum under each mission profile according to the mission segment sequence under the mission profile, to obtain a mission profile spectrum under a complete flight; S65. Sort all mission profile spectra of each complete flight to obtain the final severe gust spectrum.

7. The method for compiling severe gust spectrum based on the fusion of historical data and measured data according to claim 6 is characterized in that: The specific calculation process of equivalent load is as follows: Assume that the equivalent load of the 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: Δg=a i lg N+b i In the formula, a i 、b i is the constant of the load spectrum curve of the i-th section; Then the equivalent load is: Where: S is the slope parameter of the material SN curve, n is the number of cycles of equivalent load, Δg i ,Δg i+1 It refers to the minimum Δg and maximum Δg of the i-th curve; Equivalent load cycles:

Images