A method for compiling an aircraft gust measured severity spectrum considering gust dispersion

Abstract

A kind of aircraft gust measured severity spectrum compiling method considering gust overload dispersity, comprising the following steps: obtaining measured load data and screening overload data, obtaining the overload cumulative exceedance curve family of each mission segment;The exceedance curve family is dispersed, and the distribution function and distribution parameter of exceedance under each level load are determined;For each level load of each mission segment, establish exceedance probability distribution model, calculate the exceedance of 90% coverage according to the distribution function and distribution parameter, obtain the overload cumulative exceedance data pair of each mission segment;The overload cumulative exceedance data of each mission segment is fitted and calculated to obtain the severe gust overload cumulative exceedance curve;According to the severe gust overload cumulative exceedance curve, severe gust overload spectrum is compiled;The present application can reflect the severe use of aircraft, improve the consistency of test and use, reduce the time of fatigue test.

Description

Technical Field

[0001] This invention relates to the field of aircraft life prediction technology, and more specifically to a method for compiling a measured severity spectrum of aircraft gusts that takes into account gust dispersion. Background Technology

[0002] Currently, flight load spectrum refers to the spectrum compiled from the time history of loads experienced by an aircraft during flight. Measured flight load spectra are compiled through specialized testing modifications and flight tests to determine and verify the aircraft's design service life, and are a prerequisite for determining the fatigue life extension of aircraft structures. Gust loads account for a significant proportion of damage in flight loads, making the compilation of measured gust spectra crucial for extending the lifespan of military and civilian aircraft. The aviation industry has accumulated a large amount of measurement data, developing gust spectrum compilation methods based on gust speed exceedance curves, 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 aircraft used under the same operational requirements exhibit significant differences in load-time histories among different aircraft within a fleet, corresponding to the dispersion of load spectra. Durability analysis and testing must be conducted under a defined (unique) load spectrum. Therefore, selecting and compiling a reasonable load spectrum has become crucial for structural durability analysis and assessment. This has led to the development of the "critical load spectrum," which offers advantages such as exposing the aircraft's inherent failure characteristics and reducing testing time. Consequently, the critical load spectrum is increasingly being used in the compilation of measured load spectra.

[0004] The concepts, compilation methods, and lifetime analysis methods for average spectra are relatively mature, but there are still several key technologies to be solved in the study of severe spectra.

[0005] Therefore, how to provide a method for compiling the measured severity spectrum of aircraft gusts that takes into account gust dispersion, and can fully consider gust dispersion while ensuring safety and economy, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a method for compiling the measured severity spectrum of aircraft gusts that takes into account gust dispersion, which can reduce the fatigue testing time, and the dispersion factor used for life determination only needs to consider the dispersion factor of the structure.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A method for compiling the measured severity spectrum of aircraft gusts that takes into account gust dispersion includes the following steps:

[0009] Acquire measured load data of the aircraft group and filter out overload data to obtain a family of cumulative overload exceedance curves for each task segment;

[0010] The family of transcendental number curves is discretized, and the distribution function and distribution parameters of the transcendental number under each load level are calculated;

[0011] For each level of load in each task segment, an exceedance probability distribution model is established. Based on the distribution function and the distribution parameters, the exceedance number with a coverage rate of 90% is calculated to obtain the overload cumulative exceedance number data pair for each task segment.

[0012] The cumulative overload exceedance number data for each task segment are fitted and calculated to obtain the cumulative overload exceedance number curve for each task segment.

[0013] A severe gust overload spectrum is constructed based on the cumulative exceedance number curve of the severe gust overload.

[0014] Furthermore, the measured load data is obtained, and the cumulative overload exceedance number is calculated to obtain a family of cumulative overload exceedance number curves for each task segment. The steps include:

[0015] Confirm the aircraft mission profile; wherein the mission profile includes multiple mission segments;

[0016] The measured load data under the aircraft mission profile is obtained, and the measured load data is preprocessed to obtain overload data.

[0017] The family of cumulative overload exceedance curves for each task segment is calculated based on the overload data.

[0018] Furthermore, the preprocessing step includes:

[0019] The measured load data includes the Y-axis overload of the aircraft's center of gravity; where Y-axis is vertical.

[0020] The Y-axis overload of the aircraft's center of gravity is represented by its corresponding actual load mass G. i The ratio of the standard aircraft mass G0 for this mission segment to the flight profile diagram is corrected:

[0021] △n y0 =△n yi *G i / G0

[0022] Where, △n y0 The overload value is the corrected value according to the standard task section specifications; △n yi This is the measured overload value;

[0023] The measured overload values ​​of the motor load are filtered out to obtain the gust load overload data.

[0024] Furthermore, the measured overload values ​​of the motor load are filtered out to obtain the gust load overload data. The interpretation conditions include:

[0025] Overload that changes slowly and whose load cycle lasts for more than 2 seconds is considered a motor load overload; otherwise, it is a sudden wind load overload.

[0026] An overload with an absolute value of 4° or greater on the control surface deflection angle is a maneuver load overload; otherwise, it is a gust load overload.

[0027] For the climbing and descent mission segments, since there are fewer maneuvers, the loads are all approximated as gust loads, and the separation of gust and maneuver overload is no longer performed.

[0028] Furthermore, the measured load data is obtained and the overload data is filtered out to obtain a family of cumulative overload exceedance curves for each task segment. The steps include:

[0029] Extract peak and valley values ​​from the overload data.

[0030] Using 0.05g as the load increment, a load benchmark is set, and the peak and valley values ​​are screened and counted using the limited span-average peak value counting method to obtain the cumulative exceedance number of each load level; wherein, the cumulative exceedance number includes the cumulative exceedance number of positive gust overload and the cumulative exceedance number of negative gust overload;

[0031] Calculate the geometric mean of the cumulative exceedance number of the positive gust overload and the cumulative exceedance number of the negative gust overload:

[0032]

[0033] This yields a family of overload cumulative exceedance number curves for each task segment.

[0034] Furthermore, the family of transcendental number curves is discretized, and the distribution function and distribution parameters of the transcendental number under each load level are calculated. The steps include:

[0035] The overload cumulative exceedance number data of each task segment in the family of overload cumulative exceedance number curves is converted into standard time cumulative exceedance number data:

[0036]

[0037] The measured data corresponds to the flight time t. M The standard time for the task segment is t. s

[0038] The cumulative overload exceedance data for the standard time of each task segment are fitted to calculate the cumulative overload exceedance curve for each task segment.

[0039] Discretize the cumulative overload exceedance curve for each task segment to obtain exceedance data pairs corresponding to different sudden wind overloads;

[0040] Assume that the exceedance number corresponding to the specified sudden wind overload follows a log-normal distribution;

[0041] The maximum likelihood function estimation method is used to estimate the distribution parameters.

[0042] Furthermore, the cumulative exceedance data of the standard time for each task segment are fitted, and the fitting equation is as follows:

[0043] Δn y =a*lgN+b

[0044] Where a and b are the fitting parameters, Δn y It represents incremental overload, and N is the cumulative exceedance number.

[0045] Furthermore, the maximum likelihood function estimation method is used to estimate the distribution parameters, and the likelihood function is:

[0046]

[0047] Where, x i =lgΔN i The objective parameters (μ, σ) are solved by taking the derivatives of the likelihood functions with respect to μ and σ as zero, respectively.

[0048]

[0049]

[0050] Furthermore, the exceedance number at the 90th percentile is calculated, and this is used to fit the cumulative exceedance number curve for severe gust overload. The exceedance number for each load level with 90% coverage in each task segment is:

[0051]

[0052] Where, μ i σ i The i-th level overload n y,i The corresponding logarithmic median and standard deviation of the transcendental number.

[0053] The cumulative exceedances at each level are:

[0054]

[0055] Obtain the cumulative overload exceedance data pair (Δn) for each task segment. y ΔN 90 ) i .

[0056] Furthermore, before fitting and calculating the cumulative overload exceedance data for each task segment, the method further includes high-load truncation and low-load truncation of the cumulative overload exceedance data for each task segment.

[0057] The high load interception is: setting a high load interception value based on the high load frequency, such as using a high load that occurs once in 1000 flights as the interception value;

[0058] The low-load cutoff is achieved by setting a low-load cutoff value based on the magnitude of the load. For example, if the low-load cutoff value is set to 0.05, then small loads below 0.05g are deleted or converted into a certain level of load. Typically, the fatigue limit of critical components is determined based on the aforementioned analysis, and the overload value corresponding to 70% to 80% of the fatigue limit is cut off. Here, the fatigue limit refers to the maximum stress value that can withstand an infinite number of stress cycles without failure.

[0059] Furthermore, a severe gust overload spectrum is constructed based on the cumulative exceedance number curve of the severe gust overload, the steps of which include:

[0060] Determine the number of load levels based on the mission profile;

[0061] Based on the cumulative exceedance number curve of severe gust overload in the task segment, the load parameters of each load level are calculated, including the equivalent load and the number of equivalent load cycles.

[0062] Compile 5×5 spectra: Based on the assumption that the highest load in the glide mission segment of each flight is a normal logarithmic extreme value distribution, determine the number of occurrences of each type of flight; based on the assumption that the gale spectrum shapes of various flight types are similar, confirm the gale increment overload spectrum of each load level of each type of flight.

[0063] Based on the 5×5 spectrum, the task segment spectrum and the task profile spectrum are compiled sequentially.

[0064] The final flight-continued flight final payload spectrum was compiled based on the mission profile spectrum.

[0065] Furthermore, based on the aforementioned 5×5 spectrum, the task segment spectrum and task profile spectrum are sequentially compiled, including the following steps:

[0066] Compile the mission segment spectrum: Based on the data in the 5×5 spectrum, link the loads of each level and their corresponding frequencies under the k-th flight type of the i-th mission segment of the j-th profile, randomly and alternately select peak and valley values ​​for random pairing and arrangement to form the load spectrum of the specific flight type of the mission segment, represent the mission segment as a load pair sequence, and compile the spectrum of various flight types under all mission segments.

[0067] fi,j,k=(Δn ydn ,-Δn ydm ) h

[0068] Where, Δn ydn This represents the nth load level of this task segment, indicating the peak load; -Δn ydm Let m be the load of this task segment, representing the valley load;

[0069] Compile the task profile spectrum: Arrange the task segments of each task profile and compile all task profiles to obtain the task profile spectrum.

[0070] Compile the final payload spectrum for the flight-continued-flight mission: Construct a mission profile spectrum sequence based on the mission profile spectrum and the preset mission profile flight type vector; first, number all mission profiles in the mission profile sequence sequentially using a natural number sequence, then combine each number with a random integer sequence "x" respectively. is "Corresponding to this, press x" is The numerical values ​​are sorted to obtain the final flight-continued-flight load spectrum.

[0071] The beneficial effects of this invention are:

[0072] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method for compiling the measured severity spectrum of aircraft gusts that considers gust dispersion. This method can reduce fatigue testing time, and the dispersion factor used for life determination only needs to consider the dispersion factor of the structure. The severity spectrum is more severe than the average spectrum, and its fatigue load amplitude is larger than that of the average spectrum. Therefore, it can reduce fatigue testing time because the severity spectrum already includes the load dispersion, so only the structural dispersion needs to be considered. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0074] Figure 1 The attached figure is a schematic diagram of a method for compiling the measured severity spectrum of aircraft gusts that takes into account the dispersion of gusts, provided by the present invention.

[0075] Figure 2 The attached figure is a schematic diagram of peak and valley value detection in this invention. Detailed Implementation

[0076] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0077] like Figure 1 This invention discloses a method for compiling a measured severity spectrum of aircraft gusts that takes into account gust dispersion, comprising the following steps:

[0078] S1: Confirm the aircraft mission profile; the mission profile includes multiple mission segments; according to the aircraft's operational requirements, specify the composition and proportions of the mission profile; for each mission profile, clearly define the composition, order, proportions, and parameters of the mission segments, such as mission segments, mission segment altitude, speed, weight, flight distance, and flight time. The division of the measured data mission segments follows these principles:

[0079] a) The criterion is that the flap deflection angle becomes 0° at the start of the climb.

[0080] b) The point at which the climb ends / the point at which level flight begins and the altitude curve begins to flatten is used as the criterion;

[0081] c) The point at which level flight ends / the point at which descent begins is determined by the inflection point of the altitude curve;

[0082] d) The criterion for the end of the glide is that the flap deflection angle becomes 35°.

[0083] S2: Acquire measured load data under the aircraft mission profile and preprocess the measured load data to obtain overload data; preprocess the measured load data, including separation of gust load and maneuver load, filtering, overload standardization, etc.

[0084] In one embodiment, the preprocessing step includes:

[0085] The measured load data includes the Y-axis overload of the aircraft's center of gravity;

[0086] S21: Overload the aircraft's center of gravity in the Y direction, using the corresponding actual load mass G. i The ratio of the standard aircraft mass G0 for this mission segment to the flight profile diagram is corrected:

[0087] △n y0 =△n yi *G i / G0

[0088] Where, △n y0 The overload value is the corrected value according to the standard task section specifications; △n yiThis is the measured overload value;

[0089] S22: Filter out the measured overload values ​​of the motor load to obtain the gust load overload data.

[0090] In this implementation, the measured overload values ​​of the maneuver load are filtered out to obtain the gust load overload data. The judgment conditions adopted include: the overload changes slowly and lasts for more than 2 seconds, which is the maneuver load overload; otherwise, it is the gust load overload; the overload with an absolute value of the control surface deflection angle equal to or greater than 4° is the maneuver load overload; otherwise, it is the gust load overload. For the climb and descent mission segments, since there are fewer maneuvers, the loads are all approximately considered as gust loads, and the separation of gust and maneuver is no longer performed.

[0091] S3: Calculate the family of cumulative overload exceedance curves for each task segment based on overload data;

[0092] In one embodiment, the family of overload cumulative exceedance number curves for each task segment is calculated based on overload data, the steps of which include:

[0093] S31: The peak-valley method is used to extract peak and valley values ​​from the overload data. Peak and valley value detection involves detecting all peaks and valleys during the counting process, filtering out the data between the peaks and valleys, and retaining the corresponding sampling point numbers. For example... 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 points. If satisfied or Then the peak value (or valley value) is taken once.

[0094] S32: Set the load baseline, and use the limited span-average peak value counting method to filter and count the peak and valley values ​​to obtain the cumulative exceedance number of each load level; among which, the cumulative exceedance number includes the cumulative exceedance number of positive gust overload and the cumulative exceedance number of negative gust overload;

[0095] The restricted peak value counting method is a counting method formed by adding certain restrictions to the peak value method. Its specific processing requirements are as follows: Using the "1g" load state of each task segment as a baseline, if the valley value between two peaks (or the peak value between two valleys) does not exceed the upper deviation (lower deviation in the case of negative overload) of the "1g" baseline for that task segment, only the maximum peak value (valley value in the case of negative overload) is recorded, and the upper and lower deviations are 20% of the maximum peak value. This deviation is the restriction condition.

[0096] The peak value obtained by the cross-peak counting method is limited to be greater than 0, and the valley value is less than 0. Several load levels are selected, and the cumulative exceedance counts of positive and negative gust overloads are recorded for both positive and negative peak values, denoted as N(+Δn). y ) and N(-Δn y ).

[0097] S33: Calculate the geometric mean of the cumulative overload exceedance number of positive gust and the cumulative overload exceedance number of negative gust to obtain the family of overload exceedance number curves for each task segment.

[0098] The geometric mean is used to obtain the average cumulative exceedance number corresponding to a symmetrical sudden wind overload. This yields the family of cumulative exceedance number curves for +Δny:

[0099]

[0100] S4: Discretize the family of transcendence number curves and calculate the distribution function and distribution parameters of the transcendence number for each load level;

[0101] In another embodiment, the family of transcendence number curves is discretized, and the distribution function and distribution parameters of the transcendence number for each load level are calculated. The steps include:

[0102] S41: Convert the cumulative overload ...

[0103] The specific steps are as follows:

[0104] Since the measured load spectrum is based on the mission segment flight time, which differs from the standard time of the mission segment in the mission profile, it is necessary to convert the measured cumulative overload exceedance data of the mission segment into cumulative exceedance data of the standard time. Let the flight time corresponding to the measured data be t. M The standard time for the task segment is t. S The cumulative exceedance number N of the i-th level load obtained from the measured time. ib Then the cumulative exceedance number of the i-th level load in standard time should be:

[0105]

[0106] By substituting the cumulative overload exceedance counts for each load level using the above formula, the cumulative overload exceedance count data (Δn) for the standard time task segment can be obtained. y ,N) i .

[0107] S42: Cumulative exceedance data (Δn) for the standard time of each task segment. y ,N) iBy fitting the data, the cumulative overload exceedance curve for each task segment is calculated.

[0108] S43: Discretize the overload cumulative exceedance number curve for each task segment, and discretize it into (Δn) y From the data pairs N)i (i = 1, ..., n), the exceedance number ΔNi = Ni - Ni-1 is calculated, and the exceedance number data pairs (Δn) corresponding to different sudden wind overloads are obtained. y ,ΔN)i (i=1,…,n);

[0109] S44: Assume that the exceedance number corresponding to the specified sudden wind overload follows a log-normal distribution;

[0110] S45: The maximum likelihood function estimation method is used to estimate the distribution parameters.

[0111] The fitting equation is:

[0112] Δn y =a*lgN+b

[0113] Where a and b are fitting parameters, Δn y It represents incremental overload, and N is the cumulative exceedance number.

[0114] The likelihood function is:

[0115]

[0116] Where, x i =lgΔN i The objective parameters (μ, σ) are solved by taking the derivatives of the likelihood functions with respect to the mean μ and standard deviation σ as zero, respectively.

[0117]

[0118]

[0119] S5: For each level of load in each task segment, calculate the exceedance number with 90% coverage based on the distribution function and distribution parameters, i.e.

[0120]

[0121] Where, μ i σ i The i-th level overload n y,i The corresponding log-median and standard deviation of the transcendental number

[0122] Obtain the cumulative overload exceedance data pair (Δn) for each task segment. y ΔN 90 ) i ;

[0123] S6: Overload cumulative exceedance data pairs (Δn) for each task segment y ΔN 90 ) i The cumulative exceedance number curves of severe gust overload for each task segment were obtained by fitting calculations.

[0124] S7: Compile a severe gust overload spectrum based on the cumulative exceedance number curve of severe gust overload. Five load levels are determined using the load spectrum equivalent criterion, and a 5×5 spectrum based on the mission segment is compiled: First, a severe mission segment spectrum is compiled for typical mission segments in the mission profile; then, the mission segments constitute the mission profile severe spectrum (takeoff and landing spectrum); finally, the severe gust overload flight-continuation-flight spectrum with a period of 1000 takeoffs and landings is compiled from the mission profile severe spectrum by random proportional sorting. The core of compiling the durability severe gust spectrum lies in how to determine the severe gust overload cumulative exceedance number curve based on the mission segment.

[0125] In one embodiment, the severe sudden wind overload spectrum is compiled based on the cumulative exceedance number curve of the severe sudden wind overload, the steps of which include:

[0126] S71: Determine the load levels based on the mission profile; each mission segment of the medium-altitude flight is discretized into 5 levels. The number of discretized loads is an integer in a program block, and each mission segment is flown at least once. The representative value of each load level, i.e., the equivalent load, is determined by damage calculation for the discrete segment.

[0127] S72: Based on the cumulative exceedance number curve of severe gust overload in the task segment, calculate the load parameters for each load level, including the equivalent load and the number of equivalent load cycles;

[0128] Based on the cumulative exceedance number curve of severe sudden wind overload in the task segment, the equivalent load spectrum is calculated. Equivalent load calculation: Assume that the equivalent load for a discrete segment requiring equivalent calculation is Δn. yd The equivalent load cycle number is Neq. Using m straight lines to replace the discrete segment of the curve, the linear equation of the i-th segment is:

[0129] Δg=a i lgN+b i

[0130] In the formula, a i b i is a constant of the i-th segment of the load spectrum curve.

[0131] The equivalent load is:

[0132]

[0133] In the formula, S is the slope parameter of the SN curve for the material; for aluminum alloy, S = 2.0.

[0134] Equivalent load cycle count:

[0135]

[0136] S73: Compiling a 5×5 spectrum: Based on the assumption that the highest load in the glide phase of each flight follows a normal logarithmic extreme distribution, determine the number of occurrences y for each type of flight. i Based on the assumption that the gale spectrum shapes of various flight types are similar, the gale increment overload spectrum B of each load level for various flight types is confirmed. i，j As shown in Table 1.

[0137] Table 1 5×5 spectrum

[0138]

[0139] In the table, y1+y2+y3+y4+y5 should equal the number of times this profile appears in 1000 flights. Compile the 5×5 spectra for all mission segments under all profiles using the method described above. Furthermore, y1, y2, y3, y4, and y5 should be identical for the 5×5 spectra of each mission segment under each mission profile.

[0140] S74: Compile the task segment spectrum and task profile spectrum sequentially based on the 5×5 spectrum;

[0141] In one implementation, the task segment spectrum and task profile spectrum are sequentially compiled based on the 5×5 spectrum, and the steps include:

[0142] Compiling the mission segment spectrum: Based on the data in the 5×5 spectrum, the load levels of each level and their corresponding frequencies under a single takeoff and landing of the k-th flight type in the i-th mission segment of the j-th profile are linked. Peak and trough values ​​are randomly selected and randomly paired to form the load spectrum for the specific flight type of the mission segment, denoted as (Δn). ydn ,-Δn ydm ) h The spectrum of this mission segment (a total of j×i×k) is represented as a sequence of payload pairs, and the spectra of various flight types under all mission segments are compiled.

[0143] fi,j,k=(Δn ydn ,-Δn ydm ) h

[0144] Where, Δn ydn This represents the nth (n = 1, 2, 3, 4, 5) level load of this task segment, indicating the peak load; -Δn ydm The m-th (m = 1, 2, 3, 4, 5) load of this task segment represents the valley load;

[0145] In this embodiment, a task profile spectrum is compiled: the task segments of each task profile are arranged, and all task profiles are compiled to obtain the task profile spectrum.

[0146] S75: Compile the final flight-continued flight final payload spectrum based on the mission profile spectrum.

[0147] Compile the final flight-continued flight payload spectrum: Construct a mission profile spectrum sequence based on the mission profile spectrum and the preset mission profile flight type vector; First, number all mission profiles in the mission profile sequence sequentially with natural numbers, then associate each number with a random integer sequence "xis", sort them according to the value of xis, and obtain the final flight-continued flight payload spectrum.

[0148] In this embodiment, a random sequence “x” is randomly selected alternately from the peak, valley and total load spectrum G. i The selection of "" is achieved using a pseudo-random method based on the multiplication congruence method, and reasonable random results are given by adjusting the random parameters.

[0149] Multiplication by congruence:

[0150] y i+1 =λy i (mod2 k (1)

[0151] x i =y i / 2 m (2)

[0152] First, "y" is formed from equation (1). i "The sequence is then used to obtain the random sequence "x" from equation (2). i Calculate x i Arrange the total number in ascending order xis, and you get the random integer sequence "xis".

[0153] Regarding the parameter values ​​in equations (1) and (2), the initial value y1 is an odd number; the coefficient λ = 8t - 3, where t is any natural number, and k is determined according to the required random period "n", so that 2 k-2 ≥n; m takes the value 2. By changing the values ​​of y1, t, and k, different random number sequences can be obtained.

[0154] In another embodiment, before fitting and calculating the cumulative overload exceedance data for each task segment, the system further includes high-load truncation and low-load truncation of the cumulative overload exceedance data for each task segment.

[0155] The high load threshold is defined as a high load that occurs once in every 1000 flights of this mission segment.

[0156] Low-load cutoff refers to the following: The spectrum usually contains a large number of small-amplitude load cycles, which need to be deleted or converted to the lowest level of load based on the equivalent damage. Equivalent damage conversion means first cutting off the load, and then converting it to the lowest level of load based on the equivalent damage. Usually, the fatigue limit of the key parts is determined based on the aforementioned analysis, and the overload value corresponding to 70% to 80% of the fatigue limit is cut off.

[0157] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0158] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for compiling the measured severity spectrum of aircraft gusts considering gust dispersion, characterized in that, Includes the following steps: Acquire measured load data of the aircraft group and filter out overload data to obtain a family of cumulative overload exceedance curves for each task segment; The family of transcendental number curves is discretized, and the distribution function and distribution parameters of the transcendental number under each load level are determined; For each level of load in each task segment, an exceedance probability distribution model is established. Based on the distribution function and distribution parameters, the exceedance number with a coverage rate of 90% is calculated, and the corresponding cumulative exceedance number data pairs for each task segment with a 90% coverage rate are obtained. The cumulative overload exceedance data for each task segment are fitted to obtain the cumulative overload exceedance curve for each task segment under severe gusts of wind. Before fitting and calculating the cumulative overload exceedance data for each task segment, the data for each task segment is further subjected to high load truncation and low load truncation. The high load threshold is defined as a high load that occurs once in every 1000 flights of this mission segment. Low load removal means deleting small loads or converting equivalent damage to the lowest level load. Equivalent damage conversion means first removing the load, and then converting it to the lowest level load based on the equivalent damage. The severe sudden wind overload spectrum is constructed based on the cumulative exceedance number curve of the severe sudden wind overload; the steps include: Determine the number of load levels based on the mission profile; Based on the cumulative exceedance number curve of severe gust overload in the task segment, the load parameters of each load level are calculated, including the equivalent load and the number of equivalent load cycles. Compile 5×5 spectra: Based on the assumption that the highest load in the glide mission segment of each flight is a normal logarithmic extreme value distribution, determine the number of occurrences of each type of flight; based on the assumption that the gale spectrum shapes of various flight types are similar, confirm the gale increment overload spectrum of each load level of each type of flight. Based on the 5×5 spectrum, the mission segment spectrum and mission profile spectrum are compiled sequentially: Based on the data in the 5×5 spectrum, the loads of each level and their corresponding frequency of occurrence under the k-th flight type in the i-th mission segment of the j-th profile are linked together, and peak and valley values ​​are randomly selected and randomly paired to form the load spectrum of the specific flight type of the mission segment. The mission segment is represented as a load pair sequence, and the spectra of various flight types under all mission segments are compiled. fi,j,k= ; Where, Δn ydn This represents the nth load level of this task segment, indicating the peak load; -Δn ydm Let m be the load of this task segment, representing the valley load; Arrange the task segment spectra of each task profile and compile all task profiles to obtain the task profile spectrum. The final flight-continued flight payload spectrum is compiled based on the mission profile spectrum: a mission profile spectrum sequence is constructed based on the mission profile spectrum and the preset mission profile flight type vector; first, all mission profiles in the mission profile sequence are numbered sequentially with natural numbers, and then each number is matched with a preset random integer sequence, and sorted according to the size of each value in the sequence to obtain the final flight-continued flight payload spectrum.

2. The method for compiling the measured severity spectrum of aircraft gusts considering gust dispersion as described in claim 1, characterized in that, Obtain measured load data and calculate the cumulative overload exceedance number to obtain a family of cumulative overload exceedance number curves for each task segment. The steps include: Confirm the aircraft mission profile; wherein the mission profile includes multiple mission segments; Acquire measured load data under the mission profile of the aircraft group, and preprocess the measured load data to obtain overload data; The family of cumulative overload exceedance curves for each task segment is fitted based on the overload data.

3. The method for compiling the measured severity spectrum of aircraft gusts considering gust dispersion according to claim 2, characterized in that, The preprocessing steps include: The measured load data includes the Y-axis overload of the aircraft's center of gravity; The Y-axis overload of the aircraft's center of gravity is represented by its corresponding actual load mass G. i The ratio of the standard aircraft mass G0 for this mission segment to the flight profile diagram is corrected: △ n y0 =△ n yi ·G i / G 0； Among them, △ n y0 This is the overload value corrected according to the standard task section quality; △ n yi This is the measured overload value; After filtering out the motor overload values, we obtain the gust load overload data.

4. The method for compiling the measured severity spectrum of aircraft gusts considering gust dispersion according to claim 3, characterized in that, The measured values ​​of sudden gusts and vehicle overloads were separated to obtain the sudden gust load overload data. The separation conditions used included: Overload that changes slowly and whose load cycle lasts for more than 2 seconds is considered a motor load overload; otherwise, it is a sudden wind load overload. An overload with an absolute value of 4° or greater on the control surface deflection angle is a maneuver load overload; otherwise, it is a gust load overload. For the climbing and descent mission segments, since there are fewer maneuvers, the loads are all approximated as gust loads, and the separation of gust and maneuver overload is no longer performed.

5. The method for compiling the measured severity spectrum of aircraft gusts considering gust dispersion according to claim 2, characterized in that, The steps include: acquiring measured load data and filtering out overload data to obtain a family of cumulative overload exceedance number curves for each task segment; and obtaining the cumulative overload exceedance number curves for each task segment. Extract peak and valley values ​​from the overload data; A load baseline is set, and the peak and valley values ​​are screened and counted using the limited span-average peak value counting method to obtain the cumulative exceedance number of each load level; wherein, the cumulative exceedance number includes the cumulative exceedance number of positive gust overload and the cumulative exceedance number of negative gust overload; Calculate the geometric mean of the cumulative exceedance number of the positive gust overload and the cumulative exceedance number of the negative gust overload: N (D n y )= ; The family of overload cumulative exceedance number curves for each task segment is obtained.

6. The method for compiling the measured severity spectrum of aircraft gusts considering gust dispersion according to claim 1, characterized in that, Discretize the family of transcendental number curves and calculate the distribution function and distribution parameters of the transcendental number under each load level. The steps include: Convert the cumulative overload exceedance data for each task segment into the cumulative exceedance data for the standard time of the task segment; ； The flight time corresponding to the measured data is: t M The standard time for the task segment is t s ; The cumulative overload exceedance data for the standard time of each task segment are fitted to calculate the cumulative overload exceedance curve for each task segment. Discretize the cumulative overload exceedance curve for each task segment to obtain exceedance data pairs corresponding to different sudden wind overloads; Assume that the exceedance number corresponding to the specified sudden wind overload follows a log-normal distribution; The maximum likelihood function estimation method is used to estimate the distribution parameters.

7. The method for compiling the measured severity spectrum of aircraft gusts considering gust dispersion as described in claim 6, characterized in that, The cumulative exceedance data for the standard time of each task segment are fitted, and the fitting equation is as follows: Δ n y = a ·lg N + b; in, a , b These are the fitting parameters, Δ n y It is an incremental overload. N It is the cumulative transcendence number.

8. The method for compiling the measured severity spectrum of aircraft gusts considering gust dispersion according to claim 6, characterized in that, The distribution parameters are estimated using the maximum likelihood function estimation method, and the likelihood function is: L = = ； in, x i =lgΔ Ni ;Δ N i These are transcendental numbers at various levels; Based on the likelihood function, respectively, the mean... μ and standard deviation Solving for the objective parameters by finding the derivative to zero : ； 。 9. The method for compiling the measured severity spectrum of aircraft gusts considering gust dispersion as described in claim 8, characterized in that, Calculate the exceedance number at the 90th percentile and use it to fit the cumulative exceedance number curve for severe sudden wind overload. The exceedance numbers for each load level with 90% coverage in each task segment are: D N 90,i = ; in, The i-th level overload n y,i The corresponding log-median and standard deviation of the transcendental number The cumulative exceedances at each level are: ; Obtain the cumulative overload exceedance data pair (Δ) for each task segment. n y , N 90 ) i ; For each task segment, the cumulative overload exceedance data pair (Δ) n y , N 90 ) i The cumulative exceedance number curves for severe gust overload were obtained by fitting calculations. The fitting equation for the cumulative exceedance number curves for severe gust overload is as follows: D n y = a ∙lg N 90 + b; in, a and b These are the fitting parameters.

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