A method for compiling severe gust spectrum of aircraft based on actual measurements

Abstract

The application discloses a method for compiling a severe gust spectrum of an airplane, comprising the following steps: determining a typical mission profile according to the use requirement of a to-be-tested airplane; determining the sequence, time proportion and parameters of a mission section constituting the mission profile; collecting measured load data of the to-be-tested airplane and pre-processing the measured load data; generating a measured gust overload cumulative overshoot curve family based on the mission section according to the pre-processed measured load data; calculating the area between the measured gust overload cumulative overshoot curve corresponding to each mission section and a coordinate axis, and taking the measured gust overload cumulative overshoot curve meeting a preset condition as a severe gust overload cumulative overshoot curve; and compiling a severe gust spectrum of the to-be-tested airplane according to the severe gust overload cumulative overshoot curve based on the mission section. The severe gust spectrum of the airplane compiled by the method can fully consider the gust dispersion, can effectively reduce the time of fatigue test, and only needs to consider the dispersion coefficient of the structure when determining the service life.

Description

Technical Field

[0001] This invention relates to the field of aircraft measured load testing technology, and in particular to a method for compiling a measured severe gust spectrum for aircraft. Background Technology

[0002] Flight load spectrum refers to the spectrum compiled from the time history of loads experienced by an aircraft during flight. It is compiled through specialized testing modifications and flight tests to determine and verify the aircraft's design service life, and is 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 service life 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 among aircraft operating under the same requirements, the load-time histories of different aircraft within a fleet vary significantly, 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 spectrum," which offers advantages such as exposing the aircraft's inherent failure characteristics and reducing testing time. Consequently, the critical spectrum is increasingly used in the compilation of measured load spectra. While the concepts, compilation methods, and life analysis methods under the average spectrum are relatively mature, several key technologies related to the critical spectrum still require resolution.

[0004] Regarding specific spectrum compilation methods, there is currently no publicly available research data from abroad. Domestically, the main methods for compiling spectrums for severe aircraft gusts include:

[0005] (1) The literature “Severity spectrum compilation method based on flight subject statistical analysis” proposes a severity spectrum compilation method based on damage distribution to select severe representative take-offs and landings.

[0006] (2) The literature "A Preliminary Study on the Severity Spectrum Selection Method Based on Load Damage Dispersion" proposes that, under specified fleet life reliability requirements, the load spectrum severity PL is related to load damage and structural dispersion. For current aircraft load damage distribution parameters, the load spectrum severity PL is slightly higher than 90%.

[0007] Therefore, based on existing severe gust spectrum technology, how to provide a method for compiling aircraft measured severe gust spectrum that fully considers gust dispersion while ensuring safety and economy has become an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0008] In view of the above problems, the present invention proposes a method for compiling the measured severe gust spectrum of an aircraft that at least solves some of the above technical problems. The severe gust spectrum of an aircraft compiled by this method can effectively reduce the fatigue test time, and the dispersion factor used when determining the service life only needs to consider the dispersion factor of the structure.

[0009] This invention provides a method for compiling a severe gale spectrum measured on an aircraft, comprising the following steps:

[0010] S1. Generate a mission profile based on the usage requirements of the aircraft under test; clarify the sequence, time ratio, and parameters of the mission segments that constitute the mission profile;

[0011] S2. Collect the measured load data of the aircraft under test, and preprocess the measured load data;

[0012] S3. Based on the preprocessed measured load data, generate a family of measured cumulative exceedance number curves for the measured sudden wind overload based on the task segment.

[0013] S4. Calculate the area between the measured cumulative exceedance number curve of sudden gust overload and the coordinate axis for each task segment, and take the measured cumulative exceedance number curve of sudden gust overload that meets the preset conditions as the cumulative exceedance number curve of severe sudden gust overload; the measured cumulative exceedance number curve of sudden gust overload that meets the preset conditions is the measured cumulative exceedance number curve of sudden gust overload corresponding to the percentage of the durability severity spectrum in the area.

[0014] S5. Based on the cumulative exceedance number curve of severe gust overload, complete the severe gust measured spectrum compilation of the aircraft under test.

[0015] Further, in step S2, the measured load data is preprocessed, including:

[0016] The overload data is obtained by subtracting the first preset value from the measured load data, and the overload data is then standardized.

[0017] Separate the gust load and the motion load from the measured load data;

[0018] Obtain the peak and valley values ​​in the measured load data, filter out the measured load data between the peak and valley values, and retain the corresponding sampling point number.

[0019] Furthermore, the overload data is standardized according to the following formula:

[0020] △n y0 =△n yi *Gi / G0

[0021] In the above formula, △ny0 This is the standardized overload data; △n yi The data represents the measured overload; Gi represents the actual load mass data; and G0 represents the standard aircraft mass data.

[0022] Furthermore, the gust load and the kinetic load in the measured load data are separated in the following manner:

[0023] In the measured load data, overloads that change slowly and last for more than a preset threshold are considered as motor loads; otherwise, they are considered as sudden wind loads.

[0024] Overloads in the measured load data that include a control surface deflection angle and whose absolute value is equal to or greater than a second preset value are considered as maneuver loads; otherwise, they are considered as gust loads.

[0025] The overloads in the measured load data during the climbing and descent mission phases are all considered as sudden wind loads.

[0026] Further, step S3 includes:

[0027] The preset load state of each task segment is used as a reference to divide the baseline;

[0028] When the peak and valley values ​​in the preprocessed measured load data do not exceed the deviation of the baseline, the maximum peak value and minimum valley value in the preprocessed measured load data are recorded; the deviation includes: upper deviation and lower deviation;

[0029] Based on the maximum peak value and minimum valley value, the cumulative overload exceedance number is recorded; the cumulative overload exceedance number includes: the cumulative overload exceedance number of positive gust and the cumulative overload exceedance number of negative gust;

[0030] The average of the cumulative overload exceedance numbers is obtained by geometric mean;

[0031] The cumulative overload exceedance number of each task segment is fitted to obtain the measured cumulative overload exceedance number curve of each task segment, and a family of measured cumulative overload exceedance number curves of the gust is generated.

[0032] Furthermore, in step S3, after recording the cumulative overload exceedance number and before calculating the average of the cumulative overload exceedance number using a geometric mean, the method further includes:

[0033] The cumulative overload exceedance is converted into the cumulative exceedance of standard time.

[0034] Further, step S4 includes:

[0035] The ordinate represents the cumulative exceedance number. The ordinate axis is a logarithmic coordinate axis, and the starting coordinate is changed to 10.-7 The horizontal axis represents overload, and the horizontal axis is a linear coordinate axis with the initial coordinate set to 0; calculate the area S between the overload-cumulative exceedance number curve and the coordinate axis;

[0036] A random variable model is used, assuming that S follows a normal distribution;

[0037] For the parameter S of a certain mission segment under each measured takeoff and landing, the maximum likelihood estimation method is used to estimate the distribution parameters; the likelihood function is shown in the following equation:

[0038]

[0039] The objective parameters (μ, σ) are solved by taking the derivatives of the likelihood functions with respect to μ and σ as zero, respectively.

[0040]

[0041]

[0042] In equations (6)-(8), x i =S; S is the area; i is the index of the sample data pair; n is the total number of samples; μ is the expected value; σ is the standard deviation; σ0 is the sample standard deviation; μ0 is the mean.

[0043] Further, step S5 includes:

[0044] The number of load levels is determined based on the mission profile.

[0045] Calculate the load spectrum equivalent based on the cumulative exceedance number curve of the severe gust overload;

[0046] Determine the flight type of the mission segment; compile the mission segment spectrum based on the payload spectrum equivalent and the flight type of the mission segment;

[0047] Based on the aforementioned task segment spectrum, compile a task profile spectrum;

[0048] The mission profile spectrum is numbered sequentially according to a natural number sequence, and the numbers are sorted according to the numerical value of a preset random integer sequence to generate the severe gust test spectrum of the aircraft under test.

[0049] Further, based on the cumulative exceedance number curve of the severe gust overload, the load spectrum equivalent is calculated, including:

[0050] Discrete segments are obtained from the cumulative exceedance number curve of the severe sudden wind overload for load spectrum equivalent calculation.

[0051] By replacing the discrete segment of the curve with a straight line, the load spectrum curve is divided, and the load spectrum equivalent is calculated:

[0052]

[0053] in, Δg=a i lgN+b i

[0054] In the above formula, i represents the i-th load spectrum curve; m represents the total number of load spectrum curves; Δg represents the linear equation of all load spectrum curves; Δg i The linear equation representing the i-th segment of the load spectrum curve; Δg i +1 represents the linear equation of the (i+1)th segment of the load spectrum curve; a i and b i All are preset constants of the i-th segment of the load spectrum curve; S is the slope parameter of the material SN curve; n is the equivalent number; N is the cumulative exceedance number.

[0055] Furthermore, based on the payload spectrum equivalent and the flight type of the mission segment, a mission segment spectrum is compiled, including:

[0056] Based on the load spectrum equivalent, the load levels of each stage under a single takeoff and landing of the flight type of the mission segment, and the frequency of each stage of load, peak and valley values ​​are randomly and alternately selected.

[0057] The selected peak and valley values ​​are randomly paired and arranged to form the payload spectrum of the specific flight type of the mission segment;

[0058] The load spectrum is represented as a sequence of load pairs to generate the task segment spectrum.

[0059] Further, based on the aforementioned task segment spectrum, a task profile spectrum is compiled, including:

[0060] The load pair sequence of the task segment spectrum is sorted according to the order of the task segments to generate a task profile spectrum.

[0061] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0062] This invention provides a method for compiling a measured severe gust spectrum for an aircraft, comprising: generating a mission profile based on the usage requirements of the aircraft under test; determining the sequence, time ratio, and parameters of the mission segments constituting the mission profile; collecting measured load data of the aircraft under test and preprocessing the measured load data; generating a family of measured gust overload cumulative exceedance number curves based on the preprocessed measured load data; calculating the area between the measured gust overload cumulative exceedance number curve and the coordinate axis corresponding to each mission segment, and using the measured gust overload cumulative exceedance number curve that meets preset conditions as the severe gust overload cumulative exceedance number curve; and completing the compilation of the measured severe gust spectrum for the aircraft under test based on the severe gust overload cumulative exceedance number curve. The severe gust spectrum compiled by this method can fully consider gust dispersion, effectively reduce fatigue testing time, and the dispersion factor used during life determination only needs to consider the structural dispersion factor.

[0063] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0064] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0065] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0066] Figure 1 This is a flowchart of a method for compiling a severe gale spectrum of an aircraft based on actual measurements, provided in an embodiment of the present invention.

[0067] Figure 2 This is a curve showing the severe cumulative exceedance count during the climb task segment provided in an embodiment of the present invention.

[0068] Figure 3 The random spectrum of flight type A provided in the embodiments of the present invention. Detailed Implementation

[0069] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0070] This invention provides a method for compiling a severe gust spectrum from an aircraft measured in practice, referring to... Figure 1 As shown, it includes the following steps:

[0071] S1. Generate a mission profile based on the usage requirements of the aircraft under test; clarify the sequence, time ratio, and parameters of the mission segments that constitute the mission profile;

[0072] S2. Collect the measured load data of the aircraft under test and preprocess the measured load data;

[0073] S3. Based on the preprocessed measured load data, generate a family of measured cumulative exceedance number curves for sudden wind overload based on the task segment.

[0074] S4. Calculate the area between the measured cumulative overload of sudden wind and the coordinate axis for each task segment. The measured cumulative overload of sudden wind for the task segment that meets the preset conditions is taken as the severe cumulative overload of sudden wind for the task segment. The measured cumulative overload of sudden wind that meets the preset conditions is the measured cumulative overload of sudden wind that meets the preset percentage.

[0075] S5. Based on the cumulative exceedance number curve of severe gust overload, complete the compilation of the severe gust measured spectrum of the aircraft under test.

[0076] The severe gust spectrum of an aircraft compiled by the method for compiling the measured severe gust spectrum of an aircraft provided in this embodiment can fully take into account the gust dispersion, effectively reduce the fatigue test time, and the dispersion coefficient used when determining the life only needs to consider the dispersion coefficient of the structure.

[0077] The method for compiling severe gale spectrum data from aircraft measured in this embodiment will be described in detail below:

[0078] The core of compiling the measured spectrum of severe gusts for aircraft lies in determining the cumulative exceedance number curve of severe gust overload based on the mission segment. The compilation of the measured spectrum of severe gusts for aircraft (aircraft durability severe gust spectrum) mainly consists of five steps:

[0079] (1) Define the mission profile of the aircraft. According to the usage requirements of the aircraft under test, give the composition and proportion of the mission profile; for each mission profile, define the composition, sequence, time proportion and parameters of the mission segments that make up the mission profile (i.e., aircraft weight, altitude and speed).

[0080] (2) Load data preparation. Preprocessing of the measured load data includes separation of gust loads and kinetic loads, filtering, and overload standardization;

[0081] (3) Statistical processing of load data to obtain a family of measured cumulative exceedance number curves of sudden wind overload based on the task segment;

[0082] (4) Determination of the cumulative exceedance number curve for severe gust overload. The severity spectrum should meet the requirements of GJB 67.6A-2008 "Strength Specifications for Military Aircraft: Repeated Load, Durability and Damage Tolerance" regarding the durability severity spectrum. According to this standard, the durability severity spectrum should reflect 90% of the usage of the aircraft fleet. In this embodiment, the severity level of the severity spectrum is taken as 90%. Calculate the area S between the cumulative exceedance number curve for each measured takeoff and landing and the coordinate axis. Take the cumulative exceedance number curve for the measured takeoff and landing that is close to the severity S with a coverage rate of 90% as the cumulative exceedance number curve for severe gust overload. At the same time, correct the cumulative exceedance number curve for gust overload so that its S is close to S. 90,i The correction method is to shift the overload exceedance curve upward as a whole, that is, to correct b in equation (5).

[0083] (5) Compile severe gust overload spectrum. Determine the five load levels based on the load spectrum equivalent criterion and compile a 5×5 spectrum based on the mission segment: First, compile a severe mission segment spectrum for typical mission segments in the mission profile; then, construct a severe spectrum (take-off and landing spectrum) for the mission profile based on the mission segments; finally, compile a severe gust overload flight-continuation-flight spectrum (i.e., the measured spectrum of severe gusts on the aircraft) with a cycle of 1000 take-offs and landings by randomly sorting the severe spectrum of the mission profile in proportion.

[0084] Specifically, the task profile is determined as follows:

[0085] Determine the aircraft's mission profile, mission profile scale, and mission profile composition, and provide mission profile parameters such as mission segments, mission segment altitude, speed, weight, flight distance, and flight time. The division of mission segments based on measured data is also outlined, following these principles:

[0086] a) The criterion is that the flap deflection angle becomes 0° at the start of the climb.

[0087] 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;

[0088] c) The point at which level flight ends / the point at which descent begins is determined by the inflection point of the altitude curve;

[0089] d) The criterion for the end of the glide is that the flap deflection angle becomes 35°.

[0090] The overload spectrum of the main mission segment of the medium-altitude flight profile is compiled according to a "5×5" spectrum, that is: first, the overload spectrum is discretized into a five-level spectrum, and then the discrete spectrum is compiled into load spectra of five typical flight types with different degrees of severity. There are a total of five typical flight types.

[0091] Specifically, the measured load data is preprocessed in the following manner:

[0092] The measured center of gravity overload ny Subtracting 1 from the measured load data yields the incremental overload Δn at that moment. y Where 1 represents the equilibrium state, and subtracting 1 represents the increment in the equilibrium state.

[0093] 1) Overload data n y Standardization process:

[0094] According to the specifications of the aircraft profile diagram, the Y-axis overload data of the aircraft's center of gravity and wings are corrected by the ratio of their corresponding actual mass "Gi" to the standard aircraft mass "G0" for that mission segment in the flight profile diagram. That is:

[0095] △n y0 =△n yi *Gi / G0 (1)

[0096] In the formula, △n y0 The overload value (overload data) is the corrected (standardized) value according to the standard task section specifications; △n yi The measured overload values ​​(overload data) are represented by Gi; the actual load mass data is represented by G0; and the standard aircraft mass data is represented by G0. The actual load mass data is calculated by subtracting fuel consumption from the aircraft weight, and the fuel consumption is calculated as an overall average (the average fuel consumption is calculated from the start of takeoff taxiing to the end of landing impact).

[0097] 2) Separation of gusts and motor overload

[0098] The measured load data includes both maneuvering and gust loads, and it is necessary to separate the gust and maneuver loads. The interpretation of the maneuvering and gust load spectra during flight is determined by comprehensively considering the following three conditions:

[0099] a) Overloads that change slowly and last for more than 2 seconds are considered mechanical loads; otherwise, they are considered sudden wind loads.

[0100] b) An overload with an absolute value of 4° or greater on the control surface deflection angle is a maneuver overload; otherwise, it is a gust overload.

[0101] c) For the climbing and descent mission segments, since there are fewer maneuvers, the loads are all approximately considered as gust loads, and the separation of gust loads and maneuver loads is no longer performed.

[0102] 3) Peak and valley value collection

[0103] Peak-valley detection involves identifying all peak and trough values ​​during the counting process, filtering out the data between peaks and troughs, and retaining the corresponding sampling point numbers. Δn is retained. z1 , Δn z3 , Δn z5 , Δn z6 , Δn z7 , Δnz8 , Δn z10 Peak and valley points, remove Δn z2 , Δn z4 , Δn z9 Sampling point.

[0104] If satisfied

[0105] or

[0106] Then the peak value (or valley value) is taken once.

[0107] Specifically, the statistical analysis of the family of measured cumulative exceedance number curves for sudden wind overload is conducted in the following manner:

[0108] 1) Overload time history counting (limited peak value counting method)

[0109] 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:

[0110] 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 for negative overload) of the "1g" baseline of that task segment, only the maximum peak value (minimum valley value for negative overload) is recorded, and the upper and lower deviations are 20% of the maximum peak value. This deviation is the limiting condition.

[0111] 2) Obtaining the cumulative exceedance number of each load level

[0112] The above-mentioned method of limiting the peak value across the average value results in peak values ​​greater than 0 and valley values ​​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 ).

[0113] The discrete gust model assumes that for gusts of equal intensity (i.e., equal amplitude), the probability of positive (upward) and negative (downward) gusts is equal. Therefore, theoretically, the measured cumulative exceedance number curves for positive and negative gusts should be symmetrical. However, in practice, the cumulative exceedance number curves for positive and negative gusts often differ. In engineering, a geometric mean is used to obtain the average of the cumulative exceedance numbers corresponding to a symmetrical gust overload. This yields ±Δn. y The family of cumulative transcendental number curves is shown in equation (3).

[0114]

[0115] In the above formula, ±Δn yThe number of peaks / troughs exceeding the same level; -Δn y For valley overload; N is the cumulative exceedance number.

[0116] 3) Standardization of cumulative overload exceedances considering flight duration

[0117] Because the measured flight time of the load spectrum 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:

[0118]

[0119] By substituting the cumulative overload exceedance counts for each load level according to the above formula, the cumulative overload exceedance count data for the task segment at standard time can be obtained.

[0120] 4) Fitting of the overload cumulative exceedance curve

[0121] For each task segment, the cumulative overload exceedance data pair (Δn) y ,N) i The fitting equation is as follows:

[0122] Δn y = a*lgN+b (5)

[0123] In the above formula, Δn y denoted as incremental overload; a and b are curve fitting parameters; N is the cumulative exceedance number; and i is the index of the data pair.

[0124] The overload cumulative exceedance curve for each task segment was calculated.

[0125] 5) Calculation of the area under the cumulative overload exceedance curve

[0126] The ordinate represents the cumulative exceedance number. The ordinate axis is a logarithmic coordinate axis, and the starting coordinate is changed to 10. -7 The horizontal axis represents overload, and the horizontal axis is a linear coordinate axis with the starting coordinate set to 0; calculate the area S (triangle) between the overload-cumulative exceedance curve and the coordinate axis;

[0127] 6) Test of transcendental number distribution characteristics

[0128] A random variable model is typically used, assuming that S follows a normal distribution.

[0129] 7) Estimation of distribution parameters

[0130] For the parameter S of a specific mission segment under each measured takeoff and landing, the maximum likelihood estimation method is used to estimate the distribution parameters. The likelihood function is shown in the following equation:

[0131]

[0132] In the above formula, x i =S; S is the area; i is the index of the sample data pair; n' is the total number of samples; μ is the expected value; σ is the standard deviation; σ0 is the sample standard deviation; μ0 is the mean. The objective parameters (μ, σ) are solved by taking the derivative of the likelihood function with respect to μ and σ as zero.

[0133]

[0134]

[0135] Specifically, the severe wind surge spectrum is compiled in the following manner:

[0136] 1) Determination of the severe cumulative exceedance curve

[0137] The severity spectrum should reflect the severe usage of 90% (preset percentage) of the fleet, and the corresponding overload-cumulative exceedance number curve is the cumulative exceedance number curve corresponding to 90% reliability.

[0138] For each task segment, based on the likelihood function and target parameters (μ, σ) given above, calculate S for a coverage rate of 90%, i.e.:

[0139] S 90,i =μ i +μ 90 σ i (9)

[0140] In the above formula, μ i σ i These are the mean and standard deviation of task segment S, respectively.

[0141] After obtaining S 90,i Then, the cumulative overload exceedance curve of the mission segment with S close to this value was selected as the severe cumulative exceedance curve (severe gust overload cumulative exceedance curve). At the same time, the cumulative overload exceedance curve was corrected so that its S value was close to S. 90,i The correction method is to shift the overload exceedance curve upward as a whole, that is, to correct b in equation (5).

[0142] 2) High load cut-off, low load cut-off

[0143] The severe cumulative overload curve (severe gust overload cumulative overload curve) of the above-determined task segment is subject to high-load truncation and low-load truncation.

[0144] (1) High load interception

[0145] The high load that occurs once in 1000 flights in this mission segment is taken as the cutoff value;

[0146] (2) Low-load cutoff

[0147] The spectrum typically contains a large number of small-amplitude load cycles, which need to be removed or converted to a certain level of load based on the damage. Usually, the fatigue limit of the critical parts is determined based on the aforementioned analysis, and the overload value corresponding to 70% to 80% of the fatigue limit is removed.

[0148] 3) Compilation of severe gust load spectrum

[0149] 3.1 Determination of Load Levels

[0150] (1) Determination of load levels based on mission profile

[0151] The mission segments of the medium-altitude flight are discrete into 5 levels. The number of discrete loads is an integer in a program block, and each mission segment has at least one flight. The representative value of each load level (i.e., equivalent load) is determined by adjusting for damage in the discrete segments.

[0152] (2) Determination of equivalent load

[0153] 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 N eq If we replace the discrete segment of the curve with m straight lines, the linear equation of the i-th segment is:

[0154] Δg=a i lgN+b i (10)

[0155] In the above formula, a i b i , respectively, are constants for the i-th segment of the load spectrum curve; N is the cumulative exceedance number, which is calculated with reference to the previous calculation of the cumulative exceedance curve for severe overload.

[0156] The equivalent load is:

[0157]

[0158] In the formula:

[0159]

[0160] n is the equivalent number of times (preset value); m represents the total number of load spectrum curves; i is the i-th load spectrum curve; S is the slope parameter of the material's SN curve, S = 2.0 for aluminum alloy.

[0161] 3.2 Determination of Flight Type

[0162] The load spectra of the medium-altitude flight mission profiles were compiled according to five different flight types. The principle for determining typical flight types for medium-altitude flight is: using the glide path gale spectrum with the highest load as a benchmark, and assuming that the highest load in each glide path follows a normal logarithmic extreme distribution, the frequency of each flight type is determined (by determining y). i Based on the assumption that the gust spectrum shapes of various flight types are similar, the gust increment overload spectrum of various flight types under 1000 flights is compiled (to determine B). ij As shown in Table 1.

[0163] Table 15×5 spectrum

[0164]

[0165]

[0166] In the table, y1+y2+y3+y4+y5 should equal the number of times the mission profile appears in 1000 flights. Compile 5×5 spectra for all mission segments under all mission 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.

[0167] 3.3 Compilation of Severe Sudden Wind Overload Spectrum

[0168] (1) Compile the task segment spectrum

[0169] Based on the data in the 5×5 spectrum compiled in section 3.2, the load levels and their corresponding frequencies under the k-th flight type of a single takeoff and landing in the i-th mission segment of the j-th mission profile are linked together. Peak and trough values ​​are randomly selected alternately (i.e., peak and trough values ​​are randomly selected separately) and randomly paired to form the load spectrum for a specific flight type of the mission segment, denoted as (Δn). ydn , -Δn ydm ) h The payload spectrum (task segment spectrum) (j×i×k in total) is represented as the following payload pair sequence:

[0170] f i，j，k =(Δn) ydn , -Δn ydm ) h (13)

[0171] In the above formula, Δn ydnFor the nth (n = 1, 2, 3, 4, 5) load level of this task segment, representing the peak load; -Δn ydm This represents the m-th (m = 1, 2, 3, 4, 5) level load of the mission segment, indicating the valley load. The total number of load pairs for each flight type mission segment should be equal to the total number of cycles for each flight in Table 1 above. Following this method, spectra (load pair sequence form) for each flight type under all mission segments are compiled.

[0172] (2) Compile task profile spectrum

[0173] Let F be the load spectrum of the k-th flight type on the j-th profile. j，k F j，k This can be represented as a load pair sequence f i，j，k Sort the tasks according to the order i (i = 1, 2, 3...m) under this profile, i.e., f 1，j，k f 2，j，k f 3，j，k f 4，j，k …f m，j，k This yields the mission profile spectrum for a complete flight. The same method is used to compile mission profile spectra for all flight types under all mission profiles. All mission profile spectra are represented in vector form as follows:

[0174] B = [F] 1,1 …F 1,K F 2,1 …F 2,K F 3,1 …F 3,K …F L,1 …F L,K (14)

[0175] For example, if there are three mission segments under a hollow profile, and each mission segment has five flight types, then a total of five mission profile spectra need to be compiled for the hollow profile, representing the five flight types respectively. These are denoted as A1, B1, C1, D1, and E1. When compiling the A1 spectra, the spectra of flight type A under each mission segment are arranged together, i.e., f 1,j,k f 2,j,k f 3,j,k The A1 spectrum can be obtained by doing so, and the same method can be used to obtain the profile spectra of other types of tasks.

[0176] (3) Compile flight-continued flight spectrum

[0177] Let y represent the occurrence of the j-th profile (j=1,2…L) and the k-th flight type (k=1,2…K) in 1000 flights. j,k Next. All the y j,k Expressed in vector form as

[0178] A = [y1,1 …y 1,K y 2,1 …y 2,K y 3,1 …y 3,K …y L,1 …y L,K (15)

[0179] In the above formula, A is a vector representing the frequency of occurrence of various task profile spectra.

[0180] Vectors A and B have a one-to-one correspondence, indicating how many times the task profile spectrum appears in this 1000-times block spectrum. The total spectrum can then be represented as the task profile spectrum sequence G; G = A * B.

[0181] By reordering the mission profile spectrum for each complete flight in G, the final flight-continued-flight spectrum can be obtained. The specific method is as follows:

[0182] First, number all task profiles of G sequentially using a natural number sequence. Then, combine these numbers with a random integer sequence "x". is "Corresponding to this, press x" is Sort by numerical value (e.g., the corresponding x) is If it is 5, then the corresponding task profile spectrum is placed in the 5th position, thus obtaining the final load spectrum.

[0183] (4) Random sorting method

[0184] 3.3 Randomly alternately select the peak, valley and total load spectrum G of the random number sequence “x” 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.

[0185] Multiplication by congruence:

[0186] y i+1 =λy i (mod2 k (16)

[0187] x i =y i / 2 m (17)

[0188] First, "y" is formed from equation (16). i "The sequence is then used to obtain the random sequence "x" from equation (17). i Calculate x i The total number of columns arranged in ascending order x is This yields a random integer sequence "x" is ".

[0189] Regarding the parameter values ​​in equations (16) and (17), 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 is 2; mod represents the modulo operation. By changing the values ​​of y1, t, and k, different random number sequences can be obtained.

[0190] The following is an example of a specific practical application:

[0191] 1) Set the task profile

[0192] A typical mission profile for a certain type of aircraft is taken as medium-altitude flight. The mission is organized in units of 1000 takeoffs and landings. The mission segments of the mission profile are shown in Table 2.

[0193] Table 2 shows the task sections of the hollow profile.

[0194] Task segment Time (min) Climb 2.1 Flying 10.1 decline 2.3

[0195] 2) Obtain measured load spectrum data

[0196] A total of 30 takeoffs and landings of a certain aircraft were measured, and the flight data provided included: time, flight altitude, Y-axis overload at the center of gravity, remaining fuel in the left / right engines, flap deflection angle, and elevator deflection angle.

[0197] 3) Preprocessing of measured load spectrum data

[0198] 3.1 Overload data n y Standardization processing

[0199] The standard aircraft mass data for the mission segment is shown in Table 3 below. The actual load mass data is calculated by subtracting fuel consumption from the aircraft weight. Fuel consumption is calculated as an overall average (the average fuel consumption is calculated from the start of takeoff taxiing to the end of landing impact). The aircraft center of gravity y-axis overload data is standardized using the method described above.

[0200] Table 3 Standard Quality for Each Task Segment

[0201]

[0202]

[0203] 3.2 Peak and Valley Value Collection

[0204] Before performing the counting statistics, the peak and valley values ​​of the statistical parameters were detected according to the aforementioned method to obtain the peak and valley value pairs of sudden wind overload (Δn). y峰 ,Δn y谷 ) i .

[0205] 4) Statistics on the family of overload accumulation curves

[0206] 4.1 Overload Time History Count

[0207] With Δn y =Starting at 0.05g, with intervals of 0.05g. Using the aforementioned method, overload time histories of the standardized overload time history were counted, yielding peak-to-valley data pairs of sudden wind overload (Δn). y峰 ,Δn y谷 ) i The peak values ​​here are all positive overloads, and the valley values ​​are all negative overloads.

[0208] 4.2 Standardization of Time-Related Overload Exceedance Numbers

[0209] The aforementioned method was used to count the cumulative overload exceedances for both positive peak overload and negative trough overload. Taking flight duration into account, the cumulative overload exceedances for each level were standardized; the standard mission segment time is shown in Table 2.

[0210] 4.3 Overload Cumulative Exceedance Number Curve Fitting for Each Task Segment

[0211] The aforementioned method was used to analyze the cumulative overload exceedance data pairs (Δn) for each task segment. y ,N) i By fitting the curves, a family of overload cumulative exceedance number curves is obtained.

[0212] 5) Calculate the area S between the cumulative overload exceedance curve and the coordinate axis.

[0213] Calculation of 5.1S

[0214] The method described above is used to calculate S corresponding to the cumulative overload exceedance curve.

[0215] 5.2 S-distribution parameters

[0216] The distribution parameters μ and σ of S were obtained by statistical analysis using the aforementioned method. In this example, μ = 8.1 and σ = 1.1 for the climbing task segment S.

[0217] 6) Generate a severe cumulative exceedance curve

[0218] Based on the standardized results of the overload exceedance number considering time, calculate S for each task segment. 90,i S has a coverage rate of 90%. The overload cumulative exceedance number curve for this mission segment with S close to this value is selected as the severe cumulative exceedance number curve. Specifically, S for the climb mission segment... 90The overload cumulative exceedance number curve for the climb mission segment with measured takeoff and landing sequence number 13 is selected as the severe cumulative exceedance number curve for this mission segment, with an S value of 9.48. The correction method for this severe cumulative exceedance number curve is to increase its b value by 0.00135. The severe cumulative exceedance number curve for this mission segment is shown below. Figure 2 As shown.

[0219] 7) High-load cut-off and low-load cut-off

[0220] 7.1 High-load interception

[0221] The high load interception values ​​for each task segment are shown in Table 4 below:

[0222] Table 4 High-load interception values ​​for each task segment

[0223] Task segment High load cutoff value / g Hollow Climb 0.86 Level flight at medium altitude 0.98 Hollow gliding 0.99

[0224] 7.2 Low-load cutoff

[0225] The low-load deletion values ​​for each task segment are shown in Table 5 below:

[0226] Table 5 Low-load deletion values ​​for each task segment

[0227] Task segment Low load delete value / g Hollow Climb 0.16 Level flight at medium altitude 0.17 Hollow gliding 0.22

[0228] 8) Load spectrum compilation

[0229] 8.15×5 Spectrum Compilation

[0230] According to the compilation principles, the number of flight types in the mid-altitude flight profile under the severe gust spectrum is given in Table 6, and the gust spectrum of the climb mission segment under the mid-altitude flight profile is given in Table 7.

[0231] Table 6. Number of Flight Types in Typical Flight Profiles (1000 Flights)

[0232] Serial Number Typical flight profile A B C D E 1 Mid-altitude flight 1 7 54 278 660

[0233] Table 7. Gale Spectrum of Climbing During Mid-Altitude Flight

[0234]

[0235] 8.2 Random Arrangement of Fatigue Load Spectrum

[0236] Based on the above principles, a fatigue load spectrum (load time series) was compiled. The random spectrum (measured spectrum under severe gusts) for flight type A during the climb phase is shown below. Figure 3 As shown.

[0237] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method of compiling an aircraft measured severe gust spectrum, characterized in that, The method comprises the following steps: S1. generating a task profile according to the use requirements of a to-be-tested aircraft; determining the order, time proportion and parameters of a task section constituting the task profile; S2. collecting measured load data of the to-be-tested aircraft, and preprocessing the measured load data; S3. generating a measured gust overload cumulative overshoot curve family based on the task section according to the preprocessed measured load data; S4. calculating the area between the measured gust overload cumulative overshoot curve corresponding to each task section and the coordinate axis, and taking the measured gust overload cumulative overshoot curve meeting a preset condition as a severe gust overload cumulative overshoot curve; the measured gust overload cumulative overshoot curve meeting the preset condition is the measured gust overload cumulative overshoot curve corresponding to a preset percentage of the endurance severe spectrum in the area; S5. completing severe gust measurement spectrum compilation of the to-be-tested aircraft according to the severe gust overload cumulative overshoot curve. In the step S2, the preprocessing of the measured load data comprises: subtracting a first preset value from the measured load data to obtain overload data, and performing standardization processing on the overload data; separating gust load and maneuver load in the measured load data; obtaining peak values and valley values in the measured load data, filtering out the measured load data between the peak values and the valley values, and retaining corresponding sample point serial numbers; The step S3 comprises: taking a preset load state of each task section as a reference to divide a reference line; when the peak value and the valley value in the preprocessed measured load data are both less than the deviation of the reference line, recording the maximum peak value and the minimum valley value in the preprocessed measured load data; the deviation comprises an upper deviation and a lower deviation; recording an overload cumulative overshoot number according to the maximum peak value and the minimum valley value; the overload cumulative overshoot number comprises a positive gust overload cumulative overshoot number and a negative gust overload cumulative overshoot number; obtaining the average of the overload cumulative overshoot number by geometric mean; fitting the overload cumulative overshoot number of each task section to obtain a measured gust overload cumulative overshoot curve of each task section, and generating a measured gust overload cumulative overshoot curve family; The step S4 comprises: The ordinate axis is modified as 10 for the starting coordinate of the cumulative overshoot -7 , the abscissa axis is modified as 0 for the overload; the area S between the overload-cumulative overshoot curve and the abscissa axis is calculated; adopting a random variable model to assume that S obeys normal distribution; adopting maximum likelihood estimation method to estimate distribution parameters for S of each task section in measured take-off and landing; the likelihood function is shown in the following formula: solving target parameters (μ, σ) by deriving μ and σ from the likelihood function respectively; (6) - (8) where x i = S; S is the area; i is the serial number of the sample data pair; n is the total number of samples; μ is the expectation; σ is the standard deviation; σ0is the sample standard deviation; μ0is the mean.

2. A method of generating an aircraft measured severe gust spectrum as recited in claim 1, wherein, performing standardization processing on the overload data according to the following formula: Δn y0 = Δn yi *Gi / G0 In the above formula, Δn y0 is the overload data after standardization; Δn yi is the measured overload data; Gi is the real mass data of the load; G0 is the standard mass data of the aircraft.

3. A method of generating an aircraft measured severe gust spectrum as recited in claim 1, wherein, separating the gust load and the maneuver load in the measured load data in the following manner: taking overload in the measured load data which changes slowly and has a duration exceeding a preset threshold as maneuver load, otherwise as gust load; taking overload in the measured load data with a rudder deflection angle and an absolute value of the rudder deflection angle equal to or greater than a second preset value as maneuver load, otherwise as gust load; taking overload in the measured load data in climbing and descending task sections as gust load.

4. A method of generating an aircraft measured severe gust spectrum as recited in claim 1, wherein, The step S3 further comprises the following steps before the average of the overload cumulative overshoots is obtained by geometric mean: Converting the overload cumulative overshoots into standard time cumulative overshoots.

5. A method of generating an aircraft measured severe gust spectrum as recited in claim 1, wherein, The step S5 comprises the following steps: Determining a load series according to the mission profile; Calculating a load spectrum equivalent according to the severe gust overload cumulative overshoot curve; Determining a flight type of the mission segment; and compiling a mission segment spectrum according to the load spectrum equivalent and the flight type of the mission segment; Compiling a mission profile spectrum according to the mission segment spectrum; According to the mission profile spectrum, the mission profile spectrum is numbered according to a natural number sequence in sequence, and the numbering is sorted according to the value of the preset random integer sequence to generate a severe gust measured spectrum of the aircraft to be tested.

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