Acceleration task test run spectrum optimization method based on fuzzy evaluation method

Abstract

The invention discloses an acceleration task test run spectrum optimization method based on a fuzzy evaluation method. The method comprises the following steps: constructing an index hierarchical structure system suitable for fuzzy comprehensive evaluation of the applicability of the acceleration task test run spectrum to each key part; determining the weight of each index; establishing an evaluation set, and determining the membership degree of each index in the evaluation set by combining qualitative analysis and quantitative analysis methods; performing multi-level fuzzy comprehensive evaluation on the applicability of the whole machine of the evaluated object; quantifying a comprehensive evaluation result, defining evaluation grades for the evaluation set in a partitioned manner, and calculating a comprehensive evaluation result of the applicability of the accelerated task test run spectrum of the evaluated object to the whole machine; and the acceleration task test run spectrum with the optimal applicability is screened out. According to the method, the fuzzy comprehensive evaluation method is introduced, the applicability of the acceleration task test run spectrums of different parts to the whole machine is quantitatively reflected, and the method has the advantages of being simple, visual, concise in step and high in engineering application value.

Description

Technical Field

[0001] The invention relates to the technical field of compilation of acceleration mission test run spectra for aircraft engines, and in particular to an acceleration mission test run spectra optimization method based on a fuzzy evaluation method. Background Art

[0002] Key components of aircraft engines, such as fan discs, turbine discs, turbine blades and combustion chamber casings, all work under complex and harsh working conditions. Take turbine blades as an example. During the operation of the engine, they are subjected to high temperatures of more than 1800°C and extremely high centrifugal forces. These conditions easily lead to multiple failure modes such as thermomechanical fatigue (TMF) and creep fatigue (CF). Fan discs and turbine discs need to withstand extremely high centrifugal forces and periodic loads at high speeds, which increases the risk of low-cycle fatigue (LCF) failure. The combustion chamber casing is exposed to high-temperature gas environments for a long time and faces serious problems such as oxidation, thermal stress and creep. With the improvement of engine performance, the working environment of core components has become more severe. In order to ensure the reliable operation of aircraft engines and assess their structural integrity and high-temperature durability, the existing technology clearly requires that the engine must be subjected to a long-term test before the design is finalized, and the 1:1 long-term life test is not only time-consuming and labor-intensive, but also greatly delays the progress of engine development.

[0003] Accelerated mission test is a method to accelerate the failure process by eliminating low-power states and secondary cycles and converting them into short-term high-power states and fewer main cycles. The accelerated mission test spectrum is the core of the accelerated mission test of key components. Through a reasonably compiled test spectrum, the damage of the engine's full life cycle working state can be simulated in a relatively short time. Compared with traditional long-term life tests, accelerated mission tests greatly improve test efficiency, shorten test cycles, and have been widely used in the research and development and testing of aircraft engines. Existing accelerated mission test spectra are mostly compiled for key components. How to select the best test spectrum suitable for the entire machine is an important problem that needs to be solved urgently. Due to the different working conditions and fatigue failure modes of various components of aircraft engines, the applicability judgment of different accelerated mission test spectra is relatively complicated, and it is difficult to make a scientific judgment based on experience alone. Summary of the invention

[0004] In order to solve the above problems, the present invention proposes an acceleration task test spectrum optimization method based on fuzzy evaluation method, which can select the best acceleration task test spectrum suitable for the whole machine from the acceleration task test spectrum of each key component.

[0005] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0006] The present invention is a method for optimizing an acceleration task test spectrum based on a fuzzy evaluation method, comprising the following operations: constructing an index hierarchy system suitable for fuzzy comprehensive evaluation of the applicability of each key component of the acceleration task test spectrum;

[0007] Comprehensively score each indicator preset in the indicator hierarchy system, gradually build a judgment matrix according to the hierarchical analysis method, calculate the eigenvector and the maximum eigenvalue, and determine the weight of each indicator after consistency test;

[0008] Establish an evaluation set, and combine qualitative and quantitative analysis methods to determine the membership of each indicator in the evaluation set;

[0009] Conduct multi-level fuzzy comprehensive evaluation on the applicability of the whole machine of the evaluated object, including calculating the evaluation matrix of each component of the evaluated object spectrum through the first-level fuzzy comprehensive evaluation, and calculating the second-level fuzzy comprehensive evaluation result according to the weight of each indicator through the second-level fuzzy comprehensive evaluation;

[0010] Quantify the comprehensive evaluation results, divide the evaluation set into intervals to define the evaluation level, and calculate the comprehensive evaluation results of the applicability of the acceleration task test spectrum of the evaluated object to the whole machine;

[0011] The comprehensive evaluation results of the applicability of the acceleration task test spectrum compiled for each component to the whole machine are comprehensively compared to screen out the acceleration task test spectrum with the best applicability.

[0012] A further improvement of the present invention is that the index hierarchy system includes a target layer, a criterion layer and an index layer, wherein the target layer is established as the evaluation of the applicability of the evaluated acceleration spectrum to the whole machine, the criterion layer is established as the applicability of the acceleration spectrum to each component, and the index layer is the fatigue and damage errors when the acceleration spectrum is applied to each component.

[0013] The further improvement of the present invention is: according to the hierarchical analysis method, the judgment matrix is ​​gradually constructed, the eigenvector and the maximum eigenvalue are calculated, and the weight of each indicator is determined after the consistency test, which specifically includes:

[0014] The judgment matrix relative to each target element in the upper layer is established by the 1 to 9 degree scaling method, and the judgment matrix is ​​solved as a single-level model. The solution process includes:

[0015] Normalize each column of the judgment matrix:

[0016]

[0017] Among them, b ij Indicates the relative importance of index i and index j in the judgment matrix, b kj represents the relative importance of index k and index j in the judgment matrix, n represents the order of the judgment matrix, Indicates b ij The result after column vector normalization;

[0018] Add the normalized judgment matrix row by row:

[0019]

[0020] in, is the sum of the row vectors of the judgment matrix;

[0021] Will Perform normalization:

[0022]

[0023] Among them, w i for The normalized value, is the sum of the column vectors of the judgment matrix;

[0024] The obtained weight matrix W = (w1, w1, ... w n ) T , the calculation expression of the maximum eigenvalue of the judgment matrix is:

[0025]

[0026] Among them, W is the weight value matrix, A is the judgment matrix, (AW) i is the i-th component of the matrix product, λ max is the maximum eigenvalue of the judgment matrix;

[0027] The consistency test of the judgment matrix is ​​performed, and the expression of the consistency test index is:

[0028]

[0029] Where n is the order of the judgment matrix;

[0030] The calculation expression of consistency ratio is:

[0031]

[0032] Among them, CR is the consistency ratio of the judgment matrix, and RI is the average random consistency index;

[0033] When CR is less than 0.1, it is determined that the judgment matrix meets the consistency requirement, otherwise it is determined that the judgment matrix does not have consistency.

[0034] A further improvement of the present invention is that the calculation expression of the first-level fuzzy comprehensive evaluation is:

[0035]

[0036] Among them, B i is the i-th index of the first-level fuzzy comprehensive evaluation matrix, W i is the i-th index of the weight matrix, is the fuzzy operation symbol, R i is the i-th indicator of the evaluation matrix.

[0037] A further improvement of the present invention is that the evaluation levels and corresponding intervals include: excellent (80-100], good (60-80], medium (40-60], low (20-40], and poor [0-20].

[0038] A further improvement of the present invention is that the calculation expression for calculating the comprehensive evaluation result of the applicability of the acceleration task test spectrum of the evaluated object to the whole machine is:

[0039] H=(100,80,60,40,20)*B T

[0040] Among them, H is the comprehensive evaluation result of the applicability of the acceleration task test spectrum of the evaluated object to the whole machine, B is the evaluation matrix, and T is the matrix transpose.

[0041] The beneficial effects of the present invention are as follows: the present invention constructs a competition mechanism for the acceleration task test spectrum by introducing the fuzzy comprehensive evaluation method, aiming to consider the advantages and disadvantages of different test spectra in multiple dimensions, quantitatively reflect the applicability of the load spectrum to the whole machine, and provide systematic theoretical support for the optimization of the acceleration task test spectrum. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a schematic diagram of a method flow of an embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of a speed spectrum of an acceleration task test spectrum compiled according to turbine blades in an embodiment of the present invention;

[0044] Figure 3 It is a schematic diagram of a speed spectrum of an acceleration task test spectrum compiled according to a turbine disk in an embodiment of the present invention;

[0045] Figure 4 It is a schematic diagram of the comprehensive scoring of the acceleration task test spectrum compiled by the final components in the embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0047] like Figure 1 As shown, a method for optimizing the acceleration task test spectrum based on the fuzzy evaluation method in this embodiment includes the following operations:

[0048] Step 1: construct an index hierarchy system suitable for fuzzy comprehensive evaluation of the applicability of each key component in the acceleration task test spectrum;

[0049] Step 2: Through questionnaire consultation, comprehensively score each indicator preset in the indicator hierarchy system, gradually build a judgment matrix according to the hierarchical analysis method, calculate the eigenvector and the maximum eigenvalue, and determine the weight of each indicator after consistency test;

[0050] Step 3: Establish an evaluation set and combine qualitative and quantitative analysis methods to determine the degree of membership of each indicator in the evaluation set;

[0051] Step 4, performing a multi-level fuzzy comprehensive evaluation on the applicability of the whole machine of the evaluated object, including calculating the evaluation matrix of each component of the evaluated object spectrum through the first-level fuzzy comprehensive evaluation, and calculating the second-level fuzzy comprehensive evaluation result according to the weight of each indicator through the second-level fuzzy comprehensive evaluation;

[0052] Step 5, quantify the comprehensive evaluation results, divide the evaluation set into intervals to define the evaluation level, and calculate the comprehensive evaluation results of the applicability of the acceleration task test spectrum of the evaluated object to the whole machine;

[0053] Step 6, comprehensively compare the comprehensive evaluation results of the applicability of the acceleration task test run spectrum compiled by each component to the whole machine, and select the acceleration task test run spectrum with the best applicability.

[0054] The index hierarchy system in step 1 includes a target layer, a criterion layer and an index layer, wherein the target layer is established as the evaluation of the applicability of the acceleration spectrum to the whole machine, the criterion layer is established as the applicability of the acceleration spectrum to each component, and the index layer is the fatigue and damage errors when the acceleration spectrum is applied to each component. The index hierarchy system of this embodiment is shown in Table 1;

[0055] Table 1, Indicator hierarchy system

[0056]

[0057] Step 2 specifically includes:

[0058] The judgment matrix relative to each target element in the upper layer is established by the 1 to 9 degree scaling method, and the judgment matrix is ​​solved as a single-level model. The solution process includes:

[0059] Normalize each column of the judgment matrix:

[0060]

[0061] Among them, b ij Indicates the relative importance of index i and index j in the judgment matrix, b kj represents the relative importance of index k and index j in the judgment matrix, n represents the order of the judgment matrix, Indicates b ij The result after column vector normalization;

[0062] Add the normalized judgment matrix row by row:

[0063]

[0064] in, is the sum of the row vectors of the judgment matrix;

[0065] Will Perform normalization:

[0066]

[0067] Among them, w i for The normalized value, is the sum of the column vectors of the judgment matrix;

[0068] The obtained weight matrix W = (w1, w1, ... w n ) T , the calculation expression of the maximum eigenvalue of the judgment matrix is:

[0069]

[0070] Among them, W is the weight value matrix, A is the judgment matrix, (AW) i is the i-th component of the matrix product, λ max is the maximum eigenvalue of the judgment matrix;

[0071] The consistency test of the judgment matrix is ​​performed, and the expression of the consistency test index is:

[0072]

[0073] Where n is the order of the judgment matrix;

[0074] The calculation expression of consistency ratio is:

[0075]

[0076] Among them, CR is the consistency ratio of the judgment matrix, and RI is the average random consistency index;

[0077] When CR is less than 0.1, it is determined that the judgment matrix meets the consistency requirement, otherwise it is determined that the judgment matrix does not have consistency.

[0078] This embodiment takes the judgment matrix of AB as an example to provide the solution process of this judgment matrix. The AB matrix is ​​normalized as shown in the following table:

[0079] Table 2, AB matrix

[0080]

[0081] Normalize the column vector according to the formula Japanese style So W1=(B1,B2,B3,B4) T =(0.0794, 0.2024, 0.5596, 0.1587), W1 is the eigenvector of the judgment matrix AB, and the judgment matrix is ​​multiplied by the eigenvector. The judgment matrix is ​​subjected to a consistency check, and the consistency ratio of the judgment matrix is ​​calculated to be CR = 0.0225 < 0.1, and the judgment matrix passes the consistency check. The same method is used to calculate the weights of each indicator in layer C (indicator layer) relative to layer B (criterion layer), and the judgment matrix is ​​tested for consistency. The weight matrix of the judgment matrix B1-C after normalization is (0.8571, 0.1429); the weight matrix of the judgment matrix B2-C after normalization is (0.3333, 0.6667); the weight matrix of the judgment matrix B3-C after normalization is (0.6, 0.4); the weight matrix of the judgment matrix B4-C after normalization is (0.1667, 0.8333). All judgment matrices pass the consistency test.

[0082] Step 3 specifically includes establishing an evaluation set V = {excellent, good, medium, low, poor}, taking the creep damage error C4 of the turbine disc compiled by the accelerated task test spectrum of the turbine blade as an example, the calculation process of determining its membership is as follows: The similarity method is used to determine the fatigue creep damage error of each component of the accelerated task test spectrum. The similarity method is a method of determining the membership by calculating the similarity between an element and a reference value. The core idea is that the degree of membership depends on the closeness of the element to a certain ideal value. The higher the similarity, the greater the membership. The similarity method regards the membership of a fuzzy set as the similarity between an element and a reference point, based on the following formula:

[0083]

[0084] Among them, x is the element to be measured, x0 is the reference value, Δx is the tolerance range, and μ(x) is the membership degree of the element to be measured x.

[0085] The maximum state holding time of the acceleration task test spectrum compiled by the fan disk is 0s, the maximum state holding time of the acceleration task test spectrum compiled by the turbine disk is 1981s, the maximum state holding time of the acceleration task test spectrum compiled by the turbine blade is 2060s, and the maximum state holding time of the acceleration task test spectrum compiled by the combustion chamber casing is 2220s. x=2060, Δx is 50. When the indicator is evaluated as excellent, the reference value x0 is 1981+50; when the indicator is evaluated as good, the reference value x0 is 1981+100; when the indicator is evaluated as medium, the reference value x0 is 1981+150; when the indicator is evaluated as low, the reference value x0 is 1981+200; when the indicator is evaluated as poor, the reference value x0 is 1981+250. The calculated membership degree is normalized to obtain:

[0086] μ1=0.63,r1=0.28

[0087] μ2=0.70,r2=0.31

[0088] μ3=0.41,r3=0.18

[0089] μ4=0.29,r4=0.13

[0090] μ5=0.23,r5=0.10

[0091] Among them, μ1 is the membership of C4 to the poor evaluation set, r1 is the membership of the normalized index C4 to the poor evaluation set, μ2 is the membership of C4 to the low evaluation set, r2 is the membership of the normalized index C4 to the low evaluation set, μ3 is the membership of C4 to the middle evaluation set, r3 is the membership of the normalized index C4 to the middle evaluation set, μ4 is the membership of C4 to the good evaluation set, r4 is the membership of the normalized index C4 to the good evaluation set, μ5 is the membership of C4 to the excellent evaluation set, and r5 is the membership of the normalized index C4 to the excellent evaluation set.

[0092] The acceleration task test spectrum compiled by the turbine blade is as follows Figure 2 As shown, the speed spectrum of the acceleration task test spectrum compiled by the turbine disk is as follows Figure 3 shown.

[0093] It can be concluded that the creep damage evaluation level of the turbine disc compiled by the accelerated task test spectrum of the turbine blade is [0.28, 0.31, 0.18, 0.13, 0.10]. The membership of other quantitative indicators is calculated in the same way. According to the calculation method of the above indicator membership, the membership of each indicator is shown in the following table.

[0094] Table 3, Indicator membership

[0095]

[0096] In step 4, the first-level fuzzy comprehensive evaluation is as follows:

[0097]

[0098] Among them, B i is the i-th index of the first-level fuzzy comprehensive evaluation matrix, W i is the i-th index of the weight matrix, is the fuzzy operation symbol, R i is the i-th indicator of the evaluation matrix.

[0099] Evaluation of the applicability of the accelerated mission test spectrum to the fan disk:

[0100]

[0101] Evaluation of the suitability of the accelerated mission test spectrum for turbine discs:

[0102]

[0103] Evaluation of the suitability of the accelerated mission test spectrum for turbine blades:

[0104]

[0105] Evaluation of the applicability of the accelerated mission test spectrum to the combustion chamber casing:

[0106]

[0107] The first-level fuzzy comprehensive evaluation matrix is:

[0108]

[0109] Secondary fuzzy comprehensive evaluation:

[0110] The weight of the B-layer (criteria layer) factors relative to the A-layer (criteria layer) calculated in step 2 is:

[0111] W=(0.0794,0.2024,0.5596,0.1587)

[0112] The secondary fuzzy evaluation results are:

[0113]

[0114] In step 5, in order to facilitate the quantification of the comprehensive evaluation results, the evaluation set is defined as excellent for the interval (80-100], good for the interval (60-80], medium for the interval (40-60], low for the interval (20-40], and poor for the interval [0-20]. The comprehensive evaluation result H of the acceleration task test spectrum compiled by the turbine blade for the suitability of the whole machine is:

[0115]

[0116] In step 6, the comprehensive evaluation results of the applicability of the acceleration task test spectrum compiled by each component in this embodiment to the whole machine are as follows: Figure 4 As shown, it can be clearly seen that the turbine blade has the highest score of 68.784, followed by the turbine disk and fan disk, and the acceleration spectrum compiled according to the combustion chamber case has a relatively low score of 59.842. Therefore, it is concluded that the acceleration task test spectrum compiled according to the turbine blade is the most suitable for the whole machine.

[0117] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have the same meaning as in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless defined similarly here.

[0118] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing acceleration task test spectrum based on fuzzy evaluation method, characterized by: The following operations are included: Construct an index hierarchy system suitable for fuzzy comprehensive evaluation of the applicability of key components in the acceleration task test spectrum; Comprehensively score each indicator preset in the indicator hierarchy system, gradually build a judgment matrix according to the hierarchical analysis method, calculate the eigenvector and the maximum eigenvalue, and determine the weight of each indicator after consistency test; Establish an evaluation set, and combine qualitative and quantitative analysis methods to determine the degree of membership of each indicator in the evaluation set; Conduct multi-level fuzzy comprehensive evaluation on the applicability of the whole machine of the evaluated object, including calculating the evaluation matrix of each component of the evaluated object spectrum through the first-level fuzzy comprehensive evaluation, and calculating the second-level fuzzy comprehensive evaluation result according to the weight of each indicator through the second-level fuzzy comprehensive evaluation; Quantify the comprehensive evaluation results, divide the evaluation set into intervals to define the evaluation level, and calculate the comprehensive evaluation results of the applicability of the acceleration task test spectrum of the evaluated object to the whole machine; The comprehensive evaluation results of the applicability of the acceleration task test spectrum compiled for each component to the whole machine are comprehensively compared to screen out the acceleration task test spectrum with the best applicability.

2. The method for optimizing the acceleration task test spectrum based on fuzzy evaluation method according to claim 1, characterized in that: The index hierarchy system includes a target layer, a criterion layer and an index layer, wherein the target layer is established as the evaluation of the applicability of the evaluated acceleration spectrum to the whole machine, the criterion layer is established as the applicability of the acceleration spectrum to each component, and the index layer is the fatigue and damage errors when the acceleration spectrum is applied to each component.

3. The method for optimizing the acceleration task test spectrum based on fuzzy evaluation method according to claim 1, characterized in that: According to the hierarchical analysis method, the judgment matrix is ​​gradually constructed, the eigenvector and the maximum eigenvalue are calculated, and the weight of each indicator is determined after consistency test, which specifically includes: The judgment matrix relative to each target element in the upper layer is established by the 1 to 9 degree scaling method, and the judgment matrix is ​​solved as a single-level model. The solution process includes: Normalize each column of the judgment matrix: Among them, b ij Indicates the relative importance of index i and index j in the judgment matrix, b kj represents the relative importance of index k and index j in the judgment matrix, n represents the order of the judgment matrix, Indicates b ij The result after column vector normalization; Add the normalized judgment matrix row by row: in, is the sum of the row vectors of the judgment matrix; Will Perform normalization: Among them, w i for The normalized value, is the sum of the column vectors of the judgment matrix; The obtained weight matrix W = (w1, w1, ... w n ) T , the calculation expression of the maximum eigenvalue of the judgment matrix is: Among them, W is the weight value matrix, A is the judgment matrix, (AW) i is the i-th component of the matrix product, λ max is the maximum eigenvalue of the judgment matrix; The consistency test of the judgment matrix is ​​performed, and the expression of the consistency test index is: Among them, CI is the consistency test index; The calculation expression of consistency ratio is: Among them, CR is the consistency ratio of the judgment matrix, and RI is the average random consistency index; When CR is less than 0.1, it is determined that the judgment matrix meets the consistency requirement, otherwise it is determined that the judgment matrix does not have consistency.

4. The method for optimizing the acceleration task test spectrum based on fuzzy evaluation method according to claim 1, characterized in that: The calculation expression of the first-level fuzzy comprehensive evaluation is: Among them, B i is the i-th index of the first-level fuzzy comprehensive evaluation matrix, W i is the i-th index of the weight matrix, is the fuzzy operation symbol, R i is the i-th indicator of the evaluation matrix.

5. The method for optimizing the acceleration task test spectrum based on fuzzy evaluation method according to claim 1 is characterized by: The evaluation levels and corresponding intervals include: excellent (80-100], good (60-80], medium (40-60], low (20-40], and poor [0-20].

6. The method for optimizing the acceleration task test spectrum based on fuzzy evaluation method according to claim 5, characterized in that: The calculation expression for calculating the comprehensive evaluation result of the suitability of the acceleration task test spectrum of the evaluated object to the whole machine is: H=(100,80,60,40,20)*B T Among them, H is the comprehensive evaluation result of the applicability of the acceleration task test spectrum of the evaluated object to the whole machine, B is the evaluation matrix, and T is the matrix transpose.

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