A method and apparatus for predicting residual strength of a fiber reinforced composite

Abstract

The present application relates to the technical field of data prediction, and particularly relates to a method and device for predicting residual strength of fiber reinforced composite material. The method comprises: establishing a residual stiffness model under random fatigue for the fiber reinforced composite material to be predicted; calculating a residual stiffness damage factor under random fatigue based on the residual stiffness model under random fatigue; determining a residual strength damage factor under random fatigue according to the correlation between the residual stiffness damage factor and the residual strength damage factor and using the residual stiffness damage factor under random fatigue; establishing a residual stiffness-residual strength correlation model under random fatigue based on the residual stiffness damage factor under random fatigue and the residual strength damage factor under random fatigue; and calculating a change rule of the residual strength of the fiber reinforced composite material under random fatigue over time based on the residual stiffness-residual strength correlation model under random fatigue.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data prediction, in particular to a method and device for predicting residual strength of fiber-reinforced composite materials. BACKGROUND

[0002] Fiber-reinforced composite materials are widely used in the field of aerospace due to their excellent mechanical properties. In actual service process, the structure often bears various random loads such as noise and vibration, and damage occurs inside the material under alternating loads, which eventually leads to fatigue failure. Therefore, it is necessary to analyze the residual strength under random fatigue.

[0003] At present, the residual strength under random fatigue is analyzed by experiment, but the experiment is destructive and the experimental cost is high.

[0004] Therefore, there is an urgent need for a low-cost and low-destructive prediction method to predict the residual strength of fiber-reinforced composite materials. SUMMARY

[0005] I. Invention purposes

[0006] The embodiments of the present specification provide a method and device for predicting the residual strength of fiber-reinforced composite materials to reduce the cost and destructiveness of predicting the residual strength of fiber-reinforced composite materials.

[0007] II. Technical solutions

[0008] In a first aspect, the method and device for predicting the residual strength of fiber-reinforced composite materials according to the embodiments of the present application comprise:

[0009] A residual stiffness model under random fatigue is established for the fiber-reinforced composite material to be predicted;

[0010] Based on the residual stiffness model under random fatigue, a residual stiffness damage factor under random fatigue is calculated;

[0011] According to the correlation between the residual stiffness damage factor and the residual strength damage factor, the residual stiffness damage factor under random fatigue is used to determine the residual strength damage factor under random fatigue;

[0012] Based on the residual stiffness damage factor under random fatigue and the residual strength damage factor under random fatigue, a residual stiffness-residual strength correlation model under random fatigue is established;

[0013] Based on the residual stiffness-residual strength correlation model under random fatigue, the change rule of the residual strength of the fiber-reinforced composite material under random fatigue with time is calculated.

[0014] Preferably,

[0015] The established residual stiffness model under random fatigue is:

[0016]

[0017] In the formula, E(0) is the initial stiffness of the fiber-reinforced composite material; Q and v are both material parameters related to the stress level; n is the number of fatigue load cycles; E(n) is the residual stiffness under random fatigue;

[0018] Based on the residual stiffness model under random fatigue, the residual stiffness damage factor under random fatigue and the residual strength damage factor under random fatigue are calculated, comprising:

[0019] The residual stiffness model under random fatigue is integrated by using the critical failure cycle number and the random cycle number, and the critical failure stiffness under random fatigue and the residual stiffness under random fatigue are correspondingly obtained;

[0020] Based on the residual stiffness under random fatigue and the critical failure stiffness under random fatigue, the residual stiffness damage factor under random fatigue is calculated.

[0021] Preferably,

[0022] The residual stiffness under random fatigue of the fiber-reinforced composite material and the critical failure stiffness under random fatigue of the fiber-reinforced composite material are respectively:

[0023] E(N) = E(0)(1-QN v )

[0024] E(n) = E(0)(1-Qn v )

[0025] In the formula, E(N) is the critical failure stiffness under random fatigue; n is the random cycle number; N is the critical failure cycle number;

[0026] The residual stiffness damage factor under random fatigue is:

[0027]

[0028] In the formula, D E is the residual stiffness damage factor under random fatigue.

[0029] Preferably, the residual strength damage factor under random fatigue is determined by using the following formula:

[0030]

[0031] In the formula, D S = (D E ) mD S S(n) is the residual strength of the material under random fatigue; S is the stress level of the external load; and m is a proportional parameter.

[0032] Preferably, the establishing of the residual stiffness-residual strength correlation model under random fatigue based on the residual stiffness damage factor under random fatigue and the residual strength damage factor under random fatigue comprises:

[0033] The calculation formula of the residual strength damage factor under random fatigue is deformed to obtain the failure strength ratio of the fiber-reinforced composite material under random fatigue; and the failure strength ratio under random fatigue is differentiated to obtain a stress cycle number distribution function of the failure strength ratio under random fatigue when any stress level of the external load acts.

[0034] The stress cycle number distribution function is solved according to the cycle number of random stress in unit time and the probability density function of the cycle stress, and the residual stiffness-residual strength correlation model under random fatigue is obtained by integrating the solution result.

[0035] Preferably,

[0036] The residual stiffness-residual strength correlation model under random fatigue is:

[0037]

[0038] In the formula, n t is the cycle number of random stress in unit time; p(S) is the probability density function of the cycle stress; ΔS is the change value of the external load stress; C1 and C2 are both experimental fitting parameters; and β(S) and v(S) are both functions of the stress level.

[0039] Preferably, the obtaining of the change rule of the residual strength under random fatigue with time based on the residual stiffness-residual strength correlation model under random fatigue comprises:

[0040] The residual stiffness analysis model under random fatigue and the residual stiffness-residual strength correlation model under random fatigue are equivalently transformed, and the transformed residual stiffness analysis model under random fatigue and the residual stiffness-residual strength correlation model under random fatigue are both correlation formulas of time and failure stiffness ratio.

[0041] Based on a plurality of preset failure stiffness ratios, the time length for the change of the failure stiffness ratio to each preset failure stiffness ratio is calculated.

[0042] The calculated several experienced time lengths are substituted into the transformed random fatigue remaining stiffness-remaining strength to obtain the random fatigue remaining strength corresponding to each experienced time length;

[0043] Based on the random fatigue remaining strength corresponding to each experienced time length, the change rule of the remaining strength of the fiber-reinforced composite material with time is obtained.

[0044] In a second aspect, the present application further provides a device for predicting the remaining strength of a fiber-reinforced composite material, comprising:

[0045] A first modeling unit is configured to establish a remaining stiffness model under random fatigue for the fiber-reinforced composite material to be predicted;

[0046] A first calculation unit is configured to calculate the remaining stiffness model under random fatigue to obtain a remaining stiffness damage factor under random fatigue and a remaining strength damage factor under random fatigue;

[0047] A second modeling unit is configured to establish a remaining stiffness-remaining strength correlation model under random fatigue;

[0048] A second calculation unit is configured to calculate the remaining stiffness-remaining strength correlation model under random fatigue to obtain the change rule of the remaining strength of the predicted fiber-reinforced composite material under random fatigue with time.

[0049] In a third aspect, the present application further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method according to any one of the embodiments of the present application.

[0050] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed in a computer, causes the computer to execute the method according to any one of the embodiments of the present application.

[0051] III. Advantages

[0052] The embodiments of the present application provide a method and a device for predicting the remaining strength of a fiber-reinforced composite material. The remaining stiffness damage factor under random fatigue and the remaining strength damage factor under random fatigue are calculated by establishing a remaining stiffness model under random fatigue. The remaining stiffness damage factor under random fatigue and the remaining strength damage factor under random fatigue are solved to establish a remaining stiffness-remaining strength correlation model under random fatigue. Thus, the change rule of the remaining strength with time can be obtained by using the correlation model. It can be seen that, according to the present application, the remaining strength of a composite material under random fatigue can be predicted by modeling without strength experiments on the composite material. The present application has low cost and is not destructive. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0054] Figure 1 is a flow chart of a method for predicting residual strength of fiber-reinforced composite material provided by an embodiment of the present specification;

[0055] Figure 2 is a hardware architecture diagram of an electronic device provided by an embodiment;

[0056] Figure 3 is a schematic diagram of a device for predicting residual strength of fiber-reinforced composite material provided by an embodiment of the present specification. DETAILED DESCRIPTION

[0057] In order to make the objects, technical solutions and advantages of the embodiments of the present specification clearer, the technical solutions in the embodiments of the present specification will be described clearly and completely below with reference to the drawings in the embodiments of the present specification. Obviously, the described embodiments are some embodiments of the present specification, not all embodiments. Based on the embodiments in the present specification, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present specification.

[0058] Embodiment one

[0059] Please refer to Figure 1 The embodiments of the present specification provide a method for predicting residual strength of fiber-reinforced composite material, which comprises:

[0060] Step 100: establishing a residual stiffness model under random fatigue for the fiber-reinforced composite material to be predicted;

[0061] Step 102: calculating a residual stiffness damage factor under random fatigue based on the residual stiffness model under random fatigue;

[0062] Step 104: determining a residual strength damage factor under random fatigue by using the residual stiffness damage factor under random fatigue according to the correlation between the residual stiffness damage factor and the residual strength damage factor;

[0063] Step 106: establishing a residual stiffness-residual strength correlation model under random fatigue based on the residual stiffness damage factor under random fatigue and the residual strength damage factor under random fatigue;

[0064] Step 108: based on the random fatigue residual stiffness-residual strength correlation model, the change rule of the random fatigue residual strength of the fiber reinforced composite material with time is calculated.

[0065] In the embodiment of the application, the random fatigue residual stiffness model is established to calculate the random fatigue residual stiffness damage factor and the random fatigue residual strength damage factor, and the random fatigue residual stiffness damage factor and the random fatigue residual strength damage factor are combined to establish the random fatigue residual stiffness-residual strength correlation model, so that the change rule of the residual strength with time can be obtained by using the correlation model. It can be seen that, according to the scheme, the prediction of the residual strength of the composite material under random fatigue can be realized by modeling without strength experiment, which is low in cost and has no high destructiveness in the experimental process.

[0066] The above steps will be described below. Figure 1

[0067] First, for step 100: for the fiber reinforced composite material to be predicted, a random fatigue residual stiffness model is established.

[0068] Since the residual strength under random fatigue in the prior art can only be determined by experiment, a residual strength model under random fatigue cannot be constructed, while a residual stiffness model under random fatigue can be constructed.

[0069] In the embodiment of the application, the following random fatigue residual stiffness model is preferably established:

[0070]

[0071] In the formula, E(0) is the initial stiffness of the fiber reinforced composite material; Q and v are both parameters of the fiber reinforced composite material, which are related to the stress level; n is the number of fatigue load cycles; and E(n) is the residual stiffness under random fatigue.

[0072] Then, for step 102: based on the random fatigue residual stiffness model, the random fatigue residual stiffness damage factor is calculated.

[0073] In the embodiment of the application, when calculating the random fatigue residual stiffness damage factor, the following steps 1020-1022 can be specifically included:

[0074] 1020: the random fatigue residual stiffness model is integrated by using the critical failure cycle number and the random cycle number, respectively, to one-to-one obtain the random fatigue critical failure stiffness and the random fatigue residual stiffness;

[0075] ​In this step, first, the established random fatigue residual stiffness model is integrated by using the critical failure cycle number, that is, the upper and lower limits of integration are N and 0 in turn, and the result obtained after integration is the critical failure stiffness of the fiber reinforced composite material under random fatigue:

[0076] E(N)=E(0)(1-QN v ) (2)

[0077] In the formula, E(N) is the critical failure stiffness under random fatigue; N is the critical failure cycle number.

[0078] Then, the established random fatigue residual stiffness model is integrated by using the random cycle number, that is, the upper and lower limits of integration are n and 0 in turn, and the result obtained after integration is the residual stiffness of the fiber reinforced composite material under random fatigue:

[0079] E(n)=E(0)(1-Qn v ) (3)

[0080] In the formula, n is the random cycle number.

[0081] 1022: Based on the residual stiffness under random fatigue and the critical failure stiffness under random fatigue, the residual stiffness damage factor under random fatigue is calculated.

[0082] In the embodiment of the application, in order to obtain the residual stiffness damage factor under random fatigue, the following process can be implemented:

[0083] The formula (2) is transformed to obtain:

[0084]

[0085] The formula (4) is substituted into the formula (3) to obtain the failure stiffness ratio of the fiber reinforced composite material:

[0086]

[0087] The residual stiffness damage factor of the fiber reinforced composite material under random fatigue is defined as follows:

[0088]

[0089] Therefore, according to the formula (6), the formula (5) is transformed to obtain the function of the residual stiffness damage factor under random fatigue about the life ratio n / N of the fiber reinforced composite material:

[0090]

[0091] Step 104: Based on the correlation between the residual stiffness damage factor and the residual strength damage factor, determine the residual strength damage factor under random fatigue using the residual stiffness damage factor under random fatigue.

[0092] In this step, the residual stiffness damage factor and the residual strength damage factor of fiber-reinforced composites under constant amplitude fatigue are defined as follows:

[0093]

[0094]

[0095] In the formula, S(0) is the initial strength of the fiber-reinforced composite material; e(n) is the residual stiffness under constant amplitude fatigue; e(N) is the critical failure stiffness under constant amplitude fatigue; s is the external stress level under constant amplitude fatigue; s(n) represents the residual strength under constant amplitude fatigue.

[0096] From the above two equations, we can conclude that if n = 0, then D e =D s =0, if n=N, then D e =D s =1; that is, D e and D s The values ​​of are all in the range of 0 to 1;

[0097] Therefore, the correlation between the residual stiffness damage factor and the residual strength damage factor of fiber-reinforced composites under constant amplitude fatigue is defined as follows:

[0098] D s =(D e ) k (10)

[0099] In the formula, k is the proportional parameter under constant amplitude fatigue.

[0100] In this embodiment of the invention, the residual strength damage factor under random fatigue is defined as follows based on the above formula (9):

[0101]

[0102] In the formula, S(n) represents the residual strength under random fatigue; S is the external stress level under random fatigue.

[0103] The residual stiffness damage factor and the residual strength damage factor under random fatigue of fiber-reinforced composite materials are defined with the same relationship as in formula (10):

[0104] D S =(D E )m (12)

[0105] wherein m is a proportional parameter under random fatigue;

[0106] Substituting the correlation into formula (11), the function of the residual strength damage factor under random fatigue with respect to the life ratio n / N of the fiber-reinforced composite material can be obtained:

[0107]

[0108] Step 106: based on the residual stiffness damage factor under random fatigue and the residual strength damage factor under random fatigue, a residual stiffness-residual strength correlation model under random fatigue is established.

[0109] In the embodiment of the present application, it can be obtained from formula (8) to formula (10) that the residual stiffness damage factor and the residual strength damage factor of the fiber-reinforced composite material under constant amplitude fatigue can establish the following residual stiffness-residual strength correlation model:

[0110]

[0111] wherein, is a function of the life ratio n / N under constant amplitude fatigue;

[0112] For the residual stiffness-residual strength correlation model under constant amplitude fatigue, as long as the value of the parameter m is determined, the degradation law of the residual strength can be evaluated through the residual stiffness, and the parameter m can be obtained through at least one residual strength experiment.

[0113] In the embodiment, the residual stiffness damage factor under random fatigue and the residual strength damage factor under random fatigue shown in formula (7) and formula (13) are also functions of the life ratio n / N of the fiber-reinforced composite material, therefore, the residual stiffness-residual strength correlation model under random fatigue can be established according to the residual stiffness-residual strength correlation model under constant amplitude fatigue shown in formula (14), and the establishment process can specifically include the following steps 1060-1062:

[0114] 1060: the calculation formula of the residual strength damage factor under random fatigue is deformed to obtain the predicted failure strength ratio of the fiber-reinforced composite material under random fatigue; the failure strength ratio under random fatigue is differentiated to obtain a stress cycle number distribution function of the failure strength ratio under random fatigue when any external load stress level acts;

[0115] 1062: according to the cycle number of random stress per unit time and the probability density function of the cycle stress, the stress cycle number distribution function is solved, and the residual stiffness-residual strength correlation model under random fatigue is obtained by integrating the solution result.

[0116] In step 1060, for the convenience of subsequent establishment of the random fatigue residual stiffness-residual strength correlation model, the calculation formula (13) of the residual strength damage factor under random fatigue needs to be transformed to obtain the prediction of the failure strength ratio of the fiber-reinforced composite material under random fatigue:

[0117]

[0118] In the formula, β = mv;

[0119] The formula (7) is transformed to obtain the function relationship of n / N:

[0120]

[0121] By differentiating formula (15) under random fatigue load at any external stress level S i

[0122]

[0123] And substituting formula (16) into the above formula (17), the stress cycle number distribution function of the failure strength ratio under random fatigue when the stress cycle number is at any external stress level is obtained:

[0124]

[0125] In step 1062, since dn is the stress cycle number in the stress range (S, S+ΔS) in dt time, the residual strength and residual stiffness correlation relationship can be obtained by solving formula (18) through the stress cycle number per unit time and the probability density function of the cycle stress. The solution result is a function related to time, and is continuously integrable for the external stress level, so the residual stiffness-residual strength correlation model of the failure strength ratio related to time under random fatigue can be obtained by integrating the solution result.

[0126] The specific steps are shown in the following formula:

[0127] dn = n t dt·p(S)ΔS (19)

[0128] In the formula, n t is the cycle number of random stress per unit time; p(S) is the probability density function of the cycle stress;

[0129] Substituting formula (19) into formula (18) obtains the correlation relationship between residual strength and residual stiffness:

[0130]

[0131] ​The formula (20) is used to calculate the failure strength ratio S(n) / S(0) corresponding to a micro-stress level ΔS, therefore, the failure strength ratio function curve can be obtained by integrating the formula (20) at continuous stress levels S:

[0132]

[0133] In the formula, E(N) / E(0) is the critical failure stiffness ratio of the fiber-reinforced composite material under random fatigue;

[0134] Since it is very difficult to directly obtain the critical failure stiffness ratio of the fiber-reinforced composite material under random fatigue, the critical failure stiffness ratio is calculated by the following formula:

[0135]

[0136] In the formula, C1 and C2 are parameters fitted by the constant-amplitude fatigue experiment of the fiber-reinforced composite material; S is the external stress level; and S(0) is the static strength of the material.

[0137] The formula (22) is substituted into the formula (21) to obtain a residual stiffness-residual strength correlation model under random fatigue:

[0138]

[0139] In the model, the parameters C1 and C2 can be obtained by the constant-amplitude fatigue experiment.

[0140] Step 108: based on the residual stiffness-residual strength correlation model under random fatigue, the change rule of the residual strength of the fiber-reinforced composite material under random fatigue with time is calculated.

[0141] In the embodiment of the application, when the change rule of the residual strength with time under random fatigue is calculated, the following steps 1080-1086 can be specifically included:

[0142] 1080: the residual stiffness analysis model under random fatigue and the residual stiffness-residual strength correlation model under random fatigue are equivalently transformed, and in the transformed residual stiffness analysis model under random fatigue and the residual stiffness-residual strength correlation model under random fatigue, both are the correlation formula of time and failure stiffness ratio;

[0143] 1082: based on a plurality of preset failure stiffness ratios, the experienced time length of the change of the failure stiffness ratio to each preset failure stiffness ratio is calculated;

[0144] 1084: the plurality of experienced time lengths calculated are substituted into the transformed residual stiffness-residual strength model under random fatigue to obtain the random fatigue residual strength corresponding to each experienced time length;

[0145] 1086: Obtain the change rule of the residual strength of the fiber-reinforced composite material over time based on the random fatigue residual strength corresponding to each experienced time length.

[0146] In step 1080, since formula (23) cannot be directly calculated and solved, it is necessary to make equivalent transformation on the residual stiffness analysis model under random fatigue and the residual stiffness-residual strength correlation model under random fatigue.

[0147] Specifically, the residual stiffness analysis model under random fatigue is shown in the following formula:

[0148]

[0149] In the residual stiffness analysis model under random fatigue and the residual stiffness-residual strength correlation model under random fatigue, the right side of the equation is a function of the failure strength ratio E(n) / E(0).

[0150] That is, for formula (23), the right side of the equation can be transformed as:

[0151]

[0152] For formula (24), the right side of the equation can be transformed as:

[0153]

[0154] Substitute formula (25) into formula (23) to obtain:

[0155]

[0156] Substitute formula (26) into formula (24) to obtain:

[0157]

[0158] The transformed residual stiffness-residual strength correlation model under random fatigue (as shown in formula (27)) and the residual stiffness analysis model under random fatigue (as shown in formula (28)) are both correlation formulas of time and failure stiffness ratio.

[0159] In step 1082, in order to calculate the experienced time length dt in the correlation formula, the value of the failure stiffness ratio E(n) / E(0) needs to be preset in advance. The value of the failure stiffness ratio E(n) / E(0) can be set as: 1, 0.99, 0.98, …, 0.

[0160] The corresponding Substitute the obtained φ(1), φ(0.99), ..., φ(0) into formula (28) to calculate the time taken for the failure stiffness ratio to change to each preset failure stiffness ratio;

[0161] For example, the time it takes for the failure stiffness ratio to change from 1 to 0.99 is calculated as t1, the time it takes for the failure stiffness ratio to change from 1 to 0.98 is calculated as t2, and so on.

[0162] In step 1084, each experience duration t1, t2, ..., t n Substituting into formula (27), we obtain the random fatigue residual intensity corresponding to each duration of experience.

[0163] In one implementation of step 1086, the residual intensity of random fatigue corresponding to each duration is summed up, and the variation law of residual intensity with time under random fatigue can be obtained by numerical integration:

[0164]

[0165] In the formula, g(t) is a function of time.

[0166] It should be noted that, in addition to the numerical integration method mentioned in this manual, other methods can be used to obtain the variation law of residual strength with time under random fatigue, such as the fitting method.

[0167] Example 2

[0168] like Figure 2 , Figure 3 As shown, this invention provides a device for predicting the residual strength of fiber-reinforced composite materials. The device can be implemented via software, hardware, or a combination of both. From a hardware perspective, as... Figure 2 The diagram shown is a hardware architecture diagram of an electronic device for predicting the residual strength of fiber-reinforced composite materials according to an embodiment of the present invention. (Except for...) Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of its electronic device reading the corresponding computer program from non-volatile memory into memory and running it. This embodiment provides a device for predicting the residual strength of fiber-reinforced composite materials, comprising:

[0169] The first modeling unit 500 is configured to establish a residual stiffness model under random fatigue for a fiber-reinforced composite material to be predicted.

[0170] The first calculation unit 502 is configured to calculate the residual stiffness model under random fatigue to obtain a residual stiffness damage factor under random fatigue and a residual strength damage factor under random fatigue.

[0171] The second modeling unit 504 is configured to establish a residual stiffness-residual strength correlation model under random fatigue.

[0172] The second calculation unit 506 is configured to calculate the residual stiffness-residual strength correlation model under random fatigue to obtain a change rule of the residual strength of the fiber-reinforced composite material under random fatigue over time.

[0173] In an embodiment of the present application, the first modeling unit is specifically configured to establish the residual stiffness model under random fatigue as follows:

[0174]

[0175] wherein E(0) is the initial stiffness of the fiber-reinforced composite material; Q and v are both parameters of the fiber-reinforced composite material, which are related to the stress level; n is the random cycle number; and E(n) is the residual stiffness under random fatigue.

[0176] In an embodiment of the present application, the first calculation unit is specifically configured to perform integration on the residual stiffness model under random fatigue by using the critical failure cycle number and the random cycle number respectively to correspondingly obtain the critical failure stiffness under random fatigue and the residual stiffness under random fatigue; and calculate the residual stiffness damage factor under random fatigue based on the residual stiffness under random fatigue and the critical failure stiffness under random fatigue.

[0177] In an embodiment of the present application, when the first calculation unit performs integration on the residual stiffness model under random fatigue by using the critical failure cycle number and the random cycle number respectively to correspondingly obtain the critical failure stiffness under random fatigue and the residual stiffness under random fatigue, the first calculation unit specifically includes that the critical failure stiffness under random fatigue of the fiber-reinforced composite material and the residual stiffness under random fatigue of the fiber-reinforced composite material are respectively as follows:

[0178] E(N)=E(0)(1-QN v )

[0179] E(n)=E(0)(1-Qn v )

[0180] wherein E(N) is the critical failure stiffness under random fatigue; n is the random cycle number; and N is the critical failure cycle number.

[0181] In one embodiment of the present application, the first calculation unit, when performing the calculation of the residual stiffness damage factor under random fatigue based on the residual stiffness under random fatigue and the critical failure stiffness under random fatigue, specifically comprises that the residual stiffness damage factor under random fatigue is:

[0182]

[0183] In the formula, D E is the residual stiffness damage factor under random fatigue.

[0184] The residual strength damage factor under random fatigue is determined by using the following formula:

[0185]

[0186] In the formula, D S = (D E ) m is the correlation between the residual stiffness damage factor and the residual strength damage factor, D S is the residual strength damage factor under random fatigue; S(0) is the initial strength of the material; S(n) is the residual strength of the material under random fatigue; S is the external stress level; and m is a proportional parameter.

[0187] In one embodiment of the present application, the second modeling unit is specifically used for: deforming the calculation formula of the residual strength damage factor under random fatigue to obtain the failure strength ratio of the fiber-reinforced composite material under random fatigue; differentiating the failure strength ratio under random fatigue to obtain a stress cycle number distribution function of the failure strength ratio under random fatigue when any external stress level acts; and solving the stress cycle number distribution function according to the cycle number of random stress per unit time and the probability density function of the cycle stress, and integrating the solution result to obtain a residual stiffness-residual strength correlation model under random fatigue.

[0188] In one embodiment of the present application, the second modeling unit, when performing the modeling of the residual stiffness-residual strength correlation model under random fatigue, specifically comprises that the residual stiffness-residual strength correlation model under random fatigue is:

[0189]

[0190] In the formula, E(0) is the initial stiffness of the fiber-reinforced composite material; n is the random cycle number; N is the critical failure cycle number; E(n) is the residual stiffness of the fiber-reinforced composite material under random fatigue; S(0) is the initial strength of the fiber-reinforced composite material; S(n) is the residual strength of the fiber-reinforced composite material under random fatigue; S is the external stress level; n tis the number of cycles of random stress in a unit of time; p(S) is the probability density function of the cyclic stress; ΔS is the change value of the external load stress; C1 and C2 are both experimental fitting parameters; β(S) and v(S) are both functions of the stress level.

[0191] In an embodiment of the present application, the second calculation unit is specifically configured to: perform equivalent transformation on the residual stiffness analysis model under random fatigue and the residual stiffness-residual strength correlation model under random fatigue, and the transformed residual stiffness analysis model under random fatigue and the residual stiffness-residual strength correlation model under random fatigue are both correlation formulas of time and failure stiffness ratio; based on a plurality of preset failure stiffness ratios, calculate experienced time lengths of failure stiffness ratio changes to each preset failure stiffness ratio; input the calculated plurality of experienced time lengths into the transformed residual stiffness-residual strength model under random fatigue to obtain a random fatigue residual strength corresponding to each experienced time length; and based on the random fatigue residual strength corresponding to each experienced time length, obtain a change rule of the residual strength of the fiber-reinforced composite material with time.

[0192] In summary, in the embodiment of the present application, compared with the strength data obtained by relying on destructive experiments, the stiffness data can be obtained by non-destructive experiments, so that the residual strength curve of the fiber-reinforced composite material under random fatigue can be obtained under the premise of reducing experimental cost, and then the fatigue life and reliability problem under random fatigue can be better analyzed based on the natural failure criterion of the strength of the fiber-reinforced composite material.

[0193] It can be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the device for predicting the residual strength of the fiber-reinforced composite material. In other embodiments of the present application, the device for predicting the residual strength of the fiber-reinforced composite material can include more or fewer components than the illustration, or combine certain components, or split certain components, or different component arrangement. The illustrated components can be realized in hardware, software or a combination of software and hardware.

[0194] The information interaction, execution process and the like between the modules in the device are based on the same concept as the method embodiments of the present application, and the specific content can be referred to the description in the method embodiments of the present application, which will not be described here.

[0195] The embodiment of the present application also provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the method for predicting the residual strength of the fiber-reinforced composite material in any embodiment of the present application.

[0196] The embodiment of the present application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the method for predicting the residual strength of the fiber reinforced composite material according to any one of the embodiments of the present application.

[0197] Specifically, a system or an apparatus provided with a storage medium storing software program codes for realizing the functions of any one of the above embodiments can be provided, and a computer (or CPU or MPU) of the system or the apparatus reads out and executes the program codes stored in the storage medium.

[0198] In this case, the program codes read from the storage medium can realize the functions of any one of the above embodiments by themselves, and thus the program codes and the storage medium storing the program codes constitute a part of the present application.

[0199] Embodiments of the storage medium for providing the program codes include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program codes can be downloaded from a server computer via a communication network.

[0200] In addition, it should be clear that not only the program codes read by the computer can be executed, but also part or all of the actual operations can be completed by an operating system or the like operating on the computer based on the instructions of the program codes, so as to realize the functions of any one of the above embodiments.

[0201] In addition, it should be understood that the program codes read from the storage medium can be written into a memory provided in an expansion board inserted into the computer or a memory provided in an expansion module connected to the computer, and then part or all of the actual operations can be executed by a CPU or the like installed on the expansion board or the expansion module based on the instructions of the program codes, so as to realize the functions of any one of the above embodiments.

[0202] It should be noted that, in the present document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0203] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware, and the foregoing program can be stored in a computer readable storage medium, and the program performs the steps of the above-mentioned method embodiments when executed; and the foregoing storage medium includes various storage media that can store program codes, such as ROM, RAM, magnetic disk or optical disk.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present specification, rather than limit them; although the present specification has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present specification.

Claims

1. A method for predicting the residual strength of fiber-reinforced composite materials, characterized in that, include: For fiber-reinforced composite materials to be predicted, a residual stiffness model under stochastic fatigue is established. Based on the residual stiffness model under random fatigue, the residual stiffness damage factor under random fatigue is calculated. Based on the correlation between the residual stiffness damage factor and the residual strength damage factor, the residual strength damage factor under random fatigue is determined using the residual stiffness damage factor under random fatigue. Based on the residual stiffness damage factor and the residual strength damage factor under random fatigue, a residual stiffness-residual strength correlation model under random fatigue is established. The residual stiffness-residual strength correlation model under random fatigue is as follows: In the formula, E(0) is the initial stiffness of the fiber-reinforced composite material; n is the number of random cycles; N is the number of critical failure cycles; E(n) is the residual stiffness of the fiber-reinforced composite material under random fatigue; S(0) is the initial strength of the fiber-reinforced composite material. S(n) represents the residual strength of the fiber-reinforced composite material under random fatigue; S represents the external stress level; n t denoted as , where is the number of cycles of random stress per unit time; p(S) is the probability density function of the cyclic stress; ΔS is the change in external stress; C1 and C2 are parameters fitted by the experiment; β(S) and v(S) are functions of the stress level. Based on the residual stiffness-residual strength correlation model under random fatigue, the variation law of residual strength of the fiber-reinforced composite material under random fatigue with time is calculated.

2. The method according to claim 1, characterized in that, The established residual stiffness model under stochastic fatigue is as follows: In the formula, E(0) is the initial stiffness of the fiber-reinforced composite material; Q and v are parameters of the fiber-reinforced composite material, which are related to the stress level; n is the number of random cycles; E(n) is the residual stiffness under random fatigue. The calculation of the residual stiffness damage factor and the residual strength damage factor under random fatigue based on the residual stiffness model under random fatigue includes: By integrating the residual stiffness model under random fatigue using the critical failure cycle number and the random cycle number respectively, the critical failure stiffness and the residual stiffness under random fatigue are obtained one-to-one. Based on the residual stiffness under random fatigue and the critical failure stiffness under random fatigue, the damage factor of the residual stiffness under random fatigue is calculated.

3. The method according to claim 2, characterized in that, The critical failure stiffness and residual stiffness of the fiber-reinforced composite material under random fatigue are as follows: E(N)=E(0)(1-QN v ) E(n)=E(0)(1-Qn v ) In the formula, E(N) is the critical failure stiffness under random fatigue; n is the number of random cycles; N is the number of critical failure cycles; The residual stiffness damage factor under random fatigue is: In the formula, D E This represents the residual stiffness damage factor under random fatigue.

4. The method according to claim 3, characterized in that, The residual strength damage factor under random fatigue is determined using the following formula: Among them, D S =(D E ) m To illustrate the correlation between the residual stiffness damage factor and the residual strength damage factor, D S S(n) represents the residual strength damage factor under random fatigue; S(0) represents the initial strength of the fiber-reinforced composite material; S(n) represents the residual strength of the fiber-reinforced composite material under random fatigue; S represents the external stress level; and m represents the proportional parameter.

5. The method according to claim 1, characterized in that, The establishment of a residual stiffness-residual strength correlation model under random fatigue based on the residual stiffness damage factor and the residual strength damage factor under random fatigue includes: The calculation formula for the residual strength damage factor under random fatigue is modified to obtain the predicted failure strength ratio under random fatigue of the fiber-reinforced composite material; the failure strength ratio under random fatigue is differentiated to obtain the stress cycle number distribution function of the failure strength ratio under random fatigue under any external stress level. Based on the number of cycles of random stress per unit time and the probability density function of the cyclic stress, the stress cycle number distribution function is solved, and the solution is integrated to obtain the residual stiffness-residual strength correlation model under random fatigue.

6. The method according to claim 1, characterized in that, The residual strength-residual strength correlation model under random fatigue is used to obtain the variation law of residual strength with time under random fatigue, including: An equivalent transformation is performed on the residual stiffness analysis model under random fatigue and the residual stiffness-residual strength correlation model under random fatigue. The transformed residual stiffness analysis model under random fatigue and the residual stiffness-residual strength correlation model under random fatigue both contain correlation formulas for time and failure stiffness ratio. Based on several preset failure stiffness ratios, calculate the time taken for the failure stiffness ratio to change to each preset failure stiffness ratio; Substitute the calculated durations into the transformed random fatigue residual stiffness-residual strength model to obtain the random fatigue residual strength corresponding to each duration. Based on the random fatigue residual strength corresponding to each duration of experience, the variation law of the residual strength of the fiber-reinforced composite material with time is obtained.

7. A device for predicting the residual strength of fiber-reinforced composite materials, characterized in that, The method applied to any one of claims 1-6 includes: The first modeling unit is used to establish a residual stiffness model under random fatigue for the fiber-reinforced composite material to be predicted. The first calculation unit is used to calculate the residual stiffness model under random fatigue to obtain the residual stiffness damage factor and the residual strength damage factor under random fatigue. The second modeling unit is used to establish the residual stiffness-residual strength correlation model under random fatigue. The second calculation unit is used to calculate the residual stiffness-residual strength correlation model under random fatigue to obtain the variation law of the predicted residual strength of the fiber-reinforced composite material under random fatigue with time.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-6.

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