Heat-resistant steel creep rupture life prediction method based on data statistics

Abstract

Disclosed in the present invention is a heat-resistant steel creep rupture life prediction method based on data statistics. The method comprises the following steps: performing a creep rupture test on heat-resistant steel or consulting literature to obtain stress σ-temperature T-rupture time Tr data groups; acquiring a lgσi-Pi data group and a lgσ-P main curve equation; for each stress σi, calculating a corresponding parameter Zi deviating from a main curve; performing normal distribution fitting and hypothesis testing on Zi to obtain a mean value, a standard deviation and a probability density function of Zi; calculating the integral of the probability density function in an interval from -∞ to Zp to obtain failure probabilities corresponding to different Zp; and substituting, into an expected life equation, values such as Zp corresponding to different failure probabilities, so as to obtain a life prediction result. In the present invention, statistical analysis is performed on creep rupture data and the uncertainty of the creep rupture property of a material is comprehensively taken into consideration, such that the creep rupture life under different failure probabilities, temperatures and stresses can be relatively accurately predicted, thereby improving the resource utilization rate to the greatest extent.

Description

A method for predicting creep rupture life of heat-resistant steel based on data statistics Technical Field

[0001] The present invention belongs to the technical field of high-temperature materials and structural strength, and in particular relates to a method for predicting the creep endurance life of heat-resistant steel based on data statistics. Background Art

[0002] In industries such as thermal power generation, aerospace, and petrochemicals, a large number of components are subjected to high temperatures and high pressures during long-term service. Under the influence of temperature and stress, the microstructure and mechanical properties of the materials will slowly change, that is, the material will deteriorate. For components such as high-temperature and high-pressure steam pipes and headers in thermal power generation units, this is mainly reflected in the accumulation of creep damage and the reduction of creep endurance life. High-temperature creep cracking of the material is one of its most important failure modes. Therefore, the design of such high-temperature and high-pressure components is often based on the long-term endurance strength of the material, and the service life of the component is also mainly determined by the creep endurance life. Endurance life prediction technology is the core of the life management of high-temperature and high-pressure components.

[0003] To predict the creep life of high-temperature, high-pressure components, various life prediction models have been developed. The primary approach is to use short-term, high-temperature creep life data to extrapolate creep life at service temperatures and stresses. Developed extrapolation methods include the isotherm method, the Larson-Miller method (LM parameter method), the MH parameter method, the Monkman-Grant method (MG method), and the theta function method. These methods all fit experimental data to obtain fitted curves, then extrapolate creep life at service temperatures and stresses. However, given the dispersion of experimental data due to random factors, these extrapolated lives actually have a near 50% probability of failure.

[0004] To improve safety, current technology generally uses a safety factor method with a safety factor of 1.5. This means that the extrapolated life when the stress is 1.5 times the service stress is used as the creep endurance life of the component. However, this method is relatively conservative and often results in significant waste. Some technologies use 0.8 times the stress as the lower limit of the creep endurance data dispersion band, and use this extrapolated result as the creep endurance life. However, in reality, the data dispersion bands of different grades of materials, different manufacturers, and different test data sources vary greatly. Using 0.8 times the stress as the lower dispersion band for extrapolation may bring certain risks.

[0005] Summary of the Invention

[0006] In order to solve the technical problems existing in the prior art, the purpose of the present invention is to provide a method for predicting the creep rupture life of heat-resistant steel based on data statistics.

[0007] In order to achieve the above-mentioned purpose and the above-mentioned technical effect, the technical solution adopted by the present invention is:

[0008] A method for predicting the creep rupture life of heat-resistant steel based on data statistics comprises the following steps:

[0009] (1) Conduct creep endurance tests on heat-resistant steel or consult relevant literature to obtain a series of stress σ-temperature T-rupture time t r Data set;

[0010] (2) Using the stress σ obtained in step (1) - temperature T - fracture time t r Calculate lgσ for data set i And the corresponding LM parameter P i , and obtain lgσ i -P i Data set;

[0011] (3) Use the least squares method to calculate the lgσ obtained in step (2) i -P i The data set was fitted to obtain the lgσ-P master curve equation;

[0012] (4) For each stress σ i Calculate the parameter Z corresponding to the deviation from the main curve i ;

[0013] (5) For parameter Z i Perform normal distribution fitting and hypothesis testing to obtain its mean μ, standard deviation s and probability density function

[0014] (6) Calculate the probability density function obtained in step (5) between -∞ and Z p The integration of the interval gives different Z p The corresponding failure probability λ;

[0015] (7) The Z corresponding to different failure probabilities λ p , target temperature T, and target stress σ are substituted into the expected life equation to obtain the predicted life result.

[0016] Furthermore, in step (1), the stress σ-temperature T-fracture time t r The number of data groups is not less than 50.

[0017] Furthermore, in step (2), the LM parameter P i The calculation formula is: i =T(C+lgt r ) / 1000

[0018] Where C is a constant related to the material.

[0019] Furthermore, in step (3), the data are fitted by least squares using the fitting equation lgσ=C1-C2 exp[C3P] to obtain the lgσ-P master curve equation, where C1, C2, and C3 are constants obtained by fitting.

[0020] Furthermore, in step (4), the parameter Z i The calculation formula is: i =lgσ i -lgσ Mi

[0021] Among them, lgσ Mi is the LM parameter P i Substitute the value calculated in the master curve equation in step (3).

[0022] Furthermore, in step (6), the calculation formula for the failure probability is:

[0023] Furthermore, in step (7), the life expectancy equation is expressed as:

[0024] Furthermore, for P22 steel operating at 540°C for 20 years, the master curve equation is lgσ = 2.84669 - 0.04233exp(0.15576P).

[0025] Furthermore, for P22 steel operating at 540°C for 20 years, the probability density function f(Z) is expressed as:

[0026] The life expectancy equation is expressed as:

[0027] Furthermore, in step (7), for P22 steel that has been operating at 540°C for 20 years, when the failure probability is 1%, Z p is -0.0359, when the failure probability is 0.001%, Z p is -0.00769, when the failure probability is 5% p It is -0.0254.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention discloses a method for predicting the creep endurance life of heat-resistant steel based on data statistics, comprising the following steps: performing a creep endurance test on the heat-resistant steel or consulting literature to obtain the stress σ-temperature T-rupture time t r Data set; get lgσ i -P iData set, lgσ-P master curve equation; for each stress σ i Calculate the parameter Z corresponding to the deviation from the main curve i ; for Z i Perform normal distribution fitting and hypothesis testing to obtain Z i The mean, standard deviation and probability density function of p The integration of the interval gives different Z p The corresponding failure probability; the Z corresponding to different failure probabilities p Substituting these values ​​into the expected life equation yields the predicted lifespan. This method statistically analyzes creep endurance data, comprehensively accounting for the uncertainty of the material's creep endurance properties. This allows for relatively accurate predictions of creep endurance life under varying failure probabilities, temperatures, and stresses. This can be used to accurately determine maintenance intervals, maximize resource utilization, and achieve the goals of economically rationally using high-temperature components and ensuring safe production. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] FIG1 is a graph showing lgσ of the present invention. i -P i Data set and main curve graph. DETAILED DESCRIPTION

[0031] The present invention is described in detail below so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more precise definition of the protection scope of the present invention.

[0032] The following is a brief summary of one or more aspects to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceivable aspects and is neither intended to identify key or critical elements of all aspects nor to define the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that will be provided later.

[0033] Example 1

[0034] As shown in FIG1 , a method for predicting the creep rupture life of heat-resistant steel based on data statistics includes the following steps:

[0035] (1) Conduct creep endurance tests on heat-resistant steel or consult relevant literature to obtain a series of stress σ-temperature T-rupture time t r Data groups, the number is not less than 50;

[0036] (2) Using the stress σ obtained in step (1) - temperature T - fracture time t r Calculate lgσ for data set i And the corresponding LM parameter P i , Pi =T(20+lgt r ) / 1000, and obtain lgσ i -P i Data set;

[0037] (3) Use the least squares method to calculate the lgσ obtained in step (2) i -P i The data set was fitted to obtain the lgσ-P master curve equation;

[0038] (4) For each stress σ i Calculate the parameter Z corresponding to the deviation from the main curve i =lgσ i -lgσ Mi , where lgσ Mi P i Substitute the value calculated from the master curve equation in step (3);

[0039] (5) For parameter Z i Perform normal distribution fitting and hypothesis testing to obtain Z i The mean μ, standard deviation s and probability density function

[0040] (6) Calculate the probability density function obtained in step (5) between -∞ and Z p The integration of the interval gives different Z p The corresponding failure probability λ;

[0041] (7) The Z corresponding to different failure probabilities λ p , target temperature T, target stress σ and other numerical values ​​are substituted into the expected life equation to obtain the predicted life result.

[0042] In step (3), the data are fitted by least squares using the fitting equation lgσ=C1-C2 exp[C3P] to obtain the lgσ-P master curve equation, where C1, C2, and C3 are constants obtained by fitting.

[0043] In step (6), the calculation formula of the failure probability is:

[0044] In step (7), the life expectancy equation is expressed as:

[0045] For P22 steel operated at 540°C for 20 years, the master curve equation is lgσ=2.84669-0.04233exp(0.15576P). For P22 steel operated at 540°C for 20 years, the probability density function f(Z) is expressed as:

[0046] The life expectancy equation is expressed as:

[0047] In step (7), for P22 steel that has been operating at 540℃ for 20 years, when the failure probability is 1%, Z p is -0.0359, when the failure probability is 0.001%, Z p is -0.00769, when the failure probability is 5% p It is -0.0254.

[0048] Example 2

[0049] Creep endurance tests were conducted on P22 main steam steel pipe samples from a thermal power plant that had been in operation for about 20 years. The test temperature was 540°C, the service temperature, and the stress range was 140MPa to 90MPa. A total of 60 sets of stress σ-temperature T-rupture time t were obtained. r Data, where σ is stress (MPa), T is temperature (K), and t r is the break time (h).

[0050] For the above data, calculate lgσ i And the corresponding LM parameter P i , P i =T(20+lgt r ) / 1000, and obtain lgσ i -P i The data set is plotted with P as the horizontal axis and lgσ as the vertical axis. The least squares fitting of the data is performed using the fitting equation lgσ=C1-C2 exp[C3P] to obtain the lgσ-P master curve equation: lgσ=2.84669-0.04233exp[0.15576P], lgσ i -P i The data set and main curve are shown in Figure 1.

[0051] Then, for each σ i Calculate the parameter Z corresponding to the deviation from the main curve i =lgσ i -lgσ Mi , where lgσ Mi =2.84669-0.04233exp[0.15576P].

[0052] To Z i Perform hypothesis testing to confirm that its distribution conforms to the normal distribution, fit the data to the normal distribution, and obtain Z i The mean μ=2.34879*10 -5 , standard deviation s = 0.01542 and probability density function f(Z):

[0053] From -∞~Z p Integrate the probability density function to obtain different Z p The corresponding failure probability λ.

[0054] Finally, the Z corresponding to different failure probabilities λ p , target temperature T, target stress σ and other values ​​are substituted into the expected life equation The expected life under the corresponding failure probability, temperature and stress can be obtained.

[0055] Example 3

[0056] When the failure probability is 1%, Z p When the value is -0.0359, the temperature T is 813.15K (540℃), and the stress σ is 60MPa, the three values ​​are substituted into the expected life equation, and the expected life is calculated to be 166,000 hours.

[0057] Example 4

[0058] When the failure probability is 0.001%, Z p When the value is -0.0769, the temperature T is 813.15K (540℃), and the stress σ is 60MPa, the three values ​​are substituted into the expected life equation, and the expected life is calculated to be 80,000 hours.

[0059] Example 5

[0060] When the failure probability is 5%, Z p When the value is -0.0254, the temperature T is 813.15K (540℃), and the stress σ is 60MPa, the three values ​​are substituted into the expected life equation, and the expected life is calculated to be 1.56 million hours.

[0061] Example 6

[0062] When the failure probability is 1%, Z p When the value is -0.0359, the temperature T is 843.15K (570℃), and the stress σ is 60MPa, the three values ​​are substituted into the expected life equation, and the expected life is calculated to be 210,000 hours.

[0063] When the operating time increases and the stress increases, the probability of creep failure of high-temperature components increases significantly. The expected life under different failure probabilities varies greatly. The expected life values ​​under different failure probabilities can be used as a reference to establish maintenance and life extension assessment cycles, which is of great significance for improving the safety of high-temperature components.

[0064] Parts or structures not specifically described in the present invention may adopt existing technologies or existing products and will not be described in detail here.

[0065] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for predicting the creep rupture life of heat-resistant steel based on data statistics, characterized in that, It includes the following steps: (1) Conduct creep rupture tests on heat-resistant steels or consult relevant literature to obtain a series of stress σ - temperature T - rupture time t r data sets; (2) Use the stress σ - temperature T - fracture time t obtained in step (1) r to calculate lgσ for the data set i and the corresponding L - M parameter P i to obtain the lgσ i -P i data set; (3) Use the least squares method to perform fitting on the lgσ i -P i data group to obtain the lgσ - P master curve equation; (4) For each stress σ i calculate the parameter Z corresponding to the deviation from the master curve i ; (5)Perform normal distribution fitting and hypothesis testing on parameter Z i to obtain its mean μ, standard deviation s, and probability density function (6) Calculate the integral of the probability density function obtained in step (5) over the interval from -∞ to Z p to obtain the failure probability λ corresponding to different Z p values; (7) Substitute the Z corresponding to different failure probabilities λ, the target temperature T, and the target stress σ into the expected life equation to obtain the predicted life results. p ​ 2. A method for predicting the creep rupture life of heat-resistant steel based on data statistics according to claim 1, characterized in that, In step (1), the stress σ - temperature T - fracture time t r The number of data groups is not less than 50 groups.

3. A method for predicting the creep rupture life of heat-resistant steel based on data statistics according to claim 1, characterized in that, In step (2), the L-M parameter P i has the following calculation formula: P i = T(C + lgt r ) / 1000 where C is a constant related to the material.

4. A method for predicting the creep rupture life of heat-resistant steel based on data statistics according to claim 1, characterized in that, In step (3), the data is subjected to least-squares fitting using the fitting equation lgσ = C1 - C2exp[C3P] to obtain the lgσ-P master curve equation, where C1, C2, and C3 are constants obtained by fitting.

5. A method for predicting the creep rupture life of heat-resistant steel based on data statistics according to claim 1, characterized in that, In step (4), the parameter Z i has the following calculation formula: Z i = lgσ i - lgσ Mi where, lgσ Mi is the L-M parameter P i which is the value calculated by substituting into the master curve equation in step (3).

6. A method for predicting the creep rupture life of heat-resistant steel based on data statistics according to claim 1, characterized in that in step (6), the calculation formula for the failure probability is:

7. A method for predicting the creep rupture life of heat-resistant steel based on data statistics according to claim 1, characterized in that, In step (7), the expected life equation is expressed as:

8. A method for predicting the creep rupture life of heat-resistant steel based on data statistics according to claim 1, characterized in that, For P22 steel operating at 540 °C for 20 years, the master curve equation is lgσ = 2.84669 - 0.04233exp(0.15576P).

9. A method for predicting the creep rupture life of heat-resistant steel based on data statistics according to claim 1, characterized in that For P22 steel operating at 540 °C for 20 years, the probability density function f(Z) is expressed as: The expected life equation is expressed as:

10. A method for predicting the creep rupture life of heat-resistant steel based on data statistics according to claim 1, characterized in that, In step (7), for P22 steel operating at 540 °C for 20 years, when the failure probability is 1%, Z p is -0.0359, and when the failure probability is 0.001%, Z p is -0.00769. When the failure probability is 5%, Z p is -0.0254.

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