CN113588528A - Metal atmospheric corrosion prediction method considering dynamic characteristics of natural environment - Google Patents

Metal atmospheric corrosion prediction method considering dynamic characteristics of natural environment Download PDF

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CN113588528A
CN113588528A CN202110752355.6A CN202110752355A CN113588528A CN 113588528 A CN113588528 A CN 113588528A CN 202110752355 A CN202110752355 A CN 202110752355A CN 113588528 A CN113588528 A CN 113588528A
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马小兵
蔡义坤
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Beihang University
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Abstract

The invention provides a metal atmospheric corrosion prediction method considering the dynamic characteristics of a natural environment, which is realized by the following steps: firstly, the method comprises the following steps: establishing an environmental influence rule model of atmospheric corrosion; II, secondly: estimating and selecting parameters of an environmental influence rule model of atmospheric corrosion; thirdly, the method comprises the following steps: establishing an atmospheric natural environment model considering dynamic, relevant and random characteristics; fourthly, the method comprises the following steps: estimating environmental model parameters and verifying the model; fifthly: establishing a corrosion prediction model under the use environment condition; through the steps, the corrosion influence rule of multiple environmental factors under the atmospheric natural environment is comprehensively considered, and an optimal corrosion influence rule model can be established by combining the information criterion, so that the factors are more comprehensive, and the model is more accurate; the method has the advantages of richer natural environment information, more accurate modeling result, closer established corrosion prediction method to actual use environment, scientific method, good manufacturability, capability of being popularized and applied to different environmental conditions and higher theoretical and application values.

Description

Metal atmospheric corrosion prediction method considering dynamic characteristics of natural environment
Technical Field
The invention provides a metal atmospheric corrosion prediction method considering the dynamic characteristics of a natural environment, and relates to an atmospheric corrosion prediction method considering the characteristics of the dynamic, correlation and randomness of the natural environment. By fusing the two models, the atmospheric corrosion prediction method under the actual use environment condition can be established. The method is suitable for the fields of environmental corrosivity grading and evaluation, anticorrosion design and material selection, environmental analysis and prediction and the like.
Background
As a metal material which is most widely used, economic loss due to corrosion is very serious under actual atmospheric natural environmental conditions. Therefore, the method has very important significance for the work of environmental corrosivity evaluation, anticorrosion design and the like by researching the influence factors and the corrosion influence rule of the metal material in the atmospheric natural environment, exploring the change rule of the corrosion performance of the metal material and predicting the change rule of the corrosion performance of the metal material in different atmospheric natural environments.
However, there are two disadvantages in the current research works related at home and abroad. On the one hand, the atmospheric natural environment model is not reasonable. For the quantitative description of the atmospheric natural environment factors, three common modeling methods, namely an averaging method, a distribution method and a time sequence method, have respective characteristics and limitations. The mean value method cannot completely describe all information of the environment, and cannot reflect the nonlinear characteristics of the environmental effect, the distribution method is not suitable for the situation of large dynamic change of factors, and the modeling problem of the time sequence method on the relevant environmental factors still needs to be perfected. On the other hand, the model of the atmospheric natural environment corrosion influence law is inaccurate, and the main environmental influence factors of different metal materials may be different, and generally include temperature, humidity, pollutants and the like. There may be complex interaction effects between these different factors, and therefore, the kinds of the main environmental influencing factors need to be determined for different materials and different natural environmental conditions. Meanwhile, for the description of the environmental factors, the environmental variables of most existing corrosion models are unreasonable to select, the description of the natural environment by adopting the annual average value is too simplified, and for the corrosion influence rule models, the existing models mostly adopt a linear or generalized linear model form, so that the complex nonlinear influence rule of the comprehensive environmental factors cannot be accurately described.
Based on the method, the metal atmospheric corrosion performance prediction method including the atmospheric natural environment model and the atmospheric environmental factor corrosion influence rule model is researched and established, the atmospheric natural environment conditions are accurately described, the atmospheric corrosion influence rule is comprehensively reflected, the corrosion performance change rule is scientifically predicted, and the method has important theoretical value and engineering significance.
Disclosure of Invention
(1) Objects of the invention：
The invention aims to provide a metal atmospheric corrosion prediction method considering the dynamic characteristics of a natural environment, which aims at the atmospheric corrosion problem of metal, considers the characteristics of the dynamics, the relativity and the randomness of the natural environment, establishes an atmospheric natural environment model and accurately describes the factors of the atmospheric natural environment; meanwhile, aiming at main environmental factors influencing the metal atmospheric corrosion process, a comprehensive environmental factor corrosion influence rule model is established, the corrosion influence rule of the atmospheric natural environment is comprehensively reflected, and then the natural environment model and the influence rule model are synthesized to form a corrosion prediction model under the dynamic atmospheric natural environment condition and scientifically predict the corrosion performance change rule.
(2) Technical scheme：
The invention needs to establish the following basic settings:
setting 1: the invention mainly aims at the atmospheric corrosion problem of metal materials, main environmental influence factors comprise atmospheric temperature, relative humidity and pollutant concentration, and other environmental factors such as rainfall, irradiation, wind speed and the like are not considered temporarily;
setting 2: the interaction effect generated by the combined action of different environmental factors is not considered at all, so that the influence rule model of the environmental factors does not contain interaction items;
setting 3: the temperature, the relative humidity and the corrosion influence rule of pollutants (sulfur dioxide and chloride ions) are respectively described by an Arrhenius model, a Peck model and a dose-response model, and the detailed model form is shown in step one;
setting 4: the comprehensive environment factor model can be synthesized through models of all environment factors, or a general generalized linear, generalized logarithmic linear and generalized Eyrin (Eyring) model is adopted for building, and the detailed model form is shown in step one;
the Arrhenius model is an empirical formula created in 1889 by Arrhenius in sweden and used for describing the acceleration law of temperature; the Peck (Peck) model is an empirical formula in the form of a power function for describing the acceleration law of humidity; the generalized Eyren (Eyreg) model is an empirical formula comprehensively considering the acceleration law of a plurality of environmental factors including temperature;
the method provided by the invention mainly comprises the research contents of three aspects of atmosphere corrosion environment influence rule modeling and parameter estimation, atmosphere natural environment modeling and parameter estimation and use environment atmosphere corrosion modeling and prediction, and based on the basic settings, the metal atmosphere corrosion prediction method considering the dynamic characteristics of the natural environment is characterized in that: the method is realized by the following steps:
the method comprises the following steps: establishing an environmental influence rule model of atmospheric corrosion
Firstly, determining main influence factors of a metal material based on the actual use environment of the metal material, then respectively establishing a corrosion influence rule model of the selected environment factors, and on the basis, comprehensively considering the combined action of all the factors and establishing a comprehensive environment factor influence rule model;
the method comprises the following specific steps:
I. determining key environmental impact factors
Generally, in addition to temperature and relative humidity, it is also necessary to determine whether the effect of atmospheric pollutants needs to be considered according to the type of atmosphere; if the environment is an industrial atmospheric environment, the influence of sulfur dioxide needs to be considered, if the environment is a marine atmospheric environment, the influence of chloride ions needs to be considered, if the environment has the characteristics of industrial atmosphere and marine atmosphere, the common influence of sulfur dioxide and chloride ions needs to be considered, if the environment is a rural atmospheric environment, the influence of atmospheric pollutants does not need to be considered, and the method can be specifically selected according to the following steps;
II, establishing an influence rule model of each environmental factor
For relative humidity, the law model of its effect is the Peck (Peck) model:
r(RH)＝a·RHb (1)
wherein R (RH) is the corrosion rate, RH is the relative humidity (%) of the environment, and a and b are constants;
for temperature, its law of influence model is the Arrhenius (Arrhenius) model:
Figure BDA0003145226180000031
where r (T) is the corrosion rate, T is the ambient temperature (K), d is a constant, E ═ Ea/K，EaThe reaction activation energy can be estimated from test data, and K is a Boltzmann constant;
for sulfur dioxide concentration, the influence rule model is a dose-response model:
r(S)＝(1+f·S)g (3)
wherein r (S) is the corrosion rate, and S is the sulfur dioxide concentration (μ g.m)-3) F and g are constants;
for chloride ion deposition rate concentration, the influence law model is also a dose-response model:
r(Cl)＝(1+h·Cl)k (4)
wherein r (Cl) is the corrosion rate, and Cl is the deposition rate of chloride ion (mg.m)-2.day-1) H and k are constants;
III, establishing a comprehensive environment influence rule model
The influence rule models of the environmental factors can be multiplied to establish a comprehensive environmental influence rule model, and the form is as follows:
r(RH,T,S,Cl)＝r(RH)·r(T)·r(S)·r(Cl) (5)
equation (5) is a basic form for calculating the comprehensive influence of each environmental factor on atmospheric corrosion, and can be expanded to include the influence of more environmental factors, such as wind speed or solar radiation; meanwhile, in consideration of specific conditions which may occur under different situations, the accuracy of establishing the model by using equation (5) alone cannot be guaranteed, and therefore, the following two models can also be used:
generalized log-linear model:
r(RH,T,S,Cl)＝a·exp(b·RH+e·T+f·SO2+h·Cl) (6)
generalized Eying model:
Figure BDA0003145226180000041
the formulas (5) to (7) are general models, and when the influence of other environmental factors needs to be considered, the influence rule can be included in the models by adopting the same method;
step two: estimation and selection of environmental influence law model parameters of atmospheric corrosion
After the comprehensive environment model is established, the model parameters need to be estimated so as to determine the quantitative influence rule, and meanwhile, the optimal model needs to be selected from the alternative models according to the red blood pool information criterion (AIC) and the corrected red blood pool information criterion (AIC)C) A model information quantity determined by Bayesian Information Criterion (BIC);
wherein, AICCAnd BIC is a standard for measuring the fitting superiority of the statistical model; the AIC criterion is established and developed by Japanese statistician Chichi research and development, and is also called as Chichi information content criterion; AICCThe criterion is improved on the basis of AIC, so that estimation errors under the condition of small samples can be effectively avoided; the BIC criterion is called Bayesian information criterion, and compared with AIC, the overfitting problem under the condition of a large sample can be effectively avoided;
the method comprises the following specific steps:
I. model parameter estimation
The model forms of the formulas (5) - (7) are simple, and the parameter estimation mode can be performed by adopting a generalized linear regression or a maximum likelihood method; the step can be directly realized through MATLAB software, is very simple and convenient, and is not expanded here; (wherein the "MATLAB software" is commercial mathematical software available from MathWorks, USA for data analysis);
model selection
Comprehensively considering model fitting precision and model complexity, and judging an optimal model by adopting the information quantity; the amount of information that can be used includes AIC, AICCAnd BIC, the specific calculation formula is as follows:
Figure BDA0003145226180000051
Figure BDA0003145226180000052
Figure BDA0003145226180000053
Figure BDA0003145226180000054
wherein M is the number of parameters in the model, N is the sample size when fitting the parameters, i.e. the number of test data, RSS is the sum of squares of the residuals, SIAnd SI' the I (I ═ 1,2, …, N) test measurements and model predictions, respectively;
AIC，AICCand BIC is a statistic which comprehensively considers the complexity of the model and the prediction accuracy of the model, and AIC are used for selecting the modelCOr the model with the minimum BIC value is used as the optimal model;
step three: establishing an atmospheric natural environment model considering dynamic, relevant and random characteristics
Taking temperature, humidity and sulfur dioxide concentration as examples, in an actual natural environment, the environmental factors dynamically change and have a periodic fluctuation characteristic and a random fluctuation characteristic; in order to accurately describe the dynamic change characteristics including periodicity and randomness, the following time-varying function model is established
Figure BDA0003145226180000055
Where ξ (t) ═ t, (t), rh (t), s (t)) represents the ambient temperature, relative humidity and sulfur dioxide concentration at time t, a0＝[AT0,ARH0,AS0]Is a constant number of times, and is,
Figure BDA0003145226180000056
seasonal variation characteristics for description; a. the1＝[AT1,ARH1,AS1]For annual fluctuation amplitude, τ1＝[τT1,τRH1,τS1]And
Figure BDA0003145226180000057
the period and phase of the wave;
Figure BDA0003145226180000061
for describing the daily change; epsilon (t) ([ epsilon ]T(t),εRH(t),εS(t)]A random fluctuation item obeys a specific statistical distribution; typically, ε (t) is subject to a mean of 0 and a variance of σ2Normal distribution of (t), i.e. ε (t) to N (0, σ)2(t))；
Since the daily fluctuation term and the annual fluctuation term are periodic trigonometric functions having a mean value of 0, the mean value of the environmental factors is
Figure BDA0003145226180000062
Through analysis of a large amount of environmental data of different cities, it is found that the random fluctuation items of the temperature and the humidity can be accurately described by normal distribution, and the random fluctuation items of the sulfur dioxide concentration can be described by lognormal distribution, and can be specifically expressed as
Figure BDA0003145226180000063
Figure BDA0003145226180000064
Figure BDA0003145226180000065
Wherein the content of the first and second substances,
Figure BDA0003145226180000066
and
Figure BDA0003145226180000067
representing the variance, theta, of the distribution of the random fluctuation terms of temperature and relative humidity, respectively, at time tS(t)，
Figure BDA0003145226180000068
ηS(t) is a parameter of the lognormal distribution of the sulfur dioxide concentration at the time t;
as for the temperature and the relative humidity,
Figure BDA0003145226180000069
for sulfur dioxide
Figure BDA00031452261800000610
Figure BDA00031452261800000611
Analysis has found that the random fluctuation term of the sulfur dioxide concentration in some places can be described by an exponential distribution, namely
εS(t)～E(λS(t),ζS(t)) (16)
λS(t) and ζS(t) is the rate parameter and the position parameter of the exponential distribution of the sulfur dioxide concentration at time t, respectively, then
Figure BDA00031452261800000612
Figure BDA00031452261800000613
Step four: environmental model parameter estimation and model verification
In step three, the statistical distribution of random terms epsilon (t) of different environmental factors is different; firstly, estimating environmental model parameters according to different characteristics of epsilon (t), further generating model data by using a fitting model, comparing the model data with original observation data, and verifying the accuracy of the model; the method comprises the following specific steps:
I. environmental model parameter estimation
With respect to temperature and relative humidity, seasonal changes in temperature are closely related to the earth's revolution, while diurnal changes depend on the earth's rotation, so τ1＝1，τ21/365; analysis of a large amount of temperature observation data shows that the annual lowest temperature generally appears in the middle of one month every year, and the daily lowest temperature generally appears at two early morning hours, so that,
Figure BDA0003145226180000071
ε (t) follows a normal distribution, and the other parameters in equation (1) can be calculated in several steps:
i) calculating the annual average temperature
Figure BDA0003145226180000072
Then
Figure BDA0003145226180000073
ii) dividing all temperature data into twelve groups on a monthly basis, and calculating the average temperature T of each month1,T2,…,T12Then, then
Figure BDA0003145226180000074
iii) calculating the mean day-night temperature difference T' of the year, then AT2＝T′；
iv) substituting the parameter values obtained by the three steps into a model to generate simulation data which is in one-to-one correspondence with the recording time of the original data and is not provided with random items, and dividing the rest data into twelve groups according to the standard of each month after subtracting the simulation data from the original data to obtain epsilonT(t) calculating the variance of each group
Figure BDA0003145226180000075
v) variance of fit
Figure BDA0003145226180000076
The relation with time t;
for the deposition rates of sulfur dioxide and chloride ions, the environmental model parameters can be estimated by the following steps:
in winter, due to heating and other reasons, the combustion amount of fossil fuel is huge, the discharged sulfur dioxide is increased, in summer, the combustion amount of fossil fuel is small, the sulfur dioxide discharge amount is reduced, and analysis shows that the highest sulfur dioxide concentration appears at about the beginning of two months in one year, so tauz1＝365，
Figure BDA0003145226180000077
Meanwhile, the sulfur dioxide concentration change in each day has two peaks and two valleys, the two peaks appear about 9:00 earlier and 21:00 later, which is basically consistent with the peak time of the city, the sulfur dioxide emission of the motor vehicle is larger during the period, and the sulfur dioxide emission is relatively smaller at other time, therefore, the tau isz2＝0.5，
Figure BDA0003145226180000078
Removing annual change and daily change parts in the sulfur dioxide concentration data, and finding that the random change part, namely epsilon (t), accords with exponential distribution, and when twoThe larger the daily average value of the sulfur oxide concentration is, the larger the fluctuation of the daily variation amplitude and the random variation portion is, and thus the daily variation term Az2(t) and εz(t) all vary with time, assuming Az2(t) and εz(t) are all proportional to the daily average, and then other parameters in the formula (12) can be estimated by using a Monte Carlo method, and the specific steps are as follows:
i) calculating the annual average value of sulfur dioxide and recording the value
Figure BDA0003145226180000079
ii) the maximum and minimum values of sulfur dioxide are respectively denoted as zminAnd zmax；
iii) generating a obedient uniform distribution
Figure BDA00031452261800000710
Random number of
Figure BDA00031452261800000711
And assigning this value to Az0As initial values calculated by the Monte Carlo method, and then let
Figure BDA00031452261800000712
Figure BDA0003145226180000081
iv) subjecting the individual parameters A obtained in the preceding step toz1，Az2(t)，λz(t) substituting into the time-varying function model to generate N simulated values of sulfur dioxide concentration data, wherein N is equal to the total number of sulfur dioxide concentration data to be modeled, and counting the data to respectively fall into [ z [min,z1],…,(zi-1,zi],…,(zk-1,zmax]The number of k intervals is calculated, and statistic chi is calculated2
Figure BDA0003145226180000082
Wherein n isiAnd ni' the simulated data and the actual observed data fall into the ith interval (z)i-1,zi]The number of (2);
v) repeating the steps three to four m times until the value of x2The value is the minimum value meeting the precision requirement, and the corresponding value at the moment
Figure BDA0003145226180000083
Is Az0Is estimated as Az0Substituting into the formula of the third step to obtain Az1，Az2(t)，λz(t) an estimate of;
verification of environmental model
The method comprises the following specific steps:
i) bringing the parameter estimation result into a model, and simulating and generating environmental data of one year at intervals of three hours;
ii) fitting the Probability Density Function (PDF) of the simulated data to the original data, and respectively recording as poAnd ps；
iii) calculating the model fitting error η by the following formula:
Figure BDA0003145226180000084
wherein ξLAnd xiURespectively the lower limit and the upper limit of the xi distribution of the environmental factor;
iv) if η < 20%, the model is validated, if > 20%, the method is not applicable;
step five: establishing a corrosion prediction model under the condition of use environment
By using the environmental influence rule model and the natural environment model obtained in the first step to the fourth step, the following corrosion prediction model can be established:
Figure BDA0003145226180000085
wherein C (τ) is corrosion weight loss until time τ, r (·) is a corrosion influence law model, ξ (t) ═ t (t), rh (t), s (t), cl (t) are values of environmental factors at time t, and θ ═ a, b, …, h are corrosion influence law model parameters;
in order to verify the accuracy of the model prediction result, the actual value C of the corrosion weightlessness is calculated by utilizing the original observation data of the environmental factorsA(τ), the calculation formula is as follows:
Figure BDA0003145226180000091
wherein i is the sequence of observed values according to time sequence, and nτThe total number of observed data in time from 0 to tau;
further, a relative error γ between the predicted value and the true value is calculated as a basis for judging the prediction accuracy of the model, and the formula is as follows:
Figure BDA0003145226180000092
through the steps, the corrosion influence rule of multiple environmental factors under the atmospheric natural environment is comprehensively considered, and an optimal corrosion influence rule model can be established by combining the information criterion, so that the factors are more comprehensive, and the model is more accurate; the atmospheric natural environment model established by the invention fully considers the actual characteristics of environmental dynamics, relativity and randomness, the natural environment information is richer, the modeling result is more accurate, the established corrosion prediction method is closer to the actual use environment, the method is scientific, the manufacturability is good, and the method can be popularized and applied to different environmental conditions and has higher theoretical and application values.
(3) Advantages and effects：
The invention relates to a metal atmospheric corrosion prediction method considering the dynamic characteristics of a natural environment, in particular to a metal atmospheric corrosion prediction method considering the dynamic, related and random characteristics of the natural environment, which has the advantages that:
the method comprehensively considers the corrosion influence rule of multi-environment factors under the atmospheric natural environment, and an optimal corrosion influence rule model can be established by combining the information criterion, so that the factors are more comprehensive, and the model is more accurate;
the atmospheric natural environment model established by the invention fully considers the actual characteristics of environmental dynamics, correlation and randomness, so that the natural environment information is richer, and the modeling result is more accurate;
the corrosion prediction method established by the invention is closer to the actual use environment, the method is scientific, the manufacturability is good,
the method can be popularized and applied to different environmental conditions, and has higher theoretical and application values;
drawings
FIG. 1 is a block diagram of the method of the present invention.
FIG. 2 comparison of natural environment model simulation results with original observations.
Fig. 3 shows the corrosion weight loss prediction curve for the case.
Detailed Description
The present invention will be described in further detail with reference to examples.
A certain zinc alloy material is subjected to an accelerated corrosion test under a laboratory condition, and the corrosion current I under the conditions of different temperatures T and relative humidity RH is measuredcorrThe test data are shown in table 1.
TABLE 1 accelerated Corrosion test data for materials
Figure BDA0003145226180000101
Meanwhile, the practical effect of the atmospheric natural environment model included in the invention is shown by taking environmental data of ten years in Beijing 2007-2016 as an example. Wherein the temperature and relative humidity data are derived from NOAA in the units of deg.C and% respectively, and the data is measured and recorded every 3 hours, and the sulfur dioxide data are derived from China National Environmental Monitoring Center (CNEMC) in the unit of μ g.m-3One data was measured and recorded every 1 hour. It should be noted that, in order to ensure the accuracy of the estimation of the environmental model parameters, the raw data allows the estimation to be performed every dayShould not be less than 6 data points, i.e. at least one data is measured and recorded every 4 hours.
The technical framework of the corrosion prediction method related by the invention is shown in figure 1, and the detailed steps are as follows:
the method comprises the following steps: establishing an environmental influence rule model of atmospheric corrosion
I. Determining key environmental impact factors
In this case, only two environmental influence factors of temperature and relative humidity are involved, and the method can be applied to the corrosion prediction of metals under the rural atmospheric environment condition. Due to the limitation of the test data, the influence of other environmental factors is not considered for the moment. In theory, with sufficient support of experimental data, the method can cover all major influencing factors.
II, establishing an influence rule model of each environmental factor
For the relative humidity and the temperature, quantitative description is carried out on the influence rule by respectively adopting a Peak (Peck) model and an Arrhenius (Arrhenius) model which are represented by formulas (1) and (2);
III, establishing a comprehensive environment influence rule model
Based on the influence rule models of the environmental factors in the last step, the following comprehensive environmental influence rule models are respectively established:
and (3) combining the models:
Figure BDA0003145226180000111
generalized log-linear model:
r(RH,T)＝a·exp(b·RH+e·T) (22)
generalized Eying model:
Figure BDA0003145226180000112
step two: estimation and selection of environmental influence law model parameters of atmospheric corrosion
I. Model parameter estimation
The model parameters in equations (21) - (23) were estimated using the generalized linear regression method, and the results are shown in table 2;
TABLE 2 comprehensive environmental impact law model parameter estimation results and model information content
Model (model) lna b e AIC AICC BIC
Combined model -19.1 6.23 6140.7 -4.86 -3.66 -1.33
Generalized logarithmic linear model -42.7 0.129 0.068 -39.55 -38.35 -36.02
Generalized Eyrin (Eyring) model -2.23 0.129 5993.4 -39.94 -38.74 -36.41
Model selection
The information amount values of the respective models were calculated by equations (8) to (11), and the results are shown in table 2. According to the information criterion, in this case, AIC of generalized Ailin (Eying) modelCAnd BIC values are both minimal and therefore the optimal model in this case;
step three: establishing an atmospheric natural environment model considering dynamic, relevant and random characteristics
In this case, only two environmental factors of temperature and relative humidity are considered, and thus, the natural environment model is:
Figure BDA0003145226180000113
where ξ (t) ═ t (t), rh (t)), denotes the ambient temperature and relative humidity at time t;
step four: environmental model parameter estimation and model verification
I. Environmental model parameter estimation
Taking the natural environment of Beijing as an example, the estimation result of the environment model parameters is shown in Table 3;
TABLE 3 estimation results of natural environment model parameters and model relative errors
Figure BDA0003145226180000114
Figure BDA0003145226180000121
Verification of environmental model
And substituting the parameter estimation results in the table into a model to generate simulated environment data, wherein the data interval is one every 3 hours, the data are generated into data of one year, and the probability density function of all the generated data is fitted. Calculating a relative error value η between the simulated environmental data and the original environmental data by equation (19), the result of which is shown in table 3; as can be seen, eta is 8.7 percent and 5.2 percent respectively for the temperature and the relative humidity, which proves that the natural environment modeling method provided by the invention has high modeling precision;
step five: establishing a corrosion prediction model under the use environment condition
Integrating the modeling and calculation results of the steps, substituting the estimated parameters into the model to obtain the corrosion prediction model under the trial environment condition as follows
Figure BDA0003145226180000122
Wherein
Figure BDA0003145226180000123
Figure BDA0003145226180000124
When τ is 365, C (τ) is the cumulative amount of corrosion for one year, and the true value of the amount of corrosion is calculated by the following equation
Figure BDA0003145226180000125
Wherein, the environmental data is observed and recorded every 3 hours, and the number of times is 8 times per day, and 2820 times per year, so nτ＝2920。
The corrosion calculation results are shown in fig. 3; the predicted value of the model is, the true value is, the relative error of the model prediction is xx%, and the model is proved to have high prediction precision.

Claims (1)

1. A metal atmospheric corrosion prediction method considering the dynamic characteristics of the natural environment is provided with the following steps:
setting 1: aiming at the atmospheric corrosion problem of metal materials, environmental influence factors comprise atmospheric temperature, relative humidity and pollutant concentration, and other environmental factors are not considered;
setting 2: interaction effects generated by the combined action of different environmental factors are not considered, so that an influence rule model of the environmental factors does not contain interaction items;
setting 3: the temperature, the relative humidity and the corrosion influence rule of pollutants are respectively described by an Arrhenius model, a Peck model and a dose-response model, and the detailed model form is shown in step one;
setting 4: the comprehensive environment factor model is synthesized through models of all environment factors, or is established by adopting a general generalized linear, generalized logarithmic linear and generalized alling Eying model, and the detailed model form is shown in step one;
the method is characterized in that: the method is realized by the following steps:
the method comprises the following steps: establishing an environmental influence rule model of atmospheric corrosion
Firstly, determining influence factors based on the actual use environment of the metal material, then respectively establishing a corrosion influence rule model for the selected environment factors, and on the basis, comprehensively considering the combined action of all the factors and establishing a comprehensive environment factor influence rule model; the method comprises the following specific steps:
I. determining key environmental impact factors
In addition to temperature and relative humidity, it is also necessary to determine whether the effect of atmospheric pollutants needs to be considered according to the type of atmosphere; if the environment is an industrial atmospheric environment, the influence of sulfur dioxide needs to be considered, if the environment is a marine atmospheric environment, the influence of chloride ions needs to be considered, if the environment has the characteristics of industrial atmosphere and marine atmosphere, the common influence of sulfur dioxide and chloride ions needs to be considered, if the environment is a rural atmospheric environment, the influence of atmospheric pollutants is not considered, and the selection is specifically performed according to the following steps;
II, establishing an influence rule model of each environmental factor
For relative humidity, the model of the influence law is the Peck model:
r(RH)＝a·RHb (1)
wherein R (RH) is the corrosion rate, RH is the relative humidity (%) of the environment, and a and b are constants;
for temperature, the law model of its effect is the Arrhenius model:
Figure FDA0003145226170000011
where r (T) is the corrosion rate, T is the ambient temperature (K), d is a constant, E ═ Ea/K，EaThe reaction activation energy is estimated from test data, and K is a Boltzmann constant;
for sulfur dioxide concentration, the influence rule model is a dose-response model:
r(S)＝(1+f·S)g (3)
wherein r (S) is the corrosion rate, and S is the sulfur dioxide concentration (μ g.m)-3) F and g are constants;
for chloride ion deposition rate concentration, the influence law model is also a dose-response model:
r(Cl)＝(1+h·Cl)k (4)
wherein r (Cl) is the corrosion rate, and Cl is the deposition rate of chloride ion (mg.m)-2.day-1) H and k are constants;
III, establishing a comprehensive environment influence rule model
Multiplying the influence rule models of the environmental factors to establish a comprehensive environmental influence rule model, wherein the form of the comprehensive environmental influence rule model is as follows:
r(RH,T,S,Cl)＝r(RH)·r(T)·r(S)·r(Cl) (5)
formula (5) is a basic form for calculating the comprehensive influence of each environmental factor on atmospheric corrosion, and meanwhile, in consideration of specific conditions which may occur under different situations, the accuracy of establishing the model by using formula (5) can not be guaranteed, so that the following two models are adopted:
generalized log-linear model:
r(RH,T,S,Cl)＝a·exp(b·RH+e·T+f·SO2+h·Cl) (6)
generalized Eying model:
Figure FDA0003145226170000021
step two: estimation and selection of environmental influence law model parameters of atmospheric corrosion
After the comprehensive environment model is established, the model parameters need to be estimated so as to determine the quantitative influence rule, and meanwhile, the optimal model needs to be selected from the alternative models according to the Chichi information criterion AIC and the corrected Chichi information criterion AICCThe model information quantity determined by the Bayesian information criterion BIC; the method comprises the following specific steps:
I. model parameter estimation
The parameter estimation modes of the formulas (5) to (7) are carried out by adopting a generalized linear regression or a maximum likelihood method; the step is directly realized by MATLAB software;
model selection
Comprehensively considering model fitting precision and model complexity, and judging an optimal model by adopting the information quantity; the information amount includes AIC, AICCAnd BIC, the specific calculation formula is as follows:
Figure FDA0003145226170000031
Figure FDA0003145226170000032
Figure FDA0003145226170000033
Figure FDA0003145226170000034
wherein M is the number of parameters in the model, N is the sample size when fitting the parameters, i.e. the number of test data, RSS is the sum of squares of the residuals, SIAnd S'IRespectively an I test measured value and a model predicted value; 1,2, …, N;
step three: establishing an atmospheric natural environment model considering dynamic, relevant and random characteristics
In an actual natural environment, environmental factors such as temperature, humidity and sulfur dioxide concentration are dynamically changed, and the device has a periodic fluctuation characteristic and a random fluctuation characteristic; in order to accurately describe the dynamic change characteristics including periodicity and randomness, the following time-varying function model is established
Figure FDA0003145226170000035
Where ξ (t) ═ t, (t), rh (t), s (t)) represents the ambient temperature, relative humidity and sulfur dioxide concentration at time t, a0＝[AT0,ARH0,AS0]Is a constant number of times, and is,
Figure FDA0003145226170000036
seasonal variation characteristics for description; a. the1＝[AT1,ARH1,AS1]For annual fluctuation amplitude, τ1＝[τT1,τRH1,τS1]And
Figure FDA0003145226170000037
the period and phase of the wave;
Figure FDA0003145226170000038
for describing the daily change; epsilon (t) ([ epsilon ]T(t),εRH(t),εS(t)]A random fluctuation item obeys a specific statistical distribution; ε (t) obeys a mean of 0 and a variance of σ2Normal distribution of (t), i.e. ε (t) to N (0, σ)2(t))；
Since the daily fluctuation term and the annual fluctuation term are periodic trigonometric functions having a mean value of 0, the mean value of the environmental factors is
Figure FDA0003145226170000039
The random fluctuation terms of the temperature and the humidity are accurately described by normal distribution, and the random fluctuation terms of the sulfur dioxide concentration are described by lognormal distribution and are specifically expressed as
Figure FDA0003145226170000041
Wherein the content of the first and second substances,
Figure FDA0003145226170000042
and
Figure FDA0003145226170000043
representing the variance, theta, of the distribution of the random fluctuation terms of temperature and relative humidity, respectively, at time tS(t)，
Figure FDA0003145226170000044
ηS(t) is a parameter of the lognormal distribution of the sulfur dioxide concentration at the time t;
as for the temperature and the relative humidity,
Figure FDA0003145226170000045
for sulfur dioxide
Figure FDA0003145226170000046
It has been found by analysis that the random fluctuation term of the sulfur dioxide concentration can be described by an exponential distribution, i.e.
εS(t)～E(λS(t),ζS(t)) (16)
λS(t) and ζS(t) is the rate parameter and the position parameter of the exponential distribution of the sulfur dioxide concentration at time t, respectively, then
Figure FDA0003145226170000047
Step four: environmental model parameter estimation and model verification
In step three, the statistical distribution of random terms epsilon (t) of different environmental factors is different; firstly, estimating environmental model parameters according to different characteristics of epsilon (t), further generating model data by using a fitting model, comparing the model data with original observation data, and verifying the accuracy of the model; the method comprises the following specific steps:
I. environmental model parameter estimation
With respect to temperature and relative humidity, seasonal changes in temperature are closely related to the earth's revolution, while diurnal changes depend on the earth's rotation, so τ1＝1，τ21/365; analysis of a large amount of temperature observation data shows that the annual minimum temperature occurs in the middle of one month every year, and the daily minimum temperature occurs at about two early morning hours, so that,
Figure FDA0003145226170000048
ε (t) follows a normal distribution, and the other parameters in equation (1) are calculated in the following steps:
i) calculating the annual average temperature
Figure FDA0003145226170000049
Then
Figure FDA00031452261700000410
ii) dividing all temperature data into twelve groups on a monthly basis, and calculating the average temperature T of each month1,T2,…,T12Then, then
Figure FDA00031452261700000411
iii) calculating the mean day-night temperature difference T' of the year, then AT2＝T′；
iv) substituting the parameter values obtained by the three steps into a model to generate simulation data which is in one-to-one correspondence with the recording time of the original data and is not provided with random items, and dividing the rest data into twelve groups according to the standard of each month after subtracting the simulation data from the original data to obtain epsilonT(t) calculating the variance of each group
Figure FDA0003145226170000051
v) variance of fit
Figure FDA0003145226170000052
The relation with time t;
for the deposition rates of sulfur dioxide and chloride ions, the environmental model parameters are estimated by the following steps:
the highest sulfur dioxide concentration occurs in the beginning of the second month in one year, so τz1＝365，
Figure FDA0003145226170000053
Meanwhile, the sulfur dioxide concentration change in each day has two peaks and two valleys, wherein the two peaks appear about 9:00 and 21:00 earlier and later every day, which is consistent with the peak time of the morning and evening in the city every day, during which the vehicle emits more sulfur dioxide, and relatively less sulfur dioxide in other times, therefore, tauz2＝0.5，
Figure FDA0003145226170000054
Removing annual change and daily change parts in the sulfur dioxide concentration data, finding that the random change part, namely epsilon (t) accords with exponential distribution, and when the daily mean value of the sulfur dioxide concentration is larger, the daily change amplitude and the fluctuation of the random change part are larger, so the daily change item A is largerz2(t) and εz(t) all vary with time, let Az2(t) and εz(t) are all in direct proportion to the daily average, then other parameters in the formula (12) are estimated by a Monte Carlo method, and the specific steps are as follows:
i) calculating the annual average value of sulfur dioxide and recording the value
Figure FDA0003145226170000055
ii) the maximum and minimum values of sulfur dioxide are respectively denoted as zminAnd zmax；
iii) generating a obedient uniform distribution
Figure FDA0003145226170000056
Random number of
Figure FDA0003145226170000057
And assigning this value to Az0As initial values calculated by the Monte Carlo method, and then let
Figure FDA0003145226170000058
Figure FDA0003145226170000059
iv) subjecting the individual parameters A obtained in the preceding step toz1，Az2(t)，λz(t) substituting into the time-varying function model to generate N simulated values of sulfur dioxide concentration data, wherein N is equal to the total number of sulfur dioxide concentration data to be modeled, and counting the data to respectively fall into [ z [min,z1],…,(zi-1,zi],…,(zk-1,zmax]The number of k intervals is calculated, and statistic chi is calculated2
Figure FDA00031452261700000510
Wherein n isiAnd n'iThe simulation data and the actual observation data respectively fall into the ith interval (z)i-1,zi]The number of (2);
v) repeating the steps three to four m times until the value of x2The value is the minimum value meeting the precision requirement, and the corresponding value at the moment
Figure FDA00031452261700000511
Is Az0Is estimated as Az0Substituting into the formula of the third step to obtain Az1，Az2(t)，λz(t) an estimate of;
verification of environmental model
The method comprises the following specific steps:
i) bringing the parameter estimation result into a model, and simulating and generating environmental data of one year at intervals of three hours;
ii) fitting the Probability Density Function (PDF) of the simulated data to the original data, and respectively recording as poAnd ps；
iii) calculating the model fitting error η by the following formula:
Figure FDA0003145226170000061
wherein ξLAnd xiURespectively the lower limit and the upper limit of the xi distribution of the environmental factor;
iv) if η < 20%, the model is validated, if > 20%, the method is not applicable;
step five: establishing a corrosion prediction model under the condition of use environment
And (3) establishing the following corrosion prediction model by utilizing the environmental influence rule model and the natural environment model obtained in the first step to the fourth step:
Figure FDA0003145226170000062
wherein C (tau) is corrosion weight loss until the moment tau, r (-) is a corrosion influence rule model,
xi (t) ═ t (t), rh (t), s (t), cl (t) ] is the value of the environmental factor at time t, and θ ═ a, b, …, h is the corrosion influence law model parameter;
in order to verify the accuracy of the model prediction result, the actual value C of the corrosion weightlessness is calculated by utilizing the original observation data of the environmental factorsA(τ), the calculation formula is as follows:
Figure FDA0003145226170000063
wherein i is the sequence of observed values according to time sequence, and nτThe total number of observed data in time from 0 to tau;
further, a relative error γ between the predicted value and the true value is calculated as a basis for judging the prediction accuracy of the model, and the formula is as follows:
Figure FDA0003145226170000064
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115146216A (en) * 2022-09-01 2022-10-04 交通运输部公路科学研究所 Method for evaluating carbon-sulfur-calcium-silicate stone corrosion environment of tunnel concrete
CN116680518A (en) * 2023-08-04 2023-09-01 北京市智慧水务发展研究院 Space-time situation analysis method for surface water environment

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109238950A (en) * 2018-09-06 2019-01-18 中国兵器工业第五九研究所 Atmospheric corrosion of metal materials prediction technique based on qualitative analysis and quantitative forecast
CN110823792A (en) * 2019-11-05 2020-02-21 南京钢铁股份有限公司 Method for evaluating service life of low-alloy corrosion-resistant steel in atmospheric corrosion
CN111177947A (en) * 2020-01-12 2020-05-19 西南石油大学 Multi-factor considered CO2Corrosion prediction plate establishing method
WO2020234921A1 (en) * 2019-05-17 2020-11-26 日本電信電話株式会社 Prediction equation derivation method and prediction equation derivation device
CN112735538A (en) * 2020-12-09 2021-04-30 中国电器科学研究院股份有限公司 Metal atmospheric corrosion rate prediction algorithm based on substance transfer dynamics model

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109238950A (en) * 2018-09-06 2019-01-18 中国兵器工业第五九研究所 Atmospheric corrosion of metal materials prediction technique based on qualitative analysis and quantitative forecast
WO2020234921A1 (en) * 2019-05-17 2020-11-26 日本電信電話株式会社 Prediction equation derivation method and prediction equation derivation device
CN110823792A (en) * 2019-11-05 2020-02-21 南京钢铁股份有限公司 Method for evaluating service life of low-alloy corrosion-resistant steel in atmospheric corrosion
CN111177947A (en) * 2020-01-12 2020-05-19 西南石油大学 Multi-factor considered CO2Corrosion prediction plate establishing method
CN112735538A (en) * 2020-12-09 2021-04-30 中国电器科学研究院股份有限公司 Metal atmospheric corrosion rate prediction algorithm based on substance transfer dynamics model

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
崔梦晨等: "大气环境中碳钢腐蚀速率推测方法", 《腐蚀与防护》 *
蔡义坤等: ""Atmospheric corrosion prediction: a review"", 《DE GRUYTER》 *
蔡义坤等: ""Extrapolating short-term corrosion test results to field exposures in different environments"", 《CORROSION SCIENCE》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115146216A (en) * 2022-09-01 2022-10-04 交通运输部公路科学研究所 Method for evaluating carbon-sulfur-calcium-silicate stone corrosion environment of tunnel concrete
CN115146216B (en) * 2022-09-01 2022-11-15 交通运输部公路科学研究所 Method for evaluating carbon-sulfur-calcium-silicate stone corrosion environment of tunnel concrete
US11733153B1 (en) 2022-09-01 2023-08-22 Research Institute Of Highway Ministry Of Transport Method for evaluating environmental erosion of thaumasite in tunnel concrete
CN116680518A (en) * 2023-08-04 2023-09-01 北京市智慧水务发展研究院 Space-time situation analysis method for surface water environment
CN116680518B (en) * 2023-08-04 2023-10-20 北京市智慧水务发展研究院 Space-time situation analysis method for surface water environment

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