CN113111568A - Method for predicting stress corrosion crack propagation rate of nickel-based alloy - Google Patents
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Abstract
The invention discloses a method for predicting the stress corrosion crack propagation rate of a nickel-based alloy, which comprises the steps of inputting an original data set, processing to obtain an integrated data set, setting a super-parameter range in an XGboost model, utilizing a TPE (thermal plastic elastomer) algorithm to narrow the super-parameter range and select a group of parameter combinations, substituting the parameter combinations into the XGboost model and calculating a prediction result, and selecting whether to circulate the process according to the precision of the prediction result. The method for predicting the stress corrosion crack propagation rate of the nickel-based alloy is rapid, efficient and high in accuracy, avoids local optimal solutions, provides a technical means for predicting the stress corrosion crack propagation engineering of the reactor coolant condition of the nickel-based alloy part of the pressurized water reactor nuclear power station in China, and guarantees the nuclear safety.
Description
Technical Field
The invention relates to the technical field of nuclear power station safety evaluation, in particular to a method for predicting the stress corrosion crack propagation rate of a nickel-based alloy.
Background
The nuclear island primary loop equipment is in harsh environments such as high-temperature high-pressure water, stress, neutron irradiation and the like for a long time, stress corrosion, cracking and leakage accidents occur sometimes, and the nuclear island primary loop equipment becomes one of the key problems influencing the long-term safe operation of a nuclear power station. In early PWR nuclear power plant primary loop systems, nickel-based alloys and related welding materials were used in many locations, such as steam generator heat transfer tubes, reactor pressure vessel penetration structures, steam stabilizer nozzles, and the like. However, operation experience shows that the nickel-based alloy has Stress Corrosion Cracking (SCC) sensitivity to primary loop water, and has many SCC failure accidents of a primary loop of a nuclear power station, so that a coolant of the nuclear power station is leaked, and the safe operation of the nuclear power station is seriously threatened. Due to the universality and the severity of the phenomenon and the safety and economic problems caused by the phenomenon, establishing an accurate stress corrosion crack propagation rate prediction model is important.
At present, the existing nickel-based alloy stress corrosion prediction model comprises a slip oxidation model, an environmental fracture coupling model, a PMSCott model, an MRP-55 model and the like, but the existing model has the problems of incomplete description of influencing factors, insufficient universality and low accuracy due to the coupling effect of a plurality of factors of stress corrosion cracking.
Disclosure of Invention
In view of the above, the invention provides a method for predicting the propagation rate of the stress corrosion crack of the nickel-based alloy, which provides a technical means for predicting the propagation engineering of the stress corrosion crack of the nickel-based alloy component reactor coolant condition of the pressurized water reactor nuclear power station in China and guarantees the nuclear safety. The technical scheme is as follows:
in one aspect, the invention provides a method for predicting the propagation rate of stress corrosion cracks of a nickel-based alloy, which comprises the following steps:
s101, inputting an original data set of stress corrosion crack propagation of the nickel-based alloy, wherein the original data set comprises a plurality of groups of data;
s102, carrying out primary processing on the original data set to obtain an integrated data set, and inputting the integrated data set into an XGboost model;
s103, setting an over-parameter range of model parameters in the XGboost model;
s104, correcting the over-parameter range by utilizing a TPE probability density estimation algorithm;
s105, selecting a parameter combination in the corrected over-parameter range, inputting the parameter combination into the XGboost model, and fitting the integrated data set by using the parameter combination to obtain a model prediction result;
and S106, evaluating the error of the model prediction result, outputting the model prediction result if the error meets the precision requirement, and otherwise, continuously correcting the super-parameter range by using a TPE probability density estimation algorithm, and returning to the step S105.
Further, in step S105, the EI value of the parameter combination is calculated using a sampling function.
Further, in step S105, the parameter combination with the largest EI value is input into the XGBoost model.
Further, in step S102, the preliminary processing includes feature transformation or/and normalization processing.
Further, in step S101, the plurality of sets of data include one or more of basic material information, manufacturing process, organization structure, service environment, service performance, other information, and data source information.
Further, in step S106, the continuously correcting the out-of-parameter range by using the TPE probability density estimation algorithm includes: and correcting the over-parameter sampling range.
Further, in step S104, a parameter combination is randomly generated.
On the other hand, the invention provides a method for predicting the stress corrosion crack propagation rate of a nickel-based alloy, which comprises the following steps:
s201, inputting an original data set of stress corrosion crack propagation of the nickel-based alloy;
s202, carrying out primary processing on the original data set to obtain an integrated data set, and inputting the integrated data set into an XGboost model;
s203, setting an over-parameter range of model parameters in the XGboost model;
s204, selecting a parameter combination in the super-parameter range, and fitting the integrated data set by using the parameter combination to obtain a model prediction result;
s205, evaluating the error of the model prediction result, if the error meets the precision requirement, outputting the model prediction result, otherwise, resetting the over-parameter range, and returning to the step S204.
Further, in step S203, the setting of the out-of-parameter range of the model parameter includes: and correcting the out-of-parameter range by utilizing a TPE probability density estimation algorithm.
Further, in step S205, the resetting the out-of-parameter range includes: and correcting the over-range by utilizing a TPE probability density estimation algorithm.
The invention has the following advantages:
a. the performance prediction result of the alloy part is obtained quickly, and the accuracy of the prediction result is high;
b. the optimization result is effectively prevented from falling into a local optimal solution, so that the prediction model has good generalization capability;
c. is beneficial to ensuring the nuclear safety.
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FIG. 1 is a flowchart of a method for predicting a crack propagation rate of stress corrosion of a nickel-based alloy according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the drawings and the specific embodiments in the specification. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In one embodiment of the present invention, as shown in fig. 1, there is provided a stress corrosion crack propagation rate prediction method for a nickel-based 600 alloy, comprising the steps of:
s101, inputting an original data set of stress corrosion crack propagation of the nickel-based 600 alloy, wherein the original data set comprises a plurality of groups of data;
s102, carrying out primary processing on the original data set to obtain an integrated data set, and inputting the integrated data set into an XGboost model;
s103, setting an over-parameter range of model parameters in the XGboost model;
s104, correcting the over-parameter range by utilizing a TPE probability density estimation algorithm;
s105, selecting a parameter combination in the corrected over-parameter range, inputting the parameter combination into the XGboost model, and fitting the integrated data set by using the parameter combination to obtain a model prediction result;
and S106, evaluating the error of the model prediction result, outputting the model prediction result if the error meets the precision requirement, and otherwise, continuously correcting the super-parameter range by using a TPE probability density estimation algorithm, and returning to the step S105.
By the method of the embodiment, the over-parameter range is continuously corrected, continuously reduced and more accurate, and finally the parameter combination suitable for the prediction model is obtained.
In fact, in S106, returning to S105 may be replaced by returning to S103, i.e. resetting the hyper-parameter range during looping, and then further reducing the hyper-parameter range, so that the obtained range is more accurate and fewer loops are required. The flow of fig. 1 includes a plurality of routes, and also includes the flow of this example.
In a specific embodiment of the present invention, in step S105, the EI value of the parameter combination is calculated using a sampling function. EI, expectedprovement, the desired increment in the TPE algorithm with which the data is characterized.
In an embodiment of the present invention, in step S105, the parameter combination with the largest EI value is input into the XGBoost model, so that the accuracy of the selected parameter combination is improved, and the result can be prevented from falling into a locally optimal solution.
In a specific embodiment of the present invention, in step S102, the preliminary processing includes feature transformation or/and normalization processing, so that the data is convenient for processing by the XGBoost model.
In one embodiment of the present invention, in step S101, the plurality of sets of data include one or more of basic material information, manufacturing process, organization structure, service environment, service performance, other information, and data source information, and the most important sets may be selected according to actual needs (for efficiency or influence), or may be all included for accuracy.
In an embodiment of the invention, in step S106, the continuing to correct the out-of-parameter range by using the TPE probability density estimation algorithm includes: and correcting the sampling function, and optimizing a strategy for taking a parameter.
In an embodiment of the present invention, in step S104, a parameter combination is randomly generated, which is generally necessary when the XGBoost algorithm initially sets the range of the over-parameter, and the randomly generated parameter combination may be used for prediction or may not be used.
Another embodiment of the present invention provides a method for predicting the crack propagation rate of stress corrosion of a nickel-based 600 alloy, as shown in fig. 1, comprising the following steps:
s201, inputting an original data set of stress corrosion crack propagation of the nickel-based 600 alloy, wherein the original data set comprises a plurality of groups of data;
s202, carrying out primary processing on the original data set to obtain an integrated data set, and inputting the integrated data set into an XGboost model;
s203, setting an over-parameter range of model parameters in the XGboost model;
s204, selecting a parameter combination in the super-parameter range, and fitting the integrated data set by using the parameter combination to obtain a model prediction result;
s205, evaluating the error of the model prediction result, if the error meets the precision requirement, outputting the model prediction result, otherwise, resetting the over-parameter range, and returning to the step S204.
The direct value taking in the set hyper-parameter range is a simplification, and aims to reduce the calculation amount and obtain the prediction result more quickly.
In an embodiment of the present invention, in step S203, the setting the hyper-parameter range of the model parameter includes: and correcting the out-of-parameter range by utilizing a TPE probability density estimation algorithm.
In an embodiment of the invention, in step S205, the resetting the over-parameter range includes: and correcting the out-of-parameter range by utilizing a TPE probability density estimation algorithm.
Specifically, the invention provides an XGboost algorithm based on Bayesian optimization aiming at the SCC problem of a nickel-based 600 alloy as a nuclear power key component material under a primary loop service environment condition, wherein the nickel-based 600 alloy is adopted to model stress corrosion crack propagation experimental data under a condition of simulating a pressurized water reactor coolant, and Tree-structured park Estimator (TPE) is selected as a probability agent model of the Bayesian algorithm to self-regulate the hyper-parameters of the XGboost algorithm and improve the accuracy of model prediction.
And Step 1, acquiring a stress corrosion crack propagation original data set of the nickel-based 600 alloy, wherein the data set consists of seven parts of basic material information, a manufacturing process, an organization structure, a service environment, service performance, other information and data source information.
And Step 2, processing the original data set, including feature transformation, normalization processing and the like, and inputting the processed data into the XGboost model.
And Step 3, setting the XGboost over-parameter range to generate a group of random over-parameter combinations.
And Step 4, performing TPE probability density estimation, namely calculating E I values by using a sampling function, and selecting the next parameter combination to be evaluated according to the EI values of the preamble samples.
And Step 5, inputting the parameter combination with the maximum EI value into the XGboost prediction model for training, and outputting the model prediction result under the current over-parameters.
And Step 6, if the error of the newly selected parameter combination meets the precision requirement, stopping algorithm execution, and outputting the corresponding parameter combination and the prediction error of the model. If the accuracy requirement is not met, correcting the sampling function, and executing Step 4 again until the set accuracy requirement is met.
The technical method can be used for predicting the stress corrosion crack propagation rate of the nickel-based alloy in the nuclear power plant, such as the nickel-based 600 alloy, the 690 alloy, the 52/152 alloy, the 82/182 alloy and the like, and aiming at the problems that the nickel-based alloy SCC has multiple influence factors, the mechanism is complex and the applicability of the existing prediction model is not high, a non-parameter nickel-based alloy stress corrosion crack propagation rate prediction model reflecting the multi-dimensional data association relationship is established by utilizing a TPE-XGboost algorithm and excavating the relation between the influence factors such as the stress intensity factor, the temperature, the yield strength, the dissolved hydrogen content, the crack propagation direction, the load type, the heat treatment process and the like and the crack propagation rate. According to the technical scheme, the rapid optimization of the stress corrosion high-dimensional data set hyper-parameters can be realized, the optimization result is effectively prevented from falling into a local optimal solution, the prediction model has good generalization capability, the service state of the alloy part can be mastered more accurately, and the nuclear safety can be guaranteed.
The above description is only for the preferred embodiment of the present invention and is not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes that can be directly or indirectly applied to other related technical fields using the contents of the present specification and the accompanying drawings are included in the scope of the present invention.
Claims (10)
1. A method for predicting the propagation rate of stress corrosion cracks of a nickel-based alloy is characterized by comprising the following steps of:
s101, inputting an original data set of stress corrosion crack propagation of the nickel-based alloy, wherein the original data set comprises a plurality of groups of data;
s102, carrying out primary processing on the original data set to obtain an integrated data set, and inputting the integrated data set into an XGboost model;
s103, setting an over-parameter range of model parameters in the XGboost model;
s104, correcting the over-parameter range by utilizing a TPE probability density estimation algorithm;
s105, selecting a parameter combination in the corrected over-parameter range, inputting the parameter combination into the XGboost model, and fitting the integrated data set by using the parameter combination to obtain a model prediction result;
and S106, evaluating the error of the model prediction result, outputting the model prediction result if the error meets the precision requirement, and otherwise, continuously correcting the super-parameter range by using a TPE probability density estimation algorithm, and returning to the step S105.
2. The prediction method of claim 1, wherein in step S105, the EI value of the parameter combination is calculated using a sampling function.
3. The prediction method of claim 2, wherein in step S105, the parameter combination having the largest EI value is input into the XGBoost model.
4. The prediction method according to claim 1, wherein in step S102, the preliminary process includes a feature transformation or/and a normalization process.
5. The prediction method of claim 1, wherein in step S101, the plurality of sets of data include one or more of basic material information, manufacturing process, organization structure, service environment, service performance, other information, and data source information.
6. The prediction method of claim 2, wherein the step S106 of continuing to correct the out-of-parameter range by using the TPE probability density estimation algorithm comprises: and correcting the sampling function.
7. The prediction method of claim 1, wherein in step S104, a combination of parameters is randomly generated.
8. A method for predicting the propagation rate of stress corrosion cracks of a nickel-based alloy is characterized by comprising the following steps of:
s201, inputting an original data set of stress corrosion crack propagation of the nickel-based alloy;
s202, carrying out primary processing on the original data set to obtain an integrated data set, and inputting the integrated data set into an XGboost model;
s203, setting an over-parameter range of model parameters in the XGboost model;
s204, selecting a parameter combination in the super-parameter range, and fitting the integrated data set by using the parameter combination to obtain a model prediction result;
s205, evaluating the error of the model prediction result, if the error meets the precision requirement, outputting the model prediction result, otherwise, resetting the over-parameter range, and returning to the step S204.
9. The prediction method of claim 8, wherein the step S203, the setting of the out-of-parameter range of the model parameter comprises: and correcting the out-of-parameter range by utilizing a TPE probability density estimation algorithm.
10. The prediction method of claim 9, wherein the resetting the out-of-parameter range in step S205 comprises: and correcting the out-of-parameter range by utilizing a TPE probability density estimation algorithm.
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