A material high-temperature molten salt corrosion performance evaluation method, device, medium and product
Patent Information
- Application Number
- CN202410903118.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-05
- Publication Date
- 2026-10-30
- Estimated Expiration
- 2044-07-05
AI Technical Summary
然而，由于高温熔盐环境下的腐蚀过程受到多元因素的影响，如温度、杂质、熔盐流速、应力状态等，使得腐蚀行为呈现出显著的非线性和不确定性
[0026]本发明提供了一种材料高温熔盐腐蚀性能评价方法、设备、介质及产品，通过对静态腐蚀、流动腐蚀和应力腐蚀等多元数据的深度挖掘与整合，并借助于区间二型模糊逻辑理论的处理不确定性信息的独特优势，实现对材料在复杂高温熔盐环境下腐蚀性能的科学化、精细化和高效化的评估，以满足现代工业生产对材料防腐性能高标准的需求。本发明可有效利用各类腐蚀试验数据，实现对材料在复杂高温熔盐环境下腐蚀性能的科学化、精细化和高效化的评估，以满足现代工业生产对材料防腐性能高标准的需求。
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Figure CN118899050B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of materials science and corrosion engineering, and in particular to a method for evaluating the high-temperature molten salt corrosion performance of materials based on interval type-II fuzzy logic. Background Technology
[0002] In energy conversion and storage, nuclear engineering, chemical production, and related high-temperature corrosive environments, the resistance of materials to molten salt corrosion is becoming increasingly prominent, as it is a key factor determining the stable operation and extended service life of equipment. However, the corrosion process in high-temperature molten salt environments is influenced by multiple factors, such as temperature, impurities, molten salt flow rate, and stress state, resulting in significant nonlinearity and uncertainty in corrosion behavior.
[0003] Traditional corrosion performance evaluation methods are often based on single or limited corrosion test data, and the description of corrosion mechanism is relatively one-sided. For example, by observing the macroscopic and microscopic morphological changes of the sample and measuring mass loss, these methods are often difficult to accurately quantify the degree of corrosion of the material under the combined effect of multiple complex factors (such as temperature, flow rate, and stress state), thus affecting the accuracy and reliability of the evaluation results and failing to accurately reflect the comprehensive corrosion performance of the material under complex and ever-changing actual working conditions.
[0004] Furthermore, in actual corrosion processes, the data on many influencing factors are not clear and precise, but rather contain a certain degree of uncertainty or ambiguity, and existing methods have limited ability to process such information. Therefore, how to effectively utilize various corrosion test data, especially those containing uncertain information, to construct a comprehensive evaluation system that can both reflect the actual situation and make accurate judgments has become an urgent technical challenge to be solved. Summary of the Invention
[0005] The purpose of this invention is to provide a method, equipment, medium, and product for evaluating the high-temperature molten salt corrosion performance of materials. This method can effectively utilize various corrosion test data to achieve a scientific, precise, and efficient evaluation of the corrosion performance of materials in complex high-temperature molten salt environments, thereby meeting the high standards of corrosion resistance required by modern industrial production.
[0006] To achieve the above objectives, the present invention provides the following solution:
[0007] In a first aspect, the present invention provides a method for evaluating the high-temperature molten salt corrosion performance of materials, the method comprising:
[0008] Acquire historical high-temperature molten salt corrosion test data; the high-temperature molten salt corrosion test data includes: corrosion parameter data and corrosion degree index; the corrosion parameter data includes: test temperature, impurity concentration, molten salt flow rate, static corrosion rate, flow corrosion rate, applied load, and stress corrosion cracking time;
[0009] The corrosion parameter data and the corrosion index are converted into interval type II fuzzy sets;
[0010] Based on the aforementioned interval type II fuzzy set, and using the interval type II fuzzy set theory as the basis, an interval type II fuzzy logic reasoning model is constructed with corrosion parameter data as input and corrosion degree index as output.
[0011] The corrosion parameter data to be evaluated is input into the interval type II fuzzy logic reasoning model to obtain the corresponding corrosion degree index.
[0012] Optionally, the corrosion index inference network structure includes: a static corrosion interval type II fuzzy logic submodule, a flow corrosion fuzzy logic submodule, a stress corrosion cracking interval type II fuzzy logic submodule, and a comprehensive corrosion interval type II fuzzy logic submodule.
[0013] Optionally, the inputs to the static corrosion interval type-II fuzzy logic submodule are: experimental temperature, impurity concentration, and static corrosion rate, and the output is: static corrosion index.
[0014] Optionally, the inputs to the flow corrosion fuzzy logic submodule are: experimental temperature, impurity concentration, medium flow rate, and flow corrosion rate, and the output is: flow corrosion index.
[0015] Optionally, the inputs of the stress corrosion cracking interval type II fuzzy logic submodule are: experimental temperature, impurity concentration, applied load and stress corrosion cracking time, and the output is: stress corrosion cracking index.
[0016] Optionally, the inputs of the comprehensive corrosion range type II fuzzy logic submodule are: static corrosion index, flow corrosion index and stress corrosion cracking index, and the output is: corrosion degree index.
[0017] Optionally, the corrosion parameter data and the corrosion index are converted into interval type II fuzzy sets, specifically including:
[0018] Define interval type II fuzzy set
[0019]
[0020] in, and It is a type II fuzzy set The lower and upper membership functions of have a value range of [0,1], X is the value range of the corrosion parameter x, and u represents the parameter range level;
[0021] The corrosion parameter data and the corrosion index are converted into the interval type II fuzzy set.
[0022] In a second aspect, the present invention provides a computer device, comprising: a memory, a processor, and computer instructions stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for evaluating the high-temperature molten salt corrosion performance of a material as described in any of the preceding claims.
[0023] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for evaluating the high-temperature molten salt corrosion performance of a material as described in any of the preceding claims.
[0024] In a fourth aspect, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method for evaluating the high-temperature molten salt corrosion performance of a material as described in any of the preceding claims.
[0025] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:
[0026] This invention provides a method, equipment, medium, and product for evaluating the high-temperature molten salt corrosion performance of materials. Through in-depth mining and integration of multi-dimensional data such as static corrosion, flow corrosion, and stress corrosion, and leveraging the unique advantages of interval-type II fuzzy logic theory in handling uncertain information, it achieves a scientific, precise, and efficient evaluation of the corrosion performance of materials in complex high-temperature molten salt environments, meeting the high standards of corrosion resistance required by modern industrial production. This invention can effectively utilize various corrosion test data to achieve a scientific, precise, and efficient evaluation of the corrosion performance of materials in complex high-temperature molten salt environments, thus meeting the high standards of corrosion resistance required by modern industrial production. Attached Figure Description
[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0028] Figure 1 This is a schematic diagram of a method for evaluating the high-temperature molten salt corrosion performance of materials according to Embodiment 1 of the present invention.
[0029] Figure 2 This is a schematic diagram of the interval type II trigonometric membership function provided in Embodiment 1 of the present invention.
[0030] Figure 3 This is a schematic diagram of the interval type II fuzzy logic reasoning model provided in Embodiment 1 of the present invention.
[0031] Figure 4This is a schematic diagram of a type-two fuzzy set example provided in Embodiment 2 of the present invention.
[0032] Figure 5 This is an internal structural diagram of a computer device provided in Embodiment 3 of the present invention. Detailed Implementation
[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0034] The purpose of this invention is to provide a method, equipment, medium, and product for evaluating the high-temperature molten salt corrosion performance of materials. This method can effectively utilize various corrosion test data to achieve a scientific, precise, and efficient evaluation of the corrosion performance of materials in complex high-temperature molten salt environments, thereby meeting the high standards of corrosion resistance required by modern industrial production.
[0035] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0036] Example 1
[0037] like Figure 1 As shown in the figure, this embodiment provides a method for evaluating the high-temperature molten salt corrosion performance of materials, the method comprising:
[0038] S1. Obtain historical high-temperature molten salt corrosion test data; the high-temperature molten salt corrosion test data includes: corrosion parameter data and corrosion degree index; the corrosion parameter data includes: test temperature, impurity concentration, molten salt flow rate, static corrosion rate, flow corrosion rate, applied load, and stress corrosion cracking time.
[0039] High-temperature molten salt static corrosion, high-temperature molten salt flow corrosion, and high-temperature molten salt stress corrosion testing were used to obtain the static corrosion rate, flow corrosion rate, and stress corrosion cracking time of the tested material under specific test temperatures, impurity contents, test flow rates, and applied loads. The test apparatus consisted of a high-temperature reactor with a corrosion-resistant alloy inner liner. Analytical-grade chemical reagents were used to prepare the molten salt medium. In the static and flow corrosion tests, the tested material was processed into sheet-like specimens; in the stress corrosion test, it was processed into C-ring specimens, and an applied load was applied through bolts passing through the C-rings. All specimens were degreased with acetone and ethanol and dried before use. In the static and flow corrosion tests, the corrosion rate of the tested material was obtained by comparing the specimen mass before and after the test. In the stress corrosion test, the time from the start of the test to the appearance of cracks was observed as the stress corrosion cracking time.
[0040] S2. Convert the corrosion parameter data and the corrosion index into an interval type II fuzzy set.
[0041] In this embodiment, a type II fuzzy set is defined.
[0042]
[0043] Among them, and It is a type II fuzzy set The lower and upper membership functions of the test parameter are in the range of [0,1]. X is the range of values for the test parameter x. For example, if the test temperature range is 230℃ to 600℃, then the range of values for x is [230,600]. Figure 1 This is an example of a type-II fuzzy set. In this method, 'u' represents the parameter range level (for experimental data, such as experimental temperature, there are range levels like "high temperature", "medium temperature", "low temperature") or the corrosion degree level (for corrosion index, there are degree levels like "high corrosion degree", "medium corrosion degree", "low corrosion degree"), with each level corresponding to a type-II fuzzy set. 'x' represents the specific value of the corrosion parameter or corrosion index, such as the specific temperature value, specific flow rate value, specific corrosion rate value, etc. in the experiment.
[0044] The corrosion parameter data and the corrosion index are converted into the interval type II fuzzy set.
[0045] Specifically:
[0046] In this embodiment, based on data such as the melting and boiling points of the test molten salt, the flow rate range of the test apparatus, the mechanical properties of the tested material, the allowable corrosion rate in engineering practice, and the stress corrosion cracking time, interval type-II fuzzy sets are established for the test temperature T, impurity content Cim, test flow rate V, applied load S, static corrosion rate CRs, flow corrosion rate CRd, stress corrosion cracking time tSCC, static corrosion index CIs, flow corrosion index CId, stress corrosion cracking index CISCC, and corrosion index CIcorr. This embodiment uses the corrosion of carbon steel in binary nitrate (60% NaNO3 + 40% KNO3) as an example to construct the type-II fuzzy sets corresponding to each variable in this molten salt corrosion system. In this embodiment, the values of the above variables are divided into three levels: {low, medium, and high}, and the interval type-II fuzzy sets corresponding to different levels of variables are represented in the form of triangular and trapezoidal membership functions. Table 1 shows the type-II fuzzy sets corresponding to different levels of each variable, which are represented here using the interval type-II triangular membership function form shown below.
[0047] or [α] i ,β i ,γ i ]
[0048] or [α' i ,β i ,γ' i ]
[0049] Please see Figure 2 The specific meanings of each variable in the above formula are as follows: Let be the lower triangular membership function corresponding to the i-th level of the variable. Let α be the upper triangular membership function corresponding to the i-th level of the variable. i ,β i ,γ i These are the lower boundary point, vertex, and upper boundary point of the lower triangular membership function, respectively, α' i ,β i ,γ' i These are the lower boundary point, vertex, and upper boundary point of the membership function of the upper triangular region, respectively.
[0050] Table 1 shows the type-II fuzzy sets corresponding to different levels of each variable in the model.
[0051]
[0052]
[0053] S3. Based on the interval type II fuzzy set, and using the corrosion parameter data as input and the corrosion degree index as output, construct an interval type II fuzzy logic reasoning model according to the interval type II fuzzy set theory.
[0054] Please see Figure 3 Based on interval type-two fuzzy set theory, an interval type-two fuzzy logic inference model is constructed to establish an inference network and mapping relationship between corrosion test parameters, test results, and corrosion degree. This embodiment adopts a hierarchical fuzzy inference network structure, which can be represented by CIcorr = f(T, Cim, V, S, CRs, CRd, tSCC). The model contains four interval type-two fuzzy logic sub-modules: static corrosion interval type-two fuzzy logic sub-module IFLSs, flow corrosion fuzzy logic sub-module IFLSd, stress corrosion cracking interval type-two fuzzy logic sub-module IFLSSCC, and comprehensive corrosion interval type-two fuzzy logic sub-module. The network input parameters include test temperature T, impurity concentration Cim, molten salt flow rate V, static corrosion rate CRs, flow corrosion rate CRd, applied load S, and stress corrosion cracking time tSCC. The output parameter is the corrosion degree index CIcorr. This inference model network involves three intermediate parameters: static corrosion index CIs, flow corrosion index CId, and stress corrosion cracking index CISCC.
[0055] The corrosion index inference network structure includes: a static corrosion interval type II fuzzy logic submodule, a flow corrosion fuzzy logic submodule, a stress corrosion cracking interval type II fuzzy logic submodule, and a comprehensive corrosion interval type II fuzzy logic submodule. In this embodiment, a rule base is established based on professional knowledge of high-temperature molten salt corrosion, as shown in Tables 2-5.
[0056] The inputs to the Type II fuzzy logic submodule of the static corrosion interval are: experimental temperature, impurity concentration and static corrosion rate, and the output is: static corrosion index.
[0057] Table 2. Rule base of the Type II fuzzy logic submodule for static erosion intervals.
[0058]
[0059]
[0060] The inputs to the fuzzy logic submodule for flow corrosion are: experimental temperature, impurity concentration, medium flow rate, and flow corrosion rate, and the output is: flow corrosion index.
[0061] Table 3. Rule base of the fuzzy logic submodule for flow erosion
[0062] 1 Low Low Low Low Low 2 middle Low Low Low Low 3 high Low Low Low Low 4 Low middle Low Low Low 5 middle middle Low Low Low 6 high middle Low Low Low 7 Low high Low Low Low 8 middle high Low Low Low 9 high high Low Low Low 10 Low Low middle Low Low … … … … …
[0063] The inputs to the type II fuzzy logic submodule for the stress corrosion cracking interval are: experimental temperature, impurity concentration, applied load, and stress corrosion cracking time, and the output is: stress corrosion cracking index.
[0064] Table 4. Type II Fuzzy Logic Submodule for Stress Corrosion Cracking Range
[0065]
[0066]
[0067] The inputs of the comprehensive corrosion range type II fuzzy logic submodule are: static corrosion index, flow corrosion index and stress corrosion cracking index, and the output is: corrosion degree index.
[0068] Table 5. Type II Fuzzy Logic Submodule for Comprehensive Corrosion Range
[0069]
[0070]
[0071] S4. Input the corrosion parameter data to be evaluated into the interval type II fuzzy logic reasoning model to obtain the corresponding corrosion degree index.
[0072] Based on the established interval type II fuzzy logic reasoning model, the corrosion parameters obtained from the high-temperature molten salt corrosion test are input into the established interval type II fuzzy logic model, and the model reasoning calculation obtains the corrosion index of the tested material in a specific high-temperature molten salt environment.
[0073] Given that the test material is carbon steel, the test temperature is T = 300℃, the impurity content Cim = 0.4%, the test flow rate V = 1.2 m / s, the applied load S = 100 MPa, the static corrosion rate is CRs = 0.3 mm / year, the flow corrosion rate is CRd = 0.4 mm / year, and the stress corrosion cracking time is tSCC = 230 h, the calculated results are: CIs = 2.39, CId = 3.00, CISCC = 1.00, CIcorr = 2.00. The results indicate that under the above test conditions, carbon steel exhibits moderate static corrosion, high flow corrosion, low stress corrosion cracking, and low overall corrosion.
[0074] This embodiment effectively solves the problem that traditional corrosion performance evaluation methods are insufficient in handling complex environments and uncertain factors. It provides a more scientific, accurate and comprehensive evaluation system for material corrosion performance in high-temperature molten salt environments, which has broad application prospects and is especially suitable for evaluating the corrosion performance of metals and alloys in high-temperature molten salt environments in the fields of energy, chemical industry and nuclear energy.
[0075] Example 2
[0076] This embodiment provides a method for evaluating the high-temperature molten salt corrosion performance of materials, the method comprising:
[0077] Step 1: Test data collection stage: Obtain the static corrosion rate and corrosion depth of the material under test in the high-temperature molten salt environment (including different temperatures and different impurity contents), the flow corrosion rate at different flow rates (including different temperatures and different impurity contents), and the corrosion cracking time at different external load levels (including different temperatures and different impurity contents).
[0078] Step Two: Experimental Data Processing Stage: Based on the theory of interval type II fuzzy sets, construct interval type II fuzzy sets corresponding to the levels of experimental data and corrosion indices. This involves transforming the experimental data and corrosion indices into interval type II fuzzy sets and defining the type II fuzzy sets.
[0079]
[0080] in and It is a type II fuzzy set The lower and upper membership functions of the test parameter are in the range of [0,1]. X is the range of values for the test parameter x. For example, if the test temperature range is 230℃ to 600℃, then the range of values for x is [230,600]. Figure 4 This is an example of a type-II fuzzy set. In this method, 'u' represents the parameter range level (for experimental data, such as experimental temperature, there are range levels like "high temperature", "medium temperature", "low temperature") or the corrosion degree level (for corrosion index, there are degree levels like "high corrosion degree", "medium corrosion degree", "low corrosion degree"), with each level corresponding to a type-II fuzzy set. 'x' represents the specific value of the corrosion parameter or corrosion index, such as the specific temperature value, specific flow rate value, specific corrosion rate value, etc., in the experiment.
[0081] Step 3: Establishment of the Interval Type II Fuzzy Logic Model: The interval type II fuzzy logic model for evaluating the high-temperature solution corrosion performance of materials includes an inference network, a rule base, and an inference algorithm. The inference network adopts a layered structure. The first layer consists of the static corrosion interval type II fuzzy logic submodule IT2FLSs, the flow corrosion interval type II fuzzy logic submodule IT2FLSd, and the stress corrosion cracking interval type II fuzzy logic submodule IT2FLSSCC. The second layer consists of the comprehensive corrosion interval type II fuzzy logic submodule IT2FScorr. Each submodule can be represented by the following expression:
[0082]
[0083] In the formula, CIs, CId, CISCC, and CIcorr represent the static corrosion index, the flow corrosion index, the stress corrosion cracking corrosion index, and the comprehensive corrosion index, respectively. T is the test temperature, Cim is the impurity content, V is the test flow rate, S is the applied load, CRs is the static corrosion rate, CRd is the flow corrosion rate, and tSCC is the stress corrosion cracking time, etc. (The relationship between the test temperature T, impurity content Cim, test flow rate, etc., and the static corrosion index, the flow corrosion index, and the stress corrosion cracking corrosion index is inferred through fuzzy logic and is not explicit).
[0084] The rule base is crucial for implementing fuzzy inference, and it is built based on professional knowledge. Each rule in the rule base represents the inherent mapping relationship between input and output variables, which can be represented as an IF-THEN statement. The i-th rule can be represented as...
[0085] Rule i:IF X1 isAi and X2 is Bj and X3 is Ck THENYis Dm
[0086] Where Ai, Bj, Ck, and Dm are fuzzy sets, Ai, Bj, and Ck correspond to the antecedent, and Dm corresponds to the consequent. The above rule can be described as: if X1 is Ai, X2 is Bj, and X3 is Ck, then Y is Dm. For example:
[0087] IF T is Low and Cim is Low and CRs is High THEN CIs is High.
[0088] For the i-th rule, it can be described by the following fuzzy relation equation:
[0089]
[0090] In the formula, Let be the interval type II fuzzy set calculated according to the i-th rule, expressed in the form of membership function. Let x be the membership degree when the p-th experimental parameter takes the value xp. Let xk be the membership degree when the k-th experimental parameter takes the value xk. Let be the interval type II fuzzy set of the output variable represented in membership function form, and ∩ be the intersection operation of type II fuzzy sets.
[0091] The inference algorithm is the process by which the model uses fuzzy rules and fuzzy logic operations to perform interval-based type II fuzzy logic inference. The specific inference algorithm is as follows:
[0092] Input data fuzzification. After obtaining corrosion data of the tested material in a certain molten salt using a high-temperature molten salt corrosion test, this data is converted into corresponding upper and lower membership degrees using interval type II fuzzy sets. The specific formula is as follows:
[0093]
[0094] In the formula, inputi represents the value of the i-th input variable. For a certain level of a certain test parameter (such as a level of "high temperature" for the test temperature parameter), Let be the type II fuzzy membership degree corresponding to the value of the i-th input variable, where the membership degree is an interval. in, The membership value is the upper membership degree. This represents the membership degree value. For example, after inputting a specific experimental temperature value T0, we can obtain the degree to which that temperature belongs to the "high temperature" category. The result is a range between 0 and 1, such as [0.2, 0.4].
[0095] Inference engine. It maps input variables to output variables through rule combination and fuzzy logic operations. For example, given an output set of input = {input1, input2, ..., inputp}, where input represents a set of experimental parameter values, for the i-th rule:
[0096]
[0097] In the formula, Let be the interval type II fuzzy set calculated according to the i-th rule.
[0098] Aggregate the outputs of all rules.
[0099]
[0100] In the formula, Let μ be the interval type-II fuzzy set calculated according to the i-th rule. output Let be the output value represented by a type II fuzzy set of intervals, and ∪ be the union operation of the type II fuzzy sets of intervals. This is the set of interval type II models calculated according to the nth rule.
[0101] Type reduction and defuzzification.
[0102] To facilitate comparison of different output results, it is necessary to adjust μ. output Type downgrading and deblurring are performed to convert it into a clear value, using the following formula.
[0103]
[0104] In the formula, and μ output Let represent the upper and lower membership functions of the output, yi be the i-th output value sample, N be the number of samples collected across the entire output variable range, L and R be the switching points for the number of samples, and cL and cR be the lower and upper limits of the output value, respectively.
[0105] The final deblurred erosion index yout is:
[0106]
[0107] Step 4: Corrosion performance evaluation stage: Based on the established interval type II fuzzy logic model, the corrosion parameters obtained from the high-temperature molten salt corrosion test are input into the established interval type II fuzzy logic model. The model inferences and calculates to obtain the corrosion index of the tested material in a specific high-temperature molten salt environment. The higher the corrosion index, the worse the material's resistance to high-temperature molten salt corrosion.
[0108] Example 3
[0109] A computer device, which may be a database, may have an internal structure diagram as shown below. Figure 5 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores pending transactions. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements the high-temperature molten salt corrosion performance evaluation method for materials described in Example 1.
[0110] Example 4
[0111] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for evaluating the high-temperature molten salt corrosion performance of a material as described in Example 1.
[0112] Example 5
[0113] A computer program product includes a computer program that, when executed by a processor, implements a method for evaluating the high-temperature molten salt corrosion performance of a material as described in Example 1.
[0114] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.
[0115] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided by this invention may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided by this invention may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0116] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0117] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A method for evaluating the high-temperature molten salt corrosion performance of materials, characterized in that, The method includes: Acquire historical high-temperature molten salt corrosion test data; the high-temperature molten salt corrosion test data includes: corrosion parameter data and corrosion degree index; the corrosion parameter data includes: test temperature, impurity concentration, molten salt flow rate, static corrosion rate, flow corrosion rate, applied load, and stress corrosion cracking time; wherein, the high temperature range is 230℃ to 600℃; The corrosion parameter data and the corrosion degree index are converted into interval type II fuzzy sets; Based on the aforementioned interval type II fuzzy set, and using the interval type II fuzzy set theory as the basis, an interval type II fuzzy logic reasoning model is constructed with corrosion parameter data as input and corrosion degree index as output. The corrosion parameter data to be evaluated is input into the interval type II fuzzy logic reasoning model to obtain the corresponding corrosion degree index. The interval type II fuzzy logic reasoning model includes: a static corrosion interval type II fuzzy logic submodule, a flow corrosion fuzzy logic submodule, a stress corrosion cracking interval type II fuzzy logic submodule, and a comprehensive corrosion interval type II fuzzy logic submodule; The inputs to the static corrosion interval type II fuzzy logic submodule are: test temperature, impurity concentration and static corrosion rate, and the output is: static corrosion index; The inputs to the fuzzy logic submodule for flow corrosion are: test temperature, impurity concentration, molten salt flow rate and flow corrosion rate, and the output is: flow corrosion index; The inputs to the type II fuzzy logic submodule for the stress corrosion cracking interval are: test temperature, impurity concentration, applied load, and stress corrosion cracking time, and the output is: stress corrosion cracking index; The inputs of the comprehensive corrosion range type II fuzzy logic submodule are: static corrosion index, flow corrosion index and stress corrosion cracking index, and the output is: corrosion degree index.
2. The method for evaluating the high-temperature molten salt corrosion performance of materials according to claim 1, characterized in that, The corrosion parameter data and the corrosion degree index are converted into interval type II fuzzy sets, specifically including: Define interval type II fuzzy set : ； in, and It is a type II fuzzy set The lower and upper membership functions of have a value range of [0, 1], where X is the value range of the corrosion parameter x, and u represents the parameter range level. The corrosion parameter data and the corrosion degree index are converted into the interval type II fuzzy set.
3. A computer device, comprising: A memory, a processor, and computer instructions stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for evaluating the high-temperature molten salt corrosion performance of a material as described in claim 1 or 2.
4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for evaluating the high-temperature molten salt corrosion performance of a material as described in claim 1 or 2.
5. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for evaluating the high-temperature molten salt corrosion performance of a material as described in claim 1 or 2.