JP6607233B2 - Metal corrosion prediction method, steel structure design method, and corrosion prediction program - Google Patents

Description

  The present invention relates to a method for predicting corrosion of a metal material widely used in the industrial field by numerical analysis. Specifically, in order to predict the quality and performance life of a structure, the present invention calculates a potential distribution and a current density distribution in an electrolytic solution that is a corrosive medium using numerical analysis (numerical simulation). It is a technology related to corrosion evaluation that predicts the corrosion resistance of metals.

  Conventionally, the corrosion resistance of metal materials is evaluated by a corrosion acceleration test, an atmospheric exposure test, or an electrochemical measurement. For example, when evaluating the corrosion resistance of steel materials, test specimens and actual parts can be obtained by a salt spray test (SST) or a combined cycle test (CCT: Combined Cyclic corrosion Test) that combines wet and dry atmosphere conditions. The corrosion resistance is promoted to determine the superiority or inferiority of the corrosion resistance in a short time. On the other hand, since such a corrosion acceleration test is a severe corrosive environment compared to the environment where the steel material is actually used, it does not reproduce the order of the corrosion resistance of steel materials with different corrosion forms and types in the actual use environment There is. Therefore, an atmospheric exposure test for evaluating corrosion resistance by installing a test piece outdoors or indoors and corroding it in an environment close to actual use is also widely performed. However, since the exposure test has a long period of time until it is judged whether the corrosion resistance is superior or inferior, it is not suitable for the development of corrosion resistant materials that require early judgment of corrosion resistance.

  On the other hand, along with the recent development of computer technology and faster calculation, attempts to predict the corrosion phenomenon by computer simulation have been actively made. If corrosion of a metal can be predicted by numerical analysis, there is a possibility that it is possible to analyze the cause of corrosion and the corrosion mechanism, which are difficult to analyze by experiments, and to evaluate the corrosion resistance of the metal in a short time.

  Patent Document 1 and Patent Document 2 consider a numerical analysis method for a corrosive environment in which a metal in a corrosive medium is regarded as a continuum composed of a plurality of electrically connected analysis segments, and the polarization characteristics of the metal are determined. Disclose. In this method, the corrosion medium in the model is regarded as a Laplace field according to the Laplace equation, numerical analysis is performed, and the potential distribution of the metal as the target structure and the inflow and outflow of current are calculated. Is a method of estimating

  In Non-Patent Document 1, a technique for analyzing the corrosion of a seawater pump by a boundary element method is reported. Here, the Laplace equation is solved using the polarization curve of cast iron (FC200), which is a pump material, as a boundary condition to obtain the potential distribution and current density distribution in seawater as a corrosion medium, and the potential at the interface between cast iron and seawater. And a model to predict the corrosion of cast iron from current density is proposed. Here, the value obtained from the experiment is used for the electrical conductivity in the aqueous solution.

  Non-Patent Document 2 simulates corrosion of reinforcing bars in concrete. The mass transfer of corrosion factors such as chloride and oxygen in concrete is predicted, and further, the corrosion current density is obtained by the Tafel equation to predict the corrosion of reinforcing bars. In this model, the mass transfer is calculated using the diffusion equation by treating the concrete as pores and treating it as a uniform medium like a solution.

  Non-Patent Document 3 predicts corrosion of zinc and steel by performing numerical calculation in consideration of mass transfer and chemical reaction in contact corrosion of dissimilar metals under a thin water film of galvanizing and underlying steel sheet. In this model, the formation and distribution of corrosion products of zinc and steel are calculated every minute, and the dissolved metal ions calculated from the anode reaction given as the boundary condition and the metal ions other than metal ions calculated by mass transfer are calculated. Reactions with chemical species are predicted by equilibrium calculations using equilibrium constants for precipitation reactions of metal oxides and hydroxides, thereby predicting the distribution and amount of various corrosion products.

JP 2008-249562 A JP 2008-32421 A

Ebara Times No. 223 (2009-3) 37-45 Annual report of concrete engineering, Vol. 24, No. 1, (2002) 831-836 Materials and Environment, Vol. 61, (2012), 376-383

  Various methods are conceivable for modeling corrosion phenomena and numerical analysis methods. Of these, widely used are areas where the electrolyte solution, which is a corrosive medium, is assumed. Is obtained by solving Metal corrosion is evaluated by the flow of current and the potential difference at the interface between the electrolyte solution and the metal. As a method for solving such an equation, a difference method, a finite element method, or the like is used as a continuum model for converting an electrolyte solution region into an element. The polarization characteristics of the metal, which is the relationship between the potential of the metal surface in the electrolyte solution and the current density, are given as boundary conditions, and the distribution of the potential and current in each element and the material balance in and within the element are calculated.

  In performing such calculations, the electrical conductivity in the electrolyte solution is often calculated as constant. Alternatively, the electrical conductivity may be calculated from the concentration of chemical species dissolved in the entire electrolyte solution or each element.

  However, in the actual corrosion phenomenon, the corrosion not only proceeds as a dissolution (ionization) of the metal, but also involves the formation of so-called “rust” or “corrosion products” that form metal oxides and hydroxides. . Hereinafter, dissolved metal deposits (oxides, hydroxides, etc.) are referred to as corrosion products. Corrosion products have a large effect on the corrosion rate of metals. For example, the reason why zinc active in the electrolyte solution exhibits excellent corrosion resistance in the air environment is that the corrosion rate of metallic zinc is reduced by covering the surface with the corrosion product of zinc.

  In Patent Document 1 and Patent Document 2, the corrosion medium in the model is analyzed as a Laplace field according to the Laplace equation, and an electrical property value is given as a specific resistance of 5000 Ω · cm by taking soil as an example of the medium. In these inventions, when the medium is an aqueous solution, the corrosion polarization characteristics in the aqueous solution can be evaluated if the electrical conductivity of the aqueous solution is given. However, the electrical property values given here are conditions given only from the corrosive environment, and the influence of the formation of corrosion products due to corrosion is not considered.

  Also in Non-Patent Document 1 and Non-Patent Document 2, the corrosive medium is seawater and concrete, the respective electric conductivities are given, and the Laplace equation and the mass transport equation are solved. However, the prediction of the formation of corrosion products and the associated effects on corrosion media and corrosion are not taken into account in the calculation.

  Non-Patent Document 3 calculates the potential distribution and current density distribution in consideration of mass transport in the dissimilar metal contact corrosion of zinc and steel in an aqueous solution. In this document, the amounts of oxides and hydroxides produced are calculated using the concentrations of various chemical species in the aqueous solution that is a corrosive medium and the equilibrium constants for the precipitation reaction of oxides and hydroxides. And predict the distribution. However, the change in electrochemical properties at the interface of the metal with the aqueous solution due to the formation of the corrosion product was not considered, and the corrosion rate of the metal in the presence of the corrosion product could not be estimated accurately.

  As described above, in the prior art, the influence on the corrosion rate due to the deposition of metal corrosion products on the metal surface is not considered in the numerical calculation, and the metal corrosion can be predicted with high accuracy by numerical analysis. It wasn't.

  Accordingly, an object of the present invention is to provide a method for predicting corrosion of a metal by numerical analysis, which can predict corrosion of a metal in contact with an electrolyte solution with higher accuracy by numerical analysis.

In order to solve the above-mentioned problems, the present inventors have paid attention to the consideration of the influence on the corrosion rate caused by the deposition of metal corrosion products on the metal surface in the numerical calculation.
As a result, the electric conductivity of the electrolyte solution on the metal surface, that is, the electrolyte solution located near the interface between the electrolyte solution and the metal is set to a value lower than the electric conductivity calculated from the solute concentration of the electrolyte solution, Depending on the amount of corrosion products produced by corrosion, the electrical conductivity of the electrolyte solution located in the vicinity of the interface between the electrolyte solution and the metal is corrected to an appropriate value, so that when the metal is corroded, It is possible to simulate the effect of corrosion products deposited on the surface on the corrosion rate, and it is possible to appropriately predict the potential distribution and current density distribution when corrosion products are actually formed. Thus, the present invention has been completed.

The gist configuration of the present invention completed based on the above findings is as follows.
1. A method for predicting corrosion of a metal in contact with an electrolyte solution by numerical analysis,
Creating a calculation model of a system in which a metal and an electrolyte solution are in contact;
A step of calculating the potential distribution of the electrolyte solution and the metal at a predetermined time by numerical calculation using the calculation model;
Have
In performing the numerical calculation, the electric conductivity of the electrolyte solution located in the vicinity of the interface between the electrolyte solution and the metal in the calculation model is set to a value lower than the electric conductivity calculated from the solute concentration of the electrolyte solution. Calculating the potential distribution of the electrolyte solution and the metal by simulating the effect of corrosion products deposited on the surface of the metal during the corrosion of the metal on the corrosion rate;
A method for predicting corrosion of a metal.

2. Dividing the electrolyte solution and metal of the calculation model into a plurality of cells,
In the calculation model, the electric conductivity of the cell j of the electrolyte solution located in the vicinity of the interface between the electrolyte solution and the metal is σ _j , the volume of the cell j is V _0j , and the volume occupied by the corrosion product in the cell j is V _cj (1), wherein σ ₀ , σ _j , V _0j and V _cj satisfy the relationship of the following equation, where σ ₀ is an electrical conductivity calculated from the solute concentration of the electrolyte solution: Prediction method for metal corrosion.
σ _j = α × σ ₀ × (1-V _cj / V _0j )
However, 0.10 ≦ α ≦ 5.00.

3. 3. The method for predicting corrosion of a metal according to 1 or 2, wherein the metal is one or more metals selected from zinc, iron, aluminum, copper and nickel, or an alloy containing the metal as a main component. .

4). 4. The form of corrosion is dissimilar metal contact corrosion in which two kinds of metals come into contact with each other and the corrosion of one metal is accelerated by a potential difference between the metals. Prediction method for metal corrosion.

5. A method for designing a steel structure, wherein the material is selected by predicting corrosion resistance by the method of predicting corrosion of a metal by the numerical analysis according to any one of 1 to 4 above.

6). A method for designing a steel structure, wherein the corrosion resistance is predicted by the method for predicting corrosion of a metal by the numerical analysis according to any one of the above items 1 to 4, and the rust prevention performance is designed.

7. A corrosion prediction program for causing a computer to execute the metal corrosion prediction method according to the numerical analysis according to any one of 1 to 4 above.

  According to the metal corrosion prediction method of the present invention by numerical analysis, numerical calculation is performed in consideration of the influence on the corrosion rate due to the deposition of metal corrosion products on the metal surface. Can be predicted with higher accuracy by numerical analysis.

5 is a flowchart of a method for predicting corrosion of a metal by numerical analysis according to an embodiment of the present invention. It is a figure which shows an example of the continuous body model (calculation model) of the dissimilar metal contact corrosion of two metals. It is a figure explaining formation of the corrosion product of a metal. It is a figure which shows the model shape of the dissimilar metal contact corrosion of zinc and iron used in Example 1. FIG. 3 is a schematic diagram of an experiment for different metal contact corrosion of zinc and iron performed in Example 1. It is a figure which shows the model shape of crevice corrosion performed in Example 2. FIG.

  The present invention is a method for predicting corrosion of a metal in contact with an electrolyte solution by numerical analysis. One embodiment thereof will be described with reference to FIGS.

FIG. 1 is a flowchart of a metal corrosion prediction method by numerical analysis according to an embodiment of the present invention. FIG. 2 is a calculation model diagram of a system in which a metal and an electrolyte solution are in contact with each other in order to apply such numerical analysis. The electrolyte is formed on a state where two metals A and B are in contact with each other and are electrically short-circuited. 2 shows an example of dissimilar metal contact corrosion in the presence of a solution.
In the calculation model shown in FIG. 2, the metal and the electrolyte solution thin film in contact with the metal are divided into two-dimensional lattice-like regions (hereinafter referred to as “cells”) in these cross sections, and the boundary element method is used. The potential, current density, concentration of chemical species, etc. in each cell are calculated using the finite element method and the like, and the amount of corrosion and the precipitation reaction of the compound are predicted.
Hereinafter, a typical example of the calculation procedure will be described.

  First, in step S1, the shape of the electrolyte solution that becomes the metal and the corrosive medium is set, and the initial concentration and physical property value of the chemical species in the electrolyte solution are set. The physical property value is a diffusion coefficient used for calculating mass transport of chemical species, an ion charge, or the like.

  Next, in step S2, boundary conditions used for numerical analysis are set. Here, as a boundary condition at the interface between the metal and the electrolyte solution, a polarization curve that is a function of the potential E of the metal surface in the electrolyte solution and the current density i is set. In addition, a condition that no current flows in and out is set as a boundary condition at the interface of the electrolyte solution with the gas phase and at the end of the electrolyte solution.

ここで、Фは電位、σは電気伝導度を表す。なお、電解質溶液の電気伝導度σは、式（２）によって求められる。

ここで、Fはファラデー定数、z_k、u_k、C_kはそれぞれ、電解質溶液中のイオン種kの価数、易動度、濃度を表す。
また、イオンの易動度u_kの値は、式（３）に示すNernst-Einsteinの関係を用いて、イオン種kの拡散係数D_kから求められる。

ここで、Rは気体定数、Tは絶対温度である。また、金属部分の電気伝導度には各種金属の物性値を入力すればよい。ただし、水溶液との電気伝導度の差が大きいと解析解が収束しづらく、解析解が得られない場合がある。そのような場合には、電解質溶液の電気伝導度の１０００倍以上程度の値を入力してもよい。
さらに、得られた電位を、式（４）に代入することで、電流密度Iの分布が得られる。

Next, in step S3, the potential distribution and current density distribution of the electrolyte solution and metal at a certain time t are obtained by numerical calculation.
That is, the Laplace equation shown in the equation (1) is solved using the boundary condition set in step S2.

Here, Ф represents potential and σ represents electrical conductivity. In addition, the electrical conductivity σ of the electrolyte solution is obtained by the equation (2).

Here, F represents the Faraday constant, and z _k , u _k , and C _k represent the valence, mobility, and concentration of the ionic species k in the electrolyte solution, respectively.
In addition, the value of the ion mobility u _k can be _obtained from the diffusion coefficient D _k of the ion species k using the Nernst-Einstein relationship shown in Equation (3).

Here, R is a gas constant, and T is an absolute temperature. In addition, physical properties of various metals may be input as the electrical conductivity of the metal portion. However, if the difference in electrical conductivity with the aqueous solution is large, the analytical solution is difficult to converge and the analytical solution may not be obtained. In such a case, a value of about 1000 times or more the electric conductivity of the electrolyte solution may be input.
Furthermore, the distribution of the current density I is obtained by substituting the obtained potential into the equation (4).

ここで、tは時刻、r_kは化学種kの腐食生成物の生成における化学反応による物質変化量を表す。
なお、ここでは、電流密度および電位分布を計算するステップＳ３と、化学種の化学反応の平衡計算により、腐食生成物の生成の化学反応を考えるステップＳ５とが分かれているため、r_kは腐食反応によるイオンの生成量のみを考慮している。
そして、腐食反応によるイオンの生成量、すなわち金属の腐食量は、金属と水溶液の界面における電流密度を用いて、式（６）に示すFaradayの法則により計算される。

ここで、ｎおよびｚは腐食により溶解した金属（化学種）の物質量および価数であり、ｔは時間である。 Next, in step S4, the potential obtained from the equation (1) is substituted into the Nernst-Planck equation shown in the equation (5) to obtain the concentration change amount of each ion in each cell.

Here, t is time, r _k denotes the material variation due to chemical reactions in the production of corrosion products species k.
Here, the step S3 of calculating the current density and potential distribution by equilibrium calculations species of chemical reactions, since the step S5 to consider the chemical reaction of the formation of corrosion products are separated, r _k corrosion Only the amount of ions produced by the reaction is considered.
The amount of ions generated by the corrosion reaction, that is, the amount of corrosion of the metal is calculated according to Faraday's law shown in Equation (6) using the current density at the interface between the metal and the aqueous solution.

Here, n and z are the amount and valence of a metal (chemical species) dissolved by corrosion, and t is time.

  In actual calculation, the current density in the vicinity of the interface with the metal in the electrolyte solution is obtained by performing Step S3 at various times. Therefore, the corrosion is performed every minute using Equation (6). The distribution of the corrosion amount on the metal surface after the lapse of a predetermined time can be obtained by obtaining the amount and integrating the amounts.

  Note that when the mass transport according to the equation (5) is calculated, the electrical neutral condition that the sum of electric charges becomes zero in each cell may not be satisfied. In that case, an operation such as moving ions according to the mobility of each ion is performed so as to satisfy the electrical neutral condition.

  Next, in step S5, a reaction calculation for generating a corrosion product is performed. Specifically, the chemical equilibrium calculation may be performed based on the above calculation result, but if the reaction rate constant is known, the calculation based on the reaction rate equation may be performed.

In step S6, it is determined whether or not the time t at which the numerical calculation is performed exceeds a preset time t _set . If the set time t _set has not been exceeded, Δt is added and the process returns to step S3 to calculate the potential distribution and current density distribution in the electrolyte solution at the new time t. When the time t when the numerical calculation is performed exceeds the set time t _set , the process proceeds to step S7.

  Finally, in step S7, various calculation results are output, and the present method ends.

  Here, the potential distribution, the current density distribution, and the corrosion amount obtained by the numerical calculation above are strongly influenced by the corrosion products formed up to that time. Many corrosion products have electrical properties of insulators and semiconductors, such as oxides and hydroxides, and have extremely low electrical conductivity compared to metals and electrolyte solutions. When such a substance is generated and deposited in the electrolyte solution, the potential distribution and current density distribution in the electrolyte solution change. As a result, the corrosion reaction and the formation of corrosion products after that time also change.

  Therefore, the present invention provides a method for predicting corrosion of a metal by numerical analysis simulating the effect of a corrosion product generated on such a corrosion phenomenon.

FIG. 3 is a diagram illustrating the formation of metal corrosion products. Here, when the metal A generates metal ions A ⁺ in the solution due to corrosion, the concentration of A ⁺ increases with time, and the oxide AO or Suppose that hydroxide A (OH) is formed. As described above, most of the corrosion products are insulators or semiconductors, and are electrochemically inactive as compared to the metal before corrosion. Therefore, the corrosion reactivity differs between the interface between the metal where the corrosion product is not deposited and the electrolyte solution, and the interface between the metal where the corrosion product is deposited and the electrolyte solution.

  Therefore, in the present invention, when performing the numerical calculation in step S3, the electric conductivity of the electrolyte solution located in the vicinity of the interface between the electrolyte solution and the metal in the calculation model is expressed as the solute concentration of the electrolyte solution, that is, (2). The value is less than the electrical conductivity calculated from the equation, and this simulates the effect of corrosion products deposited on the surface of the metal during the corrosion of the metal on the corrosion rate. Calculate the current density distribution. As a result, it is possible to obtain a potential distribution and current density distribution close to the actually observed corrosion phenomenon.

Here, the electrolyte solution located in the vicinity of the interface between the electrolyte solution and the metal means that at least from the interface between the electrolyte solution and the metal in the system in which the metal and the electrolyte solution are in contact, the thickness of the electrolyte solution (mm) is 1 / 1000 (in the case of crevice corrosion, 1/1000 of the gap height (mm))] indicates the electrolyte solution existing in the region. When performing the metal corrosion prediction method by numerical analysis of the present invention, at least the electric conductivity of the electrolyte solution existing in the above region is set to a value lower than the electric conductivity calculated from the solute concentration of the electrolyte solution. However, the region where such a setting is performed is from the interface between the electrolyte solution and the metal [1/2 of the electrolyte solution thickness (mm) (in the case of crevice corrosion, the gap height (mm) 1/2)] is preferable.
For the electrolyte solution other than the vicinity of the interface, the same electrical conductivity as that in the vicinity of the interface may be set, or the electrical conductivity calculated from the solute concentration may be used as it is.

In this embodiment, the electric conductivity of the cell j of the electrolyte solution located in the vicinity of the interface between the electrolyte solution and the metal is σ _j , the volume of the cell j is V _0j , and the volume occupied by the corrosion product in the cell j the V _cj, when the electrical conductivity is calculated from the solute concentration of the electrolyte solution was a _{σ 0, σ 0, σ j} , the V _0j and V _cj, by satisfying the relation of the following equation, the electrolyte solution and the metal The electric conductivity of the electrolyte solution located in the vicinity of the interface is set.
σ _j = α × σ ₀ × (1-V _cj / V _0j )
However, 0.10 ≦ α ≦ 5.00.

Here, α is a factor depending on the form and electrical properties of the corrosion product. When α is less than 0.10, the electrical conductivity of the electrolyte solution located in the vicinity of the interface between the electrolyte solution and the metal becomes excessively small, and it is assumed that the corrosion suppression effect of the base metal by the corrosion product is large. Actually, since the corrosion product is physically destroyed and damaged, the corrosion rate is not so small and the difference between the actual corrosion phenomenon and the numerical calculation becomes large. On the other hand, when α exceeds 5.00, it is assumed that the corrosion inhibitory effect of the base metal by the corrosion product is small, and the corrosion inhibitory effect by the corrosion product accumulation is not simulated. From this viewpoint, a more preferable range of α is 0.30 or more and 2.00 or less.
Α may be constant regardless of the time t. However, since the amount of corrosion products changes with the passage of time from the start of corrosion, it is preferable to change according to the amount of corrosion products generated. .
That is,
When 0 ≦ Vcj / V0j ≦ 0.2, 1.00 ≦ α ≦ 5.00
When 0.2 <V _cj / V _0j ≦ 0.5, 0.50 ≦ α ≦ 2.00
If 0.5 <V _cj / V _0j ≦ 1.0, 0.10 ≦ α ≦ 1.00
It is preferable that

V _cj / V _0j is calculated as follows.
That is, first, in the cell j, the total amount (g or mol) of corrosion products (oxides, hydroxides, etc.) at the time t predicted to be generated by numerical calculation is obtained, and this value is determined based on these corrosions. V _cj is determined by dividing by the density of the product. For example, when the mass of the corrosion product is used, the calculation is performed by V _cj = m / d. Here, V _cj is the volume (cm ³ ) occupied by the corrosion product in cell j, m is the mass (g) of the corrosion product, and d is the density (g / cm ³ ) of the corrosion product. In addition, when a corrosion product consists of many components, you may calculate about all the components, but you may calculate using only the oxide and hydroxide which are the main components of a corrosion product. . Then, V _cj / V _0j is calculated by dividing this V _cj by the volume of the cell j: V _0j (the volume of the initial cell j originally occupied only by the aqueous solution).
In the case of the two-dimensional model, the volume of the cell and the volume occupied by the corrosion product in the cell may be read as the area.

Furthermore, in order to perform a numerical calculation close to an actual corrosion phenomenon, α may be obtained by experiment as a proportionality constant that approximates the relationship between the mixing ratio of the corrosion products and the conductivity of the electrolyte solution.
For example, when a highly pure oxide or hydroxide corresponding to the corrosion product can be prepared, the proportional constant is obtained for each component of the corrosion product, and α is obtained by the following equation.
α = Σ (α _m × V _m / Vc)
Here, α _m is the value of α of the corrosion product component m, V _m is the volume of the corrosion product component m, and Vc is the sum of the volumes of all the corrosion product components.

In addition, σ ₀ may be constant regardless of time by using the electric conductivity calculated by the equation (2) from the initial solute concentration of the electrolyte solution. Moreover, you may use the electrical conductivity calculated from the solute density | concentration in the electrolyte solution for every time.

  In addition, conditions other than the above, for example, conditions (such as cell size and shape) when the electrolyte solution and the metal in the calculation model are each divided into a plurality of cells are not particularly limited, and may be in accordance with an ordinary method.

  The metal to which the metal corrosion prediction method according to the present invention is applied is not particularly limited, but one or more metals selected from zinc, iron, aluminum, copper, and nickel, or alloys containing the metal as a main component Can be suitably applied. In addition, the metal corrosion prediction method according to the numerical analysis of the present invention is particularly effective when applied to dissimilar metal contact corrosion as shown in FIG. In this case, the polarization curve of metal A is the boundary condition at the interface between metal A and the electrolyte solution, and the polarization curve of metal B is the boundary condition at the interface between metal B and the electrolyte solution. As an example of dissimilar metal contact corrosion, the combination of zinc or galvanizing and iron or steel is mentioned.

  The deposition of the corrosion product on the metal surface is more obvious in the atmospheric environment than in the electrolyte solution, and its influence is greater. Therefore, it is suitable for numerical calculation in which the thickness of the electrolyte solution on the metal surface is less than 10 mm in order to predict atmospheric corrosion of the metal.

  In designing a steel structure, the material is selected by predicting the corrosion resistance by the above-described metal corrosion prediction method by numerical analysis. Moreover, in the design method of a steel material, corrosion resistance is predicted by the metal corrosion prediction method by the numerical analysis explained above, and the rust prevention performance is designed.

  In addition, an object of the present invention is to supply a storage medium storing software program codes for realizing the steps of the above-described embodiment to a system or apparatus, and the computer of the system or apparatus (or CPU or MPU) stores the storage medium. It is also achieved by reading and executing the program code stored on the medium. In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiment, and the storage medium storing the program and the program code constitutes the present invention.

  Examples of the storage medium for storing the program code include a flexible disk, hard disk, ROM, RAM, magnetic tape, nonvolatile memory card, CD-ROM, CD-R, DVD, optical disk, magneto-optical disk, MO, and the like. Can be considered. Also, a computer network such as a LAN (Local Area Network) or a WAN (Wide Area Network) can be used to supply the program code.

Example 1
The dissimilar metal contact corrosion of zinc and iron in contact with each other covered with NaCl aqueous solutions having various thicknesses of 0.6 mol / L, 1.2 mol / L and 2.0 mol / L is described above. Predicted by numerical calculation based on.
FIG. 4 shows a model diagram of the shape used in this example. The region Ω in the figure is a region occupied by the NaCl aqueous solution, and the initial concentration and diffusion coefficient of the chemical species to be considered in this solution are set as shown in Table 1.

Further, in the region Ω of FIG. 4, it is assumed that the equation of mass transport of Equation (5) holds, and diffusion due to a concentration gradient in an aqueous solution and migration due to electric charge are considered for each chemical species. Here, the influence of convection was ignored.
As boundary conditions for solving the equation (5), the anodic polarization curve of zinc measured in each concentration of NaCl aqueous solution is shown in Boundary 1, and the cathodic polarization curve of iron measured in each concentration of NaCl aqueous solution is shown in Boundary 2. Was used.
The other boundary has a condition that there is no current inflow / outflow (∂ / ： n = 0: where n represents a unit vector) as the boundary condition.

Under these conditions, the calculation was performed sequentially from time: 0 to time: 60 minutes to calculate the distribution of potential and current, and the concentration distribution of chemical species and reaction product concentration at that time.
In the calculation of the different metal contact corrosion, it was assumed that an anode reaction in which metal was dissolved and a cathode reaction in which oxygen was reduced occurred on zinc and iron. The anodic reaction of zinc, the anodic reaction of iron, and the reduction reaction of oxygen on zinc and iron are represented by formula (7), formula (8), and formula (9), respectively.
Zn → Zn ²⁺ + 2e ⁻ (7)
^{Fe → Fe 2+ + 2e - ····} (8)
2H ₂ O + O ₂ + 4e ⁻ → 4OH ⁻ (9)

  Based on this reaction formula, the dissolution amount of Zn was calculated from the current density at the interface between zinc and an aqueous solution, and this was integrated with respect to the calculation time to determine the corrosion amount of zinc after 60 minutes. In addition, ions generated by the corrosion reaction and ions present in the aqueous solution react to form a corrosion product. The amounts of oxides and hydroxides (corrosion products) formed in the solution at this time were determined by chemical equilibrium calculation. Table 2 shows the chemical reactions and equilibrium constants considered.

  In order to evaluate the validity of these numerical calculation results, corrosion experiments were performed under the same conditions as the numerical calculation. FIG. 5 shows a schematic diagram of a test piece in which 10 mm × 10 mm × 5 mmt zinc and iron (both purities 99.999%) used in the corrosion experiment are fixed in contact with each other. A NaCl aqueous solution having the same concentration and the same thickness as the numerically calculated conditions was formed on the surface of the test piece. In order to spread the solution as uniformly as possible on the metal surface, first, an aqueous NaCl solution diluted to 5 times is dropped on the surface of the final volume 5 times to spread it uniformly, and then the room temperature and relative humidity are immediately adjusted to RH98. The test piece was placed in a thermo-hygrostat set at% (equivalent to 0.6 mol / L), RH 93% (equivalent to 1.2 mol / L), and RH 85% (equivalent to 2.0 mol / L). A film of NaCl aqueous solution was formed on the test piece. By using a 5-fold diluted solution, repelling due to a small amount of solution was suppressed. And after putting a test piece into a constant temperature and humidity chamber, water was evaporated and it reduced uniformly as much as possible to the thickness of the target 1/5 NaCl aqueous solution.

  After the test piece was corroded for 60 minutes in a constant temperature and humidity chamber, the surface potential distribution was measured using a scanning Kelvin probe. The measurement environment was maintained at a relative humidity of room temperature and a relative humidity of 98%, 93% or 85% so as not to change the corrosion state. After completion of the measurement, the test piece was immediately washed and dried, and then the central part of the test piece shown in A-A ′ in FIG. 5 was cut in the longitudinal direction to show the corrosion state from the cross section by a scanning electron microscope (SEM). The amount of zinc corrosion was determined from the thickness.

Table 3 shows the conditions of the NaCl aqueous solution used in the calculations and experiments, the parameters for considering the influence of the corrosion products used in the calculations, and a comparison of the results of the calculations and the experiments. In the calculation, an electrolyte solution located in the vicinity of the interface between the electrolyte solution and the metal is separated from the interface between the electrolyte solution and the metal with an electrolyte solution existing in a region from 1/100 of the thickness (mm) of the electrolyte solution. did. For the electrolyte solution other than the vicinity of the interface, the electrical conductivity calculated from the solute concentration was used.
In addition, as for the potential distribution, when all 20 points were compared every 1 mm in the longitudinal direction of the test piece, the calculation results were all within ± 30 mV of the experimental result. Although not applicable, ○ when the calculation results are all within the range of ± 50 mV relative to the experimental results (high accuracy), and even when some of the calculation results are outside the range of ± 50 mV relative to the experimental results Evaluation was made as x (low accuracy).
Corrosion amount distribution is ◎ (particularly high accuracy) and ◎ when (calculated corrosion amount) / (experimental corrosion amount) = 0.75 to 1.25 at all 20 measurement points. Is not applicable, but (calculation amount) / (experimental corrosion amount) = 0.5 to 1.5 (high accuracy), (calculation amount) / (experimental corrosion amount) is The case where it was outside the range of 0.5 to 1.5 even at some measurement points was evaluated as x (low accuracy).

In all the inventive examples, the distribution of potential and corrosion amount could be predicted with high accuracy for different solution concentrations and solution thicknesses. In addition, α, which is a parameter that takes into account the influence of corrosion products, is set to an appropriate range. In 1 to 7, the distribution of potential and corrosion amount could be predicted with particularly high accuracy.
On the other hand, in the comparative example in which the decrease in the electrical conductivity of the electrolyte solution due to the formation of the corrosion product, that is, the decrease in the corrosion rate due to the deposition of the corrosion product is not taken into account, the calculation result of the distribution of the potential and the corrosion amount is accurate It was low and did not agree with the experimental results.

Example 2
The crevice corrosion of the steel material was predicted by numerical calculation based on the above embodiment. It was assumed that a steel having a gap having a length of 20 mm and a gap height d was immersed in an NaCl aqueous solution having a concentration of 0.1 mol / L, 0.6 mol / L, or 2.0 mol / L. FIG. 6 shows a model diagram of the shape used in this example. In the numerical calculation model, it was assumed that only the inner wall portion in the gap corroded and the outside of the gap did not corrode.
Here, in the region Ω in the gap of FIG. 6, it is assumed that the equation of mass transport of Equation (5) is established, and as a boundary condition for solving Equation (5), the boundary AB between the steel and the solution in the gap and In CD, the anodic polarization curve obtained from the relationship between the electric potential and the electric potential measured in a 0.6 mol / L NaCl aqueous solution was used for the steel material, and the electric potential and Current density distribution and concentration of chemical species in aqueous solution were calculated.
The solution concentration at the entrance of the gap was assumed to be equal to the solution concentration of the bulk aqueous solution. In this model, it was assumed that the cathode reaction occurred on the metal surface outside the gap, and only the anode reaction was considered.

Surface natural immersion state (self potential, 0.59 V vs SCE) specimens performs the computation in two conditions of the active dissolved state (0.50 V vs SCE), the Fe ²⁺ ions by the anode reaction, the law of Fraday It was assumed that the solution was supplied to the solution in the gap.
The region Ω of the aqueous solution in the gap in FIG. 6 was a NaCl aqueous solution, and the initial concentration and diffusion coefficient of the chemical species considered in this solution were set as shown in Table 4.

Then, by solving the mass transport equation of equation (5) using the given polarization curve as a boundary condition, the calculation is performed sequentially from time: 0 to time: 336 hours to calculate the potential and current distribution, The concentration of the chemical species and the concentration distribution of the reaction product at that time were calculated. Table 5 shows the equilibrium reaction considered in the equilibrium calculation.
In this example, only iron hydroxide Fe (OH) ₂ was considered as a corrosion product.

  Considering the thinning (corrosion amount) due to corrosion inside the crevice using Faraday's law using the electrical conductivity obtained without considering the influence of conventional corrosion products, and considering the influence of corrosion products Then, the calculation was performed by correcting the electrical conductivity of the electrolyte solution located in the vicinity of the interface between the electrolyte solution and the metal.

Moreover, the corrosion test of the steel for verifying the validity of the above numerical calculation was performed. As the steel, model steel having a chemical composition C: 0.20 mass%, Mn: 0.40 mass%, S: 0.005 mass%, and P: 0.005 mass% having a ferrite-pearlite structure equivalent to AISI 1020 steel was used. The surface of this steel material was polished to # 1000 using SiC paper, and a gap was formed by overlapping the polished surfaces with an insulating spacer interposed therebetween. The prepared gap test piece was immersed in various concentrations of NaCl aqueous solution, immersed for 336 hours, and then the sample was disassembled to investigate corrosion in the gap. The test piece was embedded in the resin, and the central part in the width direction of the gap was observed by SEM from the cross section, whereby the distribution of the erosion depth due to the corrosion of the steel surface in the gap was measured over the entire length of 20 mm at intervals of 2 mm from the entrance of the gap.
Table 6 shows a comparison between the results of the numerical calculation and the corrosion test in this example. In the calculation, the electrolyte solution located in the vicinity of the interface between the electrolyte solution and the metal was an electrolyte solution existing in the region from the interface between the electrolyte solution and the metal to [1/100 of the gap height (mm)]. For the electrolyte solution other than the vicinity of the interface, the electrical conductivity calculated from the solute concentration was used.
Here, the distribution of the corrosion amount indicates that (calculation corrosion amount) / (experimental corrosion amount) = 0.75 to 1.25 at all measurement points. Although not applicable, (calculation corrosion amount) / (experimental corrosion amount) = 0.5 to 1.5 (high accuracy), (calculation corrosion amount) / (experimental corrosion amount) The case where it was outside the range of 0.5 to 1.5 even at some measurement points was evaluated as x (low accuracy).

In any of the inventive examples, the distribution of the corrosion amount could be predicted with high accuracy regardless of the gap shape or the solution concentration of the test piece, regardless of whether the surface of the test piece was in a naturally immersed state or an active dissolution state. In addition, α, which is a parameter that takes into account the influence of corrosion products, is set to an appropriate range. In 1 to 7, the distribution of the corrosion amount could be predicted with particularly high accuracy.
On the other hand, in the comparative example in which the decrease in the corrosion rate due to the accumulation of the corrosion product is not taken into account, the accuracy of the calculation result of the distribution of the corrosion amount is low and does not match the experimental result.

  According to the method for predicting corrosion of a metal by numerical analysis of the present invention, it is possible to predict corrosion of a metal in contact with an electrolyte solution with higher accuracy by numerical analysis. Therefore, the design of metal structures and products, the evaluation of quality deterioration due to corrosion of materials and structures, and the development of corrosion-resistant materials can be carried out optimally and with high accuracy, and this is an extremely useful technology in the industry.

Claims (6)

A method for predicting corrosion of a metal in contact with an electrolyte solution by numerical analysis,
Creating a calculation model of a system in which a metal and an electrolyte solution are in contact;
A step of calculating the potential distribution of the electrolyte solution and the metal at a predetermined time by numerical calculation using the calculation model;
Have
In performing the numerical calculation, the electric conductivity of the electrolyte solution located in the vicinity of the interface between the electrolyte solution and the metal in the calculation model is set to a value lower than the electric conductivity calculated from the solute concentration of the electrolyte solution. Simulating the effect of corrosion products deposited on the surface of the metal during the corrosion of the metal on the corrosion rate, calculating the potential distribution of the electrolyte solution and the metal ,
Dividing the electrolyte solution and metal of the calculation model into a plurality of cells,
In the calculation model, the electric conductivity of the cell j of the electrolyte solution located in the vicinity of the interface between the electrolyte solution and the metal is σ _j , the volume of the cell j is V _0j , and the volume occupied by the corrosion product in the cell j is V _cj And σ ₀ , σ _j , V _0j and V _cj satisfy the relationship of the following equation, where σ ₀ is the electrical conductivity calculated from the solute concentration of the electrolyte solution : Method.
σ _j = α × σ ₀ × (1-V _cj / V _0j )
However, 0.10 ≦ α ≦ 5.00. The metal corrosion prediction method according to claim 1 , wherein the metal is one or more metals selected from zinc, iron, aluminum, copper, and nickel, or an alloy containing the metal as a main component. 3. The metal corrosion according to claim 1, wherein the corrosion is a dissimilar metal contact corrosion in which two kinds of metals come into contact with each other and the corrosion of one metal is accelerated by a potential difference between the metals. Prediction method. A method for designing a steel structure, wherein the material is selected by predicting corrosion resistance by the method of predicting corrosion of a metal by numerical analysis according to any one of claims 1 to 3 . A method for designing a steel structure, wherein corrosion resistance is predicted by the metal corrosion prediction method according to the numerical analysis according to any one of claims 1 to 3 to design rust prevention performance. The corrosion prediction program which makes a computer perform the corrosion prediction method of the metal by the numerical analysis of any one of Claims 1-3 .

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