JP6544403B2 - Method of predicting corrosion of metals by numerical analysis - Google Patents

Description

  The present invention relates to a method of predicting corrosion of metallic materials widely used in the industrial field by numerical analysis. In the present invention, specifically, in order to predict the quality and performance life of a structure, numerical distribution (numerical simulation) is used to calculate potential distribution and current density distribution in an electrolyte solution which is a corrosive medium, It is a technology related to corrosion evaluation that predicts the corrosion resistance of metals.

  Conventionally, the corrosion resistance of a metal material is evaluated by a corrosion promotion test, an air exposure test, or an electrochemical measurement. For example, in the case of evaluating the corrosion resistance of steel materials, test pieces and actual parts are obtained by a salt spray test (SST) or a combined cycle test (CCT: combined cyclic corrosion test) combining the wet and dry atmospheric conditions with this. It is carried out to promote the corrosion of the steel and to judge the superiority or inferiority of the corrosion resistance in a short time. On the other hand, because such a corrosion promotion test is a severe corrosive environment compared to the environment in which steel materials are actually used, it is not possible to reproduce the corrosion resistance hierarchy of steel materials of different corrosion types and types in actual use environments. There is. Therefore, air exposure tests for evaluating corrosion resistance are widely performed by installing test pieces outdoors or indoors and corroding them in an environment close to actual use. However, since the exposure test has a long time to determine the superiority or inferiority of the corrosion resistance, it is not suitable for the development of a corrosion resistant material for which an early judgment of the corrosion resistance is required.

  On the other hand, with recent development of computer technology and speeding up of calculation, attempts to predict corrosion phenomena by computer simulation are actively performed. If corrosion of metals can be predicted by numerical analysis, analysis of the causes of corrosion and corrosion mechanisms that are difficult to analyze in experiments may be possible, and the corrosion resistance of metals can be evaluated in a short time.

  Patent document 1 and patent document 2 consider the metal in a corrosion medium as a continuum which consists of a plurality of analysis segments electrically connected, and determine the polarization characteristic of the metal concerned numerically. Disclose. This method considers the corrosion medium in the model as a Laplace field according to the Laplace equation, performs numerical analysis, and calculates the potential distribution of the metal that is the target structure and the inflow and outflow of current to obtain the driving force for corrosion and the corrosion rate. Is a method of estimating

  In addition, Non-Patent Document 1 reports a technology for analyzing the corrosion of a seawater pump by the boundary element method. Here, the potential distribution and current distribution in the corrosive medium seawater are determined by solving the Laplace equation with the polarization curve of cast iron (FC200) as the pump material as the boundary condition, and the potential at the interface between cast iron and seawater and We propose a model to predict the corrosion of cast iron from the current density. Here, the electrical conductivity in the aqueous solution uses the value obtained from the experiment.

  Non-Patent Document 2 simulates the corrosion of reinforcing bars in concrete. The mass transfer of corrosion factors such as chloride and oxygen in concrete is predicted, and furthermore, the corrosion current density is obtained by the Tafel equation to predict the corrosion of reinforcing bars. In this model, mass transfer is calculated using the diffusion equation by treating 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 galvanizing and dissimilar metal contact corrosion under a thin water film of a base steel sheet. In this model, the formation and distribution of corrosion products of zinc and steel are calculated every minute time, and the eluted metal ions calculated from the anode reaction given as the boundary conditions are the chemical species by mass transfer calculation and The precipitation reactions that react and form metal oxides and hydroxides are predicted by equilibrium calculations using equilibrium constants, which predict the distribution and amount of various corrosion products.

JP, 2008-249562, A Japanese Patent Application Publication No. 2008-32421

Everla Times No. 223 (2009-3) 37-45 Concrete Engineering Annual Proceedings, Vol. 24, No. 1, (2002) 831-836 Materials and Environment, Vol. 61, (2012), 376-383.

  Various methods can be considered for modeling corrosion analysis and numerical analysis methods. Among these, the current density corresponding to the corrosion rate and the distribution of the potential serving as the driving force of the corrosion reaction in the region assuming the electrolyte solution which is a corrosive medium are widely practiced. It is a method to obtain by solving The corrosion of metal is evaluated by the inflow and outflow of the current at the interface between the electrolyte solution and the metal and the potential difference. As a solution method of such an equation, a difference method, a finite element method, or the like is used as a continuum model in which the electrolyte solution region is elementized. The polarization characteristics of the metal, which is the relationship between the electric potential of the metal surface in the electrolyte solution and the current density, are given as boundary conditions, and the distribution of the electric potential and current in each element, and the material balance in the element and at the boundary are calculated.

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

  In the actual corrosion phenomenon, the corrosion not only proceeds as dissolution (ionization) of the metal, but also involves the formation of so-called "rust" or "corrosion products", which form oxides and hydroxides of the metal. Hereinafter, precipitates of dissolved metal (oxides, hydroxides, etc.) are referred to as corrosion products. Corrosion products have a significant effect on the corrosion rate of metals. For example, zinc active in an electrolyte solution exerts excellent corrosion resistance in an atmospheric environment because the corrosion products of zinc cover the surface to reduce the corrosion rate of metallic zinc.

  In Patent Document 1 and Patent Document 2, the corrosive medium in the model is analyzed as Laplace field according to the Laplace equation, and soil is taken as an example of the medium, and the electrical physical property value is given as a specific resistance of 5000 Ω · cm. In the present invention, when the medium is an aqueous solution, the corrosion polarization characteristics in the aqueous solution can be evaluated if the electric conductivity of the aqueous solution is given. However, the electrical property values given here are conditions given only from the corrosive environment, and the effects of the formation of corrosion products accompanying corrosion are not taken into consideration.

  Also in Non-Patent Document 1 and Non-Patent Document 2, the electric conductivity is given to the corrosion medium as seawater and concrete, and the Laplace equation and the equation of mass transport are solved. However, the prediction of the formation of corrosion products and the consequent effects on corrosion media and corrosion are not taken into account in the calculations.

  Non-Patent Document 3 calculates potential distribution and current density distribution in consideration of mass transport in dissimilar metal catalytic corrosion of zinc and steel in an aqueous solution. In this document, the amount of oxides and hydroxides produced is calculated using the concentrations of various types of chemical species in the aqueous solution which is the corrosive medium and the equilibrium constants of the precipitation reactions of oxides and hydroxides. And predict the distribution. However, changes in the electrochemical properties at the interface of the metal with the aqueous solution due to the formation of corrosion products were not considered, and it was not possible to accurately estimate the corrosion rate of the metal in the presence of the corrosion products.

  As described above, in the prior art, the influence on the corrosion rate due to the deposition of the 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 the numerical analysis. It was not.

  Then, an object of the present invention is to provide a corrosion prediction method of metal by numerical analysis which can predict corrosion of metal in contact with an electrolyte solution with high accuracy by numerical analysis in view of the above-mentioned subject.

  In order to solve the above problems, the present inventors focused attention on the consideration of the influence on the corrosion rate due to the deposition of metal corrosion products on the metal surface in numerical calculation. Since the corrosion product is insulating, the formation of the corrosion product has focused on the fact that the overvoltage necessary for the dissolution of the metal surface is increased. As a result, by measuring the polarization curve of the metal deposited on the surface by the corrosion product formed in the target corrosive environment and using it as the boundary condition of the metal / solution interface, the formation of the corrosion product is actually realized. In light of the fact that potential distribution and current density distribution in the case involved can be appropriately predicted, the present invention has been completed.

The essential features of the present invention completed based on the above findings are as follows.
(1) A method of predicting corrosion of a metal in contact with an electrolyte solution by numerical analysis,
Measuring a polarization curve, which is a relationship between the potential E of the metal surface in the electrolyte solution and the current density i, in a state in which a corrosion product is present on the metal surface;
Determining the potential distribution in the electrolyte solution at a predetermined time in a system in which the metal and the electrolyte solution are in contact, using the polarization curve as a boundary condition by numerical calculation;
The metal corrosion prediction method by numerical analysis characterized by having.

(2) The polarization curve is measured under various conditions in which the deposition amount of the corrosion product on the metal surface is different,
Select the polarization curve when the corrosion on the metal surface is in steady state,
The metal corrosion prediction method by numerical analysis according to the above (1), wherein the selected polarization curve is used as the boundary condition.

  (3) The method according to the above (1) or (2), further including the step of obtaining the current density distribution in the electrolyte solution at the predetermined time by numerical calculation from the potential distribution in the electrolyte solution at the predetermined time. Prediction method of metal corrosion by numerical analysis of.

  (4) Of the current density distribution in the electrolyte solution, the current density in the vicinity of the interface with the metal in the electrolyte solution is used to calculate the amount of corrosion of the metal at the predetermined time according to Faraday's law. The metal corrosion prediction method by numerical analysis as described in said (3) which further has a process.

  (5) Any one of the above (1) to (4), 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. Method of predicting corrosion of metal by numerical analysis described in paragraph.

  (6) Any one of the above (1) to (5), wherein the form of the corrosion is contact corrosion by dissimilar metals in which two metals are in contact and the corrosion of one metal is accelerated by the potential difference between the metals. Method of predicting corrosion of metal by numerical analysis described in paragraph.

  (7) The method of predicting corrosion of metal by numerical analysis according to (6), wherein the two metals are a combination of zinc or zinc plating and iron or steel.

  (8) A method for designing a steel structure, characterized in that corrosion resistance is predicted by the method of predicting corrosion of a metal according to any one of the above (1) to (7), and the material is selected.

  (9) A method of designing a steel material characterized by predicting corrosion resistance by the method of predicting corrosion of a metal according to any one of the above-mentioned numerical analysis (1) to (7) to design anticorrosion performance.

  (10) A corrosion prediction program that causes a computer to execute a method for predicting metal corrosion by numerical analysis according to any one of (1) to (7) above.

  According to the method of predicting corrosion of metal by numerical analysis of the present invention, since the numerical calculation taking into consideration the influence on the corrosion rate due to the deposition of the corrosion product of the metal on the metal surface is performed, the corrosion of the metal in contact with the electrolyte solution It is possible to predict with higher accuracy by numerical analysis.

3 is a flowchart of a method of 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 continuum model of the dissimilar metal contact corrosion of two metals. It is a figure explaining formation of a metal corrosion product. It is a figure which shows the model shape of the dissimilar metal contact corrosion of zinc and iron used in the Example. (A) is a model top view of the experiment of the dissimilar metal contact corrosion of zinc and iron performed in the Example, (b) is an A-A 'sectional view of (a).

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

  FIG. 1 is a flowchart of a method of predicting corrosion of metal by numerical analysis according to an embodiment of the present invention. FIG. 2 is a model diagram of a system in which a metal and an electrolyte solution are in contact for applying such numerical analysis, and the electrolyte solution is in a state in which two metals A and B are in contact and electrically shorted. Shows an example of a model of heterogeneous metal contact corrosion in which

  In the model shown in FIG. 2, a thin film of a metal and an electrolyte solution in contact with the metal is divided into two-dimensional lattice-like regions (hereinafter referred to as "cells") in these cross sections, and the boundary element method or The potential, current density, concentration of chemical species, etc. in each cell are calculated using the finite element method etc. to predict the amount of corrosion and the precipitation reaction of the compound. Alternatively, thin films of metal and electrolyte solution may be partitioned into three-dimensional microcube regions, which may be used as cells. Note that since metal corrosion can be predicted by determining the potential distribution in the electrolyte solution, the metal may not be divided into cells, and only the electrolyte solution may be divided into cells. Hereinafter, a typical example of the calculation procedure will be described.

  First, in step S1, the shapes of the metal and the electrolyte solution to be a corrosive medium are set, and the initial concentrations and physical property values of chemical species in the electrolyte solution are set. Physical property values include the diffusion coefficient used to calculate the transport of chemical species and the charge of ions.

  Next, in step S2, boundary conditions used for numerical analysis are set. Here, as boundary conditions at the interface between the metal and the electrolyte solution, conventionally, a polarization curve represented by a function i = f (E) of the current density i of the metal surface in the electrolyte solution and the potential E was set. . On the other hand, in the present embodiment, the measurement of the polarization curve is performed in the state where a corrosion product exists on the metal surface, and the polarization curve obtained as a result is used as the boundary condition. Details will be described later.

ここで、Фは電位、C_x, D_x, z_x, u_x, R_x はそれぞれ化学種xの濃度、拡散係数、価数、移動度および反応量を表し、Fはファラデー定数を表す。 Next, in step S3, the potential distribution in the electrolyte solution at a certain time t is determined by numerical calculation. For numerical calculation, the Laplace equation shown in equation (1) or the Nernst-Plank equation shown in equation (2) is solved.

Here, Ф represents the potential, C _x , D _x , z _x , u _x , and R _x each represent the concentration of the chemical species x, diffusion coefficient, valence, mobility and reaction amount, and F represents the Faraday constant.

  At this time, at the interface between the metal and the electrolyte solution, the interface between the electrolyte solution and the gas phase and the end of the electrolyte solution are defined with the polarization curve measured in the presence of the corrosion product on the metal surface as the boundary condition. Then, numerical calculation is performed with the condition that no current flows in or out as the boundary condition.

  By discretizing equation (1) and solving, the potential in each cell at a certain time t can be calculated, that is, the potential distribution in the electrolyte solution at a certain time t can be obtained. In the case where the metal is divided into cells, the potential of each cell in the metal, that is, the potential distribution in the metal can also be obtained by the calculation.

  In addition, in order to solve the Nernst-Plank equation for mass transport shown in equation (2), it is necessary to satisfy equation (1) for each ion and molecule in each cell in the electrolyte solution, as well as electrically neutral conditions. It becomes a simultaneous equation by adding the equation of. By solving the simultaneous equations, in addition to the potentials of the cells at a certain time t, the concentrations of ions and molecules of the cells are also derived as calculation results. As a result, in addition to the potential distribution in the electrolyte solution at a certain time t, the concentration distribution of chemical species can be obtained.

Next, in step S4, from the potential distribution in the electrolyte solution at a certain time t, the current density distribution in the electrolyte solution at that time t is determined by numerical calculation. Specifically, the current density in each cell is calculated according to Ohm's law of equation (3), that is, the current density distribution in the electrolyte solution at a certain time t can be obtained. Here, σ represents the electrical conductivity of the electrolyte solution. The electrical conductivity σ is a function of the concentration of chemical species. When the potential distribution is determined by equation (2), the concentration distribution of chemical species is also obtained. Therefore, the electric conductivity for each cell obtained from the concentration of chemical species for each cell is applied to equation (3). When the potential distribution is determined by equation (1), the movement of chemical species in the solution is not taken into consideration, and the uniform electrical conductivity determined from the concentration of the solution is defined as the electrical conductivity of each cell. Apply In the case where the potential distribution in the metal is obtained, the current density distribution in the metal can also be determined by the equation (3).

  Although a typical calculation method of potential and current density is shown here, in the present invention, the method of determining the potential and current density by calculating the electrolyte solution as a potential field is not limited thereto. .

In step S5, it is determined whether the time t at which the numerical calculation has been performed exceeds the preset time t _set . If it does not exceed the set time t _set , Δt is added and the process returns to step S3 to calculate the potential distribution and current density distribution in the electrolyte solution at a new time t. If it exceeds the set time t _set , the process proceeds to step S6.

In step S6, of the current density distribution in the electrolyte solution, the current density in the vicinity of the interface with the metal in the electrolyte solution is used to corrode the metal at the set time t _set according to Faraday's law shown in equation (4). Calculate the quantity. Here, n and z are the amount and the valence of the metal (chemical species) dissolved by corrosion, and t is time. When the current density distribution in the metal is obtained, the current density near the interface with the electrolyte solution in the metal may be used. That is, the current density of the metal or electrolyte solution near the metal / electrolyte solution boundary may be used. Here, “in the vicinity of the interface” means a range in which a current density that can be considered equivalent to the current density flowing in and out of the interface between the electrolyte solution and the metal can be obtained, specifically, within 10 mm from the interface More preferably, it is an area of the electrolyte solution or metal within 1 mm.

  In the actual calculation, by repeating steps S3 and S4, the current density in the vicinity of the interface with the metal in the electrolyte solution is obtained at various times, so that the minute time is calculated using equation (4). The amount of corrosion is determined for each time, and this amount is integrated to obtain the amount of corrosion after a predetermined time has elapsed and the distribution of the amount of corrosion within the metal surface.

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

  Here, the potential distribution, the current density distribution, and the amount of corrosion 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 they have extremely low electrical conductivity compared to metals and electrolyte solutions. Formation and deposition of such a substance in the electrolyte solution changes the potential distribution and current density distribution in the electrolyte solution and the metal. As a result, the corrosion reaction and formation of corrosion products after that time also change.

  Therefore, the present invention provides a method of predicting metal corrosion by numerical analysis that reproduces the influence of the generated corrosion products on such a corrosion phenomenon.

FIG. 3 shows a diagram illustrating the formation of metal corrosion products. Here, when metal A generates metal ion A ⁺ in the solution by corrosion, the concentration of A ⁺ increases with time, and from the relationship with the solubility product of oxide or hydroxide, oxide AO or Suppose that hydroxide A (OH) is formed. As mentioned earlier, many of the corrosion products are insulators or semiconductors, which are electrochemically reactive as compared to the metal before corrosion. Therefore, the corrosion reactivity differs between the interface between the metal surface on which the corrosion product is not deposited and the electrolyte solution and the interface between the metal surface on which the corrosion product is deposited and the electrolyte solution.

  Therefore, when calculating the distribution of the potential Φ and the distribution of the current density i in the electrolyte solution by numerical calculation, the function i = f (E) of the current density i of the new metal surface in the electrolyte aqueous solution and the potential E Instead of using a certain polarization curve as the boundary condition, the polarization curve i = g (E) measured on the metal surface on which the corrosion product is deposited is used as the boundary condition. This makes it possible to obtain a potential distribution or current density distribution close to the corrosion phenomenon actually observed.

  That is, in step S2, the polarization curve [i = g (E)], which is the relationship between the potential E of the metal surface in the electrolyte solution and the current density i, is measured in the state where corrosion products are present on the metal surface. This polarization curve is used as a boundary condition at the interface between the metal and the electrolyte solution.

  It is necessary to measure the polarization curve using the same metal as the target metal of numerical analysis, and further measure the electrolyte concentration of the electrolyte solution to be target of numerical analysis in the electrolyte solution of the same electrolyte concentration. Is preferred.

  It is preferable from the viewpoint of enabling highly accurate prediction that the corrosion condition of the metal for which the polarization curve is measured is the same corrosion condition (electrolyte concentration and thickness of the electrolyte solution) as the calculation target.

  The amount and thickness of corrosion products present on the metal surface when measuring the polarization curve (ie, the corrosion time of the metal whose polarization curve is measured) are not particularly limited. As a boundary condition, the polarization curve measured on the metal surface on which the corrosion product is deposited is higher than in the boundary condition based on the polarization curve measured on the new metal surface, regardless of the amount of the corrosion product. Accurate prediction is possible. However, from the viewpoint of realizing more accurate prediction, it is preferable to use the polarization curve measured when the corrosion on the metal surface is in the steady state as the boundary condition.

  In general metal corrosion, as described above, the corrosion product starts to deposit on the metal surface after a predetermined time, and the amount of deposition increases with time, while increasing to a certain amount of deposition. The deposition of new corrosion products and removal and dissolution of existing corrosion products become comparable, and the deposition amount approaches saturation. It is preferable to use the polarization curve measured in this state as the boundary condition. However, the deposition amount of corrosion products and the corrosion time for the steady state corrosion of the metal surface vary depending on the environment to which the metal is exposed. For example, the polarization curves of the test pieces which have been subjected to a plurality of corrosion times are measured, and the polarization curve at the point when it is judged that the change of the polarization curve in the front and back corrosion time is eliminated is selected.

  The polarization characteristic used as the boundary condition may be either an internal polarization curve or an external polarization curve. Although the internal polarization curve can not be obtained by experiment, it can be determined by extrapolating from the external polarization curve, and is more preferable because more detailed electrode reactions can be given as boundary conditions.

  In the embodiment shown in FIG. 1, the same boundary conditions are used when obtaining the potential distributions at all times.

  The metal to which the method for predicting corrosion of a metal according to the numerical analysis of the present invention is applied is not particularly limited, but one or more metals selected from zinc, iron, aluminum, copper and nickel, or an alloy containing the metal as a main component Can be suitably applied. Further, it is particularly effective to apply the method of predicting corrosion of metal by numerical analysis of the present invention to contact corrosion by dissimilar metals as shown in FIG. In this case, the corrected polarization curve of metal A is taken as the boundary condition at the interface between metal A and the electrolyte solution, and the corrected polarization curve of metal B is taken as the boundary condition at the interface between metal B and the electrolyte solution. Examples of dissimilar metal contact corrosion include combinations of zinc or zinc plating with iron or steel.

  The deposition of corrosion products on metal surfaces is clearer in the atmospheric environment than in the electrolyte solution and the effect is also greater. Therefore, in order to predict atmospheric corrosion of metal, it is suitable for numerical calculation in which the thickness of the electrolyte solution on the metal surface is less than 10 mm.

  In the design of a steel structure, it is preferable to select a material by predicting the corrosion resistance by the method of predicting corrosion of a metal by the above-described numerical analysis. That is, when the amount of corrosion predicted by the numerical calculation according to the present invention can be considered to be sufficiently smaller than the required life of the steel structure with respect to the metal material, using the metal material as the material of the steel structure it can.

  Further, in the method of designing a steel material, it is preferable to design the anticorrosion performance by predicting the corrosion resistance by the method of predicting corrosion of a metal by the above-described numerical analysis. By changing the composition and structure of the steel material, the polarization curve and the component eluted in the electrolyte also change, so the amount of corrosion of the metal predicted by calculation also differs. That is, the composition and structure of the steel material can be designed such that the corrosion amount of the metal predicted by the numerical calculation according to the present invention becomes sufficiently smaller than the required life of the steel material.

(program)
An object of the present invention is to provide a system or apparatus with a storage medium storing a program code of software for realizing each process of the above-described embodiment, and a computer (or CPU or MPU) of the system or apparatus uses the storage medium. It is also achieved by reading and executing the stored program code. In this case, the program code itself read out from the storage medium implements the functions of the above-described embodiments, and the storage medium storing the program and the program code constitutes the present invention.

  Here, as a storage medium for storing the program code, for example, a flexible disk, hard disk, ROM, RAM, magnetic tape, non-volatile memory card, CD-ROM, CD-R, DVD, optical disk, magneto-optical disk, MO, etc. Is 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 heterogeneous metal catalytic corrosion of zinc and iron in contact with each other surface-covered with 0.6, 1.2, or 2.0 mol / L NaCl aqueous solution with a thickness of 0.1 mm was predicted by numerical calculation. FIG. 4 shows a model of the shape used in the present embodiment. The region Ω is the region of the aqueous NaCl solution, and the concentration of Na ⁺ and Cl ⁻ is set to 0.6, 1.2, 2.0 mol / L as the initial concentration of the species, and the initial concentration of the species to be considered in this solution Concentrations and diffusion coefficients were set as shown in Table 1. In the region Ω, it was assumed that the mass transport equation of equation (2) holds, and for each chemical species, diffusion due to concentration gradient in aqueous solution and migration due to charge were considered. The influence of convection is not considered here.

  As a boundary condition for solving Formula (2), the polarization curve measured by the method shown in "the boundary condition" of Table 2 was adopted.

That is, in the invention example, the polarization curve of the metal corroded in advance was used as the boundary condition. In the present example, the method of measuring the polarization curve of the corroded surface was applied only to zinc which is more likely to be corroded by the potential than iron. For iron, on the other hand, the polarization curve of the metal surface not intentionally corroded was used as the boundary condition under all the calculation conditions. In order to make the first corroded zinc, zinc was corroded by exposing it to a 0.6, 1.2, or 2.0 mol / L NaCl solution for 24 hours with the same thickness of 0.1 mm as the calculated corrosion conditions. After that, the surface was lightly rinsed and dried. Corrosion products of zinc were deposited on the surface of the zinc sample. Next, the polarization curve of the zinc on which the produced corrosion product was deposited was measured respectively in the same 0.6, 1.2 or 2.0 mol / L NaCl aqueous solution as the calculated corrosion conditions. Thus, the anodic polarization curve i _{a — z n} = g (E _{a — z n} ) of corroded zinc measured under each condition was taken as the boundary condition of boundary 1. On the other hand, the boundary conditions at the interface of the iron and the electrolyte solution was the cathode polarization curve i _c _ _Fe = f iron that has not been intentionally corrode (E _c _ _Fe) and the boundary conditions of the boundary 2. In the other boundaries, no current flow (∂Ф / ∂Ф n = 0: where n represents a unit vector) was used as the boundary condition.

In the comparative example, the polarization curves of surface-polished zinc and iron (in the absence of corrosion products) were measured in the same 0.6, 1.2 or 2.0 mol / L NaCl aqueous solution as the calculated corrosion conditions, respectively. Each was measured. Thus, the anodic polarization curve i _a _ _{z n} = f (E _a _ _{z n} ) of zinc measured under each condition is taken as the boundary condition of boundary 1, and the cathodic polarization curve i _c _ _Fe = f (E _c _ _Fe ) is the boundary condition of boundary 2. The boundary conditions at the iron / electrolyte solution interface are the same as those of the inventive example. In the other boundaries, no current flow (∂Ф / ∂Ф n = 0: where n represents a unit vector) was used as the boundary condition.

  Under this condition, calculation was sequentially performed from time 0 to time 1440 min to calculate potential distribution and current density distribution.

In the calculation of heterogeneous metal catalytic corrosion, it was assumed that an anodic reaction in which metal dissolves on zinc and iron and a cathodic reaction in which oxygen is reduced. The anodic reaction of zinc, the anodic reaction of iron and the reduction reaction of oxygen on zinc and iron are represented by formulas (5), (6) and (7), respectively.
^{Zn → Zn 2+ + 2e - ····} (5)
^{Fe → Fe 2+ + 2e - ····} (6)
2H ₂ O + O ₂ + 4 e ⁻ → 4 OH ⁻ ··· (7)

  Based on this reaction equation, the amount of dissolution of Zn is calculated using Faraday's law from the current density at the interface between zinc and aqueous solution, and this is integrated with respect to the calculation time, and zinc after 60, 300, 1440 min. The amount of corrosion was determined.

  In order to evaluate the validity of these numerical calculation results, corrosion experiments were conducted under the same conditions as the numerical calculation. FIG. 5 shows a schematic view of a test piece in which 10 mm × 10 mm × 5 mmt of zinc and iron (both having a purity of 99.999%) used in the corrosion test are brought into contact with each other and fixed. An aqueous solution of NaCl having the same concentration and the same thickness as the conditions calculated numerically was formed on the surface of the test piece. In order to keep the thin salt water film once formed on the metal surface in a fixed state, the test piece is promptly room temperature, relative humidity is RH 98% (equivalent to 0.6 M), RH 93% (equivalent to 1.2 M), RH 85 The test piece was corroded by placing it in a constant temperature and humidity chamber set to% (equivalent to 2.0 M).

  The test piece was corroded in a constant temperature and humidity chamber for 60, 300 and 1440 minutes, and then the potential distribution on the surface was measured using a scanning Kelvin probe. The same temperature and relative humidity as in the constant temperature and humidity chamber were maintained during measurement with the Kelvin probe so as not to change the corrosion state. After the measurement is completed, the test piece is promptly washed with distilled water and dried, and then the central portion of the test piece shown in the cross-sectional view taken along line AA 'of FIG. From the above, the amount of corrosion of zinc was determined from the thickness by observing with a scanning electron microscope (SEM).

  Table 2 shows the corrosion conditions (concentration / thickness of NaCl aqueous solution, corrosion time) of the zinc / iron contact metal used in the calculation and experiment, the measurement conditions of the polarization curve with the boundary conditions, and these as boundary conditions. The comparison of the measurement result of the electric potential and corrosion amount by a result and experiment is shown. As for the potential, the surface of zinc to iron was compared every 20 mm in the longitudinal direction of the test piece, and all 20 points were compared. Table 2 shows the potential difference at the point of the largest deviation of potential in calculation and experiment. As a result, the case where all the calculated electric potentials of 20 points became the range of +/- 50mV with respect to an experimental result was made into (circle) and the case where not so was made into x. The corrosion amount distribution measures all 10 points at 1 mm intervals in the longitudinal direction of the test piece in the same way as the potential, with the area of Zn only, and the ratio of (calculation amount of corrosion) / (experimental corrosion amount) is shown in the table. Indicates the ratio of the points most diverged from 1 and ○, when the calculated amount of corrosion / (experimental amount of corrosion) = 0.5 to 1.5 at all 10 measurement points Was evaluated as x. With respect to No. 1 to No. 9 which is the scope of the present invention, the distribution of potential and corrosion amount could be predicted with high accuracy for different solution concentrations and solution thicknesses. However, as shown in the comparative example, when the polarization curve of the as-polished metal surface was used as the boundary condition, both the potential and the amount of corrosion largely deviated from the experimental results. The actual corrosion phenomenon could be predicted with high accuracy by applying the present invention using the polarization curve of the corrosion surface as the boundary condition in consideration of the change in polarization characteristics due to the formation of the corrosion product.

(Example 2)
Similar to Example 1, the dissimilar metal catalytic corrosion of zinc and iron in an atmospheric corrosive environment was predicted by numerical calculation. The shape shown in FIG. 4 was used as a calculation model of this example. The region Ω simulates sea salt adhering in an actual atmospheric environment, is a region of a solution having a composition simulating seawater, and the initial concentration of chemical species is set as shown in Table 3. The diffusion coefficients to be considered for the chemical species contained in the composition of this solution were set as shown in Table 4. In the region Ω, it was assumed that the mass transport equation of equation (2) holds, and for each chemical species, diffusion due to concentration gradient in aqueous solution and migration due to charge were considered. The influence of convection is not considered here.

  As a boundary condition for solving the equation (2), a polarization curve measured by the method shown in "boundary condition" in Table 5 was adopted.

That is, in the invention example, the polarization curve of zinc corroded by the actual exposure test was used as the boundary condition. Using the test piece shown in FIG. 5 same as Example 1, a test piece corroded in the air exposure test was produced. The exposure test was conducted in Minami-Toda-cho, Kawasaki-ku, Kawasaki-shi, Kanagawa Prefecture, and a test piece was placed under the eaves to prevent direct rain. A four week test was conducted, recording temperature and relative humidity every minute. The surface of the test piece after 2 weeks and the test piece after 4 weeks were lightly rinsed and dried. Next, with respect to zinc on which this corrosion product was deposited, polarization curves of zinc were measured in artificial seawater (manufactured by Yahata Pharmaceutical Co., Ltd., a solution for metal corrosion test). In this way, anodic polarization curves i _{a — z n} = g (E _{a — z n} ) of corroded zinc were obtained, respectively, and used as the boundary condition of boundary 1.

In the comparative example, the surface was polished was (nonexistent corrosion products) polarization curve _{i a _ zn = f (E} a _ zn) boundary conditions of the boundary 1 of zinc.

Incidentally, the invention examples, the comparative examples both of iron and the electrolyte solution at the interface boundary conditions, the iron that has not been intentionally corrosion cathodic polarization curve _{i c _ Fe = f (E} c _ Fe) of the boundary 2 Boundary conditions. In the other boundaries, no current flow (∂Ф / ∂Ф n = 0: where n represents a unit vector) was used as the boundary condition.

  In the actual exposure test, the temperature and humidity change continuously, and the concentration and average thickness of the aqueous solution film formed on the metal surface change accordingly. For this reason, also in numerical calculation, it is desirable to set the aqueous solution concentration and thickness accompanying the change of temperature and relative humidity for every calculation unit time, and to integrate a calculation result. However, in the present embodiment, in order to reduce the calculation load, the calculation was performed with the aqueous solution concentration kept constant at almost the same concentration as the artificial seawater. Moreover, the electric conductivity σ was made to correspond to the concentration of artificial seawater, considering the chemical components contained in artificial seawater as the chemical components to be considered.

  Under this condition, the calculation was carried out in the same manner as in Example 1, and the dissimilar metal catalytic corrosion of zinc and iron in contact with each other in the four week exposure test was predicted by numerical calculation. That is, the calculation was sequentially performed from time 0 to 4 weeks, the potential distribution and the current density distribution were calculated, and the corrosion amount of zinc after each exposure test period was determined from Faraday's law.

  Similar to Example 1, the validity of the numerical calculation results is the distribution of potential by Kelvin probe and the distribution of thickness of zinc determined by observing a test piece cut in the longitudinal direction with a scanning electron microscope (SEM) investigated.

  Table 5 shows a comparison of the calculated and exposed test results. As for the potential, the surface of zinc to iron was compared every 20 mm in the longitudinal direction of the test piece, and all 20 points were compared, and Table 5 shows the potential difference at the point where the greatest deviation in potential was between calculation and experiment. The case where the calculated potentials of all 20 points fall within the range of ± 100 mV with respect to the experimental results is taken as ○, and the case where not so is taken as x. The corrosion amount distribution measures all 10 points at 1 mm intervals in the longitudinal direction of the test piece in the same way as the potential, with the area of Zn only, and the ratio of (calculation amount of corrosion) / (experimental corrosion amount) is shown in the table. Indicates the ratio of the points most diverging from 1 and ○ when the calculated amount of corrosion / (experimental amount of corrosion) = 0.25 to 2.0 at all 10 measuring points Was evaluated as x. With respect to No. 1 to No. 4 within the scope of the present invention, it was possible to accurately predict both the potential distribution and the distribution of corrosion in the exposure test under any corrosion boundary condition. Further, it can be seen that the prediction accuracy is higher when the corrosion period of the test piece used for the polarization curve is closer to the predicted period. On the other hand, as shown in the comparative example, when the polarization curve of the as-polished metal surface was used as the boundary condition, both the potential and the amount of corrosion largely deviated from the experimental results. As in Example 1, the actual corrosion phenomenon could be predicted with high accuracy by applying the present invention using the polarization curve of the corrosion surface as the boundary condition also in the actual atmospheric corrosion environment.

  According to the method of predicting corrosion of metal by numerical analysis of the present invention, it is possible to predict corrosion of metal in contact with an electrolyte solution more accurately by numerical analysis. Therefore, the design of metal structures and products, the evaluation of quality deterioration due to the corrosion of materials and structures, and the development of corrosion resistant materials can be carried out optimally and with high precision, which is an industrially extremely useful technology.

Claims (10)

A method of predicting corrosion of metal in contact with an electrolyte solution by numerical analysis,
Measuring a polarization curve, which is a relationship between the potential E of the metal surface in the electrolyte solution and the current density i, in a state in which a corrosion product is present on the metal surface;
Determining the potential distribution in the electrolyte solution at a predetermined time in a system in which the metal and the electrolyte solution are in contact, using the polarization curve as a boundary condition by numerical calculation;
I have a,
Measuring the polarization curve under various conditions in which the deposition amount of the corrosion product on the metal surface is different;
Select the polarization curve when the corrosion on the metal surface is in steady state,
A method of predicting corrosion of metal by numerical analysis , wherein the selected polarization curve is used as the boundary condition . From the potential distribution of the electrolyte solution at a predetermined time, further comprising corrosion prediction of metal by numerical analysis according to claim 1 the step of obtaining numerically the current density distribution of the electrolyte solution at a predetermined time Method. In the step of calculating the amount of corrosion of the metal at the predetermined time according to Faraday's law using the current density in the vicinity of the interface with the metal in the electrolyte solution among the current density distribution in the electrolyte solution The corrosion prediction method of metal by numerical analysis according to claim 2 .   A method of predicting corrosion of metal in contact with an electrolyte solution by numerical analysis,
  Measuring a polarization curve, which is a relationship between the potential E of the metal surface in the electrolyte solution and the current density i, in a state in which a corrosion product is present on the metal surface;
  Determining the potential distribution in the electrolyte solution at a predetermined time in a system in which the metal and the electrolyte solution are in contact, using the polarization curve as a boundary condition by numerical calculation;
Have
  The method further includes the step of obtaining the current density distribution in the electrolyte solution at the predetermined time by numerical calculation from the potential distribution in the electrolyte solution at the predetermined time.
  In the step of calculating the amount of corrosion of the metal at the predetermined time according to Faraday's law using the current density in the vicinity of the interface with the metal in the electrolyte solution among the current density distribution in the electrolyte solution The metal corrosion prediction method by numerical analysis characterized by having.   The numerical analysis according to any one of claims 1 to 4, 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. How to predict metal corrosion by   The numerical analysis according to any one of claims 1 to 5, wherein the form of the corrosion is heterogeneous metal contact corrosion in which two metals are in contact and the corrosion of one metal is accelerated by the potential difference between the metals. How to predict metal corrosion by   The method of predicting metal corrosion by numerical analysis according to claim 6, wherein the two metals are a combination of zinc or zinc plating and iron or steel.   A method of designing a steel structure, comprising selecting a material by predicting corrosion resistance by the method of predicting corrosion of metal by numerical analysis according to any one of claims 1 to 7.   A method of designing a steel material, comprising: predicting corrosion resistance by the method of predicting corrosion of metal by numerical analysis according to any one of claims 1 to 7; and designing anticorrosion performance.   The corrosion prediction program which makes a computer perform the corrosion prediction method of metal by numerical analysis of any one of Claims 1-7.

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