Dirt risk analysis method, device and equipment of reactor and storage medium

Abstract

The invention provides a fouling risk analysis method, device and equipment of a reactor and a storage medium, and relates to the technical field of nuclear reactors. The fouling risk analysis method of the reactor provided by the invention comprises the following steps: acquiring input parameters; the dirt thickness and the boron deposition mass of a target position are calculated based on the input parameters, the target position comprises a reactor core, and the dirt thickness and the boron deposition mass are used for dirt risk analysis; and outputting a calculation result. Based on a risk formation mechanism and important phenomena caused by reactor fouling, the fouling thickness used for judging the CILC risk and the boron deposition quality used for judging the CIPS risk are calculated based on the input parameters, and fouling risk analysis of the reactor is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of nuclear reactors, and in particular to a method, device, equipment and storage medium for analyzing fouling risk of a reactor. Background Art

[0002] Reactor core fouling-induced axial power deviation (CIPS) and local cladding corrosion (CILC) will affect the safety and economy of reactor operation, and is also one of the key challenges for improving the performance of pressurized water reactors, which has attracted increasing attention in the nuclear power industry. The phenomenon caused by reactor core fouling is an important factor affecting the safety and economy of reactor operation, and the risk assessment of reactor core fouling may become an important issue of concern to the industry in the future. Summary of the invention

[0003] In view of this, the present application provides a reactor fouling risk analysis method, device, equipment and storage medium to achieve reactor fouling risk analysis.

[0004] In a first aspect, the present application provides a method for analyzing the fouling risk of a reactor, comprising:

[0005] Get input parameters;

[0006] Calculate the fouling thickness and boron deposition mass of a target location based on the input parameters, wherein the target location includes a reactor core, and the fouling thickness and boron deposition mass are used for fouling risk analysis;

[0007] Output the calculation results.

[0008] In a second aspect, the present application provides a reactor fouling risk analysis device, comprising:

[0009] Pre-processing module, used to obtain input parameters;

[0010] A numerical solution module, used for calculating the dirt thickness and the boron deposition mass at the target position based on the input parameters, wherein the dirt thickness and the boron deposition mass are used for dirt risk analysis;

[0011] Post-processing module, used to output calculation results.

[0012] In a possible implementation, the calculating the dirt thickness and boron deposition quality at the target position based on the input parameters includes:

[0013] Calculate the mass evaporation rate based on the input parameters;

[0014] calculating the dirt thickness at the target location and the total concentration of corrosion products in the coolant system based on the mass evaporation rate;

[0015] Calculate the mass of boron deposition based on the thickness of the core fouling;

[0016] Determine whether the target parameters meet the convergence conditions. If the target parameters do not meet the convergence conditions, update the input parameters for iterative calculation.

[0017] In a possible implementation, the determining whether the target parameter satisfies the convergence condition includes:

[0018] Determining whether an error between the calculated mass evaporation rate and a first preset value is less than a first threshold;

[0019] It is determined whether an error between the calculated total concentration of corrosion products in the coolant system and a second preset value is less than a second threshold value.

[0020] In a possible implementation, the calculating the mass evaporation rate based on the input parameters includes:

[0021] When the fouling thickness is greater than a critical value, the mass evaporation rate is calculated based on the heat flux density on the cladding surface, the heat flux density on the fouling surface, the vapor phase enthalpy value on the fouling surface, and the liquid phase enthalpy value on the fouling surface;

[0022] When the fouling thickness is less than a critical value, the mass evaporation rate is calculated based on the saturated boiling heat flux component, the boiling starting point heat flux component and the latent heat of vaporization.

[0023] In a possible implementation, the calculating the dirt thickness at the target location and the total concentration of corrosion products in the coolant system based on the mass evaporation rate includes:

[0024] Calculate the total release rate of corrosion products in the coolant system based on the mainstream pH value in the coolant system;

[0025] Calculate the nickel solubility near the wall of the target location based on the pH value of the fluid near the wall of the target location;

[0026] A mass transfer model for the target location is constructed based on the mass evaporation rate, the near-wall nickel solubility of the target location, the total concentration of corrosion products in the coolant system, and the mass flow rate of fouling deposits transferred at the target location;

[0027] constructing a mass balance model of the coolant system based on the total concentration of corrosion products in the coolant system, the mass flow rate of fouling deposits delivered at the target location, and the total release rate of corrosion products in the coolant system;

[0028] The fouling thickness at the target location and the total concentration of corrosion products in the coolant system are calculated based on the mass transfer model of the target location and the mass balance model of the coolant system.

[0029] In a possible implementation, the target location further includes a steam generator; and constructing a mass transfer model for the target location based on the mass evaporation rate, the near-wall nickel solubility of the target location, the total concentration of corrosion products in the coolant system, and the mass flow rate of fouling deposition transfer at the target location includes:

[0030] A mass transfer model of the core is constructed based on the mass evaporation rate, the nickel solubility near the core wall, the total concentration of corrosion products in the coolant system, and the mass flow rate of fouling deposition transfer in the core;

[0031] The mass transfer model of the steam generator is constructed based on the near-wall nickel solubility of the steam generator, the total concentration of corrosion products in the coolant system, and the mass flow rate transferred by the fouling deposition of the steam generator.

[0032] In a possible implementation, obtaining the input parameters includes:

[0033] Determine whether the time point reaches the end time;

[0034] If the time point does not reach the end time, set the initial value of the input parameter;

[0035] updating the time point, and updating the time step when the calculated fouling thickness of the steam generator is less than a third threshold;

[0036] The initial values ​​of the input parameters are interpolated based on the time step and the time point to obtain the input parameters required for calculation.

[0037] In a third aspect, the present application provides a computing device, including:

[0038] at least one processor; and

[0039] At least one memory having instructions stored thereon, which, when executed individually or collectively by the at least one processor, cause the computing device to perform the method according to the first aspect.

[0040] In a fourth aspect, the present application provides a computer storage medium having instructions stored thereon, which, when executed individually or collectively by at least one processor of a computing device, causes the computing device to execute the method described in the first aspect.

[0041] Compared with the prior art, this application has the following advantages:

[0042] The fouling risk analysis method of the reactor provided in the present application includes obtaining input parameters; calculating the fouling thickness and boron deposition mass of the target position based on the input parameters, the target position includes the core, and the fouling thickness and the boron deposition mass are used for fouling risk analysis; and outputting the calculation results. The present application is based on the risk formation mechanism and important phenomena caused by reactor fouling, and calculates the fouling thickness used to judge CILC risk and the boron deposition mass used to judge CIPS risk based on the input parameters to realize the fouling risk analysis of the reactor. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings are included to provide a further understanding of the present application. They are included and constitute a part of the present application. The accompanying drawings illustrate embodiments of the present application and together with the present specification serve to explain the principles of the present application. In the accompanying drawings:

[0044] Figure 1 It is a schematic flow chart of a reactor fouling risk analysis method provided in an embodiment of the present application;

[0045] Figure 2 It is a flow chart of another method for analyzing fouling risk of a reactor provided in an embodiment of the present application;

[0046] Figure 3 is a schematic diagram of a dirt grid division provided in an embodiment of the present application;

[0047] Figure 4 It is a structural schematic diagram of a reactor fouling risk analysis device provided in an embodiment of the present application.

[0048] Figure 5 It is a structural diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0050] As shown in this application, unless the context clearly indicates an exception, the words "a", "an", "a kind" and / or "the" do not refer to the singular, but also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0051] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0052] Unless otherwise specifically stated, the relative arrangement, numerical expressions and numerical values ​​of the parts and steps set forth in these embodiments do not limit the scope of the present application. Meanwhile, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to actual proportional relationships. The technology, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but in appropriate cases, the technology, methods and equipment should be considered as a part of the specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings, and therefore, once a certain item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0053] In addition, although the terms used in this application are selected from well-known and commonly used terms, some of the terms mentioned in this application specification may be selected by the applicant at his or her discretion, and their detailed meanings are explained in the relevant parts of the description of this article. In addition, it is required to understand this application not only by the actual terms used, but also by the meaning implied by each term.

[0054] Flowcharts are used in this application to illustrate the operations performed by the apparatus or equipment according to the embodiments of the application. It should be understood that the preceding or following operations are not necessarily performed precisely in order. On the contrary, the steps may be processed in reverse order or simultaneously. At the same time, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0055] When the reactor is in operation, corrosion products (nickel, iron, etc.) on the coolant system pipes will be released into the coolant and deposited on the surface of the fuel assembly cladding to form scale. Boron and lithium hydroxide accumulate in the porous gaps of the scale. When boron accumulates to a certain extent, it will precipitate on the scale, resulting in a decrease in the power of the upper part of the fuel assembly, and the power peak will move downward, that is, the CIPS phenomenon occurs. If the scale deposition in a local position is thick, the coolant will not be able to flow through the scale and cool the surface of the fuel cladding, resulting in a high temperature of the fuel cladding. If the fuel cladding is at a very high temperature for a long time, it will accelerate the corrosion and failure of the fuel cladding, resulting in the CILC phenomenon. When the reactor power is increased, the average temperature of the reactor coolant increases, or the core power peak factor increases, the CIPS / CILC phenomenon is very easy to occur, and usually occurs in high-power fuel assemblies in the core.

[0056] The embodiment of the present application realizes the fouling risk analysis of the reactor by calculating the fouling thickness used to judge the CILC risk and the boron deposition mass used to judge the CIPS risk.

[0057] Figure 1 Schematic diagram of a reactor fouling risk analysis method provided in an embodiment of the present application. Figure 1 As shown, the reactor fouling risk analysis method includes the following steps:

[0058] Step S110: Obtain input parameters.

[0059] Input parameters include some parameters required for subsequent calculations. In actual operation, the user can provide input parameters by providing input text files, calculation results of other modules (such as computational fluid dynamics software, etc.), restart file information, etc.

[0060] In some embodiments, please refer to Figure 2 , obtaining input parameters includes the following steps:

[0061] Step S111, determine whether the time point reaches the end time. If the time point reaches the end time, then end.

[0062] Step S112: If the time point does not reach the end time, set the initial value of the input parameter.

[0063] In some embodiments, certain parameter values ​​that require solutions may be iteratively updated during the iterative calculation process, such as dirt thickness, and their initial values ​​need to be set at each time step. For example, the initial values ​​of the input parameters of the current time step may be set based on the calculation results of the previous time step.

[0064] Step S113: update the time point, and update the time step when the calculated dirt thickness of the steam generator is less than a third threshold value.

[0065] Each time a time step is calculated, the time point of the current calculation is updated. In some embodiments, the time step is also updated based on the calculated dirt thickness of the steam generator to improve the calculation accuracy. For example, when the calculated dirt thickness of the steam generator is less than the third threshold, the time step is shortened, such as halving the time step, or reducing the time step to one third or one quarter of the original time step.

[0066] Step S114: interpolate the initial values ​​of the input parameters based on the time step and the time point to obtain the input parameters required for calculation.

[0067] It should be noted that the values ​​of some input parameters are independent of time, so time interpolation may not be performed on these input parameters, and only time interpolation may be performed on input parameters that are related to time (such as boron and lithium concentrations in the coolant).

[0068] Step S120: Calculate the dirt thickness and boron deposition mass at the target position based on the input parameters, wherein the calculated dirt thickness and boron deposition mass are used for dirt risk analysis.

[0069] The target location includes at least the core, so that the fouling thickness of the core and the boron deposition mass of the core can be calculated. The target location may also include the steam generator, so that the fouling thickness of the steam generator can be calculated. Since the CIPS phenomenon is irrelevant to the steam generator, only the boron deposition mass of the core is calculated.

[0070] In some embodiments, the target location has a large space. To ensure the accuracy of the calculation result, the target location can be divided into multiple nodes, and calculations are performed for each node. Accordingly, in the embodiments of the present application, calculations are performed for the target location, which means calculations are performed for multiple nodes of the divided target location.

[0071] Please refer to Figure 2 , the calculation of the dirt thickness and boron deposition quality at the target location based on the input parameters includes the following steps:

[0072] Step S121, calculating the mass evaporation rate based on the input parameters.

[0073] In some embodiments, when the fouling thickness is greater than a critical value, the mass evaporation rate is calculated based on the heat flux density on the cladding surface, the heat flux density on the fouling surface, the vapor phase enthalpy value on the fouling surface, and the liquid phase enthalpy value on the fouling surface. The calculation relationship of the mass evaporation rate is shown as follows:

[0074] (1)

[0075] Where: G evap is the mass evaporation rate, in kg / m 2 ·s;

[0076] q w is the heat flux density on the shell surface, in W / m 2 ;

[0077] q surf is the heat flux density on the dirt surface, in W / m 2 ;

[0078] H l is the liquid phase enthalpy of the dirt surface, in J / kg;

[0079] H v It is the vapor phase enthalpy of dirt surface, in J / kg.

[0080] Among them, q w ,q surf , H l , H v is the input parameter.

[0081] When the dirt thickness is less than the critical value, the mass evaporation rate is calculated based on the saturated boiling heat flux component, the boiling starting point heat flux component and the latent heat of vaporization. The calculation relationship of the mass evaporation rate is shown as follows:

[0082] (2)

[0083] Where: G evap is the mass evaporation rate, in kg / m 2 ·s;

[0084] q nb is the saturated boiling heat flux component, in W / m 2 ;

[0085] q bi is the heat flux component at the boiling starting point, in W / m 2 ;

[0086] h fg is the latent heat of vaporization, in J / kg.

[0087] Among them, q nb ,q bi 、h fg is the input parameter.

[0088] It is understandable that in the first iterative calculation step, an initial value of the dirt thickness can be set, and in subsequent iterative calculations, the initial value of the dirt thickness in the current step can be set based on the dirt thickness calculated in the previous step.

[0089] It should be noted that the relationship formula listed in the embodiments of the present application is only an exemplary relationship formula with better results. Each relationship formula can also be replaced by other calculation relationship formulas.

[0090] Step S122: Calculate the dirt thickness at the target location and the total concentration of corrosion products in the coolant system based on the mass evaporation rate.

[0091] In some embodiments, calculating the fouling thickness at the target location and the total concentration of corrosion products in the coolant system based on the mass evaporation rate comprises the following steps:

[0092] Step S1221: Calculate the total release rate of corrosion products in the coolant system based on the mainstream pH value in the coolant system.

[0093] An example of a relationship for calculating the total release rate of corrosion products in a coolant system is as follows:

[0094] G dis =f1(PH dis ) (3)

[0095] Where: G dis is the total release rate of corrosion products in the coolant system, in kg / m 2 ·s;

[0096] f1 is the calculation function of the total release rate of corrosion products in the coolant system;

[0097] PH dis is the mainstream pH value in the coolant system. This parameter is an input parameter.

[0098] It should be noted that, in the above embodiment, the total release rate of corrosion products in the coolant system is calculated based on the mainstream pH value in the coolant system. In other embodiments, the total release rate of corrosion products in the coolant system may also be calculated based on other calculation formulas.

[0099] Step S1222: Calculate the nickel solubility near the wall of the target position based on the pH value of the fluid near the wall of the target position.

[0100] An example of the calculation relationship for the near-wall nickel solubility at the target location is as follows:

[0101] C Ni =f2(PH Ni ) (4)

[0102] Where: C Ni is the near-wall nickel solubility at the target location, in ppb;

[0103] f2 is the calculation function of nickel solubility near the wall at the target position;

[0104] PH Ni is the pH value of the fluid near the wall at the target location. This parameter is an input parameter.

[0105] It should be noted that, in the above embodiment, the nickel solubility near the wall of the target position is solved based on the calculation relationship related to the pH value of the fluid near the wall of the target position. In some other embodiments, the calculation can also be performed based on other calculation relationships of nickel solubility.

[0106] Step S1223: construct a mass transfer model for the target location based on the mass evaporation rate, the near-wall nickel solubility of the target location, the total concentration of corrosion products in the coolant system, and the mass flow rate of the fouling deposits transferred at the target location.

[0107] The mass transfer model considers the mass transfer caused by turbulent mixing and boiling circulation in the near-wall layer, as well as the mass transfer from the near-wall layer to the wall caused by deposition. In some embodiments, the target location includes the core. The mass transfer model of the core can be constructed based on the mass evaporation rate, the near-wall nickel solubility of the core, the total concentration of corrosion products in the coolant system, and the mass flow rate of fouling deposition transfer in the core. An example of the relationship of the mass transfer model of the core is as follows:

[0108] (5)

[0109] Where: is the mass transfer efficiency due to boiling;

[0110] G evap is the mass evaporation rate, in kg / s·m 2 ;

[0111] Con is the total concentration of corrosion products in the coolant system, in ppb;

[0112] Con wnode is the nickel solubility near the core wall, in ppb;

[0113] F is the factor converting nickel solubility into total concentration;

[0114] is the mass transfer coefficient caused by the mixing of the core near wall layer and the mainstream, in kg / s·m 2 ;

[0115] G node The mass flow rate of fouling deposition in the core, in kg / s·m 2 .

[0116] in, , G evap , and F are input parameters.

[0117] In some embodiments, the target location includes a steam generator. A mass transfer model for the steam generator can be constructed based on the near-wall nickel solubility of the steam generator, the total concentration of corrosion products in the coolant system, and the mass flow rate transferred by the fouling deposition of the steam generator.

[0118] An example of a relationship for a mass transfer model for a steam generator is:

[0119] (6)

[0120] Where: Con is the total concentration of corrosion products in the coolant system, in ppb;

[0121] Con wall is the nickel solubility near the wall of the steam generator, in ppb;

[0122] F is the factor converting nickel solubility into total concentration;

[0123] is the mass transfer coefficient caused by the mixing of the near-wall layer and the mainstream of the steam generator, in kg / s·m 2 ;

[0124] G sed The mass flow rate transferred for the fouling deposition of the steam generator, in kg / s·m 2 ;

[0125] Among them, F and is the input parameter.

[0126] Step S1224: construct a mass balance model of the coolant system based on the total concentration of corrosion products in the coolant system, the mass flow rate of dirt deposits transferred at the target location, and the total release rate of corrosion products in the coolant system.

[0127] In an exemplary embodiment, the mass balance model of the coolant system considers the total release of corrosion products in the coolant system and the fouling mass at the target location, and the relationship is as follows:

[0128] (7)

[0129] Where: m total is the mass of coolant in the coolant system, in kg / m 2 ;

[0130] Con is the total concentration of corrosion products in the coolant system, in ppb;

[0131] t is time, in seconds;

[0132] G targetThe mass flow rate delivered to the target location for dirt deposition, in kg / s·m 2 ;

[0133] G dis is the total release rate of corrosion products in the coolant system, in kg / s·m 2 .

[0134] Among them, m total is a known parameter and can be input by the user.

[0135] In some embodiments, the mass balance model considers the mass removed by the downflow system in addition to the mass transfer on the core and steam generator surface and the source term of corrosion product release. An example of the relationship of the mass transfer model of the downflow system is as follows:

[0136] (8)

[0137] Where: G ld The mass flow rate of dirt deposits in the downstream system, in kg / s·m 2 ;

[0138] G sld is the discharge flow rate, in kg / s·m 2 ;

[0139] Con ld is the nickel solubility in the downstream system, in ppb;

[0140] F p is the particle removal rate;

[0141] F s is the solute removal rate.

[0142] Among them, G sld 、F p and F s Is the input parameter. ld It can be calculated based on the fluid pH value of the downflow system and the relationship between nickel solubility and fluid pH value.

[0143] An example of a relationship for a mass balance model for a coolant system is as follows:

[0144] (9)

[0145] Where: m total is the mass of coolant in the coolant system, in kg / m 2 ;

[0146] Con is the total concentration of corrosion products in the coolant system, in ppb;

[0147] t is time, in seconds;

[0148] G node The mass flow rate of fouling deposition in the core, in kg / s·m 2 ;

[0149] G sed The mass flow rate transferred for the fouling deposition of the steam generator, in kg / s·m 2 ;

[0150] G ld The mass flow rate of dirt deposits in the downstream system, in kg / s·m 2 ;

[0151] G dis is the total release rate of corrosion products in the coolant system, in kg / s·m 2 .

[0152] Among them, m total is the input parameter.

[0153] When constructing the mass transfer model, the embodiments of the present application take into account the inflow and outflow of coolant and corrosion products in the near-wall coolant layer; the effects of turbulent mixing and boiling circulation, which cause mass transfer; the dissolution and deposition of dirt on the wall; and the increase or decrease of corrosion products in the coolant. These processes make the process of dirt deposition from the mainstream coolant to the wall more detailed and the mass transfer process more accurate.

[0154] Step S1225: Calculate the dirt thickness at the target location and the total concentration of corrosion products in the coolant system based on the mass transfer model at the target location and the mass balance model of the coolant system.

[0155] In some embodiments, the target location includes the core and the steam generator. Based on the mass transfer model of the steam generator, the mass transfer model of the core, the mass transfer model of the downflow system, and the mass balance model of the coolant system, the mass flow rate of the fouling deposition transfer of the steam generator and the mass flow rate of the fouling deposition transfer of the core are solved. That is, the combined relationship (5), (6), (8) and (9) can be solved to obtain G node , G sed and Con.

[0156] The fouling thickness can be calculated based on the calculated mass flow rate of fouling deposition transfer. In some embodiments, the fouling mass per unit area can be calculated based on the mass flow rate of fouling deposition transfer, and then the fouling thickness can be calculated based on the fouling mass per unit area and the fouling density. For example, the fouling thickness of the core can be calculated using equations (10) and (11). The calculation equation for the fouling thickness of the steam generator is similar, where G is replaced bynode Replace with G sed That's it.

[0157] (10)

[0158] (11)

[0159] Where: m node is the mass per unit area of ​​core dirt, in kg / m 2 ;

[0160] G node The mass flow rate of fouling deposition in the core, in kg / s·m 2 ;

[0161] t is time, in seconds;

[0162] THC node is the thickness of the core fouling, in m;

[0163] is the dirt density, in kg / m 3 .

[0164] in, is the input parameter.

[0165] In the embodiment of the present application, the dirt thickness is directly solved by a given dirt density, without considering the dirt density at different positions separately, thereby reducing the amount of calculation.

[0166] The embodiment of the present application considers the fouling quality inside the core, inside the steam generator and inside the downflow system at the same time, and comprehensively considers the location where the fouling is generated and disappears in the primary circuit, so that the fouling distribution in the entire circuit is more reasonable and the calculated fouling thickness is more accurate. It is understandable that in some other embodiments, the arrangement in the reactor circuit may be different, and the steam generator and / or downflow system may not be considered, thereby omitting the corresponding calculation steps.

[0167] Step S123: Calculate the boron deposition mass based on the fouling thickness of the core.

[0168] The embodiment of the present application considers two boron deposition mechanisms, namely boron-lithium compound deposition and boron adsorption, and combines these two deposition mechanisms to calculate the total boron deposition mass, thereby realizing the boron deposition mass calculation of the reactor, making the calculation result more accurate.

[0169] The mass of boron-lithium compounds deposited inside the dirt, that is, the mass of boron-lithium compounds deposited inside the dirt, can be calculated through a certain relationship. For example, the mass of boron-lithium compounds deposited inside the dirt can be calculated based on the thickness of the boron deposition layer, the boron concentration in the coolant, and the concentration factor.

[0170] The mass of boron adsorbed on the dirt surface, i.e., the mass of boron adsorbed on the dirt surface, can be calculated by a certain relationship, such as the mass of boron adsorbed on the surface of the cladding. In an exemplary embodiment, the mass of boron adsorbed on the dirt surface can be obtained based on the relationship between the boron adsorption mass fitted according to the test data, the boron concentration on the surface of the cladding, and the mass of the dirt.

[0171] An example of a calculation relationship for boron deposition quality is as follows:

[0172] (12)

[0173] Where M b is the mass of boron deposition, in kg;

[0174] M b,1 is the mass of boron-lithium compound deposition, in kg;

[0175] k B is the concentration factor;

[0176] C coolant B is the boron concentration in the coolant, in ppb;

[0177] is the coolant density, in kg / m 3 ;

[0178] d B is the thickness of the boron deposition layer, in m;

[0179] A is the calculated area of ​​the cladding surface, in m 2 ;

[0180] M b,2 is the boron adsorption mass, in kg;

[0181] C clad b is the boron concentration on the cladding surface, in ppb.

[0182] Among them, k B , C coolant B , , A, C clad b is the input parameter.

[0183] The thickness of the boron deposition layer inside the dirt can be calculated based on the dirt thickness, such as based on the relationship between the boron deposition layer thickness inside the dirt and the dirt thickness fitted according to the test data, or based on the concentration of the target substance (lithium, boron, etc.) inside the dirt and the dirt thickness. In an exemplary embodiment, please refer to Figure 3, dirt is deposited on the surface of the fuel assembly cladding. The grid is evenly divided based on the given dirt thickness, and the thickness of each grid layer is dx. The grid is divided in sequence from the dirt surface (close to the fluid side) to the cladding surface and the parameters at each grid layer are determined. For example, the dirt is evenly divided into N-1 layers of grids, and the N-1 layers of grids share N surfaces, namely surfaces x(1), x(2), ..., x(n), x(n+1), ..., x(N). The thickness of the nth grid layer is dx(n). It can be understood that, considering the large size of the surface of the fuel assembly cladding, each grid layer can also be divided into multiple grids in a direction perpendicular to the direction of dirt deposition, and calculations are performed cyclically for each grid. In the above embodiment, the grid is divided in sequence from the dirt surface to the cladding surface. In some other embodiments, the grid can also be divided in sequence from the cladding surface to the dirt surface, and the embodiments of the present application are not limited to this.

[0184] Based on the concentration distribution of the target substance i inside the dirt, it is determined whether the target substance i at the grid has reached the critical concentration, that is, whether it exceeds the solubility of the deposited boron-lithium compound, thereby obtaining the thickness d of the boron deposition layer. B . Boron deposition layer thickness d B The calculation formula example is as follows:

[0185] (13)

[0186] Among them, d B is the thickness of the boron deposition layer, in m;

[0187] d c is the dirt thickness, in m;

[0188] x(j) is the thickness from the dirt surface to the jth grid layer, assuming that the concentration of target substance i at the jth grid layer exceeds the solubility of the deposited boron-lithium compound. If the thickness of each grid layer is dx, then the thickness from the dirt surface to the jth grid layer is j·dx, in meters.

[0189] The solubility of the deposited boron-lithium compound can be used as an input parameter or obtained based on the internal temperature of the dirt. For example, the solubility of the deposited boron-lithium compound is obtained based on an empirical relationship related to temperature:

[0190] SI = f3(T) (14)

[0191] Wherein, SI is the solubility of the deposited boron-lithium compound, in ppb;

[0192] f3 is the calculation function of the solubility of the deposited boron-lithium compound;

[0193] T is the internal temperature of the dirt, in °C.

[0194] Among them, T is the input parameter.

[0195] Step S124: determine whether the target parameters meet the convergence condition. If the target parameters do not meet the convergence condition, update the input parameters for iterative calculation. Otherwise, proceed to the next step.

[0196] The target parameter may include one or more parameters obtained during the calculation process. In an exemplary embodiment, the target parameter includes the calculated mass evaporation rate and the total concentration of corrosion products in the coolant system, so that the solution process is more accurate. Specifically, judging whether the target parameter meets the convergence condition includes: judging whether the error between the calculated mass evaporation rate and the first preset value is less than the first threshold; judging whether the error between the calculated total concentration of corrosion products in the coolant system and the second preset value is less than the second threshold. When the error between the calculated mass evaporation rate and the first preset value is less than the first threshold, the convergence condition is met. When the error between the calculated total concentration of corrosion products in the coolant system and the second preset value is less than the second threshold, the convergence condition is met. When both the mass evaporation rate and the total concentration of corrosion products in the coolant system meet the convergence condition, the current time step calculation is completed, otherwise the input parameters are updated to perform iterative calculation of the current time step.

[0197] Step S130: output the calculation result.

[0198] In some embodiments, the calculation result of the current time step may be output after each time step is calculated. In other embodiments, the calculation result may be output after the end time is reached at the time point and the calculation of all time steps is completed. The form of outputting the calculation result may be set as required.

[0199] Figure 4 Schematic diagram of the structure of a reactor fouling risk analysis device provided in an embodiment of the present application. The reactor fouling risk analysis device is used to achieve the above Figure 1 or Figure 2 As shown in the method. Figure 4 As shown, the fouling risk analysis device 400 of the reactor includes a pre-processing module 410, a numerical solution module 420 and a post-processing module 430, wherein the pre-processing module 410 is used to obtain input parameters; the numerical solution module 420 is used to calculate the fouling thickness and boron deposition mass at the target position based on the input parameters, wherein the fouling thickness and boron deposition mass are used for fouling risk analysis; and the post-processing module 430 is used to output the calculation results.

[0200] Figure 5 Schematic diagram of a computing device provided in an embodiment of the present application. Figure 5As shown, computing device 500 includes one or more processors 510 , one or more memories 520 coupled to processor 510 , and one or more communication modules 540 coupled to processor 510 .

[0201] The communication module 540 is used for two-way communication. The communication module 540 has at least one antenna to facilitate communication. The communication interface may represent any interface necessary for communicating with other network elements.

[0202] Processor 510 may be of any type suitable for the local technology network, and may include, as non-limiting examples, one or more of: a general purpose computer, a special purpose computer, a microprocessor, a digital signal processor (DSP), and a processor based on a multi-core processor architecture. Computing device 500 may have multiple processors, such as application specific integrated circuit chips, which are driven in time to a clock that synchronizes a master processor.

[0203] The memory 520 may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, read-only memory (ROM) 524, electrically programmable read-only memory (EPROM), flash memory, hard disk, compact disk (CD), digital video disk (DVD), and other magnetic and / or optical memories. Examples of volatile memories include, but are not limited to, random access memory (RAM) 522 and other volatile memories that do not persist during the duration of a power outage.

[0204] The computer program 530 includes computer executable instructions that are executed by the associated processor 510. The computer program 530 may be stored in the ROM 524. The processor 510 may perform any suitable actions and processes by loading the computer program 530 into the RAM 522.

[0205] The embodiments of the present application can be implemented by a computer program 530, so that the computing device 500 can execute the reference Figure 1 or Figure 2 Any process disclosed in the discussion. The embodiments of the present application may also be implemented by hardware or by a combination of software and hardware.

[0206] In some embodiments, the computer program 530 may be tangibly embodied in a computer-readable medium, which may be contained in the computing device 500 (e.g., memory 520) or other storage device accessible to the computing device 500. The computing device 500 may load the computer program 530 from the computer-readable medium to the RAM 522 for execution. The computer-readable medium may include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc. The computer-readable medium has the computer program 530 stored thereon.

[0207] Generally, various embodiments of the present application may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software, which may be performed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of the present application are shown and described as block diagrams, flow charts, or using some other graphical representations, it should be understood that, as non-limiting examples, the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general hardware or controllers or other computing devices, or some combination thereof.

[0208] The present application also provides at least one computer program product tangibly stored on a non-transitory computer readable storage medium. The computer program product includes computer executable instructions, such as instructions included in program modules, which are executed in a device on a target real or virtual processor to perform the above-mentioned reference Figure 1 or Figure 2 The method described. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or separated between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0209] The program code for executing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing equipment so that when the program code is executed by the processor or controller, the function / operation specified in the flow chart and / or the block diagram is realized. The program code can be executed completely on the machine as an independent software package, partially on the machine, partially on the machine, partially on a remote machine, partially on a remote machine, or all on a remote machine or server.

[0210] In the context of this application, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform various processes and operations as described above. Examples of carriers include signals, computer readable media, etc.

[0211] The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or apparatuses, or any suitable combination of the foregoing. More specific examples of computer readable storage media include an electrical connection having one or more conductors, a portable computer floppy disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0212] In addition, although the operations are described in a specific order, this should not be construed as requiring to perform these operations in the specific order or sequence shown, or to perform all the operations shown, to obtain the desired result. In some cases, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these details should not be interpreted as limiting the scope of the application, but can be interpreted as a description of the specific features of a particular embodiment. Some of the features described in the context of a separate embodiment may also be implemented in combination in a single embodiment. On the contrary, the various features described in the context of a single embodiment may also be implemented in multiple embodiments individually or in any suitable sub-combination.

[0213] Although the present application has been described in language specific to structural features and / or methodological acts, it should be understood that the present application defined in the appended claims is not necessarily limited to the specific features or acts described above. Instead, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A method for analyzing the fouling risk of a reactor, characterized in that: include: Get input parameters; Calculate the fouling thickness and boron deposition mass of a target location based on the input parameters, wherein the target location includes a reactor core, and the fouling thickness and boron deposition mass are used for fouling risk analysis; Output the calculation results.

2. The method according to claim 1, characterized in that The calculating the dirt thickness and boron deposition quality at the target position based on the input parameters comprises: Calculate the mass evaporation rate based on the input parameters; calculating the dirt thickness at the target location and the total concentration of corrosion products in the coolant system based on the mass evaporation rate; Calculate the mass of boron deposition based on the thickness of the core fouling; Determine whether the target parameters meet the convergence conditions. If the target parameters do not meet the convergence conditions, update the input parameters for iterative calculation.

3. The method according to claim 2, characterized in that The determining whether the target parameter meets the convergence condition includes: Determining whether an error between the calculated mass evaporation rate and a first preset value is less than a first threshold; It is determined whether an error between the calculated total concentration of corrosion products in the coolant system and a second preset value is less than a second threshold value.

4. The method according to claim 2, characterized in that The calculation of mass evaporation rate based on input parameters comprises: When the fouling thickness is greater than a critical value, the mass evaporation rate is calculated based on the heat flux density on the cladding surface, the heat flux density on the fouling surface, the vapor phase enthalpy value on the fouling surface, and the liquid phase enthalpy value on the fouling surface; When the fouling thickness is less than a critical value, the mass evaporation rate is calculated based on the saturated boiling heat flux component, the boiling starting point heat flux component and the latent heat of vaporization.

5. The method according to claim 2, characterized in that The calculating the dirt thickness at the target location and the total concentration of corrosion products in the coolant system based on the mass evaporation rate comprises: Calculate the total release rate of corrosion products in the coolant system based on the mainstream pH value in the coolant system; Calculate the nickel solubility near the wall of the target location based on the pH value of the fluid near the wall of the target location; A mass transfer model for the target location is constructed based on the mass evaporation rate, the near-wall nickel solubility of the target location, the total concentration of corrosion products in the coolant system, and the mass flow rate of fouling deposits transferred at the target location; constructing a mass balance model of the coolant system based on the total concentration of corrosion products in the coolant system, the mass flow rate of fouling deposits delivered at the target location, and the total release rate of corrosion products in the coolant system; The fouling thickness at the target location and the total concentration of corrosion products in the coolant system are calculated based on the mass transfer model of the target location and the mass balance model of the coolant system.

6. The method according to claim 5, characterized in that The target location also includes a steam generator; the mass transfer model of the target location is constructed based on the mass evaporation rate, the near-wall nickel solubility of the target location, the total concentration of corrosion products in the coolant system, and the mass flow rate of the fouling deposition transfer at the target location, including: A mass transfer model of the core is constructed based on the mass evaporation rate, the nickel solubility near the core wall, the total concentration of corrosion products in the coolant system, and the mass flow rate of fouling deposition transfer in the core; The mass transfer model of the steam generator is constructed based on the near-wall nickel solubility of the steam generator, the total concentration of corrosion products in the coolant system, and the mass flow rate transferred by the fouling deposition of the steam generator.

7. The method according to claim 6, characterized in that The obtaining of input parameters includes: Determine whether the time point reaches the end time; If the time point does not reach the end time, set the initial value of the input parameter; updating the time point, and updating the time step when the calculated fouling thickness of the steam generator is less than a third threshold; The initial values ​​of the input parameters are interpolated based on the time step and the time point to obtain the input parameters required for calculation.

8. A reactor fouling risk analysis device, characterized in that: include: Pre-processing module, used to obtain input parameters; A numerical solution module, used for calculating the dirt thickness and the boron deposition mass at the target position based on the input parameters, wherein the dirt thickness and the boron deposition mass are used for dirt risk analysis; Post-processing module, used to output calculation results.

9. A computing device, characterized in that: include: at least one processor; as well as At least one memory having instructions stored thereon, which, when executed individually or collectively by the at least one processor, cause the computing device to perform the method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer storage medium stores instructions, which, when executed individually or collectively by at least one processor of a computing device, cause the computing device to perform the method according to any one of claims 1 to 7.

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