Method, device, equipment and storage medium for fouling risk analysis of reactor

Abstract

The present application provides a method, device, equipment and storage medium for analyzing the fouling risk of a reactor, relating to the technical field of nuclear reactors. The method for analyzing the fouling risk of a reactor provided by the present application includes obtaining input parameters; calculating the fouling thickness and boron deposition mass at a target position based on the input parameters, where the target position includes the reactor core, and the fouling thickness and the boron deposition mass are used for fouling risk analysis; and outputting the calculation results. Based on the risk formation mechanism and important phenomena caused by reactor fouling, the present application calculates the fouling thickness for judging the CILC risk and the boron deposition mass for judging the CIPS risk based on the input parameters, so as to realize the fouling risk analysis of the reactor.

Description

Technical Field

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

[0002] Axial power shift (CIPS) and local cladding corrosion (CILC) caused by fouling in the reactor core will affect the safety and economy of reactor operation. At the same time, it is also one of the key challenges in improving the performance of pressurized water reactors, which has attracted more and more attention in the nuclear power industry. The phenomena caused by fouling in the reactor core are important influencing factors for the safety and economy of reactor operation, and the evaluation of fouling risk in the reactor core may become an important issue that the industry will focus on in the future. Summary of the Invention

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

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

[0005] Obtaining input parameters;

[0006] Calculating the fouling thickness and boron deposition mass at a target position based on the input parameters, where the target position includes the reactor core, and the fouling thickness and the boron deposition mass are used for fouling risk analysis;

[0007] Outputting the calculation result.

[0008] In a second aspect, this application provides a device for analyzing the fouling risk of a reactor, including:

[0009] A preprocessing module for obtaining input parameters;

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

[0011] A postprocessing module for outputting the calculation result.

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

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

[0014] Calculating the fouling thickness at the target position and the total concentration of corrosion products in the coolant system based on the mass evaporation rate;

[0015] Calculating the boron deposition mass based on the fouling thickness in the reactor core;

[0016] Determine whether the target parameter meets the convergence condition. If the target parameter does not meet the convergence condition, update the input parameter and perform iterative calculation.

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

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

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

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

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

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

[0023] In a possible implementation, the calculating the fouling thickness at the target position 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 near-wall nickel solubility at the target position based on the fluid pH value near the wall at the target position;

[0026] Construct a mass transfer model at the target position based on the mass evaporation rate, the near-wall nickel solubility at the target position, the total concentration of corrosion products in the coolant system, and the mass flow rate of fouling deposition transfer at the target position;

[0027] Construct a mass balance model for the coolant system based on the total concentration of corrosion products in the coolant system, the mass flow rate of fouling deposition transfer at the target position, and the total release rate of corrosion products in the coolant system;

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

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

[0030] Constructing a mass transfer model for the core 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 in the core;

[0031] Constructing a mass transfer model for the steam generator 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 of fouling deposition in the steam generator.

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

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

[0034] If the time point does not reach the end time, setting an initial value of the input parameters;

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

[0036] Interpolating the initial value of the input parameters 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, storing instructions thereon, which when executed by the at least one processor alone or jointly, cause the computing device to execute the method as described in the first aspect.

[0040] In a fourth aspect, the present application provides a computer storage medium, storing instructions thereon, which when executed by at least one processor of a computing device alone or jointly, cause the computing device to execute the method as described in the first aspect.

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

[0042] The fouling risk analysis method for a reactor provided in this application includes obtaining input parameters; calculating the fouling thickness and boron deposition mass at a target location based on the input parameters, where the target location includes the reactor core, and the fouling thickness and the boron deposition mass are used for fouling risk analysis; and outputting the calculation results. Based on the risk formation mechanism and important phenomena caused by reactor fouling, this application calculates the fouling thickness for judging CILC risk and the boron deposition mass for judging CIPS risk based on input parameters, thereby realizing the fouling risk analysis of the reactor. Description of the Drawings

[0043] The accompanying drawings are provided to further understand this application. They are incorporated and form a part of this application. The accompanying drawings illustrate embodiments of this application and, together with this specification, serve to explain the principles of this application. In the accompanying drawings:

[0044] Figure 1 is a schematic flowchart of a fouling risk analysis method for a reactor provided by an embodiment of this application;

[0045] Figure 2 is a schematic flowchart of another fouling risk analysis method for a reactor provided by an embodiment of this application;

[0046] Figure 3 is a schematic diagram of a fouling grid division provided by an embodiment of this application;

[0047] Figure 4 is a schematic structural diagram of a fouling risk analysis device for a reactor provided by an embodiment of this application.

[0048] Figure 5 is a schematic structural diagram of a computing device provided by an embodiment of this application. Detailed Embodiments

[0049] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some examples or embodiments of this application. For those of ordinary skill in the art, without creative efforts, this application can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the drawings represent the same structure or operation.

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

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

[0052] Unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that, for the sake of convenience of description, the dimensions of the various parts shown in the drawings are not drawn in actual proportional relationship. Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the said technologies, methods, and devices should be regarded as part of the specification. In all the examples shown and discussed here, any specific value should be construed as merely exemplary, and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

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

[0054] Flowcharts are used in the present application to illustrate the operations performed by the apparatus or device according to the embodiments of the present application. It should be understood that the operations before or below do not necessarily need to be performed precisely in sequence. On the contrary, they can be performed in reverse order or simultaneously. At the same time, other operations can be added to these processes, or one or several steps of operations can be removed from these processes.

[0055] During reactor operation, corrosion products (such as nickel and iron) on the coolant system pipes will be released into the coolant and deposit on the surface of the fuel assembly cladding to form dirt. Boron and lithium hydroxide accumulate in the pore-like gaps of the dirt. When boron accumulates to a certain extent, it will precipitate onto the dirt, resulting in a decrease in the power of the upper part of the fuel assembly. As a result, the power peak will shift downward, that is, the CIPS phenomenon occurs. If the dirt deposition is thick at a local position, the coolant will not be able to flow through the dirt and cool the surface of the fuel cladding, resulting in a relatively high temperature of the fuel cladding. If the fuel cladding is at a high temperature for a long time, it will accelerate the corrosion and failure of the fuel cladding, thus leading to the CILC phenomenon. When the reactor power is increased, the average temperature of the reactor coolant is increased, or the core power peak factor is increased, the CIPS / CILC phenomenon is very likely to occur, and it usually occurs in the high-power fuel assemblies in the core.

[0056] In the embodiments of the present application, the fouling risk analysis of the reactor is realized by calculating the dirt thickness for judging the CILC risk and the boron deposition mass for judging the CIPS risk.

[0057] Figure 1 It is a schematic flow chart of a fouling risk analysis method for a reactor provided by the embodiments of the present application. As Figure 1 shown, the fouling risk analysis method for the reactor includes the following steps:

[0058] Step S110, obtain input parameters.

[0059] The input parameters include some parameters required for subsequent calculations. In actual operation, the input parameters can be provided by the user through an input text file, the calculation results of other modules (such as computational fluid dynamics software, etc.), and restart file information.

[0060] In some embodiments, please refer to Figure 2 , obtaining the 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, end.

[0062] Step S112, if the time point does not reach the end time, set the initial value of the input parameters.

[0063] In some embodiments, the parameter values to be solved can be iteratively updated during the iterative calculation process, such as the dirt thickness, etc. The initial value needs to be set at each time step. For example, the initial value of the input parameters at the current time step can be set according to 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 the third threshold.

[0065] After each time step of calculation is completed, the current calculation time point is updated. In some embodiments, the time step is also updated based on the calculated fouling thickness of the steam generator to improve the calculation accuracy. For example, when the calculated fouling thickness of the steam generator is less than a third threshold, the time step is shortened. For example, the time step can be halved, or the time step can be changed to one-third or one-fourth of the original, etc.

[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 the calculation.

[0067] It should be noted that for some input parameters whose values are independent of time, these input parameters do not need to be interpolated with respect to time, and only the input parameters related to time (such as the boron and lithium concentrations in the coolant, etc.) are interpolated with respect to time.

[0068] Step S120: Calculate the fouling thickness and boron deposition mass at the target location based on the input parameters. Among them, the calculated fouling thickness and boron deposition mass are used for fouling risk analysis.

[0069] The target location includes at least the reactor core to calculate the fouling thickness of the reactor core and the boron deposition mass in the reactor core. The target location may also include the steam generator to calculate the fouling thickness of the steam generator. Since the CIPS phenomenon is independent of the steam generator, only the boron deposition mass in the reactor core is calculated.

[0070] In some embodiments, the target location has a large space. For the accuracy of the calculation results, the target location can be divided into multiple nodes, and calculations are performed for each node separately. Correspondingly, in the embodiments of the present application, performing calculations for the target location means performing calculations for multiple nodes of the divided target location.

[0071] Please refer to Figure 2 , calculating the fouling thickness and boron deposition mass at the target location based on the input parameters includes the following steps:

[0072] Step S121: Calculate the mass evaporation rate based on the input parameters.

[0073] In some embodiments, when the fouling thickness is greater than the 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 enthalpy value on the fouling surface, and the liquid enthalpy value on the fouling surface. The calculation relationship of the mass evaporation rate is shown as follows:

[0074] (1)

[0075] In the formula: G evap is the mass evaporation rate, with the unit of kg / m 2 ·s;

[0076] q w is the heat flux density on the cladding surface, with the unit of W / m 2 ;

[0077] q surf is the heat flux density on the fouling surface, with the unit of W / m 2 ;

[0078] H l is the liquid-phase enthalpy value on the fouling surface, with the unit of J / kg;

[0079] H v is the vapor-phase enthalpy value on the fouling surface, with the unit of J / kg.

[0080] Among them, q w 、q surf 、H l 、H v are input parameters.

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

[0082] (2)

[0083] In the formula: G evap is the mass evaporation rate, with the unit of kg / m 2 ·s;

[0084] q nb is the saturated boiling heat flux density component, with the unit of W / m 2 ;

[0085] q bi is the boiling inception point heat flux density component, with the unit of W / m 2 ;

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

[0087] Among them, q nb 、q bi 、h fg are input parameters.

[0088] It can be understood that in the first-step iterative calculation, an initial value can be set for the fouling thickness. In the subsequent iterative calculation process, the initial value of the fouling thickness in the current step can be set based on the fouling thickness obtained in the previous step.

[0089] It should be noted that the relational expressions listed in the embodiments of the present application are only exemplary relational expressions with better results. Each relational expression can also be replaced with other calculation relational expressions.

[0090] Step S122: Calculate the fouling thickness at the target position 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 position and the total concentration of corrosion products in the coolant system based on the mass evaporation rate includes 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 the calculation relational expression for the total release rate of corrosion products in the coolant system is as follows:

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

[0095] In the formula: G dis is the total release rate of corrosion products in the coolant system, with the unit of 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 embodiments, the total release rate of corrosion products in the coolant system is calculated based on the mainstream pH value in the coolant system. In some other embodiments, it can also be calculated based on other calculation relational expressions of the total release rate of corrosion products in the coolant system.

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

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

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

[0102] In the formula: C Ni is the near-wall nickel solubility at the target position, with the unit of ppb;

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

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

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

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

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

[0108] (5)

[0109] In the formula: is the mass transfer efficiency caused by boiling;

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

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

[0112] Con wnode is the nickel solubility near the wall of the core, with the unit of ppb;

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

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

[0115] G node is the mass flow rate of fouling deposition transferred at the core, with the unit of kg / s·m 2 .

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

[0117] In some embodiments, the target location includes a steam generator. A mass transfer model of 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 deposit of the steam generator.

[0118] An example of the relationship of the mass transfer model of the steam generator is as follows:

[0119] (6)

[0120] In the formula: Con is the total concentration of corrosion products in the coolant system, with the unit of ppb;

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

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

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

[0124] G sed is the mass flow rate transferred by the fouling deposit of the steam generator, with the unit of kg / s·m 2 ;

[0125] Among them, F and are input parameters.

[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 transferred by the fouling deposit 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 its relationship is as follows:

[0128] (7)

[0129] In the formula: m total is the mass of the coolant in the coolant system, with the unit of kg / m 2 ;

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

[0131] t is the time, with the unit of s;

[0132] G targetThe mass flow rate transferred for dirt deposition at the target location, 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] where m total is a known parameter that can be input by the user.

[0135] In some embodiments, the mass balance model considers, in addition to mass transfer on the core and steam generator surfaces and the corrosion product release source term, the mass removed through the letdown system. Among them, an example of the relational expression of the mass transfer model of the letdown system is as follows:

[0136] (8)

[0137] In the formula: G ld is the mass flow rate of dirt deposition transfer in the letdown system, in kg / s·m 2 ;

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

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

[0140] F p is the particulate removal rate;

[0141] F s is the dissolved matter removal rate.

[0142] where G sld , F p and F s are input parameters. Con ld can be calculated based on the fluid pH value of the letdown system in combination with the relational expression between nickel solubility and fluid pH value.

[0143] An example of the relational expression of the mass balance model of the coolant system is as follows:

[0144] (9)

[0145] In the formula: m total is the coolant mass 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, with the unit of s;

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

[0149] G sed is the mass flow rate of fouling deposition transfer in the steam generator, with the unit of kg / s·m 2 ;

[0150] G ld is the mass flow rate of fouling deposition transfer in the letdown system, with the unit of kg / s·m 2 ;

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

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

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

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

[0155] In some embodiments, the target position 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 letdown system, and the mass balance model of the coolant system, solve for the mass flow rate of fouling deposition transfer in the steam generator and the mass flow rate of fouling deposition transfer in the core. That is, by combining equations (5), (6), (8), and (9), G node , G sed and Con can be solved.

[0156] The fouling thickness can be calculated based on the calculated mass flow rate of fouling deposition transfer. In some embodiments, the mass per unit area of fouling can be calculated based on the mass flow rate of fouling deposition transfer, and then the fouling thickness can be calculated based on the mass per unit area of fouling and the fouling density. Exemplarily, the fouling thickness of the core can be calculated through equations (10) and (11). The calculation relationship of the fouling thickness of the steam generator is the same. Replace Gnode Replace it with G sed That's all.

[0157] (10)

[0158] (11)

[0159] In the formula: m node is the mass per unit area of fouling in the core, with the unit of kg / m 2 ;

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

[0161] t is time, with the unit of s;

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

[0163] is the fouling density, with the unit of kg / m 3 .

[0164] Among them, is the input parameter.

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

[0166] The embodiment of the present application simultaneously considers the fouling mass inside the core, inside the steam generator, and inside the letdown system, comprehensively considering the parts where fouling is generated and disappears in the primary loop, making the fouling distribution in the entire loop more reasonable and the calculated fouling thickness more accurate. It can be understood that in some other embodiments, the layout in the reactor loop may be different, and the steam generator and / or the letdown system may not be considered, thus omitting the corresponding calculation steps.

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

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

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

[0170] The boron adsorption mass on the fouling surface, i.e., the mass of boron adsorbed on the fouling surface, can be calculated through a certain relational expression. For example, the boron adsorption mass can be calculated based on the boron concentration on the cladding surface. In an exemplary embodiment, the mass of boron adsorbed on the fouling surface can be obtained based on the relational expression of boron adsorption mass with the boron concentration and fouling mass on the cladding surface fitted according to test data.

[0171] An example of the calculation relational expression for boron deposition mass is as follows:

[0172] (12)

[0173] In the formula, M b is the boron deposition mass, with the unit of kg;

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

[0175] k B is the concentration factor;

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

[0177] is the coolant density, with the unit of kg / m 3 ;

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

[0179] A is the area of the calculation region on the cladding surface, with the unit of m 2 ;

[0180] M b,2 is the boron adsorption mass, with the unit of kg;

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

[0182] Among them, k B 、C coolant B 、 、A、C clad b are input parameters.

[0183] The thickness of the boron deposition layer inside the fouling can be obtained based on the fouling thickness. For example, it can be obtained based on the relational expression of the boron deposition layer thickness inside the fouling with the fouling thickness fitted according to test data, or based on the concentration of target substances (such as lithium and boron) inside the fouling and the fouling thickness. In an exemplary embodiment, please refer to Figure 3, dirt deposits on the surface of the fuel assembly cladding. Based on the given dirt thickness, the grid is evenly divided, and the thickness of each layer of the grid is dx. The grid is divided from the dirt surface (close to the fluid side) to the cladding surface in sequence, and the parameters at each layer of the grid are determined. For example, if the dirt is evenly divided into N - 1 layers of grids, these N - 1 layers of grids share N surfaces, namely surfaces x(1), x(2), …, x(n), x(n + 1), …, x(N). Among them, the thickness of the nth layer of the grid is dx(n). It can be understood that considering the large size of the fuel assembly cladding surface, each layer of the grid can also be divided into multiple grids in the direction perpendicular to the dirt deposition direction, and the calculation is performed for each grid in a loop. In the above - mentioned embodiment, the grid is divided from the dirt surface to the cladding surface in sequence. In some other embodiments, the grid can also be divided from the cladding surface to the dirt surface in sequence, and the embodiments of the present application do not limit this.

[0184] Based on the concentration distribution of the target substance i inside the dirt, it is judged whether the target substance i at the grid reaches the critical concentration, that is, whether it exceeds the solubility of the deposited boron - lithium compound, so as to obtain the thickness d of the boron deposition layer B . The thickness d of the boron deposition layer B The calculation formula is exemplified as follows:

[0185] (13)

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

[0187] d c is the dirt thickness, with the unit of m;

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

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

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

[0191] Among them, SI is the solubility of the deposited boron - lithium compound, with the unit of ppb;

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

[0193] T is the temperature inside the dirt, with the unit of °C.

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

[0195] Step S124: Determine whether the target parameter meets the convergence condition. If the target parameter does not meet the convergence condition, update the input parameter 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, making the solution process more accurate. Specifically, determining whether the target parameter meets the convergence condition includes: determining whether the error between the calculated mass evaporation rate and the first preset value is less than the first threshold; determining 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 calculation for the current time step is completed; otherwise, update the input parameter for 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 the calculation of each time step is completed. In some other embodiments, the calculation result may be output after the time point reaches the end time and the calculations for all time steps are completed. The form of outputting the calculation result can be set as needed.

[0199] Figure 4 It is a schematic structural diagram of a fouling risk analysis device for a reactor provided by an embodiment of the present application. The fouling risk analysis device for a reactor is used to implement the above Figure 1 or Figure 2 the method shown. As Figure 4 shown, the fouling risk analysis device 400 for a reactor includes a pre-processing module 410, a numerical solution module 420, and a post-processing module 430. Among them, 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, where the fouling thickness and boron deposition mass are used for fouling risk analysis; the post-processing module 430 is used to output the calculation result.

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

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

[0202] Processor 510 can be any type suitable for the local technical network and, by way of non-limiting example, can include one or more of the following: general-purpose computer, dedicated computer, microprocessor, digital signal processor (DSP), and processor based on a multi-core processor architecture. Computing device 500 can have multiple processors, such as an application-specific integrated circuit chip, which is clocked in time to synchronize with the main processor.

[0203] Memory 520 can 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, optical disc (CD), digital video disc (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 a power outage.

[0204] Computer program 530 includes computer-executable instructions executed by the relevant processor 510. Computer program 530 can be stored in ROM 524. Processor 510 can perform any appropriate actions and processes by loading computer program 530 into RAM 522.

[0205] Embodiments of the present application can be implemented by computer program 530 such that computing device 500 can execute any of the disclosed processes referred to Figure 1 or Figure 2 discussed. Embodiments of the present application can 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 included in a computing device 500 (e.g., the memory 520) or other storage devices accessible to the computing device 500. The computing device 500 may load the computer program 530 from the computer-readable medium into 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 program 530 is stored on the computer-readable medium.

[0207] Generally, the 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 executed by a controller, a microprocessor, or other computing devices. Although the various aspects of the embodiments of the present application are shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that, by way of non-limiting example, the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general-purpose 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 methods described above with reference to Figure 1 or Figure 2 the methods described. Generally, 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 the program modules may be combined or separated as needed among the program modules. The machine-executable instructions for the program modules may be executed within a local or distributed device. In a distributed device, the program modules may be located in local and remote storage media.

[0209] The program code for performing the methods of the present application may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program code is executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code may be executed entirely 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 entirely on a remote machine or server.

[0210] In the context of the present application, the computer program code or related data can be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like.

[0211] The computer-readable media can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable media can 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 the computer-readable storage media include electrical connections with one or more wires, portable computer floppy disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0212] Furthermore, although the operations are described in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in sequential order, or that all of the illustrated operations be performed, 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 construed as limitations on the scope of the present application, but rather can be construed as descriptions of features specific to particular embodiments. Certain features described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment can also be implemented separately or in any suitable sub-combination in multiple embodiments.

[0213] Although the present application has been described in language specific to structural features and / or methodological acts, it should be understood that the application defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, 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 calculation results; Wherein, 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; And the calculation of mass evaporation rate based on input parameters includes: 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 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; And the calculating of 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.

2. The method according to claim 1, 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.

3. The method according to claim 1, 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.

4. The method according to claim 3, 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.

5. 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; Wherein, 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; And the calculation of mass evaporation rate based on input parameters includes: 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 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; And the calculating of 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. 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 4.

7. 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 4.

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