Multi-working-condition intelligent calibration method for feed water heater model of nuclear power plant

Abstract

The invention relates to the technical field of feed water heater model calibration, aims to solve the problems that results vary from person to person, efficiency is low and calibration precision is poor under multiple working conditions in a traditional manual calibration mode, and discloses a multi-working-condition intelligent calibration method for a nuclear power plant feed water heater model. A feed water heater model and an intelligent optimization algorithm are transformed and integrated, data interaction between the model and the optimization algorithm is opened, standardized calibration of a single working condition is performed, the state of the model is initialized and the performance of the model is calibrated by using reference data of a single working condition one by one based on a mechanism and experience, and intelligent calibration of multiple working conditions is performed. And carrying out multi-working-condition calibration on the basis of a calibration result by adopting a population optimization method, and outputting steady-state data under each working condition. According to the method, the multi-working-condition weighted error of the model is further optimized by using a group optimization method, the calibration precision is improved, the number of optimization iterations can be greatly reduced, and the optimization efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of feedwater heater model calibration, and in particular to a multi-operating-condition intelligent calibration method for a nuclear power plant feedwater heater model. Background Art

[0002] Feedwater heaters, key components of nuclear power plant regenerative systems, heat feedwater and thereby improve the overall thermal efficiency of the unit. Feedwater heater simulation is an efficient, economical, and safe method for analyzing operational characteristics, and is widely used in design verification, transient analysis, and operator training.

[0003] During the simulation process, a simulation model must first be established. Subsequently, the model's performance and initialization state are calibrated based on reference data, such as design, experimental, or operational data. The calibrated model is considered to be essentially consistent with the physical entity and can then be used to simulate other steady-state and transient processes. As a direct factor in determining computational accuracy and efficiency, simulation models have become a focus of research both domestically and internationally, and have undergone significant development. On the one hand, modeling methods such as segmented, multi-node dynamic, and heat transfer microelement have been proposed. On the other hand, artificial intelligence-based data models and multi-scale coupling technologies are being used to further improve the computational efficiency and accuracy of the model.

[0004] In contrast, there is little research on the model calibration and initialization process. However, in actual engineering applications, this process also has a non-negligible impact. Model state initialization refers to adjusting the model state parameters to be completely consistent with the reference data. The state parameters mainly include: 1) structural parameters, 2) thermal hydraulic parameters, such as pressure, temperature, specific enthalpy, dryness, flow rate, heat transfer, and heat dissipation, and 3) performance parameters, such as liquid phase heat dissipation coefficient, steam heat dissipation coefficient, cylinder heat dissipation coefficient, vapor-liquid phase heat transfer coefficient, tube convection heat transfer coefficient, shell-side convection heat transfer coefficient, shell-side condensation heat transfer coefficient, and shell-side non-condensable gas heat transfer coefficient. Model performance calibration refers to the process of determining model performance parameters based on reference data.

[0005] Since the feedwater heater model has a large number of performance parameters, which can be as many as nearly ten, the model performance calibration process is a multi-solution problem. However, traditional manual calibration methods usually only select reference data for one operating condition, and repeatedly adjust these performance parameters according to the engineer's experience until the model state is consistent with the reference data. The calibration results vary from person to person. In fact, the reference data covers a variety of operating conditions. This traditional manual calibration method does not consider the applicability of the model under other operating conditions, resulting in low model accuracy under other operating conditions. Therefore, studying the multi-operating condition intelligent calibration technology of the feedwater heater model to achieve standard and intelligent efficient and high-precision model calibration has important engineering value. Summary of the Invention

[0006] The purpose of the present invention is to provide a multi-operating-condition intelligent calibration method for a nuclear power plant feedwater heater model, so as to solve the problems of different results from different people, low efficiency and poor calibration accuracy under multiple operating conditions in the traditional manual calibration method, and to achieve standardized, efficient and high-precision model performance calibration and state initialization.

[0007] The present invention addresses the multi-solution problem of model performance calibration under a single working condition. It uses experimental mechanisms and calibration experience to determine a portion of performance parameters and calculates the remaining performance parameters by constructing an iterative format, thereby achieving more standardized and efficient calibration.

[0008] In order to achieve the above object, the present invention provides the following technical solutions:

[0009] A multi-operating-condition intelligent calibration of a nuclear power plant feedwater heater model, including:

[0010] S1. Read reference data of N working conditions;

[0011] S2. Transform and integrate the feedwater heater model with the intelligent optimization algorithm to facilitate data interaction between the model and the optimization algorithm. The optimization algorithm can call the feedwater heater model for calculations and evaluate the optimization effect based on the calculation results. Furthermore, the optimization algorithm can obtain the performance calibration results of the model under each single operating condition and create an initial population based on these calibration results.

[0012] S3. Perform standardized calibration for a single working condition. Based on the mechanism and experience, use the reference data of a single working condition one by one to initialize the model state and calibrate the model performance.

[0013] S4: Perform intelligent calibration of multiple working conditions, using population optimization method to carry out multi-working condition calibration based on the S3 calibration results;

[0014] S5. Output steady-state data under each working condition.

[0015] In some embodiments, the reference data includes an interface status of the feedwater heater, structural and status parameters of the feedwater heater, and calibration weights of various operating conditions.

[0016] In some embodiments, S2 includes:

[0017] S2.1. For the feedwater heater model, divide the model variables into performance calibration coefficients, output parameters, and state parameters, and provide a parameter output interface;

[0018] S2.2. For the intelligent optimization algorithm, obtain data from the parameter output interface of the model described in S2.1, and configure the algorithm's population, parameters to be optimized, and target parameters for optimization based on the data.

[0019] In some embodiments, a bee colony algorithm is used in S2.2, including:

[0020] S2.2.1. Take the number of working conditions N+x in S1 as the leader bee population size count, where x is 0 to 20%*N;

[0021] S2.2.2. Use the performance calibration coefficient in the feedwater heater model as the parameter to be optimized, and determine the dimension of the parameter to be optimized and the optimization range, i.e., the upper and lower limits of the parameter;

[0022] S2.2.3. The sum of the relative errors between the model output parameters and the reference data is weighted according to the working condition weight and used as the target parameter for optimization.

[0023] In some embodiments, S3 includes:

[0024] S3.1. Check mass balance;

[0025] S3.2. Calculate steam and water loading;

[0026] S3.3. Calculate the heat transfer area of ​​the heat transfer tubes in the condensation zone and the hydrophobic zone;

[0027] S3.4. Check the energy balance and add the product of the flow rate and specific enthalpy of all interfaces of the feedwater heater to obtain the total heat dissipation of the feedwater heater;

[0028] S3.5. Calculate the total heat transfer of the heat transfer tube;

[0029] S3.6. Calculate heat dissipation and interphase heat transfer;

[0030] S3.7. Calculate the heat exchange between the steam and the heat transfer tubes in the condensation zone;

[0031] S3.8. Calculate the heat exchange between the condensate and the heat transfer pipes in the cooling zone;

[0032] S3.9. Calculate the heat transfer coefficient of the heat transfer tube wall based on the heat transfer tube wall thickness and thermal conductivity;

[0033] S3.10. Use empirical relationships to estimate tube-side convective heat transfer calibration parameters;

[0034] S3.11. Calculate the heat transfer calibration parameters of the shell-side hydrophobic region using the LMTD method;

[0035] S3.12, if k sub ≤0, then it means k t The estimated value is too small and needs to be multiplied by the correction factor z and then returned to S3.11 for recalculation. sub >0, go to the next step;

[0036] S3.13. Calculate the heat transfer calibration coefficient in the shell-side condensation zone using the LMTD method;

[0037] S3.14, if k con ≤0, then it means k t The estimated value is too small and needs to be multiplied by the correction factor z and then returned to S3.11 for recalculation. con >0, go to the next step;

[0038] S3.15. Calculate the heat dissipation and interphase heat transfer performance calibration coefficients.

[0039] In some embodiments, according to h f *A f The heat dissipated by the steam-water phase is distributed in the ratio of f The heat transfer area A will be estimated based on empirical formulas such as DB relationship. f The volume ratio of the vapor and liquid phases will be calculated, and the interphase heat exchange will be estimated according to the heat dissipation*y. y can be 0.1 to 1. The calculation formula is as follows:

[0040] Q=h f A f (T sat -T amb )

[0041] Where Q is the heat dissipation, h is the heat transfer coefficient, A is the heat transfer area, f is the steam or liquid phase, T sat is the saturation temperature, T amb is the ambient temperature;

[0042] The DB relation is as follows:

[0043] Nu t =0.023*Re 0.8 *Pr n

[0044] Where Nu t is the Nusselt number, Re is the Reynolds number, and Pr is the Prandtl number.

[0045] In some embodiments, the heat exchange rate Q between the steam and the heat transfer tube in the junction area is con The calculation formula is as follows:

[0046] Q con =[G di *H di +G si *H si -(G di +G si )*H l,sat ]-Q sloss -Q sl

[0047] Where Q sloss is the heat dissipation of steam, Q sl is the interphase heat transfer, H l,sat is the liquid phase saturation specific enthalpy.

[0048] In some embodiments, the heat exchange rate Q between the condensed water in the cooling zone and the heat transfer pipe is sub The calculation formula is as follows:

[0049] Q sub =G do *(H l,sat -H do )-Q lloss +Q sl

[0050] Where Q lloss The heat dissipated by condensed water.

[0051] In some embodiments, the heat transfer calibration parameter k of the shell-side hydrophobic region is sub The calculation formula is as follows:

[0052] Q sub =G ti *cp ti *EFF*(T sat -T ti )

[0053]

[0054]

[0055] Where, cp ti The constant-pressure specific heat is obtained by querying the saturated physical property table for the tube side inlet temperature, EFF is the heat transfer efficiency, cmin and cmax are the smaller and larger values ​​of the tube side and shell side flow rate * constant-pressure specific heat, respectively.

[0056] In some embodiments, S4 includes:

[0057] S4.1. Initialize the relevant parameters of the swarm optimization algorithm;

[0058] S4.2. Calculate the weighted values ​​of the multi-operating-condition errors of the individuals in the population;

[0059] S4.3. Half of the individuals with smaller error weight values ​​constitute the leading bee population, and the rest serve as the following bee population;

[0060] S4.4, search and generate new leader bee populations;

[0061] S4.5, search and generate new follower bee populations;

[0062] S4.6. Determine whether to generate a scout bee population. When an individual in a swarm (including all swarms, leader swarms, follower swarms, and scout swarms) remains unchanged for n consecutive iterations, the individual is transformed into a scout bee and randomly explores unknown areas within the optimization domain.

[0063] S4.7. Output the current optimal individual, i.e., the individual with the smallest error weighted value;

[0064] S4.8. If the weighted error meets the accuracy requirement or exceeds the maximum number of iterations, proceed to step S5; otherwise, return to S4.2.

[0065] In some embodiments, the search method used in S4.4 is as follows:

[0066] U i =v i +r(v i -v j )

[0067] Where, v i Leading the contemporary bee individual i,v j is a randomly selected individual from the contemporary leader bee population, r is a random number, U i The new location resulting from the search.

[0068] Compared with the prior art, the multi-operating-condition intelligent calibration method for a nuclear power plant feedwater heater model provided by the present invention has the following beneficial effects:

[0069] To improve the accuracy of model performance calibration under multiple operating conditions, this method utilizes a swarm optimization approach to further optimize the model's weighted errors across multiple operating conditions, thereby enhancing calibration accuracy. Furthermore, to reduce optimization time, which can often take several hours, this method incorporates the calibration results for each single operating condition into the initial population of the optimization process, significantly reducing the number of optimization iterations and significantly improving optimization efficiency.

[0070] Through mechanistic calibration of a single operating condition, the present invention can complete a rough adjustment of model performance and initialization of the model state within 0.5 seconds. Subsequently, based on the rough adjustment results for each operating condition, the optimal optimization region is quickly identified. This not only effectively avoids optimization regions that violate physical laws but also significantly improves optimization efficiency, reducing the weighted error across multiple operating conditions to approximately 1% within 10 minutes. Ultimately, the model can be calibrated and steady-state calculation data generated within tens of minutes according to a standardized process.

[0071] Compared with manual calibration, this method has significant advantages. On the one hand, it can complete several days' work in tens of minutes. On the other hand, the process is highly standardized and normalized, ensuring the quality of calibration and improving accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for the technical description.

[0073] Figure 1 A flow chart of the multi-operating-condition intelligent calibration method for a nuclear power plant feedwater heater model provided by the present invention;

[0074] Figure 2 A schematic diagram of the structural relationship of each module provided by the present invention;

[0075] Figure 3 This is a thermal balance design diagram of the No. 6 high-pressure feedwater heater of a pressurized water reactor nuclear power plant using existing technology;

[0076] Figure 4 This is a schematic diagram of the optimization curve provided by the present invention. DETAILED DESCRIPTION

[0077] The following is further explained in detail through specific implementation methods.

[0078] like Figure 1 As shown, the present invention provides a multi-operating-condition intelligent calibration method for a nuclear power plant feedwater heater model. This method utilizes both mechanistic and empirical methods to efficiently initialize the model's state and preliminarily calibrate its performance. Model parameters are divided into three parts: state parameters are retained to verify calibration accuracy, while calibration coefficients and output parameters are encapsulated as a coupling interface for connecting to an optimization algorithm. After the model is connected to an intelligent optimization algorithm, such as bee swarm optimization, via the coupling interface, the preliminarily calibrated dataset serves as the optimization algorithm's initial population. The weighted calibration errors for each operating condition are used as the optimization target parameters. Ultimately, the intelligent optimization algorithm is used to further improve calibration accuracy in a short period of time.

[0079] The main process of this method includes:

[0080] S1. Read reference data of N working conditions, such as design data, operation data, and experimental data. The reference data includes:

[0081] (1) Interface status of the feedwater heater. The interfaces include the drain inlet, drain outlet, steam inlet, heat transfer tube inlet, and heat transfer tube outlet. The status refers to the flow rate and specific enthalpy. For experimental data and operating data, the specific enthalpy value can be obtained by querying the saturation property table based on the temperature measurement results.

[0082] (2) Structure and state parameters of the feedwater heater. Structure refers to the outer diameter, wall thickness and heat exchange area of ​​the heat transfer tube, cylinder surface area, cylinder length, heat transfer tube bundle height, and shell volume; state parameters refer to the water level on the shell side, steam space pressure or temperature.

[0083] (3) Calibration weight of each working condition.

[0084] S2. Transform and integrate the feedwater heater model with the intelligent optimization algorithm to facilitate data exchange between the model and the optimization algorithm. The optimization algorithm can call the feedwater heater model for calculations and evaluate the optimization effect based on the calculation results. Furthermore, the optimization algorithm can obtain the performance calibration results of the model under each single operating condition and create an initial population based on these calibration results. This includes the following steps:

[0085] S2.1. For the feedwater heater model, the model variables are divided into performance calibration coefficients, output parameters, and state parameters.

[0086] Performance calibration coefficients include but are not limited to: tube side heat transfer performance calibration parameter k t , shell side condensation zone heat transfer performance calibration parameter k con , shell side drain cooling zone heat transfer performance calibration parameter k sub , Steam heat dissipation performance calibration parameter k sloss , condensation water heat dissipation performance calibration parameter k llosss , interphase heat transfer performance calibration parameter k sl .

[0087] k t For example, its meaning is as follows:

[0088] Nu t =k t *Re 0.8 *Pr n

[0089] Among them, Nu t is the tube-side Nusselt number, Re is the Reynolds number of the tube-side fluid, and Pr is the Prandtl number of the tube-side fluid. When the fluid is heated, n is 0.4, and when it is cooled, n is 0.3. In particular, when n is treated as a variable, it can also be used as a performance calibration factor.

[0090] The output parameters mainly include: tube side outlet temperature, shell side drain outlet temperature, shell side saturation temperature, heat transfer capacity of heat transfer tube, heat transfer capacity of heat transfer tube in condensation zone, and heat transfer capacity of heat transfer tube in drain zone.

[0091] The remaining parameters are state parameters used to characterize the operating status of the equipment, such as the shell-side steam and condensate loading, the heat transfer area and wall thickness of the heat transfer tube, the surface area of ​​the cylinder, and the physical properties of the fluid.

[0092] S2.2. For intelligent optimization algorithms, data is obtained from the model's parameter output interface, and based on this data, the algorithm's population, parameters to be optimized, and target parameters for optimization are configured. For example, the bee colony algorithm includes:

[0093] S2.2.1. Take the number of working conditions N+x in S1 as the leader bee population size count, where x is 0 to 20%*N.

[0094] S2.2.2. Use the performance calibration coefficient in the feedwater heater model as the parameter to be optimized, and determine the dimension of the parameter to be optimized and the optimization range, i.e., the upper and lower limits of the parameter.

[0095] S2.2.3. The sum of the relative errors between the model output parameters and the reference data is weighted according to the working condition weight and used as the target parameter for optimization.

[0096] S3. Perform standardized calibration of a single working condition. Based on the mechanism and experience, use the reference data of a single working condition one by one to initialize the state of the model and calibrate the performance of the model.

[0097] Under a single working condition, the steps for model state initialization and performance calibration are as follows:

[0098] S3.1. Check the mass balance. If the sum of the feedwater heater interface flows (i.e., the net flow) exceeds 1% of the drain flow, the reference data is incorrect and the calculation is terminated.

[0099] S3.2. Calculate the steam and water loadings. First, calculate the steam and water volumes by linear interpolation based on the water level and volume table in the reference data. Then, query the saturated physical property table based on the steam space pressure or temperature in the reference data to obtain the density values ​​of saturated steam and water. Combined with the volume values ​​calculated (the table interpolation calculation above), the steam and water loadings can be determined. First, calculate the steam and water volumes based on the water level and volume table in the reference data. The above calculation is based on the input data (the input data contains a table of water level and volume relationship points).

[0100] S3.3. Calculate the heat transfer area of ​​the heat transfer tubes in the condensation and drain zones. Based on the water level and tube bundle layout information in the reference data, calculate the area of ​​the heat transfer tube bundle exposed to the steam space and the area of ​​the heat transfer tube bundle submerged in the condensate.

[0101] S3.4. Verify the energy balance. According to the following formula, add the product of the flow rate and specific enthalpy of all the interfaces of the feedwater heater to obtain the total heat dissipation of the feedwater heater.

[0102] Q loss =G di *H di +G si *H si +G ti *H ti -G do *H do -G to *H to

[0103] Among them, Q loss is the total heat dissipation, G is the mass flow rate, H is the specific enthalpy, subscript di represents the drain inlet, subscript si represents the steam inlet, subscript ti represents the tube side feed water inlet, subscript to represents the tube side feed water outlet, and subscript do represents the drain outlet.

[0104] S3.5. Calculate the total heat transfer capacity Qt of the heat transfer tube. The calculation formula is as follows:

[0105] Q t =G to *H to -G ti *H ti

[0106] S3.6. Calculate the heat dissipation and interphase heat transfer. According to the following formula, according to h f *A f The heat dissipation of the steam and water phases is distributed in the proportion of f The heat transfer area A will be estimated based on empirical formulas such as DB relationship. f The calculation is based on the volume ratio of the vapor and liquid phases. The interphase heat transfer is estimated as heat dissipation * y, where y can be 0.1 to 1. Since the interphase heat transfer accounts for a small proportion under steady-state conditions, interphase heat transfer calibration is not required.

[0107] Q=h f A f (T sat -T amb )

[0108] Where Q is the heat dissipation, h is the heat transfer coefficient, A is the heat transfer area, f represents the steam or liquid phase, T sat is the saturation temperature, T amb is the ambient temperature.

[0109] The DB relation is as follows:

[0110] Nu t =0.023*Re 0.8 *Pr n

[0111] Among them, Nu t is the Nusselt number, Re is the Reynolds number, and Pr is the Prandtl number.

[0112] S3.7. Calculate the heat exchange rate Q between the steam and the heat transfer tube in the condensation zone con Calculated according to the following formula:

[0113] Q con =[G di *H di +G si *H si-(G di +G si )*H l,sat ]-Q sloss -Q sl

[0114] Among them, Q sloss is the heat dissipation of steam, Q sl is the interphase heat transfer, H l,sat is the liquid phase saturation specific enthalpy, which is obtained by querying the saturation physical property table based on the shell side temperature in the reference data.

[0115] S3.8. Calculate the heat exchange rate Q between the condensate and the heat transfer tube in the cooling zone sub , calculated according to the following formula:

[0116] Q sub =G do *(H l,sat -H do )-Q lloss +Q sl

[0117] Among them, Q lloss The heat dissipated by condensed water.

[0118] S3.9. Calculate the heat transfer coefficient h of the heat transfer tube wall based on the heat transfer tube wall thickness and thermal conductivity w Calculated according to the following formula:

[0119]

[0120] Where λ is the thermal conductivity of the tube wall and δ is the thickness of the tube wall.

[0121] S3.10. Use empirical relationships such as the DB formula to estimate the tube-side convective heat transfer calibration parameter k. t .

[0122] S3.11. Calculation of the heat transfer calibration parameter k in the shell-side hydrophobic region using the LMTD method sub .

[0123] Q sub =G ti *cp ti *EFF*(T sat -T ti )

[0124]

[0125]

[0126] Among them, cp tiThe constant-pressure specific heat is obtained by querying the saturated physical property table for the tube side inlet temperature, EFF is the heat transfer efficiency, cmin and cmax are the smaller and larger values ​​of the tube side and shell side flow rate * constant-pressure specific heat, respectively.

[0127] S3.12, if k sub ≤0, then it means k t If the estimated value is too small, it needs to be multiplied by the correction factor z and then returned to S3.11 for recalculation. The value of z is 0.01 to 0.5. sub >0, go to the next step.

[0128] S3.13. Calculate the heat transfer calibration coefficient k in the shell-side condensation zone using the LMTD method con . With k sub The calculation of is similar.

[0129] S3.14, if k con ≤0, then it means k t If the estimated value is too small, it needs to be multiplied by the correction factor z and then returned to S3.11 for recalculation. The value of z is 0.01 to 0.5. con >0, go to the next step.

[0130] S3.15. Calculate the heat dissipation and interphase heat transfer performance calibration coefficients. Use the vapor phase heat dissipation correction coefficient k sloss For example, as shown below:

[0131] Q sloss =k sloss *A sloss *(T sat -T amb )

[0132] Among them, Q sloss is the vapor phase heat dissipation coefficient, A sloss is the vapor phase heat dissipation surface area.

[0133] S4: Perform intelligent calibration of multiple working conditions, using population optimization method to carry out multi-working condition calibration based on the S3 calibration results. The specific steps are as follows:

[0134] S4.1. Initialize the relevant parameters of the swarm optimization algorithm: The swarm population count, the dimension dim of the problem to be optimized, the parameter boundaries (i.e., the optimization domain), and the target parameters are all given in step S2. First, set the maximum number of iterations and the number of follower bees, n (n, calculated according to the following formula). Then, initialize the swarm population. The N sets of corrected parameter configurations for a single operating condition calculated in step S3 are directly used as the leader bee population. The remaining individuals in the population are randomly generated within the optimization domain.

[0135]

[0136] Among them, intup means round up.

[0137] S4.2. Calculate the multi-condition error weights of individuals in the population.

[0138] S4.3. Half of the individuals with smaller error weight values ​​constitute the leading bee population, and the rest constitute the following bee population.

[0139] S4.4. Search to generate a new leader bee population. The search method is as follows:

[0140] U i =v i +r(v i -v j )

[0141] Among them, v i Leading the contemporary bee individual i,v j is a randomly selected individual from the contemporary leader bee population; r is a random number ranging from -1 to 1; U i For the new position generated by the search, if the target parameter value of the position is smaller, the original individual will be replaced as the new leader bee.

[0142] S4.5. Search and generate new follower bee populations.

[0143] S4.6. Determine whether to generate a scout bee population. When there is an individual in the swarm that remains unchanged for n consecutive iterations, the individual is transformed into a scout bee and randomly explores unknown areas within the optimization domain.

[0144] S4.7. Output the current optimal individual, i.e., the individual with the smallest error weighted value.

[0145] S4.8. If the weighted error meets the accuracy requirement or exceeds the maximum number of iterations, proceed to step S5; otherwise, return to S4.2.

[0146] S5. Output steady-state data under each working condition.

[0147] The output parameters and state parameters calculated in step S3, along with the performance calibration coefficients optimized in step S4, are input into the feedwater heater model and iteratively calculated until the steady state is reached. All model parameters are exported and saved. The saved steady-state data has been calibrated with the reference data and can be used as a baseline for simulation analysis.

[0148] Example

[0149] Taking the design data of the No. 6 high-pressure feedwater heater of a pressurized water reactor nuclear power plant as an example, the specific implementation process of the present invention is described below. The specific process is as follows:

[0150] S1. Read Figure 3Steady-state design data for a total of five operating conditions, including 100%, 90%, 70%, 50%, and 30% power steps in the thermal balance design diagram. The reference data should include:

[0151] (1) Interface status of the feedwater heater. The interfaces include the drain inlet, drain outlet, steam inlet, heat transfer tube inlet, and heat transfer tube outlet. The status refers to the flow rate and specific enthalpy. For experimental data and operating data, the specific enthalpy value can be obtained by querying the saturation property table based on the temperature measurement results.

[0152] (2) Structure and state parameters of the feedwater heater. Structure refers to the outer diameter, wall thickness and heat exchange area of ​​the heat transfer tube, cylinder surface area, cylinder length, heat transfer tube bundle height, and shell volume; state parameters refer to the water level on the shell side, steam space pressure or temperature.

[0153] (3) The weight of the 100% power step data is set to 0.6, and the weight of the other working conditions is 0.1.

[0154] S2. Transform and integrate the feedwater heater model with the intelligent optimization algorithm. This includes:

[0155] 1) For the feedwater heater model, the model variables are divided into performance calibration coefficients, output parameters, and state parameters.

[0156] The performance calibration parameters are: tube side heat transfer performance calibration parameter k t , shell side condensation zone heat transfer performance calibration parameter k con , shell side drain cooling zone heat transfer performance calibration parameter k sub , Steam heat dissipation performance calibration parameter k sloss , condensation water heat dissipation performance calibration parameter k llosss , interphase heat transfer performance calibration parameter k sl .

[0157] The output parameters mainly include: tube side outlet temperature, shell side drain outlet temperature, shell side saturation temperature, heat transfer capacity of heat transfer tube, heat transfer capacity of heat transfer tube in condensation zone, and heat transfer capacity of heat transfer tube in drain zone.

[0158] The remaining parameters are status parameters, which are used to characterize the operating status of the device.

[0159] 2) For the bee swarm algorithm:

[0160] 2.1) The number of working conditions of S1 5 + x is used as the size of the leading bee population, where x is 0.

[0161] 2.2) The performance calibration coefficient in the feedwater heater model is used as the parameter to be optimized, and the dimension of the parameter to be optimized is determined to be 6, and the optimization range, that is, the upper and lower limits of the parameter are both 0 to 100.

[0162] 2.3) The tube side outlet temperature, saturation temperature, and shell side outlet temperature are selected as the output parameters for error calculation. The sum of the relative errors between the output parameters and the reference data is weighted according to the operating condition weight and used as the target parameter for optimization.

[0163] S3. Perform standardized calibration of a single working condition. Based on the mechanism and experience, use the reference data of a single working condition one by one to initialize the state of the model and calibrate the performance of the model.

[0164] Under a single working condition, the steps for model state initialization and performance calibration are as follows:

[0165] 1) Check the mass balance. The sum of the feedwater heater interface flow rates, i.e., the net flow rate, is 0.0 kg / s. Continue the calculation.

[0166] 2) Calculate steam and water loading. First, calculate the steam and water volumes based on the water level and volume table in the reference data. Then, query the saturated physical properties table based on the steam space pressure or temperature in the reference data to obtain the density values ​​of saturated steam and water. Combined with the calculated volume values, the steam and water loading quantities can be determined.

[0167] 3) Calculate the heat transfer area of ​​the heat transfer tubes in the condensation zone and the hydrophobic zone. Based on the water level and the layout height of the heat transfer tube bundle in the reference data, calculate the area of ​​the heat transfer tube bundle exposed to the steam space and the area of ​​the heat transfer tube bundle submerged in the condensate.

[0168] 4) Check the energy balance. According to the following formula, add the product of the flow rate and specific enthalpy of all the interfaces of the feedwater heater to obtain the total heat dissipation of the feedwater heater.

[0169] 5) Calculate the total heat transfer capacity Q of the heat transfer tube t .

[0170] 6) Calculate the heat dissipation and interphase heat transfer. The ambient temperature is 20°C.

[0171] 7) Calculate the heat exchange Q between steam and heat transfer tube in the condensation zone con .

[0172] 8) Calculate the heat exchange Q between the condensate and the heat transfer pipe in the cooling area sub .

[0173] 9) Calculate the heat transfer coefficient h of the heat transfer tube wall based on the heat transfer tube wall thickness and thermal conductivity w .

[0174] 10) Use the DB formula to estimate the tube side convection heat transfer calibration coefficient k t .

[0175] 11) Calculate the heat transfer calibration coefficient k of the shell side hydrophobic area using the LMTD method sub .

[0176] 12)k sub >0, represents k t The estimated value is reasonable, so proceed to the next step of calculation.

[0177] 13) Calculate the heat transfer calibration coefficient k in the shell side condensation zone using the LMTD method con .

[0178] 14)k con >0, represents k t The estimated value is reasonable, so proceed to the next step of calculation.

[0179] 15) Calculate the calibration coefficients for heat dissipation and interphase heat transfer performance.

[0180] S4: Perform intelligent calibration of multiple working conditions, using population optimization method to carry out multi-working condition calibration based on the S3 calibration results. The specific steps are as follows:

[0181] 1) Initialize the relevant parameters of the swarm optimization algorithm. The swarm population, the dimension of the problem to be optimized, the parameter boundaries (i.e., the optimization domain), and the target parameters for optimization are all given in step S2. First, set the maximum number of iterations to 10,000 and the number of generations of follower bees to 15. Then, use the five sets of corrected parameter configurations for a single operating condition as the initial population of leading bees.

[0182] 2) Calculate the multi-condition error weighted values ​​of the individuals in the population.

[0183] 3) Half of the individuals with smaller error weight values ​​constitute the leading bee population, and the rest serve as the following bee population.

[0184] 4) Search and generate new leader bee populations.

[0185] 5) Search and generate new follower bee populations.

[0186] 6) Search to generate scout bees.

[0187] 7) Output the current optimal individual, that is, the individual with the smallest error weighted value.

[0188] 8) If the weighted error meets the accuracy requirement or exceeds the maximum number of iterations, proceed to step S5, otherwise return to step 2) of step S4.

[0189] S5. Output steady-state data under each working condition.

[0190] The output parameters and state parameters calculated in step S3, along with the performance calibration coefficients optimized in step S4, are input into the feedwater heater model and iteratively calculated until the steady state is reached. All model parameters are exported and saved. The saved steady-state data has been calibrated with the reference data and can be used as a baseline for simulation analysis.

[0191] In this embodiment, the multi-condition calibration error change and time consumption change curves are as follows: Figure 4 As shown, when the number of optimization iterations is 1, it represents step S3, i.e., the calibration result of a single working condition, and the weighted error shown is the minimum value of the weighted error of multiple working conditions in the parameter configuration set obtained in step S3.

[0192] Table 1 Calibration effect comparison table

[0193] / Methods Pure optimization algorithm Weighted error 1.0828％ 1.46％ time consuming 9.55 minutes 9.5 hours

[0194] Table 1 shows the comparison results between the present invention and a pure optimization algorithm, where the pure optimization algorithm refers to the performance of the model calibrated directly using the bee colony optimization algorithm. It is clear that the present invention has significant advantages in both computational accuracy and efficiency.

[0195] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed in the present invention should be covered by the scope of protection of the present invention.

Claims

1. A multi-operating-condition intelligent calibration of a feedwater heater model for a nuclear power plant, characterized in that: include: S1. Read reference data of N working conditions; S2. Transform and integrate the feedwater heater model with the intelligent optimization algorithm to open up data interaction between the model and the optimization algorithm; S3, perform standardized calibration of a single working condition, and use the reference data of a single working condition one by one based on mechanism and experience to initialize the state of the model and calibrate the performance of the model; S4, conduct intelligent calibration of multiple working conditions, and use population optimization method to carry out multi-condition calibration based on the calibration results of S3; S5. Output steady-state data under each working condition.

2. The multi-operating-condition intelligent calibration of the feedwater heater model of a nuclear power plant according to claim 1, characterized in that S2 include: S2.

1. For the feedwater heater model, the model variables are divided into performance calibration coefficients, output parameters, and state parameters, and a parameter output interface is provided; S2.

2. For the intelligent optimization algorithm, data is obtained from the parameter output interface of the model described in S2.1, and the algorithm population, parameters to be optimized, and target parameters for optimization are configured based on the data.

3. The multi-operating-condition intelligent calibration of a feedwater heater model for a nuclear power plant according to claim 2, characterized in that: S2.2 uses a bee colony algorithm, including: S2.2.1, take the number of working conditions N+x of S1 as the leader bee population size count, and x is 0~20%*N; S2.2.

2. The performance calibration coefficient in the feedwater heater model is used as the parameter to be optimized, and the dimension of the parameter to be optimized and the optimization range, i.e., the upper and lower limits of the parameter, are determined; S2.2.

3. The sum of the relative errors between the model output parameters and the reference data is weighted according to the working condition weight and used as the target parameter for optimization.

4. The multi-operating-condition intelligent calibration of a feedwater heater model for a nuclear power plant according to claim 1, characterized in that S3 include: S3.

1. Check mass balance; S3.

2. Calculate the steam and water loading; S3.

3. Calculate the heat transfer area of ​​the heat transfer tubes in the condensation zone and the hydrophobic zone; S3.

4. Check the energy balance and add the product of the flow rate and specific enthalpy of all interfaces of the feedwater heater to obtain the total heat dissipation of the feedwater heater; S3.

5. Calculate the total heat transfer of the heat transfer tube; S3.

6. Calculate the heat dissipation and interphase heat transfer; S3.

7. Calculate the heat exchange between the steam and the heat transfer tube in the condensation zone; S3.

8. Calculate the heat exchange between condensed water and heat transfer tubes in the cooling zone; S3.

9. Calculate the heat transfer coefficient of the heat transfer tube wall based on the thickness and thermal conductivity of the heat transfer tube wall; S3.

10. Use empirical relationships to estimate tube-side convective heat transfer calibration parameters; S3.11, using the LMTD method to calculate the heat transfer calibration parameters of the shell side hydrophobic area; S3.12, if k sub ≤0, then k t The estimated value is too small and needs to be multiplied by the correction factor z and then returned to S3.11 for recalculation. sub >0, go to the next step; S3.13, calculate the heat transfer calibration coefficient of the shell side condensation zone using the LMTD method; S3.14, if k con ≤0, then k t The estimated value is too small and needs to be multiplied by the correction factor z and then returned to S3.11 for recalculation. con >0, go to the next step; S3.

15. Calculate the heat dissipation and interphase heat transfer performance calibration coefficients.

5. The multi-operating-condition intelligent calibration of a feedwater heater model for a nuclear power plant according to claim 4, characterized in that: In S3.6, according to h f *A f The heat dissipation of the steam-water phase is distributed in the ratio of f The heat transfer area A will be estimated based on empirical formulas such as DB relationship. f The heat transfer between phases will be estimated based on the volume ratio of the vapor and liquid phases and the heat dissipation*y. y can be 0.1 to 1. The heat dissipation calculation formula is as follows: Q=h f A f (T sat -T amb ) In the formula, h is the heat transfer coefficient, A is the heat transfer area, f is the steam or liquid phase, T sat is the saturation temperature, T amb is the ambient temperature; The DB relation is as follows: Not t =0.023*Re 0.8 *Pr n In the formula, Nu t is the Nusselt number, Re is the Reynolds number, and Pr is the Prandtl number.

6. The multi-operating-condition intelligent calibration of a feedwater heater model for a nuclear power plant according to claim 4, characterized in that: In S3.7, the heat exchange between the steam and the heat transfer tube in the junction area is Q con The calculation formula is as follows: Q con =[G di *H di +G si *H si -(G di +G si )*H l,sat ]-Q sloss -Q sl In the formula, Q sloss is the heat dissipation of steam, Q sl is the heat transfer between phases, H l,sat is the liquid phase saturation specific enthalpy.

7. The multi-operating-condition intelligent calibration of a feedwater heater model for a nuclear power plant according to claim 4, characterized in that: In S3.8, the heat exchange between the condensed water and the heat transfer tube in the cooling zone is Q sub The calculation formula is as follows: Q sub =G do *(H l,sat -H do )-Q lloss +Q sl In the formula, Q lloss The heat dissipated by condensed water.

8. The multi-operating-condition intelligent calibration of a feedwater heater model for a nuclear power plant according to claim 4, characterized in that: In S3.11, the heat transfer calibration parameter k in the shell-side hydrophobic region sub The calculation formula is as follows: Q sub =G ti *cp ti *EFF*(T sat -T ti ) In the formula, cp ti The constant-pressure specific heat is obtained by querying the saturated physical property table for the tube side inlet temperature, EFF is the heat transfer efficiency, cmin and cmax are the smaller and larger values ​​of the tube side and shell side flow rate * constant-pressure specific heat, respectively.

9. The multi-operating-condition intelligent calibration of a feedwater heater model for a nuclear power plant according to claim 1, characterized in that S4 include: S4.

1. Initialize the relevant parameters of the swarm optimization algorithm; S4.2, calculating the multi-condition error weighted values ​​of the individuals in the population; S4.3, half of the individuals with smaller error weight values ​​constitute the leading bee population, and the rest serve as the following bee population; S4.4, search and generate new leader bee populations; S4.5, search and generate new follower bee population; S4.6, determine whether to generate a scout bee population. When there is an individual in the bee colony that remains unchanged for n consecutive iterations, the individual is transformed into a scout bee and randomly explores unknown areas in the optimization domain; S4.7, output the current optimal individual, i.e., the individual with the smallest error weighted value; S4.

8. If the weighted error meets the accuracy requirement or exceeds the maximum number of iterations, go to step S5, otherwise return to S4.

2.

10. The multi-operating-condition intelligent calibration of a feedwater heater model of a nuclear power plant according to claim 1, characterized in that: The search method used in S4.4 is as follows: U i =v i +r(v i -v j ) Where, vi is the contemporary leader bee individual i, v j is a randomly selected individual from the current leader bee population, r is a random number, U i The new location resulting from the search.

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