Gas prediction method and system of high-temperature gas cooled reactor fuel loading and unloading system
By constructing an initial CFD model and using data assimilation technology to update boundary conditions, the problem of inaccurate gas flow prediction in the high-temperature gas-cooled reactor fuel loading and unloading system is solved, achieving higher prediction accuracy and reliability, and providing valuable information for system development. experience.
Patent Information
- Application Number
- CN202411135754.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-19
- Publication Date
- 2025-01-24
AI Technical Summary
The existing gas flow prediction methods for high-temperature gas-cooled reactor fuel loading and unloading systems have problems such as inaccurate prediction results, high computing resources consumption, difficulty in real-time monitoring and online adjustment, and lack of an effective calibration mechanism.
By collecting physical property data and sensor data of the fuel loading and unloading system, an initial CFD model is constructed for gas prediction. Data assimilation technology is used to update the model boundary conditions, and multiple iterative predictions are performed. Finally, accurate gas prediction results are generated and evaluated through independent verification data. The credibility of the prediction results.
It significantly improves the accuracy and reliability of gas flow prediction, reduces the uncertainty of the initial stage of the model, ensures the credibility of the prediction results, and provides valuable experience for the development of the high -temperature and cold stack fuel loading system.
Smart Images
Figure CN119358433A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the field of nuclear energy technology, in particular to a gas prediction method and system for a high temperature gas-cooled reactor fuel loading and unloading system. Background Art
[0002] With the advancement of nuclear energy technology, especially the development of high temperature gas-cooled reactors, the accurate prediction of gas flow characteristics in fuel loading and unloading systems has become increasingly important. The pebble bed high temperature gas-cooled reactor occupies an important position in the fourth generation of nuclear reactor technology with its unique fuel element design and efficient thermal performance. At present, the commonly used methods for studying gas flow characteristics in this type of reactor include experimental measurement, empirical formulas, and simple numerical simulation techniques. These methods can provide basic information about gas flow to a certain extent, but for complex and changeable flow conditions, especially those involving multiphase flow and turbulence, they often find it difficult to provide sufficiently accurate data. In recent years, with the development of computer technology, computational fluid dynamics (CFD) technology has been widely used in various engineering fields, including the nuclear energy industry. Based on the basic principles of fluid mechanics and numerical simulation technology, the CFD model can perform high-precision simulation of complex fluid flows, thereby providing a more accurate gas prediction method for the fuel loading and unloading system of the high temperature gas-cooled reactor.
[0003] Although CFD technology has shown certain advantages in predicting the gas flow of the fuel loading and unloading system of the high-temperature gas-cooled reactor, it still faces some challenges in practical applications. First, due to the complexity of the internal flow field of the fuel loading and unloading system, the traditional CFD model may not be able to fully capture all the factors affecting the gas flow, resulting in a certain deviation between the predicted results and the actual situation. Secondly, CFD models usually require a lot of computing resources and a long simulation time, which limits their application in real-time monitoring and online adjustment. In addition, traditional CFD models are often simulated based on fixed boundary conditions, which may change with time and operating conditions during actual operation. Therefore, the simulation results under fixed boundary conditions may not accurately reflect the actual situation of dynamic changes. Finally, the traditional CFD simulation lacks an effective calibration mechanism and cannot use real-time monitoring data to correct and optimize the model, which limits the accuracy of the prediction results. Summary of the invention
[0004] In view of the problems existing in the existing gas prediction method of the high temperature gas-cooled reactor fuel loading and unloading system, the present invention is proposed. Therefore, the problem to be solved by the present invention is how to provide a gas prediction method and system for the high temperature gas-cooled reactor fuel loading and unloading system.
[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:
[0006] In a first aspect, the present invention provides a gas prediction method for a high-temperature gas-cooled reactor fuel loading and unloading system, which includes collecting physical property data of the fuel loading and unloading system, acquiring sensor data within the gas prediction time to construct a data set; constructing a CFD model of the fuel loading and unloading system based on the physical property data to perform gas prediction and generate preliminary gas prediction results; performing data assimilation on the sensor data set and the preliminary gas prediction results, and updating the CFD model of the fuel loading and unloading system according to the assimilation results; performing gas prediction again based on the updated CFD model of the fuel loading and unloading system to generate a final gas prediction result, comparing the final gas prediction result with independent verification data, and evaluating the final gas prediction result.
[0007] As a preferred solution of the gas prediction method for the high-temperature gas-cooled reactor fuel loading and unloading system of the present invention, the physical property data includes the material properties, geometric dimensions and surface characteristics of the fuel loading and unloading system; the acquisition of sensor data within the gas prediction moment to construct a data set includes the following steps: determining the acquisition frequency of sensor data according to the dynamic characteristics of the system and the prediction accuracy requirements, and selecting the sensor type according to the monitored physical quantity; connecting the sensor to the data acquisition system, configuring the data acquisition equipment, setting the acquisition frequency and sampling interval parameters; starting the data acquisition system to record sensor data, timestamping and position-marking the acquired sensor data, and continuously monitoring the data acquisition process during the entire gas prediction cycle; performing data preprocessing on the acquired temperature, pressure and flow values; integrating data from different sensors to construct a data set, and saving the processed data in a database for data assimilation.
[0008] As a preferred solution of the gas prediction method for the high-temperature gas-cooled reactor fuel loading and unloading system described in the present invention, the gas prediction of constructing a CFD model of the fuel loading and unloading system includes the following steps: constructing a fluid flow CFD model in the fuel loading and unloading system using computational fluid dynamics software based on the physical property data of the fuel loading and unloading system; determining the control equations of the CFD model of the fuel loading and unloading system, the control equations including the mass conservation equation, the momentum conservation equation and the energy conservation equation; setting model boundary conditions according to the inlet, outlet and wall of the fuel loading and unloading system, using the boundary conditions as constraints, and iteratively solving the control equations of the CFD model through a numerical solution under the constraints until the model converges, and obtaining preliminary gas prediction results including flow field distribution, pressure distribution and temperature distribution.
[0009] As a preferred solution of the gas prediction method for the high-temperature gas-cooled reactor fuel loading and unloading system described in the present invention, the data assimilation includes the following steps: using the Kalman filter algorithm to fuse the sensor data set with the preliminary prediction results, and making predictions through the data assimilation correction model; initializing the state vector and covariance matrix of the Kalman filter, and using the CFD model of the fuel loading and unloading system to calculate the state vector and covariance matrix at the prediction time; using the observation values in the sensor data set to assimilate the state vector and covariance matrix at the prediction time; obtaining the assimilated state vector and covariance matrix, and using the assimilated state vector and covariance matrix to update the boundary conditions of the CFD model
[0010] As a preferred solution of the gas prediction method for the high temperature gas-cooled reactor fuel loading and unloading system of the present invention, the method of updating the boundary conditions of the CFD model using the assimilated state vector and covariance matrix includes the following steps: performing state vector difference evaluation, calculating the relative difference between the assimilated state vector and the state vector at the prediction time, and if the relative difference of the boundary condition exceeds the set limit, entering the boundary condition update process, performing covariance matrix credibility evaluation, otherwise maintaining the value of the original boundary condition; if the covariance matrix element corresponding to the boundary condition is less than the first boundary threshold, calculating the new value of the first boundary condition, and the calculation formula is:
[0011] x n1 =0.9x a +0.1x0
[0012] In the formula, x n is the new value of the first boundary condition, that is, the updated boundary condition, x a is the assimilated state vector, x0 is the state vector at the prediction time; if the covariance matrix element corresponding to the boundary condition is above the first boundary threshold, the new value of the second boundary condition is calculated, and the calculation formula is:
[0013] x n2 =0.5(x a +x0)
[0014] In the formula, x n The new value of the second boundary condition is used; the coupling coefficient matrix is used to comprehensively adjust the updated boundary conditions; a local sensitivity analysis is performed on each boundary condition to evaluate the degree of impact on the overall simulation results; based on the historical update effect, the weights of the assimilated state vector and the state vector at the prediction time are dynamically adjusted to obtain the final boundary condition results, and the updated boundary conditions are re-entered into the CFD model
[0015] As a preferred solution of the gas prediction method for the high-temperature gas-cooled reactor fuel loading and unloading system of the present invention, the gas prediction based on the updated model includes the following steps: applying the updated boundary conditions to the CFD model; re-solving the mass conservation equation, momentum conservation equation and energy conservation equation of the CFD model; extracting the solution results from the CFD model to obtain the final gas prediction results.
[0016] As a preferred solution of the gas prediction method for the high-temperature gas-cooled reactor fuel loading and unloading system of the present invention, wherein: the evaluation of the final gas prediction result includes the following steps: selecting independent verification data corresponding to the prediction time period, and preprocessing the independent verification data; setting evaluation indicators, the evaluation indicators include mean absolute error, root mean square error, relative error and correlation coefficient, and pairing the final gas prediction result with the independent verification data at the same time and space point; using the paired data to calculate all evaluation indicators, draw a time series comparison chart of the predicted value and the actual value, and generate a spatial distribution chart of the prediction error; analyzing the distribution characteristics of the error in time and space, and performing sensitivity analysis on the key parameters of the CFD model based on the time and space distribution characteristics of the error combined with the system physical model; calculating the confidence interval of the prediction result based on the covariance matrix, converting the confidence interval into a standard deviation to calculate the relative uncertainty, and the calculation formula is:
[0017]
[0018] Among them, U i is the relative uncertainty of the ith prediction point, σ i is the standard deviation, is the predicted value, U is the average relative uncertainty, and n is the total number of prediction points. Based on the evaluation indicators and analysis results, the model is comprehensively rated and the prediction result credibility score is calculated. The calculation formula is:
[0019]
[0020] In the formula, Sc f is the prediction result credibility score, U is the average prediction uncertainty, and U max is the maximum acceptable uncertainty, obtained based on the historical performance of the prediction model; when the prediction result credibility score Sc f ≥ credible limit Sc f1 When the prediction result credibility score Sc f <Confidence limit Sc f1, the final gas prediction result is considered unreliable, the frequency of sensor data acquisition is increased, the CFD model is updated with the new value of the first boundary condition, the current coupling coefficient matrix is continued to be used for comprehensive adjustment of the boundary conditions, the weight of the assimilated state vector in the final boundary condition calculation is increased, and the secondary prediction result credibility score calculation is performed. If the secondary prediction result credibility score Sc f ≥ credible limit Sc f1 If the reliability score of the secondary prediction result Sc f <Confidence limit Sc f1 When the final gas prediction result is considered unreliable, the CFD model is updated with the new value of the second boundary condition, the coupling coefficient matrix is re-evaluated and adjusted, the data acquisition process is continuously monitored, and the processed data is saved in the database.
[0021] In a second aspect, the present invention provides a gas prediction system for a high-temperature gas-cooled reactor fuel loading and unloading system, which includes: a data acquisition module, which is used to collect physical property data of the fuel loading and unloading system, and collect sensor data sets sensed by multiple sensors deployed at the fuel loading and unloading system at the prediction time; a preliminary result generation module, which is used to build a CFD model of the fuel loading and unloading system based on the physical property data, and use the CFD model of the fuel loading and unloading system to perform gas prediction and generate preliminary gas prediction results; a data assimilation module, which is used to perform data assimilation on the sensor data set and the preliminary gas prediction results, and update the CFD model according to the assimilation results; a final result generation module, which is used to perform gas prediction again based on the updated CFD model to generate a final gas prediction result; an evaluation module, which is used to compare the final gas prediction result with the predetermined independent verification data based on a predetermined prediction error range, and evaluate the final gas prediction result.
[0022] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, the steps of a gas prediction method for a high temperature gas-cooled reactor fuel loading and unloading system are implemented.
[0023] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, the steps of a gas prediction method for a high temperature gas-cooled reactor fuel loading and unloading system are implemented.
[0024] The beneficial effect of the present invention is that by comprehensively collecting the physical property data of the fuel loading and unloading system, the real state of the system can be accurately reflected, the uncertainty in the initial stage of model establishment can be reduced, and the reliability of the entire prediction process can be improved. Through the preliminary prediction of the CFD model, information about the gas flow characteristics can be quickly obtained, which significantly improves the accuracy of the prediction, so that the CFD model can more accurately reflect the real state of the fuel loading and unloading system, ensure the reliability of the prediction results, and provide experience for the development of the high temperature gas-cooled reactor fuel loading and unloading system. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0026] Figure 1 Flowchart of the gas prediction method for the high temperature gas-cooled reactor fuel loading and unloading system. DETAILED DESCRIPTION
[0027] In order to make the above-mentioned purposes, features and advantages of the present invention more understandable, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.
[0028] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.
[0029] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.
[0030] Example 1
[0031] Reference Figure 1 , which is the first embodiment of the present invention, provides a gas prediction method for a high temperature gas-cooled reactor fuel loading and unloading system, comprising:
[0032] S1: Collect the physical property data of the fuel loading and unloading system and obtain the sensor data within the gas prediction time to build a data set;
[0033] Specifically, physical property data include material properties, geometric dimensions, and surface characteristics of the fuel handling system;
[0034] Determine the frequency of sensor data collection based on the system's dynamic characteristics and prediction accuracy requirements, and select sensor types based on the physical quantities being monitored, including temperature sensors, pressure sensors, and flow sensors;
[0035] Connect sensors to the data acquisition system, configure data acquisition equipment, and set acquisition frequency and sampling interval parameters;
[0036] Start the data acquisition system to record sensor data, time-stamp and position-identify the acquired sensor data, and continuously monitor the data acquisition process throughout the gas prediction cycle;
[0037] Perform data preprocessing on the acquired temperature, pressure and flow values, including data cleaning, data standardization and missing value filling;
[0038] The data from different sensors are integrated to construct a data set, and the processed data are saved in the database in preparation for data assimilation.
[0039] S2: Build a CFD model of the fuel loading and unloading system based on physical property data for gas prediction and generate preliminary gas prediction results;
[0040] Specifically, a CFD model of fluid flow in the fuel loading and unloading system is constructed using computational fluid dynamics software based on the physical property data of the fuel loading and unloading system;
[0041] Determine the control equations of the CFD model of the fuel loading and unloading system. The control equations include the mass conservation equation, momentum conservation equation, and energy conservation equation. The calculation formula is:
[0042]
[0043]
[0044] Where ρ is the gas density, u is the velocity vector, p is the ambient pressure, τ is the stress tensor, f is the external force vector, E is the total energy, k is the thermal conductivity, T is the temperature, and φ is the heat source term;
[0045] The model boundary conditions are set according to the inlet, outlet and wall of the fuel loading and unloading system. The boundary conditions are used as constraints. The control equations of the CFD model are iteratively solved by numerical solution under the constraints until the model converges, and preliminary gas prediction results including flow field distribution, pressure distribution and temperature distribution are obtained.
[0046] S3: Data assimilate the sensor data set with the preliminary gas forecast results and update the CFD model of the fuel loading and unloading system based on the assimilation results;
[0047] Specifically, the sensor data set is fused with the preliminary prediction results using the Kalman filter algorithm, and the prediction is performed by correcting the model through data assimilation;
[0048] Initialize the state vector and covariance matrix of the Kalman filter, and use the CFD model of the fuel loading and unloading system to calculate the state vector and covariance matrix at the prediction time. The calculation formula is:
[0049] X k =F k-1 ·X k-1 +B k-1 ·u k-1
[0050]
[0051] Among them, F k-1 is the dynamic model matrix, B k-1 is the control input influence matrix, u k-1 is the control input vector, Q k-1 is the process noise covariance matrix, X k is the state vector at the prediction time, P k is the covariance matrix at the prediction time, k is the prediction time, X k-1 is the state vector at the last prediction moment, P k-1 is the covariance matrix of the previous prediction moment, is the transpose of the dynamic model matrix;
[0052] The state vector and covariance matrix at the prediction time are assimilated using the observations in the sensor data set. The assimilated state vector and covariance matrix are:
[0053] X=X k +K(ZH·X k )
[0054] P=(IK·H)P k
[0055] Where X is the updated state vector, P is the updated covariance matrix, K is the Kalman gain, H is the observation model matrix, Z is the observation value, and I is the identity matrix;
[0056] The assimilated state vector and covariance matrix are obtained, and the boundary conditions of the CFD model are updated using the assimilated state vector and covariance matrix.
[0057] Perform state vector difference evaluation, calculate the relative difference between the assimilated state vector and the state vector at the prediction time, if the relative difference of the boundary conditions exceeds the set limit, enter the boundary condition update process and perform covariance matrix credibility evaluation, otherwise keep the original boundary condition value;
[0058] If the covariance matrix element corresponding to the boundary condition is less than the first boundary threshold, the new value of the first boundary condition is calculated using the following formula:
[0059] x n1 =0.9x a +0.1x0
[0060] In the formula, x n is the new value of the first boundary condition, that is, the updated boundary condition, x a is the assimilated state vector, x0 is the state vector at the prediction time;
[0061] If the covariance matrix element corresponding to the boundary condition is above the first boundary threshold, the new value of the second boundary condition is calculated using the following formula:
[0062] x n2 =0.5(x a +x0)
[0063] In the formula, x n is the new value of the second boundary condition;
[0064] Considering the mutual influence between different types of boundary conditions, the coupling coefficient matrix is used to comprehensively adjust the updated boundary conditions;
[0065] Calculate the uncertainty range of boundary conditions based on the covariance matrix, perform local sensitivity analysis on each boundary condition, and evaluate its impact on the overall simulation results;
[0066] Based on the historical update effect, the weights of the assimilated state vector and the state vector at the prediction time are dynamically adjusted to obtain the final boundary condition results, and the updated boundary conditions are re-input into the CFD model.
[0067] S4: Perform gas prediction again based on the updated model to generate a final gas prediction result, compare the final gas prediction result with the independent verification data, and evaluate the final gas prediction result.
[0068] Specifically, the updated boundary conditions are applied to the CFD model;
[0069] Resolve the mass conservation equation, momentum conservation equation, and energy conservation equation of the CFD model;
[0070] Extract the solution results from the CFD model to obtain the final gas prediction results.
[0071] Selecting independent verification data corresponding to the forecast time period and preprocessing the independent verification data;
[0072] Set evaluation indicators, including mean absolute error, root mean square error, relative error, and correlation coefficient, and pair the final gas prediction results with independent verification data at the same time and space points;
[0073] Use paired data to calculate all evaluation indicators, draw a time series comparison chart of predicted values and actual values, and generate a spatial distribution map of prediction errors;
[0074] Analyze the distribution characteristics of errors in time and space, and conduct sensitivity analysis on key parameters of the CFD model based on the temporal and spatial distribution characteristics of errors combined with the system physical model;
[0075] The confidence interval of the prediction result is calculated based on the covariance matrix, and the confidence interval is converted into standard deviation to calculate the relative uncertainty. The calculation formula is:
[0076]
[0077] Among them, U i is the relative uncertainty of the ith prediction point, σ i is the standard deviation, is the predicted value, U is the average relative uncertainty, and n is the total number of prediction points;
[0078] Based on the evaluation indicators and analysis results, the model is comprehensively rated and the credibility score of the prediction result is calculated. The calculation formula is:
[0079]
[0080] In the formula, Sc f is the prediction result credibility score, U is the average prediction uncertainty, and U max is the maximum acceptable uncertainty, obtained based on the historical performance of the forecast model;
[0081] When the prediction result credibility score Sc f ≥ credible limit Sc f1 When , the current sensor data acquisition frequency and sampling interval parameters are maintained, and the final gas prediction result is considered to be credible;
[0082] When the prediction result credibility score Sc f <Confidence limit Sc f1, the final gas prediction result is considered unreliable, the frequency of sensor data acquisition is increased, the CFD model is updated with the new value of the first boundary condition, the current coupling coefficient matrix is continued to be used for comprehensive adjustment of the boundary conditions, the weight of the assimilated state vector in the final boundary condition calculation is increased, and the credibility score of the secondary prediction result is calculated. If the credibility score of the secondary prediction result Sc f ≥ credible limit Sc f1 If the reliability score of the secondary prediction result Sc f <Confidence limit Sc f1 When the final gas prediction result is considered unreliable, the CFD model is updated with the new value of the second boundary condition, the coupling coefficient matrix is re-evaluated and adjusted, the data acquisition process is continuously monitored, and the processed data is saved in the database.
[0083] Furthermore, the present embodiment also provides a gas prediction system for a high-temperature gas-cooled reactor fuel loading and unloading system, comprising: a data acquisition module, used to collect physical property data of the fuel loading and unloading system, and to collect sensor data sets sensed by multiple sensors deployed at the fuel loading and unloading system at the prediction moment; a preliminary result generation module, used to construct a CFD model of the fuel loading and unloading system based on the physical property data, and to perform gas prediction using the CFD model of the fuel loading and unloading system to generate preliminary gas prediction results; a data assimilation module, used to perform data assimilation on the sensor data set and the preliminary gas prediction results, and to update the CFD model according to the assimilation results; a final result generation module, used to perform gas prediction again based on the updated CFD model to generate a final gas prediction result; and an evaluation module, used to compare the final gas prediction result with the predetermined independent verification data based on a predetermined prediction error range, and to evaluate the final gas prediction result.
[0084] This embodiment also provides a computer device, which is applicable to the gas prediction method of a high-temperature gas-cooled reactor fuel loading and unloading system, and includes: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement all or part of the steps of the method described in the embodiment of the present invention as proposed in the above embodiment.
[0085] This embodiment also provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method in any optional implementation of the above embodiment is executed. Wherein, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk.
[0086] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.
[0087] From the above, it can be seen that this method can accurately reflect the real state of the system by comprehensively collecting the physical property data of the fuel loading and unloading system, and provide high-quality data support for subsequent model construction. Reduce the uncertainty in the initial stage of model establishment and improve the reliability of the entire prediction process. Through the preliminary prediction of the CFD model, information about the gas flow characteristics can be quickly obtained, which significantly improves the accuracy of the prediction, allowing the CFD model to more accurately reflect the real state of the fuel loading and unloading system, ensure the reliability of the prediction results, and provide experience for the development of high-temperature gas-cooled reactor fuel loading and unloading systems.
[0088] Example 2
[0089] Referring to Table 1, which is a second embodiment of the present invention, this embodiment provides a gas prediction method for a high temperature gas-cooled reactor fuel loading and unloading system. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.
[0090] Table 1 Test data table
[0091]
[0092] From the above, it can be seen that this method can accurately reflect the real state of the system by comprehensively collecting the physical property data of the fuel loading and unloading system, and provide high-quality data support for subsequent model construction. Reduce the uncertainty in the initial stage of model establishment and improve the reliability of the entire prediction process. Through the preliminary prediction of the CFD model, information about the gas flow characteristics can be quickly obtained, which significantly improves the accuracy of the prediction, allowing the CFD model to more accurately reflect the real state of the fuel loading and unloading system, ensure the reliability of the prediction results, and provide experience for the development of high-temperature gas-cooled reactor fuel loading and unloading systems.
[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.
Claims
1. A gas prediction method for a high temperature gas-cooled reactor fuel loading and unloading system, characterized in that: include, Collect physical property data of the fuel loading and unloading system and obtain sensor data within the gas prediction time to build a data set; Based on the physical property data, a CFD model of the fuel loading and unloading system is constructed to perform gas prediction and generate preliminary gas prediction results; Assimilating the sensor data set with the preliminary gas prediction result, and updating the CFD model of the fuel loading and unloading system according to the assimilation result; Based on the updated CFD model of the material handling system, gas prediction is performed again to generate a final gas prediction result, the final gas prediction result is compared with the independent verification data, and the final gas prediction result is evaluated.
2. The gas prediction method for a high temperature gas-cooled reactor fuel loading and unloading system according to claim 1, characterized in that: The physical property data includes material properties, geometric dimensions and surface features of the fuel loading and unloading system; the acquisition of sensor data within the gas prediction time to construct a data set includes the following steps: Determine the frequency of sensor data collection based on the system's dynamic characteristics and prediction accuracy requirements, and select the sensor type based on the monitored physical quantity; Connect sensors to the data acquisition system, configure data acquisition equipment, and set acquisition frequency and sampling interval parameters; Start the data acquisition system to record sensor data, time-stamp and position-identify the acquired sensor data, and continuously monitor the data acquisition process throughout the gas forecast period; Perform data preprocessing on the acquired temperature, pressure and flow values; The data from different sensors are integrated to construct a data set, and the processed data are saved in the database in preparation for data assimilation.
3. The gas prediction method for a high temperature gas-cooled reactor fuel loading and unloading system according to claim 2, characterized in that: The construction of a CFD model of a fuel loading and unloading system for gas prediction comprises the following steps: Based on the physical property data of the fuel loading and unloading system, a CFD model of the fluid flow in the fuel loading and unloading system is constructed using computational fluid dynamics software; Determining control equations of a CFD model of a fuel loading and unloading system, wherein the control equations include a mass conservation equation, a momentum conservation equation, and an energy conservation equation; The model boundary conditions are set according to the inlet, outlet and wall of the fuel loading and unloading system. The boundary conditions are used as constraints. The control equations of the CFD model are iteratively solved by numerical solution under the constraints until the model converges, and preliminary gas prediction results including flow field distribution, pressure distribution and temperature distribution are obtained.
4. The gas prediction method for a high temperature gas-cooled reactor fuel loading and unloading system according to claim 3, characterized in that: The data assimilation comprises the following steps: The sensor data set is fused with the preliminary prediction results using the Kalman filter algorithm, and the prediction is made by correcting the model through data assimilation; Initialize the state vector and covariance matrix of the Kalman filter, and use the CFD model of the fuel loading and unloading system to calculate the state vector and covariance matrix at the prediction time; Assimilate the state vector and covariance matrix at the prediction time using the observations in the sensor dataset; The assimilated state vector and covariance matrix are obtained, and the boundary conditions of the CFD model are updated using the assimilated state vector and covariance matrix.
5. The gas prediction method for a high temperature gas-cooled reactor fuel loading and unloading system according to claim 4, characterized in that: The method of updating the boundary conditions of the CFD model using the assimilated state vector and covariance matrix includes the following steps: Perform state vector difference evaluation, calculate the relative difference between the assimilated state vector and the state vector at the prediction time, if the relative difference of the boundary conditions exceeds the set limit, enter the boundary condition update process and perform covariance matrix credibility evaluation, otherwise keep the original boundary condition value; If the covariance matrix element corresponding to the boundary condition is less than the first boundary threshold, the new value of the first boundary condition is calculated using the following formula: x n1 =0.9x a +0.1x0 In the formula, x n is the new value of the first boundary condition, that is, the updated boundary condition, x a is the assimilated state vector, x0 is the state vector at the prediction time; If the covariance matrix element corresponding to the boundary condition is above the first boundary threshold, the new value of the second boundary condition is calculated using the following formula: x n2 =0.5(x a +x0) In the formula, x n is the new value of the second boundary condition; Comprehensively adjust the updated boundary conditions using the coupling coefficient matrix; Conduct local sensitivity analysis on each boundary condition to assess its impact on the overall simulation results; Based on the historical update effect, the weights of the assimilated state vector and the state vector at the prediction time are dynamically adjusted to obtain the final boundary condition results, and the updated boundary conditions are re-input into the CFD model.
6. The gas prediction method for a high temperature gas-cooled reactor fuel loading and unloading system according to claim 5, characterized in that: The gas prediction based on the updated model comprises the following steps: Apply the updated boundary conditions to the CFD model; Resolve the mass conservation equation, momentum conservation equation, and energy conservation equation of the CFD model; Extract the solution results from the CFD model to obtain the final gas prediction results.
7. The gas prediction method for a high temperature gas-cooled reactor fuel loading and unloading system according to claim 6, characterized in that: The evaluation of the final gas prediction result comprises the following steps: Select independent verification data corresponding to the prediction time period, and pre-process the independent verification data; Set evaluation indicators, including mean absolute error, root mean square error, relative error, and correlation coefficient, and pair the final gas prediction results with independent verification data at the same time and space points; Use paired data to calculate all evaluation indicators, draw a time series comparison chart of predicted values and actual values, and generate a spatial distribution map of prediction errors; Analyze the distribution characteristics of errors in time and space, and conduct sensitivity analysis on key parameters of the CFD model based on the temporal and spatial distribution characteristics of errors combined with the system physical model; The confidence interval of the prediction result is calculated based on the covariance matrix, and the confidence interval is converted into standard deviation to calculate the relative uncertainty. The calculation formula is: Among them, U i is the relative uncertainty of the ith prediction point, σ i is the standard deviation, is the predicted value, U is the average relative uncertainty, and n is the total number of prediction points; Based on the evaluation indicators and analysis results, the model is comprehensively rated and the credibility score of the prediction result is calculated. The calculation formula is: In the formula, Sc f is the prediction result credibility score, U is the average prediction uncertainty, and U max is the maximum acceptable uncertainty, obtained based on the historical performance of the forecast model; When the prediction result credibility score Sc f ≥ credible limit Sc f1 When , the current sensor data acquisition frequency and sampling interval parameters are maintained, and the final gas prediction result is considered to be credible; When the prediction result credibility score Sc f <Confidence limit Sc f1 , the final gas prediction result is considered unreliable, the frequency of sensor data acquisition is increased, the CFD model is updated with the new value of the first boundary condition, the current coupling coefficient matrix is continued to be used for comprehensive adjustment of the boundary conditions, the weight of the assimilated state vector in the final boundary condition calculation is increased, and the credibility score of the secondary prediction result is calculated. If the credibility score of the secondary prediction result Sc f ≥ credible limit Sc f1 When , the final gas prediction result is considered to be credible; If the reliability score of the secondary prediction result Sc f <Confidence limit Sc f1 When the final gas prediction result is considered unreliable, the CFD model is updated with the new value of the second boundary condition, the coupling coefficient matrix is re-evaluated and adjusted, the data acquisition process is continuously monitored, and the processed data is saved in the database.
8. A gas prediction system for a high temperature gas-cooled reactor fuel loading and unloading system, based on the gas prediction method for a high temperature gas-cooled reactor fuel loading and unloading system according to any one of claims 1 to 7, characterized in that: include, A data acquisition module, used to collect physical property data of the fuel loading and unloading system, and to collect sensor data sets sensed by various sensors deployed at the fuel loading and unloading system within the prediction time; A preliminary result generation module is used to build a CFD model of the fuel loading and unloading system based on the physical property data, and use the CFD model of the fuel loading and unloading system to perform gas prediction and generate preliminary gas prediction results; The data assimilation module is used to assimilate the sensor data set with the preliminary gas prediction results and update the CFD model based on the assimilation results; The final result generation module is used to perform gas prediction again based on the updated CFD model to generate the final gas prediction result; The evaluation module is used to compare the final gas prediction result with the pre-determined independent verification data based on the pre-determined prediction error range to evaluate the final gas prediction result.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the gas prediction method for a high temperature gas-cooled reactor fuel loading and unloading system according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the gas prediction method for a high temperature gas-cooled reactor fuel loading and unloading system according to any one of claims 1 to 7 are implemented.