Dynamic test platform and data processing method for thermal-hydraulic characteristics of transverse liquid sodium-cooled fuel assembly

Abstract

The present application relates to the field of nuclear reactor engineering, and particularly relates to a kind of transverse liquid sodium-cooled fuel assembly thermal hydraulic characteristics dynamic test platform, the platform includes, simulation fuel assembly module, it is arranged in transverse mode, for simulating the single-box fuel assembly of sodium-cooled reactor under transverse condition;Liquid sodium coolant circulation module is used to provide and control the flow, temperature and pressure of liquid sodium coolant;Heating power simulation module is used to simulate the power output of fuel assembly, and adjust its power level;Measurement module is arranged at the inlet, outlet and internal subchannel position of the simulation fuel assembly module, for real-time monitoring thermal hydraulic parameter;Data analysis module is connected with the measurement module, for multidimensional dynamic analysis of real-time collected thermal hydraulic parameter;The present application provides high-precision data basis for fuel assembly thermal design optimization by integrating the innovative test architecture of multi-module collaborative work.

Description

Technical Field

[0001] This invention relates to the field of nuclear reactor engineering technology, and in particular to a dynamic testing platform and data processing method for the thermal-hydraulic characteristics of horizontally placed liquid sodium-cooled fuel assemblies. Background Technology

[0002] Sodium-cooled reactors (SCRs) are characterized by high power density and relatively small volume, making them promising candidates for marine nuclear power. However, under special conditions such as underwater operation, the reactor core is typically arranged laterally due to limitations in space and structural layout. During accident or low-flow conditions, the coolant flow and heat transfer characteristics within transversely arranged single-box fuel assemblies differ from those of vertically arranged assemblies, necessitating thermal-hydraulic analysis. Computational fluid dynamics (CFD) methods are commonly used for SCR analysis of SCR cores, but the required mesh size is too large, leading to computational time consumption. Subchannel-based SCRs offer an effective alternative; however, traditional SCRs fail to account for the unique effects of transverse core placement, such as the impact of buoyancy on lateral flow between subchannels, radial temperature distribution within assemblies, and coolant heat transfer characteristics. Therefore, a buoyancy model needs to be added to existing SCRs to conduct thermal-hydraulic analysis of SCR single-box fuel assemblies in sodium-cooled reactors.

[0003] However, existing technologies still have the following problems:

[0004] Without considering the effect of buoyancy caused by temperature difference on lateral flow after the change of gravity direction, the calculation accuracy is insufficient when simulating transverse cores, especially under low flow or natural circulation conditions, and it is impossible to accurately predict the key safety parameters inside the component. Summary of the Invention

[0005] To address this issue, the present invention provides a dynamic testing platform and data processing method for the thermal-hydraulic characteristics of a transverse liquid sodium-cooled fuel assembly. This overcomes the problem in the prior art where the buoyancy force caused by the temperature difference after the change of gravity direction is not considered, resulting in insufficient calculation accuracy when simulating a transverse reactor core, especially under low flow or natural circulation conditions, and thus failing to accurately predict key safety parameters inside the assembly.

[0006] To achieve the above objectives, this invention provides a dynamic testing platform for the thermal-hydraulic characteristics of a horizontally placed liquid sodium-cooled fuel assembly. It includes:

[0007] A simulated fuel assembly module, arranged laterally, is used to simulate a single fuel assembly in a sodium-cooled reactor under lateral conditions.

[0008] A liquid sodium coolant circulation module, connected to the simulated fuel assembly module, is used to provide and control the flow rate, temperature, and pressure of the liquid sodium coolant;

[0009] A heating power simulation module, which is connected to the simulated fuel assembly module, is used to simulate the power output of the fuel assembly and adjust its power level;

[0010] A measurement module, located at the inlet, outlet, and internal sub-channels of the simulated fuel assembly module, is used to monitor thermal-hydraulic parameters in real time.

[0011] The data analysis module, which is connected to the measurement module, is used to perform multi-dimensional dynamic analysis of the real-time collected thermal-hydraulic parameters.

[0012] Furthermore, the simulated fuel assembly module adopts a multi-bar bundle wound fuel assembly structure, with the bar bundles arranged closely in a triangular pattern.

[0013] Furthermore, the data analysis module integrates a sub-channel analysis program, which calculates the influence of buoyancy on lateral flow based on the Boussinesq approximation model and outputs mass, energy, and momentum exchange data between sub-channels.

[0014] Furthermore, the subchannel analysis program includes a buoyancy calculation module, which calculates the lateral flow rate driven by buoyancy through the elevation difference and temperature difference between adjacent subchannels, and updates the subchannel conservation equation.

[0015] Furthermore, the data analysis module performs multi-dimensional dynamic analysis of the real-time collected thermal-hydraulic parameters, including:

[0016] Real-time monitoring and recording of the changing trends of thermal hydraulic parameters over time, including the transient response of coolant temperature, pressure, and flow rate;

[0017] The subchannel analysis program generates two-dimensional and three-dimensional temperature, velocity and pressure field distribution maps inside the component, and identifies high-temperature regions and flow dead zones.

[0018] Based on the Boussinesq approximation model, the contribution rate of buoyancy force to lateral flow is calculated, and its impact on the radial temperature distribution non-uniformity of the component is evaluated.

[0019] The system dynamically outputs key safety parameters, including the highest temperature at the sub-channel outlet, the peak temperature of the fuel cladding, and the coolant temperature rise gradient, and compares the differences between vertical and horizontal installation conditions.

[0020] Furthermore, the data analysis module also integrates a dynamic feedback control unit, which dynamically adjusts the power distribution of the heating power simulation module and the flow rate of the liquid sodium coolant circulation module based on the real-time analyzed thermal-hydraulic parameters.

[0021] Furthermore, the dynamic feedback control unit employs an adaptive PID algorithm, and its control strategy includes:

[0022] When the temperature non-uniformity at the sub-channel outlet exceeds the preset threshold, the heating power of the low-temperature sub-channel area is increased or the coolant flow distribution is adjusted.

[0023] Based on the calculation results of the lateral flow rate driven by buoyancy, the parameters of the mixing model between sub-channels are corrected in real time to improve the simulation accuracy.

[0024] Furthermore, the test platform also includes a CFD verification module, which is used to verify the test results using computational fluid dynamics methods and compare the thermal-hydraulic characteristics under horizontal and vertical conditions.

[0025] A data processing method for a dynamic testing platform for the thermal-hydraulic characteristics of a horizontally placed liquid sodium-cooled fuel assembly includes:

[0026] Step S1: Collect multi-source data from the measurement module in real time, including temperature, pressure and flow data of coolant at component inlet, outlet and each internal sub-channel, as well as power data from the heating power simulation module, and synchronize the data in time.

[0027] Step S2: Input the collected sub-channel temperature distribution data and the pre-stored component geometric structure data into the sub-channel analysis program. Based on the Boussinesq approximation model, calculate online the buoyancy force driving the lateral flow rate between adjacent sub-channels due to elevation and temperature differences.

[0028] Step S3: Based on the results of steps S1 and S2, perform multi-dimensional dynamic analysis;

[0029] Step S4: Compare the analysis results with the benchmark data of the CFD verification module to correct the mixed model parameters in the sub-channel analysis program online; at the same time, generate control commands to adaptively adjust the heating power distribution and coolant flow rate through the dynamic feedback control unit.

[0030] Step S5: Based on the corrected model and dynamic test data, output a thermal-hydraulic safety characteristic assessment report of the horizontal fuel assembly. The report includes a quantitative analysis of the buoyancy effect, safe operating boundaries, and comparison conclusions with vertical conditions.

[0031] Compared with existing technologies, the advantages of this invention are as follows: Each rod integrates an electromagnetic induction heater or a high-power resistance heating wire, which can achieve uniform heating inside the rod, accurately match the spatial distribution characteristics of nuclear fuel fission heat generation, and the heating power can be flexibly adjusted. It can simulate both constant heat generation under steady-state operation and power changes under transient conditions, solving the problem that traditional heating methods are difficult to accurately simulate the heat generation law of nuclear fuel, and providing stable and reliable heat source conditions for the study of thermal hydraulic parameters. The fuel rods are made of high-temperature nickel-based alloys, which can withstand the high temperature during the heating process and have excellent corrosion resistance and radiation resistance. The wire winding and hexagonal sleeve are made of stainless steel, which takes into account both strength and erosion resistance, and can withstand the flow impact and high temperature of coolant for a long time, avoiding experimental interruption or safety risks caused by material failure, extending the service life of the module, and reducing experimental costs.

[0032] Furthermore, this invention employs the Boussinesq approximation model to accurately calculate the impact of buoyancy on lateral flow, precisely reflecting the complex flow structure generated by the interaction of gravity and temperature gradient, providing a reliable physical basis for reactor safety analysis. It is deeply integrated into the data analysis framework as a DLL or Python / C++ module, achieving seamless integration with the measurement system. The program simultaneously solves the continuity, momentum, and energy equations, considering the strong coupling relationship between flow, heat transfer, and buoyancy, improving the overall accuracy of the simulation. It supports various inlet conditions and power distribution modes, including uniform and non-uniform power distributions. Wall temperature data can supplement and validate the calculations, improving the model's predictive ability and reliability. Detailed mass, energy, and momentum exchange data are output at each calculation time step, including key parameters such as lateral mass flow rate, enthalpy exchange rate, and momentum exchange rate. These data provide precise input for dynamic feedback control, supporting the implementation of multivariable coordinated control strategies. By comprehensively considering the influence of buoyancy, turbulence, and geometric factors on the flow, it can more accurately predict mixing phenomena between sub-channels.

[0033] Furthermore, this invention uses the Boussinesq approximation method to accurately calculate the driving head generated by the density and elevation differences between adjacent sub-channels, enabling accurate prediction of lateral flow caused by buoyancy. It is suitable for secondary flow analysis of transverse components. By injecting the lateral flow rate driven by buoyancy as a source term into the sub-channel conservation equation, it achieves complete coupling of mass, energy, and momentum, significantly improving the model's prediction accuracy for complex flow structures. The introduction of a lateral flow coefficient, considering the geometry and turbulence characteristics of the sub-channel gaps, more realistically reflects the influence of structures such as filament windings on the flow, avoiding errors caused by overly simplified flow assumptions. As the core subroutine of the sub-channel analysis program, it automatically executes calculations at each time step, ensuring that simulation results are updated synchronously with experimental conditions. Robust numerical methods are employed to guarantee computational stability. Not only is the mass conservation equation updated, but the energy and momentum equations are also corrected simultaneously, ensuring the integrity and consistency of the physical process and avoiding physical distortions that may result from single-equation corrections. It provides accurate lateral mass, energy, and momentum exchange data, providing reliable input to the dynamic feedback control unit and supporting real-time optimization of power distribution and coolant flow allocation strategies.

[0034] Furthermore, by monitoring and recording the transient responses of coolant temperature, pressure, and flow rate in real time, this invention can fully capture the parameter change trends of the system from startup and steady-state operation to transient disturbances, avoiding the problem of missing key transient information in traditional static analysis. This not only provides real-time data support for subsequent control strategy adjustments but also helps identify potential risks in the transient process, laying the foundation for system safety early warning. By generating two-dimensional and three-dimensional temperature, velocity, and pressure field distribution maps inside the components using the sub-channel analysis program, abstract thermal-hydraulic data can be transformed into intuitive visualization results, quickly identifying high-temperature regions and flow dead zones. This solves the problem of traditional data analysis's difficulty in accurately locating local anomalies, providing a clear target direction for optimizing heating power distribution and adjusting coolant flow distribution, reducing safety hazards caused by local overheating or poor flow. Based on the Boussinesq approximation model, the contribution rate of buoyancy to lateral flow is calculated, and its impact on the radial temperature distribution non-uniformity of the components is evaluated. This quantifies the proportion of buoyancy in complex flow fields, providing a key basis for optimizing the sub-channel mixing model and improving simulation accuracy.

[0035] Furthermore, this invention dynamically adjusts the power distribution and coolant flow rate based on thermal-hydraulic parameters to ensure that the system always operates within a safe range. The PID parameters can be automatically adjusted according to changes in operating conditions, avoiding the problem of poor control effect of fixed parameters under different operating conditions. Non-uniformity is judged by temperature standard deviation, enabling targeted power and flow rate adjustments to improve temperature field uniformity. Key parameters such as temperature, pressure, and flow rate are tracked in real time, and abnormal conditions are warned in a timely manner. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the dynamic testing platform for the thermal-hydraulic characteristics of a horizontally placed liquid sodium-cooled fuel assembly according to the present invention.

[0037] Figure 2 This is a flowchart illustrating the data processing method of the present invention applied to the dynamic testing platform for the thermal-hydraulic characteristics of a horizontally placed liquid sodium-cooled fuel assembly. Detailed Implementation

[0038] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0039] It should be noted that the data in this embodiment are all derived from a comprehensive analysis and evaluation of historical data from the six months prior to this determination and the corresponding historical determination results by the system described in this invention. Those skilled in the art will understand that the system described in this invention can determine the above-mentioned parameters for a single item by selecting the value with the highest proportion based on the data distribution as the preset standard parameter, using weighted summation to obtain the value as the preset standard parameter, substituting each historical data point into a specific formula and using the value obtained by that formula as the preset standard parameter, or other selection methods, as long as the system described in this invention can clearly define different specific situations in the single-item determination process through the obtained values.

[0040] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0041] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0042] Please see Figures 1-2 As shown, Figure 1 This is a schematic diagram of the dynamic testing platform for the thermal-hydraulic characteristics of a horizontally placed liquid sodium-cooled fuel assembly according to the present invention. Figure 2 This is a flowchart illustrating the data processing method of the present invention applied to the dynamic testing platform for the thermal-hydraulic characteristics of a horizontally placed liquid sodium-cooled fuel assembly.

[0043] The present invention provides a dynamic testing platform for the thermal-hydraulic characteristics of a horizontally placed liquid sodium-cooled fuel assembly, comprising:

[0044] A simulated fuel assembly module, arranged laterally, is used to simulate a single fuel assembly in a sodium-cooled reactor under lateral conditions.

[0045] A liquid sodium coolant circulation module, connected to the simulated fuel assembly module, is used to provide and control the flow rate, temperature, and pressure of the liquid sodium coolant;

[0046] A heating power simulation module, which is connected to the simulated fuel assembly module, is used to simulate the power output of the fuel assembly and adjust its power level;

[0047] A measurement module, located at the inlet, outlet, and internal sub-channels of the simulated fuel assembly module, is used to monitor thermal-hydraulic parameters in real time.

[0048] The data analysis module, which is connected to the measurement module, is used to perform multi-dimensional dynamic analysis of the real-time collected thermal-hydraulic parameters.

[0049] Specifically, the simulated fuel assembly module adopts a multi-bar bundle wound fuel assembly structure, with the bar bundles arranged closely in a triangular pattern.

[0050] In this embodiment of the invention, the simulated fuel assembly module adopts a simplified model of a 19-bar bundle wire-wound fuel assembly. The 19 simulated fuel rods are arranged in a triangular pattern and tightly packed within a hexagonal sleeve. The entire assembly is mounted horizontally on a robust support frame, ensuring its axis is parallel to the ground to simulate lateral conditions. The rods are made of high-temperature nickel-based alloy. Each rod integrates an electromagnetic induction heater or a high-power resistance heating wire, which can uniformly heat the rod from the inside, accurately simulating the fission heat generation of nuclear fuel. The outer diameter of the rod is 8 mm, and the length of the active heating section is 2.0 meters to provide fully developed thermal-hydraulic conditions. Each fuel rod is tightly wound with a 1.2 mm diameter stainless steel wire with a wire pitch of 200 mm. The main function of the wire is to maintain a regular spacing between the rod bundles, forming defined sub-channels and promoting lateral mixing of coolant between the sub-channels. The hexagonal sleeve is made of stainless steel and houses the entire rod bundle, forming the boundary for coolant flow.

[0051] In this invention, each rod integrates an electromagnetic induction heater or a high-power resistance heating wire, enabling uniform heating within the rod and accurately matching the spatial distribution characteristics of nuclear fuel fission heat generation. The heating power is flexibly adjustable, simulating both constant heat generation under steady-state operation and power changes under transient conditions. This solves the problem of traditional heating methods failing to accurately simulate the heat generation patterns of nuclear fuel, providing a stable and reliable heat source for the study of thermal-hydraulic parameters. The fuel rods are made of high-temperature nickel-based alloys, capable of withstanding high temperatures during heating and possessing excellent corrosion and radiation resistance. The wire winding and hexagonal sleeve are made of stainless steel, balancing strength and erosion resistance, allowing them to withstand the flow impact and high temperatures of the coolant over long periods. This avoids experimental interruptions or safety risks due to material failure, extends the module's lifespan, and reduces experimental costs.

[0052] Specifically, the data analysis module integrates a sub-channel analysis program, which calculates the impact of buoyancy on lateral flow based on the Boussinesq approximation model and outputs mass, energy, and momentum exchange data between sub-channels.

[0053] In this embodiment of the invention, the sub-channel analysis program is deeply integrated into the software framework of the data analysis module in the form of a dynamic link library (DLL) or a Python / C++ calculation module. The raw data (temperature, pressure, flow rate) collected in real time by the measurement module is sent to the data analysis module through the data acquisition system. It undergoes preprocessing such as filtering and unit conversion, and is then automatically mapped to the geometric mesh and boundary conditions defined by the sub-channel analysis program. Inlet conditions include: total flow rate, temperature, and pressure at the component inlet; power distribution is obtained from the heating power simulation module, supporting both uniform and non-uniform power distribution; wall temperature is supplemented and verified by thermocouple data embedded in the fuel rod surface; the application of the Boussinesq approximation model is also included: This approximation model assumes that the density change (Δρ) of the fluid is crucial only in the gravity term of the momentum equation, while the fluid can be considered incompressible in other terms; in the momentum equation of the program, the buoyancy term is treated as: ;in, and Here are the reference density and reference temperature (usually taken as inlet conditions); g is the gravitational acceleration vector, which, under transverse conditions, is perpendicular to the component axis; β is the thermal expansion coefficient of liquid sodium; T is the temperature of the local computing unit; the program solves the following set of coupled conservation equations:

[0054] Continuity equation: (Simplified to the Boussinesq approximation) ).

[0055] Momentum equation: Among them, the last equation The term refers to the buoyancy source term; in the horizontally placed component, since the direction of gravity is perpendicular to the flow direction, this source term directly drives the fluid to generate a transverse secondary flow in a plane perpendicular to the main flow direction.

[0056] Energy equation: Where Q is the volumetric heat source.

[0057] In this embodiment of the invention, the turbulence and mixing model is adopted. Two-equation or algebraic stress models are used to simulate turbulence; forced mixing caused by wire winding is modeled as an additional transverse momentum source term and turbulence enhancement; the subchannel analysis program outputs the following key exchange data at each computation time step for multi-dimensional dynamic analysis:

[0058] Quality exchange data: Outputs the lateral mass flow rate (kg / s) at each sub-channel interface;

[0059] Energy exchange data: Output the enthalpy exchange rate (W) at each sub-channel interface, calculated from the transverse mass flow rate and fluid enthalpy;

[0060] Momentum exchange data: outputs the transverse momentum exchange rate and turbulent shear stress between sub-channels.

[0061] This invention employs the Boussinesq approximation model to accurately calculate the impact of buoyancy on lateral flow, precisely reflecting the complex flow structure generated by the interaction of gravity and temperature gradient, providing a reliable physical basis for reactor safety analysis. It is deeply integrated into the data analysis framework as a DLL or Python / C++ module, achieving seamless interface with the measurement system. The program simultaneously solves the continuity, momentum, and energy equations, considering the strong coupling relationship between flow, heat transfer, and buoyancy, improving the overall accuracy of the simulation. It supports various inlet conditions and power distribution modes, including uniform and non-uniform power distributions. Wall temperature data can supplement and validate the calculations, improving the model's predictive ability and reliability. Detailed mass, energy, and momentum exchange data are output at each calculation time step, including key parameters such as lateral mass flow rate, enthalpy exchange rate, and momentum exchange rate. This data provides precise input for dynamic feedback control, supports the implementation of multivariable coordinated control strategies, and comprehensively considers the influence of buoyancy, turbulence, and geometric factors on the flow, enabling more accurate prediction of mixing phenomena between sub-channels.

[0062] Specifically, the subchannel analysis program includes a buoyancy calculation module, which calculates the lateral flow rate driven by buoyancy through the elevation difference and temperature difference between adjacent subchannels and updates the subchannel conservation equation.

[0063] In this embodiment of the invention, the buoyancy calculation module is a core subroutine that the subchannel analysis program must call at each time step (or iteration step). It is activated after the basic flow field and temperature field predictions for the current step are completed, and is specifically used to calculate and correct for lateral mixing driven by buoyancy. Input data includes:

[0064] T_i,T_j: The volume average temperature of two adjacent sub-channels i and j at the current axial grid z;

[0065] (x_i,y_i,z),(x_j,y_j,z): Geometric center coordinates of sub-channels i and j. In the horizontal component, the y-direction is the direction of gravity.

[0066] g: Gravitational acceleration vector (0, -g, 0);

[0067] ρ_ref and β: reference density and coefficient of thermal expansion;

[0068] The essence of buoyancy-driven force is the net pressure difference stemming from density differences. For adjacent sub-channels i and j, the driving pressure head ΔP_bouy at the center of their connecting gap is calculated as follows:

[0069] Calculating the density difference: According to the Boussinesq approximation, the density difference is linearly determined by the temperature difference. ;

[0070] Calculate the effective elevation difference: The effective height at which buoyancy does work is the projected distance between the two sub-channels in the direction of gravity. ;

[0071] Calculate the buoyancy force driving the pressure head: ;

[0072] Substituting into the above formula, we obtain the core calculation formula: ;

[0073] The buoyancy-driven pressure head ΔP_bouy will overcome the lateral flow resistance of the fluid, generating a lateral mass flow rate m_dot_bouy, which can be described by a simplified hydraulic model:

[0074] m_dot_bouy=C_b×sign(ΔP_bouy)×sqrt(ρ_ref×|ΔP_bouy|);

[0075] C_b: Transverse flow coefficient, which is determined by experimental data and represents the flow characteristics of the subchannel gap (including the influence of wire winding). It can be expressed as a function of the gap geometry and turbulence intensity.

[0076] sign(ΔP_bouy): determines the flow direction. For example, if sub-channel j is above i (y_j>y_i) and has a higher temperature (T_j>T_i), then ΔP_bouy is negative and sign is negative, indicating that the fluid flows from the high-temperature channel j above to the low-temperature channel i below.

[0077] The calculated m_dot_bouy will be injected as a source term into the conservation equation of the sub-channel. The mass conservation equation is as follows: For sub-channel i, the source term of its mass equation is the algebraic sum of m_dot_bouy of all its adjacent channels (positive for inflow, negative for outflow); Energy conservation equation: h_i is the enthalpy of subchannel i, and h_mix is ​​the enthalpy of the mixed fluid; Axial momentum equation: ; It is the axial velocity of the mixed fluid.

[0078] This invention utilizes the Boussinesq approximation method to accurately calculate the driving head generated by density and elevation differences between adjacent sub-channels. This enables accurate prediction of lateral flow caused by buoyancy, making it suitable for secondary flow analysis of transversely mounted components. By injecting the lateral flow rate driven by buoyancy as a source term into the sub-channel conservation equations, it achieves complete coupling of mass, energy, and momentum, significantly improving the model's prediction accuracy for complex flow structures. The introduction of a lateral flow coefficient, considering the geometry and turbulence characteristics of the sub-channel gaps, more realistically reflects the influence of structures such as filament windings on the flow, avoiding errors caused by overly simplified flow assumptions. As the core subroutine of the sub-channel analysis program, it automatically executes calculations at each time step, ensuring that simulation results are updated synchronously with experimental conditions. Robust numerical methods are employed to guarantee computational stability. Not only are the mass conservation equations updated, but the energy and momentum equations are also simultaneously corrected, ensuring the integrity and consistency of the physical processes and avoiding physical distortions that may result from single-equation corrections. It provides accurate lateral mass, energy, and momentum exchange data, providing reliable input to the dynamic feedback control unit and supporting real-time optimization of power distribution and coolant flow allocation strategies.

[0079] Specifically, the data analysis module performs multi-dimensional dynamic analysis of the real-time collected thermal-hydraulic parameters, including:

[0080] Real-time monitoring and recording of the changing trends of thermal hydraulic parameters over time, including the transient response of coolant temperature, pressure, and flow rate;

[0081] The subchannel analysis program generates two-dimensional and three-dimensional temperature, velocity and pressure field distribution maps inside the component, and identifies high-temperature regions and flow dead zones.

[0082] Based on the Boussinesq approximation model, the contribution rate of buoyancy force to lateral flow is calculated, and its impact on the radial temperature distribution non-uniformity of the component is evaluated.

[0083] The system dynamically outputs key safety parameters, including the highest temperature at the sub-channel outlet, the peak temperature of the fuel cladding, and the coolant temperature rise gradient, and compares the differences between vertical and horizontal installation conditions.

[0084] This invention, by real-time monitoring and recording of the transient responses of coolant temperature, pressure, and flow rate, can comprehensively capture the parameter change trends of the system from startup and steady-state operation to transient disturbances. This avoids the problem of traditional static analysis missing key transient information, providing real-time data support for subsequent control strategy adjustments and helping to identify potential risks during transient processes, laying the foundation for system safety early warning. By generating two-dimensional and three-dimensional temperature, velocity, and pressure field distribution maps within the components using a sub-channel analysis program, abstract thermal-hydraulic data can be transformed into intuitive visualizations. This allows for rapid identification of high-temperature regions and flow dead zones, solving the problem of traditional data analysis's difficulty in accurately locating local anomalies. It provides a clear target direction for optimizing heating power distribution and adjusting coolant flow distribution, reducing safety hazards caused by local overheating or poor flow. Based on the Boussinesq approximation model, the contribution rate of buoyancy to lateral flow is calculated, and its impact on the radial temperature distribution non-uniformity of the components is evaluated. This quantifies the proportion of buoyancy in complex flow fields, providing a key basis for optimizing sub-channel mixing models and improving simulation accuracy.

[0085] Specifically, the data analysis module also integrates a dynamic feedback control unit, which dynamically adjusts the power distribution of the heating power simulation module and the flow rate of the liquid sodium coolant circulation module based on the real-time analyzed thermal-hydraulic parameters.

[0086] Specifically, the dynamic feedback control unit employs an adaptive PID algorithm, and its control strategy includes:

[0087] When the temperature non-uniformity at the sub-channel outlet exceeds the preset threshold, the heating power of the low-temperature sub-channel area is increased or the coolant flow distribution is adjusted.

[0088] Based on the calculation results of the lateral flow rate driven by buoyancy, the parameters of the mixing model between sub-channels are corrected in real time to improve the simulation accuracy.

[0089] In this embodiment of the invention, the temperature non-uniformity at the sub-channel outlet is the standard deviation of the outlet temperatures of all sub-channels. The thermal-hydraulic parameters include, but are not limited to, "temperature parameters, pressure parameters, and flow rate parameters." The dynamic feedback control unit analyzes the thermal-hydraulic parameters in real time to determine whether the current system thermal state meets the preset operating requirements. If the parameters deviate from the target range, it adjusts the power distribution of the heating power simulation module accordingly (e.g., reducing power in areas with excessive heat load and increasing power in areas with insufficient heat load), and simultaneously adjusts the flow rate of the liquid sodium coolant circulation module (e.g., increasing the flow rate to enhance heat dissipation when parameters exceed limits and reducing the flow rate to maintain thermal balance when parameters are too low). The target range of parameters is determined by referring to the design specifications, safety guidelines, and operating license requirements of the reactor or related thermal system to determine the safety boundaries and normal operation of each thermal-hydraulic parameter. The adjustment range; the adjustment amount can be calculated by comprehensively considering the degree of deviation of the parameters from the target range (deviation magnitude), the duration of the deviation (integral term), and the rate of change of the deviation (differential term) to obtain the adjustment amount of heating power and coolant flow rate; the mixing model parameters between sub-channels include, but are not limited to, "lateral mixing coefficient, mass, momentum and energy exchange coefficients between adjacent sub-channels, buoyancy correction coefficient, geometric factor, and turbulence model parameters"; real-time correction of the mixing model parameters between sub-channels includes real-time monitoring of the outlet temperature, pressure, and flow rate of each sub-channel; calculating the temperature standard deviation as a non-uniformity index, comparing the predicted lateral flow rate between sub-channels with the measured data or high-precision CFD results using the current model, and when the prediction deviation exceeds the threshold (e.g., >5%), correcting the mixing coefficient according to the error direction and magnitude, and using an adaptive algorithm to avoid over-correction.

[0090] Specifically, the test platform also includes a CFD verification module, which is used to verify the test results using computational fluid dynamics methods and compare the thermal-hydraulic characteristics under horizontal and vertical conditions.

[0091] This invention dynamically adjusts power distribution and coolant flow rate based on thermal-hydraulic parameters to ensure the system always operates within a safe range. PID parameters can be automatically adjusted according to changes in operating conditions, avoiding the problem of poor control effect of fixed parameters under different operating conditions. Non-uniformity is judged by temperature standard deviation, enabling targeted power and flow rate adjustments to improve temperature field uniformity. Key parameters such as temperature, pressure, and flow rate are tracked in real time, providing timely warnings of abnormal conditions.

[0092] Specifically, a data processing method applied to the dynamic testing platform for the thermal-hydraulic characteristics of the horizontally placed liquid sodium-cooled fuel assembly includes:

[0093] Step S1: Collect multi-source data from the measurement module in real time, including temperature, pressure and flow data of coolant at component inlet, outlet and each internal sub-channel, as well as power data from the heating power simulation module, and synchronize the data in time.

[0094] Step S2: Input the collected sub-channel temperature distribution data and the pre-stored component geometric structure data into the sub-channel analysis program. Based on the Boussinesq approximation model, calculate online the buoyancy force driving the lateral flow rate between adjacent sub-channels due to elevation and temperature differences.

[0095] Step S3: Based on the results of steps S1 and S2, perform multi-dimensional dynamic analysis;

[0096] Step S4: Compare the analysis results with the benchmark data of the CFD verification module to correct the mixed model parameters in the sub-channel analysis program online; at the same time, generate control commands to adaptively adjust the heating power distribution and coolant flow rate through the dynamic feedback control unit.

[0097] Step S5: Based on the corrected model and dynamic test data, output a thermal-hydraulic safety characteristic assessment report of the horizontal fuel assembly. The report includes a quantitative analysis of the buoyancy effect, safe operating boundaries, and comparison conclusions with vertical conditions.

[0098] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic testing platform for the thermal-hydraulic characteristics of a horizontally placed liquid sodium-cooled fuel assembly, characterized in that, include: A simulated fuel assembly module, arranged laterally, is used to simulate a single fuel assembly in a sodium-cooled reactor under lateral conditions. A liquid sodium coolant circulation module, connected to the simulated fuel assembly module, is used to provide and control the flow rate, temperature, and pressure of the liquid sodium coolant; A heating power simulation module, which is connected to the simulated fuel assembly module, is used to simulate the power output of the fuel assembly and adjust its power level; A measurement module, located at the inlet, outlet, and internal sub-channels of the simulated fuel assembly module, is used to monitor thermal-hydraulic parameters in real time. The data analysis module, which is connected to the measurement module, is used to perform multi-dimensional dynamic analysis of the real-time collected thermal-hydraulic parameters. The data analysis module integrates a sub-channel analysis program, which calculates the influence of buoyancy on lateral flow based on the Boussinesq approximation model and outputs mass, energy and momentum exchange data between sub-channels. The subchannel analysis program includes a buoyancy calculation module, which calculates the lateral flow driven by buoyancy through the elevation difference and temperature difference between adjacent subchannels and updates the subchannel conservation equation. The data analysis module performs multi-dimensional dynamic analysis of the real-time collected thermal-hydraulic parameters, including: Real-time monitoring and recording of the changing trends of thermal hydraulic parameters over time, including the transient response of coolant temperature, pressure, and flow rate; The subchannel analysis program generates two-dimensional and three-dimensional temperature, velocity and pressure field distribution maps inside the component, and identifies high-temperature regions and flow dead zones. Based on the Boussinesq approximation model, the contribution rate of buoyancy force to lateral flow is calculated, and its impact on the radial temperature distribution non-uniformity of the component is evaluated. The system dynamically outputs key safety parameters, including the highest temperature at the sub-channel outlet, the peak temperature of the fuel cladding, and the coolant temperature rise gradient, and compares the differences between vertical and horizontal installation conditions.

2. The dynamic testing platform for the thermal-hydraulic characteristics of a horizontally placed liquid sodium-cooled fuel assembly according to claim 1, characterized in that, The simulated fuel assembly module adopts a multi-bar bundle wound fuel assembly structure, with the bar bundles arranged closely in a triangular pattern.

3. The dynamic testing platform for the thermal-hydraulic characteristics of a horizontally placed liquid sodium-cooled fuel assembly according to claim 1, characterized in that, The data analysis module also integrates a dynamic feedback control unit, which dynamically adjusts the power distribution of the heating power simulation module and the flow rate of the liquid sodium coolant circulation module based on the real-time analyzed thermal-hydraulic parameters.

4. The dynamic testing platform for the thermal-hydraulic characteristics of a horizontally placed liquid sodium-cooled fuel assembly according to claim 3, characterized in that, The dynamic feedback control unit employs an adaptive PID algorithm, and its control strategy includes: When the temperature non-uniformity at the sub-channel outlet exceeds the preset threshold, the heating power of the low-temperature sub-channel area is increased or the coolant flow distribution is adjusted. Based on the calculation results of the lateral flow rate driven by buoyancy, the parameters of the mixing model between sub-channels are corrected in real time to improve the simulation accuracy.

5. The dynamic testing platform for the thermal-hydraulic characteristics of a horizontally placed liquid sodium-cooled fuel assembly according to claim 1, characterized in that, The test platform also includes a CFD verification module, which is used to verify the test results using computational fluid dynamics methods and compare the thermal-hydraulic characteristics under horizontal and vertical conditions.

6. A data processing method applied to the dynamic testing platform for the thermal-hydraulic characteristics of a horizontally placed liquid sodium-cooled fuel assembly as described in any one of claims 1-5, characterized in that, Step S1: Collect multi-source data from the measurement module in real time, including temperature, pressure and flow data of coolant at component inlet, outlet and each internal sub-channel, as well as power data from the heating power simulation module, and synchronize the data in time. Step S2: Input the collected sub-channel temperature distribution data and the pre-stored component geometric structure data into the sub-channel analysis program. Based on the Boussinesq approximation model, calculate online the buoyancy force driving the lateral flow rate between adjacent sub-channels due to elevation and temperature differences. Step S3: Based on the results of steps S1 and S2, perform multi-dimensional dynamic analysis; Step S4: Compare the analysis results with the benchmark data of the CFD verification module to correct the mixed model parameters in the sub-channel analysis program online; at the same time, generate control commands to adaptively adjust the heating power distribution and coolant flow rate through the dynamic feedback control unit of the data analysis module. Step S5: Based on the corrected model and dynamic test data, output a thermal-hydraulic safety characteristic assessment report of the horizontal fuel assembly. The report includes a quantitative analysis of the buoyancy effect, safe operating boundaries, and comparison conclusions with vertical conditions.

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