Parameter Optimization Method, Device and Electronic Equipment for Helium-Xenon Cooling Reactor

Abstract

The present invention provides a method, device and electronic equipment for parameter optimization of a helium-xenon cooled reactor, relating to the technical field of nuclear reactors. The method for parameter optimization of the helium-xenon cooled reactor comprises the following steps: obtaining an effective sample set of the helium-xenon cooled reactor; wherein, the effective sample set includes the parameters to be optimized of the helium-xenon cooled reactor; determining the parameter relationship between the parameters to be optimized and the overall reactor efficiency and the overall reactor mass of the helium-xenon cooled reactor based on the effective sample set; and determining the optimal solution of the parameters to be optimized based on a preset multi-objective optimization algorithm and the parameter relationship. The present invention can quickly obtain the optimal solution of the parameters to be optimized, improve the overall reactor efficiency on the basis of minimizing the overall reactor mass as much as possible for the helium-xenon cooled reactor, and improve the parameter optimization effect of the helium-xenon cooled reactor.

Description

Technical Field

[0001] The present invention relates to the technical field of nuclear reactors, and in particular, to a method, device and electronic equipment for optimizing parameters of a helium-xenon cooled reactor. Background Art

[0002] A helium-xenon cooled reactor is a small nuclear reactor that uses a helium-xenon mixed gas as a coolant and combines a Brayton thermoelectric conversion system (hereinafter referred to as a helium-xenon cooled reactor). Due to the limitations of its miniaturization and mobility, it is very important to optimize the mass and efficiency of the nuclear reactor. At present, the determination of the design parameters of the nuclear reactor mainly adopts empirical or semi-empirical design methods, and it is difficult to give the optimal scheme of the reactor. Therefore, how to optimize the design of the parameters of the helium-xenon cooled reactor with the reactor mass and efficiency of the helium-xenon cooled reactor as the optimization objectives has become an urgent problem to be solved. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method, device and electronic equipment for optimizing parameters of a helium-xenon cooled reactor, which can quickly solve the optimal solution of the parameters to be optimized, improve the overall efficiency of the helium-xenon cooled reactor on the basis of minimizing the overall mass of the helium-xenon cooled reactor, and improve the parameter optimization effect of the helium-xenon cooled reactor.

[0004] To achieve the above purpose, the technical solutions adopted in the embodiments of the present invention are as follows:

[0005] In the first aspect, an embodiment of the present invention provides a method for optimizing parameters of a helium-xenon cooled reactor, including: obtaining an effective sample set of the helium-xenon cooled reactor; wherein, the effective sample set includes the parameters to be optimized of the helium-xenon cooled reactor; determining the parameter relationship between the parameters to be optimized and the overall efficiency and overall mass of the helium-xenon cooled reactor based on the effective sample set; and determining the optimal solution of the parameters to be optimized based on a preset multi-objective optimization algorithm and the parameter relationship.

[0006] Further, an embodiment of the present invention provides a first possible implementation manner of the first aspect, wherein the method for optimizing parameters of the helium-xenon cooled reactor further includes: obtaining the parameters to be optimized of the helium-xenon cooled reactor; wherein, the parameters to be optimized include any one or more of the helium-xenon gas mixing ratio, system temperature ratio, recuperator heat recovery rate, compressor pressure ratio, compressor isentropic efficiency, compressor mechanical efficiency, turbine isentropic efficiency, turbine mechanical efficiency, core radius and interlayer channel width; generating a plurality of array samples of the parameters to be optimized based on the value range of the parameters to be optimized to obtain a sample set; and performing numerical simulation calculations on each sample in the sample set to determine an effective sample set that meets the reactor design conditions.

[0007] Further, an embodiment of the present invention provides a second possible implementation manner of the first aspect. In this manner, the step of performing numerical simulation calculations on each array sample in the sample set to determine an effective sample set that meets the reactor design conditions includes: screening out a sample set that simultaneously meets the thermal-hydraulic design constraints, weight constraints, and neutronics constraints from the sample set to obtain an effective sample set.

[0008] Further, an embodiment of the present invention provides a third possible implementation manner of the first aspect. In this manner, the step of screening out a sample set that simultaneously meets the thermal-hydraulic design constraints, weight constraints, and neutronics constraints from the sample set to obtain an effective sample set includes: performing a thermal cycle calculation based on each array sample in the sample set, and screening out a first sample set that meets the thermal-hydraulic design constraints from the sample set according to the thermal cycle calculation results; calculating the overall reactor efficiency and the overall reactor mass corresponding to each sample array in the first sample set based on the thermal cycle calculation results, and screening out a second sample set that meets the weight constraints from the first sample set; performing a neutronics calculation based on the second sample set to determine the effective multiplication factor corresponding to each sample array in the second sample set, and screening out an effective sample set that meets the neutronics constraints from the second sample set based on the effective multiplication factor.

[0009] Further, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect. In this manner, the step of determining the parameter relationship between the parameter to be optimized and the overall reactor efficiency and the overall reactor mass of the helium-xenon cooled reactor based on the effective sample set includes: using an approximate model algorithm to perform sample fitting on the effective samples, and establishing a functional relationship between the parameter to be optimized and the overall reactor efficiency and the overall reactor mass to obtain an objective function.

[0010] Further, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect. In this manner, the step of determining the optimal solution of the parameter to be optimized based on a preset multi-objective optimization algorithm and the parameter relationship includes: performing an optimization calculation on the objective function based on a preset multi-objective optimization algorithm, and determining the value of the parameter to be optimized corresponding to the maximum overall reactor efficiency and the minimum overall reactor mass to obtain an optimal solution set of the parameter to be optimized.

[0011] Further, an embodiment of the present invention provides a sixth possible implementation manner of the first aspect. In this manner, the approximate model algorithm includes any one of a polynomial of the response surface method, stepwise regression, neural network, and Kriging model.

[0012] In a second aspect, an embodiment of the present invention further provides a parameter optimization device for a helium-xenon cooled reactor, including: an acquisition module configured to acquire an effective sample set of the helium-xenon cooled reactor; wherein the effective sample set includes parameters to be optimized of the helium-xenon cooled reactor; a first determination module configured to determine a parameter relationship between the parameters to be optimized and the overall reactor efficiency and the overall reactor mass of the helium-xenon cooled reactor based on the effective sample set; a second determination module configured to determine an optimal solution of the parameters to be optimized based on a preset multi-objective optimization algorithm and the parameter relationship.

[0013] In a third aspect, an embodiment of the present invention provides an electronic device, including: a processor and a storage device; a computer program is stored on the storage device, and the computer program, when run by the processor, executes the method according to any one of the first aspect.

[0014] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when run by a processor, executes the steps of the method according to any one of the first aspect above.

[0015] An embodiment of the present invention provides a parameter optimization method, device and electronic device for a helium-xenon cooled reactor. The parameter optimization method for the helium-xenon cooled reactor includes the following steps: acquiring an effective sample set of the pre-established helium-xenon cooled reactor; wherein the effective sample set includes parameters to be optimized of the helium-xenon cooled reactor; determining a parameter relationship between the parameters to be optimized and the overall reactor efficiency and the overall reactor mass of the helium-xenon cooled reactor based on the effective sample set; determining an optimal solution of the parameters to be optimized based on a preset multi-objective optimization algorithm and the parameter relationship. In the above parameter optimization method for the helium-xenon cooled reactor, by determining the parameters to be optimized of the helium-xenon cooled reactor and performing numerical simulation on the mathematical model of the helium-xenon cooled reactor, a parameter relationship between the parameters to be optimized and the overall reactor efficiency and the overall reactor mass of the helium-xenon cooled reactor can be obtained. Based on this parameter relationship and the preset multi-objective optimization algorithm, the optimal value of the parameters to be optimized can be quickly solved, improving the overall reactor efficiency on the basis of minimizing the overall reactor mass of the helium-xenon cooled reactor, and improving the parameter optimization effect of the helium-xenon cooled reactor.

[0016] Other features and advantages of the embodiments of the present invention will be described in the subsequent description, or some features and advantages can be inferred from the description or determined without doubt, or can be learned by implementing the above technologies of the embodiments of the present invention.

[0017] To make the above objects, features and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given below in conjunction with the accompanying drawings for detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 Shows a flowchart of a parameter optimization method for a helium-xenon cooled reactor provided by an embodiment of the present invention;

[0020] Figure 2 Shows a schematic diagram of a Brayton cycle provided by an embodiment of the present invention;

[0021] Figure 3 Shows a flowchart of the overall system optimization of a helium-xenon cooled reactor provided by an embodiment of the present invention;

[0022] Figure 4 Shows a schematic structural diagram of a parameter optimization device for a helium-xenon cooled reactor provided by an embodiment of the present invention;

[0023] Figure 5 Shows a diagram of the change of cycle efficiency with the helium-xenon mixing ratio provided by an embodiment of the present invention;

[0024] Figure 6 Shows a diagram of the change of cycle efficiency with the core outlet temperature provided by an embodiment of the present invention;

[0025] Figure 7 Shows a schematic diagram of the change of cycle efficiency with the pressure ratio provided by an embodiment of the present invention;

[0026] Figure 8 Shows a schematic diagram of the change of cycle efficiency with the heat regeneration degree of the regenerator provided by an embodiment of the present invention.

[0027] Icon:

[0028] 201 - Reactor; 202 - Turbine; 203 - Regenerator; 204 - Pre-cooler; 205 - Compressor; 206 - Generator. Specific Embodiments

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will describe the technical solutions of the present invention in conjunction with the drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention.

[0030] At present, in order to select appropriate design parameters for a helium-xenon cooled reactor to optimize its comprehensive performance, such as minimizing mass and maximizing efficiency, a full-system optimization is required. The determination of nuclear reactor design parameters involves coupled iterations of disciplines such as nuclear physics and thermal-hydraulics, and comprehensive considerations of factors such as safety and reliability. At the same time, it involves a large number of devices, and there are strong coupling relationships between the devices. The order of magnitude of the parameters describing the devices varies greatly. Therefore, to solve this optimization problem of a complex system, nonlinear, strongly coupled, and somewhat rigid nature, it is necessary to design an optimization method specifically. Existing reactor parameter design methods are less targeted at helium-xenon cooled reactors and have problems such as complex calculation methods leading to poor parameter optimization effects.

[0031] To address this issue, an embodiment of the present invention provides a parameter optimization method, device, and electronic device for a helium-xenon cooled reactor. This technology can be applied to improve the parameter optimization effect of a helium-xenon cooled reactor. The following provides a detailed introduction to the embodiments of the present invention.

[0032] This embodiment provides a parameter optimization method for a helium-xenon cooled reactor. This method can be applied to electronic devices such as computers. Refer to Figure 1 the flowchart of the parameter optimization method for a helium-xenon cooled reactor shown. This method mainly includes the following steps S102 to step S106:

[0033] Step S102, obtain an effective sample set of the helium-xenon cooled reactor.

[0034] The above effective sample set can be established in advance or in real time. The above effective sample set includes the parameters to be optimized for the helium-xenon cooled reactor. Obtain the parameters to be optimized for the helium-xenon cooled reactor. The parameters to be optimized include parameters such as the helium-xenon gas mixing ratio, system temperature ratio, recuperator heat recovery rate, compressor pressure ratio, compressor isentropic efficiency, compressor mechanical efficiency, turbine isentropic efficiency, turbine mechanical efficiency, core radius, and sandwich channel width.

[0035] Generate multiple array samples of the parameters to be optimized based on the value ranges of the parameters to be optimized to obtain a sample set. Each parameter to be optimized has a variable range, which is set according to the physical meaning of the parameter and existing reference calculation results. Generate a sample space for the parameters to be optimized based on the variable ranges of the parameters to be optimized, that is, take values within the variable ranges of each parameter to be optimized, and take values for each parameter to be optimized within the variable range respectively to form an array (x1, x2, x3... xn). Perform numerical simulation calculations on each sample in the sample set to determine an effective sample set that meets the reactor design conditions.

[0036] Select a sample set that simultaneously satisfies the thermal design constraints, weight constraints, and neutron physics constraints from the sample set to obtain an effective sample set. That is, calculate the total reactor mass and total reactor efficiency (y1, y2) obtained when the above array satisfies the thermal design constraints, weight constraints, and neutron physics constraints, and take {(x1, x2, x3……xn), (y1, y2)} as an effective sample. Take multiple values from the variable range of each parameter to be optimized (the values can be evenly taken or unevenly taken), and generate multiple effective samples respectively according to the above method to obtain an effective sample set.

[0037] Step S104, determine the parameter relationship between the parameter to be optimized and the total reactor efficiency and total reactor mass of the helium-xenon cooled reactor based on the effective sample set.

[0038] Perform curve fitting based on the total reactor efficiency and total reactor mass corresponding to each parameter to be optimized in the above effective sample set, and establish the functional relationship between the parameter to be optimized and the total reactor efficiency and total reactor mass, so as to obtain the variation law of the total reactor efficiency and total reactor mass with the parameter to be optimized.

[0039] Step S106, determine the optimal solution of the parameter to be optimized based on the preset multi-objective optimization algorithm and parameter relationship.

[0040] Perform optimization calculation on the parameter relationship between the parameter to be optimized and the total reactor efficiency and total reactor mass based on the preset multi-objective optimization algorithm, calculate the value of the parameter to be optimized corresponding to the maximum total reactor efficiency and the minimum total reactor mass, and record this value as the optimal solution of the parameter to be optimized, so as to improve the reactor efficiency and reduce the total reactor mass. The above preset multi-objective optimization algorithm can be an optimization algorithm such as a genetic algorithm, a particle swarm algorithm, or a BSO algorithm (also known as a beetle algorithm, a longhorn beetle swarm algorithm).

[0041] The parameter optimization method of the helium-xenon cooled reactor provided in this embodiment can obtain the parameter relationship between the parameter to be optimized of the helium-xenon cooled reactor and the total reactor efficiency and total reactor mass by determining the parameter to be optimized of the helium-xenon cooled reactor and performing numerical simulation on the mathematical model of the helium-xenon cooled reactor. Based on this parameter relationship and the preset multi-objective optimization algorithm, the optimal solution of the parameter to be optimized can be quickly solved, improving the total reactor efficiency on the basis of minimizing the total reactor mass of the helium-xenon cooled reactor and improving the parameter optimization effect of the helium-xenon cooled reactor.

[0042] In a feasible implementation manner, this embodiment provides an implementation manner for screening out a sample set that simultaneously satisfies the thermal design constraints, weight constraints, and neutron physics constraints from the sample set to obtain an effective sample set, which can be specifically performed according to the following steps (1) to (3):

[0043] Step (1): Perform a thermodynamic cycle calculation based on each array sample in the sample set, and screen out the first sample set that meets the thermal design constraints from the sample set according to the results of the thermodynamic cycle calculation.

[0044] See, for example, Figure 2 the schematic diagram of the Brayton cycle shown in the figure. The above-mentioned helium-xenon cooled reactor includes a reactor 201, a turbine 202, a recuperator 203, a precooler 204, a compressor 205, and a generator 206, Figure 2 showing the thermodynamic cycle relationship between the various devices of the helium-xenon cooled reactor.

[0045] According to the parameters to be optimized in each array sample, namely the helium-xenon gas mixing ratio, the system temperature ratio, the recuperator heat recovery ratio, the compressor pressure ratio, the compressor isentropic efficiency, the compressor mechanical efficiency, the turbine isentropic efficiency, the turbine mechanical efficiency, and the core radius value, iteratively calculate the thermodynamic cycle process of the reactor under the current parameters. Use the limit temperature of the thermal design criterion to set the thermal judgment condition. If the sample array does not meet the thermal design constraints, update a set of samples according to the variable range of the parameters to be optimized and perform a recalculation. Take the samples in the sample set that meet the thermal constraint conditions as the first sample set.

[0046] When calculating the simulation of the thermodynamic cycle process, the core part can be simulated by a single-channel program; the compressor, precooler, recuperator, and turbine are calculated using heat transfer relations; it is considered that the state points of each device in the entire circulation process of the coolant are in a steady-state process, that is, the cycle operates under steady state; for Figure 2 the processes with the same numbers marked in the thermodynamic cycle process are considered to have the same temperature and pressure, such as the gas working medium at the core outlet and the turbine inlet having the same temperature and pressure.

[0047] The above thermal design constraints include: the maximum temperature of the cladding of the coolant channel in the core does not exceed 1400K; the Brayton cycle efficiency of the reactor reaches 40% or more.

[0048] Step (2): Calculate the overall reactor efficiency and the overall reactor mass corresponding to each sample array in the first sample set based on the results of the thermodynamic cycle calculation, and screen out the second sample set that meets the weight constraint from the first sample set.

[0049] Based on the thermodynamic cycle process established in the above steps, calculate the overall reactor mass and the overall reactor efficiency of each sample array in the first sample set, and judge whether the currently calculated overall reactor mass and the masses of each subsystem are less than the corresponding weight upper limits. If so, determine that the weight constraint conditions are met, and take the samples in the first sample set that meet the weight constraint conditions as the second sample set.

[0050] The above-mentioned total reactor mass includes the compressor weight, turbine weight, recuperator weight, precooler weight, core region weight, and pressure-bearing layer weight. According to the physical characteristics and geometric shapes of each sub-component, the weights of the above sub-components are calculated separately and summed up as the total system weight, that is, the total reactor mass.

[0051] The above weight constraint conditions include the upper mass limit requirements for each subsystem of the reactor. For example, the upper mass limit of the core can be 7 tons, the upper mass limit of the energy conversion system is 5 tons, the upper mass limit of the shielding system is 14 tons, and the upper mass limit of the total reactor mass is 26 tons.

[0052] Step (3): Perform neutron physics calculations based on the second sample set to determine the effective multiplication factor corresponding to each sample array in the second sample set, and screen out the effective sample set that meets the neutron physics constraints from the second sample set based on the effective multiplication factor.

[0053] The neutron physics constraint is manifested as reactor criticality. The effective multiplication factor must be greater than 1 in order for the reactor to maintain an operating state.

[0054] Based on the core radius and the basic core unit structure of each sample data in the second sample set, calculate the effective multiplication factor corresponding to each sample data. When the effective multiplication factor is greater than 1, it is determined that the neutron physics constraint conditions are met. The sample arrays in the second sample set that meet the neutron physics constraint conditions are used as the effective sample set, and this effective sample set is the sample that simultaneously meets the thermal design constraints, weight constraints, and neutron physics constraints.

[0055] In a feasible implementation manner, this embodiment provides a specific implementation manner for determining the parameter relationship between the parameters to be optimized, the overall reactor efficiency, and the overall reactor mass of the helium-xenon cooled reactor based on the effective sample set: using the approximate model algorithm to perform sample fitting on the effective samples, establishing the functional relationship between the parameters to be optimized, the overall reactor efficiency, and the overall reactor mass, and obtaining the objective function.

[0056] The above approximate model algorithm can be a polynomial of the response surface method; any one of algorithms such as stepwise regression, neural network model, and Kriging model. Using the approximate model algorithm to establish the relationship between the variables to be optimized, the overall reactor mass, and the overall reactor efficiency, the approximate model form is: [Y1, Y2] = f(X1, X2, ……, X10) (X1 to X10 correspond to the above 10 parameters to be optimized), where the objective function f is a determined form obtained by fitting the sample set. That is to say, given a set of basic parameter values, the objective function value corresponding to this set of parameters can be obtained through f.

[0057] By adopting the approximate model algorithm, on the one hand, it is possible to avoid repeatedly performing time-consuming numerical simulation calculations during the optimization process; on the other hand, during the optimization process, the parameters can take values between two sets of samples and estimate the corresponding objective function values to facilitate the search for the optimal solution, and the discrete data in the effective sample set is fitted into a function form with continuously changing parameters.

[0058] In a feasible implementation manner, this embodiment provides a specific implementation manner for determining the optimal solution of the parameters to be optimized based on a preset multi-objective optimization algorithm and parameter relationship: perform optimization calculation on the objective function based on the preset multi-objective optimization algorithm, determine the values of the parameters to be optimized corresponding to the maximum full-reactor efficiency and the minimum full-reactor mass, and obtain the optimal solution set of the parameters to be optimized.

[0059] By using the preset multi-objective optimization algorithm to find the optimal solution of the parameters to be optimized for the reactor, multiple groups of optimal parameter combinations of the parameters to be optimized can be obtained, and a set of data is selected from the multiple groups of optimal parameters as the optimal solution of the reactor according to the actual situation of the reactor.

[0060] Judge the comprehensive performance of the parameters to be optimized of the helium-xenon cooled reactor when taking the above optimal solution. If the user is not satisfied with the comprehensive performance, the variable range of each parameter to be optimized of the reactor can be reset, and the above step S102 is returned to re-obtain values from the variable range to establish an effective sample set until the user is satisfied with the comprehensive performance of the reactor corresponding to the optimal solution.

[0061] The above parameter optimization method of the helium-xenon cooled reactor provided by this embodiment can automatically obtain the optimal of the reactor by establishing an effective sample set that simultaneously satisfies the thermal design constraints, weight constraints, and neutron physics constraints, and performing multi-objective optimization on the effective sample set fitting function; by taking the helium-xenon gas mixing ratio as the parameter to be optimized to find the optimal solution, the influence of different helium-xenon gas mixing ratios on the reactor is considered, which improves the rationality and reliability of the parameter optimization design of the helium-xenon cooled reactor.

[0062] Based on the foregoing embodiments, this embodiment provides an example of optimizing the entire system of a helium-xenon cooled reactor by applying the foregoing parameter optimization method of the helium-xenon cooled reactor. Refer to the Figure 3 flow chart of the entire system optimization of the helium-xenon cooled reactor shown in the figure, and the specific steps can be executed according to the following steps 1 to step 8:

[0063] Step 1: Obtain a set of parameters to be optimized for the helium-xenon cooled reactor to obtain a sample array.

[0064] The parameters to be optimized (i.e., the basic parameters of the reactor) include the helium-xenon gas mixing ratio, system temperature ratio, recuperator heat recovery ratio, compressor pressure ratio, compressor isentropic efficiency, compressor mechanical efficiency, turbine isentropic efficiency, turbine mechanical efficiency, core radius, and interlayer channel width.

[0065] Through thermodynamic calculation formulas and other equations, the unique physical thermal-hydraulic state of the helium-xenon cooled reactor can be determined (i.e., calculated) using the above parameters to be optimized.

[0066] The process of optimizing the entire system of the helium-xenon cooled reactor is to change these parameters to be optimized so that the total mass of the helium-xenon cooled reactor is minimized and the overall efficiency is maximized.

[0067] Considering the physical meaning of the parameters to be optimized, each parameter to be optimized has a variable range. This variable range is set based on its physical meaning, expert experience, existing reference calculation results, etc.

[0068] Obtain the variable ranges of the parameters to be optimized, and generate a parameter sample space for the parameters to be optimized, that is, take points (including uniform or non-uniform sampling) within the variable range of each parameter to be optimized. For each parameter to be optimized, take a value within the variable range to form an array, then a set of basic parameters (x1, x2, x3... xn) is obtained.

[0069] Use a set of basic parameters (x1, x2, x3... xn) for calculation, and obtain the calculation results, i.e., the system mass and efficiency (y1, y2) under the condition of meeting each constraint; take {(x1, x2, x3... xn), (y1, y2)} as a valid sample, and multiple valid samples will be calculated and generated subsequently to form a valid sample set.

[0070] Step 2: Determine the thermodynamic cycle process and judge whether the sample array meets the thermal-hydraulic design constraints.

[0071] According to the values of the given sample array, i.e., the helium-xenon gas mixing ratio, system temperature ratio, recuperator heat recovery rate, compressor pressure ratio, compressor isentropic efficiency, compressor mechanical efficiency, turbine isentropic efficiency, turbine mechanical efficiency, and core radius values, iteratively calculate the thermodynamic cycle process of the reactor under the current sample array. Use the limit temperature of the thermal-hydraulic design criterion to set the thermal-hydraulic judgment condition. If not met, update a set of parameters according to the parameter range set in Step 1 and recalculate. If the condition is met, proceed to Step 3. The above helium-xenon gas mixing ratio can be 85% helium - 15% xenon (volume ratio), or the volume ratio of helium to xenon is 72:28.

[0072] The core part is simulated by a single-channel program; the compressor, pre-cooler, recuperator, and turbine are calculated using heat transfer relations; it is considered that the state points of each device in the entire coolant cycle process are in a steady-state process, that is, the cycle operates under steady state. The thermal-hydraulic design constraints of the reactor refer to: ① The maximum temperature of the coolant channel cladding in the core does not exceed 1400K; ② The Brayton cycle efficiency of the reactor reaches 40% or more.

[0073] Step 3: Calculate the system weight and efficiency, and determine whether the current calculation meets the weight constraint condition.

[0074] Based on the result of the thermodynamic cycle established in Step 2, the system thermal efficiency can be obtained; using the basic parameter of the interlayer channel width, the system weight can be calculated. The system weight (i.e., the total reactor mass) includes: the weight of the compressor, the weight of the turbine, the weight of the recuperator, the weight of the precooler, the weight of the core region, and the weight of the pressure-bearing layer. According to the physical characteristics and geometric shapes of each sub-component, the weights of the above sub-components are calculated separately and summed up as the total system weight.

[0075] There are upper limits on the weights for each subsystem (core, energy conversion system, shielding system), which are the weight constraint conditions. Use the weight constraint conditions to set the judgment conditions. If the weight constraint conditions are not met, update a set of parameters according to the parameter range set in Step 1 and recalculate. If the conditions are met, proceed to Step 4.

[0076] Step 4: Calculate the neutron physical state of the core, and determine whether the current calculation meets the neutron physical constraint condition.

[0077] Based on the given core radius and the basic unit structure of the core, the effective multiplication factor is calculated. Use the neutron physical constraint conditions to set the judgment conditions. If the neutron physics is not met, update a set of parameters according to the parameter range set in Step 1 and recalculate. If the conditions are met, proceed to Step 5.

[0078] The neutron physical constraint is manifested as reactor criticality. The effective multiplication factor must be greater than 1 to keep the reactor in an operating state.

[0079] Step 5: Obtain a valid sample of the reactor design that meets the thermal-hydraulic design constraints, weight constraints, and neutron physical constraints for this set of sample arrays, and add it to the sample set. After traversing all the variable range values of the parameters to be optimized, finally obtain n valid sample arrays to form a valid sample set.

[0080] Step 6: Use the approximate model algorithm to establish the relationship between the optimization variables and the overall reactor efficiency and overall reactor mass, and obtain an approximate model.

[0081] The approximate model algorithm includes polynomials such as the response surface method; stepwise regression, neural networks, and Kriging models, etc. The form of the approximate model is: [Y1,Y2] = f(X1,X2,……,X10), where the function f is a determined form obtained by fitting the sample set. That is to say, given a set of values of the basic parameters, the corresponding objective function value of this set of parameters can be immediately obtained through f.

[0082] Using an approximate model is essentially to fit the discrete data in the effective sample set in step 5 into a form with continuously varying parameters. On the one hand, it is to avoid repeatedly performing time-consuming numerical simulation calculations during the optimization process; on the other hand, it is to enable the parameters to take values between two sets of samples during the optimization process and estimate the corresponding objective function values to facilitate the search for the optimal solution.

[0083] Step 7: Using the approximate model, adopt a multi-objective optimization algorithm to find the optimal values of the parameters to be optimized for the reactor, and obtain multiple best parameter combinations, that is, the Pareto optimal solution set; the multi-objective optimization algorithm can be, for example: genetic algorithm, particle swarm algorithm, BSO algorithm (also known as the beetle algorithm, longhorn beetle swarm algorithm). In the optimal solution set, select one as the final reactor optimization result according to the actual situation.

[0084] Step 8: If not satisfied with the comprehensive performance of the reactor calculated from the initial parameter settings, it is possible to return to step 1 to reset the parameter change range of the reactor. Repeat the solution process from 1 to 7 until satisfactory comprehensive performance of the reactor is obtained.

[0085] Corresponding to the parameter optimization method of the helium-xenon cooled reactor provided in the above embodiment, an embodiment of the present invention provides a parameter optimization device for a helium-xenon cooled reactor. Refer to Figure 4 the structural schematic diagram of a parameter optimization device for a helium-xenon cooled reactor shown in

[0086] An acquisition module 41, configured to acquire an effective sample set of the helium-xenon cooled reactor; wherein, the effective sample set includes the parameters to be optimized for the helium-xenon cooled reactor.

[0087] A first determination module 42, configured to determine the parameter relationship between the parameters to be optimized and the overall reactor efficiency and overall reactor mass of the helium-xenon cooled reactor based on the effective sample set.

[0088] A second determination module 43, configured to determine the optimal solution of the parameters to be optimized based on the preset multi-objective optimization algorithm and the parameter relationship.

[0089] The above-mentioned parameter optimization device for the helium-xenon cooled reactor provided in this embodiment can, by determining the parameters to be optimized for the helium-xenon cooled reactor and performing numerical simulation on the mathematical model of the helium-xenon cooled reactor, obtain the parameter relationship between the parameters to be optimized for the helium-xenon cooled reactor and the overall reactor efficiency and overall reactor mass. Based on this parameter relationship and the preset multi-objective optimization algorithm, the optimal solution of the parameters to be optimized can be quickly solved, improving the overall reactor efficiency on the basis of minimizing the overall reactor mass of the helium-xenon cooled reactor and enhancing the parameter optimization effect of the helium-xenon cooled reactor.

[0090] In one implementation manner, the above device further includes:

[0091] A sample establishment module for obtaining the parameters to be optimized of a helium-xenon cooling reactor; wherein, the parameters to be optimized include any one or more of the helium-xenon gas mixing ratio, system temperature ratio, recuperator heat recovery ratio, compressor pressure ratio, compressor isentropic efficiency, compressor mechanical efficiency, turbine isentropic efficiency, turbine mechanical efficiency, core radius, and interlayer channel width; generating multiple array samples of the parameters to be optimized based on the value ranges of the parameters to be optimized to obtain a sample set; performing numerical simulation calculations on each sample in the sample set to determine an effective sample set that meets the reactor design conditions.

[0092] In one implementation, the above sample establishment module is further configured to screen out a sample set that simultaneously meets the thermal design constraints, weight constraints, and neutron physics constraints from the sample set to obtain an effective sample set.

[0093] In one implementation, the above sample establishment module is further configured to perform a thermodynamic cycle calculation based on each array sample in the sample set, screen out a first sample set that meets the thermal design constraints from the sample set according to the thermodynamic cycle calculation results; calculate the overall reactor efficiency and overall reactor mass corresponding to each sample array in the first sample set based on the thermodynamic cycle calculation results, and screen out a second sample set that meets the weight constraints from the first sample set; perform neutron physics calculations on the second sample set to determine the effective multiplication factor corresponding to each sample array in the second sample set, and screen out an effective sample set that meets the neutron physics constraints from the second sample set based on the effective multiplication factor.

[0094] In one implementation, the above first determination module 42 is further configured to perform sample fitting on the effective samples using an approximate model algorithm to establish a functional relationship between the parameters to be optimized, the overall reactor efficiency, and the overall reactor mass to obtain an objective function.

[0095] In one implementation, the above second determination module 43 is further configured to perform an optimization calculation on the objective function based on a preset multi-objective optimization algorithm to determine the values of the parameters to be optimized corresponding to the maximum overall reactor efficiency and the minimum overall reactor mass, and obtain an optimal solution set of the parameters to be optimized.

[0096] In one implementation, the above approximate model algorithm includes any one of a polynomial of the response surface method, stepwise regression, neural network model, and Kriging model.

[0097] The parameter optimization device of the above-mentioned helium-xenon cooling reactor provided in this embodiment can automatically obtain the optimal solution of the reactor by establishing an effective sample set that simultaneously satisfies thermal design constraints, weight constraints, and neutron physics constraints, and performing multi-objective optimization on the effective sample set fitting function; by using the helium-xenon gas mixing ratio as the parameter to be optimized to find the optimal solution, the influence of different helium-xenon gas mixing ratios on the reactor is considered, which improves the rationality and reliability of the parameter optimization design of the helium-xenon cooling reactor.

[0098] The device provided in this embodiment has the same implementation principle and technical effects as those in the previous embodiment. For a brief description, for the parts not mentioned in the device embodiment, reference can be made to the corresponding content in the previous method embodiment.

[0099] In a specific implementation manner, this embodiment provides an example for analyzing the thermal efficiency of a helium-xenon cooling Brayton cycle:

[0100] In this example, the system parameters for calculating the Brayton cycle efficiency can refer to the default values of the cycle parameters shown in Table 1 below. The highest pressure and the compressor outlet pressure in the thermodynamic cycle are default selected as 2 MPa.

[0101] Table 1 Default values of cycle parameters

[0102]

[0103]

[0104] Helium-xenon mixture ratio

[0105] By increasing the proportion of xenon in the helium-xenon mixture, the aerodynamic load of the compressor (related to the enthalpy rise of the working fluid in the compressor) can be reduced, thereby reducing the size of the compressor. With the incorporation of xenon, the specific heat capacity at constant pressure of the mixed working fluid decreases, thereby reducing the enthalpy rise. However, at the same time, the viscosity of the mixed working fluid increases and the core pressure drop increases, thereby reducing the thermal efficiency. When the volume ratio of helium to xenon is 72:28, the molar mass of the mixed working fluid can be calculated by the following formula:

[0106] M mix = αM He +(1 - α)M Xe

[0107] Where, M mix is the molar mass of the mixed working fluid, α is the volume fraction of helium, M He is the molar mass of helium (4 g / mol), M Xe is the molar mass of xenon (131.29 g / mol). By changing different helium-xenon mixing ratios to calculate the cycle efficiency, see Figure 5The diagram showing the variation of cycle efficiency with the helium-xenon mixing ratio. When the volume ratio of helium to xenon is changed to 85:15, the molar mass of the mixed gas is 23 g / mol, and the cycle thermal efficiency reaches 40.89% at this time. As the proportion of xenon incorporated increases, the mixed molar mass is 40 g / mol, and the efficiency will be 33.3% at this time.

[0108] System temperature ratio

[0109] The lowest temperature point in the cycle is located at the outlet of the precooler and the inlet of the compressor, which is 26.85 °C (300 K). The highest temperature point in the cycle is located at the core outlet, which is 850 °C (1123.5 K). See the Figure 6 diagram showing the variation of cycle efficiency with the core outlet temperature as shown Figure 6 shows the variation law of cycle efficiency with the outlet temperature under the selection of default parameters. Keeping the lowest temperature of the cycle, i.e., the compressor inlet temperature, unchanged, the increase in the core outlet temperature will affect the cycle thermal efficiency in two aspects. First, with the core inlet temperature unchanged, a higher outlet temperature makes the total thermal power of the cycle higher. Second, the core outlet temperature will also make the inlet temperature of the turbine higher, thus affecting the output work of the turbine.

[0110] Compression ratio

[0111] The compression ratio γ of the compressor refers to the ratio of the outlet pressure to the inlet pressure of the compressor, and it is also the ratio of the highest pressure to the lowest pressure in the entire cycle loop. For an adiabatic process, the relationship between the inlet and outlet temperatures of the compressor can be expressed by the following formula:

[0112]

[0113] where, φ 4-5 is the average adiabatic coefficient of the compressor thermal process, T4 is the actual inlet working fluid temperature of the compressor (which is also the outlet temperature of the precooler), P4 is the actual inlet working fluid pressure of the compressor, P5 is the actual outlet working fluid pressure of the compressor, that is, P5 is the highest pressure in the entire loop ( Figure 2 the 1-2-3-4-5-6-1 cycle loop in 5s ), and T is

[0114] the outlet working fluid temperature of the compressor in the adiabatic process. Figure 7 The larger the compression ratio of the compressor, under the same compressor efficiency, the greater the cycle work consumed by the compressor, thus reducing the thermal efficiency of the entire cycle. See the schematic diagram showing the variation of cycle efficiency with the compression ratio as shown Figure 7 The compression ratio can be selected as 2.0 for the sample array in combination with

[0115] The increase in the pressure ratio of the compressor affects the thermal efficiency of the Brayton cycle in two aspects. First, at the same inlet temperature, it will cause a higher outlet temperature of the compressor, thus consuming more cycle work. Second, the increase in the outlet temperature of the compressor will also increase the inlet temperature of the reactor core. Therefore, with the outlet temperature of the reactor core remaining unchanged, the total thermal power of the cycle will decrease.

[0116] Regenerator heat recovery ratio

[0117] The regenerator heat recovery ratio is an important parameter affecting the thermodynamic process in the regenerator. In a regenerator without considering flow splitting, its thermodynamic process can be described by the following several formulas. First is the energy conservation of the heat exchange between the cold side and the hot side fluids in the regenerator:

[0118]

[0119] Among them, is Figure 2 the average constant-pressure specific heat capacity of the thermodynamic process of 2-3 (hot side of the regenerator), is Figure 2 the average constant-pressure specific heat capacity of the thermodynamic process of 5-6 (cold side of the regenerator). The specific heat capacity of the working fluid can be obtained from the helium-xenon gas property table. T2 is the inlet working fluid temperature of the hot side of the regenerator and also the outlet working fluid temperature of the turbine; T3 is the outlet working fluid temperature of the hot side of the regenerator and also the inlet working fluid temperature of the precooler; T5 is the inlet working fluid temperature of the cold side of the regenerator and also the outlet working fluid temperature of the compressor; T6 is the outlet working fluid temperature of the cold side of the regenerator and also the inlet working fluid temperature of the reactor core.

[0120] The heat recovery ratio α of the regenerator r is defined as the ratio of the actual heat recovery of the cycle to the maximum heat recovery that can be achieved. Then the calculation formula for the heat recovery ratio is:

[0121]

[0122] Among them, H5 is the specific enthalpy of the working fluid at the outlet of the compressor (also the specific enthalpy of the working fluid at the inlet of the cold side of the regenerator), H6 is the specific enthalpy of the working fluid at the outlet of the cold side of the regenerator (also the specific enthalpy of the working fluid at the inlet of the reactor core), H2 is the specific enthalpy of the working fluid at the outlet of the turbine (also the specific enthalpy of the working fluid at the inlet of the hot side of the regenerator), C p,2 is the constant-pressure specific heat capacity of the working fluid at the inlet of the hot side of the regenerator, and C p,5 is the constant-pressure specific heat capacity of the working fluid at the inlet of the cold side of the regenerator.

[0123] The inlet temperature of the cold side of the regenerator can be considered equal to the outlet temperature of the compressor, and the inlet temperature of the hot side is considered equal to the outlet temperature of the turbine. Then the above formula can directly solve for the outlet temperatures of the hot side and the cold side of the regenerator. It can be seen that the selection of the heat recovery ratio directly affects the outlet temperature of the cold side, that is, the inlet temperature of the reactor core, thus affecting the thermal power and efficiency of the entire cycle.

[0124] See Figure 8 The schematic diagram of the cycle efficiency changing with the regenerator reheat degree is shown. In the Brayton cycle design with helium-xenon mixed gas as the circulating working fluid, the regenerator reheat degree can range from 0.767 to 0.95, and the reheat degree that can be selected from the above sample array is 0.95.

[0125] The thermal design criteria of gas-cooled reactors are different from those of pressurized water reactors. Gas-cooled reactors do not have the critical boiling problem of the fuel element surface like pressurized water reactors. The thermal design criteria of gas-cooled reactors are mainly that the maximum temperature of the fuel element surface, the maximum temperature of the center, and the maximum thermal stress of the fuel element and structural components do not exceed the allowable value. In one embodiment, the materials of various parts of the reactor can refer to the materials table of various parts of the reactor shown in Table 2 below:

[0126] Table 2 List of materials for each part of the reactor

[0127]

[0128]

[0129] The fuel element is made of uranium carbide alloy, the matrix material is graphite, and the coolant channel cladding material is molybdenum alloy. Among the above three materials, molybdenum alloy has the worst temperature tolerance, so in the preliminary thermal cycle design, only the maximum temperature of the coolant channel cladding in the core is considered not to exceed 1400K.

[0130] If the specified electric power output is used as the preliminary design target of the Brayton cycle, two approaches can be considered to achieve this goal. One is to try to improve the thermal efficiency of the cycle under the premise of fixing the total thermal power of the cycle; the other is to improve the total thermal power of the core under the premise of fixing the thermal efficiency. It is also possible to comprehensively consider the two approaches to achieve the final output electric power index. This embodiment chooses the first approach to achieve, that is, first fix the total thermal efficiency of the core, which mainly involves the selection of the mass flow rate of the core coolant and the inlet and outlet temperatures. After fixing the total thermal power of the core, the thermal efficiency of the cycle is improved by adjusting the cycle parameters to achieve the final electric power target. Under the corresponding cycle thermal efficiency, the calculation of the cycle will give the cycle design pressure loss of the core according to the calculation method of the pressure loss. Therefore, the actual pressure loss of the core should not exceed this design pressure loss. Otherwise, the actual pressure loss is greater than the pressure loss limit given by the design, and other cycle parameters remain unchanged. The core outlet pressure is reduced, and the output power of the turbine is reduced, so that the specified thermal efficiency cannot be achieved.

[0131] Combined with the design criteria mentioned above, in the single-channel calculation program of the core, the actual pressure drop of the core and the temperature calculation of the coolant cladding become particularly important, which mainly involves the heat transfer relationship and pressure drop relationship of the helium-xenon circulating working fluid.

[0132] The heat transfer relationship between helium and xenon. Under the same pipeline and the same molar mass flow rate, the heat transfer coefficient h of the mixed working fluid and the heat transfer coefficient h of the pure helium working fluid He The ratio is defined as the relative heat transfer coefficient, which can be expressed by the basic physical property parameters of the mixed working fluid:

[0133]

[0134] where μ is the viscosity of the mixed working fluid, M mix is the average molar mass of the mixed working fluid, C p is the specific heat capacity at constant pressure of the mixed working fluid, and λ is the thermal conductivity of the mixed working fluid.

[0135] The heat transfer coefficient h of the pure helium working fluid He is calculated according to the empirical relationship proposed by Taylor. The error of this empirical relationship with the experimental value is kept within 10% under low heat flux heating and within 20% under high heat flux heating.

[0136]

[0137] where c = 0.57 - [1.59 / (z / D)], z is the distance from the inlet, D is the inner diameter of the pipeline, Nu b 、Re b 、Pr b are the Nusselt number, Reynolds number, and Prandtl number of the mainstream respectively, T s and T b are the wall temperature and the mainstream temperature respectively, and b is the fluid average (bulk).

[0138] For a certain fixed core radius R, if the core height H is too long, the pressure drop inside the core will be too large, which may be greater than the core design pressure drop determined to obtain a 40% thermal efficiency; if the core height H is too small, the linear power density will be too high, which may exceed the allowable temperature of the material. For a fixed core height H, if the core radius R is too small, the flow velocity of the working fluid in the core coolant channel will increase and the pressure drop will increase, which may exceed the allowable pressure drop of the efficiency design. If the radius R is too large, there are no upper limitations except for the increase in size. Therefore, for a certain fixed core radius, the allowable range of the core height is between the maximum value and the minimum value.

[0139] Weight pressure accounting

[0140] The mass of the compressor and the turbine is related to the diameter:

[0141]

[0142] The diameter of the compressor is estimated by the following formula:

[0143]

[0144] The mass of the auxiliary component is related to the total output power and can be calculated by the following formula:

[0145] M aux = ξ aux ·P

[0146] Where: T1 and T2 are the inlet and outlet temperatures respectively, Ψ is the compressor head coefficient, n is the compressor speed in rmp, is the specific mass of the energy conversion system, and ξ ECS is the compressor head coefficient, and ξ aux is the specific mass of the auxiliary system.

[0147] The masses of the recuperator and the pre-cooler are related to the area:

[0148] M rcp = ξ rcp ·A rcp

[0149] The area of the recuperator is calculated by the following formula:

[0150]

[0151] Where, is the heat transfer rate in kW, U is the overall heat coefficient in kW / m2K (overall heat coefficient), and LMTD is the log mean temperature difference.

[0152] The mass of the core region is obtained by multiplying the volume of the corresponding region by the density of the material in the corresponding region.

[0153] Neutron physics calculation:

[0154] Criticality calculation. According to the equivalent area, the number of coolant-fuel element units that the core needs to accommodate is 774 / 1507. The core neutron calculation program arranges the coolant-fuel element units in a regular hexagon. The results of the criticality calculation are shown in Table 3 below. When the core radius is 80 cm, changing the peripheral material of the matrix to the reflector material Be, Keff rises to 1.11925 (+ / -0.00071).

[0155] Table 3 Criticality calculation results table

[0156] Core radius (unit: cm) <![CDATA[k eff > 50 0.98326+ / -0.00079 65 1.04565+ / -0.00078 80 1.07894+ / -0.00082

[0157] Considering that when the coolant channel radius is 3 mm, the coolant flow rate in the core is too high, the coolant channel radius is adjusted to 4 mm, and the cladding thickness remains 1 mm. The core radius is designed to be 0.6 m and the height is 1.2 m. At this time, the number of units calculated by the thermal engineering program is 1507, and the 65 cm radius core in the neutron program can accommodate these units to reach criticality.

[0158] An embodiment of the present invention provides an electronic device, including a processor and a memory. A computer program that can run on the processor is stored in the memory. When the processor executes the computer program, the steps of the method provided in the above embodiment are implemented.

[0159] An embodiment of the present invention provides a computer-readable medium. The computer-readable medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to implement the method described in the above embodiment.

[0160] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process of the above-described system can refer to the corresponding process in the foregoing embodiment, and will not be described herein again.

[0161] A computer program product of a method, device, and electronic device for parameter optimization of a helium-xenon cooling reactor provided by an embodiment of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the foregoing method embodiment. For the specific implementation, reference can be made to the method embodiment, and details will not be described herein again.

[0162] In addition, in the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0163] If the above function is implemented in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc that can store program code.

[0164] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0165] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present invention, which are used to illustrate the technical solutions of the present invention, rather than limiting them. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions described in the foregoing embodiments or easily conceive of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A parameter optimization method for a helium-xenon cooling reactor, characterized in that, including: obtaining an effective sample set of a helium-xenon cooled reactor; wherein the effective sample set includes the parameters to be optimized of the helium-xenon cooled reactor; determining the parameter relationship between the parameters to be optimized and the overall efficiency and overall mass of the helium-xenon cooled reactor based on the effective sample set; determining the optimal solution of the parameters to be optimized based on a preset multi-objective optimization algorithm and the parameter relationship; the method further includes: obtaining the parameters to be optimized of the helium-xenon cooled reactor; wherein the parameters to be optimized include any one or more of the helium-xenon gas mixing ratio, system temperature ratio, recuperator heat recovery rate, compressor pressure ratio, compressor isentropic efficiency, compressor mechanical efficiency, turbine isentropic efficiency, turbine mechanical efficiency, core radius, and sandwich channel width; generating a plurality of array samples of the parameters to be optimized based on the value range of the parameters to be optimized to obtain a sample set; performing numerical simulation calculations on each sample in the sample set to determine an effective sample set that meets the reactor design conditions; the step of determining the parameter relationship between the parameters to be optimized and the overall efficiency and overall mass of the helium-xenon cooled reactor based on the effective sample set includes: using an approximate model algorithm to perform sample fitting on the effective samples, and establishing a functional relationship between the parameters to be optimized and the overall efficiency and overall mass to obtain an objective function.

2. The method according to claim 1, wherein the step of performing numerical simulation calculations on each array sample in the sample set to determine an effective sample set that meets the reactor design conditions includes: screening out a sample set that simultaneously meets the thermal design constraints, weight constraints, and neutron physics constraints from the sample set to obtain an effective sample set.

3. The method according to claim 2, wherein the step of screening out a sample set that simultaneously meets the thermal design constraints, weight constraints, and neutron physics constraints from the sample set to obtain an effective sample set includes: performing a thermodynamic cycle calculation based on each array sample in the sample set, and screening out a first sample set that meets the thermal design constraints from the sample set according to the thermodynamic cycle calculation results; calculating the overall efficiency and overall mass corresponding to each sample array in the first sample set based on the thermodynamic cycle calculation results, and screening out a second sample set that meets the weight constraints from the first sample set; performing neutron physics calculations based on the second sample set to determine the effective multiplication factor corresponding to each sample array in the second sample set, and screening out an effective sample set that meets the neutron physics constraints from the second sample set based on the effective multiplication factor.

4. The method according to claim 1, wherein the step of determining the optimal solution of the parameters to be optimized based on a preset multi-objective optimization algorithm and the parameter relationship includes: performing an optimization calculation on the objective function based on a preset multi-objective optimization algorithm, and determining the value of the parameters to be optimized corresponding to the maximum overall efficiency and the minimum overall mass to obtain the optimal solution set of the parameters to be optimized.

5. The method according to claim 1, characterized in that, the approximate model algorithm includes any one of a polynomial of the response surface method, stepwise regression, a neural network model, and a Kriging model.

6. A parameter optimization device for a helium-xenon cooling reactor, characterized in that, including: an obtaining module, configured to obtain an effective sample set of a helium-xenon cooled reactor; wherein the effective sample set includes the parameters to be optimized of the helium-xenon cooled reactor; The first determination module is configured to determine the parameter relationship between the parameter to be optimized, the overall reactor efficiency, and the overall reactor mass of the helium-xenon cooling reactor based on the effective sample set; The second determination module is configured to determine the optimal solution of the parameter to be optimized based on a preset multi-objective optimization algorithm and the parameter relationship; The sample establishment module is configured to obtain the parameter to be optimized of the helium-xenon cooling reactor; wherein, the parameter to be optimized includes any one or more of the helium-xenon gas mixing ratio, the system temperature ratio, the recuperator heat recovery rate, the compressor pressure ratio, the compressor isentropic efficiency, the compressor mechanical efficiency, the turbine isentropic efficiency, the turbine mechanical efficiency, the core radius, and the interlayer channel width; generate a plurality of array samples of the parameter to be optimized based on the value range of the parameter to be optimized to obtain a sample set; perform numerical simulation calculations on each sample in the sample set to determine an effective sample set that meets the reactor design conditions; The first determination module is configured to perform sample fitting on the effective samples by using an approximate model algorithm, establish a functional relationship between the parameter to be optimized, the overall reactor efficiency, and the overall reactor mass, and obtain an objective function.

7. An electronic device, characterized in that, Comprising: A processor and a storage device; A computer program is stored on the storage device, and the computer program, when run by the processor, executes the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it executes the steps of the method according to any one of claims 1 to 5 above.

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