A simulation optimization method and system for a relativistic klystron, and an electronic device

Abstract

The present invention provides a simulation optimization method and system for a relativistic klystron, and an electronic device, including: obtaining simulation design parameters of the relativistic klystron input by a user; selecting an optimization objective, optimization parameters and a multi-objective optimization algorithm for the relativistic klystron; according to the simulation design parameters, optimization objective, optimization parameters and the multi-objective optimization algorithm, using the self-developed klystron large-signal simulation software KlyH based on a one-dimensional disk model to perform multi-objective optimization on the relativistic klystron, and visually displaying the optimized simulation results, greatly improving the simulation speed and achieving multi-objective optimization of the relativistic klystron; being able to perform bandwidth simulation on the relativistic klystron based on the simulation design parameters of the relativistic klystron input by the user. Through designing a clear visual interface, the user can efficiently complete the simulation and optimization tasks without complex programming skills, significantly reducing the technical usage threshold.

Description

Technical Field

[0001] The present invention relates to the fields of high-power microwaves, computer science, and technology, and particularly to a simulation optimization method and system for a relativistic klystron, and an electronic device. Background Art

[0002] In the field of electronic science and technology, a relativistic klystron amplifier (RKA) has the advantages of high peak power, high efficiency, high gain, and stable output microwave frequency and phase, and is of great significance for promoting the practical application of HPM systems. The RKA is an electro-vacuum type 0 microwave device that uses beam-wave interaction to achieve radio frequency (RF) amplification, and has broad application prospects in technical fields such as particle accelerators, pulsed radars, and power synthesis. The high-gain and high-efficiency characteristics of the RKA give it important advantages in applications that require high-power microwave signals.

[0003] Performing numerical calculations on a klystron using a computer can quickly and effectively achieve the design of the klystron. Especially for high-efficiency klystrons, the one-dimensional disk model was the earliest model used for klystron numerical calculations. With the improvement of computer calculation speed and storage capacity, there are now various 2D and 3D PIC particle simulation software, mainly represented by software such as Magic, CHIPIC, CST, and Arsenal. Since a large number of complex parameter dimensions need to be considered in the design of high-efficiency RKAs, using traditional optimization methods to design high-efficiency RKAs will result in problems such as long design time and a large difference between the design efficiency and the target efficiency.

[0004] Therefore, the existing technology still needs to be further developed. Summary of the Invention

[0005] The purpose of the present invention is to overcome the above technical deficiencies and provide a simulation optimization method and system for a relativistic klystron, and an electronic device to solve the problems existing in the prior art.

[0006] To achieve the above technical purpose, according to the first aspect of the present invention, a simulation optimization method for a relativistic klystron is provided, including:

[0007] S100. Obtain the simulation design parameters of the relativistic klystron input by the user;

[0008] S200. Select the optimization objective, optimization parameters, and multi-objective optimization algorithm of the relativistic klystron;

[0009] S300. According to the simulation design parameters, optimization objectives, optimization parameters, and multi-objective optimization algorithm, use the klystron large-signal simulation software KlyH based on the one-dimensional disk model to perform multi-objective optimization on the relativistic klystron, and visually display the optimized simulation results.

[0010] Specifically, the method further includes:

[0011] Set up a user operation interface for configuring the simulation design parameters of the relativistic klystron and displaying the simulation results of the relativistic klystron.

[0012] Specifically, the method for obtaining the simulation design parameters of the relativistic klystron input by the user includes:

[0013] Obtain the simulation design parameters of the relativistic klystron input by the user in the user operation interface;

[0014] The simulation design parameters include the microwave parameters, calculation parameters, electron beam parameters, and resonator modulation parameters of the relativistic klystron.

[0015] Specifically, the klystron large-signal simulation software KlyH based on the one-dimensional disk model uses a numerical program based on the one-dimensional disk model developed in Fortran language as the kernel. The klystron large-signal simulation software KlyH based on the one-dimensional disk model is used to perform one-dimensional simulation on the simulation design parameters input by the user. The method for performing one-dimensional simulation on the simulation design parameters input by the user includes:

[0016] Package the core algorithm program of the numerical program based on the one-dimensional disk model into a dynamic link library, and use the dynamic link library to perform one-dimensional simulation on the simulation design parameters input by the user to obtain the output parameters of the optimization objective.

[0017] Specifically, S300 includes:

[0018] The multi-objective optimization algorithm calls the dynamic link library, reads the simulation design parameters of the relativistic klystron, configures the basic parameters of the multi-objective optimization algorithm, and performs multi-objective optimization simulation on the relativistic klystron according to the selected optimization parameters and optimization objectives.

[0019] Specifically, the multi-objective optimization algorithm includes: non-dominated sorting genetic algorithm or optimal solution multi-objective particle swarm algorithm.

[0020] Specifically, the user operation interface includes a multi-objective optimization interface. The performing optimization simulation on the relativistic klystron according to the selected optimization parameters and optimization objectives includes:

[0021] The multi-objective optimization interface is provided with optimization objective options and optimization parameter options. The user selects the optimization objective from the optimization objective options and the optimization parameters from the optimization parameter options according to actual needs.

[0022] Specifically, the user operation interface includes a bandwidth simulation interface, which is used to perform bandwidth simulation on the relativistic klystron according to the simulation design parameters of the relativistic klystron input by the user and display the simulation results.

[0023] According to the second aspect of the present invention, there is provided a simulation optimization system for a relativistic klystron, including:

[0024] An acquisition module (100): used to acquire the simulation design parameters of the relativistic klystron input by the user;

[0025] A simulation module (200): used to select the optimization objective, optimization parameters and multi-objective optimization algorithm of the relativistic klystron; and used to perform multi-objective optimization on the relativistic klystron by using the klystron large-signal simulation software KlyH based on the one-dimensional disk model according to the simulation design parameters, optimization objective, optimization parameters and multi-objective optimization algorithm, and visually display the optimized simulation results.

[0026] According to the third aspect of the present invention, there is provided an electronic device, including: a memory; and a processor, where computer-readable instructions are stored on the memory, and when the computer-readable instructions are executed by the processor, the above-mentioned simulation optimization method for the relativistic klystron is implemented.

[0027] Beneficial effects:

[0028] The present invention provides a simulation optimization method for a relativistic klystron. By acquiring the simulation design parameters of the relativistic klystron input by the user, selecting the optimization objective, optimization parameters and multi-objective optimization algorithm of the relativistic klystron, performing one-dimensional simulation on the relativistic klystron by using the KlyH software, and visually displaying the optimized simulation results, the present invention also provides a friendly user operation interface, supports parameter configuration and simulation result display, combines the core algorithm program of one-dimensional simulation of the self-developed KlyH software with the multi-objective optimization algorithm, greatly improves the simulation speed, realizes multi-objective optimization of the relativistic klystron, and through designing a clear visual interface, the user can efficiently complete the simulation and optimization tasks without complex programming skills, significantly reduces the technical use threshold, solves the technical problems of long design time and large difference between the design efficiency and the target efficiency in the traditional method, can provide strong support for the research and engineering practice in related fields, and greatly improves the intelligence, usability and reliability of the present invention. Description of the Drawings

[0029] Figure 1 It is the flowchart of the simulation optimization method of the relativistic klystron provided in the specific embodiment of the present invention;

[0030] Figure 2 It is the schematic diagram of the system composition of the simulation optimization system of the relativistic klystron provided in the specific embodiment of the present invention;

[0031] Figure 3 It is the working principle diagram of the KlyH one-dimensional simulation provided in the specific embodiment of the present invention;

[0032] Figure 4 It is the schematic diagram of a part of the interface of the KlyH software provided in the specific embodiment of the present invention;

[0033] Figure 5 It is the schematic diagram of the cavity parameter interface of the KlyH software provided in the specific embodiment of the present invention;

[0034] Figure 6 It is the schematic diagram of the output parameter interface of the KlyH software provided in the specific embodiment of the present invention;

[0035] Figure 7 It is the flowchart of the KlyH one-dimensional simulation interface provided in the specific embodiment of the present invention;

[0036] Figure 8 It is the schematic diagram of the simulation result of the KlyH one-dimensional simulation interface provided in the specific embodiment of the present invention;

[0037] Figure 9 It is the schematic diagram of the cavity parameters of the KlyH one-dimensional simulation interface provided in the specific embodiment of the present invention;

[0038] Figure 10 It is the schematic diagram of the output parameters of the KlyH one-dimensional simulation interface provided in the specific embodiment of the present invention;

[0039] Figure 11 It is the schematic diagram of the multi-objective optimization tab interface provided in the specific embodiment of the present invention;

[0040] Figure 12 It is the schematic diagram of the simulation result of the multi-objective optimization tab interface provided in the specific embodiment of the present invention;

[0041] Figure 13 It is the schematic diagram of the modulation parameters of each resonator before optimization provided in the specific embodiment of the present invention;

[0042] Figure 14 It is the schematic diagram of the optimization result details interface provided in the specific embodiment of the present invention;

[0043] Figure 15It is a schematic diagram of the bandwidth simulation tab interface provided in the specific implementation manner of the present invention;

[0044] Figure 16 It is a schematic diagram of the simulation result of the bandwidth simulation tab interface provided in the specific implementation manner of the present invention;

[0045] Figure 17 It is a flowchart of the relativistic klystron KlyH software simulation provided in the specific implementation manner of the present invention;

[0046] Figure 18 It is a flowchart of the relativistic klystron KlyH software bandwidth simulation provided in the specific implementation manner of the present invention. Specific implementation manner

[0047] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. Based on the embodiments in this application, other similar embodiments obtained by those of ordinary skill in the art without creative efforts shall all fall within the scope of protection of this application. In addition, the directional terms mentioned in the following embodiments, such as "up", "down", "left", "right", etc., are only the directions referring to the accompanying drawings. Therefore, the directional terms used are for illustration rather than to limit the present invention.

[0048] The present invention will be further described below in conjunction with the accompanying drawings and preferred embodiments.

[0049] Please refer to Figure 1 , this embodiment provides a simulation optimization method for a relativistic klystron, including:

[0050] S100. Obtain the simulation design parameters of the relativistic klystron input by the user;

[0051] Specifically, the method further includes:

[0052] Design a user operation interface for configuring the simulation design parameters of the relativistic klystron and displaying the simulation results of the relativistic klystron.

[0053] It should be further noted that, referring to Figures 4 - 6 , the user operation interface in this embodiment is a graphical user interface developed using the Java language, including an input parameter area, a simulation result display area, a cavity parameter display area, and an operation button area. As Figures 4 - 6 shown, the input parameter area is used to input the simulation design parameters of the relativistic klystron, and the simulation result display area is used to display the simulation results, including one-dimensional simulation, multi-objective optimization, and bandwidth simulation. The operation button area includes a simulation run button and a clear button.

[0054] It can be understood that the graphical user operation interface in the present invention has an intuitive operation process and an easy-to-understand interface layout. Users can input simulation design parameters, set optimization goals, view simulation results, and make real-time adjustments and optimizations through simple operations. This interface not only supports conventional parameter configuration but also allows users to make personalized settings according to specific requirements, such as the selection of optimization goals and the adjustment of simulation models. Through a clear visualization interface, users can efficiently complete simulation and optimization tasks without complex programming skills, significantly reducing the technical usage threshold and improving the operability of the system.

[0055] Specifically, the user operation interface includes a bandwidth simulation interface, which is used to perform bandwidth simulation on a relativistic klystron according to the simulation design parameters of the relativistic klystron input by the user and display the simulation results.

[0056] See Figure 4 , in some specific embodiments, in addition to an input parameter area, a simulation result display area, and an operation button area, the user operation interface further includes a bandwidth simulation interface, which is used to perform bandwidth simulation on a relativistic klystron according to the simulation design parameters of the relativistic klystron input by the user and display the simulation results.

[0057] See Figure 15 , in the simulation optimization method of the relativistic klystron in this embodiment, as Figure 15 shown, the bandwidth simulation interface includes a sweep frequency range setting area, a sweep frequency step setting area, a bandwidth simulation result display area, and a bandwidth simulation operation button. The sweep frequency range setting area is used to set the upper and lower limits of the frequency for bandwidth simulation, the sweep frequency step setting area is used to set the sweep frequency step for bandwidth simulation, as Figure 16 shown, the bandwidth simulation result display area is used to display parameters such as the output power, efficiency, and gain of the relativistic klystron at different frequencies, and the bandwidth simulation operation button is used to start the bandwidth simulation calculation.

[0058] Specifically, the method for obtaining the simulation design parameters of the relativistic klystron input by the user includes:

[0059] Obtaining the simulation design parameters of the relativistic klystron input by the user in the user operation interface;

[0060] The simulation design parameters include the microwave parameters, calculation parameters, electron beam parameters, and resonator modulation parameters of the relativistic klystron.

[0061] See Figures 8 - 10, in the simulation optimization method of the relativistic klystron in this embodiment, obtain the simulation design parameters of the relativistic klystron input by the user in the user operation interface. The simulation design parameters include the microwave parameters, calculation parameters, electron beam parameters, and resonator modulation parameters of the relativistic klystron, specifically as follows:

[0062] The microwave parameters include: input microwave power (operating frequency), input microwave frequency (injection power);

[0063] The calculation parameters include: the number of disks, the number of times to push the disks per high-frequency period, the maximum number of iterations, the initial value of the total number of times to push the disks, etc.;

[0064] The electron beam parameters include: beam voltage, beam current, beam radius, drift tube radius;

[0065] The resonator modulation parameters include: the resonant frequency of the resonator, the axial position of the resonator, the characteristic impedance of the resonator, the gap width, the harmonic number;

[0066] According to the above simulation design parameters, the one-dimensional simulation results, cavity parameters, and output parameters are respectively as Figures 8 - 10 shown.

[0067] In this embodiment, in addition to the microwave parameters, calculation parameters, electron beam parameters, and resonator modulation parameters, the simulation design parameters also include bandwidth simulation parameters. The bandwidth simulation parameters include: lower frequency limit, upper frequency limit, frequency step, etc. Among them, the lower frequency limit range can be set to 0.8 to 0.95 times the operating frequency, the upper frequency limit range can be set to 1.05 to 1.2 times the operating frequency, and the frequency step range can be set to 0.01 GHz to 0.1 GHz.

[0068] S200. Select the optimization objectives, optimization parameters, and multi-objective optimization algorithm of the relativistic klystron.

[0069] S300. According to the simulation design parameters, optimization objectives, optimization parameters, and multi-objective optimization algorithm, use the klystron large-signal simulation software KlyH based on the one-dimensional disk model to perform multi-objective optimization on the relativistic klystron, and visually display the optimized simulation results.

[0070] Specifically, the klystron large-signal simulation software KlyH based on the one-dimensional disk model takes a numerical program based on the one-dimensional disk model developed in Fortran language as the kernel. The klystron large-signal simulation software KlyH based on the one-dimensional disk model is used to perform one-dimensional simulation on the simulation design parameters input by the user. The method for performing one-dimensional simulation on the simulation design parameters input by the user includes:

[0071] The core algorithm program of the numerical program based on the one-dimensional disk model is encapsulated into a dynamic link library, and the dynamic link library is used to perform one-dimensional simulation on the simulation design parameters input by the user to obtain the output parameters of the optimization target.

[0072] See Figure 17 , in this embodiment, a numerical program based on the one-dimensional disk model is independently developed in Fortran language, and the core algorithm program of the numerical program based on the one-dimensional disk model is encapsulated into a dynamic link library. The simulation design parameters input by the user are subjected to one-dimensional simulation to obtain output parameters such as efficiency and power, and the simulation results of the relativistic klystron are visually displayed.

[0073] It can be understood that the one-dimensional disk model is a classical model for relativistic klystron simulation. This model simplifies the electron beam into a series of charged disks, and by solving the motion equations of these disks in the electromagnetic field, the interaction process between the electron beam and the electromagnetic field is simulated. KlyH of this application is a large-signal simulation software for klystrons developed based on the one-dimensional disk model. It is developed in JAVA language and uses the numerical program based on the one-dimensional disk model independently developed in Fortran language as the kernel. It can efficiently simulate the working process of the relativistic klystron, and can efficiently perform calculation tasks such as large-signal simulation, bandwidth analysis and efficiency evaluation. Through highly optimized numerical calculation methods, KlyH software greatly improves the simulation speed, supports rapid scanning of large-scale parameter spaces and multi-objective optimization calculations. This model can accurately simulate the behavior of the relativistic klystron under different working conditions and generate accurate simulation results, ensuring the scientificity and accuracy of design decisions, and greatly meeting the requirements of efficient and high-precision simulation.

[0074] The core algorithm of KlyH one-dimensional simulation includes the following parts:

[0075] (1) One-dimensional electron disk model: For a solid electron beam, the following assumptions are made: It is considered to be composed of many layers of electrons, each layer is an infinitely thin electron disk, and the behavior of each electron on the disk is the same, without transverse motion, and the disk will not deform during the motion, just like a rigid body. It is assumed that the disk is transparent, that is, other disks are allowed to pass through. When two disks are infinitely close, the force between them is zero, the focusing magnetic field is infinitely large, and the electron beam works in the space charge limited current state;

[0076] (2) Position and velocity model of electron disks: Calculated according to kinematic theory through relativistic motion equations;

[0077] (3) Resonator gap impedance model: Calculate the gap impedances of the input cavity, output cavity and intermediate cavity according to the equivalent circuit;

[0078] (4) Induced current and gap voltage model: By performing Fourier expansion on the induced current, the induced current components of each harmonic can be obtained for calculating the induced current. The gap voltage is calculated based on parameters such as the gap impedance and the induced current.

[0079] (5) Output power and efficiency calculation model: Based on data such as the high-frequency voltage, current, and external quality factor of the output cavity, parameters such as the output power and efficiency of the relativistic klystron are calculated.

[0080] The process of encapsulating the core algorithm program of KlyH one-dimensional simulation into a dynamic link library is as follows:

[0081] (1) Use a Fortran compiler to compile the core algorithm program of KlyH one-dimensional simulation into a dynamic link library file.

[0082] (2) Call the functions in the dynamic link library through the JNA mechanism in the JAVA program.

[0083] (3) Define a data structure for passing data between the JAVA program and the dynamic link library.

[0084] (4) Implement a parameter conversion function to convert the simulation design parameters input by the user into the format required by the core algorithm program of KlyH one-dimensional simulation.

[0085] The process of converting the simulation design parameters input by the user into a relativistic klystron simulation instance is as follows:

[0086] (1) Read the microwave parameters, calculation parameters, electron beam parameters, and resonator modulation parameters input by the user.

[0087] (2) Construct the initialization data of the relativistic klystron based on these parameters.

[0088] (3) Set the initial parameters of the electron beam, including beam voltage, beam current, beam radius, drift tube radius, etc.

[0089] (4) Set the initial parameters of the input microwave, including the input microwave power 、 input microwave frequency, etc.

[0090] (5) Set the calculation parameters, including the number of disks, the number of times to push the disks per high-frequency cycle, the maximum number of iterations, the initial value of the total number of times to push the disks, etc.

[0091] (6) Set the modulation parameters of each resonator, including the characteristic impedance R / Q of the resonator, the resonant frequency, the axial position of the resonator, the external quality factor of the resonator, etc.

[0092] (7) Pass the constructed one-dimensional input parameters to the core algorithm program of KlyH one-dimensional simulation for calculation.

[0093] After the core algorithm program of the KlyH one-dimensional simulation is completed, it will return the simulation results of the relativistic klystron, including parameters such as output power, efficiency, gain, and bandwidth.

[0094] In some specific embodiments, see Figures 15 - 16 and Figure 18 , the core algorithm program of the KlyH simulation in this embodiment can not only perform single-frequency point simulation, but also perform bandwidth simulation. The process of bandwidth simulation is as follows:

[0095] (1) Read the sweep parameters such as the starting frequency, ending frequency, and sweep step size set by the user;

[0096] (2) Starting from the starting frequency, at intervals of the sweep step size, calculate the output power, efficiency, gain, etc. of the relativistic klystron at different frequencies in turn;

[0097] (3) Save the calculation results to a data file and display them in the form of a chart in the bandwidth simulation result display area.

[0098] The bandwidth simulation results include: frequency-output power curve, frequency-efficiency curve, frequency-gain curve, etc. Through these curves, users can intuitively understand the performance changes of the relativistic klystron at different frequencies, so as to determine the bandwidth characteristics of the relativistic klystron.

[0099] It can be understood that this embodiment adopts a framework that combines the dynamic link library of KlyH one-dimensional simulation with the multi-objective optimization algorithm, supports efficient multi-objective optimization calculation. This framework seamlessly connects the optimization algorithm and the simulation program through an interface to achieve balanced optimization between multiple design goals. The optimization process has an adaptive function, which can adjust the optimization strategy according to the simulation results and perform optimization calculations in real time; the system has high fault tolerance. When calculation anomalies occur during the simulation optimization process, it can promptly feedback to the user to ensure the stability and efficiency of the entire optimization process. This framework can not only support common optimization tasks, but also expand new goals according to needs to meet the complex requirements of different design scenarios.

[0100] Preferably, the multi-objective optimization algorithm includes the non-dominated sorting genetic algorithm (NSGA-II) or the optimal solution multi-objective particle swarm optimization algorithm (OMOPSO).

[0101] It should be noted here that the non-dominated sorting genetic algorithm (NSGA-II) is a multi-objective optimization algorithm based on Pareto optimal solutions. Its core idea is to retain Pareto optimal solutions in the population through non-dominated sorting and crowding degree calculation, so as to achieve multi-objective optimization. The main steps of the NSGA-II algorithm include:

[0102] (1) Initialize the population;

[0103] (2) Perform non - dominated sorting on the population and divide the population into different ranks;

[0104] (3) Calculate the crowding degree of each individual to distinguish the quality of individuals in the same rank;

[0105] (4) Generate a new population through selection, crossover, and mutation operations;

[0106] (5) Merge the new population with the original population, and perform non - dominated sorting and crowding degree calculation again;

[0107] (6) Select the individuals with higher ranks to form a new population;

[0108] (7) Repeat steps (4) to (6) until the termination condition is met.

[0109] The Optimal - solution Multi - objective Particle Swarm Optimization (OMOPSO) algorithm was proposed by Carlos Artemio Coello Coello in 2002. This algorithm is an extension based on the Particle Swarm Optimization algorithm, aiming to solve multi - objective optimization problems. OMOPSO is the optimal - solution multi - objective particle swarm algorithm proposed based on MOPSO. The OMOPSO algorithm constructs a non - dominated solution set based on the Pareto domination relationship, uses an external set to save the currently found non - dominated solution set, proposes the concept of strong ε - domination based on ε - domination and uses it to update the external set, enabling the algorithm to maintain good distribution. By improving the selection methods of the global best and individual best in the algorithm, and at the same time adopting a new particle update strategy to accelerate the convergence of the solution set, adding an adaptive mutation operator to avoid falling into local non - inferior optimal solutions, and proposing a non - dominated set construction method based on quicksort to accelerate the algorithm's running efficiency. The main framework of the OMOPSO algorithm is as follows:

[0110] (1) Initialization

[0111] Randomly generate a population containing N particles. Each particle represents a solution. Initialize its position and velocity, and calculate the objective function value of each particle. At the same time, initialize the individual best solution of each particle to its current position, and initialize the global best solution to the best particle in the population.

[0112] (2) Iteration process (the iteration reaches the preset maximum number of generations or the solution converges)

[0113] The velocity of each particle is adjusted according to the individual and global optimal solutions, and the position is updated accordingly. If the new position of the particle is better than its individual optimal solution, the individual optimal solution is updated. Then, the particle is mutated with a certain probability to increase diversity and avoid being trapped in a local optimum. The updated particle needs to recalculate the objective function value. Next, through non-dominated sorting, all non-dominated particles in the population are identified and added to the non-dominated set. If the new position of the particle is better than its current individual optimal solution, the individual optimal solution is updated, and the non-dominated particles are stored in the external set to maintain the diversity of the solution set. Finally, based on the particles in the external set, the global optimal solution is updated.

[0114] In some specific embodiments, the multi-objective optimization algorithm may further include a multi-objective differential evolution algorithm (MODE) in addition to the non-dominated sorting genetic algorithm and the optimal solution multi-objective particle swarm algorithm.

[0115] The multi-objective differential evolution algorithm is a multi-objective optimization algorithm based on differential evolution. Its core idea is to retain Pareto optimal solutions in the population through differential mutation and greedy selection, thereby achieving multi-objective optimization. The main steps of the MODE algorithm include:

[0116] (1) Initialize the population;

[0117] (2) For each individual, generate a trial individual through differential mutation and crossover operations;

[0118] (3) Evaluate the fitness of the trial individual and compare it with the original individual;

[0119] (4) If the trial individual dominates the original individual, replace the original individual with the trial individual; if the original individual dominates the trial individual, retain the original individual; if neither dominates the other, add the trial individual to the population;

[0120] (5) Perform non-dominated sorting on the population and retain the individuals with higher rankings;

[0121] (6) Repeat steps 2 to 5 until the termination condition is met.

[0122] Specifically, the S300 includes:

[0123] The multi-objective optimization algorithm calls the dynamic link library, reads the simulation design parameters of the relativistic klystron, configures the basic parameters of the multi-objective optimization algorithm, and performs multi-objective optimization simulation on the relativistic klystron according to the optimization parameters and optimization objectives selected by the user.

[0124] Furthermore, the basic parameters of the algorithm include the population size (number of seeds), maximum number of iterations, mutation probability, crossover probability, etc.

[0125] Specifically, the user operation interface includes a multi-objective optimization interface, and optimizing and simulating the relativistic klystron according to the optimization parameters and objectives selected by the user includes:

[0126] The multi-objective optimization interface is provided with an optimization objective option and an optimization parameter option. The user selects an optimization objective from the optimization objective option and an optimization parameter from the optimization parameter option according to actual needs.

[0127] See Figure 11 In the simulation optimization method of the relativistic klystron simulation model in this embodiment, the optimization objective options include parameters such as interaction length and efficiency. The user can select one or more optimization objectives according to actual needs. For example, the efficiency and interaction length can be optimized simultaneously, or the efficiency and interaction length can be optimized separately;

[0128] Such as Figures 11 - 12 As shown, the optimization parameter options include basic parameters and cavity parameters, specifically including: parameters such as operating frequency, injection power, beam voltage, beam current, characteristic impedance of each resonant cavity, cavity position, and resonant frequency. The user can select one or more optimization parameters according to actual needs. For example, the operating frequency and cavity parameters can be optimized simultaneously, or the beam voltage, beam current, and cavity parameters can be optimized simultaneously. As Figure 12 shown, the simulation results of output efficiency and total interaction length are obtained.

[0129] Furthermore, the configuration parameters of the multi-objective optimization algorithm include: population size, maximum number of iterations, crossover probability, mutation probability, etc. Among them, the population size range can be set from 50 to 500, the maximum number of iterations range can be set from 100 to 1000, the crossover probability range can be set from 0 to 1, and the mutation probability range can be set from 0 to 1.

[0130] The optimization process of the multi-objective optimization algorithm is as follows:

[0131] (1) Read the optimization objectives and parameters selected by the user;

[0132] (2) Generate an initial population or particle swarm according to the range of optimization parameters;

[0133] (3) For each individual or particle, call the dynamic link library of KlyH one-dimensional simulation for simulation calculation to obtain the corresponding optimization objective value;

[0134] (4) Evaluate and rank the individuals or particles according to the optimization objective values;

[0135] (5) Generate new individuals or particles through operations such as selection, crossover, mutation, or velocity-position update;

[0136] (6) Repeat steps (3) to (5) until the termination condition is met;

[0137] (7) Output the Pareto optimal solution set, i.e., the optimized relativistic klystron model.

[0138] The optimized relativistic klystron model includes parameters such as the optimized operating frequency, injection power, beam voltage, beam current, characteristic impedance of each resonator, position of the cavity, resonance frequency, etc., as well as performance indicators such as the corresponding output power, efficiency, gain, etc.

[0139] Please refer to Figures 3 - 16 , and the working principle of the present invention is illustrated by specific examples as follows:

[0140] 1. Design and implement a lightweight and efficient numerical program for one-dimensional simulation of KlyH. Its working principle is as Figure 3 shown, and its general steps are as follows:

[0141] Step 1: Start and input data;

[0142] The input data includes initial values such as DC electron beam parameters, high-frequency parameters of the resonator, total number of resonators, injection microwave power and frequency, number of disks, maximum number of iterations, and total number of disk pushes;

[0143] Step 2: Initialize and assign initial values to the gap voltage and induced current of each resonator;

[0144] Step 3: Calculate the electron load conductance and the gap impedance of each resonator to prepare for the subsequent calculation of the gap voltage;

[0145] Step 4: Iteratively calculate the gap voltage, induced current, and electron load of the resonator, and determine whether the gap voltage converges. If it does not converge, push the disk to recalculate the induced current, electron load, and gap voltage until convergence;

[0146] Step 5: Determine whether it is the output cavity. If so, calculate the efficiency and gain, and output the data and images. Otherwise, return to Step 4.

[0147] The numerical program based on the one-dimensional disk model is developed using the Fortran language and encapsulated into a dynamic link library for easy interaction with the upper-layer user operation interface and multi-objective optimization algorithms.

[0148] This numerical program is based on the mature one-dimensional large-signal disk model and resonator theory, and can simulate the performance of relativistic klystrons under high-frequency and high-power operating conditions, including key parameters such as efficiency, output power, and gain.

[0149] In specific implementation, first, the numerical program initializes and assigns the design parameters input by the user, then conducts large-signal simulation, calculates the corresponding output data, and passes the calculation results to the upper-layer user operation interface for displaying the corresponding physical characteristic data and charts.

[0150] 2. Design and implement a clear and user-friendly user operation interface. Its overall interface is as Figures 4 - 6 shown:

[0151] The general process implementation of the tab interface for one-dimensional simulation of KlyH in the user operation interface is as Figure 7 shown, supporting one-dimensional large-signal simulation of relativistic klystrons with multiple cavities.

[0152] The user configures the simulation design parameters through the user operation interface. The simulation design parameters include calculation parameters (number of disks, number of times to push the disk per high-frequency cycle, maximum number of iterations, initial value of the total number of times to push the disk), electron beam parameters (beam voltage, beam current, beam radius, drift tube radius), microwave parameters (input microwave power, input microwave frequency), modulation parameters of each resonator cavity (resonant frequency of the resonator cavity, axial position of the resonator cavity, characteristic impedance of the resonator cavity, gap width, harmonic number). After the parameter configuration is completed, click the run button on the toolbar to perform parameter legality verification. After passing the data verification, call the dynamic link library of the underlying KlyH one-dimensional simulation to conduct one-dimensional large-signal simulation of the relativistic klystron.

[0153] After the simulation ends, process the returned data to generate visualizable data, specifically as Figures 8 - 10 shown:

[0154] It mainly includes the curve graph of the variation of the current modulation coefficient along the Z direction, the curve graph of the velocity distribution of the electron disks along the Z direction, the distribution graph of the electron disk phase along the Z direction, the distribution graph of the axial position of the electron disks at the total time, the curve graph of the velocity distribution of the electron disks at the total time, the curve graph of the induced current of each resonator cavity changing with time in a high-frequency cycle, and the gap impedance, loaded quality factor, gap voltage amplitude and phase of each resonator cavity, as well as the output power, efficiency and gain. Its data display interface is as Figures 8 - 10 shown. Click the clear button on the toolbar to clear the simulation data.

[0155] The multi-objective optimization tab interface in the overall user operation interface is as Figure 11 and Figure 12 shown. The user can optimize based on the current simulated klystron, and the optimization objectives and parameters are selectable.

[0156] As Figures 11 - 12 shown, the optimization parameters include basic parameters and cavity parameters. The parameter floating range refers to the range of floating above and below its existing indicators.

[0157] The algorithm configuration section includes algorithm selection and algorithm parameter configuration. The optional algorithms include the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Optimal Solution Multi-objective Particle Swarm Optimization (OMOPSO) algorithm. The algorithm parameters mainly include the number of seeds (population size), the maximum number of iterations, the mutation probability, the crossover probability, etc.:

[0158] For example, if optimizing a 7-cavity single-beam klystron, the steps to determine the klystron instance parameters are as follows: The simulation design parameters include calculation parameters: the number of disks is 64, each high-frequency cycle pushes the disk 32 times, the maximum number of iterations is 45, and the initial value of the total number of times of pushing the disk is 1000;

[0159] Beam parameters: beam voltage V0 = 372 kV, beam current I0 = 115 A, beam radius R

[0160] = 2.24 mm, drift tube radius R b = 3.6 mm; t = 3.6 mm;

[0161] Microwave parameters: input microwave power P0 = 118.75 W, input microwave frequency f0 = 10 GHz;

[0162] The modulation parameters of each resonant cavity are as Figure 13 shown, where f is the resonant frequency of the resonant cavity, z is the relative input cavity position of the resonant cavity in the axial direction, Q ext is the external quality factor of the resonant cavity. To meet the engineering design requirements, the Q of the bunching cavity ext is taken as 950000, and R / Q is the characteristic impedance of the resonant cavity;

[0163] The simulation efficiency of this relativistic klystron instance is 45.52%, the total interaction length is 18.83 cm. The optimization objectives are determined to be the electron beam drift tube interval length and the output efficiency of the klystron. The optimization parameters are the resonant frequency (f) of the cavity, the drift tube length (l) between the cavities, and the external quality factors (Q ext ) of the input and output cavities. The optimization range is as Figures 11 - 12 shown.

[0164] Determine that the number of seeds of the OMOPSO algorithm is 100, the maximum number of iterations is 10, the mutation probability is 0.07, and the crossover probability is 0.9. Use OMOPSO for multi-objective optimization. When the number of iterations is reached or convergence occurs, output the Pareto solution set. Simulate the optimal design solution selected from the Pareto solution set. Figure 14 This is the detailed interface of the optimization result. The optimized efficiency is 54.72%, and the total interaction length is 17.20 cm. The output efficiency is improved, and the length of the electron beam drift tube interval is shortened. It can be seen that the present invention can provide an intuitive basis for engineering decisions.

[0165] In the overall user operation interface, the bandwidth simulation tab interface is as Figure 15 and Figure 16 shown, Figure 15 and Figure 16 contains input parameters and output data. The input parameters include the sweep frequency range and the sweep frequency step. After the user sets the simulation parameters according to the requirements and clicks the run button below, waiting for the simulation to end, the output results can be obtained. Figure 16 shows the simulation results, that is, the output results include the -3dB bandwidth and the -1dB bandwidth, as well as the output power - frequency graph, efficiency - frequency graph, and gain - frequency graph on the right.

[0166] The user interface in this embodiment is implemented through the Java programming language and is based on the graphical user interface technology (GUI), with an intuitive operation interface:

[0167] In specific implementation, the user first inputs the simulation design parameters through the user operation interface, such as the operating frequency of the klystron, electron beam parameters, resonator characteristics, etc. The user operation interface will display the current parameter settings and related simulation status in real time. The user operation interface supports quick adjustment of parameters, viewing of simulation data, and optimization of results through simple mouse clicks and input box operations.

[0168] For the optimization task, the user can select the objective function, such as maximizing efficiency, minimizing tube length, etc., and adjust the optimization direction in real time according to the simulation results.

[0169] This user operation interface supports customizing the optimization objective and optimization parameters, enabling the user to perform flexible design and optimization according to specific requirements.

[0170] 3. This embodiment has a fault - tolerant framework and interface design.

[0171] The multi - objective optimization framework in this embodiment combines modern optimization methods such as genetic algorithms or particle swarm optimization algorithms. The core algorithm program of KlyH one - dimensional simulation and the multi - objective optimization algorithm perform data interaction through an interface. In the specific implementation process, the core algorithm program of KlyH simulation provides the simulation results as the input data for the optimization algorithm's objectives. The optimization algorithm weighs among multiple objective functions (such as efficiency, tube length), adjusts the simulation design parameters through multiple iterations, and finally finds the optimal solution. The multi - objective optimization algorithm adjusts the parameters according to the simulation results of each iteration and gradually converges to the optimal design. The entire framework is designed modularly, enabling the optimization process and the simulation process to be flexibly separated, extended, and modified, with good adaptability.

[0172] It should be noted here that this embodiment provides a simulation optimization method for a relativistic klystron. By obtaining the simulation design parameters of the relativistic klystron input by the user, selecting the optimization objectives, optimization parameters and multi-objective optimization algorithm of the relativistic klystron, using the KlyH software to simulate and simulate the relativistic klystron, and visually displaying the optimized simulation results. The present invention also provides a friendly user operation interface, supporting parameter configuration and simulation result display. Combining the core algorithm program of the one-dimensional simulation of the self-developed KlyH software with the multi-objective optimization algorithm greatly improves the simulation speed, realizes the multi-objective optimization of the relativistic klystron. By designing a clear visual interface, users can efficiently complete the simulation and optimization tasks without complex programming skills, significantly reducing the technical usage threshold, solving the technical problems of long design time and large difference between design efficiency and target efficiency in traditional methods, providing strong support for research and engineering practice in related fields, and greatly improving the intelligence, usability and reliability of the present invention.

[0173] Please refer to Figure 2 , this embodiment provides a simulation optimization system for a relativistic klystron, and the system includes:

[0174] Acquisition module 100: used to acquire the simulation design parameters of the relativistic klystron input by the user;

[0175] Simulation module 200: used to select the optimization objectives, optimization parameters and multi-objective optimization algorithm of the relativistic klystron; and used to perform multi-objective optimization on the relativistic klystron using the KlyH software according to the simulation design parameters, optimization objectives, optimization parameters and multi-objective optimization algorithm, and visually display the optimized simulation results.

[0176] It should be noted here that this embodiment provides a simulation optimization system for a relativistic klystron. By obtaining the simulation design parameters of the relativistic klystron input by the user, selecting the optimization objectives, optimization parameters and multi-objective optimization algorithm of the relativistic klystron, using the KlyH software to perform multi-objective optimization on the relativistic klystron, and visually displaying the optimized simulation results. The present invention also provides a friendly user operation interface, supporting parameter configuration and simulation result display. Combining the core algorithm program of the one-dimensional simulation of the self-developed KlyH software with the multi-objective optimization algorithm greatly improves the simulation speed, realizes the multi-objective optimization of the relativistic klystron. By designing a clear visual interface, users can efficiently complete the simulation and optimization tasks without complex programming skills, significantly reducing the technical usage threshold, solving the technical problems of long design time and large difference between design efficiency and target efficiency in traditional methods, providing strong support for research and engineering practice in related fields, and greatly improving the intelligence, usability and reliability of the present invention.

[0177] In a preferred embodiment, the present application further provides an electronic device, which includes:

[0178] a memory; and a processor, where computer-readable instructions are stored on the memory, and when the computer-readable instructions are executed by the processor, the simulation optimization method of the relativistic klystron as described above is implemented.

[0179] The present invention can also be implemented as a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method of the embodiments of the present invention are caused to be executed.

[0180] It should be noted here that the present invention obtains the simulation design parameters of the relativistic klystron input by the user, selects the optimization objectives, optimization parameters and multi-objective optimization algorithm of the relativistic klystron, performs multi-objective optimization on the relativistic klystron using the KlyH software, and visually displays the optimized simulation results. The present invention also provides a friendly user operation interface, supports parameter configuration and simulation result display, and combines the core algorithm program of the one-dimensional simulation of the self-developed KlyH software with the multi-objective optimization algorithm, greatly improving the simulation speed, realizing the multi-objective optimization of the relativistic klystron. By designing a clear visual interface, users can efficiently complete the simulation and optimization tasks without complex programming skills, significantly reducing the technical usage threshold, solving the technical problems of long design time and large difference between the design efficiency and the target efficiency in the traditional method, providing strong support for the research and engineering practice in related fields, and greatly improving the intelligence, usability and reliability of the present invention.

[0181] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these process, method, product or device.

[0182] The above-described technical features can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification as long as such combination does not exist in contradiction.

[0183] The specific embodiments of the present invention described above do not constitute a limitation on the protection scope of the present invention. Any other corresponding changes and deformations made according to the technical concept of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A simulation optimization method for a relativistic klystron, characterized in that, Including: S100. Obtain the simulation design parameters of the relativistic klystron input by the user; S200. Select the optimization objective, optimization parameters and multi-objective optimization algorithm of the relativistic klystron; S300. According to the simulation design parameters, optimization objective, optimization parameters and multi-objective optimization algorithm, use the klystron large-signal simulation software KlyH based on the one-dimensional disk model to perform multi-objective optimization on the relativistic klystron, and visually display the optimized simulation results; The method further includes: Set a user operation interface for configuring the simulation design parameters of the relativistic klystron and displaying the simulation results of the relativistic klystron; The klystron large-signal simulation software KlyH based on the one-dimensional disk model takes a numerical program based on the one-dimensional disk model developed in Fortran language as the kernel. The klystron large-signal simulation software KlyH based on the one-dimensional disk model is used to perform one-dimensional simulation on the simulation design parameters input by the user. The method for performing one-dimensional simulation on the simulation design parameters input by the user includes: Encapsulate the core algorithm program of the numerical program based on the one-dimensional disk model into a dynamic link library, and use the dynamic link library to perform one-dimensional simulation on the simulation design parameters input by the user to obtain the output parameters of the optimization objective; The S300 includes: The multi-objective optimization algorithm calls the dynamic link library, reads the simulation design parameters of the relativistic klystron, configures the basic parameters of the multi-objective optimization algorithm, and performs multi-objective optimization simulation on the relativistic klystron according to the optimization parameters and optimization objective selected by the user.

2. The simulation optimization method of the relativistic klystron according to claim 1, characterized in that The method for obtaining the simulation design parameters of the relativistic klystron input by the user includes: Obtain the simulation design parameters of the relativistic klystron input by the user in the user operation interface; The simulation design parameters include the microwave parameters, calculation parameters, electron beam parameters and resonator modulation parameters of the relativistic klystron.

3. The simulation optimization method of the relativistic klystron according to claim 1, wherein The multi-objective optimization algorithm includes: non-dominated sorting genetic algorithm or optimal solution multi-objective particle swarm algorithm.

4. The simulation optimization method of the relativistic klystron according to claim 1, characterized in that The user operation interface includes a multi-objective optimization interface. The performing optimization simulation on the relativistic klystron according to the optimization parameters and optimization objective selected by the user includes: The multi-objective optimization interface is provided with an optimization objective option and an optimization parameter option. The user selects the optimization objective in the optimization objective option and the optimization parameters in the optimization parameter option according to actual needs.

5. The simulation optimization method of the relativistic klystron according to claim 1, wherein The user operation interface includes a bandwidth simulation interface, and the bandwidth simulation interface is used to perform bandwidth simulation on the relativistic klystron according to the simulation design parameters of the relativistic klystron input by the user and display the simulation results.

6. A simulation optimization system for a relativistic klystron, characterized in that, Adopt the simulation optimization method of the relativistic klystron according to any one of claims 1 to 5. The system includes: An acquisition module (100) for acquiring the simulation design parameters of the relativistic klystron input by the user; Simulation module (200): configured to select optimization objectives, optimization parameters, and multi-objective optimization algorithms for a relativistic klystron; and configured to perform multi-objective optimization on the relativistic klystron using the klystron large-signal simulation software KlyH based on a one-dimensional disk model according to the simulation design parameters, optimization objectives, optimization parameters, and multi-objective optimization algorithms, and visually display the optimized simulation results.

7. An electronic device, characterized in that, Comprising: A memory; And a processor, wherein computer-readable instructions are stored on the memory, and when the computer-readable instructions are executed by the processor, the simulation optimization method of the relativistic klystron according to any one of claims 1 to 5 is implemented.

Images