Optimization design method of beam bunching system of electron accelerator for replacement injection scheme of new generation of positron and positron colliders

Abstract

The invention discloses an electron accelerator bunching system optimization design method for a new-generation positron-positron collision machine replacement injection scheme, and belongs to the field of electron beam injectors. According to the method, a genetic algorithm is adopted, and the output beam bunching length and energy divergence of a bunching system serve as fitness functions; the optimization target is that the beam bunch length and the energy divergence are converged to the minimum at the same time, and a preliminary design result of beam bunching system layout and element parameters is obtained; then, beam dynamics software is used for further carrying out multi-particle simulation, a preliminary design result obtained by the genetic algorithm is verified, constraint conditions in the multi-target genetic algorithm are continuously corrected, iterative computation is carried out, and through multiple times of iterative computation, a multi-particle model is obtained; the optimal design result of the electron accelerator bunching system layout and elements meeting the clash ring injection requirement is obtained; according to the method provided by the invention, the optimal design result of the electron accelerator bunching system layout and element parameters meeting the clash ring injection requirement can be quickly obtained.

Description

Technical Field

[0001] The present invention belongs to the field of electron beam injectors, and more specifically, relates to an optimization design method for an electron accelerator bunching system for a replacement injection scheme of a new generation of electron-positron colliders. Background Art

[0002] In recent years, electron-positron colliders have provided technical support for understanding the nature of strong interactions, exploring matter-antimatter asymmetry, and searching for new physics beyond the standard model. The new generation of colliders has extremely high requirements for the performance of injected beams. In order to meet the brightness requirements of positron-electron collisions, the electron beam injected into the collider is required to have a large charge, high current, short bunch length, and small bunch energy divergence. Therefore, the new generation of colliders around the world use a displacement injection scheme to bunch and accelerate the low-energy DC bunches generated by the electron gun until the ultra-high injection requirements of the collider are met. Under the displacement injection scheme, the bunching system, as the pre-stage device of the electron linear accelerator of the displacement injection scheme, consists of several subharmonic bunchers and traveling wave bunchers. The resonant frequency of the subharmonic buncher is 1 / n of the main acceleration frequency (called the nth harmonic). The multi-section subharmonic buncher performs preliminary bunching of the DC beam output by the electron gun in the longitudinal direction, and compresses the DC beam to within 1 / 4 cycle of the main acceleration frequency before entering the traveling wave buncher. In the conventional displacement injection bunching system, a combination of two subharmonic bunchers is used. The frequency of the first subharmonic buncher is 1 / 18 of the main acceleration frequency, and the frequency of the second subharmonic buncher is 1 / 6 of the main acceleration frequency. The traveling wave buncher performs preliminary acceleration on the bunch until the energy requirement of the subsequent main acceleration section is met. At the same time, the bunch length will gradually decrease in the traveling wave buncher. In order to ensure that the bunch entering the collider has a large charge and high current, the bunching system needs to have a high capture rate.

[0003] For built beam equipment, it is best to optimize beam performance. Compared with the design process, which can also be intuitively evaluated through electromagnetic calculations and beam simulations, optimization may be limited by the lack of observation and adjustment methods on existing test platforms. And considering that there are multiple parameters involved and there are interactions between these parameters, it is usually necessary to perform optimization by manual repeated calculations, which is inefficient. Summary of the invention

[0004] In view of the above defects or improvement needs of the prior art, the present invention provides an optimization design method for an electron accelerator bunching system for a new generation of electron-positron collider replacement injection scheme, which can ensure that the output bunch of the bunching system has sufficient energy and a high capture rate, while compressing the length of the DC bunch generated by the hot cathode electron gun as much as possible, reducing the energy divergence of the output bunch, and meeting the injection requirements of high-quality beams for the new generation of electron-positron collider replacement injection scheme.

[0005] To achieve the above object, according to a first aspect of the present invention, a method for optimizing the design of an electron accelerator bunching system for a replacement injection scheme of a new generation of electron-positron collider is provided, comprising:

[0006] S1, using a reference point-based non-dominated sorting genetic algorithm with an elite strategy, taking the first performance of the beam output by the bunching system as the optimization target, and optimizing the setting range of each bunching system parameter corresponding to the beam with the best first performance;

[0007] Wherein, the first performance includes bunch length and bunch energy divergence, the optimization goal is to minimize both bunch length and bunch energy divergence, the bunching system parameters include the phase, cavity pressure and drift length of the first and second harmonic cavities; the fitness function of the non-dominated sorting genetic algorithm is a calculation model of bunch length and energy divergence, and in the optimization process, each population individual corresponds to a bunch distribution, and the gene length of a single population individual is determined by the possible value points of each bunching system parameter;

[0008] S2, using multi-particle beam dynamics simulation software to calculate the second performance of the beam corresponding to the value combination of all bunching system parameters and determine whether it meets the performance requirements; wherein the parameter values ​​in the value combination are all within the current setting range; the second performance includes bunch energy and capture rate;

[0009] S3, if not, then increase the lower limit of the current setting range and lower the upper limit to update the current setting range, and use it as the initial setting range of each beamforming system parameter, and return to S1; if yes, then output the current setting range and use it as the optimal setting range of each beamforming system parameter.

[0010] According to a second aspect of the present invention, there is provided an electronic device, comprising: a computer-readable storage medium and a processor;

[0011] The computer-readable storage medium is used to store executable instructions;

[0012] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method as described in the first aspect.

[0013] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a processor to execute the method as described in the first aspect.

[0014] According to a fourth aspect of the present invention, there is provided a computer program product, comprising a computer program or instructions, wherein when the computer program or instructions are executed by a processor, the method according to the first aspect is implemented.

[0015] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0016] The method provided by the present invention uses the initial beam parameters generated by the electron gun and the injection requirement of the collision ring as constraint conditions and optimization targets respectively, uses the calculation model of the bunch length energy divergence output by the bunching system as the fitness function, and the optimization target is that the bunch length and the energy divergence converge to the minimum at the same time, and obtains the preliminary design results of the bunching system layout and component parameters to achieve preliminary optimization of the bunching system layout and component parameters; then, beam dynamics software is used to further carry out multi-particle simulation, and the single particle design result obtained by the genetic algorithm is verified, so as to continuously correct the constraint conditions in the multi-objective genetic algorithm, and perform the next iterative calculation. After multiple iterative calculations, the optimal design results of the electron accelerator bunching system layout and components that meet the collision ring injection requirement under the multi-particle model are obtained; the method provided by the present invention gets rid of the difficulty and tediousness of manual calculation, and can quickly obtain the optimal design results of the electron accelerator bunching system layout and component parameters that meet the collision ring injection requirement.

[0017] As a further preference, the method provided by the present invention uses numerical analysis software to solve the single particle motion equation, and analyzes and obtains the preliminary setting range of parameters of the subharmonic buncher and the traveling wave buncher in the bunching system, which can further improve the design efficiency.

[0018] As a further preference, the method provided by the present invention uses electromagnetic field calculation software and beam dynamics software to carry out multi-particle simulation to verify the single-particle design results, fully considering the strong space charge effect of the high-current electron beam and the tail field effect of the accelerating tube, so as to make the design results more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic flow chart of a method for optimizing the design of an electron accelerator bunching system for a replacement injection scheme of a new generation electron-positron collider provided by an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of the multi-objective genetic algorithm NSGA-III process flow for the layout and parameter optimization design of electron linear accelerator components for a new generation of electron-positron collider replacement injection scheme provided by an embodiment of the present invention;

[0021] Figure 3 It is a schematic diagram of a two-dimensional model of a bunching system of a high-current and high-charge electron beam injector;

[0022] Figure 4 A result graph of non-dominated solution sets of bunch length and energy divergence obtained by a multi-objective genetic algorithm provided by an embodiment of the present invention;

[0023] Figure 5The electron beam phase motion trajectory diagram obtained by solving the multi-objective genetic algorithm provided by the embodiment of the present invention;

[0024] Figure 6 An electron beam energy gain graph obtained by solving a multi-objective genetic algorithm provided by an embodiment of the present invention;

[0025] Figure 7 The phase space diagram of the electron beam at the exit of the electron gun and two subharmonic bunchers obtained by solving the multi-objective genetic algorithm provided by the embodiment of the present invention;

[0026] Figure 8 Phase space diagrams of the electron beam at the inlet and outlet of the traveling wave buncher obtained by solving the multi-objective genetic algorithm provided by the embodiment of the present invention;

[0027] Fig. 9 The embodiment of the present invention provides an optimization parameter evolution diagram of an electron beam in a bunching system obtained by solving the multi-objective genetic algorithm results using the three-dimensional multi-particle simulation software Parmela. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0029] The electron-positron collider requires the injected beam to have extremely high energy, low energy divergence and short bunch length. Under the displacement injection scheme, the DC beam generated by the electron gun is longitudinally compressed into a short bunch by the bunching system, so that the electron bunch can obtain the maximum energy gain in the accelerating tube. At the same time, the energy divergence of the bunch directly affects the acceleration effect of the subsequent acceleration stage. Therefore, it is very important to optimize the design parameters of the bunching system so that the beam entering the accelerating tube has a shorter bunch length and lower energy divergence.

[0030] The parameter settings of the multi-section harmonic buncher include parameters such as the resonant cavity pressure, frequency, RF phase, and drift segment length. The design and optimization are difficult and generally require repeated manual calculations for optimization, and the results obtained are often not ideal.

[0031] Based on this, an embodiment of the present invention provides an optimization design method for an electron accelerator bunching system for a replacement injection scheme of a new generation of electron-positron collider, including:

[0032] S1, using a reference point-based non-dominated sorting genetic algorithm with an elite strategy, taking the first performance of the beam output by the bunching system as the optimization target, and optimizing the setting range of each bunching system parameter corresponding to the beam with the best first performance;

[0033] Among them, the first performance includes bunch length and bunch energy divergence, the bunching system parameters include the phase, cavity pressure and drift length of the first and second harmonic cavities; the fitness function of the non-dominated sorting genetic algorithm is a calculation model of the bunch length and energy divergence output by the bunching system, and in the optimization process, each population individual corresponds to a bunch distribution, and the gene length of a single population individual is determined by the possible value points of each bunching system parameter (i.e., the phase, cavity pressure and drift length of the first and second harmonic cavities).

[0034] The bunching system is a conventional displacement injection bunching system, including two subharmonic bunchers and one traveling wave buncher.

[0035] The theoretical motion model of the beam in the bunching system is:

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] Equations (1) and (2) give the motion equations of a single electron in a subharmonic buncher, where e is the charge of the electron, m is 0 c 2 is the rest energy of the electron, eV 0 It represents the energy of electrons emitted by the electron gun, V g is the cavity pressure of the subharmonic buncher, represents the RF phase of the subharmonic buncher, L represents the length of the drift segment, n represents the harmonic order of the subharmonic buncher, f represents the main acceleration frequency, and the frequency of the resonant cavity is f / n. The subscript i represents the electrons at different positions in the longitudinal direction, the total number of electrons output by the electron gun is N, and the total number of electrons output by the bunching system is M. The DC beam emitted by the electron gun occupies different phases in the longitudinal direction. After entering the subharmonic buncher, the speed v i Modulation occurs, and the corresponding phase changes after the drift distance L.

[0045] Formula (3) gives the calculation method of the relative velocity β, relativistic factor γ and kinetic energy W of a single particle.

[0046] Equations (4) and (5) give the motion equations of a single particle in the traveling wave buncher, where z is the longitudinal position of the traveling wave accelerator tube, represents the particle phase, λ is the free space wavelength of the electromagnetic wave, β p is the cavity phase velocity. E is the electric field gradient of the traveling wave buncher.

[0047] Formula (6) uses η to represent the capture rate. The high capture rate of the bunching system is a requirement for the displacement injection scheme to inject a large charge bunch. Formulas (7) and (8) use represents the bunch length, σ δ It represents the cluster energy divergence, which is the fitness function of the genetic algorithm.

[0048] The multi-objective genetic algorithm is used to optimize the design of the bunching system parameters. The optimization goal is to minimize the output beam length and energy divergence of the bunching system. The component parameters corresponding to the best beam quality are the algorithm-optimized bunching system design results.

[0049] In the process of multi-objective algorithm optimization, the genetic operator operations for obtaining the offspring population include: selection operation based on reference point sorting, crossover operation using a single-point crossover method, and mutation operation using a single-point mutation method.

[0050] A non-dominated sorting genetic algorithm based on reference points with an elite strategy is used. The energy divergence and bunch length of the bunching system output converge to the optimal direction as the optimization goal, and the best beam quality under different parameter settings is optimized. Among them, the cavity length, phase, and drift length of the two subharmonic bunchers are used as input setting parameters, and the parameter range to be set is obtained according to the final optimization result. The mutual influence between the optimization objectives is taken into account. It may not be optimal for a single objective, but it can maintain the overall optimal solution.

[0051] The beam-forming system setting parameters are obtained by solving the final optimization results. Since the final optimal solution is presented as the Pareto frontier, the input setting parameters are a small range, and the specific values ​​are selected according to the actual engineering needs. Similarly, the specific values ​​of other beam-forming system parameters such as the phase and electric field gradient of the traveling wave beam-forming system or the parameters required to be set in the theoretical motion model of the beam in the beam-forming system are selected according to the actual engineering needs or set according to experience. That is, the initial setting range of each beam-forming system parameter can be set according to experience. In order to further improve the design efficiency, as a further preferred embodiment of the present invention, the initial setting range of each beam-forming system parameter is obtained as follows:

[0052] A motion model of electrons in a bunching system is established in numerical analysis software, a set of to-be-selected values ​​of any bunching system parameter is constructed, the values ​​of other bunching system parameters are fixed, each to-be-selected value of any bunching system parameter is substituted into the motion model to obtain the second performance of the corresponding beam, and the value interval of the to-be-selected values ​​that meet the second performance requirements is used as the initial setting range of the corresponding any bunching system parameter; wherein, if the initial setting range of the other bunching system parameters is not determined, their values ​​are set arbitrarily, otherwise the values ​​in the initial setting range are traversed according to a preset step size, and the values ​​are respectively used as their values.

[0053] Specifically, by building a motion model of electrons in a bunching system and solving it using numerical analysis software (such as Matlab), the second performance of the output beam under different initial beam parameters and bunching system component setting parameters can be quickly obtained, and the value range of the bunching system component parameters where the second performance meets the performance requirements is used as its initial range, which greatly simplifies the design process of the injector.

[0054] Numerical analysis software is used to establish and solve the motion equations of electrons in the bunching system. The bunching parameters are directly given as numerical values ​​and can be easily adjusted and modified. The bunching effect can be solved intuitively, which greatly simplifies the design process.

[0055] For example, assuming that the initial setting range of the phase, cavity pressure and drift length of the first and second harmonic cavities are unknown, if the phase φ of the first harmonic cavity needs to be determined 1 The initial setting range is, first set the values ​​of other parameters to fixed values ​​based on experience, and then set φ 1 The set of values ​​to be selected (which can be set based on experience or the actual settable range of the first harmonic cavity) is respectively 1 Substitute each value in the set of to-be-selected values ​​into the motion model to obtain the second performance of the corresponding beam, and take the value interval of the to-be-selected value that meets the second performance requirement as φ 1 If you need to determine the cavity pressure V of the second harmonic cavity 2, at this time only φ 1 The initial setting range of V is known, and the initial setting range of other parameters is unknown. Therefore, according to experience, 2 The values ​​of other parameters except φ are fixed, among which, 1 The value of traverses φ according to the preset step size 1 Initial setting range, then set V 2 The set of values ​​to be selected, respectively V 2 Substitute each value in the set of to-be-selected values ​​into the motion model to obtain the second performance of the corresponding beam, and take the value interval of the to-be-selected value that meets the second performance requirement as V 2 Initial setting range; the method for obtaining the initial setting range of other parameters is similar.

[0056] S2, using multi-particle beam dynamics simulation software to calculate the second performance of the beam corresponding to the value combination of all bunching system parameters and determine whether it meets the performance requirements; wherein the parameter values ​​in the value combination are all within the current setting range; the second performance includes bunch energy and capture rate;

[0057] S3, if not, then increase the lower limit of the current setting range and lower the upper limit to update the current setting range, and use it as the initial setting range of each beamforming system parameter, and return to S1; if yes, then output the current setting range and use it as the optimal setting range of each beamforming system parameter.

[0058] In steps S2-S3, Matlab is used to call three-dimensional multi-particle beam dynamics simulation software (such as Parmela) to verify the genetic algorithm optimization results.

[0059] The three-dimensional multi-particle beam dynamics simulation should take into account the tail field effect in the accelerating tube and the strong space charge effect under strong current conditions. Although the simulation results will have a certain deviation from the single-particle calculation model, the parameter change trend is consistent with the single-particle model optimization result. Therefore, preferably, in step S2, the multi-particle beam dynamics simulation software is used to calculate the second performance of the beam corresponding to the value combination of all bunching system parameters, including:

[0060] A bunching system model is established using three-dimensional electromagnetic field simulation software, and the actual three-dimensional electromagnetic field distribution in the bunching system is calculated. Then, the motion process of the beam under the actual three-dimensional electromagnetic field distribution is simulated using multi-particle beam dynamics simulation software, thereby calculating the second performance of the beam corresponding to the combination of values ​​of all bunching system parameters.

[0061] Specifically, a bunching system model is established using three-dimensional electromagnetic field simulation software (such as Superfish), and the tail field effect is fully considered to calculate the actual three-dimensional electromagnetic field distribution in the bunching system; then the multi-particle beam dynamics simulation software Parmela is used to solve the motion process of the beam under the actual three-dimensional electromagnetic field distribution calculated by Superfish under the strong space charge effect, and the design results of the bunching system optimized by the algorithm are verified. On the premise of ensuring the output bunch energy and capture rate, the parameter setting range of the subharmonic buncher is gradually iterated and corrected. After multiple iterative operations, the optimal design results of the bunching system parameter settings can be obtained.

[0062] The subharmonic buncher places the input beam in different acceleration phases so that the energy obtained by the particles at the head of the beam is less than that of the particles at the tail, so that the particles are gradually caught up by the latter in the subsequent drift section of the resonant cavity, thereby achieving a longitudinal bunching effect. Therefore, the bunching effect of the subharmonic buncher mainly depends on the cavity pressure, phase and subsequent drift section length of the resonant cavity in the buncher. The bunching effect of the traveling wave buncher depends on the beam parameters output by the subharmonic buncher. The appropriate phase and electric field gradient of the traveling wave buncher can further accelerate the bunch in the traveling wave buncher. In this process, the bunch length is further compressed, and the energy divergence is gradually reduced. Based on this, preferably, step S3 also includes: adjusting the phase and electric field gradient of the traveling wave buncher.

[0063] That is, when verifying the design results of the bunching system optimized by the algorithm, the parameter setting range of the subharmonic buncher and the fixed parameters of the traveling wave buncher are gradually and iteratively corrected on the premise of ensuring the output bunch energy and capture rate. After multiple iterative calculations, the optimal design results of the bunching system parameter settings can be obtained.

[0064] Considering that the tail field effect and space charge effect are ignored in the calculation of the single-particle model, the output energy result is too high. Therefore, in order to ensure that the energy in the multi-particle simulation meets the standard, the electric field gradient of the traveling wave buncher should be increased during the iteration (for example, gradually increasing from 10MV / m); usually, in order to ensure the output energy, the phase of the traveling wave buncher is set at the maximum acceleration phase of 90°, but due to the space charge effect, the multi-particle simulation may cause the loss of particles at the tail of the bunch at 90°, that is, the capture rate is reduced. Usually, a slight advance injection is used to suppress this phenomenon, that is, the phase is gradually reduced from 90°. Based on this, preferably, before returning to S1, it also includes:

[0065] The phase and electric field gradient of the traveling wave buncher are respectively reduced and increased according to their respective adjustment steps, and the updated values ​​are used as initial values ​​of the phase and electric field gradient.

[0066] In this way, the phase and electric field gradient of the traveling wave buncher are also optimized.

[0067] Preferably, the method provided by the present invention can also store the dynamic parameters (position, phase and energy) of each particle during the beam motion process by solving the single particle motion equation. Matlab is used to develop a computer visualization program to display ① the change of energy and phase along the beam line direction during the motion of each particle, ② the longitudinal phase space of the beam, and ③ the change of beam length and energy divergence along the beam line direction. The visualization interface is convenient for designers to observe the changing trend of beam motion and adjust the simulation setting parameters in time.

[0068] The method provided by the present invention is further illustrated by taking a specific example as follows.

[0069] The NSGA-III algorithm uses a reference point-based selection mechanism instead, so the algorithm ensures the diversity of solutions. Its execution process is as follows: Figure 1 As shown. The optimization result of the genetic algorithm is related to the selection of the independent variable parameter interval. According to the requirements of the collider replacement injection scheme, the initial length of the DC beam output by the electron gun is set to 1296°. After preliminary estimation (i.e., the motion model of electrons in the bunching system is established in the numerical analysis software, and the values ​​of each bunching system parameter to be selected are substituted into the motion model to obtain the second performance of the corresponding beam, and the initial setting range of the corresponding bunching system parameter is determined according to the values ​​to be selected that meet the second performance requirements), the cavity pressure setting interval of the first subharmonic buncher is [60,150] kV, the injection phase interval is [-90,-10]°, and the drift length interval is [0.4, 1.2] m. The cavity pressure setting interval of the second subharmonic buncher is [80,150] kV, the injection phase interval is [-120,-40]°, and the drift length interval is [0.1, 0.8] m.

[0070] After the population is initialized, determine whether the first-generation sub-population is generated. If not, then after calculating the fitness function (i.e., outputting the bunch length and energy divergence), performing fast non-dominated sorting, and executing the genetic operator, the population is generated. Regarding fast non-dominated sorting, first set the number of layers of non-dominated sorting to 1, place all non-dominated individuals in it, then increase the number of layers by 1, place all non-dominated individuals in the remaining individuals in it, and so on, until all individuals have layers. Then merge the generated sub-population with the parent population, calculate the fitness function of the merged population, and obtain the stratification result after fast non-dominated sorting. Then calculate the reference point distance of the individual, and fill the individuals with small non-dominated sequence numbers and small reference point distances into the new parent population until the initial population size. This set of individuals is the generated new generation sub-population. Regarding genetic operations, selection operations are performed based on reference points; single-point crossover is used for crossover operations, and the crossover probability is generally 0.5-1.0, and 0.8 is taken in this example; single-point mutation is used for mutation operations, and the mutation probability is generally 0-0.05, and 0.02 is taken in this example. If the population reproduction generation does not reach the preset number of iterations, it will continue. If it reaches the preset number of iterations, the solution set with the smallest non-dominated sequence number, that is, the Pareto solution set, is output, and the result with the shortest bunch length and the smallest bunch energy divergence can be obtained in the end. Regarding the termination generation, the calculation is terminated when the population reproduction generation in this example is 50.

[0071] The results of algorithm optimization are given in the form of Pareto frontier solution set, that is, the optimization results of bunch parameters (and bunch energy divergence and length) and the parameter settings of bunching system components under the corresponding parameters are an interval range. Matlab is used to call the multi-particle beam dynamics simulation software Parmela to solve all the cases in the solution set. In this example, the output bunch energy is greater than 11MeV and the capture rate is greater than 99.5% as the judgment conditions (i.e. the performance requirements of the second performance). If they are not satisfied, the constraints of the subharmonic buncher phase, cavity pressure, and drift length in the algorithm are adjusted (i.e. the initial setting range of the above parameters) and the initial setting values ​​of the phase and electric field gradient of the traveling wave buncher, and the next algorithm solution is executed. The adjustment step of the parameters in each iteration can be set manually. In this example, the adjustment steps of the subharmonic buncher phase, cavity pressure, and drift length are 1°, 2kV, and 0.1m, respectively. The adjustment steps of the phase and electric field gradient of the traveling wave buncher are 0.5° and 0.2MV / m, respectively. The choice of step size needs to comprehensively consider the solution accuracy and solution time. The smaller the solution step size, the higher the solution accuracy will be, but it will take longer to solve.

[0072] Figure 3The schematic diagram of the structure of the bunching system includes two subharmonic bunchers and one traveling wave buncher. The two subharmonic bunchers are represented by SHB1 and SHB2 respectively, and the traveling wave buncher is represented by TW-buncher. The main acceleration frequency is 2.9982MHz, the frequency of the first subharmonic buncher is 1 / 18 of the main acceleration frequency, and the frequency of the second subharmonic buncher is 1 / 6 of the main acceleration frequency.

[0073] The non-dominated solution set of energy divergence and bunch length is as follows Figure 4 As shown, the abscissa is the energy divergence and the ordinate is the bunch length. It can be seen that the energy divergence is less than 25% and the bunch length is less than 125° under the parameters of the two subharmonic bunchers optimized by the method provided by the present invention.

[0074] Figure 5 Shows the selection Figure 4 When one of the solutions is solved, the phase space distribution of the bunch at the exit of the electron gun, the first subharmonic buncher and the exit of the second subharmonic buncher is shown. The horizontal axis represents the phase of each particle relative to the central particle, and the vertical axis represents the energy deviation of each particle relative to the central particle, that is, the energy divergence δ. The results show that with the optimized parameters of the two subharmonic bunchers, the bunch has good compression performance, and the energy divergence is maintained within an acceptable range. The bunching effect mainly comes from the first subharmonic buncher, and the second subharmonic buncher further compresses the bunch length, while the energy divergence of the bunch is effectively reduced. The bunch length of the DC beam at the exit of the electron gun is 1296°, and the bunch length at the exit of the second subharmonic buncher is 26.72°. The compression ratio reaches 48.5, and the energy divergence is only 7.43%, which meets the injection conditions of the traveling wave buncher.

[0075] Figure 6 , Figure 7 Shown respectively Figure 3 When one of the solutions is solved, the phase motion trajectory and energy gain of a single particle in the traveling wave buncher are observed using the beam motion visualization program. Figure 6 It can be seen that when the electron beam moves in front of the traveling wave buncher, the electron phase gradually oscillates and shrinks. After the electron energy gradually increases and the speed approaches the speed of light, the particle phase gradually stabilizes and the phase length no longer changes significantly.

[0076] Figure 8 The phase space diagram of the electron beam at the entrance and exit of the traveling wave buncher observed using a beam motion visualization program is shown. At the exit of the bunching system, the bunch length is further compressed to 5.1°. At the same time, there is no particle loss in the entire injector bunching system, and the capture rate of the system reaches 100%.

[0077] Fig. 9 The calculation results of the three-dimensional multi-particle beam dynamics software Parmela are shown with the final bunching system layout and component parameter settings. The horizontal axis is the kinetic energy of the bunch, the vertical axis is the solid line, and the dotted line is the energy divergence of the bunch. The bunch length is mainly compressed in the first two subharmonic bunchers, and the energy variation range is not large at this time. In the traveling wave buncher, the energy increases significantly, and the bunch length and energy divergence are further reduced.

[0078] In summary, the method provided by the present invention uses a multi-objective genetic algorithm with better convergence, and a single-particle simulation model of electrons moving in a subharmonic buncher and a traveling wave buncher, and jointly calculates with a multi-particle beam dynamics simulation to solve the output of the electron accelerator bunching system using a reference point-based non-dominated sorting genetic algorithm with an elite strategy. The optimization goal is to converge the two parameters of the bunch length and bunch energy divergence output by the bunching system to the optimal direction. The initial setting range of the bunching system parameters is used as the restriction condition of the genetic algorithm to obtain the bunching system parameter setting results under the best beam quality, and the results are further verified by beam simulation software. The output results are optimized by repeated iterations to obtain the optimal parameter setting results.

[0079] An embodiment of the present invention provides an electronic device, including: a computer-readable storage medium and a processor;

[0080] The computer-readable storage medium is used to store executable instructions;

[0081] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method described in any of the above embodiments.

[0082] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the method described in any of the above embodiments.

[0083] An embodiment of the present invention provides a computer program product, including a computer program or instructions, and when the computer program or instructions are executed by a processor, the method described in any of the above embodiments is implemented.

[0084] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An optimization design method for an electron accelerator bunching system for a new generation electron-positron collider replacement injection scheme, characterized in that: include: S1, using a reference point-based non-dominated sorting genetic algorithm with an elite strategy, taking the first performance of the beam output by the bunching system as the optimization target, and optimizing the setting range of each bunching system parameter corresponding to the beam with the best first performance; Wherein, the first performance includes bunch length and bunch energy divergence, the optimization goal is to minimize both bunch length and bunch energy divergence, the bunching system parameters include the phase, cavity pressure and drift length of the first and second harmonic cavities; the fitness function of the non-dominated sorting genetic algorithm is a calculation model of bunch length and energy divergence, and in the optimization process, each population individual corresponds to a bunch distribution, and the gene length of a single population individual is determined by the possible value points of each bunching system parameter; S2, using multi-particle beam dynamics simulation software to calculate the second performance of the beam corresponding to the value combination of all bunching system parameters and determine whether it meets the performance requirements; wherein the parameter values ​​in the value combination are all within the current setting range; the second performance includes bunch energy and capture rate; S3, if not, then increase the lower limit of the current setting range and lower the upper limit to update the current setting range, and use it as the initial setting range of each beamforming system parameter, and return to S1; if yes, then output the current setting range and use it as the optimal setting range of each beamforming system parameter.

2. The method according to claim 1, characterized in that The initial setting range of each beamforming system parameter is obtained as follows: A motion model of electrons in a bunching system is established in numerical analysis software, a set of values ​​to be selected for any bunching system parameter is constructed, the values ​​of other bunching system parameters are fixed, each value to be selected for any bunching system parameter is substituted into the motion model to obtain the second performance of the corresponding beam, and the value interval of the value to be selected that meets the second performance requirement is used as the initial setting range of the corresponding any bunching system parameter; wherein, if the initial setting range of the other bunching system parameters is not determined, its value is set arbitrarily, otherwise the values ​​in its initial setting range are traversed and the values ​​are used as their values ​​respectively.

3. The method according to claim 1, characterized in that The adjustment steps of the phase, cavity pressure and drift length of the first and second harmonic bunchers are 1°, 2kV and 0.1m respectively.

4. The method according to claim 1, characterized in that In step S2, multi-particle beam dynamics simulation software is used to calculate the second performance of the beam corresponding to the value combination of all bunching system parameters, including: A bunching system model is established using three-dimensional electromagnetic field simulation software, and the actual three-dimensional electromagnetic field distribution in the bunching system is calculated. Then, the movement process of the beam in the actual three-dimensional electromagnetic field distribution is simulated using multi-particle beam dynamics simulation software, so as to calculate the second performance of the beam corresponding to the combination of values ​​of all bunching system parameters.

5. The method according to claim 1, characterized in that Before returning to S1, the method further includes: respectively reducing and increasing the phase and electric field gradient of the traveling wave buncher according to respective adjustment steps, and using the updated values ​​as initial values ​​of the phase and electric field gradient.

6. The method according to claim 5, characterized in that The adjustment steps of the phase and electric field gradient of the traveling wave buncher are 0.5° and 0.2MV / m respectively.

7. The method according to claim 1, characterized in that Also includes: The dynamic parameters of each particle during the beam motion are stored, and the changes of energy and phase along the beam line direction, the longitudinal phase space and beam length of each particle during the beam motion, and the changes of energy divergence along the beam line direction are displayed through a visualization program.

8. An electronic device, characterized in that: include: A computer readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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