CN111709183B - Neutron tube acceleration system optimization method based on genetic algorithm - Google Patents
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Abstract
The invention discloses a neutron tube acceleration system optimization method based on a genetic algorithm, which comprises the steps of firstly taking geometric structural parameters of a neutron tube acceleration system as parameters to be optimized, randomly generating N binary numbers to represent the parameters to be optimized to form an individual, constructing an initial population, constructing a geometric model of the neutron tube acceleration system, calculating beam current performance indexes of the neutron tube acceleration system corresponding to the genetic individual, and establishing a target function; measuring fitness of the genetic individual by using the objective function value; carrying out iteration to carry out genetic operation, generating a new population, calculating the fitness of each individual of the new population until the iteration stopping condition is met, and outputting an optimal solution as a value of a parameter to be optimized; the invention calculates the beam performance index of the neutron tube accelerating system by utilizing a finite element method, then applies a genetic algorithm, and takes the beam performance of the neutron tube accelerating system as a target to globally optimize the geometric structure parameter of the neutron tube accelerating system, thereby obtaining the neutron tube accelerating system under the optimized geometric structure parameter.
Description
Technical Field
The invention belongs to the technical field of neutron tube acceleration systems, and particularly relates to a genetic algorithm-based neutron tube acceleration system optimization method.
Background
The neutron tube is a small accelerator, which is formed by fully sealing an ion source, an accelerator, a target and a storage in a glass vacuum tube or a ceramic vacuum tube; has the advantages of compact structure, high safety, convenient use and the like; currently, neutron tubes are widely used in the fields of neutron photography, neutron therapy, petroleum logging and the like.
The neutron tube accelerating system leads out beam current from ion source plasma, and focuses and accelerates the beam current through electric field force, so that the beam current performance of the target surface is affected; the beam intensity and uniformity are important indexes for evaluating beam performance; in the prior art, a finite element simulation method is generally adopted to optimize parameters of the structure of an acceleration system, so that the uniformity of beam current is improved, and the performance of a neutron tube is further improved; in the prior art, when parameter optimization is performed by adopting a finite element simulation method, an optimal solution cannot be obtained often, so that the uniformity of beam current is low, the service life and performance of a target are damaged, and the service life and performance of a neutron tube are further influenced.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides an optimal design method of a neutron tube acceleration system and the neutron tube acceleration system, and aims to solve the technical problems that the optimal solution is often not obtained when the neutron tube acceleration system parameter is optimized by adopting a finite element simulation method in the prior art, so that the beam uniformity is low, and the service life and the performance of a neutron tube are further influenced.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention provides a neutron tube acceleration system optimization method based on a genetic algorithm, which comprises the following steps:
step 1, initializing
Taking geometric structure parameters of a neutron tube acceleration system as parameters to be optimized, randomly generating N binary numbers to represent the parameters to be optimized, and forming an individual to form an initial population;
step 2, calculating individual fitness
Converting the obtained binary number to generate a decimal representation neutron tube acceleration system geometric structure parameter; constructing a neutron tube acceleration system geometric model according to the decimal representation neutron tube acceleration system geometric parameters; calculating beam performance indexes of neutron tube acceleration systems corresponding to genetic individuals by adopting a finite element method;
establishing an objective function according to beam performance indexes of a neutron tube acceleration system; then, the fitness of the genetic individuals is measured according to the objective function value;
and step 3, carrying out iteration to carry out genetic operation, generating a new population, calculating the fitness of each individual of the new population until the iteration stop condition is met, outputting an optimal solution which meets the iteration stop condition and is used as the value of the parameter to be optimized, and manufacturing a neutron tube acceleration system according to the value of the parameter to be optimized.
Further, in step 1, parameters to be optimized include an acceleration gap, an extremely small diameter of an acceleration electrode, a length of the acceleration electrode, an aperture of a leading-out electrode, a magnet ring slope aperture, a magnet conductive cylinder slope aperture and an aperture of the acceleration electrode of the neutron tube acceleration system.
Further, in step 2, beam performance indexes of the neutron tube acceleration system corresponding to the genetic individual include beam unevenness, beam off-target number and beam radius;
the mathematical expression of the objective function is:
objective function ObjV = target/base + outtarget;
wherein target is beam current unevenness; outtarget is the beam off-target number, and base is the beam radius; the smaller the objective function value, the better the adaptability of the genetic individual;
the mathematical expression of the beam non-uniformity target is as follows:
wherein o is non-uniformity; z is Z x In an ideal state, the number of particles in a certain ring x on the target surface; a is that x Is the area of a certain circular ring x on the target surface; a is that Total (S) Is the total area of the target surface; u (U) x The actual particle number in a certain ring x on the target surface; x=1, 2, …, n.
Furthermore, the objective function is solved by using a finite element method and Matlab software.
Further, in step 3, genetic manipulation includes selection, crossover and mutation; wherein, the selection operation adopts a mechanism combining elite selection and roulette according to the fitness value of the genetic individual; the crossing operation adopts a uniform crossing mode, and individuals are randomly selected to implement crossing or column crossing; the mutation operation adopts a bit mutation mechanism.
Further, in step 3, the iteration stop condition is satisfied to reach the maximum evolution algebra, and at this time, the optimal solution is the parameter to be optimized corresponding to the individual with the minimum fitness in the current population.
Further, in step 3, the iteration stop condition is satisfied, that the fitness reaches the set requirement, and at this time, the optimal solution is the parameter to be optimized corresponding to the individual whose fitness reaches the set requirement.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a neutron tube accelerating system optimizing method based on a genetic algorithm, which utilizes a finite element method to calculate beam current performance indexes of a neutron tube accelerating system, then applies the genetic algorithm, takes the beam current performance of the neutron tube accelerating system as a target, and carries out global optimizing on geometric structure parameters of the neutron tube accelerating system; according to the performance index requirement of the neutron tube accelerating system, the invention automatically searches the overall optimal structural size, thereby obtaining the neutron tube accelerating system under the optimized geometric structure parameter; the problem that the optimal solution can not be obtained often when the parameter optimization of the neutron tube acceleration system is carried out by adopting a finite element simulation method is solved, and the beam performance of the neutron tube is obviously improved; a large amount of scheme selection time is saved, the working efficiency is improved, and the service life and the performance of the neutron tube are effectively improved.
Furthermore, the extraction pole side and the acceleration pole side of an acceleration region in the neutron tube acceleration system have larger influence on particle beam focusing, and according to the results of electric potential and particle tracks, the beam performance of the neutron tube acceleration system can be effectively improved by taking an acceleration gap, an extremely small diameter of an acceleration electrode, the length of the acceleration electrode, the aperture of the extraction pole, the caliber of a magnetic ring slope, the caliber of a magnetic conduction cylinder slope and the aperture of the acceleration electrode in the neutron tube acceleration system as parameters to be optimized, so that the service life and the performance of the neutron tube are further improved; the geometric parameters of the seven neutron tube acceleration systems form an individual of a genetic algorithm, the genetic algorithm selects cross variation according to the fitness of the individual, the probability that the individual genes with good fitness are inherited by offspring is high, and the genetic algorithm is the optimal solution.
Furthermore, beam unevenness, beam off-target number and beam radius are selected as beam performance indexes of the neutron tube accelerating system, the beam performance can be intuitively reflected, the advantages and disadvantages of the accelerating system can be evaluated through the beam unevenness, the beam radius and the beam off-target number, and the smaller the objective function value is, the better the individual is proved.
Furthermore, the objective function is calculated by combining a finite element method with Matlab software, the advantages of COMSOL simulation and MATLAB programming are fully combined, the optimization requirement of a magnetic nanoparticle simulation test platform using magnetic nanoparticle devices with different sizes can be widely met, and a magnetic nanoparticle measurement simulation model meeting the requirements can be directly generated and stored through parameter transfer among different software.
In conclusion, the finite element method, the genetic algorithm and the uniformity evaluation method are combined with each other, the acceleration system of the neutron tube is optimally designed by means of COMSOL and MATLAB software, the acceleration system structure with good performance is finally obtained, the performance of the neutron tube is improved, the method is used for guiding the design and manufacture of the neutron tube, and the practicability is high.
Drawings
FIG. 1 is a schematic flow chart of a neutron tube acceleration system optimization method according to the present invention;
FIG. 2 is a flowchart of an individual fitness evaluation method in the neutron tube acceleration system optimization method according to the present invention;
FIG. 3 is a diagram showing a distribution of beam particles on a target surface of an optimal solution neutron tube acceleration system according to an embodiment;
fig. 4 is a schematic diagram illustrating convergence of a genetic algorithm of the neutron tube acceleration system according to the embodiment.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects solved by the invention more clear, the following specific embodiments are used for further describing the invention in detail. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1 and 2, the optimization method of the neutron tube acceleration system based on the genetic algorithm comprises the following steps:
step 1, initializing
Taking geometric structure parameters of a neutron tube acceleration system as parameters to be optimized, randomly generating N binary numbers to represent the parameters to be optimized, and forming an individual to form an initial population; the parameters to be optimized comprise an acceleration gap, an extremely small diameter of an acceleration electrode, a length of the acceleration electrode, an aperture of a leading-out electrode, a caliber of a magnetic ring slope, a caliber of a magnetic conduction cylinder slope and an aperture of the acceleration electrode of the neutron tube acceleration system.
Step 2, calculating individual fitness
Converting the obtained binary number to generate a decimal representation neutron tube acceleration system geometric structure parameter; constructing a neutron tube acceleration system geometric model according to the decimal representation neutron tube acceleration system geometric parameters; calculating beam performance indexes of neutron tube acceleration systems corresponding to genetic individuals by adopting a finite element method; the beam performance index of the neutron tube acceleration system comprises beam unevenness, beam off-target number and beam radius.
Establishing an objective function according to beam current performance evaluation indexes of a neutron tube acceleration system; then, the fitness of the genetic individuals is measured according to the objective function value; wherein, the mathematical expression of the objective function is:
objective function ObjV = target/base + outtarget;
wherein target is beam current unevenness; outtarget is the beam off-target number, and base is the beam radius; the smaller the objective function value, the better the adaptability of the genetic individual;
the mathematical expression of the beam non-uniformity target is as follows:
wherein o is non-uniformity; z is Z x In an ideal state, the number of particles in a certain ring x on the target surface; a is that x Is the area of a certain circular ring x on the target surface; a is that Total (S) Is the total area of the target surface; u (U) x The actual particle number in a certain ring x on the target surface; x=1, 2, …, n.
And (3) the beam off-target number is obtained by reading the three-dimensional coordinates of the particles and counting whether the coordinates are obtained on the target according to the finite element simulation calculation result.
The beam radius is obtained by calculating the distance from the coordinates of the particles on the target surface to the coordinates of the center of the target surface.
Step 3, carrying out genetic operation iteratively to generate a new population, calculating the fitness of each individual of the new population until the iteration stop condition is met, outputting an optimal solution which meets the iteration stop condition and is used as the value of the parameter to be optimized, and manufacturing a neutron tube acceleration system according to the value of the parameter to be optimized; wherein genetic manipulation includes selection, crossover and mutation; wherein, the selection operation adopts a mechanism combining elite selection and roulette according to the fitness value of the genetic individual; the crossing operation adopts a uniform crossing mode, and individuals are randomly selected to implement crossing or column crossing; the mutation operation adopts a position mutation mechanism; when the iteration stopping condition is met and the maximum evolution algebra is reached, the optimal solution is the parameter to be optimized corresponding to the individual with the minimum adaptability in the current population; when the iteration stop condition is met and the fitness reaches the set requirement, the optimal solution is the parameter to be optimized corresponding to the individual with the fitness reaching the set requirement.
The invention provides a neutron tube accelerating system optimizing method based on a genetic algorithm, which utilizes a finite element method to calculate beam current performance indexes of a neutron tube accelerating system, then applies the genetic algorithm, takes the beam current performance of the neutron tube accelerating system as a target, and carries out global optimizing on geometric structure parameters of the neutron tube accelerating system; according to the performance index requirement of the neutron tube accelerating system, the invention automatically searches the overall optimal structural size, thereby obtaining the neutron tube accelerating system under the optimized geometric structure parameter; the method solves the problem that when the parameter optimization of the neutron tube acceleration system is carried out by adopting a finite element simulation method, the optimal solution can not be obtained, the beam performance of the neutron tube acceleration system is obviously improved, a large amount of scheme selection time is saved, the working efficiency is improved, and the service life and the performance of the neutron tube are effectively improved.
Examples
The structure related to particle acceleration in the neutron tube acceleration system mainly comprises an extraction electrode, an acceleration electrode and a target, wherein the extraction electrode is grounded, and the acceleration electrode is high in voltage; the extraction electrode and the accelerating electrode form potential difference, particles at the outlet of the extraction electrode move towards the accelerating electrode under the action of an electric field, and a particle beam is formed; after the acceleration process, the particle beam continuously moves upwards to the target under the inertia effect, and the radius of the particle beam is larger and larger in the process of drifting on the target, when the radius of the particle beam is larger, the effective area of the target is larger, and the service life of the target is longer under the same condition, so that the service life of the neutron tube is longer; the geometric structure of the neutron tube accelerating system is parameterized by utilizing the mutual coordination of finite elements and genetic algorithm, and the service life and the performance of the neutron tube accelerating system and the neutron tube are further improved by improving the uniformity of beam current distribution on a target.
The neutron tube acceleration system optimization method based on the genetic algorithm of the embodiment comprises the following steps:
step 1, selecting an acceleration gap D and an extremely small diameter D of an acceleration electrode in a neutron tube acceleration system 2 The length L of the accelerating electrode and the aperture phi of the leading-out electrode 2 Magnet ring slope caliber fm 2 Groove diameter fm of magnetic conductive cylinder 3 Accelerating electrode aperture D 1 As a parameter to be optimized of a neutron tube acceleration system; respectively setting the value ranges and the precision of the seven parameters to be optimized; the value range is set according to engineering practical experience and structure size without conflict, parameter accuracy is set according to genetic algorithm simulation experience, and the parameter range is generally divided into 2 10 The parts are used as parameter precision.
Taking the value of each group of parameters to be optimized as an individual in the population, namely taking an acceleration gap D and an acceleration electrode minimum diameter D in each group of neutron tube acceleration systems 2 The length L of the accelerating electrode and the aperture phi of the leading-out electrode 2 Magnet ring slope caliber fm 2 Groove diameter fm of magnetic conductive cylinder 3 Accelerating electrode aperture D 1 As an individual in the population, setting control parameters of a genetic algorithm; wherein, the maximum evolution algebra is 100, the initial population size is 50, the crossover probability is 0.7, and the mutation probability is 0.1.
In the neutron tube accelerating system, the extraction electrode side of the accelerating region and the extraction electrode side and the accelerating electrode side of the accelerating region are relatively loud to the particle beam focusing process, so that the beam distribution on a target is influenced, and therefore, an accelerating gap D and an extremely small diameter D of an accelerating electrode in the neutron tube accelerating system are selected 2 The length L of the accelerating electrode and the aperture phi of the leading-out electrode 2 Magnet ring slope caliber fm 2 Groove diameter fm of magnetic conductive cylinder 3 Accelerating electrode aperture D 1 As the optimized variable of the geometric parameters of the neutron tube acceleration system, the uniformity and the beam radius of the beam are improved, the off-target number of the beam is reduced, and the service life and the performance of the neutron tube are further improved.
Step 2, determining the individual length according to the precision set in the step 1, and randomly generating N binary numbers to represent parameters to be optimized by combining a random function to form an individual to form an initial population;
step 3, converting binary numbers into decimal numbers for individuals in the initial population, converting seven parameters to be optimized in each individual into real numbers in corresponding value ranges, and calculating to obtain individual fitness;
the individual fitness calculating process comprises the following specific steps:
for each individual, the MATLAB software is matched with the COMSOL simulation software, seven parameters to be optimized of the corresponding individual are transmitted to the COMSOL simulation software, other model parameters, material properties, boundary conditions and the like are set in the COMSOL simulation software through a program, and the construction and calculation of a neutron tube acceleration system geometric model are carried out; invoking COMSOL software to calculate particle coordinates to obtain three performance indexes of beam performance of the neutron tube acceleration system, wherein the three performance indexes comprise beam non-uniformity target, beam off-target number outtarget and beam radius base;
specifically, for each individual, converting seven parameters to be optimized in each individual into real numbers in corresponding value ranges, establishing a three-dimensional finite element model of the neutron tube acceleration system, and dividing grids on the basis of the model structure of the neutron tube acceleration system; the model structure is provided with a grounding electrode, an accelerating electrode is electrified, and the target plane is connected with potential.
After the material selection, the boundary condition application and the grid division operation are carried out, the electric field simulation calculation of the neutron tube acceleration system is carried out, a large number of particles move towards a certain direction to form beam current under the action of an electromagnetic field, the simulation of the whole beam current by adopting partial particles is preferable because of the overlarge particle order, and the effect of space charge is not needed to be considered for the beam current intensity of the neutron tube.
The simulation of the particle track is completed on the basis of previous potential calculation, electric field force is added in a particle motion area in a particle tracking module of COMSOL simulation software, particle attributes such as mass, charge and the like are set, particle inlet boundary conditions are added at an outlet of a leading-out electrode, 10000 particles are released, particle density is subjected to Gaussian distribution, and the particles move towards a target direction under the action of the electric field force; and wall boundary conditions are added to the ion entrance face of the target model to freeze the particles on the surface without continuing forward motion.
When the particle beam is distributed on the target surface, wherein R is the distance between the particles and the target center, and R is used for calculating the uniformity of the particles on the target; the uniformity of the particle beam on the target is evaluated by adopting the non-uniformity, the lower the non-uniformity is, the more uniform is proved, and the non-uniformity calculation principle is as follows:
assume that the radius is R max 10000 particles in the circle, R is max Dividing into 10 parts;
in an ideal state, 10000 particles calculate R according to the area ratio 1/10 There should be a particle number Z in the circle of =0.665 mm 1 =10000×(1/10) 2 Let =100, assume actual situation Z 1 The absolute value of the subtraction of the two is the non-uniform number z=100-14=86;
in an ideal state, R 1/10 -R 2/10 Should have Z in the circular ring according to area calculation 1 =10000×[(2/10)2-(1/10)2]=300, assuming actual situation Z 1 =184, the absolute value of the subtraction of the two z=300-184=126.
And so on until the last ring R is calculated 9/10 -R max The difference Z between the ideal value and the actual value of each ring is divided by the total particle number 10000, which is the non-uniformity in the ring. Assuming that the calculated total value of Z is 4496, unevenness o=z/10000=0.449 can be obtained. The smaller the non-uniformity geometry, the better the acceleration system.
The mathematical expression of the beam non-uniformity target is:
wherein o is non-uniformity; z is Z x In an ideal state, the number of particles in a certain ring x on the target surface; a is that x Is the area of a certain circular ring x on the target surface; a is that Total (S) Is the total area of the target surface; u (U) x The actual particle number in a certain ring x on the target surface; x is the number of equally divided circular rings of the target surface, and x=1, 2, … and 10.
And (3) the beam off-target number is obtained by reading the three-dimensional coordinates of the particles and counting whether the coordinates are obtained on the target according to the finite element simulation calculation result.
The beam radius is obtained by calculating the distance from the particle coordinates on the target surface to the center coordinates of the target surface; wherein the beam radius is selected to cover 90% of the distance of the particle from the bulls-eye; that is, assuming that 10000 particles are spaced from the centroid, 9000 th particles are selected in a small-to-large arrangement, and the beam radius is determined.
According to the three performance indexes of the beam performance of the neutron tube acceleration system, an objective function ObjV is constructed, the fitness of each individual is carried out, and the performance of the neutron tube acceleration system corresponding to each individual in the population is measured.
Wherein, the mathematical expression of the objective function ObjV is:
objective function ObjV = target/base + outtarget;
wherein target is beam current unevenness; outtarget is the beam off-target number, and base is the beam radius; the smaller the objective function value, the better the fitness of the genetic individual.
In order to accelerate the convergence of the genetic algorithm, three performance indexes of beam performance are amplified, wherein once the beam off-target number outtarget is not equal to 0, the beam off-target number outtarget is equal to infinity, and the purpose is to screen out genetic individuals which can be off-target, so that genes of the genetic individuals can not be effectively transmitted to the next generation; exponentially amplifying the beam uniformity target and the beam radius base; the smaller the objective function value, the better the individual, the smaller the objective function value, the smaller the unevenness, the larger the beam radius, and the beam off-target number is zero.
Step 4, after the fitness calculation of the population is completed, iterative genetic operations of selection, crossing and variation are performed, wherein the genetic operations comprise selection, crossing and variation; wherein, the selection operation adopts a mechanism combining elite selection and roulette according to the fitness value of the genetic individual; the crossing operation adopts a uniform crossing mode, and individuals are randomly selected to implement crossing or column crossing; the mutation operation adopts a bit mutation mechanism.
After the genetic operation is finished, a new population is generated, binary gene bit strings of each individual in the new population are separated, decoding is carried out, the binary gene bit strings are separated into seven parameters to be optimized again, binary is converted into decimal, and the fitness of each individual in the new population is calculated according to the fitness calculation method in the step 3.
Step 5, decoding the new genetic population into geometric parameters of a neutron tube acceleration system through genetic operation, carrying out neutron tube bundle flow performance analysis of the new population and adaptability evaluation of the genetic population, selecting and evolving the genetic population according to the direction favorable for improving the neutron tube bundle flow performance, determining the genetic population of the next generation, and realizing continuous evolution of the population;
step 6, continuously evolving the population, satisfying iteration stop conditions, namely when the set maximum evolution algebra is reached, decoding the individual with the minimum fitness to obtain an acceleration interval D and an acceleration electrode minimum diameter D corresponding to the individual 2 The length L of the accelerating electrode and the aperture phi of the leading-out electrode 2 Magnet ring slope caliber fm 2 Groove diameter fm of magnetic conductive cylinder 3 Accelerating electrode aperture D 1 The actual values of the seven parameters to be optimized are used as the optimal solution of the geometric parameters to be optimized of the neutron tube accelerating system, and the neutron tube accelerating system is manufactured according to the values of the parameters to be optimized;
as shown in figures 3 and 4, after 100 generations of 300 individual iterative computation, the optimal solution of the population is stabilized, and the adaptability of the optimal solution is stabilized at 10 -4 Converging a genetic algorithm; wherein, in the initial stage, the adaptability of the population is basically stabilized near 894.4, slightly reduced but not obviously, at about 50 generations, the adaptability of some individuals of the population is better due to crossover and mutation, the optimal solution adaptability of the population is reduced in a cliff type, then the gene of the population is rapidly spread in the population, and finally the adaptability of the optimal solution is stabilized at 10 -4 The optimal solution is obtained, and the particles are paved on the whole target surface at 100 generations; when the fitness is 0.14 around 60 generations, the maximum radius of the particles is only 0.5mm from the edge of the target, and the optimal solution of 100 generations requires too high dimensional accuracy, 10 -5 The magnitude of mm, combined with engineering practice, selects about 60 generations, and the fitness is 0.14 as the optimal solution of the problem; compared with a neutron tube acceleration system in an unoptimized state, the beam unevenness in the optimal solution is smaller than that in the unoptimized state, the beam radius in the optimal solution is larger than that in the unoptimized state, the maximum distance of an optimal Jie Zhongli sub-from the target is larger than that of particles in the unoptimized state, and in addition, the beam off-target number in the optimal solution is zero; decoding an individual with the minimum fitness as an optimal solution to obtain the optimized geometric structure parameters of the neutron tube acceleration system, wherein the neutron tube bundle flow performance is greatly improved under the structure of the optimal solution; the electric potential in the neutron tube accelerating system is greatly improved before the focusing capability of the accelerating electrode is intersected, so that the particle aggregation is improvedJiao Neng force expands the radius of the particle distribution on the target surface; the radius of the particles on the target surface is obviously enlarged, and in addition, the off-target particle number is zero, so that the pressure resistance of the neutron tube is improved, and the service life and the performance of the neutron tube are improved.
The method adopts the combination of a finite element method, a genetic algorithm and a uniformity evaluation method, optimizes the accelerating system of the neutron tube by means of COMSOL simulation software and MATLAB software, finally obtains a better accelerating system structure, prolongs the service life and performance of the neutron tube, is used for guiding the design and manufacture of the neutron tube, and has strong practicability.
The above embodiment is only one of the implementation manners capable of implementing the technical solution of the present invention, and the scope of the claimed invention is not limited to the embodiment, but also includes any changes, substitutions and other implementation manners easily recognized by those skilled in the art within the technical scope of the present invention.
Claims (6)
1. The neutron tube acceleration system optimization method based on the genetic algorithm is characterized by comprising the following steps of:
step 1, initializing
Taking geometric structure parameters of a neutron tube acceleration system as parameters to be optimized, randomly generating N binary numbers to represent the parameters to be optimized, and forming an individual to form an initial population;
step 2, calculating individual fitness
Converting the obtained binary number to generate a decimal representation neutron tube acceleration system geometric structure parameter; constructing a neutron tube acceleration system geometric model according to the decimal representation neutron tube acceleration system geometric parameters; calculating beam performance indexes of neutron tube acceleration systems corresponding to genetic individuals by adopting a finite element method;
establishing an objective function according to beam performance indexes of a neutron tube acceleration system; then, the fitness of the genetic individuals is measured according to the objective function value;
step 3, carrying out genetic operation iteratively to generate a new population, calculating the fitness of each individual of the new population until the iteration stop condition is met, outputting an optimal solution which meets the iteration stop condition and is used as the value of the parameter to be optimized, and manufacturing a neutron tube acceleration system according to the value of the parameter to be optimized;
in the step 2, beam performance indexes of the neutron tube acceleration system corresponding to the genetic individual comprise beam unevenness, beam off-target number and beam radius;
the mathematical expression of the objective function is:
objective function ObjV = target/base + outtarget;
wherein target is beam current unevenness; outtarget is the beam off-target number, and base is the beam radius; the smaller the objective function value, the better the adaptability of the genetic individual;
the mathematical expression of the beam non-uniformity target is as follows:
wherein o is non-uniformity; z is Z x In an ideal state, the number of particles in a certain ring x on the target surface; a is that x Is the area of a certain circular ring x on the target surface; a is that Total (S) Is the total area of the target surface; u (U) x The actual particle number in a certain ring x on the target surface; x=1, 2, …, n.
2. The method for optimizing a neutron tube acceleration system based on a genetic algorithm according to claim 1, wherein in the step 1, parameters to be optimized include an acceleration gap, an extremely small diameter of an acceleration electrode, an acceleration electrode length, an aperture of a leading-out electrode, a magnet ring slope caliber, a magnet tube slope caliber and an acceleration electrode aperture of the neutron tube acceleration system.
3. The optimization method of the neutron tube acceleration system based on the genetic algorithm according to claim 1, wherein the finite element method and Matlab software are used for solving an objective function.
4. The method of claim 1, wherein in step 3, the genetic operation includes selection, crossover and mutation; wherein, the selection operation adopts a mechanism combining elite selection and roulette according to the fitness value of the genetic individual; the crossing operation adopts a uniform crossing mode, and individuals are randomly selected to implement crossing or column crossing; the mutation operation adopts a bit mutation mechanism.
5. The optimization method of neutron tube acceleration system based on genetic algorithm according to claim 1, wherein in the step 3, the iteration stop condition is met to reach the maximum evolution algebra, and the optimal solution is the parameter to be optimized corresponding to the individual with the minimum fitness in the current population.
6. The optimization method of neutron tube acceleration system based on genetic algorithm according to claim 1, wherein in the step 3, the condition of stopping iteration is satisfied, that is, the fitness reaches the set requirement, and the optimal solution is the parameter to be optimized corresponding to the individual whose fitness reaches the set requirement.
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Non-Patent Citations (1)
Title |
---|
王翠 ; 乔双 ; .中子管用三电极离子聚焦系统的仿真研究.东北师大学报(自然科学版).2016,(02),全文. * |
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