A neutron multiplicity measuring device 3 He counter tube adaptation method

Abstract

The application relates to the field of nuclear radiation detection, in particular to a neutron multiplicity measuring device 3 He counter tube adaptation method. The technical problem to be solved by the application is to provide a neutron multiplicity measuring device 3 He counter tube adaptation method, which provides a heuristic adaptation method for determining the length, quantity, interval distance and layout mode of 3He counter tubes under the premise of meeting the size requirement of a measuring cavity and detection efficiency, and minimizes the total amount of counter tubes.

Description

Technical Field

[0001] This invention relates to the field of nuclear radiation detection, and in particular to a neutron multiplicity measurement device. 3 He counter tube adaptation method. Technical Background

[0002] With the development of the nuclear industry, the amount of uranium and plutonium materials worldwide is increasing, making nuclear proliferation a widespread concern in the international community. Since both uranium and plutonium can produce fission neutrons, non-destructive analysis using neutron detection technology combined with isotopic abundance is the most common method in nuclear safeguards. In my country, the detection of uranium and plutonium materials in nuclear facilities, as well as the classification and treatment of large amounts of radioactive solid waste generated during long-term operation and decommissioning, has gradually become a challenging task. The development of nuclear radiation detection technology has made neutron measurement technology a crucial non-destructive analysis technique in this field, holding significant importance in nuclear safeguards, particularly in nuclear material accounting.

[0003] Neutrons possess strong penetrating power and are difficult to shield, making them the only feasible and rapid detection technology suitable for analyzing medium- and high-density, large-volume samples. They also show broad application prospects in the classification and detection of low- and intermediate-level solid radioactive waste. In the precise quantitative analysis of U / Pu materials in the aforementioned fields, including routine sample analysis, warehouse inventory, and closed-loop balance calculations for U / Pu production lines, this technology plays a positive role.

[0004] Neutron multiplicity measurement is a rapid NDA technique that accurately and quantitatively analyzes nuclear materials by measuring the multiplicity distribution of fission neutrons. The measurement process does not require standard calibration, thus avoiding the potential influence of standard samples on the measurement results. Fission neutrons are temporally correlated; the number of neutrons released in a single fission event follows a probability distribution, i.e., a multiplicity distribution. This method can distinguish between fission neutrons and non-fission neutrons, minimizing interference from non-fission neutrons and the influence of the matrix material on the measurement. Neutron multiplicity measurement can be expressed by the following equation:

[0005] Singles=Fεν sf,1 (1+α)M (1)

[0006]

[0007]

[0008] In the formula, Singles, Doubles, and Triples represent the single, double, and triple count rates, respectively, and ν sf,1 ν sf,2and ν sf,3 These are the first, second, and third factorial moments of the spontaneous fission emission neutron number distribution, ν i1 ν i2 、 and ν i3 These are the first, second, and third factorial moments of the induced fission emission neutron number distribution, respectively; ε is the detector's neutron detection efficiency; and f... d f t These are the detector's double and triple coincidence gate factors, M is the multiplication coefficient, α is the ratio of the number of neutrons to the number of spontaneously fissioning neutrons, and F is the average reaction rate when spontaneously fissioning neutrons occur.

[0009] To ensure that neutron multiplexing measurement technology can meet the practical needs of different measurement objects, the neutron multiplexing measurement device needs to be designed according to the volume of the object to be measured and the required detection efficiency. When neutron multiplexing measurement devices meet the same or similar main structure, electronic system, and measurement cavity requirements, the main factor affecting the detection efficiency is the detection components. 3 The selection of a He counter tube includes important factors such as 3 The length, number, spacing, and layout of He objects—different combinations of these factors—directly affect detection efficiency. 3 The length and number of He counter tubes are positively correlated with detection efficiency. 3 The spacing and layout of the He counter tubes were determined, and then simulations were performed using MCNPX to obtain the number of detected neutrons, verifying whether the detection efficiency could be met, and determining the appropriate level based on the verification results. 3 An adaptation scheme for the He counter tube is needed. Therefore, a neutron multiplicity measurement device for different measurement cavities is required. 3 The He counter tube adaptation method can meet the need for rapid adaptation. Summary of the Invention

[0010] The technical problem to be solved by the present invention is: to provide a neutron multiplicity measurement device. 3 The He counter tube adaptation method, under the premise of meeting the requirements of measurement cavity size and detection efficiency, determines 3 The length, number, spacing, and layout of the counting tubes should be optimized to minimize the total number of counting tubes used.

[0011] The concept and technical solution of this invention are described below:

[0012] Step 1: Determine the size of the measurement cavity and the required neutron detection efficiency ε, and determine the objective function and constraint function that minimizes the total number of counting tubes used;

[0013] Step 2: Determine the diameter of the measuring cavity. 3 The range of the number of He counting tubes;

[0014] Step 3: Determine the height of the measuring cavity. 3 The range of values ​​for the length of the He counter tube;

[0015] Step 4: Confirm 3 The layout of the He counter tube can be selected;

[0016] Step 5: Confirm 3 The range of values ​​for the He counter tube spacing;

[0017] Step 6: With 3 The length, number, spacing, and layout of the counting tubes are combined to represent individuals in the population. 3 He uses the quantity as the fitness function, selects the encoding and decoding method of individuals, population size, number of iterations, selection factor, crossover factor, and mutation factor, and then uses MCNP simulation to determine whether the detection efficiency of each individual meets the requirements. Individuals that do not meet the constraints are deleted, and individuals are added according to certain rules. The calculation is repeated until the conditions for the method to end are met.

[0018] Step 7: The output must meet the detection efficiency requirement ε, and 3 He used the method with the least amount of material as the size of the measuring cavity. 3 He counter tube adaptation method.

[0019] The specific implementation steps are as follows:

[0020] Step S1: Determine the required size of the measurement cavity φA×B, where A is the diameter of the measurement cavity and B is the height of the measurement cavity, and determine the detection efficiency δ. ε The required value;

[0021] Step S2, establish 3 The objective function and constraint function of the He counter tube adaptation method

[0022]

[0023] Where L is 3 The length of the He counter tube, N is... 3 The number of He counters, ε is the detection efficiency value calculated by MCNP simulation, d i The value of the counting tube spacing is determined, and j is the number of concentric circles corresponding to the layout method;

[0024] Step S3: Determine based on the diameter of the measuring cavity. 3 The number of He counters is in the range of [N] l N h ], where N l The lower limit of the number of counting tubes N h The upper limit of the number of counting tubes N i The value representing the number of counter tubes used each time;

[0025] Step S4: Based on the size of the measuring cavity, first determine... 3 The range of values ​​for the length of the He counter tube is [L l L h ], where L l This is the lower limit of the counting tube length, and L l =0.75B, L h L is the upper limit of the length of the counting tube, and L h =1.25B, L i The value for the length of the counting tube is determined each time;

[0026] Step S5, determine the set of optional layout methods {p 1,...j};

[0027] Step S6, according to 3 The diameter of the counting tube must be determined first. 3 The range of values ​​for the He counter tube spacing is [d l d h ], where d l Let d be the lower limit of the spacing between the counting tubes. l =1,d h Let d be the upper limit of the spacing between the counting tubes. h =1.6A, d i The value of the counter tube spacing is determined for each time;

[0028] Step S7: Generate an initial population. The size of the initial population is pop_size, the number of iterations is T, and the initial population P(t) =<p j N i ,L i ,d i >, t=0, perform gene compensation for individuals with gene deletions;

[0029] Step S8: Calculate the fitness function f(x) = min L × N for each individual based on the objective function;

[0030] Step S9, apply the constraint equation N(1+d) to each individual in the population. i If the condition is greater than or equal to 3.2 × j × A, individuals that do not meet the constraint are deleted and P'(t) is generated.

[0031] Step S10, set P'(t) = <p j N i ,L i ,d iThe input terms are combined and fed into the MCNP model, and the MCNP simulation is used to determine whether the detection efficiency meets the requirements. If ε < δ, then... ε Delete individuals that do not meet the constraints and generate P"(t);

[0032] Step S11: Sort P"(t) in ascending order according to their fitness values;

[0033] Step S12: Extract the individual with the lowest fitness into G(t) and save it to the belief space;

[0034] Step S13: Determine the conditions for calling the operator, and determine whether the random parameter is greater than the preset value. If it is greater than the preset value, call the crossover operator to perform crossover operation; otherwise, call the mutation operator to perform mutation operation, and form the generated new individuals with the old individuals that meet the constraints into a new population, ensuring that the population size pop_size meets the requirements.

[0035] Step S14: Determine whether t = T is true, i.e., whether the iteration count is met. If not, t = t + 1 and return to S8. If it is true, go to S15.

[0036] Step S15: Output the individual with the smallest fitness value as the adaptation scheme.

[0037] Preferably, step 14 is to determine whether |G(t)-G(t-1)|≤a%G(t-1) is true, that is, the change in fitness value between generations is less than or equal to the set value a. If it is not true, t=t+1 and return to S8. If it is true, go to S15. The set value a satisfies 0.1≤a≤1.

[0038] Preferably, step 14 is to determine whether |G(t)-G(t-1)|≤a%G(t-1) or t=T is true, that is, whether the change in fitness value between generations is less than or equal to the set value a or whether the number of iterations is satisfied. If not satisfied, t=t+1 and return to S8. If satisfied, go to S15. The set value a satisfies 0.1≤a≤1.

[0039] The advantage of this invention compared to the prior art is that it provides a neutron multiplicity measurement device for different measurement cavities. 3 The He counter tube adaptation method of this invention fully considers 3 The positive correlation between the length and number of He counters and the detection efficiency of neutron multiplicity measurement, and the nonlinear relationship between the spacing and layout of He counters and the detection efficiency of neutron multiplicity measurement, are explored. Based on national standards or practical needs, the detection efficiency ε is determined, aiming to minimize the total number of counters used. This invention, combined with the basic principles of a cultural genetic algorithm and addressing the specific technical problems it aims to solve, provides a neutron multiplicity measurement device. 3This paper proposes a method for population generation and modification based on constraint propagation, combining the characteristics of the technical problem to be solved with practical needs. This method ensures population diversity and, while avoiding rapid population convergence, achieves faster population generation compared to existing technologies. 3 Adaptation solution for He counter tube. Attached Figure Description

[0040] Figure 1 A neutron multiplicity measurement device 3 He counter tube adaptation method flowchart Detailed Implementation

[0041] The following is in conjunction with the appendix Figure 1 For neutron multiplicity measurement devices in different measurement cavities 3 The He counter tube adaptation method is described in detail below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Many specific details are set forth in the following description to provide a thorough understanding of the invention. However, the invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0042] The calculation method of the present invention includes the following steps:

[0043] Step S1: Determine the required size of the measurement cavity φA×B, where A = 40 is the diameter of the measurement cavity and B = 40 is the height of the measurement cavity, and determine the detection efficiency δ. ε =30%;

[0044] Step S2, establish 3 The objective function and constraint function of the He counter tube adaptation method

[0045]

[0046] Where L is 3 The length of the He counter tube, N is... 3 The number of He counters, ε is the detection efficiency value calculated by MCNP simulation, d i The value of the counting tube spacing is determined, and j is the number of concentric circles corresponding to the layout method;

[0047] Step S3: Determine based on the diameter of the measuring cavity. 3 The number of He counters ranges from [25, 52];

[0048] Step S4: Determine the size of the measuring cavity. 3 The length of the He counter tube ranges from [30, 50];

[0049] Step S5: Determine the set of optional layout methods {p1 = single loop, p2 = double loop};

[0050] Step S6, according to 3 He counter tube diameter, determined 3 The range of the He counter tube spacing is [1, 64];

[0051] Step S7: Generate an initial population. The initial population size is pop_size = 10, the number of iterations is T = 100, and the initial population contains individuals. <p j N i ,L i ,d i >The encoding is performed using a decimal number. Individuals are randomly generated according to the value range. The length of an individual is 8. The highest bit is the layout method bit, the second and third bits are the number of counting tubes, the fourth and fifth bits are the length of the counting tubes, and the sixth to eighth bits are the value range of the spacing between the counting tubes. One decimal place is taken. Gene compensation is performed on individuals with gene deletions.

[0052] Step S8: Calculate the fitness function f(x) = min L × N for each individual based on the objective function;

[0053] Step S9, apply the constraint equation N(1+d) to each individual in the population. i If the condition is greater than or equal to 3.2 × j × A, individuals that do not meet the constraint are deleted and P'(t) is generated.

[0054] Step S10, set P'(t) = <p j N i ,L i ,d i The input terms are combined and fed into the MCNP model, and the MCNP simulation is used to determine whether the detection efficiency meets the requirements. If ε < δ, then... ε Delete individuals that do not meet the constraints and generate P"(t);

[0055] Step S11: Sort P"(t) in ascending order according to fitness value L×N;

[0056] Step S12: Extract the individual with the lowest fitness into G(t) and save it to the belief space;

[0057] Step S13: Determine the conditions for calling the operator, set the preset value to 0.5, the random parameter to 0.6, set the random parameter to be greater than the preset value, call the crossover operator to perform crossover operation, and form the generated new individuals with the old individuals that meet the constraints into a new population, ensuring that the population size pop_size meets the requirements;

[0058] Step S14: Determine if t = 100 is true, i.e., if the iteration count is met. If not, t = t + 1. If not, return to S8. If it is true, go to S15.

[0059] Step S15: Output an individual <2,50,48,4.5> with a fitness value of 2400 and a detection efficiency of 30.5% as the adaptation scheme.

[0060] The above embodiments are provided merely for the purpose of describing the present invention and are not intended to limit the scope of the invention. All equivalent substitutions and modifications made without departing from the spirit and principles of the invention should be covered within the scope of the invention.

Claims

1. A neutron multiplicity measurement device 3 He counter tube adaptation method, characterized in that Includes the following steps: Step S1: Determine the required size of the measurement cavity φA×B, where A is the diameter of the measurement cavity and B is the height of the measurement cavity, and determine the detection efficiency δ. ε The required value; Step S2, establish 3 The objective function and constraint function of the He counter tube adaptation method min L×N Where L is 3 The length of the He counter tube, N is... 3 The number of He counters, ε is the detection efficiency value calculated by MCNP simulation, d i The value of the counting tube spacing is determined, and j is the number of concentric circles corresponding to the layout method; Step S3: Determine based on the diameter of the measuring cavity. 3 The number of He counters is in the range of [N] l N h ], where N l The lower limit of the number of counting tubes N h The upper limit of the number of counting tubes N i The value representing the number of counter tubes used each time; Step S4: Based on the size of the measuring cavity, first determine... 3 The range of values ​​for the length of the He counter tube is [L l L h ], where L l This is the lower limit of the counting tube length, and L l =0.75B, L h L is the upper limit of the length of the counting tube, and L h =1.25B, L i The value for the length of the counting tube is determined each time; Step S5, determine the set of optional layout methods {p 1,...j }; Step S6, according to 3 The diameter of the counting tube must be determined first. 3 The range of values ​​for the He counter tube spacing is [d l d h ], where d l Let d be the lower limit of the spacing between the counting tubes. l =1,d h Let d be the upper limit of the spacing between the counting tubes. h =1.6A, d i The value of the counter tube spacing is determined for each time; Step S7: Generate an initial population. The size of the initial population is pop_size, the number of iterations is T, and the initial population P(t) = <p j N i ,L i ,d i >, t=0, perform gene compensation for individuals with gene deletions; Step S8: Calculate the fitness function f(x) = min L × N for each individual based on the objective function; Step S9, apply the constraint equation N(1+d) to each individual in the population. i If the condition is greater than or equal to 3.2 × j × A, individuals that do not meet the constraint are deleted and P'(t) is generated. Step S10, set P'(t) = <p j N i ,L i ,d i The input terms are combined and fed into the MCNP model, and the MCNP simulation is used to determine whether the detection efficiency meets the requirements. If ε < δ, then... ε Delete individuals that do not meet the constraints and generate P"(t); Step S11: Sort P"(t) in ascending order according to their fitness values; Step S12: Extract and save the individual with the lowest fitness to the belief space G(t); Step S13: Determine the conditions for calling the operator, and determine whether the random parameter is greater than the preset value. If it is greater than the preset value, call the crossover operator to perform crossover operation; otherwise, call the mutation operator to perform mutation operation, and form the generated new individuals with the old individuals that meet the constraints into a new population, ensuring that the population size pop_size meets the requirements. Step S14: Determine whether t = T is true, i.e., whether the iteration count is satisfied. If not, t = t + 1 and return to step S8. If it is satisfied, proceed to step S15. Step S15: Output the individual with the smallest fitness value as the adaptation scheme.

2. A neutron multiplicity measurement device according to claim 1 3 He counter tube adaptation method, characterized in that Step S14 is to determine whether |G(t)-G(t-1)|≤a%G(t-1) is true, that is, the change in fitness value between generations is less than or equal to the set value a. If it is not true, t=t+1 and return to step S8. If it is true, go to step S15. The set value a satisfies 0.1≤a≤1.

3. A neutron multiplicity measurement device according to claim 1 3 He counter tube adaptation method, characterized in that Step S14 is to determine whether |G(t)-G(t-1)|≤a%G(t-1) or t=T is true, that is, whether the change in fitness value between generations is less than or equal to the set value a or whether the number of iterations is satisfied. If not satisfied, t=t+1 and return to step S8. If satisfied, go to step S15. The set value a satisfies 0.1≤a≤1.

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