Hydrogen trap characteristic parameter determination method based on genetic algorithm

Abstract

The invention discloses a hydrogen trap characteristic parameter determination method based on a genetic algorithm, and relates to the technical field of hydrogen trap characteristic calculation. Placing the sample in a hydrogen filling device for hydrogen filling treatment, wherein the hydrogen concentration in the sample is saturated; transferring the sample into a heating thermal desorption device for hydrogen desorption treatment, collecting overflowed hydrogen through a mass spectrometer to obtain an actually measured TDS curve, and analyzing the category number of hydrogen traps in the material; according to the McNabb and Foster theories, in combination with an Oriani hypothesis, a calculated TDS curve is obtained; establishing an error function between the actually measured TDS curve and the calculated TDS curve; and according to the error function, a genetic algorithm is adopted to invert and solve the concentration and binding energy of various hydrogen traps. The method can accurately predict the concentration of various hydrogen traps in the material and the characteristic parameters of binding energy, and provides technical support for hydrogen embrittlement prevention design, safety evaluation and the like of steel for hydrogen energy storage and transportation equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen trap characteristic calculation, in particular to a method for determining hydrogen trap characteristic parameters based on a genetic algorithm. Background Art

[0002] Steel used in hydrogen storage and transportation equipment is exposed to long-term high-pressure / ultra-high-pressure hydrogen environments. Hydrogen may penetrate the material and be captured by various hydrogen traps, leading to decreased fracture toughness and accelerated fatigue crack growth rates. The extent of this material degradation is related to the diffusible hydrogen concentration, which is determined by the hydrogen trap concentration, the number of hydrogen atoms accommodated per hydrogen trap site, and the hydrogen trap binding energy. These characteristic parameters of hydrogen traps are crucial for revealing the damage effects of hydrogen on steel used in hydrogen storage and transportation equipment during service.

[0003] Currently, the temperature-dependent desorption method is commonly used to obtain the hydrogen desorption rate versus temperature curve (TDS curve) of hydrogen-charged materials at different heating rates. The relationship between the peak temperature and heating rate in each TDS curve is then determined, and the hydrogen trap binding energy is then calculated based on the Kissinger theory. However, this method has several limitations: 1) it is only applicable at relatively low heating rates; 2) it only measures the hydrogen trap binding energy, not the concentration of each hydrogen trap; and 3) for hydrogen traps with similar binding energies, the high degree of overlap in the thermal desorption peaks leads to large errors in the decoupling process, resulting in inaccurate calculations of the hydrogen trap binding energy. Therefore, it is urgent to develop a method to determine the characteristic parameters of hydrogen traps in steels used in hydrogen energy storage and transportation equipment, accurately predict the hydrogen transport process within the material, and provide technical support for hydrogen embrittlement prevention design and safety assessment of steels used in hydrogen energy storage and transportation equipment. Summary of the Invention

[0004] In order to overcome the defects in the above-mentioned prior art, the present invention provides a method for determining the characteristic parameters of hydrogen traps based on genetic algorithms, which can accurately predict the characteristic parameters of the concentration and binding energy of various types of hydrogen traps in the material, and provide technical support for the anti-hydrogen embrittlement design and safety assessment of steel used in hydrogen energy storage and transportation equipment.

[0005] To achieve the above object, the present invention adopts the following technical solutions, including: A method for determining characteristic parameters of hydrogen traps based on a genetic algorithm comprises the following steps: S1, cutting samples of steel for hydrogen storage and transportation equipment; S2, placing the sample in a hydrogen charging device and performing hydrogen charging treatment until the hydrogen concentration inside the sample reaches saturation; S3, the sample after hydrogenation treatment is transferred to a temperature-raising thermal desorption device for hydrogen desorption treatment, and the overflowed hydrogen is collected by a mass spectrometer to obtain the measured hydrogen desorption rate With temperature The change curve of TDS is the measured TDS curve, and the number of hydrogen trap types in the material is analyzed; S4, according to McNabb and Foster theory, combined with the Oriani hypothesis, the lattice interstitial site hydrogen occupancy is obtained The partial differential equation of The partial differential equation of the sample was used to obtain the curve of hydrogen flux changing with temperature, and then the hydrogen desorption rate was calculated. With temperature The change curve of is the calculated TDS curve; S5, establish the error function between the measured TDS curve and the calculated TDS curve ; S6, according to the error function That is, the objective function, and the genetic algorithm is used to inversely solve the concentration and binding energy of various hydrogen traps.

[0006] Preferably, in step S1, the thickness L of the sample is ≤ 1 mm, and the radius R of the sample is ≥ 5L.

[0007] Preferably, step S4 is specifically as follows: Hydrogen occupancy of lattice interstitial sites The partial differential equation is: ; in, The first The number of hydrogen-like traps, i.e. the concentration of hydrogen-like traps; For the Binding energy of hydrogen-like traps; is the number of hydrogen trap categories, ; Represents space; is the heating rate of the heating thermal desorption device; is the sample radius; 、 is the material constant; is the number of lattice interstitial sites per unit volume; To establish the Hydrogen occupancy of hydrogen-like traps and The coefficient of correlation; is the diffusion coefficient; Set the boundary conditions as follows: = 0, at the sample boundary position, Initial =0; the initial = ; Surface hydrogen flux of the sample for: ; The two surfaces of the specimen are There is hydrogen flux everywhere, and the hydrogen flux on the two surfaces is the same, hydrogen desorption rate = surface hydrogen flux × 2; at a given heating rate Next, time Convert to temperature Expression , is the initial temperature, and the surface hydrogen flux of the sample is obtained , and then the calculated hydrogen desorption rate is obtained With temperature The change curve of .

[0008] Preferably, in step S5, the error function is specifically: ; in, is the number of data points, The measured TDS curve The value of the data point, To calculate the TDS curve The value of a data point.

[0009] Preferably, step S6 is specifically as follows: S61, set the The range of hydrogen-like trap concentration is [ , ], No. The binding energy of hydrogen-like traps ranges from [ , ], the error limit is ; Set the population size of the genetic algorithm to , the genetic algebra is , the chromosome length is , the crossover probability is , the mutation probability is ; The initial value of each individual in the population is given based on a random number within the value range [ , ], Represents an individual, =1,…, , For the the concentration of hydrogen-like traps; For the Binding energy of hydrogen-like traps; is the kth individual The initial value of the hydrogen-like trap concentration, is the kth individual Initial value of the binding energy of hydrogen-like traps; S62, based on the concentration and binding energy of various hydrogen traps in individual k, obtain the calculated hydrogen desorption rate of individual k With temperature The error between the calculated TDS curve and the measured TDS curve of individual k is calculated. , get the individual fitness; S63, select population The minimum error of all individuals in , judge the minimum error Is it less than the error limit? Or whether the current genetic generation exceeds ; If so, take the individual corresponding to the minimum error as the output to obtain the concentration and binding energy of each type of hydrogen trap; if not, calculate the concentration and binding energy of each type of hydrogen trap in each individual of the next generation based on fitness, crossover probability and mutation probability, and return to step S62.

[0010] Preferably, in step S2, the sample is placed in a high-pressure gas-phase hydrogen charging device for hydrogen charging treatment at a temperature of In an environment, high-purity nitrogen is first used to replace several times, and then high-purity hydrogen is used to replace several times, and then high-purity hydrogen is filled to make the pressure in the high-pressure gas phase hydrogen filling device reach ,place time, so that the hydrogen concentration inside the sample reaches saturation.

[0011] Preferably, in step S3, the sample after hydrogenation is placed in a heating thermal desorption device for hydrogen desorption at a heating rate of Heat up to the maximum temperature , then keep warm During this process, the overflowed hydrogen is collected by a quadrupole mass spectrometer to obtain the measured TDS curve.

[0012] Preferably, in step S3, the number of hydrogen trap categories is determined by measuring the number and peak shape of desorption peaks of the TDS curve and combining it with the microstructure analysis of the material; if the TDS curve contains N desorption peaks, the number of hydrogen trap categories is at least N; if the peak shape of a desorption peak is obviously asymmetric, it means that it is composed of M different types of hydrogen trap desorption peaks superimposed here, and the value of M is determined by microstructure analysis of the material.

[0013] The present invention also provides a computer program product, which includes a computer program / instruction, and when the computer program / instruction is executed by a processor, it implements the method for determining characteristic parameters of hydrogen traps based on a genetic algorithm.

[0014] The present invention also provides a readable storage medium having a computer program stored thereon, and when the computer program is executed, the method for determining characteristic parameters of hydrogen traps based on a genetic algorithm is implemented.

[0015] The advantages of the present invention are: The present invention can accurately predict the concentration and characteristic parameters of binding energy of various hydrogen traps in the material, providing technical support for the anti-hydrogen embrittlement design and safety assessment of steel used in hydrogen energy storage and transportation equipment.

[0016] The present invention can not only obtain the number of hydrogen trap categories and binding energy in the material, but also obtain the concentration of various hydrogen traps, while the traditional temperature-raising thermal desorption method can only obtain the number of hydrogen trap categories and binding energy in the material.

[0017] The present invention can effectively distinguish hydrogen traps with similar binding energies. These hydrogen traps often exhibit significant TDS curve overlap during thermal desorption experiments. Manual decoupling using the Kissinger method can easily lead to significant errors and non-unique decoupling results. However, the present invention directly determines the characteristic parameters of binding energy and concentration for different types of hydrogen traps, achieving a globally optimal solution without the need for manual decoupling and avoiding the problem of large errors.

[0018] The present invention studies the hydrogen desorption law of the material during the heating process from a theoretical perspective. Compared with the experimental research process which is limited by equipment parameters, such as the lower and upper limits of the heating rate, the present invention can break through the limitations and study the influence of factors such as the heating rate on the hydrogen desorption process from a theoretical perspective under a wide parameter range.

[0019] The present invention uses a genetic algorithm to determine the characteristic parameters of the concentration and binding energy of various hydrogen traps. The genetic algorithm starts the search from a set of initial solutions (population) rather than a single solution. Compared with some traditional local search algorithms (such as gradient descent), which are prone to falling into local optimal solutions, the genetic algorithm of the present invention can effectively skip local optimal areas and continue to search for the global optimal solution through the diversity of the population and genetic operations (selection, crossover, and mutation). BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of a method for determining characteristic parameters of hydrogen traps based on a genetic algorithm according to the present invention.

[0021] Figure 2 Comparison between the measured TDS curve and the calculated TDS curve. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] Depend on Figure 1 As shown, the present invention provides a method for determining characteristic parameters of hydrogen traps based on a genetic algorithm, comprising the following steps: S1, using the electric spark cutting method, cut the steel with a radius of , thickness is The thin slice sample is polished to make the surface roughness reach Among them, the thickness of the sample L ≤ 1mm, and the radius of the sample R ≥ 5L.

[0024] S2, place the sample in a high-pressure gas-phase hydrogen charging device, close the high-pressure gas-phase hydrogen charging device, and perform hydrogen charging treatment at a temperature of In the environment, 0.3MPa high-purity nitrogen is used to replace 5 times, and then 0.3MPa high-purity hydrogen is used to replace 5 times, and then high-purity hydrogen is filled to make the pressure in the hydrogen filling device reach ,place time, so that the hydrogen concentration inside the sample reaches saturation.

[0025] S3, after exhausting the hydrogen, open the high-pressure gas phase hydrogen charging device and quickly transfer the sample to the heating thermal desorption device for hydrogen desorption treatment. Set the heating rate to , the maximum temperature is , the holding time is , start heating, starting from room temperature at a heating rate Continue heating to the maximum temperature , then keep warm During this process, the overflowed hydrogen was collected by a quadrupole mass spectrometer to obtain the measured hydrogen desorption rate With temperature The change curve of the measured TDS curve is used to analyze the number of hydrogen trap categories in the material and record the number of hydrogen trap categories as .

[0026] Hydrogen traps in a material are typically found in typical microstructural structures, such as vacancies, dislocations, grain boundaries, phase boundaries, and inclusions. The number of hydrogen trap types is determined by the number and shape of desorption peaks in the TDS curve, combined with material microstructural analysis. If a TDS curve contains N desorption peaks, the number of hydrogen trap types is at least N. If the shape of a desorption peak is significantly asymmetric, it indicates that it is composed of the superposition of desorption peaks from M different types of hydrogen traps. The value of M is determined by microstructural characterization using methods such as XRD, SEM, EBSD, and TEM.

[0027] S4, according to McNabb and Foster theory, combined with the Oriani hypothesis, the lattice interstitial site hydrogen occupancy is obtained The partial differential equation is as follows: ; in, The first The number of hydrogen-like traps, i.e. the concentration of hydrogen-like traps; For the Binding energy of hydrogen-like traps; Represents space; is the heating rate; is the sample radius; 、 is the material constant; is the number of lattice interstitial sites per unit volume; To establish the Hydrogen occupancy of hydrogen-like traps and The coefficient of correlation; is the diffusion coefficient.

[0028] The McNabb and Foster theory is a nonequilibrium hydrogen diffusion equation that accounts for the effects of hydrogen traps on hydrogen diffusion. It can describe the diffusion of hydrogen atoms in interstitial sites (normal sites) in the lattice and the adsorption / desorption behavior at trap sites. The Oriani hypothesis is a thermodynamic equilibrium hypothesis regarding hydrogen between normal and trap sites. It states that the distribution of hydrogen at normal and trap sites satisfies chemical potential equilibrium, i.e., their chemical potentials are equal, thus establishing an equilibrium relationship. Interstitial sites (normal sites) in the lattice are interstitial locations where atoms can diffuse freely. Trap sites are locations where hydrogen atoms are easily trapped (such as dislocations and vacancies).

[0029] Set the boundary conditions as follows: = 0, the initial = At the sample boundary, Department, =0.

[0030] After solving the above equation, the surface hydrogen flux of the sample is obtained: , as shown below: ; Among them, the two surfaces of the thin sample are There is hydrogen flux everywhere, and the hydrogen flux on the two surfaces is the same, hydrogen desorption rate = surface hydrogen flux × 2; The above formula (the hydrogen occupancy rate of the lattice interstitial site is The partial differential equation of Next, time Can be converted to temperature Expression , is the initial temperature, and the surface hydrogen flux of the thin film sample is obtained , and then the calculated hydrogen desorption rate is obtained With temperature The change curve of , that is, calculate the TDS curve.

[0031] S5, establish error function , as shown below: ; in, is the number of data points; The first The value of the data point, To calculate the TDS curve The value of a data point.

[0032] S6, according to the error function That is, the objective function, and the genetic algorithm is used to inversely solve the concentration and binding energy of various hydrogen traps.

[0033] S61, set constraints: The range of hydrogen-like trap concentration is [ , ], No. The binding energy of hydrogen-like traps ranges from [ , ], the error limit is ; Set the population size of the genetic algorithm to , the genetic algebra is , the chromosome length is , the crossover probability is , the mutation probability is ; The initial value of each individual in the population is given based on a random number within the value range [ , ], Represents an individual, =1,…, ; is the kth individual The initial value of the hydrogen-like trap concentration, is the kth individual Initial value of the binding energy of hydrogen-like traps.

[0034] S62, solve the hydrogen occupancy rate of the lattice interstitial sites based on the concentration and binding energy of various hydrogen traps in each individual k The partial differential equation is then used to calculate the hydrogen desorption rate for each individual k With temperature The change curve and calculate the error , get the fitness of each individual k (The smaller the error, the higher the fitness).

[0035] S63, select population The minimum error of all individuals in , judge the minimum error Is it less than the error limit? or current genetic generation Whether it exceeds If yes, then take the individual corresponding to the minimum error as the output, and get the concentration and binding energy of each type of hydrogen trap; if no, calculate the next generation according to fitness, crossover probability and mutation probability. The concentration and binding energy of each type of hydrogen trap in each individual of the generation are calculated, and the process returns to step S62.

[0036] After the genetic algorithm inversion solution is completed, the concentration of various hydrogen traps can be determined and binding energy , .

[0037] Example 1 The method for determining the characteristic parameters of hydrogen traps in this embodiment includes the following steps: S11, a cylindrical specimen with a radius of 5 mm and a length of 1 mm was cut from low-alloy high-strength steel using the electric spark cutting method, and the surface of the specimen was polished to a surface roughness of 0.4 μm.

[0038] S12: Place the sample in a high-pressure gas-phase hydrogen charging device, close the high-pressure gas-phase hydrogen charging device, and perform hydrogen charging treatment. Under a temperature environment of 25°C, replace it with 1MPa high-purity nitrogen for 5 times, then replace it with 1MPa high-purity hydrogen for 5 times, and then fill it with high-purity hydrogen to make the pressure in the hydrogen charging device reach 110MPa. Place it for 480 hours to make the hydrogen concentration inside the sample reach saturation.

[0039] S13, after evacuating the hydrogen, open the high-pressure gas phase hydrogen charging device and quickly transfer the sample to the temperature-raising thermal desorption device for hydrogen desorption treatment. Set the heating rate to 2K / s, the maximum temperature to 600℃, the holding time to 0h, start heating, and collect the overflowed hydrogen through the quadrupole mass spectrometer to obtain the measured hydrogen desorption rate. The curve of change with temperature T, that is, the measured TDS curve, shows that the number of hydrogen trap categories in the material is 2.

[0040] S14, according to McNabb and Foster theory, combined with the Oriani hypothesis, the lattice interstitial site hydrogen occupancy is obtained The partial differential equation is as follows: ; Set the boundary conditions as follows: = 0, the initial = At the sample boundary, 5mm, =0.

[0041] After solving the above equation, the hydrogen flux on the sample surface is obtained: , as shown below: ; The calculated hydrogen desorption rate is then obtained With temperature The change curve of is the calculated TDS curve.

[0042] S15, establish error function , as shown below: ; S16, according to the error function That is, the objective function, and the genetic algorithm is used to inversely solve the concentration and binding energy of various hydrogen traps.

[0043] S161, set the value range of the first type hydrogen trap concentration to [1.61×10 2 mol / m 3 ,2.41×10 2 mol / m 3 ], the value range of the first type of hydrogen trap binding energy is [2×104 J / mol,3×10 4 J / mol], the concentration of the second type of hydrogen trap ranges from [23.6 mol / m 3 ,35.4mol / m 3 ], the value range of the second type of hydrogen trap binding energy is [6×10 4 J / mol,7×10 4 J / mol], with an error limit of 1×10 -11 ; The population size of the genetic algorithm is set to 200, the number of generations is 100, the chromosome length is 25, the crossover probability is 0.7, and the mutation probability is 0.002; The initial value of each individual in the population is given based on a random number within the value range.

[0044] S162, solve the hydrogen occupancy rate of the lattice interstitial sites based on the concentration and binding energy of each type of hydrogen trap in each individual The partial differential equation is then used to calculate the TDS curve of each individual according to the formula for the hydrogen flux on the sample surface. The error between the calculated TDS curve and the measured TDS curve of each individual is calculated according to the error function to obtain the fitness of each individual (the smaller the error, the higher the fitness).

[0045] S163, select the minimum error value of all individuals in the population, and determine whether the minimum error value is less than the error limit 1×10 -11 Or whether the number of generations exceeds 100. If so, the individual corresponding to the minimum error value is taken as the output to obtain the concentration and binding energy of each type of hydrogen trap; if not, the concentration and binding energy of each type of hydrogen trap in each individual of the next generation are calculated based on the fitness, crossover probability, and mutation probability, and the process returns to step S162.

[0046] In this embodiment, after the genetic algorithm inversion solution is completed, the calculated TDS curve of the individual corresponding to the minimum error value is compared with the measured TDS curve. Figure 2 As shown, the concentration of the first type of hydrogen trap is 2.01×10 2 mol / m 3 The binding energy of the first type of hydrogen trap is 30 kJ / mol; the concentration of the second type of hydrogen trap is 29.5 mol / m 3 , the binding energy of the second type of hydrogen trap is 67kJ / mol.

[0047] The above are only preferred embodiments of the present invention and are 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 scope of protection of the present invention.

Claims

1. A method for determining characteristic parameters of hydrogen traps based on genetic algorithm, characterized in that: The steps include: S1, cutting samples of steel for hydrogen storage and transportation equipment; S2, placing the sample in a hydrogen charging device and performing hydrogen charging treatment until the hydrogen concentration inside the sample reaches saturation; S3, the sample after hydrogenation treatment is transferred to a temperature-raising thermal desorption device for hydrogen desorption treatment, and the overflowed hydrogen is collected by a mass spectrometer to obtain the measured hydrogen desorption rate With temperature The change curve of TDS is the measured TDS curve, and the number of hydrogen trap types in the material is analyzed; S4, according to McNabb and Foster theory, combined with the Oriani hypothesis, the lattice interstitial site hydrogen occupancy is obtained The partial differential equation of The partial differential equation of the sample was used to obtain the curve of hydrogen flux changing with temperature, and then the hydrogen desorption rate was calculated. With temperature The change curve of is the calculated TDS curve; S5, establish the error function between the measured TDS curve and the calculated TDS curve ; S6, according to the error function That is, the objective function, and the genetic algorithm is used to inversely solve the concentration and binding energy of various hydrogen traps.

2. The method for determining characteristic parameters of hydrogen traps based on genetic algorithm according to claim 1, characterized in that: In step S1 , the thickness of the sample L is ≤ 1 mm, and the radius of the sample R is ≥ 5L.

3. The method for determining hydrogen trap characteristic parameters based on genetic algorithm according to claim 2, characterized in that: Step S4 is specifically as follows: Hydrogen occupancy of lattice interstitial sites The partial differential equation is: ； in, The first The number of hydrogen-like traps, i.e. the concentration of hydrogen-like traps; For the Binding energy of hydrogen-like traps; is the number of hydrogen trap categories, ; Represents space; is the heating rate of the heating thermal desorption device; is the sample radius; 、 is the material constant; is the number of lattice interstitial sites per unit volume; To establish the Hydrogen occupancy of hydrogen-like traps and The coefficient of correlation; is the diffusion coefficient; Set the boundary conditions as follows: = 0, at the sample boundary position, Initial =0; the initial = ; Surface hydrogen flux of the sample for: ； The two surfaces of the specimen are There is hydrogen flux everywhere, and the hydrogen flux on the two surfaces is the same, hydrogen desorption rate = surface hydrogen flux × 2; at a given heating rate Next, time Convert to temperature Expression , is the initial temperature, and the surface hydrogen flux of the sample is obtained , and then the calculated hydrogen desorption rate is obtained With temperature The change curve of .

4. The method for determining characteristic parameters of hydrogen traps based on genetic algorithm according to claim 1, characterized in that: In step S5, the error function is specifically: ； in, is the number of data points, The measured TDS curve The value of the data point, To calculate the TDS curve The value of a data point.

5. The method for determining hydrogen trap characteristic parameters based on genetic algorithm according to claim 1 or 3, characterized in that: Step S6 is specifically as follows: S61, set the The range of hydrogen-like trap concentration is [ , ], No. The binding energy of hydrogen-like traps ranges from [ , ], the error limit is ; Set the population size of the genetic algorithm to , the genetic algebra is , the chromosome length is , the crossover probability is , the mutation probability is ; The initial value of each individual in the population is given based on a random number within the value range [ , ], Represents an individual, =1,…, , For the the concentration of hydrogen-like traps; For the Binding energy of hydrogen-like traps; is the kth individual The initial value of the hydrogen-like trap concentration, is the kth individual Initial value of the binding energy of hydrogen-like traps; S62, based on the concentration and binding energy of various hydrogen traps in individual k, obtain the calculated hydrogen desorption rate of individual k With temperature The error between the calculated TDS curve and the measured TDS curve of individual k is calculated. , get the individual fitness; S63, select population The minimum error of all individuals in , judge the minimum error Is it less than the error limit? Or whether the current genetic generation exceeds ; If so, take the individual corresponding to the minimum error as the output to obtain the concentration and binding energy of each type of hydrogen trap; if not, calculate the concentration and binding energy of each type of hydrogen trap in each individual of the next generation based on fitness, crossover probability and mutation probability, and return to step S62.

6. The method for determining characteristic parameters of hydrogen traps based on genetic algorithm according to claim 1, characterized in that: In step S2, the sample is placed in a high pressure gas phase hydrogen charging device for hydrogen charging. In an environment, high-purity nitrogen is first used to replace several times, and then high-purity hydrogen is used to replace several times, and then high-purity hydrogen is filled to make the pressure in the high-pressure gas phase hydrogen filling device reach ,place time, so that the hydrogen concentration inside the sample reaches saturation.

7. The method for determining characteristic parameters of hydrogen traps based on genetic algorithms according to claim 1, characterized in that: In step S3, the sample after hydrogenation is placed in a heating thermal desorption device for hydrogen desorption at a heating rate of Heat up to the maximum temperature , then keep warm During this process, the overflowed hydrogen is collected by a quadrupole mass spectrometer to obtain the measured TDS curve.

8. The method for determining characteristic parameters of hydrogen traps based on genetic algorithms according to claim 1, characterized in that: In step S3, the number of hydrogen trap categories is determined by measuring the number and shape of desorption peaks of the TDS curve and combining it with material microstructure analysis. If the TDS curve contains N desorption peaks, the number of hydrogen trap categories is at least N. If the shape of a desorption peak is obviously asymmetric, it means that it is composed of M different types of hydrogen trap desorption peaks superimposed here, and the value of M is determined by microstructure analysis of the material.

9. A computer program product, characterized in that The invention comprises a computer program / instruction, which, when executed by a processor, implements a method for determining characteristic parameters of hydrogen traps based on a genetic algorithm as claimed in any one of claims 1 to 8.

10. A readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the method for determining characteristic parameters of hydrogen traps based on a genetic algorithm according to any one of claims 1 to 8 is implemented.

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