Method for correcting and checking theoretical conductivity data of metallic materials

Abstract

This invention discloses a method for correcting and verifying theoretical electrical conductivity data of metallic materials, comprising: obtaining theoretical electrical conductivity data of metallic materials based on a metallic material conductivity model; conducting an underwater electro-explosion experiment on a metallic sample prepared using the metallic material to obtain the experimental expansion trajectory of the metallic sample, and obtaining multiple sets of experimental electrical conductivity data corresponding to different mass density and temperature data of the metallic material based on the experimental expansion trajectory; correcting the theoretical electrical conductivity data with the experimental electrical conductivity data as the optimization target to obtain corrected theoretical electrical conductivity data; and substituting the corrected theoretical electrical conductivity data into a magnetohydrodynamic numerical simulation to compare the experimental and simulated expansion trajectory and resistive voltage, thereby completing the verification of the theoretical electrical conductivity data of metallic materials and providing higher precision data support for various discharge experiments and simulation calculations of metallic materials.

Description

Technical Field

[0001] This invention relates to the field of material property parameter calculation and numerical simulation technology, and in particular to a method for correcting and verifying theoretical electrical conductivity data of metallic materials. Background Technology

[0002] Electrical conductivity is a core parameter characterizing the carrier transport properties of metallic materials, and it has significant application value in fields such as power transmission, electronic device development, and numerical simulation. Traditional conductivity models are based on ideal lattices and free electron approximations, deriving macroscopic conductivity expressions through electron-phonon scattering mechanisms. However, microscopic defects in actual metallic materials, such as grain boundaries, dislocations, impurity phases, and surface oxide layers, significantly alter electron transport paths, leading to a systematic deviation between the model's predicted values ​​(theoretical conductivity data) and experimental measurements (experimental conductivity data). Currently, there is a wealth of research on the calculation of electrical conductivity of metallic materials both domestically and internationally, with various models and calculation methods proposed. Against this backdrop, developing an effective method for correcting and verifying theoretical conductivity data of metallic materials, and addressing the systematic deviation between the theoretical and experimental conductivity data output by conductivity models, is urgently needed in the fields of material property parameter calculation and numerical simulation to improve the accuracy of theoretical conductivity data output by conductivity models. Summary of the Invention

[0003] This invention provides a method for correcting and verifying theoretical conductivity data of metallic materials, which solves the problem of systematic deviation between theoretical conductivity data output by the conductivity model of metallic materials and experimental conductivity data.

[0004] This invention provides a method for correcting theoretical conductivity data of metallic materials, comprising:

[0005] S200. Obtain the theoretical electrical conductivity data of the metal material based on the electrical conductivity model of the metal material;

[0006] S400. An underwater electric explosion experiment is conducted on the metal sample prepared using the metal material to obtain the experimental expansion trajectory of the metal sample, and multiple sets of experimental conductivity data corresponding to different mass density data and temperature data of the metal material are obtained based on the experimental expansion trajectory.

[0007] S600. The theoretical conductivity data is corrected using the experimental conductivity data as the optimization target to obtain the corrected theoretical conductivity data.

[0008] Furthermore, the metal sample is a metal wire;

[0009] Methods for obtaining data on different mass densities include:

[0010] The underwater electro-explosion experiment was conducted on the metal wire to obtain the experimental expansion trajectory of the metal wire. Assuming that the metal wire is uniformly distributed in the radial direction, the volume of the metal wire at different times was calculated based on the experimental expansion trajectory, and multiple sets of mass density data of the metal wire at different times were obtained.

[0011] Furthermore, multiple sets of experimental conductivity data for the metallic material at different times are calculated, including:

[0012] The voltage and load current of the load area during the underwater electric explosion experiment are obtained, and the total inductance of the load area is measured by conducting multiple short-circuit experiments in advance. Based on the voltage, load current and total inductance of the load area, the resistive voltage and load current of the metal wire are calculated, and multiple sets of load resistance values ​​of the metal wire at different times are obtained.

[0013] Based on multiple sets of load resistance data of the metal wire at different times, combined with the expansion radius data of the metal wire and the length of the metal wire set in the experiment, multiple sets of experimental conductivity data of the metal wire at different times are calculated.

[0014] The expansion radius data of the metal wire was obtained by diagnostic equipment during the underwater electro-explosion experiment.

[0015] Furthermore, methods for obtaining temperature data for metallic materials include:

[0016] Fluid simulation of the underwater electric explosion experiment of the metal wire was carried out. It was assumed that the load current flowed uniformly through the metal wire. The resistive voltage of the metal wire and the energy corresponding to the load current obtained from the underwater electric explosion experiment were uniformly injected into the simulation region of the metal wire. The state equation data of the metal material were combined with iterative solution to obtain multiple sets of simulated temperature data of the fluid at different times during the discharge process and simulation results of the expansion trajectory of the metal wire.

[0017] The experimental expansion trajectory of the metal wire is compared with the simulation result of the expansion trajectory of the metal wire to obtain a first error value. If the first error value is less than the first preset error value, then the multiple sets of simulated temperature data of the fluid at different times are determined as multiple sets of temperature data of the metal material at different times, wherein the first preset error value is less than or equal to 10%.

[0018] Furthermore, if the first error value is greater than or equal to the first preset error value, a smaller diameter metal wire is selected for underwater electro-explosion experiments and fluid numerical simulation calculations to satisfy the assumption that the load current flows uniformly through the metal wire.

[0019] Obtaining the experimental expansion trajectory of the metal wire includes:

[0020] The underwater electro-explosion experiment was conducted on the metal wire, and the optical diagnosis of the wire explosion was performed using X-ray flash photography. The experimental expansion trajectory of the metal wire was obtained by analyzing and calculating the X-ray images of the metal wire at different discharge times.

[0021] Further, the step of correcting the theoretical conductivity data using the experimental conductivity data as the optimization target to obtain the corrected theoretical conductivity data includes:

[0022] Using the aforementioned sets of experimental conductivity data as optimization targets, an optimization function is employed, combined with a nonlinear least squares fitting method, to optimize and correct the theoretical conductivity data, minimizing the error between the corrected theoretical conductivity data and the experimental conductivity data.

[0023] Furthermore, the optimization function is a two-dimensional Gaussian function.

[0024] This invention also provides a method for verifying the corrected theoretical conductivity data of metallic materials obtained in any of the above embodiments, comprising:

[0025] The corrected theoretical conductivity data is substituted into the magnetohydrodynamic numerical simulation to perform a one-dimensional magnetohydrodynamic numerical simulation of the electro-explosion of metallic materials in water, and the simulated expansion trajectory of the metallic materials is obtained. The simulated expansion trajectory is compared with the experimental expansion trajectory to obtain a second error value. If the second error value is less than the second preset error value, the accuracy of the theoretical conductivity data of the metallic materials is verified.

[0026] Furthermore, it also includes:

[0027] In the numerical simulation of one-dimensional magnetohydrodynamics of electro-explosion in water using the experimental load current as excitation, the numerical simulation result of resistive voltage is obtained. The numerical simulation result of resistive voltage is compared with the resistive voltage result obtained through experiment to obtain a third error value. If the third error value is less than the third preset error value and the second error value is less than the second preset error value, the accuracy of the theoretical conductivity data of the metallic material is verified.

[0028] As can be seen from the above technical solutions, the present invention has the following advantages:

[0029] On the one hand, this embodiment obtains multiple sets of mass density data, multiple sets of temperature data, and multiple sets of experimental conductivity data of the metal material at different times through an underwater electro-explosion experiment. That is, it obtains multiple sets of experimental conductivity data at different densities and temperatures. Using the multiple sets of experimental conductivity data at different densities and temperatures as optimization targets, the theoretical conductivity data obtained from the conductivity model based on the metal material is corrected. The corrected theoretical conductivity data has a reduced deviation under conditions of high plasma density or low temperature, and can describe the complete physical process of the metal material.

[0030] On the other hand, this embodiment substitutes the corrected theoretical conductivity data into the magnetohydrodynamic numerical simulation to compare the simulated expansion trajectory with the experimental expansion trajectory, thereby verifying the theoretical conductivity data of metallic materials. This solves the problem of systematic deviation between the theoretical conductivity data and experimental conductivity data output by existing metallic material conductivity models, and provides higher-precision data support for various discharge experiments and simulation calculations of metallic materials. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating a method for correcting theoretical electrical conductivity data of metallic materials according to an embodiment of the present invention.

[0033] Figure 2 A flowchart illustrating a method for correcting and verifying theoretical conductivity data of metallic materials, as provided in another embodiment of the present invention;

[0034] Figure 3a To calculate the linear mixing regularity conductivity model of tungsten in this embodiment of the invention, the surface of tungsten conductivity data as a function of mass density and temperature is given;

[0035] Figure 3b This invention describes the evolution of the expansion trajectory of a tungsten wire at different discharge moments, obtained by combining an underwater electric explosion experiment with X-ray flash radiography.

[0036] Figure 3c In this embodiment of the invention, fluid numerical simulation was used to perform numerical calculations on an electro-explosion experiment of a metal wire in water to obtain fluid temperature change curves at different discharge times;

[0037] Figure 3dThe distribution of experimental conductivity and theoretical conductivity data calculated in this embodiment of the invention;

[0038] Figure 3e This is a distribution diagram showing the results of optimizing and correcting theoretical conductivity data using the nonlinear least squares fitting method in an embodiment of the present invention.

[0039] Figure 3f The comparison results of experimental and numerical simulation expansion curves are given in the embodiments of the present invention;

[0040] Figure 3g The comparison results of experimental and numerical simulation resistive voltage waveforms are presented in the embodiments of the present invention. Detailed Implementation

[0041] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0042] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the present application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0043] The inventors discovered that while widely used conductivity models such as Spitzer and Lee-More are well-suited for fully ionized, non-degenerate plasmas, they exhibit significant deviations and limitations under conditions of high plasma density and low temperature. The theoretical conductivity data output by these models cannot fully describe the physical processes of conductivity in metallic materials. For various material conductivity models, a correction process based on experimentally measured conductivity data is needed to address the model's computational deficiencies. Simultaneously, verification methods are required to validate the accuracy of the theoretical conductivity data. To correct the deviations and limitations of the theoretical conductivity data output by these models, experimental data is essential for refining and verifying the theoretical conductivity data and improving physical assumptions. This necessitates an experimental verification system capable of in-situ characterizing the conductivity response of local regions during the dynamic evolution of plasma, capturing real-time variations in conductivity under different conditions. This provides authentic, comprehensive, and accurate experimental evidence for optimizing and correcting the theoretical conductivity data, ensuring that the corrected theoretical conductivity data more closely reflects the actual physical processes of the theoretical conductivity data for metallic materials.

[0044] Please see Figure 1 , Figure 1 A flowchart illustrating a method for correcting theoretical electrical conductivity data of metallic materials, provided as an embodiment of the present invention.

[0045] The widely used Spitzer and Lee-More conductivity models are well-suited for fully ionized, non-degenerate plasmas. However, when plasma density is high and temperature is low, the theoretical conductivity data output by these models exhibits significant deviations, demonstrating limitations and making them unsuitable for describing the complete physical processes of metallic materials. Therefore, for various material conductivity models, it is necessary to correct and validate the theoretical conductivity data output by the models based on experimentally measured conductivity data.

[0046] This invention provides a method for correcting theoretical conductivity data of metallic materials, comprising:

[0047] S200. Obtain theoretical electrical conductivity data of metallic materials based on the electrical conductivity model of metallic materials;

[0048] It should be noted that a conductivity model is a mathematical and physical model describing the electrical conductivity behavior of a material. It consists of three parts: core equations, constitutive relations, and model parameters, and is the source of theoretical conductivity data. Models such as the Spitzer conductivity model and the Lee-More conductivity model calculate theoretical conductivity using a fixed model structure and parameters. Theoretical conductivity data are numerical results obtained through mathematical calculations after substituting initial parameters into the conductivity model; they are the model's output.

[0049] S400. Conduct an underwater electric explosion experiment on a metal sample prepared using metallic materials to obtain the experimental expansion trajectory of the metal sample. Based on the experimental expansion trajectory, obtain multiple sets of mass density data, multiple sets of temperature data, and multiple sets of experimental conductivity data of the metal material at different times.

[0050] It should be noted that the multiple sets of mass density data, multiple sets of temperature data, and multiple sets of experimental conductivity data at different times refer to the fact that at the same moment, these three physical quantities—mass density, temperature, and conductivity—correspond one-to-one, with mass density and temperature as independent variables and conductivity as the dependent variable, as presented in the table below:

[0051] Table 1. Schematic diagram of the interrelation of multiple sets of mass density data, multiple sets of temperature data, and multiple sets of experimental conductivity data at different times.

[0052]

[0053] It should be further explained that the expansion trajectory refers to the spatial path and pattern of the expansion / contraction of a metallic material's shape and dimensions (length, curvature, etc.) as the temperature increases / decreases. During the underwater electro-explosion process, the temperature of the metallic sample changes, and its shape and dimensions change accordingly. This embodiment obtains the experimental expansion trajectory of the metallic sample through an underwater electro-explosion experiment, and obtains multiple sets of mass density data, multiple sets of temperature data, and multiple sets of experimental conductivity data of the metallic material at different times based on the experimental expansion trajectory.

[0054] S600. The theoretical conductivity data is corrected based on the experimental conductivity data as the optimization target to obtain the corrected theoretical conductivity data.

[0055] As can be seen from the above, in the specific implementation of this embodiment, multiple sets of mass density data, temperature data, and experimental conductivity data of the metallic material at different times are obtained through underwater electro-explosion experiments. That is, multiple sets of experimental conductivity data at different densities and temperatures are acquired. These multiple sets of experimental conductivity data at different densities and temperatures are used as optimization targets to correct the theoretical conductivity data obtained from the conductivity model based on the metallic material. The corrected theoretical conductivity data shows reduced deviation under conditions of high plasma density or low temperature, and can describe the complete physical process of the metallic material. This embodiment corrects the theoretical conductivity data of the metallic material based on multiple sets of experimental conductivity data at different densities and temperatures, improving the accuracy of the relevant physical property parameters of the metallic material. This provides higher-precision data support for various discharge experiments and simulation calculations of the metallic material, solves the problem of the experimental verification system needing in-situ characterization capability of local conductivity dynamic response, and ensures that the theoretical conductivity data has sufficient experimental data support.

[0056] In a more specific embodiment, the method for obtaining multiple sets of mass density data at different times includes:

[0057] The metal sample is a metal wire;

[0058] An underwater electro-explosion experiment was conducted on a metal wire to obtain its experimental expansion trajectory. Assuming that the metal wire is uniformly distributed in the radial direction, the volume of the metal wire at different times was calculated based on the experimental expansion trajectory, resulting in multiple sets of mass density data for calculating the metal wire at different times.

[0059] It is understood that in this embodiment, an underwater electro-explosion experiment was conducted on a metal wire to obtain the expansion trajectory of the metal wire. Based on the assumption that the mass density of the metal wire is uniformly distributed in its radial direction during the discharge process, the mass density ρ of the metal wire at different times was calculated.

[0060] It should be noted that this assumes the metal wire expands uniformly in the radial direction during discharge, meaning its mass density should be the same radially at any given moment; the current flows uniformly through the wire during discharge, without a significant skin effect; and in the metal wire electro-explosion experiment in water, it is assumed that the mass density of the metal wire is uniformly distributed radially. The physical basis for this assumption is that the discharge process of the metal wire electro-explosion in water is extremely fast (microseconds or even nanoseconds), and the axial length of the metal wire is much larger than its radial diameter (typically, the length-to-diameter ratio can reach over 1000:1, for example, a diameter of tens of micrometers and a length of several centimeters). This geometric characteristic determines that the radial physical process naturally tends towards uniformity.

[0061] 1) The skin effect of the current is negligible (in the initial stage): In the initial stage of discharge, the penetration depth of the current in the metal wire is much greater than the diameter of the wire. The current is uniformly distributed radially, so the radial Joule heat generation rate is basically uniform, and there will be no drastic local temperature / density differences. The skin effect is the phenomenon that when alternating current passes through a conductor, the current is not uniformly distributed across the cross-section of the conductor, but rather concentrates on the surface layer of the conductor.

[0062] 2) Radial relaxation time is much shorter than axial time: The radial dimension of the metal wire is extremely small. The thermal motion, collision and momentum exchange of particles in the radial relaxation time (the time to reach uniformity) is much shorter than the overall time scale of the explosion. Therefore, the radial density, temperature and pressure can quickly reach a uniform state. Macroscopically, it can be considered that the radial direction is always uniform.

[0063] 3) Uniform constraint effect of water medium: When the metal wire is immersed in water, the radial pressure of the water on the metal wire is circumferentially symmetrical, and will not produce additional compression or stretching in a certain radial direction, thus further maintaining the radial uniformity.

[0064] As can be seen from the above, the "small size" and "fast relaxation" of the radial direction of the metal wire make "radial uniformity" an approximation that closely reflects the actual physical process, rather than a subjective assumption completely detached from the experiment. The calculation of the mass density ρ of the metal wire at different times requires certain assumptions: the metal wire expands uniformly in the radial direction during discharge, meaning its mass density should be the same along the radial direction at the same moment; the current flows uniformly through the metal wire during discharge, without a significant skin effect; mass density equals mass divided by volume, and the volume changes at different times during expansion. Assuming the metal wire is uniformly distributed in the radial direction, multiple sets of mass density data ρ at different times can be determined.

[0065] Therefore, by using a metal wire to conduct an underwater electro-explosion experiment and assuming that the metal wire is uniformly distributed in the radial direction, this embodiment can obtain more accurate mass density data of the metal material at different times.

[0066] In a more specific embodiment, obtaining multiple sets of experimental conductivity data at different densities and temperatures through experiments includes:

[0067] An underwater electro-explosion experiment was conducted on a metal wire made of metallic materials. The experimental expansion trajectory of the metal wire was obtained by combining X-ray flash radiography optical diagnosis. Based on the experimental expansion trajectory, multiple sets of mass density data of the metal wire at different times were calculated.

[0068] During the underwater electric explosion experiment, the expansion radius of the metal wire was obtained by combining the results of the underwater electric explosion experiment with the electrical measurement results of the metal wire in the underwater electric explosion experiment. Multiple sets of conductivity data of the metal wire at different discharge times were calculated.

[0069] Understandably, in this embodiment, an underwater electro-explosion experiment is conducted on a metal wire, and X-ray flash radiography is used for optical diagnosis of the wire explosion. The expansion trajectory of the metal wire is calculated by analyzing X-ray images of the wire at different discharge moments. Based on the assumption that the metal wire is uniformly distributed radially during the discharge process, multiple sets of mass density data ρ of the metal wire at different moments are calculated. The expansion trajectory of the metal wire in underwater electro-explosion in this embodiment must be obtained using X-ray flash radiography because this process has an extremely fast timescale (microseconds), extreme environments (high-pressure water medium, strong shock waves, high-temperature plasma), and the metal wire / plasma is completely encased in opaque water. Conventional optical observation methods cannot penetrate the water medium to capture its internal expansion morphology, while X-ray flash radiography can overcome these limitations, achieving high-precision, transient, and non-invasive measurement of the expansion trajectory. The ultra-short exposure and high spatial resolution characteristics of X-ray flash radiography perfectly match the transient process of metal wire electro-explosion.

[0070] In a more specific embodiment, the length of the metal wire is set to a fixed value of 5 cm, and the diameter of the metal wire can take several different values ​​for underwater electro-explosion experiments and numerical simulations, such as 100 μm, 200 μm, etc.

[0071] In a more specific embodiment, the digital imaging panel used in X-ray flash radiographic diagnostics has a resolution of 20 μm.

[0072] In a more specific embodiment, multiple sets of experimental conductivity data for the metallic material at different times are calculated, including:

[0073] Using the electrical parameter measurement system in the underwater electric explosion experiment of metal wire, the voltage and load current of the load area during the experiment are obtained. Multiple short-circuit experiments are conducted in advance to determine the total inductance of the load area. Based on the voltage, load current and total inductance of the load area, the resistive voltage and load current of the metal wire are calculated, and multiple sets of load resistance data of the metal wire at different times are obtained.

[0074] Based on multiple sets of load resistance data of the metal wire at different times, combined with the expansion radius data of the metal wire and the length of the metal wire set in the experiment, multiple sets of experimental conductivity data of the metal wire at different times were calculated.

[0075] Understandably, in practical implementation, this embodiment utilizes the electrical parameter measurement system of the underwater metal wire electric explosion experiment to obtain the voltage and load current of the load area during the experiment. Multiple short-circuit experiments are conducted beforehand to determine the total inductance of the load area. The resistive voltage and load current of the metal wire are calculated, thereby obtaining the load resistance value at different times. The core formula for calculating conductivity is: (σ is conductivity, L is length, R is resistance, and S is cross-sectional area). Therefore, by combining the load resistance value at different times with the results of the metal wire expansion radius given by optical diagnostics and the metal wire length set in the experiment, multiple sets of experimental conductivity data at different times can be calculated.

[0076] It should be noted that the resistive voltage (voltage drop across the resistance) and load current (current flowing through the metal wire) of the metal wire are derived from the current, voltage and total inductance of the load area using Kirchhoff's Voltage Law (KVL).

[0077] In a more specific embodiment, the method for obtaining multiple sets of temperature data of a metallic material at different times includes:

[0078] Fluid simulation of an underwater electro-explosion experiment of a metal wire was carried out using fluid numerical simulation software. Assuming that the load current flows uniformly through the metal wire, the resistive voltage of the metal wire and the energy corresponding to the load current obtained from the underwater electro-explosion experiment were uniformly injected into the simulation region of the metal wire. The state equation data of the metal material were combined with iterative solution to obtain multiple sets of simulated temperature data of the fluid at different times during the discharge process and simulation results of the expansion trajectory of the metal wire.

[0079] The experimental expansion trajectory of the metal wire is compared with the simulation results of the expansion trajectory of the metal wire to obtain the first error value. If the first error value is less than the first preset error value, the multiple sets of simulated temperature data of the fluid at different times are determined as the multiple sets of temperature data of the metal material at different times. The first preset error value needs to be less than or equal to 10% based on the experimental accuracy and material properties.

[0080] Understandably, the experimental expansion trajectory of the metal wire is compared with the simulation results to obtain a first error value. If this error value is less than the first preset error value, it indicates that under the current experimental conditions, the load current in the metal wire satisfies the assumption of uniform distribution, and the simulation model can effectively reproduce the dynamic behavior of the metal wire. Based on this, the temperature data at different times obtained from the simulation can be used as a reliable estimate of the actual temperature of the metal material.

[0081] This embodiment can only directly obtain multiple sets of mass density data and multiple sets of experimental conductivity data of the metal material at different times through underwater electric explosion experiments, but cannot directly obtain multiple sets of temperature data of the metal material at different times. This embodiment further combines underwater electric explosion experiments with fluid numerical simulation to calculate multiple sets of temperature data of the metal material at different times, and finally obtains multiple sets of experimental conductivity data with mass density and temperature as independent variables. Therefore, this embodiment obtains multiple sets of experimental conductivity data with mass density and temperature as independent variables by combining underwater electric explosion experiments with fluid numerical simulation, thereby correcting the theoretical conductivity data of the metal material and improving the accuracy of the relevant physical property parameters of the metal material, providing higher precision data support for various discharge experiments and simulation calculations of metal materials.

[0082] It should be noted that the underwater electro-explosion experiment of metal samples involves injecting a pulsed high current into a metal sample immersed in water, causing it to undergo a rapid phase transition and generate high-temperature, high-pressure plasma / metal vapor. This pulsed power experiment drives strong shock waves, bubble pulsation, and chemical reactions. The core principle is to utilize the high impedance and incompressibility of water to enhance energy concentration and mechanical effects. The fluid numerical simulation calculation for the underwater electro-explosion experiment of metal samples focuses on the electro-thermal-mechanical-multiphase coupling process that occurs when a large current is passed through a metal sample in water. It uses numerical methods to solve fluid dynamics and related physical equations, reproducing and analyzing key phenomena such as underwater shock wave propagation, bubble evolution, and flow field interactions on a computer. Its core is to couple the electromagnetic energy deposition, metal phase transition, plasma expansion, and fluid dynamic response of the water medium in the electro-explosion, overcoming the limitations of measuring the entire flow field and extreme parameters (such as GPa-level pressure and 10,000 K-level temperature) in the experiment, providing quantitative support for experimental mechanism analysis and parameter optimization.

[0083] In practice, the purpose of this comparison is to verify the reliability of the fluid numerical simulation itself, with the core objective of obtaining accurate temperature data T. The input to the fluid simulation is the experimentally measured energy deposition, and its reliability needs to be verified by independent observation. The expansion trajectory is an observable macroscopic quantity. If the simulation matches the experimental trajectory, it proves that the simulation's characterization of processes such as energy deposition, shock waves, and material expansion is reliable. Therefore, we can accept its calculated key parameter—fluid temperature T—which is difficult to measure directly with high precision. This is a necessary prerequisite for subsequently establishing data correlation and correcting theoretical conductivity data.

[0084] In summary, X-ray flash radiography was used to optically diagnose the expansion process of the metal wire, obtaining its expansion trajectory / curve. Combined with the electrical parameter measurement system and the total inductance of the load region determined by short-circuit parameters, the load current and resistive voltage waveforms flowing through the metal wire were calculated, and the experimental conductivity values ​​at different discharge moments for each experiment were calculated. A one-dimensional fluid numerical simulation of the electric explosion experiment of the metal wire in water was performed to obtain the simulated expansion trajectory of the metal wire. The fluid temperature T at different discharge moments can be obtained, thus allowing the determination of multiple sets of experimental conductivity data with mass density and temperature as independent variables.

[0085] In a more specific embodiment, the expression for the magnetohydrodynamic equations is:

[0086] (1)

[0087] (2)

[0088] (3)

[0089] (4)

[0090] Where ρ is the density of the magnetohydrodynamic fluid, v is the velocity of the fluid, T is the temperature, B is the magnetic field, g is the volume force per unit mass, such as gravity, λ is the thermal conductivity, η is the resistivity, p* is the total pressure, E is the total energy per unit mass, and τ is the viscosity tensor.

[0091] In a more specific embodiment, if the first error value is greater than or equal to the first preset error value, a smaller diameter metal wire is selected for the underwater electro-explosion experiment and fluid numerical simulation calculation, assuming that the load current flows uniformly through the metal wire.

[0092] Understandably, in specific implementation, if the first error value is greater than or equal to the first preset error value, the smaller the radial dimension of the metal wire, the more uniform the radial density, temperature, and pressure can be, satisfying the assumption that the load current flows uniformly through the metal wire, i.e., satisfying the assumption of discharge uniformity. This makes the experimental expansion trajectory of the metal wire more closely match the simulation results of the metal wire expansion trajectory, thereby reducing the first error value to be less than the first preset error value, and obtaining the fluid temperature corresponding to the mass density calculated in the metal wire water electro-explosion experiment during the fluid numerical simulation calculation.

[0093] In a more specific embodiment, the theoretical conductivity data is corrected using experimental conductivity data as the optimization target to obtain corrected theoretical conductivity data, including:

[0094] Using multiple sets of experimental conductivity data as optimization targets, an optimization function is used in conjunction with a nonlinear least squares fitting method to optimize and correct the theoretical conductivity data, so that the error between the corrected theoretical conductivity data and the experimental conductivity data is minimized.

[0095] Understandably, in practice, there will be some deviation between experimental and theoretical conductivity data. Using the experimental conductivity data as the optimization objective, an optimization function is determined, and a nonlinear least squares fitting method is used to optimize and correct the theoretical conductivity data, minimizing the error between the experimental and theoretical conductivity results. It should be noted that the nonlinear least squares fitting method demonstrates significant advantages in optimizing and correcting theoretical conductivity data. Its core value lies in its ability to accurately adapt to the nonlinear correlation between conductivity and influencing factors (such as concentration, temperature, and component ratios), overcoming the limitation of linear fitting being only applicable to simple linear relationships. It can flexibly and accurately optimize parameters for complex theoretical conductivity data. This method aims to minimize the sum of squared residuals between experimental and theoretical data, effectively reducing the interference of random errors on the fitting results and improving the accuracy and stability of parameter estimation. Even if the experimental data contains some noise or dispersion, iterative optimization can yield corrected theoretical conductivity data that highly matches the experimental results. Meanwhile, it can directly fit the nonlinear theoretical equations related to conductivity without the need for complex linearization transformation of theoretical conductivity data, avoiding additional errors introduced during the transformation process, preserving the physical meaning of theoretical conductivity data to the greatest extent, and providing reliable mathematical support for accurate correction of conductivity data and subsequent mechanism analysis and law summarization.

[0096] In a more specific embodiment, the optimization function used in the theoretical conductivity data correction process is an expression of a two-dimensional Gaussian function:

[0097] (5)

[0098] In the formula, the total number of unknown parameters that need to be fitted using the least squares method is A,μ. x ,μ y ,σ x ,σ y The six parameters B control the amplitude, center position, width in the x and y directions, and minimum value of the two-dimensional Gaussian function, respectively.

[0099] Understandably, in practical implementation, using a two-dimensional Gaussian function as the optimization function in the process of correcting theoretical conductivity data has multiple core advantages, such as adaptability, smoothness, and physical correlation, which can significantly improve the accuracy and rationality of the correction of theoretical conductivity data. First, the two-dimensional Gaussian function has continuous and smooth nonlinear characteristics, and its surface is bell-shaped and symmetrically distributed. It can accurately fit the gradual change law of conductivity in two-dimensional space (such as the planar distribution of materials, the regional concentration field of solutions, etc.). It is especially suitable for scenarios where conductivity changes continuously with two variables (such as temperature-concentration, spatial coordinates x and y) and decays / diffused from the center to the surrounding areas, perfectly matching the physical characteristics of non-abrupt and continuous distribution of conductivity in actual systems. Secondly, the physical meaning of the function parameters is clear: the peak value corresponds to the region of maximum conductivity, the mean value represents the center of conductivity distribution, and the variance reflects the diffusion range and rate of change of conductivity in two dimensions. By fitting and optimizing these parameters, the spatial distribution characteristics of conductivity can be directly quantified. The corrected theoretical conductivity data not only has smaller errors but also retains clear physical meaning, facilitating subsequent analysis of the conductivity change mechanism. Furthermore, the two-dimensional Gaussian function has good convergence and noise resistance. Its mathematical form is concise and its differentiability is excellent. In the iterative optimization process of nonlinear least squares fitting, it can quickly converge to the optimal solution. Simultaneously, it can effectively smooth random noise in experimental data, avoiding interference from local outliers on the correction results of theoretical conductivity data. This allows the corrected theoretical conductivity data to not only closely match experimental data but also possess better stability and universality.

[0100] In a more specific embodiment, the optimization objective expression using the nonlinear least squares fitting method is as follows:

[0101] (6)

[0102] In the formula, the experimental resistivity data determined through experiments is y. i For metallic materials, the theoretical conductivity function f(x,β) calculated by the conductivity model minimizes the error between the theoretical conductivity data and the experimental conductivity data.

[0103] This invention also provides a method for verifying corrected electrical conductivity data of metallic materials, comprising:

[0104] The corrected theoretical conductivity data is substituted into the magnetohydrodynamic numerical simulation to perform a one-dimensional magnetohydrodynamic numerical simulation of the electro-explosion of metallic materials in water, and the simulated expansion trajectory of the metallic materials is obtained. The simulated expansion trajectory is compared with the experimental expansion trajectory to obtain a second error value. If the second error value is less than the second preset error value, the accuracy of the theoretical conductivity data of the metallic materials is verified.

[0105] Understandably, in practice, the expansion trajectory directly reflects the accuracy of the material's fluid dynamics behavior and equation of state under high temperature and high pressure; if the simulation results can reproduce the experimental expansion trajectory, it proves that the theoretical conductivity data is accurate.

[0106] It should be noted that the experimental expansion trajectory is the experimental expansion trajectory of the metal sample obtained by performing an underwater electric explosion experiment on the metal sample prepared using metal materials in the above embodiments.

[0107] In a more specific embodiment, the experimental load current is used as the excitation fed into the metal material in the one-dimensional magnetohydrodynamic numerical simulation of electro-explosion in water to obtain the resistive voltage numerical simulation calculation result. The resistive voltage numerical simulation calculation result is compared with the resistive voltage result obtained through experiment to obtain a third error value. If the third error value is less than the third preset error value and the second error value is less than the second preset error value, the accuracy of the theoretical conductivity data of the metal material is verified.

[0108] Understandably, in practical implementation, for extreme transient processes with strong coupling of multiple physics fields, such as the electric explosion of a metal wire, the conformity of a single physical quantity may mask deviations in the model from other physical mechanisms. In this invention, the expansion trajectory and resistive voltage characterize two fundamentally different physical processes: the expansion trajectory directly reflects the fluid dynamics behavior of the material under high temperature and high pressure and the accuracy of the equation of state; while the resistive voltage directly characterizes the transient electromagnetic response characteristics of the material.

[0109] It should be noted that the experimental load current is the load current of the load area obtained during the experiment using the electrical parameter measurement system in the underwater electric explosion experiment of the metal wire in the above embodiment; the resistive voltage result obtained through the experiment is the voltage and load current of the load area obtained during the experiment using the electrical parameter measurement system in the underwater electric explosion experiment of the metal wire in the above embodiment, and the total inductance of the load area is determined by conducting multiple short-circuit experiments in advance, and the resistive voltage of the metal wire is calculated; the one-dimensional magnetohydrodynamic (MHD) numerical simulation of underwater electric explosion of metal materials simplifies the multiple physical processes of underwater electric explosion of metal wire, such as electromagnetic, thermal, phase transition, and hydrodynamic, to a one-dimensional spatial dimension (such as radial one-dimensional in cylindrical coordinates). By solving the magnetohydrodynamic control equations, a numerical method is used to simulate the entire process of the metal wire from energization, phase transition, plasma formation, to driving underwater shock waves and bubble evolution on a computer.

[0110] In a more specific embodiment, both the second preset error value and the third preset error value can be 10%.

[0111] Therefore, this invention innovatively proposes a dual verification method combining the expansion trajectory and resistive voltage. The advantages of this method are:

[0112] 1) Improved the completeness of physical process coverage. The theoretical conductivity data to be verified must not only be accurate in static or quasi-static physical property tests, but also work effectively in dynamic, multi-field coupled real physical environments. The theoretical conductivity data must be verified by both thermodynamic driving processes (expansion) and electromagnetic response processes (pressure drop).

[0113] 2) Verification through two independent physical channels significantly improves the theoretical conductivity data after correction and verification by this process, enhancing the reliability and prediction accuracy of subsequent results when used in complex magnetohydrodynamics or particle simulations involving metal wire electro-explosion, such as Z-pinch, plasma switch, and high-pressure material synthesis.

[0114] In a more specific embodiment, the verification method specifically includes:

[0115] A one-dimensional magnetohydrodynamic (MHD) numerical simulation of an electric explosion experiment involving a metal wire in water was conducted by combining the corrected conductivity data σ(ρ,T) with the equation of state data of the metallic material into a magnetohydrodynamic equation system, using the load current as the excitation feed. The numerical simulation results provide calculated thermodynamic quantities such as density, temperature, and pressure; electromagnetic quantities such as current density and magnetic field; and spatial evolution results such as the simulated expansion trajectory of the metal wire. Comparing the numerical simulation results of the expansion trajectory and resistive voltage with experimentally measured results demonstrates the accuracy of the physical properties used, such as conductivity data, thus completing the verification of the accuracy of the theoretical conductivity data.

[0116] This embodiment also calculated the resistive voltage of the metal wire. The error was less than the preset value after comparison with the experimentally measured resistive voltage waveform, proving that the conductivity data is highly accurate and reliable. If the simulation results can simultaneously reproduce the experimental expansion trajectory and resistive voltage, it proves that: 1) the theoretical conductivity data itself is accurate; 2) the theoretical conductivity data can be well coupled with the magnetohydrodynamic equations and other physical properties, correctly predicting complex physical processes. This is the final verification of the applicability of the theoretical conductivity data.

[0117] In summary, this invention combines underwater metal wire electric explosion experiments with fluid and magnetohydrodynamic numerical simulations to correct and verify the theoretical conductivity data of metallic materials, thereby improving the accuracy of relevant physical property parameters of metallic materials and providing higher precision data support for various discharge experiments and simulation calculations of metallic materials.

[0118] Please see Figure 2 This invention provides a method for correcting and verifying theoretical conductivity data of metallic materials:

[0119] Step 1: Write and solve the code based on the conductivity model of metallic materials to obtain the theoretical conductivity data of metallic materials.

[0120] Step 2, Experimental Stage (Obtaining the mass density ρ and experimental resistivity data σ of the metal wire):

[0121] An electric explosion experiment of a metal wire in water was conducted on a corresponding metal material, and the optical diagnosis of the wire explosion was performed using X-ray flash photography. The experimental expansion trajectory of the metal wire was calculated by analyzing the X-ray images of the metal wire at different discharge times. Based on the assumption that the metal wire is uniformly distributed in its radial direction during the discharge process, the mass density ρ of the metal wire at different times was calculated.

[0122] Using an electrical parameter measurement system for an underwater metal wire electric explosion experiment, the voltage and current in the load region during the experiment were obtained. Multiple short-circuit experiments were conducted beforehand to determine the total inductance of the load region. The resistive voltage waveform and load current of the metal wire were calculated, thus yielding the load resistance values ​​at different times. Combining the results of the metal wire expansion radius provided by optical diagnostics with the wire length set in the experiment, multiple sets of experimental resistivity data σ were calculated. exp ;

[0123] It should be noted that multiple underwater metal wire explosion experiments can be conducted to obtain multiple sets of experimental resistivity data corresponding to multiple experiments.

[0124] Step 3: Simulation and Verification Phase (Obtaining a credible temperature T):

[0125] Fluid simulation (i.e., one-dimensional fluid numerical simulation of an electric explosion experiment with a metal wire in water) was performed using fluid numerical simulation software. Assuming the load current flows uniformly through the metal wire, energy was uniformly injected into the simulated region of the metal wire using the measured resistive voltage and load current. The thermodynamic quantities of the fluid, such as temperature and mass density, during the discharge process were iteratively solved using the metal material's equation of state data, along with the simulated expansion trajectory of the metal wire. The expansion trajectory of the metal wire was compared; if the comparison results showed high accuracy, the simulation results were considered reliable, thus allowing the fluid temperature T corresponding to the experimentally calculated mass density during the simulation process to be obtained.

[0126] Step 4: For the theoretical conductivity data obtained from the conductivity model of metallic materials, multiple sets of experimental conductivity data are used as optimization targets to determine the optimization function in the calculation process. The nonlinear least squares fitting method is used to optimize and correct the theoretical conductivity data so that the error between the conductivity data and the experimental conductivity results is minimized, thereby obtaining the corrected results of the theoretical conductivity data of metallic materials.

[0127] Step 5: Data Association Stage

[0128] A one-dimensional magnetohydrodynamic (MHD) numerical simulation of an electric explosion experiment involving a metal wire in water was conducted by combining the corrected conductivity data σ(ρ,T) with the equation of state data of the metallic material into a magnetohydrodynamic equation system, using the load current as the excitation feed. The numerical simulation results provide calculated thermodynamic quantities such as density, temperature, and pressure; electromagnetic quantities such as current density and magnetic field; and spatial evolution results such as the simulated expansion trajectory of the metal wire. Comparing the numerical simulation results of the expansion trajectory and resistive voltage with experimentally measured results demonstrates the accuracy of the physical properties used, such as conductivity data. If the comparison error between the expansion trajectory and the load resistive voltage is less than a specified value, the conductivity data is considered accurate and reliable; otherwise, it is considered inaccurate and unreliable. This process completes the verification of the accuracy of the theoretical conductivity data.

[0129] In a more specific embodiment, a microsecond-level pulse discharge system consisting of a 20kV high-voltage DC power supply and a 4μF charging capacitor is used to discharge the load area. A Tektronix high-voltage probe and a standard Person coil are used to measure experimental electrical parameters. Hard X-ray (>10keV) flash radiography is used for optical diagnosis of the wire expansion process. One-dimensional fluid numerical simulation calculations are performed using the finite element analysis software Autodyn, and one-dimensional magnetohydrodynamic numerical simulation calculations are performed using the radiation magnetohydrodynamic software FLASH. The overall design scheme is feasible.

[0130] In a more specific embodiment, combined with Figures 3a-3g This paper describes the effect of the method for correcting and verifying the theoretical conductivity data of metallic materials of the present invention on the correction and accuracy verification of the theoretical conductivity data of tungsten metallic materials.

[0131] First, the linear mixed regular conductivity model of tungsten was calculated, such as... Figure 3a As shown, the surface curves of tungsten's electrical conductivity as a function of mass density and temperature are presented, with the temperature range being 10kK-20kK and the density range being 0.01g / cm3-4g / cm3.

[0132] After obtaining the theoretical electrical conductivity data of tungsten, an underwater electro-explosion experiment was conducted using a 100 μm tungsten filament. X-ray flash radiography was then used to optically diagnose the expansion process of the tungsten filament. Figure 3b As shown, the evolution process of the tungsten filament at different discharge times is presented. Based on the expansion trajectory, the expansion curve of the tungsten filament can be obtained, and the mass density at the corresponding time can be calculated. Combining the electrical parameter measurement results, filament length, and filament expansion radius, multiple sets of experimental conductivity values ​​can be calculated.

[0133] Subsequently, a one-dimensional fluid numerical simulation was performed on the electric explosion experiment of a metal wire in water. The simulated expansion trajectory of the metal wire was obtained, and the calculated error compared with the experimentally determined expansion trajectory curve was less than 10%, indicating that the numerical simulation results are reliable. Furthermore, the fluid temperature T at different discharge moments can be obtained, such as... Figure 3c As shown, multiple sets of experimental conductivity data with mass density and temperature as independent variables can be determined.

[0134] The distribution of experimental conductivity and theoretical conductivity data is as follows: Figure 3d As shown, there is a certain deviation between the two. Using the experimental conductivity data as the optimization objective, an optimization function is determined. A nonlinear least squares fitting method is used to optimize and correct the theoretical conductivity data. The correction results are shown below. Figure 3e As shown, the deviation between the experimental conductivity and the theoretical conductivity data has been greatly reduced, and the theoretical conductivity data of tungsten metal has been successfully corrected.

[0135] After obtaining the corrected conductivity data, a one-dimensional magnetohydrodynamic numerical simulation of an underwater electro-explosion of a metal wire was performed using the simultaneous magnetohydrodynamic equations. The simulated expansion trajectory of the metal wire was calculated, and the calculation error was less than 10% after comparing it with the experimentally measured expansion trajectory curve. At the same time, the resistive voltage of the metal wire was calculated, and the error was less than 10% after comparing it with the experimentally measured resistive voltage waveform. Therefore, the conductivity data can be considered to be highly accurate and reliable. Figure 3f The comparison results of the expansion curves from experiments and numerical simulations are presented. Figure 3g The results of comparing the resistive voltage waveforms obtained from experiments and numerical simulations are presented.

[0136] In summary, on the one hand, the method used in the embodiments of the present invention to correct and further verify the theoretical conductivity data of metallic materials adopts a comprehensive approach combining experiments and numerical simulations, and uses high-precision and high-resolution X-ray flash radiography technology, which can accurately capture the expansion and change process of the metal wire at the nanosecond level; different finite element analysis software is used to perform fluid numerical simulation and magnetohydrodynamic numerical simulation of the experimental process, which can provide a more comprehensive and in-depth analysis of the expansion process of the metal wire and reveal the physical phenomena.

[0137] On the other hand, the method for correcting and further verifying the theoretical conductivity data of metallic materials used in the embodiments of the present invention improves the accuracy and reliability of the physical property parameters of metallic materials by correcting the theoretical conductivity data, and effectively improves the accuracy of the numerical simulation results of magnetohydrodynamics in multiphysics coupling scenarios; further verification of the conductivity data confirms the reliability of the conductivity data, and provides favorable support for the correctness of the multiphysics coupling numerical simulation results based on the physical property parameters of the metallic materials.

[0138] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for correcting theoretical electrical conductivity data of metallic materials, characterized in that, include: S200. Obtain the theoretical electrical conductivity data of the metal material based on the electrical conductivity model of the metal material; S400. An underwater electric explosion experiment is conducted on the metal sample prepared using the metal material to obtain the experimental expansion trajectory of the metal sample, and multiple sets of experimental conductivity data corresponding to different mass density data and temperature data of the metal material are obtained based on the experimental expansion trajectory. S600. The theoretical conductivity data is corrected using the experimental conductivity data as the optimization target to obtain the corrected theoretical conductivity data.

2. The method for correcting theoretical conductivity data of metallic materials according to claim 1, characterized in that, The metal sample is a metal wire; Methods for obtaining data on different mass densities include: The underwater electro-explosion experiment was conducted on the metal wire to obtain the experimental expansion trajectory of the metal wire. Assuming that the metal wire is uniformly distributed in the radial direction, the volume of the metal wire at different times was calculated based on the experimental expansion trajectory, and multiple sets of mass density data of the metal wire at different times were obtained.

3. The method for correcting theoretical conductivity data of metallic materials according to claim 2, characterized in that, Calculate multiple sets of experimental conductivity data for the metallic material at different times, including: The voltage and load current of the load area during the underwater electric explosion experiment are obtained, and the total inductance of the load area is measured by conducting multiple short-circuit experiments in advance. Based on the voltage, load current and total inductance of the load area, the resistive voltage and load current of the metal wire are calculated, and multiple sets of load resistance values ​​of the metal wire at different times are obtained. Based on multiple sets of load resistance data of the metal wire at different times, combined with the expansion radius data of the metal wire and the length of the metal wire set in the experiment, multiple sets of experimental conductivity data of the metal wire at different times are calculated. The expansion radius data of the metal wire was obtained by diagnostic equipment during the underwater electro-explosion experiment.

4. The method for correcting theoretical conductivity data of metallic materials according to claim 2, characterized in that, Methods for obtaining temperature data for metallic materials include: Fluid simulation of the underwater electric explosion experiment of the metal wire was carried out. It was assumed that the load current flowed uniformly through the metal wire. The resistive voltage of the metal wire and the energy corresponding to the load current obtained from the underwater electric explosion experiment were uniformly injected into the simulation region of the metal wire. The state equation data of the metal material were combined with iterative solution to obtain multiple sets of simulated temperature data of the fluid at different times during the discharge process and simulation results of the expansion trajectory of the metal wire. The experimental expansion trajectory of the metal wire is compared with the simulation result of the expansion trajectory of the metal wire to obtain a first error value. If the first error value is less than the first preset error value, then the multiple sets of simulated temperature data of the fluid at different times are determined as multiple sets of temperature data of the metal material at different times, wherein the first preset error value is less than or equal to 10%.

5. The method for correcting theoretical conductivity data of metallic materials according to claim 4, characterized in that, If the first error value is greater than or equal to the first preset error value, then a smaller diameter metal wire is selected for underwater electro-explosion experiments and fluid numerical simulation calculations to satisfy the assumption that the load current flows uniformly through the metal wire.

6. The method for correcting theoretical conductivity data of metallic materials according to claim 2, characterized in that, Obtaining the experimental expansion trajectory of the metal wire includes: The underwater electro-explosion experiment was conducted on the metal wire, and the optical diagnosis of the wire explosion was performed using X-ray flash photography. The experimental expansion trajectory of the metal wire was obtained by analyzing and calculating the X-ray images of the metal wire at different discharge times.

7. The method for correcting theoretical conductivity data of metallic materials according to claim 1, characterized in that, The step of correcting the theoretical conductivity data using the experimental conductivity data as the optimization target to obtain the corrected theoretical conductivity data includes: Using the aforementioned sets of experimental conductivity data as optimization targets, an optimization function is employed, combined with a nonlinear least squares fitting method, to optimize and correct the theoretical conductivity data, minimizing the error between the corrected theoretical conductivity data and the experimental conductivity data.

8. The method for correcting theoretical conductivity data of metallic materials according to claim 7, characterized in that, The optimization function is a two-dimensional Gaussian function.

9. A method for verifying the corrected theoretical conductivity data of metallic materials obtained according to any one of claims 1-8, characterized in that, include: The corrected theoretical conductivity data is substituted into the magnetohydrodynamic numerical simulation to perform a one-dimensional magnetohydrodynamic numerical simulation of the electro-explosion of metallic materials in water, and the simulated expansion trajectory of the metallic materials is obtained. The simulated expansion trajectory is compared with the experimental expansion trajectory to obtain a second error value. If the second error value is less than the second preset error value, the accuracy of the theoretical conductivity data of the metallic materials is verified.

10. The verification method according to claim 9, characterized in that, Also includes: In the numerical simulation of one-dimensional magnetohydrodynamics of electro-explosion in water using the experimental load current as excitation, the numerical simulation result of resistive voltage is obtained. The numerical simulation result of resistive voltage is compared with the resistive voltage result obtained through experiment to obtain a third error value. If the third error value is less than the third preset error value and the second error value is less than the second preset error value, the accuracy of the theoretical conductivity data of the metallic material is verified.

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