Multivariable coupling control method and system for testing the explosive impact characteristics of rocks and composite materials

Abstract

This invention discloses a method and system for testing the explosive impact properties of rocks and composite materials using multivariable coupling control. This method relates to the technical field of rock explosive property testing and includes the following steps: dividing the rock material gradient based on a gradient sensitivity function and generating an initial grid; densifying the explosion hole and crack path regions of the initial grid based on multi-physics field response characteristics to obtain a densified grid; constructing a multi-field coupling model based on the densified grid, the RHT model, and the heat conduction equation, and outputting simulation results; and conducting explosion experiments. By comparing the simulation results with the experimental data, the dynamic strength and fracture toughness parameters of the material are inverted. This invention addresses the problem of inaccurate simulation of the properties of rocks and composite materials subjected to explosive impact.

Description

Technical Field

[0001] The present invention relates to the technical field of rock explosion characteristic testing, and more particularly to a method and system for testing rock and composite material explosion impact characteristics using multivariable coupling control. Background Art

[0002] Rocks and composite materials show significant commonalities in explosion impact property tests: both are typical inhomogeneous materials, exhibiting nonlinear mechanical responses and multi-mode failure behaviors under explosion impact loads, and the dynamic processes involve energy transfer and force-heat-damage coupled evolution from microstructure to macroscale.

[0003] At the application level, defense, military, energy development, and infrastructure applications place stringent demands on the safety and reliability of materials under extreme loads, pushing testing beyond single loading conditions to complex environments such as high ground stress, high temperature, and multi-directional impact. Technically, traditional single testing methods struggle to capture cross-scale failure mechanisms and multi-field coupling effects, prompting the evolution of testing systems toward a strategy of "multi-dimensional loading (planar / three-dimensional stress states) - multi-scale characterization (from mineral grains / fiber filaments to structural components) - and multi-technical integration (combining experimental testing with numerical simulation and machine learning)." Simultaneously, innovations in material design concepts are forcing testing technology to push boundaries, requiring precise quantification of interface properties, component sensitivity, and failure thresholds under complex load paths to support the development of high-performance explosion-resistant materials and the safety assessment of major projects.

[0004] For example, the invention patent publication number CN106546481B discloses a method for testing the mechanical properties of rock-like materials. The main steps are: first, prepare a rock-like material test specimen; perform tensile and shear tests on specimens of different sizes to obtain a specimen containing a complete failure section; then divide the specimen into several scanning specimens according to the specifications of the scanning electron microscope; scan the scanning specimens with an electron microscope to obtain microscopic morphologies of the failure section at different magnifications, which serve as identification points for the tensile stress and shear stress of the failure section; then scan the same material specimen under any load condition with an electron microscope; and by matching the identification points on the failure section, obtain the stress distribution of the failure section under the corresponding load conditions. Compared with the prior art, the present invention adds multi-walled carbon nanotubes during the specimen preparation process, eliminating the need for metal film spraying during electron microscope scanning, thus avoiding contamination of the specimen section and making the test more convenient and accurate.

[0005] For example, the invention patent publication number CN117538377B discloses a device and method for testing the dynamic characteristics of synergistic suppression of combustible explosion release by rapidly responding to multiple parameters. The device includes a visualization explosion system, an explosion suppression system, a fire arresting system, an ignition system, an air supply system, a powder spraying system, an explosion venting system, a pressure acquisition system, a temperature acquisition system, a high-speed infrared acquisition system, an image acquisition system, a schlieren acquisition system, an oil bath heating system, a synchronous control system, and a program control and data acquisition system. The invention utilizes rapid-response explosion suppression equipment to study the performance and dynamic characteristics of rapidly responding fire arresting and explosion suppression synergistic suppression of gas / dust explosion release under the influence of multiple parameters. By studying the dynamic characteristics of synergistic suppression of combustible explosion release by rapidly responding to multiple parameters and its explosion suppression performance test device, the invention fills a gap in the field of rapid-response explosion suppression synergistic suppression testing devices and technologies for gas-dust explosions.

[0006] The above disclosed technical solutions have at least the following technical problems:

[0007] In numerical simulations, existing rock explosion characteristic testing methods generally ignore the refinement and encryption of simulation grids, especially the implementation of targeted grid refinement at key locations such as the explosion action area and rock fracture development zones. This makes it difficult for the model to accurately capture the local stress concentration, material damage and other microscopic mechanical behaviors of rocks under explosion loads.

[0008] In addition, thermal effects, as an indispensable influencing factor in the explosion process, have not been fully considered. The high temperature generated by the explosion will significantly change the thermodynamic parameters of the rock, inducing a strong coupling of thermal stress and mechanical response. However, existing methods often ignore the dynamic interaction process between the temperature field and the stress field, resulting in the inability to fully reveal the crack initiation, expansion and energy dissipation mechanism of the rock under the high temperature and high pressure coupling environment.

[0009] The above two deficiencies together cause the simulation results to deviate from the actual explosion effects, making it difficult to accurately reflect the multi-field coupled dynamic response characteristics of rock materials under complex explosion loads.

[0010] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0011] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for testing the explosion impact characteristics of rocks and composite materials with multivariable coupling control. The method solves the problem of inaccurate simulation of the characteristics test of rocks and composite materials under explosion impact by multi-dimensional targeted encryption and refinement of the rock material explosion characteristics test analysis grid, and uses the encrypted grid for simulation of the multi-field coupling model.

[0012] To achieve the above object, the present invention provides the following technical solutions:

[0013] A method for testing the explosive impact characteristics of rocks and composite materials using multivariable coupling control includes the following steps: dividing the rock material gradient based on a gradient sensitivity function and generating an initial grid; encrypting the explosion hole and crack path areas of the initial grid based on multi-physical field response characteristics to obtain an encrypted grid; constructing a multi-field coupling model based on the encrypted grid, a RHT model, and a heat conduction equation, and outputting simulation results; and conducting an explosion experiment to invert the material's dynamic strength and fracture toughness parameters by comparing the simulation results with experimental data.

[0014] In a preferred embodiment, the rock material gradient is divided based on the gradient sensitivity function and the initial grid is generated, specifically: the elastic modulus distribution field of the rock material is obtained, and the grid density control parameter is calculated based on the gradient sensitivity function; and the initial grid is generated according to the grid density control parameter.

[0015] In a preferred embodiment, the explosion hole and crack path areas of the initial grid are encrypted based on the multi-physical field response characteristics to obtain an encrypted grid. Specifically, based on the first grid, the explosion hole area grid is encrypted by analyzing the explosion shock wave propagation characteristics to obtain a second grid. The first grid is based on the initial grid and is obtained by encrypting the heat-affected zone grid through heat conduction characteristics analysis; based on the second grid, the crack path area grid is encrypted by predicting rock material damage to obtain an encrypted grid.

[0016] In a preferred embodiment, a multi-field coupling model is constructed based on the encrypted grid, RHT model and heat conduction equation, and simulation results are output. Specifically, a multi-field coupling model is constructed based on the coupling of temperature field, stress field and damage field, the stress field and damage field are coupled based on the RHT model, and the temperature field and the coupled stress field are coupled through the heat conduction equation; the encrypted grid is input into the multi-field coupling model, and multi-field simulation results are output, wherein the multi-field simulation results include stress field simulation results, temperature field simulation results and damage field simulation results.

[0017] In a preferred embodiment, the explosion experiment is conducted, and the dynamic strength and fracture toughness parameters of the material are inverted by comparing the simulation results with the experimental data. Specifically, rock material samples are selected for explosion experiments to obtain experimental data, and the experimental data include the temperature field, stress field and damage field obtained from the experiment; a group of material dynamic strength and fracture toughness parameters are set as a particle; particles are randomly generated and numerical simulations are performed to calculate the simulated temperature field, stress field and damage field, and compare them with the experimental data; the particles are iteratively solved based on the particle swarm algorithm to obtain the material dynamic strength and fracture toughness parameters when the difference between the multi-field simulation results and the experimental data is minimized.

[0018] In a preferred embodiment, the first grid is specifically obtained by: obtaining the thermal conductivity, specific heat capacity and thermal expansion coefficient of the rock material; according to the thermal conductivity and specific heat capacity, obtaining the range of the heat-affected zone based on the heat conduction equation; according to the thermal expansion coefficient, obtaining the thermal stress field distribution based on the thermoelasticity equation; according to the thermal stress field distribution, based on the adaptive finite element method, encrypting the heat-affected zone of the initial grid to obtain the first grid.

[0019] In a preferred embodiment, the second grid is specifically obtained by: obtaining the distribution field of explosive detonation energy, rock material density and rock material elastic modulus; obtaining the shock wave propagation velocity based on the explosive detonation energy and the rock material elastic modulus based on the Navier-Stokes equation and the wave equation; obtaining the shock wave wavelength based on the shock wave propagation velocity and the highest frequency obtained; and according to the shock wave wavelength, encrypting the explosion hole area of the first grid based on the hierarchical gradient grid model to obtain the second grid.

[0020] In a preferred embodiment, the second grid is based on rock material damage prediction, and the crack path area grid is encrypted to obtain an encrypted grid, specifically: CT scanning data, static fracture toughness and yield strength of the rock material are obtained; based on the Weibull statistical model, the initial damage distribution field of the rock material is generated according to the CT scanning data; according to the fracture mechanics criterion, the static fracture toughness and yield strength are combined to obtain the upper limit of the crack tip grid size; based on the extended finite element method, the crack propagation process is simulated to obtain the crack path area; the crack path area of the second grid is encrypted to obtain the encrypted grid.

[0021] A multivariable coupled-controlled rock and composite material explosion impact characteristic testing system comprises: an initial mesh division module for dividing the rock material gradient based on a gradient sensitivity function and generating an initial mesh; a mesh encryption module for encrypting the explosion hole and crack path areas of the initial mesh based on multi-physical field response characteristics to obtain an encrypted mesh; an explosion characteristic simulation module for constructing a multi-field coupling model based on the encrypted mesh, a RHT model, and a heat conduction equation, and outputting simulation results; and an experimental inversion module for conducting explosion experiments and inverting the material's dynamic strength and fracture toughness parameters by comparing the simulation results with the experimental data.

[0022] The technical effects and advantages of the multivariable coupled controlled rock and composite material explosion impact characteristics testing method and system of the present invention are as follows:

[0023] This method uses multi-dimensional, targeted encryption and refinement of the rock material explosion characteristic test and analysis grid, helping to accurately capture the local stress, heat conduction, and damage evolution characteristics of rocks and composite materials during the explosion process, thereby improving the simulation accuracy of key areas. The encrypted grid is then used in simulations using a multi-field coupling model. Combining the RHT model with the heat conduction equation in this multi-field coupling model helps comprehensively consider the interactions of the temperature-stress-damage multi-physics field, thereby faithfully reproducing the dynamic response of rocks and composite materials under explosive loads. By comparing explosion experiments with simulation results and inverting the material's dynamic strength and fracture toughness parameters using a particle swarm algorithm, material characteristic data more closely aligned with actual working conditions is obtained. This effectively addresses the issue of inaccurate characteristic test simulations of rocks and composite materials under explosive impact, thereby improving the accuracy of material parameters obtained using characteristic test methods and providing reliable theoretical support and technical reference for rock engineering blasting design. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic flow chart of a method for testing the explosion impact characteristics of rocks and composite materials using multivariable coupling control provided in an embodiment of the present invention.

[0025] Figure 2 Schematic diagram of the structure of a rock and composite material explosion impact characteristics testing system with multivariable coupling control provided by an embodiment of the present invention.

[0026] Figure 3 and Figure 4 An encrypted grid graph provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will provide a clear and complete description of 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0028] Example 1, Figure 1 A method for testing the explosion impact characteristics of rocks and composite materials using multivariable coupling control is provided, comprising the following steps:

[0029] S1, divide the rock material gradient based on the gradient sensitivity function and generate the initial grid;

[0030] S2, based on the multi-physics field response characteristics, the explosion hole and crack path areas of the initial grid are encrypted to obtain an encrypted grid;

[0031] S3, based on the refined grid, RHT model and heat conduction equation, builds a multi-field coupling model and outputs simulation results;

[0032] S4, and conduct explosion experiments, and inversely calculate the dynamic strength and fracture toughness parameters of the material by comparing the simulation results with the experimental data.

[0033] This embodiment uses multi-dimensional, targeted encryption and refinement of the rock material explosion characteristic test and analysis grid to accurately capture the local stress, heat conduction, and damage evolution characteristics of rocks and composite materials during the explosion process, thereby improving simulation accuracy in key areas. The encrypted grid is then used in simulations using a multi-field coupling model. The RHT model and the heat conduction equation are integrated into the multi-field coupling model to comprehensively consider the interactions of the temperature, stress, and damage multi-physics fields, thereby faithfully reproducing the dynamic response of rocks and composite materials under explosive loads. By comparing explosion experiments with simulation results and inverting the material dynamic strength and fracture toughness parameters using a particle swarm algorithm, material characteristic data more closely aligned with actual working conditions is obtained. This effectively addresses the issue of inaccurate simulations of rock and composite material characteristic tests under explosive impact, thereby improving the accuracy of material parameters obtained using characteristic testing methods and providing reliable theoretical support and technical reference for rock engineering blasting design.

[0034] S1, divide the rock material gradient based on the gradient sensitivity function and generate the initial grid.

[0035] In this embodiment, the rock material gradient is divided based on the gradient sensitivity function and the initial grid is generated, specifically:

[0036] Obtain the elastic modulus distribution field of rock materials and calculate the grid density control parameters based on the gradient sensitivity function;

[0037] Generate the initial mesh according to the mesh density control parameters.

[0038] In this embodiment, the elastic modulus distribution field of the rock material is obtained by:

[0039] A three-dimensional grayscale image of a rock material sample is obtained by CT scanning, and the three-dimensional grayscale image is converted into a density distribution field based on the empirical relationship between grayscale value and density value;

[0040] The density distribution field is converted into the elastic modulus distribution field based on the density-elastic modulus formula;

[0041] In this embodiment, the density-elastic modulus formula is specifically calculated as follows:

[0042]

[0043] Where, is the elastic modulus, is the local density, and is an empirical constant of rock materials.

[0044] In this embodiment, the grid density control parameter is calculated based on the gradient sensitivity function, and the specific calculation formula is:

[0045]

[0046] Where, is the grid density control parameter, is the Euclidean norm, is the gradient of the elastic modulus.

[0047] In this embodiment, the initial grid is generated according to the grid density control parameter, specifically:

[0048] Construct a uniform grid for rock material property analysis and preset control thresholds;

[0049] The uniform grid in the area where the grid density control parameter is higher than the control threshold is encrypted to obtain the initial grid.

[0050] S2, based on the multi-physics field response characteristics, the explosion hole and crack path areas of the initial grid are encrypted to obtain an encrypted grid.

[0051] In this embodiment, the explosion hole and crack path regions of the initial grid are encrypted based on the multi-physics field response characteristics to obtain an encrypted grid, specifically:

[0052] Based on the initial mesh, the mesh of the heat-affected zone is encrypted by analyzing the heat conduction characteristics to obtain the first mesh;

[0053] Based on the first grid, the grid of the explosion hole area is encrypted by analyzing the propagation characteristics of the explosion shock wave to obtain the second grid;

[0054] Based on the second grid, the grid of the crack path area is encrypted through rock material damage prediction to obtain an encrypted grid.

[0055] In this embodiment, the first grid is obtained by:

[0056] Obtain thermal conductivity, specific heat capacity and thermal expansion coefficient of rock materials;

[0057] According to thermal conductivity and specific heat capacity, the range of heat affected zone is obtained based on heat conduction equation;

[0058] According to the thermal expansion coefficient, the thermal stress field distribution is obtained based on the thermoelastic equation;

[0059] According to the distribution of thermal stress field, the heat affected zone of the initial mesh is encrypted based on the adaptive finite element method to obtain the first mesh.

[0060] In this embodiment, according to the distribution of the thermal stress field, the heat-affected zone of the initial grid is encrypted based on the adaptive finite element method to obtain the first grid, specifically:

[0061] According to the distribution of thermal stress field, the thermal stress field is reconstructed based on the node average method to obtain a smooth thermal stress field;

[0062] based on Error estimation compares the thermal stress field and the smooth thermal stress field to obtain the reconstruction error;

[0063] A reconstruction threshold is preset, and the area where the reconstruction error exceeds the reconstruction threshold is marked as the area to be encrypted. The grid in the area to be encrypted is encrypted based on the h-adaptive encryption method to obtain the first grid.

[0064] In this embodiment, the second grid is obtained by:

[0065] Obtain the explosive detonation energy, rock material density and rock material elastic modulus distribution field;

[0066] According to the explosive detonation energy and the elastic modulus of the rock material, the shock wave propagation velocity is obtained based on the Navier-Stokes equation and the wave equation;

[0067] The shock wave wavelength is obtained based on the shock wave propagation speed and the highest frequency obtained;

[0068] According to the shock wave wavelength, the explosion hole area of the first grid is encrypted based on the hierarchical gradient grid model to obtain the second grid.

[0069] In this embodiment, the shock wave propagation velocity is obtained based on the explosive detonation energy and the elastic modulus of the rock material, based on the Navier-Stokes equation and the wave equation, specifically:

[0070] Based on the detonation energy of explosives and the elastic modulus of rock materials, the expansion process of explosion products is simulated using the Navier-Stokes equation to calculate the initial impact pressure of high-pressure gas on rock.

[0071] The initial shock pressure is substituted into the one-dimensional wave equation of rock material as the boundary condition, and the wave equation is solved using the finite difference method to obtain the shock wave propagation velocity.

[0072] In this embodiment, the specific formula of the layered gradient grid model is:

[0073]

[0074] Where, is the grid size of the i-th layer, is the first layer grid size, is the gradient factor that controls the change of grid size.

[0075] It should be noted that the explosion hole area refers to the area where the rock material is deformed due to the explosion, including the explosion center area and the explosion wave propagation area.

[0076] In this embodiment, based on the second grid, the crack path area grid is encrypted by predicting the damage of the rock material to obtain an encrypted grid, specifically:

[0077] Obtain CT scanning data, static fracture toughness and yield strength of rock materials;

[0078] Based on the Weibull statistical model, the initial damage distribution field of rock materials is generated according to CT scanning data;

[0079] According to the fracture mechanics principle, combined with the static fracture toughness and yield strength, the upper limit of the crack tip mesh size is obtained;

[0080] The crack propagation process is simulated based on the extended finite element method to obtain the crack path area;

[0081] The crack path region of the second grid is encrypted to obtain an encrypted grid.

[0082] In this embodiment, the crack tip mesh size is constrained by the following conditions based on the fracture mechanics principle:

[0083]

[0084] Where, is the crack tip mesh size, is the static fracture toughness, is the yield strength.

[0085] In this embodiment, the crack path region of the second grid is encrypted to obtain an encrypted grid, specifically:

[0086] Dynamically refine the crack path area of the second grid according to the crack tip grid size;

[0087] The crack path evolution is tracked by the level set function to ensure that the mesh matches the crack geometry.

[0088] S3, based on the encrypted grid, RHT model and heat conduction equation, builds a multi-field coupling model and outputs the simulation results.

[0089] In this embodiment, a multi-field coupling model is constructed based on the encrypted grid, RHT model and heat conduction equation, and simulation results are output, specifically:

[0090] Couple stress field and damage field based on RHT model;

[0091] Then, the temperature field and the coupled stress field are coupled through the heat conduction equation to obtain a multi-field coupling model that couples the temperature field, stress field, and damage field.

[0092] The encrypted grid is input into the multi-field coupling model, and the multi-field simulation results are output, wherein the multi-field simulation results include stress field simulation results, temperature field simulation results and damage field simulation results.

[0093] S4, and conduct explosion experiments, and inversely calculate the dynamic strength and fracture toughness parameters of the material by comparing the simulation results with the experimental data.

[0094] In this embodiment, the explosion experiment is carried out, and the dynamic strength and fracture toughness parameters of the material are inverted by comparing the simulation results with the experimental data, specifically:

[0095] Selecting rock material samples to conduct explosion experiments to obtain experimental data, wherein the experimental data includes a temperature field, a stress field, and a damage field obtained from the experiment;

[0096] Set a set of material dynamic strength and fracture toughness parameters as a particle;

[0097] Randomly generate particles and perform numerical simulations to calculate the simulated temperature, stress, and damage fields, and compare them with experimental data;

[0098] Based on the particle swarm algorithm, the particles are iteratively solved to obtain the dynamic strength and fracture toughness parameters of the material when the difference between the multi-field simulation results and the experimental data is minimal.

[0099] It should be noted that the dynamic strength of the material refers to the compressive strength of the rock material at high strain rate, which characterizes the material's ability to resist damage under dynamic loads; the fracture toughness parameter refers to dynamic fracture toughness.

[0100] Table 1 shows the comparison between the simulation results and experimental data of a certain experiment in this embodiment.

[0101] Table 1

[0102]

[0103] The RHT model (Riedel-Hillmeier-Thoma constitutive model) is a core model for describing the mechanical behavior of quasi-brittle materials such as rock and concrete under dynamic loads such as impact and explosion. This model, based on an improved HJC model, accurately characterizes the correlation between the strain type and stress state of the material. It also couples the strain rate effect with the damage accumulation mechanism, effectively simulating the plastic deformation, crack propagation, and failure process of the material under high strain rates. Its core advantage lies in its consideration of anisotropic damage evolution under complex stress paths. It is particularly adept at simulating rock response under extreme conditions such as cyclic loading and blasting loading. For example, it addresses the energy superposition law of double-hole blasting, the optimization of pre-splitting blasting hole spacing, and the crack propagation pattern of cyclic blasting.

[0104] The coefficient of thermal expansion is a physical quantity that characterizes the change in volume or length of an object due to temperature changes. The coefficient of thermal expansion is very critical to the practical application of materials. Considering the coefficient of thermal expansion of materials can help avoid structural deformation and damage caused by temperature changes. For example, in engine components working in high-temperature environments, or building structures that generate thermal stress due to temperature changes, the coefficient of thermal expansion is a factor that needs to be considered.

[0105] Thermoelasticity equations are a set of mathematical equations that describe the coupled interaction between heat and elasticity. They comprehensively consider the relationship between an object's elastic deformation and temperature changes. In thermoelasticity theory, an object's elastic properties are temperature-dependent. Temperature changes cause thermal expansion or contraction, which in turn generates thermal stress. Thermoelasticity equations quantitatively characterize this coupled effect of thermal and mechanical behavior. They are crucial for understanding and predicting the mechanical response of materials in thermal environments.

[0106] The adaptive finite element method (AFM) is a numerical method used to improve computational accuracy and efficiency in finite element analysis. Based on the traditional finite element method, it dynamically adjusts the finite element mesh based on the characteristics of the problem being solved and the error estimate of the computational results. Specifically, the mesh is refined in areas with large errors and appropriately coarsened in areas with small errors. While ensuring computational accuracy, the AFM rationally allocates computing resources, avoiding overcomputation in unnecessary areas. This significantly improves computational efficiency, effectively solving complex engineering and scientific problems, and providing a more reliable analytical tool for dealing with complex situations such as nonlinearity and large deformations.

[0107] The h-adaptive refinement method is a key technique in the adaptive finite element method (FEM). Based on finite element analysis, this method refines or coarsens the finite element mesh based on error estimates of the calculation results. This method refines the mesh in areas with large errors within the calculation region, while appropriately reducing the mesh size in areas with smaller errors to improve computational accuracy. In practical applications, this method is particularly suitable for solving problems with highly localized features and is an effective means of improving the efficiency and accuracy of FEM calculations.

[0108] In the field of rock material explosion analysis, the Navier-Stokes equations are a crucial tool for describing the movement of fluids (such as air) surrounding rock and within its pores during an explosion. These equations account for factors such as fluid viscosity, pressure gradients, and inertial forces. They effectively describe the propagation, reflection, and refraction of shock waves generated at the moment of an explosion within a fluid medium, as well as the complex mechanical behavior of fluid-rock interactions. For example, they describe the influence of fluids on rock fragmentation morphology and crack propagation during rock fragmentation under explosive shock. In rock material explosion analysis, they provide a critical theoretical foundation and analytical tools for studying explosion effects, assessing the stability of rock structures under explosive conditions, and optimizing blasting strategies.

[0109] The wave equation refers to the wave equation for explosive shock waves. It is a mathematical expression that describes the propagation of explosive shock waves in a medium. It comprehensively considers physical properties such as the medium's density, elastic modulus, and wave velocity, and can accurately characterize phenomena such as shock wave propagation, reflection, refraction, and energy dissipation. In the field of explosive engineering, this equation can be used to predict the effects of shock waves on structures. Solving and analyzing the wave equation helps study the dynamic response of materials under shock waves and explore the mechanisms of material damage, deformation, and failure. It is a key tool for understanding and mastering the physical processes of explosive shock waves.

[0110] In material strength distribution analysis, the Weibull statistical model characterizes the probabilistic characteristics of material strength through a two-parameter probability distribution (the shape parameter is the Weibull modulus, and the scale parameter is the characteristic strength). Its probability density function describes the probability of occurrence of different strength values, while the survival function corresponds to the material's reliability (probability of not failing) under a specific stress. This model is suitable for modeling material strength data and can quantify the dispersion and average level of the strength distribution through parameter estimation. This model can then be used to predict the probability of failure of a material under a given stress, assess structural reliability, or optimize material design. It is a core tool for analyzing strength distribution and reliability in materials science.

[0111] The Extended Finite Element Method (XFEM) is a numerical calculation method developed from the traditional FEM. It overcomes the limitations of displacement continuity in the traditional FEM. By introducing special shape functions to describe the singularities at the crack tip and the discontinuities along the crack surface, it eliminates the need for continuous re-meshing as the crack propagates and effectively simulates dynamic processes such as crack initiation and propagation. The XFEM exhibits unique advantages, enabling more accurate prediction of structural failure modes and serving as an important tool for resolving crack-related issues.

[0112] Error estimation (Zienkiewicz-Zhu) is a key error assessment method in finite element analysis. It constructs a more accurate reference solution based on the finite element solution and estimates the error by calculating the difference between the finite element solution and the reference solution. This method effectively assesses the accuracy of finite element calculation results. Through a posteriori error estimation, it provides a basis for determining whether the finite element mesh needs to be refined and for optimizing the mesh. This improves the accuracy of the finite element solution, making the finite element analysis results more accurate and reliable, and providing a more valuable reference for problem solving.

[0113] Example 2, Figure 2 The present invention provides a rock and composite material explosion impact characteristics testing system with multivariable coupling control, comprising:

[0114] Initial meshing module, used to divide rock material gradients based on gradient sensitivity functions and generate initial meshes;

[0115] A mesh encryption module is used to encrypt the explosion hole and crack path areas of the initial mesh based on the multi-physics field response characteristics to obtain an encrypted mesh;

[0116] Explosion characteristics simulation module, used to build a multi-field coupling model based on the refined grid, RHT model and heat conduction equation, and output simulation results;

[0117] The experimental inversion module is used to conduct explosion experiments and invert the dynamic strength and fracture toughness parameters of the material by comparing the simulation results with the experimental data.

[0118] Figure 3 and Figure 4 This is an encrypted grid diagram provided by an embodiment of the present invention. The red dots in the diagram represent the encrypted grid in the explosion hole area, the yellow dots represent the encrypted grid in the heat-affected zone, and the blue dots represent the encrypted grid in the crack path area.

[0119] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0120] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0121] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0122] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0123] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0124] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. 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 testing the explosion impact characteristics of rocks and composite materials using multivariable coupling control, characterized in that: The following steps are involved: The rock material gradient is divided based on the gradient sensitivity function, and an initial grid is generated. Specifically, the elastic modulus distribution field of the rock material is obtained, and a grid density control parameter is calculated based on the gradient sensitivity function; and an initial grid is generated based on the grid density control parameter; The explosion hole and crack path regions of the initial mesh are encrypted based on the multi-physics field response characteristics to obtain an encrypted mesh. Specifically, based on the first mesh, the mesh of the explosion hole region is encrypted by analyzing the explosion shock wave propagation characteristics to obtain a second mesh. The first mesh is based on the initial mesh and the mesh of the heat-affected zone is encrypted by analyzing the heat conduction characteristics. Based on the second grid, the grid of the crack path area is encrypted through the prediction of rock material damage to obtain an encrypted grid; Based on the refined grid, the RHT model and the heat conduction equation, a multi-field coupling model is constructed, and simulation results are output. Specifically, the multi-field coupling model is constructed based on the coupling of the temperature field, the stress field and the damage field, wherein the stress field and the damage field are coupled based on the RHT model, and the temperature field and the coupled stress field are coupled through the heat conduction equation; the refined grid is input into the multi-field coupling model, and multi-field simulation results are output, wherein the multi-field simulation results include stress field simulation results, temperature field simulation results and damage field simulation results; Explosion experiments were also carried out, and the dynamic strength and fracture toughness parameters of the material were inverted by comparing the simulation results with the experimental data.

2. The method for testing the explosion impact characteristics of rocks and composite materials using multivariable coupling control according to claim 1 is characterized in that: The explosion experiment is carried out, and the dynamic strength and fracture toughness parameters of the material are inverted by comparing the simulation results with the experimental data, specifically: Selecting rock material samples to conduct explosion experiments to obtain experimental data, wherein the experimental data includes a temperature field, a stress field, and a damage field obtained from the experiment; Set a set of material dynamic strength and fracture toughness parameters as a particle; Randomly generate particles and perform numerical simulations to calculate the simulated temperature, stress, and damage fields, and compare them with experimental data; Based on the particle swarm algorithm, the particles are iteratively solved to obtain the dynamic strength and fracture toughness parameters of the material when the difference between the multi-field simulation results and the experimental data is minimal.

3. The method for testing the explosion impact characteristics of rocks and composite materials using multivariable coupling control according to claim 2 is characterized in that: The first grid is obtained by: Obtain thermal conductivity, specific heat capacity and thermal expansion coefficient of rock materials; According to thermal conductivity and specific heat capacity, the range of heat affected zone is obtained based on heat conduction equation; According to the thermal expansion coefficient, the thermal stress field distribution is obtained based on the thermoelastic equation; According to the distribution of thermal stress field, the heat affected zone of the initial mesh is encrypted based on the adaptive finite element method to obtain the first mesh.

4. The method for testing the explosion impact characteristics of rocks and composite materials using multivariable coupling control according to claim 3 is characterized in that: The specific method for obtaining the second grid is: Obtain the explosive detonation energy, rock material density and rock material elastic modulus distribution field; According to the explosive detonation energy and the elastic modulus of the rock material, the shock wave propagation velocity is obtained based on the Navier-Stokes equation and the wave equation; The shock wave wavelength is obtained based on the shock wave propagation speed and the highest frequency obtained; According to the shock wave wavelength, the explosion hole area of the first grid is encrypted based on the hierarchical gradient grid model to obtain the second grid.

5. The method for testing the explosion impact characteristics of rocks and composite materials using multivariable coupling control according to claim 4 is characterized in that: Based on the second grid, the crack path area grid is encrypted through rock material damage prediction to obtain an encrypted grid, specifically: Obtain CT scanning data, static fracture toughness and yield strength of rock materials; Based on the Weibull statistical model, the initial damage distribution field of rock materials is generated according to CT scanning data; According to the fracture mechanics principle, combined with the static fracture toughness and yield strength, the upper limit of the crack tip mesh size is obtained; The crack propagation process is simulated based on the extended finite element method to obtain the crack path area; The crack path region of the second grid is encrypted to obtain an encrypted grid.

6. A system for testing the explosive impact characteristics of rocks and composite materials using the multivariable coupled control method according to any one of claims 1 to 5, comprising: Initial meshing module, used to divide rock material gradients based on gradient sensitivity functions and generate initial meshes; A mesh encryption module is used to encrypt the explosion hole and crack path areas of the initial mesh based on the multi-physics field response characteristics to obtain an encrypted mesh; Explosion characteristics simulation module, used to build a multi-field coupling model based on the refined grid, RHT model and heat conduction equation, and output simulation results; The experimental inversion module is used to conduct explosion experiments and invert the dynamic strength and fracture toughness parameters of the material by comparing the simulation results with the experimental data.

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