A method and system for predicting thermal damage of granite based on a pfc-based particle model
A numerical model of granite was constructed using the PFC2D model to simulate the mechanical behavior of rocks under high-temperature conditions. This solved the problem of predicting thermal damage in granite, enabled thermal damage assessment under high-temperature conditions, and provided guidance for deep geological energy development.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHANGZHOU UNIV
- Filing Date
- 2023-10-10
- Publication Date
- 2026-06-23
AI Technical Summary
Existing technologies are insufficient to effectively predict rock damage and fractures caused by thermal expansion and thermal cracking of granite under high-temperature conditions, which affects the construction safety of geothermal or deep energy development.
A numerical model of granite was constructed using the grain-based model (GBM) in PFC2D. Contact models of the grain interior and grain boundaries were set up. Mechanical experiments of the rock under high temperature conditions were simulated by uniform heating. Thermal damage was calculated and a functional relationship model between thermal damage and volumetric thermal strain was established.
This paper presents a simple and easy-to-use method to predict thermal damage in granite through experimental measurements, which can guide the site selection and construction of deep geological energy resources and reduce construction risks.
Smart Images
Figure CN117634134B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of high-temperature wellbore stability analysis technology, and in particular to a particle model based on PFC and a method and system for predicting thermal damage to granite. Background Technology
[0002] Geothermal energy is a green and sustainable geothermal resource with abundant reserves. According to the World Energy Assessment, the global available geothermal energy reaches 600,000 EJ annually. Hot dry rock is a typical geothermal resource, mainly located in granite at depths of 3000m to 10000m, with temperatures reaching 150℃-650℃. On the other hand, with the depletion of shallow Earth resources, the development of deep energy resources is receiving increasing attention. Temperatures in deep or ultra-deep reservoirs can reach 180℃ or higher. During geothermal or deep energy development, rock damage and fractures caused by thermal expansion and thermal fracturing pose significant challenges to construction safety. For example, thermal expansion or thermal fracturing can lead to instability in the rock surrounding the borehole, requiring workers to spend more time dealing with accidents and incurring millions in additional costs. Summary of the Invention
[0003] In view of the problems existing in the above-mentioned assessment of the heterogeneity of rock grain size, the present invention is proposed.
[0004] Therefore, the problem to be solved by this invention is to provide a method for constructing a rock numerical model using the grain-based model (GBM) in PFC2D and calibrating it according to experimental data to obtain the microscopic parameters in the model.
[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:
[0006] In a first aspect, embodiments of the present invention provide a particle model based on PFC and a method for predicting thermal damage in granite. This includes constructing granite models with different grain sizes and mineral compositions, setting contact models within the grains and at grain boundaries, calibrating the microscopic parameters of the granite models, and generating granite samples with different grain sizes and heterogeneity. A confining pressure is applied to the rock boundary wall, and the rock is heated uniformly to simulate the mechanical experiment of the rock under high-temperature conditions, obtaining stress-strain curves and microcrack propagation. Based on the simulation results, the microcrack propagation and mechanical damage behavior of granite with different grain sizes and heterogeneity are revealed. Thermal damage is calculated through changes in the elastic modulus, and the main factors determining the thermal damage of granite with different grain sizes and heterogeneity are analyzed. A functional relationship model between the thermal damage of granite and volumetric thermal strain is established.
[0007] As a preferred embodiment of the PFC-based particle model and granite thermal damage prediction method described in this invention, the numerical model uses two contact models: a parallel bonding model is used within the grains; and a smooth joint model is used to characterize the contact along the grain boundaries, which is assigned to the model by the grain boundary structure established by the fracture import from-geometry command.
[0008] As a preferred embodiment of the PFC-based particle model and granite thermal damage prediction method of the present invention, the generation of granite samples with different grain sizes and heterogeneity includes calculating the heterogeneity index H of the sample using the following formula, which describes the heterogeneity of the numerical sample:
[0009]
[0010]
[0011] Where m and d i and d a These represent the total number of mineral particles, particle size, and average particle size, respectively; A i Let A be the area of the i-th grain; A is the cross-sectional area of the rock.
[0012] As a preferred embodiment of the PFC-based particle model and granite thermal damage prediction method of the present invention, wherein the calculation of thermal damage through elastic modulus change includes the following formula under uniaxial thermal damage conditions:
[0013] D T =1-(E T / E0)
[0014] The formula under confining pressure conditions is as follows:
[0015] σ1=(1-D T E0ε1+μ(σ2+σ3)
[0016] Where E0 is the elastic modulus; E T σ is the elastic modulus after thermal loading; μ is Poisson's ratio; σ1, σ2, and σ3 are the nominal stress components, respectively; DT is thermal damage; and ε1 is axial strain.
[0017] As a preferred embodiment of the PFC-based particle model and granite thermal damage prediction method described in this invention, the process of establishing a functional relationship model between granite thermal damage and volumetric thermal strain is as follows: grain size or heterogeneity index affects thermal damage by changing strain; the relationship between thermal damage and ε is obtained through fitting. v The relationship between them is expressed in the following formula:
[0018]
[0019] Where, ε v For volumetric thermal strain, a, b, and c are model parameters related to peak damage, curvature, and curve shape, respectively.
[0020] When the fit R 2 When the range is 0.9942 to 0.9999, it indicates that the model fits the curve well.
[0021] As a preferred embodiment of the PFC-based particle model and granite thermal damage prediction method described in this invention, the construction of granite models with different grain sizes and mineral compositions includes the following steps: generating an initial disk model with the desired mineral composition and grain size distribution; constructing rigid blocks directly from the disk using Voronoi in the latest PFC 7.0; obtaining the grain geometry after deleting the initially generated disk; establishing the grain boundary structure using the fractureimport from-geometry command based on the grain geometry; filling the model with disks of 0.4mm-0.6mm radius, and grouping disks within the same mineral grain into a group to represent each mineral grain.
[0022] As a preferred embodiment of the PFC-based particle model and granite thermal damage prediction method of the present invention, the granite microstructure parameters include basic physical property parameters of all particle units, parallel bond parameters, and smooth bond parameters.
[0023] Secondly, to further address the problems in assessing the heterogeneity of rock grain size, the present invention provides a PFC-based grain model and a granite thermal damage prediction system, comprising: a model building module for constructing initial granite models with different grain sizes and mineral compositions, and setting contact models within grains and grain boundaries; a sample generation module for generating granite samples with different grain sizes and heterogeneity; an operation module for applying confining pressure at the model boundaries and uniformly heating the model to simulate high-temperature conditions; a mechanical response module for calculating and outputting the mechanical response under loaded stress and heating conditions; a damage assessment module for calculating the change in elastic modulus and assessing the degree of thermal damage of different granite samples; and a model establishment module for establishing a functional relationship model between thermal damage and volumetric thermal strain based on the simulation results.
[0024] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the PFC-based particle model and granite thermal damage prediction method as described in the first aspect of the present invention.
[0025] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the PFC-based particle model and granite thermal damage prediction method as described in the first aspect of the present invention.
[0026] The beneficial effects of this invention are as follows: Based on the PFC discrete element method, a multi-mineral granite particle model is established. This model can effectively simulate the mechanical behavior and microcrack propagation process of rocks under thermo-mechanical coupling conditions. The model establishment method is simpler and easier to operate than previous methods. By explicitly considering particle size and heterogeneity, an empirical formula for predicting the thermal damage of granite is proposed. This formula does not require mechanical experiments; it only requires simple experimental measurements on heat-loaded granite to easily determine the thermal damage of granite in a high-temperature environment. From an engineering perspective, this formula provides guidance for the site selection, design, and construction of deep geological energy resources. Attached Figure Description
[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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. Wherein:
[0028] Figure 1 This is a microstructure diagram of the LdB granite in Example 1.
[0029] Figure 2 This is a numerical model diagram of granite based on grains in Example 1.
[0030] Figure 3 This is the initial disk filling diagram in Example 1.
[0031] Figure 4 A rigid block diagram is generated for Example 1.
[0032] Figure 5 This is a diagram of the polygonal grain structure in Example 1.
[0033] Figure 6 The smaller disk in Example 1 is filled to generate the final model diagram.
[0034] Figure 7 The diagrams show two contact models for the numerical sample in Example 1: (a) smooth-joint model and (b) liner parallel bond model.
[0035] Figure 8This is a damage envelope diagram of the granite in Example 2.
[0036] Figure 9 This is a numerical model with different heterogeneity indices in Example 1. (Different colors represent different minerals; S is the grain size; H is the heterogeneity index) Figure.
[0037] Figure 10 The figure shows the stress-strain curve and microcrack propagation process (uniaxial compression test) in Example 1.
[0038] Figure 11 This is a graph showing the relationship between volumetric strain and thermal damage in different heterogeneous granite samples in Example 2.
[0039] Figure 12 The graph shows the relationship between volumetric strain and thermal damage in granite samples with different grain sizes in Example 2.
[0040] Figure 13 The graph shows the changes of parameters a, b, and c with the heterogeneity index in Example 2.
[0041] Figure 14 The graph shows the variation of parameters a, b, and c with crystal grain size in Example 2. Detailed Implementation
[0042] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0043] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.
[0044] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.
[0045] Example 1
[0046] Reference Figures 1-10 This is the first embodiment of the present invention, which provides a method for predicting thermal damage to granite based on a PFC-based particle model, specifically including the following steps:
[0047] S1: Construct granite models with different grain sizes and mineral compositions, set contact models for the interior of grains and grain boundaries, calibrate the microscopic parameters of the granite models, and generate granite samples with different grain sizes and heterogeneity.
[0048] Specifically, images of the Lac du Bonnet (LdB) granite are as follows: Figure 1 As shown, granite grain structures are mostly polygonal; first, an initial disk model of the rock with the required mineral composition and grain size distribution is generated, such as... Figure 3 As shown; secondly, in the latest PFC 7.0, Voronoi is used to directly construct rigid blocks from the disk in the first step, as follows. Figure 4 As shown; after deleting the initially generated disk, the grain geometry is obtained. Based on the grain geometry, the grain boundary structure is established using the "fracture import from-geometry" command, as shown. Figure 5 As shown; then, disks with smaller radii are filled into the model, and disks within the same grain structure are grouped together to represent each grain as shown. Figure 6 As shown; the generated numerical model is as follows Figure 2 As shown.
[0049] Two contact models are used in the numerical model, such as Figure 7 As shown; a parallel bonding model is used inside the grains to produce deformable and brittle grains; a smooth joint model is used to characterize the contact along the grain boundaries, which is given to the model by the initially established grain boundary structure.
[0050] Based on a comparison of the results of Brazilian splitting, uniaxial, and triaxial compression experiments with simulation results, the micro-parameters of the granite model were calibrated, such as... Figure 8 and Figure 10 As shown, granite samples with different grain sizes and heterogeneity were generated, such as... Figure 9 As shown.
[0051] Preferably, generating granite samples with different grain sizes and heterogeneity includes calculating the heterogeneity index H of the sample using the following formula, which describes the heterogeneity of the numerical sample:
[0052]
[0053]
[0054] Where m and d i and d a These represent the total number of mineral particles, particle size, and average particle size, respectively; A i Let A be the area of the i-th grain; A is the cross-sectional area of the rock.
[0055] S2: Apply confining pressure to the rock boundary wall and heat the rock by uniform heating to simulate the mechanical experiment of the rock under high temperature conditions, and obtain stress-strain curves and microcrack propagation.
[0056] Preferably, confining pressure is first applied to the rock boundary wall, and then temperature is applied to the rock boundary, with an initial temperature of 25°C, increasing by 1°C every 100 steps to achieve uniform heating.
[0057] S3: Based on the simulation results, the microcrack propagation and mechanical damage behavior of granites with different grain sizes and heterogeneity are revealed. The thermal damage is calculated by the change of elastic modulus. The main factors that determine the thermal damage of granites with different grain sizes and heterogeneity are analyzed. A functional relationship model between the thermal damage of granites and volumetric thermal strain is established.
[0058] Preferably, thermal damage is calculated by measuring changes in the elastic modulus, and the relationship between strain and thermal damage is analyzed. The calculation of thermal damage by measuring changes in the elastic modulus includes the following formula under uniaxial thermal damage conditions:
[0059] D T =1-(E T / E0)
[0060] The formula under confining pressure conditions is as follows:
[0061] σ1=(1-D T E0ε1+μ(σ2+σ3)
[0062] Where E0 is the elastic modulus; E T σ is the elastic modulus after thermal loading; μ is Poisson's ratio; σ1, σ2, and σ3 are the nominal stress components, respectively; DT is thermal damage; and ε1 is axial strain.
[0063] Analysis of the above formula reveals that the main factor affecting thermal damage is thermal expansion strain. Grain size or heterogeneity index mainly affects thermal damage by changing strain. The relationship between thermal damage and εv was obtained through fitting. If the fit R2 ranges from 0.9942 to 0.9999, it indicates that the model fits the curve well.
[0064] Preferably, the process of establishing a functional relationship model between thermal damage and volumetric thermal strain in granite is as follows: grain size or heterogeneity index affects thermal damage by changing strain; the relationship between thermal damage and ε is obtained through fitting. v The relationship between them is expressed in the following formula:
[0065]
[0066] Where, ε vLet a, b, and c represent the volumetric thermal strain, and let a, b, and c be the model parameters related to peak damage, curvature, and curve shape, respectively. Parameter b decreases relatively linearly with decreasing grain size or increasing heterogeneity. Parameters a and c do not exhibit obvious variation patterns. For different samples, the fitting parameters for a change relatively little, and the same constant can be assigned. The calculation range for parameter c is large, possibly due to errors in calculating thermal damage using the elastic modulus. Assuming parameter c is also constant for different samples, to improve the accuracy of simulation and calculation, 10 sets of data were analyzed: the average value of parameter a is 1, and the average value at a confining pressure of 60 MPa is 0.4; the values of parameter c are 2 and 3, respectively. Therefore, it can be inferred that a and c are mainly controlled by the confining pressure.
[0067] This embodiment also provides a PFC-based particle model and a granite thermal damage prediction system, including a model building module for constructing initial granite models with different grain sizes and mineral compositions, and setting contact models within grains and grain boundaries; a sample generation module for generating granite samples with different grain sizes and heterogeneity; an operation module for applying confining pressure at the model boundaries and uniformly heating the model to simulate high-temperature conditions; a mechanical response module for calculating and outputting the mechanical response under loaded stress and heating conditions; a damage assessment module for calculating the change in elastic modulus and assessing the degree of thermal damage of different granite samples; and a model establishment module for establishing a functional relationship model between thermal damage and volumetric thermal strain based on the simulation results.
[0068] This embodiment also provides a computer device applicable to the prediction method of PFC-based particle model and granite thermal damage, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the prediction method of PFC-based particle model and granite thermal damage as proposed in the above embodiment.
[0069] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.
[0070] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for predicting granite thermal damage based on a PFC-based particle model as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.
[0071] In summary, this invention establishes a multi-mineral granite particle model based on the PFC discrete element method. This model can effectively simulate the mechanical behavior and microcrack propagation process of rocks under thermo-mechanical coupling conditions. The model establishment method is simpler and easier to operate compared to previous methods. By explicitly considering grain size and heterogeneity, an empirical formula for predicting the thermal damage of granite is proposed. This formula does not require mechanical experiments; it only requires simple experimental measurements on heat-loaded granite to easily determine the thermal damage of granite in a high-temperature environment. From an engineering perspective, this formula provides guidance for the site selection, design, and construction of deep geological energy resources.
[0072] Example 2
[0073] Reference Figures 11-14 This is the second embodiment of the present invention. Based on the first embodiment, in order to verify its beneficial effects, experimental comparison data of the particle model and granite thermal damage prediction method based on PFC of the present invention and the prior art are provided.
[0074] The following are the experimental data from our invention, as shown in the table below:
[0075] Table 1 Experimental Data
[0076]
[0077] It can be seen that the experimental data and the simulation data are basically consistent, indicating that the calculation results of the model are relatively reliable. Experimental results are generally obtained by conducting mechanical experiments after thermal loading and calculating thermal damage through changes in the elastic modulus. This model only needs to obtain the thermal expansion strain through thermal loading experiments to calculate thermal damage, eliminating the need for mechanical experiments.
[0078] like Figure 11 and Figure 12 The graph showing the relationship between thermal damage and volumetric thermal strain reveals the same curve pattern in samples with different grain sizes or heterogeneity indices. Three cross sections can be observed: a gradually increasing initial stage, followed by a linear cross section, and then the damage tends to be constant. As the grain size decreases or the heterogeneity increases, the slope of the linear cross section becomes higher. In these samples, there are more grain boundaries, which are prone to fracture during thermal loading, leading to more thermal damage. It can be inferred that the main factor affecting thermal damage is thermal expansion strain.
[0079] The model parameters a, b, and c vary with grain size as follows: Figure 13 and 14 As shown, it can be observed that, regardless of whether under confining pressure or without confining pressure, parameter b exhibits a relatively linear decrease with decreasing grain size or increasing heterogeneity. Parameters a and c do not show significant changes. For different samples, the fitting parameter of a varies relatively little and can be assigned the same constant. The range of parameter c is relatively large, possibly due to errors in calculating thermal damage using the elastic modulus. Assuming that parameter c is also constant for different samples, and to improve the accuracy of simulation and calculation, ten sets of data are analyzed together. For samples without confining pressure, the average values of parameter a are 1 and 0.4, and the values of parameter c are 2 and 3, respectively. It can be inferred that a and c are mainly controlled by confining pressure, which determines the magnitude of thermovolute strain.
[0080] It should be noted that 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A particle model based on PFC and a method for predicting thermal damage in granite, characterized in that: include: Granite models with different grain sizes and mineral compositions were constructed, contact models of the grain interior and grain boundaries were set, the microscopic parameters of the granite models were calibrated, and granite samples with different grain sizes and heterogeneity were generated. A confining pressure was applied to the rock boundary wall, and the rock was heated by uniform heating to simulate the mechanical experiment of the rock under high temperature conditions, and the stress-strain curve and microcrack propagation were obtained. The simulation results reveal the microcrack propagation and mechanical damage behavior of granites with different grain sizes and heterogeneity. The thermal damage is calculated by the change of elastic modulus. The factors that determine the thermal damage of granites with different grain sizes and heterogeneity are analyzed. A functional relationship model between the thermal damage of granites and volumetric thermal strain is established. The process of establishing the functional relationship model between thermal damage and volumetric thermal strain of granite is as follows: Grain size or heterogeneity index affects thermal damage by altering strain; The thermal damage D was obtained through fitting. T With ε v The relationship between them is expressed in the following formula: ； Where, ε v For volumetric thermal strain, a, b, and c are model parameters related to peak damage, curvature, and curve shape, respectively. When the fit R 2 When the range is 0.9942 to 0.9999, it indicates that the model fits the curve well.
2. The method for predicting thermal damage to granite based on the PFC-based particle model as described in claim 1, characterized in that: The grain-based granite model uses two contact models: a parallel bonding model is used within the grains; and a smooth joint model is used to characterize the contact along the grain boundaries, which is assigned to the model by the grain boundary structure established by the fracture import from-geometry command.
3. The method for predicting thermal damage to granite based on the PFC-based particle model as described in claim 1, characterized in that: The generation of granite samples with different grain sizes and heterogeneity includes, The heterogeneity index H of the sample is calculated using the following formula, which describes the heterogeneity of the numerical sample: ； ； Where m and d i and d a These represent the total number of mineral particles, particle size, and average particle size, respectively; A i Let A be the area of the i-th grain; A is the cross-sectional area of the rock.
4. The method for predicting thermal damage to granite based on the PFC-based particle model as described in claim 3, characterized in that: The calculation of thermal damage through changes in elastic modulus includes, The formula under uniaxial thermal damage conditions is as follows: D T = 1-( E T / E 0)； The formula under confining pressure conditions is as follows: ； Where E0 is the elastic modulus; E T σ1, σ2, and σ3 are the elastic modulus after heat loading; μ is Poisson's ratio; σ1, σ2, and σ3 are the nominal stress components, respectively; D T ε represents thermal damage; ε1 represents axial strain.
5. The method for predicting thermal damage to granite based on a PFC-based particle model as described in any one of claims 1, 2, or 4, characterized in that: The construction of granite models with different grain sizes and mineral compositions includes the following steps: Generate an initial disk model with the desired mineral composition and grain size distribution; In the latest PFC 7.0, rigid blocks are constructed directly from disks using Voronoi. After deleting the initially generated disk, the grain geometry is obtained; Based on the grain geometry, use the `fracture import from-geometry` command to establish the grain boundary structure; Disks with a radius of 0.4mm-0.6mm were filled into the model, and the disks within the same mineral grain were grouped together to represent each mineral grain.
6. The method for predicting thermal damage to granite based on the PFC-based particle model as described in claim 3, characterized in that: The microstructure parameters of the granite include all grain unit physical properties, parallel bond parameters, and smooth bond parameters.
7. A particle model based on PFC and a prediction system for thermal damage in granite, based on the particle model based on PFC and the prediction method for thermal damage in granite according to any one of claims 1 to 6, characterized in that: include: The model building module is used to build initial granite models with different grain sizes and mineral compositions, and to set the contact models of the grain interior and grain boundaries; The sample generation module is used to generate granite samples with different grain sizes and heterogeneity. The operation module is used to apply confining pressure to the model boundary and uniformly heat the model to simulate high-temperature conditions; The mechanical response module is used to calculate and output the mechanical response under applied stress and heating conditions; The damage assessment module is used to calculate the change in elastic modulus and assess the degree of thermal damage to different granite samples. The model building module is used to establish a functional relationship model between thermal damage and volumetric thermal strain based on simulation results.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the PFC-based particle model and granite thermal damage prediction method as described in any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the PFC-based particle model and granite thermal damage prediction method as described in any one of claims 1 to 6.