Rock thermal damage evolution simulation method based on stress corrosion model

Abstract

The invention relates to the technical field of rock and soil mechanics, and discloses a rock thermal damage evolution simulation method based on a stress corrosion model, which comprises the following steps: step 1, preparing a rock sample, measuring the average particle size of mineral particles through a microscope, and calculating the average particle diameter by combining the measurement results of a plurality of rock slices; x-ray diffraction (XRD) is used for analyzing and determining mineral components in the rock and relative contents of the mineral components so as to obtain microscopic material parameters of the rock; 2, constructing a discrete element numerical model of the rock, determining the size of particles in discrete element simulation according to the average particle diameter, generating a particle set, and identifying different mineral particles with different colors respectively; the invention provides a temperature-stress corrosion dynamic coupling mechanism, and the thermal damage prediction precision is effectively improved. A temperature variable T is embedded into a particle flow stress corrosion model for the first time by establishing a temperature-dependent damage rate equation.

Description

Technical Field

[0001] This invention relates to the field of geotechnical mechanics, and in particular to a method for simulating the thermal damage evolution of rocks based on a stress corrosion model. Background Technology

[0002] With the needs of China's economic and social development, rock, as an important material in engineering structures, is increasingly being mined across various fields, including civil engineering, water conservancy and hydropower, transportation and railways, oil mining, and even military and civil defense projects. Microwave-assisted rock breaking technology has therefore been widely applied in deep mineral resource mining, geothermal drilling, and efficient tunnel excavation. Compared to traditional mechanical rock breaking, microwave-assisted rock breaking significantly reduces mechanical energy consumption during rock fracturing by inducing thermal fracturing through heating mineral grain boundaries.

[0003] In practical engineering, the time-dependent deformation and failure behavior of rocks is a fundamental law determining the long-term stability of rock structures. Some rock structures in engineering projects, such as underground rock tunnels, steep rock slopes, and geological carbon dioxide storage structures, require long-term stability. Although theoretical and experimental research on rock time-dependent deformation has been extensively conducted over the past half-century, research on microscopic simulation methods for real-time thermal damage effects in microwave-assisted rock breaking technology is limited, and the microscopic differences in time-dependent deformation and failure remain unclear.

[0004] Traditional discrete element models have the following drawbacks: 1. They ignore the regulatory effect of temperature on the micro-stress corrosion rate, leading to distorted predictions of thermal damage evolution; 2. They lack a quantitative evaluation method for the residual strength of rocks based on cooling paths (such as nonlinear cooling); 3. Traditional time step strategies (such as PSC adaptive step) are inefficient when simulating long-term thermal damage.

[0005] Therefore, it is essential to establish a method for simulating the thermal damage evolution of rocks. To address the aforementioned issues, we propose a method for simulating the thermal damage evolution of rocks based on a stress corrosion model. Summary of the Invention

[0006] To address the technical problems mentioned in the background section, this invention provides a method for simulating the thermal damage evolution of rocks based on a stress corrosion model, comprising the following steps: Step 1: Prepare rock samples; measure the average particle size of mineral particles using a microscope, and calculate the average particle diameter by combining the measurement results of multiple thin rock sections; use X-ray diffraction (XRD) analysis to determine the mineral components and their relative contents in the rock in order to obtain the microscopic material parameters of the rock. Step 2: Construct a discrete element numerical model of the rock; determine the particle size in the discrete element simulation based on the average particle diameter mentioned above and generate a particle set, with different mineral particles identified by different colors; use particle flow software (PFC) to discretize the rock into a spherical particle system, and realize the cementation connection between particles through a parallel bonded contact model; Step 3: Establish a temperature-dependent stress corrosion model; Introduce the ambient temperature variable into the parallel bond stress corrosion (PSC) model in PFC, construct a functional relationship between the stress corrosion damage evolution rate and temperature, and realize real-time simulation of thermo-mechanical coupled damage. Step 4: Simulate the thermal damage evolution process of rock; set the temperature degradation function and temperature-time loading function for the material parameters of the model; with a fixed time step. The thermal effects calculation is performed cyclically. In each time step, the micro tensile stress of all contact points is calculated sequentially, and the damage amount of the bond diameter is updated according to the temperature-dependent stress corrosion rate formula to generate microcracks. Damage update is performed only for contacts that meet the bond evolution activation condition, while contacts that do not meet the condition remain unchanged in the time step. Step 5: Mechanical response analysis; After completing the thermal damage loading, apply uniaxial compressive load to the obtained thermally damaged rock model to perform numerical simulation, and extract the stress-strain curve and failure mode of the rock. Step 6: Cooling path optimization; After thermal damage evolution and uniaxial loading, compare and analyze the peak stress of rock samples under different cooling paths (linear uniform cooling, fast-then-slow convex function cooling, and slow-then-fast concave function cooling) to determine the optimal cooling strategy.

[0007] In this scheme, step 1 involves measuring the diameter of mineral particles in rock slices using a microscope and a ruler, and taking the average value of multiple measurements as the average particle size; XRD analysis of the diffraction patterns of the rock slice samples is used, and the mineral components and their relative contents are determined by comparing them with a standard database.

[0008] Furthermore, in step 2, a non-circular, fragile grain is constructed by bonding a large number of deformable and breakable polygonal particle clusters along adjacent boundaries to replace the traditional circular particle model; a parallel bonding contact model is used inside the particle cluster to simulate the cementation inside the grain; a smooth joint contact model is used at the grain boundary, and the contact points on the grain boundary are defined as smooth joint contact points to reflect the relatively weak bonding characteristics at the grain boundary.

[0009] Furthermore, in step 3, a stress corrosion activation condition is set in the parallel bonded contact when the maximum tensile stress at the contact point is reached. satisfy Time-triggered stress corrosion (TTC) The micro-activation stress threshold, (For parallel bond tensile strength); in smooth joint contact, when the stress level at the contact point is greater than or equal to m, the following condition applies: Time-triggered adhesion evolution ( (This is the ratio of micro-activated stress level), and a functional relationship between the stress corrosion damage evolution rate and temperature is established.

[0010] Furthermore, step 4 employs a fixed time step. The system performs thermal damage evolution calculations in seconds, replacing the adaptive time step strategy of the traditional PSC model, thereby improving the computational efficiency of simulating long-term thermal damage processes.

[0011] Furthermore, in step 4, a degradation function of the rock's elastic modulus with temperature is established. In the formula The initial Young's modulus, The degradation factor is a temperature-dependent factor, and the mechanical parameters of particle contact in the discrete element model are dynamically adjusted based on this degradation function.

[0012] Furthermore, in step 5, the micro-parameters of the particles are calibrated through numerical experiments and comparative experiments. Numerical simulations of uniaxial tension, uniaxial compression, triaxial compression, and fatigue creep are performed at room temperature. The stiffness and strength of the particle contact parameters are adjusted so that the simulated macroscopic stress-strain curve of the rock basically matches the corresponding room temperature experimental curve. The micro-parameters of the discrete element model are determined through the above parameter calibration method.

[0013] Furthermore, in step 6, the cooling path is set to three forms: convex function (rapid cooling followed by gradual cooling), linear function (constant cooling rate), and concave function (slow cooling followed by accelerated cooling). Under the condition of the same total cooling time, the peak compressive strength of the rock under different paths is calculated, and it is verified that the peak stress of the rock is the highest under the convex function path.

[0014] Furthermore, a temperature-dependent material constant is introduced into the temperature-dependent stress corrosion model. Furthermore, the constant was set to approach zero under high-temperature conditions to simulate a purely thermally induced stress corrosion failure process without mechanical stress.

[0015] This invention also proposes a rock thermal damage evolution simulation system based on a stress corrosion model, comprising:

[0016] The data acquisition module is used to perform the operation of step 1 of claim 1, and acquire the average particle diameter of the rock and the relative content of each mineral component; the model construction module is used to perform the operation of step 2 of claim 1, and construct a discrete element numerical model of the rock; the model definition module is used to perform the operation of step 3 of claim 1, and establish a temperature-dependent stress corrosion model (HSC model); the damage simulation module is used to perform the operation of step 4 of claim 1, and simulate the thermal damage evolution process of the rock; the mechanical analysis module is used to perform the operation of step 5 of claim 1, and perform mechanical response analysis on the model after thermal damage; the cooling optimization module is used to perform the operation of step 6 of claim 1, and compare and analyze different cooling paths to determine the optimal cooling strategy.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0018] This invention achieves dynamic coupling between temperature and stress corrosion, significantly improving the accuracy of thermal damage prediction. By establishing a temperature-dependent damage rate equation, it embeds the temperature variable T into the granular flow stress corrosion model for the first time, effectively reflecting the influence of temperature on damage evolution. This makes the simulation process closer to the actual physical mechanism and significantly improves the accuracy of thermal damage prediction.

[0019] This invention combines the elastic modulus degradation function to achieve coupled simulation of the entire process of thermal expansion and microcrack propagation. Verification results show that, under different temperature conditions ranging from 200℃ to 600℃, the uniaxial compressive stress-strain results obtained from the thermal damage simulation experiment are consistent with the experimental data trends, and the key mechanical parameters show good agreement, proving the effectiveness and reliability of this model over a wide temperature range.

[0020] This invention reveals the regulation law of cooling path on the residual strength of rock, providing a theoretical basis for the optimization of engineering cooling processes. The study found that non-uniform cooling paths have a significant impact on the residual strength of rock. Specifically, a convex function path, employing rapid cooling followed by slow cooling, helps suppress the further propagation and proliferation of microcracks, thus resulting in a relatively higher peak residual strength in the rock; while a concave function path, employing slow cooling followed by rapid cooling, may lead to varying degrees of reduction in rock strength due to thermal stress concentration.

[0021] This invention develops a highly efficient fixed-time-step algorithm, which significantly improves the computational efficiency of long-term thermal damage simulation. Addressing the weak stress-driven nature of thermal damage, the adaptive time-step strategy of the traditional PSC model is replaced with a fixed step size (e.g., 30 seconds), effectively reducing the computational overhead caused by adaptive time-step adjustments and thus significantly improving the computational efficiency of simulating long-term thermal damage.

[0022] This invention enables visualization of damage evolution at the grain scale, providing microscopic mechanism support for engineering protection design. Based on the spatial distribution of microcracks statistically analyzed using discrete crack networks (DFN), it is possible to accurately locate thermally damaged sensitive areas (such as mineral grain boundaries). By recording the location, orientation, and number of each microcrack unit, dynamic monitoring and visualization of the entire process of crack initiation, propagation, and penetration during rock thermal damage are achieved, providing a powerful tool for understanding damage mechanisms and guiding engineering protection design. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the particle-based model provided by the present invention.

[0024] Figure 2 This is a schematic diagram of the parallel bond stress corrosion (PSC) model of the present invention.

[0025] Figure 3 This is a schematic diagram of two particles fixed together by smooth joints in the presence of particles according to the present invention.

[0026] Figure 4 A flowchart simulating the bonding evolution process provided by the present invention.

[0027] Figure 5 The flowchart of the thermal stress corrosion model (HSC) for real-time thermal damage effect provided by the present invention.

[0028] Figure 6 Numerical calibration and gradation curve of the sample.

[0029] Figure 7 This is a comparison graph of experimental stress-strain and simulated stress-strain at room temperature.

[0030] Figure 8 This is a schematic diagram comparing failure modes under different temperature conditions.

[0031] Figure 9 This is a schematic diagram showing the changes in peak stress and elastic modulus with heating rate.

[0032] Figure 10 This is a schematic diagram of stress-strain curves under different temperatures and conditions.

[0033] Figure 11 This is a schematic diagram of different cooling paths at 200℃. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0035] Example 1:

[0036] In this embodiment, the method for simulating the thermal damage evolution of rocks based on the stress corrosion model is described in detail with examples.

[0037] 1. Model Establishment

[0038] (A1) After slicing, the average grain size and mineral grain content of the actual rock were observed using a microscope. X-ray diffraction (XRD) was performed on the rock slices using X-ray diffraction equipment to analyze their diffraction patterns. The measured patterns were compared with those in the database to further determine the relative content of each mineral grain.

[0039] (A2) The particle size of the PFC discrete unit is determined based on the average particle size, generating a particle assembly set. Different mineral particles can be represented by different colors. Fragile, non-circular grains are created by bonding numerous deformable and breakable polygonal particle clusters along their adjacent edges, replacing the simulation of circular particles in traditional PFC. Internally, a parallel bonded contact model is used, where PFC particles are bonded into particle clusters. A smooth joint contact model is used for grain boundaries, defining the contact points on the grain boundaries as smooth joint contact points.

[0040] (A3) Identify particle boundaries and internal particles, and assign parameter values ​​to the particle contact between the discrete element boundaries and the internal particles. A constant that varies with temperature and chemical environment is used as the temperature function. To simulate thermal damage under high temperature conditions, we define this constant to approach infinitesimal to simulate the real stress corrosion process without stress.

[0041]

[0042] in ω(T) represents a material constant that changes with temperature, and ω(T) represents a function related to temperature.

[0043] (A4) The performance degradation method was used to study the Young's modulus of rocks at different temperatures.

[0044]

[0045] Where E represents the deformation modulus at the target temperature, and p(T) represents the degradation factor, which varies with the target temperature. This represents the original characteristics of the deformation modulus.

[0046] (A5) The original PSC program establishes a separate cumulative time and adaptively estimates the time step by calculation.

[0047]

[0048] Among them, t f It is the estimated time for the next failure contact, n. c It is the number of cycles until the connection breaks.

[0049] 2. Numerical Implementation

[0050] (B1) First, the numerical sample is run to achieve a static equilibrium state, and the degree of equilibrium is controlled by the equilibrium ratio limit set by the user.

[0051] (B2) Determine whether the stress corrosion condition has been met - when the maximum tensile stress exceeds the micro-activation stress, the stress corrosion mechanism is triggered.

[0052] (B3) After the stress corrosion activation condition is met, the system will calculate the function value of the temperature change.

[0053] (B4) Within a fixed time step, the decrease in the parallel bond diameter and the number of cracks caused by this change will be calculated simultaneously.

[0054] (B5) Within this time step, the contact pairs that satisfy the adhesion evolution activation are parallel adhesives.

[0055]

[0056] For smooth joints

[0057]

[0058]

[0059] Contacts that do not meet the conditions will skip the above calculations.

[0060] (B4) After the time step calculation is completed, the sample exits the time step program and continues to run until it reaches equilibrium. This process will continue until the set time is reached.

[0061] 3. Numerical simulation of thermal damage experiments

[0062] (C1) The sample size was 100 mm × 50 mm, with 2507 particles. The particle size distribution curve shows a more uniform particle distribution, with a smaller proportion of large particles. It should be noted that the microstructure of the particles in the simulation is not derived from scanned images of actual rock strata.

[0063] (C2) This study uses a linear parallel bonding model as the bonding mechanism between particles. Unlike the traditional linear contact bonding model, this model connects particles through parallel springs, forming a "surface contact" structure that can transmit contact forces and bending forces.

[0064] (C3) Due to the lack of a unified formula to establish a quantitative relationship between particle microscopic parameters and material macroscopic parameters, continuous parameter tuning is necessary. This involves a trial-and-error process to match microscopic parameters with macroscopic properties. The macroscopic and long-term mechanical properties of the numerical model were calibrated based on uniaxial experimental results at laboratory room temperature (25℃). The simulation parameters of the numerical model are shown in Table 1.

[0065] Table 1 Simulation parameters of the numerical model

[0066]

[0067] (C4) After inputting the above parameters into the HSC program, considering the disturbance damage during sample extraction and preparation, as well as the initial stress damage during pre-compression, the HSC model can well characterize the internal damage characteristics of the sample before uniaxial compression. When using the HSC model to calculate room temperature initial damage, the initial damage should be minimized as much as possible; the smaller the parameter value, the greater the damage; the larger the parameter value, the smaller the damage. This process mainly consists of... The smaller the parameter value, the greater the damage; the larger the parameter value, the smaller the damage. A comparison between the model obtained through uniaxial compression simulation and the experimental results at room temperature shows that the simulated curves and the experimental stress-strain curves are in good agreement, proving the rationality of the above parameter settings.

[0068] (C5) The rock sample was heated to the target temperature at a heating rate of 10℃ / min and held at that temperature for 4 hours. It was then cooled to room temperature at a slower rate, with the entire process taking approximately 24 hours. According to the experimental standards, the total program duration was set to 24 hours, and the time step was set to 30 seconds / step.

[0069] (C6) For different target temperatures, we need to set different... Only with the expression and the E value can ideal simulation results be obtained (Table 2). First, it is necessary to establish the functional relationship between temperature and time. Assume that the temperature change satisfies... = kt.

[0070]

[0071] Where k0 represents the value of k at the initial temperature, ΔT represents the absolute value of the initial temperature change, and T is the target temperature. The magnitude of the degradation factor p(T) is related to the rock properties. Since the elastic modulus of coarse-grained marble gradually decreases with increasing target temperature in practical applications, the degradation factor p(T) in this study also shows a gradual decreasing trend. Therefore, the model specimen only needs to adjust the value of k and the degradation factor p(T) according to different target temperatures. The HSC program simulation at different temperatures... The expression and E value are set in the PFC simulation software as follows.

[0072] Table 2. At different target temperatures Expression value and E value

[0073]

[0074] Example 2:

[0075] 1. Granite mineral samples were sliced ​​to obtain detailed information on their grain size. The weighted average grain size of the four main minerals was 2.87 mm. These four minerals are K-feldspar, plagioclase, quartz, and biotite, with contents of 45%, 20%, 30%, and 5%, respectively. Different mineral grains were represented by four colors. Uniaxial compression tests and static fatigue tests under unconstrained conditions were performed on the samples to calibrate the model parameters of the particle size distribution sample.

[0076] 2. Using PFC2D software, circular particle geometry corresponding to the particle image is generated. Fragile, non-circular grains are created by bonding numerous deformable and breakable polygonal particle clusters along their adjacent edges, replacing the traditional circular particle simulation in PFC. Internally, a parallel bonding contact model is used to bond PFC particles together. A smooth joint contact model is used for grain boundaries, defining contact points on the grain boundaries as smooth joint contact points.

[0077]

[0078]

[0079] In the above formula, , , , Let I and J represent the normal stress, shear stress, normal force, and shear force at the contact point, respectively; I and J represent the moment of inertia and polar inertia at the contact point, respectively. and These represent the normal torque and shear torque at the contact point, respectively; R is the parallel bond contact radius; A is the parallel bond contact area (in PFC2D, the contact is circular, i.e., A = πR²); β represents the moment of inertia coefficient (usually set to 1.0).

[0080] 3. Due to the long bonding evolution time, performing PFC in a fully dynamic mode is impractical, where each PFC time step corresponds to the same bonding evolution time step. A separate cumulative time was established, and an adaptive procedure was used to determine the bonding evolution time step. The adaptive procedure determines the time step, which decreases during the primary stress-induced damage formation stage and increases during the relatively static phase of the damage. The time step is estimated using the following equation.

[0081]

[0082] Among them, t f It is the estimated time of the next failure, n. c It is the number of cycles until the connection breaks.

[0083] First, the numerical specimen must undergo mechanical cycling to reach static equilibrium. The static equilibrium condition is controlled by the user-specified equilibrium ratio limit. When the specimen is in static equilibrium, before bond evolution begins, the bond evolution activation conditions for each parallel bonded contact and smooth joint contact are checked sequentially within the estimated time step. For parallel bonded contacts, when the maximum tensile stress ( ) greater than micro-activation stress ( ), lower than the parallel bond tensile strength ( When adhesion evolution is activated ( ), ≤ < For smooth jointed contact, the activation condition is m. s ≤m<1. For those satisfying the adhesion evolution activation condition, the adhesion diameter and contact performance are calculated according to the corresponding formula. After the calculation is completed for this time step, wait for the mechanical cycle and equilibrium to re-enter, and at the same time, estimate the elapsed time t of the next failed contact. f The time step is adaptively determined. The next failed contact can be a parallel bond or a smooth joint contact, after time t. f A shorter failure time should be selected between parallel bonding and smooth joint contact. This allows the sample time to progress continuously, repeating the cycle to reach the set time requirement.

[0084] 4. Using the numerical model and calculation methods described above, a series of fatigue tests were conducted under different constraint pressures and driving stress ratios. These included creep axial strain under constraint pressures of 0, 5, 10, and 15 MPa; the change in failure time with constraint pressure at different driving stress ratios of 0.6, 0.7, and 0.8; and the changes in grain boundary and intragranular microcracks with creep strain under different driving stress ratios.

[0085] 5. Based on the simulation results, numerical simulation results show that increasing the confining pressure and reducing the driving stress ratio can prolong the failure time and improve the long-term strength. The number of grain boundaries and microcracks within particles both increase with the increase of the driving stress ratio. Based on the proposed discrete element analysis method, the creep simulation of rock particle bonding evolution has been well validated.

[0086] Example 3:

[0087] This embodiment verifies the impact of different heating methods on rock damage.

[0088] 1. In this stage, three gradient heating rates (1℃ / min, 5℃ / min, and 20℃ / min) were set and maintained for 4 hours respectively. Then, the sample was cooled to the initial temperature (0℃) for uniaxial compression testing. Due to the change in initial temperature, the k value will be adjusted accordingly. The k value was calculated to be 11.1 at 200℃, 9.4 at 400℃, and 9.25 at 600℃ using the formula.

[0089] 2. Two experimental groups were set up with 0℃ as the initial temperature. The first group was heated to the target temperature at a heating rate of 10℃ / minute and then kept at the high temperature for 24 hours for uniaxial compression testing; the second group was heated to the target temperature at the same heating rate, kept at that temperature for 4 hours, and then cooled to the initial temperature at a lower cooling rate before uniaxial compression testing.

[0090] 3. Considering that the cooling process is not necessarily linear, it is necessary to study the impact of different cooling paths on rock strength. We still use a heating mode of 10°C per minute, maintaining a constant temperature for 4 hours before cooling. For different temperature conditions, we define three curves: a slow-fast alternating curve 'cooling path degree -1', a linear and gentle curve 'cooling path degree 0', and a fast-slow alternating curve 'cooling path degree 1'. At the midpoint of the overall cooling process, the convex curve is 40% higher than the gentle curve, while the concave curve is 40% lower than the gentle curve. This section finally sets the cooling temperature to 5°C, and the temperature-time relationship corresponding to different curves can be determined through fitting.

[0091] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0092] The above embodiments illustrate the application of the method of the present invention under different working conditions in a progressive manner. The same or similar parts between the embodiments have been described above and will not be repeated here. Through the method of the present invention, a fully coupled simulation of the macroscopic mechanical response and microscopic crack evolution of rock thermal damage can be achieved, and the simulation results agree well with the experimental data.

[0093] Symbol explanation: - The amount of damage to the contact bonding bond diameter; - Maximum tensile stress at the contact point; - Micro-activation stress threshold; - Parallel bond tensile strength; -Shear stress at the contact point; -Smooth joint shear strength; - Smooth joint micro-activation stress ratio; -Material parameters; -Time step; -Young's modulus at room temperature; -Young's modulus at high temperature; -Young's modulus degradation factor; -temperature; -time; - Heating rate; -Contact normal force and tangential force; - Moment of inertia at the contact point; -Contact point bending moment; - Parallel bonding radius; -Contact area; - Coefficient of rotational inertia; - Estimate failure time; - Number of loops, etc.

[0094] Detailed description of the attached diagram:

[0095] Figure 1 Schematic diagram of the particle-based model:

[0096] Conclusion and Explanation: This figure illustrates the basic structure of the discrete element numerical model constructed in this invention. The model consists of numerous polygonal particle clusters, which bond together to form non-circular grains, more realistically simulating the morphology of mineral grains in natural rocks. Different colored particles in the figure represent different mineral compositions, intuitively reflecting the heterogeneous characteristics of the rock. The contact between particles employs a parallel bonding model (simulating intragranular cementation) and a smooth joint model (simulating relatively weak grain boundaries), providing a geometric and physical basis for subsequent thermo-mechanical coupling damage simulation.

[0097] Figure 2 Schematic diagram of parallel bond stress corrosion (PSC) model;

[0098] Conclusion and Explanation: This figure illustrates in detail the mechanical transmission mechanism of the parallel bonded contact model. This model is considered as an elastic beam with constant normal and tangential stiffness, capable of transmitting forces (…). ) and torque ( Its stress calculation takes into account the combined effects of normal force, shear force, bending moment, and torque, enabling more accurate calculation of tensile stress at the contact point. ) and shear stress ( This is the key to determining whether stress corrosion has been activated and thus triggered damage evolution.

[0099] Figure 3 Schematic diagram of two particles fixed in contact by smooth joints.

[0100] Conclusion and Explanation: This figure illustrates a smooth joint contact model, specifically designed to simulate grain boundary behavior. It allows for relative sliding and separation of particles on either side at the contact surface, and its strength properties (tensile strength) are considered. and shear strength The stiffness is independent of the grains themselves. This accurately reflects the characteristic that grain boundaries in rocks are usually mechanically weak points, ensuring that thermal damage and microcracks can preferentially occur and propagate along grain boundaries, consistent with physical experimental observations.

[0101] Figure 4 Flowchart of the adhesion evolution process simulation

[0102] Conclusion and Explanation: This flowchart clearly illustrates the core damage evolution simulation process of this invention. Its core lies in using a fixed time step ( The system iterates through all contacts, replacing the adaptive step size of the traditional PSC model. Within each time step, the system sequentially checks the stress state of all contacts, only resolving those that meet the activation condition. or The bonding diameter damage is updated based on the contact area. This strategy significantly improves the computational efficiency and stability of long-term thermal damage simulation.

[0103] Figure 5 Flowchart of the real-time thermal damage effect thermal stress corrosion model (HSC)

[0104] Conclusion and Explanation: This figure illustrates the logical flow of the innovative thermally induced stress corrosion (HSC) model of this invention. The key improvement of this model lies in introducing the temperature variable (T) into the stress corrosion damage evolution rate. After each step of mechanical equilibrium and stress calculation, the system updates the material parameters (e.g., ...) based on the current temperature field. The system integrates the temperature field and the stress field in real time and bidirectionally, thereby enabling accurate simulation of the damage process under the combined action of thermal stress and chemical corrosion.

[0105] Figure 6 Numerical calibration and gradation curve of the sample

[0106] Conclusions and Interpretation: This figure shows the particle size distribution (gradation) curve of the numerical sample. The curve indicates that the particle size distribution in the model is uniform, with a relatively small proportion of large particles. This ensures the rationality of the model's macroscopic mechanical response and avoids distortions in mechanical behavior caused by uniform or discontinuous particle sizes. This gradation is the basis for subsequent parameter calibration and for matching the model's macroscopic behavior with real rock.

[0107] Figure 7 Comparison of experimental stress-strain and simulated stress-strain at room temperature

[0108] Conclusions and Explanations: This figure, by comparing the stress-strain curves obtained from physical experiments and numerical simulations at room temperature, verifies the effectiveness and accuracy of the discrete element model and its parameter calibration method constructed in this invention. The two curves are basically consistent in the elastic stage, the plastic stage, and even the peak strength, proving that the numerical model can faithfully reflect the macroscopic mechanical behavior of real rocks, laying a solid foundation for subsequent reliable thermal damage simulations.

[0109] Figure 8 Schematic diagram comparing failure modes under different temperature conditions: (a) 200℃ (b) 400℃ (c) 600℃

[0110] Conclusions and Explanations: This set of figures visually illustrates the failure morphology of the rock model after thermal damage at different target temperatures. It can be observed that as the temperature increases, the number of microcracks in the model increases significantly, and the failure modes become more complex and fragmented. This qualitatively demonstrates that the HSC model of this invention can successfully simulate the intensifying effect of temperature on the degree of rock damage and failure modes; high temperatures lead to a sharp decline in rock integrity.

[0111] Figure 9 Schematic diagram showing the changes in peak stress and elastic modulus with heating rate.

[0112] Conclusions and Interpretation: This figure quantitatively reveals the influence of heating rate on the macroscopic mechanical properties of rocks. With increasing heating rate, both the peak stress and elastic modulus of the rock show a decreasing trend. This indicates that rapid thermal shock causes more severe damage to the rock. The model in this invention successfully captures this important thermodynamic phenomenon, providing a quantitative basis for evaluating the stability of rocks under different heating conditions.

[0113] Figure 10 Schematic diagram of stress-strain curves under different temperatures and conditions

[0114] Conclusions and Interpretation: This figure comprehensively illustrates the stress-strain curves simulated under different temperature conditions and with or without thermal damage. The curves clearly show that: 1) after thermal damage, both the strength and stiffness of the rock significantly degrade; 2) the higher the temperature, the more severe this performance degradation. This figure quantitatively demonstrates the ability of the method of this invention to predict the influence of thermal history on the residual mechanical properties of rocks.

[0115] Figure 11 Schematic diagram of different cooling paths at 200℃

[0116] Conclusion and Explanation: This figure embodies another important innovation of this invention, comparing the effects of three different cooling paths (convex function, linear function, and concave function) on the residual strength of rock at the same target temperature (200℃). The simulation results clearly indicate that using a convex function cooling path (fast at first, then slow) can effectively suppress thermal stress concentration and microcrack propagation during the cooling process, thereby enabling the rock to achieve the highest peak residual strength. This discovery provides direct theoretical guidance and decision support for optimizing cooling processes in engineering to protect the stability of surrounding rock.

[0117] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0118] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0119] Those skilled in the art will recognize that the modules and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art 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.

[0120] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0122] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0123] 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 within the protection scope of the present invention.

Claims

1. A method for simulating the thermal damage evolution of rocks based on a stress corrosion model, characterized in that, Includes the following steps: Step 1: Prepare rock samples; measure the average particle size of mineral particles using a microscope, and calculate the average particle diameter by combining the measurement results of multiple thin rock sections; use X-ray diffraction analysis to determine the mineral components and their relative contents in the rock, so as to obtain the microscopic material parameters of the rock. Step 2: Construct a discrete element numerical model of the rock; determine the size of the particles in the discrete element simulation by the average particle diameter and generate a particle set, with different mineral particles identified by different colors; use particle flow software to discretize the rock into a spherical particle system, and realize the cementation connection between particles through a parallel bonded contact model; Step 3: Establish a temperature-dependent stress corrosion model; Introduce the ambient temperature variable into the parallel bonded stress corrosion model in the granular flow software, construct a functional relationship between the stress corrosion damage evolution rate and temperature, and realize real-time simulation of thermo-mechanical coupled damage. Step 4: Simulate the thermal damage evolution of rock; set the temperature degradation function and temperature-time loading function for the material parameters of the model; With a fixed time step The thermal effects calculation is performed cyclically. In each time step, the micro tensile stress of all contact points is calculated sequentially, and the damage amount of the bond diameter is updated according to the temperature-dependent stress corrosion rate formula to generate microcracks. Damage update is performed only for contacts that meet the bond evolution activation condition, while contacts that do not meet the condition remain unchanged in the time step. Step 5: Mechanical response analysis; After completing the thermal damage loading, apply uniaxial compressive load to the obtained thermally damaged rock model to perform numerical simulation, and extract the stress-strain curve and failure mode of the rock. Step 6: Cooling path optimization; After thermal damage evolution and uniaxial loading, compare and analyze the peak stress of rock samples under different cooling paths to determine the optimal cooling strategy.

2. The method according to claim 1, characterized in that, In step 1, the diameter of mineral particles in the rock slices is measured using a microscope and a ruler, and the average value of multiple measurements is taken as the average particle size. The diffraction pattern of the rock slice samples is analyzed using XRD, and the mineral components and their relative contents are determined by comparing them with a standard database.

3. The method according to claim 1, characterized in that, In step 2, a large number of deformable and breakable polygonal particle clusters are bonded along adjacent boundaries to construct non-circular fragile grains to replace the traditional circular particle model; a parallel bonding contact model is used inside the particle clusters to simulate the cementation inside the grains; a smooth joint contact model is used at the grain boundaries, and the contact points on the grain boundaries are defined as smooth joint contact points to reflect the relatively weak bonding characteristics at the grain boundaries.

4. The method according to claim 1, characterized in that, In step 3, a stress corrosion activation condition is set in the parallel bonded contact when the maximum tensile stress at the contact point is reached. satisfy Time-triggered stress corrosion, where: The micro-activation stress threshold, For parallel bond tensile strength; in smooth joint contact, when the stress level ratio at the contact point is greater than or equal to m, it satisfies Time triggers adhesion evolution, where: The ratio of micro-activated stress level is defined; and the functional relationship between stress corrosion damage evolution rate and temperature is set.

5. The method according to claim 1, characterized in that, In step 4, a fixed time step is used. The system performs thermal damage evolution calculations in seconds, replacing the adaptive time step strategy of the parallel bond stress corrosion model in traditional granular flow software, thereby improving the computational efficiency of simulating long-term thermal damage processes.

6. The method according to claim 1, characterized in that, In step 4, a degradation function of the rock's elastic modulus with temperature is established. In the formula: The initial Young's modulus, The degradation factor is a temperature-dependent factor, and the mechanical parameters of particle contact in the discrete element model are dynamically adjusted based on this degradation function.

7. The method according to claim 1, characterized in that, In step 5, the micro-parameters of the particles are calibrated by numerical experiment-experiment comparison under room temperature conditions through various numerical simulations including uniaxial tension, uniaxial compression, triaxial compression and fatigue creep. The stiffness and strength of the particle contact are adjusted so that the simulated macroscopic stress-strain curve of the rock basically matches the corresponding room temperature experimental curve. The micro-parameters of the discrete element model are determined by the above parameter calibration method.

8. The method according to claim 1, characterized in that, In step 6, the cooling path settings include: convex function - rapid cooling followed by gradual cooling, linear function - constant cooling rate, and concave function - slow cooling followed by accelerated cooling. Under the condition of the same total cooling time, the peak compressive strength of the rock under different paths is calculated, and it is verified that the peak stress of the rock is the highest under the convex function path.

9. The method according to claim 1, characterized in that, Introduce temperature-dependent material constants into the temperature-dependent stress corrosion model. Furthermore, the constant was set to approach zero under high-temperature conditions to simulate a purely thermally induced stress corrosion failure process without mechanical stress.

10. A rock thermal damage evolution simulation system based on a stress corrosion model, characterized in that, include: The data acquisition module is used to obtain the average grain diameter of the rock and the relative content of each mineral component; The model building module is used to build discrete element numerical models of rocks; The model definition module is used to establish a temperature-dependent stress corrosion model; the damage simulation module is used to simulate the thermal damage evolution process of rocks; and the mechanical analysis module is used to perform mechanical response analysis on the model after thermal damage. The cooling optimization module is used to compare and analyze different cooling paths to determine the optimal cooling strategy.

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