A crack distribution quantitative analysis method and system for discrete element simulation output data files

Abstract

The application discloses a crack distribution quantitative analysis method and system for discrete element simulation output data files, relates to the technical field of discrete element numerical simulation data post-processing and damage quantitative analysis, and comprises the following steps: receiving an output data directory, identifying an initial reference file and a subsequent time step file, analyzing a particle data block and a bond data block, removing non-sample particles or abnormal particles based on sample particle filtering rules, performing endpoint effectiveness verification, numbering pair normalization and set processing on a bond numbering pair, constructing an initial effective bond set and a current surviving bond set, calculating a global or local bond damage variable based on the intersection and difference set of the initial effective bond set and the current surviving bond set, and outputting a damage evolution result. The application can reduce manual post-processing errors, avoid repeated statistics or misjudgment cracks caused by inconsistent bond numbering sequences, and improve the consistency, reviewability and traceability of damage analysis.

Description

Technical Field

[0001] This invention relates to the field of discrete element numerical simulation data post-processing technology, specifically to a method and system for quantitative analysis of crack distribution in discrete element simulation output data files. Background Technology

[0002] The Discrete Element Method (DEM) is commonly used for numerical simulations of rock, concrete, sand, granular materials, and geotechnical engineering structures. In DEM models containing bond relationships, the bonds, contact bonds, or cementation structures between particles can be disrupted by loading, seepage, water absorption and softening, joint expansion, or other external forces. After the simulation is complete, DEM software typically outputs a data file containing particle data, bond data, wall data, and other relevant information from multiple time steps.

[0003] Existing analytical methods typically focus on discrete element model construction, material parameter degradation, or failure simulation under specific conditions. However, for the generated multi-time-step output data files, there is still a lack of a post-processing method that can automatically identify the baseline state, uniformly analyze particle and bond data, normalize bond number pairs, and calculate global and local damage variables. In particular, the same bond relationship may be recorded in opposite endpoint order in different time steps. Directly comparing the original bond number pairs can easily lead to duplicate statistics or misjudgment of cracks. Therefore, it is necessary to provide a method and system for quantitative analysis of crack distribution in discrete element simulation output data files. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of the embodiments of the present invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this section, the abstract and title of the invention. Such simplifications or omissions shall not be used to limit the scope of the present invention.

[0005] Given that existing discrete element simulation output data post-processing methods have problems such as difficulty in automatically identifying the baseline state, uniformly analyzing particle and bonding data, standardizing bonding number pairs and calculating global and local damage variables, and the tendency to generate duplicate statistics or misjudgment of cracks by directly comparing the original bonding number pairs, this invention is proposed.

[0006] Therefore, the problem to be solved by this invention is how to perform automated and verifiable quantitative analysis of crack distribution from discrete element multi-time step output data containing bonding relationships.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a method for quantitative analysis of crack distribution in discrete element simulation output data files, comprising, S1: Receive the discrete element simulation output data directory, identify the initial reference file and multiple subsequent time step files from the data directory, establish a time step file sequence according to the time step number, and determine the target time step file from the time step file sequence according to the preset target time step number; S2: Parse the particle data block and the bonding data block in the initial reference file and the target time step file respectively to obtain particle data and bonding number pairs, wherein the particle data includes at least the particle number, particle coordinates, particle radius and particle color group; S3: Based on the preset sample particle filtering rules, the particle data is filtered and wall particles, loading plate particles and abnormal particles are removed to determine the set of valid sample particles. S4: Based on the set of valid sample particles, perform endpoint validity verification and normalization on the bonding number pairs to construct an initial set of valid bonding pairs. The current survival set at the target time step ; S5: Based on the initial effective bond set Adhesive set with current survival The intersection and difference of the data are used to calculate the number of surviving bonds, the number of broken bonds, and the bond damage variables at the current time step, and output the quantitative analysis results of crack distribution.

[0008] Secondly, embodiments of the present invention provide a quantitative analysis system for crack distribution in discrete element simulation output data files, comprising: The data catalog identification module is used to receive the discrete element simulation output data catalog, identify the initial baseline file and multiple subsequent time step files from the data catalog, establish a time step file sequence according to the time step number, and determine the target time step file from the time step file sequence according to the preset target time step number. The file parsing module is used to parse the particle data blocks and bonding data blocks in the initial reference file and the target time step file respectively, to obtain particle data including at least particle number, particle coordinates, particle radius and particle color group, as well as bonding number pairs. The sample particle filtering module filters particle data based on preset sample particle filtering rules, and removes wall particles, loading plate particles and abnormal particles to determine the set of valid sample particles. The adhesive set construction module is used to perform endpoint validity verification and normalization on adhesive number pairs based on the valid sample particle set, and to construct the initial valid adhesive set. The current survival set at the target time step ; The damage variable calculation module is used to calculate the damage variable based on the initial effective bond set. Adhesive set with current survival The intersection and difference of the data are used to calculate the number of surviving bonds, the number of broken bonds, and the bond damage variable at the current time step. The local damage analysis module is used to filter the set of particle numbers falling within a rectangular ROI based on the ROI range, and then analyze them in the initial effective bond set. and the currently surviving set of adhesives The number of internal bonds that both ends of the particle number belong to the set is counted, and the baseline number of internal bonds, the number of surviving internal bonds and the local damage variable within the ROI are calculated. The results output module is used to output the quantitative analysis results of structured crack distribution. The results include at least the time step, the baseline file identifier, the target time step file identifier, the baseline bond quantity, the number of surviving bonds, the number of fractured bonds, and the damage variable.

[0009] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the above-described method for quantitative analysis of crack distribution in discrete element simulation output data files.

[0010] 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 above-described method for quantitative analysis of crack distribution in discrete element simulation output data files.

[0011] Compared with existing technologies, the advantages of this invention are as follows: This invention can automatically identify and select the baseline file and subsequent time step files, reducing the workload of manually processing discrete element output data; it improves the stability of parsing mixed output files through block state machine parsing and anomaly skipping mechanisms; it avoids the influence of walls, loading plates, or abnormal particles on damage statistics through sample particle filtering and endpoint validity verification; and it avoids the influence of adhesive numbering on normalization processing. It can prevent misclassification as different bonds; it can calculate damage variables based on the intersection and difference of the initial effective bond set and the current surviving bond set, thereby improving the consistency of statistical standards; it can support the automatic calculation of multi-time step damage evolution sequences, damage growth rate and peak time step markers; it can further support quantitative statistics of ROI local bond damage and structured result output, which is convenient for tracing input files, verifying statistical objects and performing secondary analysis. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the 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: Figure 1 A flowchart of the overall method for quantitative analysis of crack distribution in discrete element simulation output data files; Figure 2 A schematic diagram of the damage calculation logic of the benchmark set and the current set for a quantitative analysis method of crack distribution in discrete element simulation output data files; Figure 3 A schematic diagram of the global bond damage evolution analysis results of a quantitative analysis method for crack distribution in discrete element simulation output data files; Figure 4 A schematic diagram of the internal adhesion statistics of the ROI for a quantitative analysis method of crack distribution in discrete element simulation output data files. Figure 5 This diagram illustrates the results of ROI local bond damage analysis using a quantitative analysis method for crack distribution in discrete element simulation output data files. Detailed Implementation

[0013] 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. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0014] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort should fall within the scope of protection of this invention.

[0015] 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.

[0016] As mentioned in the background section, existing post-processing methods for discrete element method (DIM) output data still lack a method that can automatically identify the baseline state, uniformly analyze particle and bond data, standardize bond number pairs, and calculate damage variables for multi-time-step output data files. To address these issues, this invention provides a method for quantitative analysis of crack distribution in discrete element method (DIM) output data files.

[0017] Example 1

[0018] Reference Figures 1-2 , Figure 1 This is a flowchart illustrating a method for quantitative analysis of crack distribution in discrete element simulation output data files according to an embodiment of the present invention. Figure 1 As shown, a quantitative analysis method for crack distribution in discrete element simulation output data files includes: S1: Receive the discrete element simulation output data directory, identify the initial reference file and multiple subsequent time step files from the data directory, establish a time step file sequence according to the time step number, and determine the target time step file from the time step file sequence according to the preset target time step number.

[0019] S1.1: Receive the discrete element simulation output data directory, read the file names and file paths in the data directory, and form a list of files to be identified.

[0020] In this embodiment of the application, the system receives a data directory containing an initial baseline file and multiple subsequent time step files from the discrete element simulation output, wherein the files in the data directory are used to describe the particle state and bonding state of the discrete element model at different evolution stages.

[0021] S1.2: In the list of files to be identified, identify candidate initial benchmark files whose filenames are prefixed with "all_" and whose filenames are suffixed with "_ini.dat", and extract the time step number from the filenames of the candidate initial benchmark files.

[0022] In one implementation, the system extracts the time step number from the numeric field in the file name.

[0023] S1.3: When there is only one candidate initial benchmark file, the candidate initial benchmark file is directly determined as the initial benchmark file; when there are multiple candidate initial benchmark files, the initial benchmark file is determined according to the time step number and the preset benchmark file selection rules.

[0024] In this embodiment of the application, the preset reference file selection rule is as follows: when there are multiple candidate initial reference files, the one with the largest time step number is selected as the initial reference file.

[0025] S1.4: In the list of files to be identified, identify candidate time step files whose filenames have the prefix "all_" and the suffix ".dat", and which do not belong to the initial baseline file, and extract the time step number from the filename of the candidate time step file.

[0026] S1.5: Sort the candidate time step files in ascending order according to the time step number to establish a time step file sequence; select the target time step file from the time step file sequence according to the preset target time step number. If the target time step number is not specified, take the last candidate time step file in the time step file sequence.

[0027] In this embodiment, the system provides a target time step number configuration interface; when the user specifies a target time step number, the system selects the file that matches the number from the sorted sequence of time step files as the target time step file; when the user does not specify a number, the system defaults to using the last candidate time step file in the sequence as the target time step file.

[0028] S2: Parse the particle data block and the bonding data block in the initial reference file and the target time step file respectively to obtain particle data and bonding number pairs. The particle data includes at least the particle number, particle coordinates, particle radius and particle color group.

[0029] In this embodiment, the system reads the initial reference file and the target time step file line by line, and establishes a parsing state based on the block title. The parsing state includes particle parsing state, bonding parsing state, and skip state. The method of parsing the data file using a block state machine is as follows: when a particle data block identifier is read, the system enters the particle parsing state and parses the particle number, particle coordinates, particle radius, and particle color group from the data line of the particle data block; when a bonding data block identifier is read, the system enters the bonding parsing state and parses the particle numbers at both ends of the bonding data block from the data line of the bonding data block to obtain bonding number pairs; when a wall block identifier or an unknown block identifier is read, the system enters the skip state and does not extract particle data and bonding number pairs from the data lines in the skip state; skip processing is performed on blank lines, data lines that do not start with a number, data lines in the skip state, and data lines with parsing errors to obtain particle data and bonding number pairs corresponding to the initial reference file and the target time step file.

[0030] S3: Based on the preset sample particle filtering rules, the particle data is filtered, and wall particles, loading plate particles and abnormal particles are removed to determine the set of valid sample particles.

[0031] In this embodiment, a set of valid sample particles is determined according to preset sample particle filtering rules. These rules include one or more of particle color groups, target particle groups, and coordinate anomaly thresholds. Both particle color groups and coordinate anomaly thresholds are configurable parameters. The coordinate anomaly threshold, configurable to 25000.0, is used to remove particles whose coordinates exceed a reasonable range due to data anomalies. Based on particle color groups, wall particles, loading plate particles, and non-sample particles are identified from the particle data and removed. Based on the coordinate anomaly threshold, anomaly judgment is performed on the particle coordinates in the removed particle data, and particles whose coordinates exceed the threshold are identified as abnormal particles. Particle numbers are extracted from the particle data after removing wall particles, loading plate particles, non-sample particles, and abnormal particles to obtain the set of valid sample particles.

[0032] S4: Based on the set of valid sample particles, perform endpoint validity verification and normalization on the bonding number pairs to construct an initial set of valid bonding pairs. The current survival set at the target time step .

[0033] In the embodiments of this application, the initial effective bond set The construction method includes: reading the bonding number pairs corresponding to the initial benchmark file; performing endpoint validity verification on each bonding number pair; and determining whether the particle numbers at both ends of each bonding number pair belong to the valid sample particle set; when the particle numbers at both ends of the bonding number pair belong to the valid sample particle set, the bonding number pair is determined as a valid bonding number pair; normalizing the valid bonding number pairs; sorting the valid bonding number pairs according to the size relationship of the particle numbers at both ends, so that bonding number pairs with reversed endpoint order but the same particle numbers at both ends (e.g., ...) and The corresponding bonding number pairs are from the same specification; the bonding number pairs are then processed by set-based deduplication to obtain the initial valid bonding set. .

[0034] Current live bond set The construction method is as follows: For the adhesive number corresponding to the target time step file, perform endpoint validity verification, normalization processing, and set-based deduplication processing repeatedly to obtain the current surviving adhesive set under the target time step. The damage calculation logic between the baseline set and the current set can be as follows: Figure 2 As shown.

[0035] For the same bonding relationship, the discrete element output file may contain different time steps. or The system records each adhesive number pair in a predefined, ordered format before performing cross-time step comparisons. For example, it sorts each pair by endpoint number and then performs settling on this basis. Identifying them as having the same bonding relationship can avoid duplicate statistics or misjudgment of cracks.

[0036] S5: Based on the initial effective bond set Adhesive set with current survival The intersection and difference of the data are used to calculate the number of surviving bonds, the number of broken bonds, and the bond damage variables at the current time step, and output the quantitative analysis results of crack distribution.

[0037] Global damage variable calculation: Let Represents the initial effective bond set The reference bonding quantity in This represents the number of baseline bonds that remain alive at the target time step t. Indicates the number of reference bonds that fracture at the target time step t; bond damage variable. or equivalent .

[0038] It should be noted that when When the value is 0, the system stops damage calculation or returns a message indicating that there is no valid reference bond, in order to avoid meaningless calculations or division by zero errors.

[0039] Damage evolution index calculation: Repeat the above process for multiple target time steps and calculate the amount of new fracture. Damage increment Damage growth rate Δs represents the adjacent evolution interval. When the time step number only indicates the discrete evolution order, the difference between adjacent time step numbers is used. When the output file or external configuration contains actual physical time, load displacement, or load step size information, the actual time difference, load displacement difference, or load step difference is used. The system marks all time steps. The time step with the maximum value is taken as the peak time step.

[0040] Output results: Output quantitative analysis results of structured crack distribution. The results should include at least the time step, baseline file identifier, target time step file identifier, baseline bond quantity, surviving bond quantity, fractured bond quantity, newly added fracture quantity, damage variables, damage increment, damage growth rate, peak time step marker, ROI range, and set of particle numbers involved in the statistics.

[0041] ROI Local Damage Calculation: In one implementation, the system determines the set of particle numbers within the ROI based on the current particle coordinates at the target time step, used to evaluate the degree of local damage within the current spatial region. In another implementation, the set of particle numbers within the ROI can also be determined based on the particle coordinates at the initial baseline state, used to track the damage development of the initial local region during subsequent evolution. Regardless of the method used to determine the ROI particles, the system uses the same rule to count the number of internal bonds in the initial effective bond set and the current surviving bond set.

[0042] For any adhesion relation (i,j), only if And j∈ Only then is the adhesion included in the internal adhesion of the ROI, where This represents the set of particle numbers within the ROI. If only one end of a particle is located within the ROI, or if both ends of a particle are located outside the ROI, then the adhesion within the ROI is not counted. The statistical relationship of adhesion within the ROI can be seen as follows: Figure 4 As shown.

[0043] Based on the above rules, the system obtains , and ,in and These represent the baseline internal bond set within the ROI and the currently surviving internal bond set within the ROI, respectively. Indicates the amount of internal bonding within the reference area of ​​the ROI. This indicates the number of currently surviving internal bonds within the ROI. Indicates the number of fractured bonds within the ROI; local damage variable. ,in This represents the local damage variable of the ROI. When... If this occurs, the system will stop calculating local damage or return a message indicating that there is no effective reference bonding.

[0044] Based on the teachings of the above embodiments, other aspects of the present invention also propose a quantitative analysis system for crack distribution in discrete element simulation output data files, comprising: The data catalog identification module is used to receive the discrete element simulation output data catalog, identify the initial baseline file and multiple subsequent time step files from the data catalog, establish a time step file sequence according to the time step number, and determine the target time step file from the time step file sequence according to the preset target time step number. The file parsing module is used to parse the particle data blocks and bonding data blocks in the initial reference file and the target time step file respectively, to obtain particle data including at least particle number, particle coordinates, particle radius and particle color group, as well as bonding number pairs. The sample particle filtering module filters particle data based on preset sample particle filtering rules, and removes wall particles, loading plate particles and abnormal particles to determine the set of valid sample particles. The adhesive set construction module is used to perform endpoint validity verification and normalization on adhesive number pairs based on the valid sample particle set, and to construct the initial valid adhesive set. The current survival set at the target time step ; The damage variable calculation module is used to calculate the damage variable based on the initial effective bond set. Adhesive set with current survival The intersection and difference of the data are used to calculate the number of surviving bonds, the number of broken bonds, and the bond damage variable at the current time step. The local damage analysis module is used to filter the set of particle numbers falling within a rectangular ROI based on the ROI range, and then analyze them in the initial effective bond set. and the currently surviving set of adhesives The number of internal bonds that both ends of the particle number belong to the set is counted, and the baseline number of internal bonds, the number of surviving internal bonds and the local damage variable within the ROI are calculated. The results output module is used to output the quantitative analysis results of structured crack distribution. The results include at least the time step, the baseline file identifier, the target time step file identifier, the baseline bond quantity, the number of surviving bonds, the number of fractured bonds, and the damage variable.

[0045] This embodiment also provides a computer device suitable for a quantitative analysis method of crack distribution based on discrete element simulation output data files, 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 realize the quantitative analysis method of crack distribution based on discrete element simulation output data files as proposed in the above embodiment.

[0046] 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.

[0047] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the method for quantitative analysis of crack distribution in discrete element simulation output data files as proposed in the above embodiments.

[0048] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0049] Example 2

[0050] Reference Figures 3-5 This is the second embodiment of the present invention, which provides a method for quantitative analysis of crack distribution in discrete element simulation output data files. To verify the beneficial effects of the present invention, a biaxial compression example is used for illustration.

[0051] In an optional embodiment, the implementation process of the quantitative analysis method for crack distribution of the present invention is illustrated using the simulation results of a ZDEM biaxial compression bonded particle sample as input data.

[0052] The simulation uses two-dimensional discrete element particle samples, which are composed of spherical particles. The bonding relationship between particles is used to characterize the cementation structure or micro-connection inside the sample. After the simulation is completed, ZDEM outputs a data directory containing the initial reference file and multiple subsequent time step files. The files include particle data blocks, bonding data blocks, wall data blocks and other auxiliary information.

[0053] In this simulation, a boundary structure consisting of granular walls is set outside the sample; the top and bottom walls are used to apply vertical compression, while the left and right side walls are used to provide lateral constraints or confining pressure; the model's computational domain is set to 1.0 to 9000.0 in the x direction and 1.0 to 15000.0 in the y direction; the main body of the sample is located in the region of x=2000.1 to 5999.9 and y=3001.0 to 10999.0; the top wall, bottom wall, and left and right side walls are represented by granular walls of different color groups, which facilitates the differentiation of wall particles from the sample body particles during subsequent analysis.

[0054] Regarding material parameters, the Hertz contact model was used for the sample particles, with a particle density of 2.5e3, a shear modulus of 2.9e9, a Poisson's ratio of 0.2, and a local damping coefficient of 0.4. For bonding parameters, the interparticle friction coefficient was set to 2.5, the normal and tangential bond moduli were both set to 2.0e8, the tensile strength was set to 1.1e7, and the shear strength was set to 2.4e7. A confining pressure of approximately 5 MPa was applied to the left and right side walls, allowing free horizontal movement; a downward velocity was applied to the top wall, and an upward velocity was applied to the bottom wall, thus creating a bidirectional vertical compressive loading.

[0055] It should be noted that the nonlinear elastic contact model is based on Hertz contact theory and belongs to the category of nonlinear elastic contact models. It can describe the nonlinear relationship between force and overlap when particles interact in the normal and tangential directions.

[0056] During the simulation, the system saves the initial state and multiple subsequent time step states. The states can be represented as the initial baseline file all_*_ini.dat and the time step files all_*.dat. This invention does not alter the discrete element simulation solution process; instead, after the simulation is complete, it reads the output data directory and performs unified post-processing on the particle and bonding data. The sample undergoes overall shortening and local deformation under compressive loading, and the internal particle structure and bonding state evolve with time steps.

[0057] The system first scans the output data directory, identifies the initial baseline file, and sorts subsequent time step files according to their time step numbers. Then, the system parses the initial baseline file, extracting particle numbers, particle coordinates, particle radii, particle color groups, and bond number pairs. Based on preset sample particle filtering rules, the system removes wall particles, loading plate particles, and particles with abnormal coordinates. It then performs number normalization and settling on the bond number pairs that pass endpoint validity verification, thereby constructing the initial valid bond set. .

[0058] For each subsequent time step file, the system uses the same parsing rules, particle filtering rules, and numbering normalization rules to construct the current live aggregate set. The system will and A set comparison is performed to calculate the number of surviving baseline bonds, the cumulative number of fractured bonds, and damage variables at the current time step. The damage calculation logic for the baseline set and the current set can be as follows: Figure 2 As shown.

[0059] In this embodiment, as shown in Table 1, the initial baseline bond count identified by the system is 4045. As the vertical compression loading progresses, the number of surviving bonds gradually decreases, while the cumulative number of fractured bonds gradually increases. At time step 10800, the number of surviving bonds is 4045, the cumulative number of fractures is 0, and the damage variable is 0; at time step 13200, the number of surviving bonds decreases to 3526, the cumulative number of fractures increases to 519, and the damage variable reaches 12.8307%; at the final time step 18000, the number of surviving bonds is 2818, the cumulative number of fractures is 1227, and the damage variable reaches 30.3337%. The global bond damage evolution analysis results are as follows: Figure 3 As shown.

[0060] Table 1. Global Bond Damage Evolution Results of Biaxial Compression Example

[0061] Furthermore, the system calculates the damage growth rate based on the damage increment between adjacent time steps and the time step interval, and automatically marks the time step with the largest damage growth rate. In this embodiment, the number of new fractures corresponding to time step 13200 is 458, and the damage variable increment is 12.8307%, which is the stage with the fastest damage growth in this set of simulations. This result indicates that the sample underwent significant concentrated development of bond failure at this stage.

[0062] Based on the global damage evolution analysis, the system further performs ROI (Region of Interest) bonding damage statistics on local areas. Taking time step 14800 as an example, the system determines a rectangular ROI region in the particle spatial distribution map and filters particles falling within the ROI according to the particle coordinates at the current time step, forming a set of particle numbers within the ROI. Subsequently, the system performs initial effective bonding set... and the currently surviving set of adhesives The statistics show the number of particles bonded within an ROI, where both particle numbers at either end belong to the set of particle numbers within the ROI. The statistical relationship of bonding within an ROI can be seen as follows: Figure 4 As shown.

[0063] In the ROI statistical results, the system identified 259 particles within the ROI, 536 particles with internal bonding within the ROI, 244 particles with broken bonding within the ROI, and a local damage variable of 45.6324%. The results of the ROI local bonding damage analysis are as follows: Figure 5As shown, the local damage variable is higher than the global damage variable at the same stage, indicating that a stronger local bond failure concentration occurred within the selected ROI region.

[0064] Through the above embodiments, the present invention can automatically identify the baseline state and time step sequence from the output data of discrete element biaxial compression simulation, uniformly analyze particle data and bonding data, remove interference from walls and abnormal particles, calculate the global bonding damage evolution sequence, and further perform quantitative damage statistics on the local ROI regions of key time steps. This embodiment demonstrates that the present invention can achieve global damage evolution identification, rapid damage growth stage location, and quantitative evaluation of local damage regions within the same data processing framework, thereby improving the automation, consistency, and verifiability of discrete element simulation output data post-processing.

[0065] 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 method for quantitative analysis of crack distribution in discrete element simulation output data files, characterized in that, include: S1: Receive the discrete element simulation output data directory, identify the initial reference file and multiple subsequent time step files from the data directory, establish a time step file sequence according to the time step number, and determine the target time step file from the time step file sequence according to the preset target time step number; S2: Parse the particle data block and the bonding data block in the initial reference file and the target time step file respectively to obtain particle data and bonding number pairs, wherein the particle data includes at least particle number, particle coordinates, particle radius and particle color group; S3: Based on the preset sample particle filtering rules, the particle data is filtered, and wall particles, loading plate particles and abnormal particles are removed to determine the effective sample particle set. S4: Based on the set of valid sample particles, perform endpoint validity verification and normalization on the bonding number pairs to construct an initial set of valid bonding particles. The current survival set at the target time step ; S5: Based on the initial effective bond set Adhesive set with current survival The intersection and difference of the data are used to calculate the number of surviving bonds, the number of broken bonds, and the bond damage variables at the current time step, and output the quantitative analysis results of crack distribution.

2. The method for quantitative analysis of crack distribution in discrete element simulation output data files as described in claim 1, characterized in that, In step S1, the initial reference file is identified based on the initial marker and time step number in the file name; files with the prefix all_ and the suffix _ini.dat are scanned as candidate initial reference files, and the time step number is extracted through the numeric field in the file name. When there is only one candidate initial reference file, the candidate initial reference file is directly determined as the initial reference file. When multiple candidate initial reference files exist, the candidate file whose time step number meets the preset rules is selected as the initial reference file, and the file whose file name prefix is ​​all_ and whose suffix is ​​.dat and does not belong to the initial reference file is selected as the candidate time step file. The time step file sequence is established in ascending order of time step number.

3. The method for quantitative analysis of crack distribution in discrete element simulation output data files as described in claim 2, characterized in that, In step S2, a block state machine is used to parse the discrete element simulation output data file; when the particle data block identifier is read, the particle parsing state is entered; when the bonding data block identifier is read, the bonding parsing state is entered; when the wall block identifier or an unknown block identifier is read, the skip state is entered; and skip processing is performed on blank lines, data lines that do not start with a number, data lines in the skip state, and data lines with parsing errors.

4. The method for quantitative analysis of crack distribution in discrete element simulation output data files as described in claim 3, characterized in that, In step S3, the preset sample particle filtering rules include determining the effective sample particle set based on one or more of particle color groups, target particle groups, and coordinate anomaly thresholds, in order to remove wall particles, loading plate particles, non-sample particles, or particles with coordinate anomalies.

5. The method for quantitative analysis of crack distribution in discrete element simulation output data files as described in claim 4, characterized in that, In step S4, endpoint validity is checked for each pair of bonding numbers. Only when the particle numbers at both ends of the bonding belong to the set of valid sample particles is the pair of bonding numbers included in the candidate set of valid bonding numbers. The pair of bonding numbers is then converted into a standardized form according to a preset endpoint order so that id1,id2 and id2,id1 are identified as the same bonding relationship. The standardized pair of bonding numbers is then set up or deduplicated.

6. The method for quantitative analysis of crack distribution in discrete element simulation output data files as described in claim 5, characterized in that, In step S5, let Represents the initial effective bond set The reference bonding quantity in This represents the number of baseline bonds that remain alive at the target time step t. This indicates the number of reference bonds that fractured at the target time step t.

7. The method for quantitative analysis of crack distribution in discrete element simulation output data files as described in claim 6, characterized in that, The bond damage variable satisfies the following formula: ； Or the aforementioned bond damage variable satisfies: 。 8. A quantitative analysis system for crack distribution in discrete element simulation output data files, based on the quantitative analysis method for crack distribution in discrete element simulation output data files according to any one of claims 1 to 7, characterized in that, include: The data directory identification module is used to receive the discrete element simulation output data directory, identify the initial reference file and multiple subsequent time step files from the data directory, establish a time step file sequence according to the time step number, and determine the target time step file from the time step file sequence according to the preset target time step number. The file parsing module is used to parse the particle data block and the bonding data block in the initial reference file and the target time step file respectively, to obtain particle data including at least particle number, particle coordinates, particle radius and particle color group, as well as bonding number pairs; The sample particle filtering module filters the particle data based on preset sample particle filtering rules, and removes wall particles, loading plate particles and abnormal particles to determine the set of valid sample particles. The adhesive set construction module is used to perform endpoint validity verification and normalization processing on the adhesive number pairs based on the valid sample particle set, and construct an initial valid adhesive set. The current survival set at the target time step ; The damage variable calculation module is used to calculate the damage variable based on the initial effective bond set. Adhesive set with current survival The intersection and difference of the data are used to calculate the number of surviving bonds, the number of broken bonds, and the bond damage variable at the current time step. The local damage analysis module is used to filter the set of particle numbers falling within the rectangular ROI based on the ROI range, and to select the initial effective bond set. and the currently surviving set of adhesives The number of internal bonds that both ends of the particle number belong to the set is counted, and the baseline number of internal bonds, the number of surviving internal bonds and the local damage variable within the ROI are calculated. The results output module is used to output the quantitative analysis results of structured crack distribution. The results include at least the time step, the reference file identifier, the target time step file identifier, the reference bond quantity, the number of surviving bonds, the number of fractured bonds, and the damage variable.

9. 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 quantitative analysis method for crack distribution in discrete element simulation output data files as described in any one of claims 1 to 7.

10. 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 quantitative analysis method for crack distribution in discrete element simulation output data files as described in any one of claims 1 to 7.

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