Cellular automata modeling method, system and device for porous metals
By establishing a three-dimensional regular cellular mesh and assigning a gradient porosity distribution to the porous metal model, the problem of the inability to accurately describe non-uniform diffusion characteristics in existing technologies is solved, and accurate simulation and continuous evolution characteristics of porous metal diffusion systems are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGDONG UNIV OF TECH
- Filing Date
- 2026-04-03
- Publication Date
- 2026-07-14
AI Technical Summary
Existing cellular automata models cannot accurately characterize the non-uniform diffusion features in diffusion-type systems, cannot represent the evolution process of local enhancement-local decay in complex diffusion networks, and lack an effective coupling mechanism between the continuous diffusion field and the discrete state field, resulting in a large deviation between simulation results and actual system behavior.
By establishing a three-dimensional regular cellular mesh and using porosity parameters as control variables, solid candidate cells are generated and assigned material properties to form a porous nanometal cell model with gradient porosity distribution. By combining gradient information and connectivity evolution rules, the continuous evolution characteristics of microscopic local connectivity and macroscopic diffusion processes are realized.
It achieves an accurate description of porous metal diffusion systems, reduces the deviation between simulation results and actual behavior, and balances the consistency between local physical driving forces and global diffusion dynamics.
Smart Images
Figure CN122392657A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of nanometals, specifically to a method, system, and apparatus for modeling cellular automata of porous metals. Background Technology
[0002] Existing cellular automata models generally employ fixed neighborhoods and simple counting rules for state updates. Their model structures are overly idealized and fail to reflect the real physical driving forces in diffusion systems. For example, when there are significant concentration, temperature, or energy gradients in the system, diffusion behavior often exhibits clear directionality and varying strengths. However, traditional models, lacking gradient response mechanisms, cannot accurately characterize these non-uniform diffusion features, leading to simulation results that deviate significantly from actual system behavior.
[0003] Furthermore, existing models typically treat local connectivity as fixed or binary logic, neglecting the dynamic changes in connection strength between cells due to differences in physical quantities. This prevents the models from representing the "local enhancement-local decay" evolution process in complex diffusion networks, thus limiting their application in continuous field coupling problems such as material diffusion, heat conduction, and interface evolution. Moreover, traditional rules often only express "diffusion" or "growth" behavior in isolation, lacking a mechanism to effectively couple the continuous diffusion field with the discrete state field. This makes it impossible to simultaneously characterize gradient-driven connection changes at the microscopic level and the overall diffusion dynamics at the macroscopic level, resulting in models that struggle to balance physical plausibility and computational accuracy.
[0004] Based on the above shortcomings, there is an urgent need for a new modeling method that can simultaneously consider diffusion gradient, connectivity changes, and state evolution laws within the framework of cellular automata, thereby achieving a more accurate, continuous, and physically consistent description of real diffusion systems. Summary of the Invention
[0005] The purpose of this disclosure is to provide a method, system, and apparatus for modeling cellular automata of porous metals, so as to solve at least one technical problem in the prior art.
[0006] The technical solution disclosed herein is: A method for modeling cellular automata for porous metals, comprising: A three-dimensional regular cellular mesh is established, and solid candidate cells are generated using the porosity parameter as a control variable. Material properties are uniformly assigned to the candidate cells, completing the transformation from porous structure to actual material phase, and obtaining a porous nano metal cell model with gradient porosity distribution.
[0007] The process of establishing a three-dimensional regular cellular mesh and generating solid candidate cells using the porosity parameter P as a control variable includes: The porosity parameter P varies with the spatial coordinate Z direction to form a preset gradient function relationship; the gradient function can be a linear function, such as y=kx.
[0008] For any cell, based on the porosity P corresponding to its Z-direction position, the cell is determined in a probabilistic manner to be either in a porous state or a candidate state of solidity. The larger the porosity P value, the higher the probability that the cell is determined to be a candidate state of solidity, thus forming a pore distribution feature that changes continuously along the Z-direction in space.
[0009] Set the number of modeling layers and the length and width of the porous nanometal cell model, and define the cell state; Obtain the current void ratio P in the nth layer, and traverse all cells in the (n-1)th layer; if any cell has more than 8 × P void neighbors, then the cell at the corresponding position in the (n-2)th layer is in the solid state, otherwise it is converted to the void state; n = 2, 3, 4, ...
[0010] The process of uniformly assigning material properties to the candidate cells to complete the transformation from porous structure to actual material phase includes: Set the number of modeling layers and the length and width of the porous nanometal cell model, and define the type of material; Obtain the probability of generating materials at the current location and determine the state of the next layer; Generate a random number Q, and generate the corresponding material based on the random number Q.
[0011] The probability of obtaining material generated at the current location includes: Using the first layer of material as a base, the proportion is linearly reduced from the inside out; The second layer of material is added synchronously and linearly, achieving a continuous transition of the structure from dense to porous.
[0012] We can refer to the process of human bones from the inside out, from loose to dense; assuming that the materials to be generated are material 1 and material 2, we propose that in the process from layer 0 to N, material 1 is the base element, that is, the probability of material 1 changes linearly from 0 to N, from dense 1 to 0.2 at the final N, while material 2 changes linearly from 0 to 0.8.
[0013] In this disclosure: the state of the current location is first constructed, such as a solid or a void. After all N layers of modeling are completed, materials are generated for the solids in each layer. It is a process from solid to specific material.
[0014] The process of generating a random number Q and generating corresponding materials based on the random number Q includes: Obtain the random number Q; The random number Q is compared with the material probability interval of any layer of generated material; The material probability range into which the random number Q falls is taken as the material of the current layer.
[0015] A system based on the aforementioned cellular automata modeling method for porous metals includes: Control unit; The modeling unit interacts with the control unit to establish a three-dimensional regular cellular mesh and generates candidate solid cells using the porosity parameter as a control variable. The unit is assigned material properties to the candidate cells of the entity, and the data interaction with the modeling unit is carried out to complete the transformation from the porous structure to the actual material phase, so as to obtain a porous nano metal cell model with gradient porosity distribution.
[0016] An electronic device for modeling cellular automata of porous metals, comprising: Storage media, used to store computer programs The processing unit exchanges data with the storage medium and executes the computer program to perform the steps of the cellular automata modeling method for porous metals as described above when modeling cellular automata for porous metals.
[0017] A computer-readable storage medium: The computer-readable storage medium stores a computer program. When the computer program is run, it executes the steps of the cellular automata modeling method for porous metals as described above.
[0018] The beneficial effects of this disclosure include at least the following: The cellular automata modeling method for porous metals disclosed herein generates candidate cells by establishing a three-dimensional regular cellular mesh and using porosity parameters as control variables. Material properties are then uniformly assigned to these candidate cells, completing the transformation from porous structure to actual material phase, resulting in a porous nanoscale metal cell model with a gradient porosity distribution. This disclosure couples gradient information with connectivity evolution rules, enabling the model to reflect both the local connectivity characteristics between microscopic cells and the continuous evolution characteristics of macroscopic diffusion processes. It accurately describes the non-uniform connectivity evolution in diffusion-type systems caused by concentration, temperature, or energy gradients. Simultaneously, it balances the consistency between local physical driving forces and global diffusion dynamics, reducing the deviation between simulation results and actual diffusion behavior. Attached Figure Description
[0019] Figure 1 This is a flowchart of the first stage of the cellular automata modeling method for porous metals described in this disclosure; Figure 2This is a flowchart of the second stage of the cellular automata modeling method for porous metals described in this disclosure; Figure 3 This is a block diagram of the system described in this disclosure. Detailed Implementation
[0020] The present disclosure will now be further explained with reference to the accompanying drawings.
[0021] Terminology Explanation: Cellular automata: Composed of regular cellular grids, each cell in the regular grid takes a finite number of discrete states, follows the same action rules, and is synchronously updated according to determined local rules.
[0022] Porosity: The proportion of voids in a region relative to all other locations.
[0023] Gradient property: The gradient property referred to in this article is the change of porosity with the Z-axis of the coordinate system.
[0024] Porous metals: Porous metals refer to metals or metal alloys that contain a large number of pores (including open pores, closed pores, or mixed pore structures). The porosity is usually between 20% and 98%, and the pore size can cover the range of micrometers to millimeters.
[0025] Existing cellular automata models are mostly based on neighborhood counting rules for state updates, which cannot accurately describe the evolution of non-uniform connectivity in diffusion systems caused by concentration, temperature or energy gradients. At the same time, traditional models are difficult to balance the consistency between local physical driving forces and global diffusion dynamics, resulting in significant deviations between simulation results and actual diffusion behavior.
[0026] Therefore, a new method is needed to couple gradient information with connectivity evolution rules so that the model can reflect both the local connectivity characteristics between micro-cells and the continuous evolution characteristics of the macro-diffusion process. Specific Implementation
[0027] This disclosure provides an embodiment: A cellular automata modeling method for porous metals is proposed to construct porous nanometal models with gradient pore structures. Specifically, the method includes a pore generation stage and a material phase determination stage.
[0028] (1) In the first stage, a three-dimensional regular cellular mesh is established, and the porosity parameter P is used as the control variable, where P changes with the spatial coordinate Z direction to form a preset gradient function relationship. For each cell, according to the porosity P corresponding to its Z position, the cell is determined to be in a porous state or a solid candidate state in a probabilistic manner; where the larger the value of P, the higher the probability that the cell is determined to be a solid candidate state, thus forming a pore distribution feature that changes continuously along the Z direction in space.
[0029] like Figure 1 Based on the given curve of porosity P versus the Z-axis, the cell evolution is performed from layer 1 to layer N: For the nth layer: Based on the PZ curve, the porosity P of the current layer is obtained. Then, the following evolution is performed by traversing all cells in the (n-1)th layer: For the cell at the current position: If a cell has more than 8*P empty neighbors (the other eight cells surrounding the cell), then the cell at that position in the next layer is in a solid state; otherwise, it is converted to an empty state.
[0030] (2) In the second stage, material properties are uniformly assigned to the candidate cells generated in the first stage to complete the transformation from porous structure to actual material phase, and finally a porous nano metal cell model with gradient porosity distribution is obtained.
[0031] Through the above two-stage generation mechanism, this disclosure can achieve a controllable gradient distribution of porosity along a specific direction while maintaining the flexibility of cellular automata modeling. Compared with the traditional cell generation method based on fixed neighborhood rules, it is more suitable for describing the formation and evolution of non-uniform pore structures in nanometals.
[0032] like Figure 2 Given a curve showing the probability of material generation as a function of the Z-axis, material generation is performed. For example, the curves showing the probability of copper and silver generation as a function of the Z-axis. At any Z-axis, the sum of the probabilities of multiple materials being generated is 1. Specific Implementation
[0033] This disclosure also provides an embodiment: like Figure 3 A system based on the cellular automata modeling method for porous metals includes: a control unit 100, a modeling unit 200, and an assignment unit 300; the modeling unit 200 interacts with the control unit 100 to establish a three-dimensional regular cellular mesh and generate solid candidate cells using porosity parameters as control variables; the assignment unit 300 interacts with the modeling unit 200 to uniformly assign material properties to the solid candidate cells, completing the transformation from porous structure to actual material phase, and obtaining a porous nanometal cell model with gradient porosity distribution. Specific Implementation
[0034] This disclosure also provides an embodiment: An electronic device for modeling cellular automata of porous metals includes: a storage medium and a processing unit; wherein the storage medium is used to store a computer program; the processing unit exchanges data with the storage medium and is used to execute the computer program through the processing unit when modeling cellular automata of porous metals, performing the steps of the cellular automata modeling method for porous metals as described in Specific Embodiment 1.
[0035] A computer-readable storage medium: the computer-readable storage medium stores a computer program; When the computer program is run, it executes the steps of the cellular automata modeling method for porous metals as described in Specific Embodiment 1.
[0036] It should be clarified that, in this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.
[0037] The above disclosures only cover a few specific implementation scenarios. However, this disclosure is not limited to these, and any variations that can be conceived by those skilled in the art should fall within the protection scope of this disclosure. The serial numbers in this disclosure are for descriptive purposes only and do not represent the superiority or inferiority of the implementation scenarios.
Claims
1. A method for modeling cellular automata of porous metals, characterized in that, include: A three-dimensional regular cellular mesh is established, and solid candidate cells are generated using the porosity parameter as a control variable. Material properties are uniformly assigned to the candidate cells, completing the transformation from porous structure to actual material phase, and obtaining a porous nano metal cell model with gradient porosity distribution.
2. The cellular automata modeling method for porous metals according to claim 1, characterized in that, The process of establishing a three-dimensional regular cellular mesh and generating solid candidate cells using the porosity parameter P as a control variable includes: The porosity parameter P varies with the spatial coordinate Z direction to form a preset gradient function relationship; For any cell, based on the porosity P corresponding to its Z-direction position, the cell is determined in a probabilistic manner to be either in a porous state or a candidate state of solidity. The larger the porosity P value, the higher the probability that the cell is determined to be a candidate state of solidity, thus forming a pore distribution feature that changes continuously along the Z-direction in space.
3. The cellular automata modeling method for porous metals according to claim 2, characterized in that, include: Set the number of modeling layers and the length and width of the porous nanometal cell model, and define the cell state; Obtain the current void ratio P in the nth layer, and traverse all cells in the (n-1)th layer; if any cell has more than 8 × P void neighbors, then the cell at the corresponding position in the (n-2)th layer is in the solid state, otherwise it is converted to the void state; n = 2, 3, 4, ...
4. The method for modeling cellular automata of porous metals according to claim 1, characterized in that, The process of uniformly assigning material properties to the candidate cells to complete the transformation from porous structure to actual material phase includes: Set the number of modeling layers and the length and width of the porous nanometal cell model, and define the type of material; Obtain the probability of generating materials at the current location and determine the state of the next layer; Generate a random number Q, and generate the corresponding material based on the random number Q.
5. The cellular automata modeling method for porous metals according to claim 4, characterized in that, The process of obtaining the probability of generating material at the current location and determining the state of the next layer includes: Using the first layer of material as a base, the proportion is linearly reduced from the inside out; The second layer of material is added synchronously and linearly, achieving a continuous transition of the structure from dense to porous.
6. The cellular automata modeling method for porous metals according to claim 4, characterized in that: After constructing the state of the current location and completing the modeling of all N layers, materials are generated for the entities in each layer.
7. The method for modeling cellular automata of porous metals according to claim 4, characterized in that, The process of generating a random number Q and generating corresponding materials based on the random number Q includes: Obtain the random number Q; The random number Q is compared with the material probability interval of any layer of generated material; The material probability range into which the random number Q falls is taken as the material of the current layer.
8. A system based on the cellular automata modeling method for porous metals according to any one of claims 1-7, characterized in that, include: Control unit; The modeling unit interacts with the control unit to establish a three-dimensional regular cellular mesh and generates candidate solid cells using the porosity parameter as a control variable. The unit is assigned material properties to the candidate cells of the entity, and the data interaction with the modeling unit is carried out to complete the transformation from the porous structure to the actual material phase, so as to obtain a porous nano metal cell model with gradient porosity distribution.
9. An electronic device for modeling cellular automata of porous metals, characterized in that, include: Storage media, used to store computer programs The processing unit exchanges data with the storage medium and executes the computer program during the modeling of cellular automata for porous metals, performing the steps of the cellular automata modeling method for porous metals as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program. When the computer program is run, it performs the steps of the cellular automata modeling method for porous metals as described in any one of claims 1-7.