A method and apparatus for predicting the microbial barrier efficiency of a gas barrier film reinforcing layer material

Abstract

The present application relates to the technical field of air-tight film, in particular to a kind of air-tight film reinforcing layer material microbial barrier efficiency prediction method and device.The scheme is simulated by fusing CT scanning reconstruction, voxel grid modeling and CFD simulation, the motion trajectory of microorganism particles in flow field, the barrier efficiency of material to microorganism in environment is quantified, the rapid low-cost evaluation of material antibacterial and mildewproof performance is realized, the theoretical basis is provided for material structure optimization, the structure-performance action mechanism is clarified, and high-performance material research and development is promoted.

Description

Technical Field

[0001] This invention relates to the field of airtight membrane technology, and in particular to a method and apparatus for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material. Background Technology

[0002] Functional airtight membranes are thin-film materials widely used in construction, wearable textiles, and other fields. Based on the substrate type, airtight membranes can be broadly classified into composite nonwoven fabrics, polymer films, paper-based membranes, and aluminum foil, with composite nonwoven fabrics being the most widely used.

[0003] Composite nonwoven airtight membranes are multi-layered functional materials, typically employing a "sandwich" layered structure. Different structural layers are integrated through hot-pressing or adhesive bonding processes. The composite structure mainly includes a reinforcing layer composed of nonwoven fabric and a functional layer composed of polymer membrane material. This type of material combines the flexibility of nonwoven fabric with the airtight properties of polymer membrane material. Its core advantage lies in achieving a multi-functional balance of "airtightness, waterproofing, breathability (selective), and weather resistance" through material combination, while optimizing construction convenience and environmental adaptability.

[0004] Evaluating the performance of functional airtight membranes requires a multi-dimensional indicator system, which can be systematically divided into five categories: mechanical properties, airtight and waterproof properties, moisture permeability control properties, antibacterial protection properties, and weather resistance and durability properties. Each indicator independently characterizes the material properties, and also determines the overall functional performance through synergistic effects. Among them, antibacterial protection properties are mainly evaluated through antimicrobial rate and antifungal rating.

[0005] In the application of airtight membrane materials, the antibacterial and antifungal properties of the reinforcing layer material (such as spunlace nonwoven fabric) are crucial. Traditionally, the evaluation of the antibacterial and antifungal effects of such materials has mainly relied on experimental methods. However, experimental methods have significant limitations, such as high cost, long execution cycle, and difficulty in deeply revealing the intrinsic mechanism between material structure and antibacterial and antifungal effects.

[0006] Currently, while some methods exist for characterizing the internal structure of materials, accurate modeling at the pore scale remains challenging for materials with complex porous structures, such as spunlace nonwovens. Furthermore, when simulating the movement of bacteria or fungi within materials, the lack of effective simulation methods that incorporate the material's three-dimensional spatial structure makes it difficult to accurately predict the material's inhibitory effect on microorganisms. Therefore, there is an urgent need for a rapid, low-cost method that can deeply reveal the mechanisms underlying antibacterial and antifungal effects. Summary of the Invention

[0007] This invention provides a method and apparatus for predicting the microbial barrier efficiency of airtight membrane reinforcement layer materials, in order to solve the problems of high cost and long cycle of existing experimental methods.

[0008] In a first aspect, embodiments of the present invention provide a method for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material, comprising:

[0009] The airtight membrane reinforcement material to be predicted was subjected to CT scanning, and the scanning results were obtained;

[0010] Based on the scan results, a three-dimensional voxel mesh is generated;

[0011] CFD simulation was performed on the three-dimensional voxel mesh to predict the microbial barrier efficiency.

[0012] Secondly, embodiments of the present invention also provide a device for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material, comprising:

[0013] The scanning module is used to perform CT scans on the airtight membrane reinforcement material to be predicted and obtain the scan results;

[0014] A generation module is used to generate a three-dimensional voxel mesh based on the scanning results;

[0015] The prediction module is used to perform CFD simulation on the three-dimensional voxel grid to predict the microbial barrier efficiency.

[0016] This invention provides a method and apparatus for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material. By integrating CT scan reconstruction, voxel mesh modeling, and CFD simulation, the method simulates the motion trajectory of microbial particles in a flow field, quantifies the material's barrier efficiency against microorganisms in the environment, and achieves rapid and low-cost evaluation of the material's antimicrobial and antifungal performance. This provides a theoretical basis for material structure optimization, clarifies the structure-performance interaction mechanism, and promotes the research and development of high-performance materials. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material provided in an embodiment of the present invention;

[0019] Figure 2 This is a hardware architecture diagram of an electronic device provided in an embodiment of the present invention;

[0020] Figure 3 This is a structural diagram of a device for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material provided in an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of a sample of the airtight membrane reinforcement material to be predicted, provided in an embodiment of the present invention.

[0022] Figure 5 This is a CT scan image of the airtight membrane reinforcement material to be predicted, provided in an embodiment of the present invention.

[0023] Figure 6 This is a schematic diagram of the reconstructed structure of the airtight membrane reinforcement material to be predicted, provided in an embodiment of the present invention.

[0024] Figure 7 This is a schematic diagram of the simulation results of the airtight membrane reinforcement material provided in the embodiments of the present invention for the barrier efficiency against microorganisms of different particle sizes. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0026] Please refer to Figure 1 This invention provides a method for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material, the method comprising:

[0027] Step 100: Perform a CT scan on the airtight membrane reinforcement material to be predicted and obtain the scan results;

[0028] Step 102: Based on the scanning results, generate a three-dimensional voxel mesh;

[0029] Step 104: Perform CFD simulation on the three-dimensional voxel mesh to predict the microbial barrier efficiency.

[0030] In this embodiment, by integrating CT scan reconstruction, voxel mesh modeling, and CFD simulation, the motion trajectory of microbial particles in the flow field is simulated, the material's barrier efficiency against microorganisms in the environment is quantified, and the material's antibacterial and antifungal performance is evaluated rapidly and at low cost. This provides a theoretical basis for material structure optimization, clarifies the structure-performance interaction mechanism, and promotes the research and development of high-performance materials.

[0031] In one embodiment of the present invention, the airtight membrane reinforcement material is a cubic sample, and moisture is removed by vacuum drying to ensure the structural stability of the airtight membrane reinforcement material during CT scanning.

[0032] In some implementations, CT scans may employ high-resolution 3D micron-level computed tomography and digital radiography systems, without specific limitations.

[0033] In one embodiment of the present invention, a three-dimensional voxel mesh is generated based on the scanning results, including:

[0034] Based on the scanning results, the distribution pattern of fibers within the material is statistically analyzed (such as the distribution ratio of fibers in different directions).

[0035] The fiber and pore regions are separated by a threshold segmentation algorithm, the three-dimensional volume data is reconstructed by a filtered back projection algorithm, the geometric morphology of the pore surface is extracted by the Marching Cubes algorithm, and an STL format model file that meets the accuracy requirements is generated.

[0036] The model file is mesh optimized to remove redundant triangular faces, the voxel mesh resolution is set, and the octree voxelization algorithm is used to generate a three-dimensional voxel matrix (where the fiber phase is assigned a value of 1 and the pore phase is assigned a value of 0).

[0037] Calculate the porosity and fiber orientation distribution of the three-dimensional voxel matrix and compare it with the distribution pattern;

[0038] If the comparison results meet the accuracy requirements, CFD simulation is performed on the 3D voxel mesh; otherwise, the voxel mesh resolution is increased and mesh optimization is performed on the model file.

[0039] In one embodiment of the present invention, CFD simulation is performed on a three-dimensional voxel mesh to predict the microbial barrier efficiency, including:

[0040] A three-dimensional voxel mesh is embedded within a cuboid computational domain. At the entrance and exit points of the three-dimensional voxel mesh, the cuboid computational domain is extended along the thickness direction of the three-dimensional voxel mesh.

[0041] Set the inlet to a velocity inlet and the outlet to a pressure outlet, set the remaining boundaries to symmetric or periodic boundary conditions, and set the simulation convergence residual.

[0042] Define the microbial particle size distribution (e.g., single particle size or distribution pattern) and the number of microbial particles released;

[0043] Particles are uniformly released at the inlet section of the cuboid computational domain to simulate the movement of microbial particles with airflow within the reinforcing layer material, and the microbial barrier efficiency is predicted.

[0044] In one embodiment of the present invention, the microbial barrier efficiency is determined by the following formula:

[0045]

[0046]

[0047] In the formula, η is the microbial barrier efficiency, N is the number of microbial particles, the subscripts in and out represent the inlet and outlet positions, m is the particle mass (kg), and v is the particle velocity (m·s). -1 ), u represents fluid velocity (m·s) -1 ), t is time (s), μ is dynamic viscosity (kg·m³) -1 ·s -1 R represents the particle radius (m), C c Represents the Cunningham correction factor, where D is the particle diffusion coefficient (m 2 ·s), dW represents 3D Wiener measure (s -0.5 ), where F is the force required to accelerate the fluid around the particle.

[0048] The second formula is the microbial trajectory obtained by solving the particle motion equation.

[0049] Based on this, through parameter statistics and quantitative analysis, the antibacterial and antifungal effect of the airtight membrane reinforcement layer material can be accurately evaluated using particle tracking results.

[0050] In summary, the above technical solution has the following key points:

[0051] 1) Precise modeling method integrating multiple technologies: It integrates high-resolution CT tomography, three-dimensional geometric reconstruction and voxel mesh generation technology to achieve micron-level precise characterization of the pore structure of spunlace nonwoven fabric, breaking through the bottleneck of traditional methods in modeling complex fiber spatial distribution, and providing a real and reliable three-dimensional structural basis for subsequent flow field simulation.

[0052] 2) Fluid-structure interaction microbial motion simulation technology: Based on CFD flow field calculation, bacteria / fungi are simplified into spherical particles, and their motion trajectory under multiple mechanisms such as collision, interception, and diffusion in fiber pores is quantitatively simulated. Combined with specific particle size distribution, dynamic prediction of the antibacterial and antifungal effect of materials can be achieved.

[0053] 3) Low-cost and efficient material antibacterial and antifungal evaluation system: By replacing traditional experiments with numerical simulation, the material performance evaluation cycle is shortened. At the same time, it can reveal the quantitative relationship between structural parameters such as fiber spacing and porosity and antibacterial performance, providing a traceable theoretical basis and data support for material design.

[0054] The above technical solution has the following beneficial effects:

[0055] 1) Compared with traditional experimental evaluation methods, the antibacterial and antifungal effect prediction method proposed in this invention breaks through the bottleneck of experimental methods relying on physical sample preparation and long testing cycles, and shortens the material performance evaluation cycle through computer simulation. This method is applicable to spunlace nonwoven materials with different fiber densities and different process parameters, and can quickly simulate multiple structural schemes without repeated sample preparation, thus reducing the material research and development cycle.

[0056] 2) This invention establishes a quantitative correlation model between material microstructure (such as fiber orientation factor and average fiber spacing) and antibacterial efficiency by coupling CT reconstruction and CFD simulation, providing a traceable theoretical basis for structural optimization and improving the scientific and intelligent level of research and development.

[0057] 3) Taking a certain type of spunlace nonwoven fabric as an example, the method proposed in this invention is used to predict and evaluate the antibacterial and antifungal effects of the material. The pretreated sample to be tested is as follows: Figure 4 As shown, Figure 5 This is a CT scan image of a portion of the sample. The reconstructed sample feature structure based on the scan data can be found in [reference needed]. Figure 6 CFD simulations can be used to obtain the material's barrier efficiency against microorganisms of different particle sizes. See [link to relevant documentation]. Figure 7 The number of microorganisms of each particle size released at the entrance of the computational domain is 10,000.

[0058] like Figure 2 , Figure 3 As shown, this invention provides a device for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material. The device can be implemented via software, hardware, or a combination of both. From a hardware perspective, such as... Figure 2 The diagram shown is a hardware architecture diagram of an electronic device for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material according to an embodiment of the present invention. (Except for...) Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of the electronic device in which it is located reading the corresponding computer program from the non-volatile memory into the memory for execution.

[0059] This embodiment provides a device for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material, comprising:

[0060] The scanning module 300 is used to perform CT scans on the airtight membrane reinforcement material to be predicted and obtain the scan results.

[0061] The generation module 302 is used to generate a three-dimensional voxel mesh based on the scanning results;

[0062] The prediction module 304 is used to perform CFD simulation on the three-dimensional voxel grid to predict the microbial barrier efficiency.

[0063] In this embodiment of the invention, the scanning module 300 can be used to execute step 100 in the above method embodiment, the generation module 302 can be used to execute step 102 in the above method embodiment, and the prediction module 304 can be used to execute step 104 in the above method embodiment.

[0064] In one embodiment of the present invention, the airtight membrane reinforcement material is a cubic sample, and moisture is removed by vacuum drying.

[0065] In one embodiment of the present invention, the generation module is configured to perform the following operations:

[0066] Based on the scanning results, the distribution pattern of fibers within the material was statistically analyzed;

[0067] The fiber and pore regions are separated by a threshold segmentation algorithm, the three-dimensional volume data is reconstructed by a filtered back projection algorithm, the geometric morphology of the pore surface is extracted by the Marching Cubes algorithm, and an STL format model file that meets the accuracy requirements is generated.

[0068] The model file is mesh optimized, redundant triangular faces are removed, the voxel mesh resolution is set, and an octree voxelization algorithm is used to generate a three-dimensional voxel matrix.

[0069] Calculate the porosity and fiber orientation distribution of the three-dimensional voxel matrix and compare it with the distribution pattern;

[0070] If the comparison results meet the accuracy requirements, then perform CFD simulation on the three-dimensional voxel mesh; otherwise, increase the voxel mesh resolution and perform mesh optimization on the model file.

[0071] In one embodiment of the present invention, the prediction module is configured to perform the following operations:

[0072] The three-dimensional voxel mesh is embedded within a cuboid computational domain, and the cuboid computational domain is extended along the thickness direction of the three-dimensional voxel mesh at the entrance and exit positions of the three-dimensional voxel mesh.

[0073] Set the inlet as a velocity inlet and the outlet as a pressure outlet, set the remaining boundaries as symmetrical or periodic boundary conditions, and set the simulation convergence residual.

[0074] Set the microbial particle size distribution and the number of microbial particles released;

[0075] Particles are uniformly released at the inlet section of the cuboid computational domain to simulate the movement of microbial particles with airflow within the reinforcing layer material, thereby predicting the microbial barrier efficiency.

[0076] In one embodiment of the present invention, the microbial barrier efficiency is determined by the following formula:

[0077]

[0078]

[0079] In the formula, η is the microbial barrier efficiency, N is the number of microbial particles, the subscripts in and out represent the inlet and outlet positions, m is the particle mass, v is the particle velocity, u represents the fluid velocity, t is time, μ is the dynamic viscosity, R represents the particle radius, and C... c dW represents the Cunningham correction factor, D is the particle diffusion coefficient, dW represents the 3D Wiener measure, and F is the force required to accelerate the fluid around the particle.

[0080] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a device for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material. In other embodiments of the present invention, a device for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0081] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0082] This invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a method for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material according to any embodiment of this invention.

[0083] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program causes the processor to perform a method for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material according to any embodiment of this invention.

[0084] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0085] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0086] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0087] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0088] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0089] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or electronic device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or electronic device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or electronic device that includes said element.

[0090] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various storage media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material, characterized in that, include: The airtight membrane reinforcement material to be predicted was subjected to CT scanning, and the scanning results were obtained; Based on the scan results, a three-dimensional voxel mesh is generated; CFD simulation was performed on the three-dimensional voxel mesh to predict the microbial barrier efficiency. The three-dimensional voxel mesh is embedded within a cuboid computational domain, and the cuboid computational domain is extended along the thickness direction of the three-dimensional voxel mesh at the entrance and exit positions of the three-dimensional voxel mesh. Set the inlet as a velocity inlet and the outlet as a pressure outlet, set the remaining boundaries as symmetrical or periodic boundary conditions, and set the simulation convergence residual. Set the microbial particle size distribution and the number of microbial particles released; Particles are uniformly released at the inlet section of the cuboid computational domain to simulate the movement of microbial particles with airflow within the reinforcing layer material, thereby predicting the microbial barrier efficiency.

2. The method according to claim 1, characterized in that, The airtight membrane reinforcement material is a cubic sample, and moisture is removed by vacuum drying.

3. The method according to claim 1, characterized in that, The step of generating a three-dimensional voxel mesh based on the scan results includes: Based on the scanning results, the distribution pattern of fibers within the material was statistically analyzed; The fiber and pore regions are separated by a threshold segmentation algorithm, the three-dimensional volume data is reconstructed by a filtered back projection algorithm, the geometric morphology of the pore surface is extracted by the Marching Cubes algorithm, and an STL format model file that meets the accuracy requirements is generated. The model file is mesh optimized, redundant triangular faces are removed, the voxel mesh resolution is set, and an octree voxelization algorithm is used to generate a three-dimensional voxel matrix. Calculate the porosity and fiber orientation distribution of the three-dimensional voxel matrix and compare it with the distribution pattern; If the comparison results meet the accuracy requirements, then perform CFD simulation on the three-dimensional voxel mesh; otherwise, increase the voxel mesh resolution and perform mesh optimization on the model file.

4. The method according to claim 1, characterized in that, The microbial barrier efficiency is determined by the following formula: In the formula, η For microbial barrier efficiency, N The subscripts 'in' and 'out' represent the number of microbial particles, respectively, and their distributions indicate the inlet and outlet locations. m Let v be the particle mass, v be the particle velocity, and u be the fluid velocity. t For time, μ For dynamic viscosity, R Indicates particle radius, C c Represents the Cunningham correction factor. D dW represents the particle diffusion coefficient, dW represents the 3D Wiener measure, and F is the force required to accelerate the fluid around the particle.

5. A device for predicting the microbial barrier efficiency of an airtight membrane reinforcement layer material, characterized in that, include: The scanning module is used to perform CT scans on the airtight membrane reinforcement material to be predicted and obtain the scan results; A generation module is used to generate a three-dimensional voxel mesh based on the scanning results; The prediction module is used to perform CFD simulation on the three-dimensional voxel mesh to predict the microbial barrier efficiency. The prediction module is used to perform the following operations: The three-dimensional voxel mesh is embedded within a cuboid computational domain, and the cuboid computational domain is extended along the thickness direction of the three-dimensional voxel mesh at the entrance and exit positions of the three-dimensional voxel mesh. Set the inlet as a velocity inlet and the outlet as a pressure outlet, set the remaining boundaries as symmetrical or periodic boundary conditions, and set the simulation convergence residual. Set the microbial particle size distribution and the number of microbial particles released; Particles are uniformly released at the inlet section of the cuboid computational domain to simulate the movement of microbial particles with airflow within the reinforcing layer material, thereby predicting the microbial barrier efficiency.

6. The apparatus according to claim 5, characterized in that, The airtight membrane reinforcement material is a cubic sample, and moisture is removed by vacuum drying.

7. The apparatus according to claim 6, characterized in that, The generation module is used to perform the following operations: Based on the scanning results, the distribution pattern of fibers within the material was statistically analyzed; The fiber and pore regions are separated by a threshold segmentation algorithm, the three-dimensional volume data is reconstructed by a filtered back projection algorithm, the geometric morphology of the pore surface is extracted by the Marching Cubes algorithm, and an STL format model file that meets the accuracy requirements is generated. The model file is mesh optimized, redundant triangular faces are removed, the voxel mesh resolution is set, and an octree voxelization algorithm is used to generate a three-dimensional voxel matrix. Calculate the porosity and fiber orientation distribution of the three-dimensional voxel matrix and compare it with the distribution pattern; If the comparison results meet the accuracy requirements, then perform CFD simulation on the three-dimensional voxel mesh; otherwise, increase the voxel mesh resolution and perform mesh optimization on the model file.

8. The apparatus according to claim 5, characterized in that, The microbial barrier efficiency is determined by the following formula: In the formula, η For microbial barrier efficiency, N The subscripts 'in' and 'out' represent the number of microbial particles, respectively, and their distributions indicate the inlet and outlet locations. m Let v be the particle mass, v be the particle velocity, and u be the fluid velocity. t For time, μ For dynamic viscosity, R Indicates particle radius, C c Represents the Cunningham correction factor. D dW represents the particle diffusion coefficient, dW represents the 3D Wiener measure, and F is the force required to accelerate the fluid around the particle.

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