A computational fluid dynamics-discrete phase model simulation evaluation method for the transport and deposition characteristics of contaminated dust in the intersection area of underground roadway and laboratory

Abstract

The application discloses a kind of underground roadway and laboratory intersection area pollution dust migration and deposition characteristics of computational fluid dynamics-discrete phase model simulation evaluation method, belong to high-level radioactive waste geological disposal engineering and underground space dust pollution simulation evaluation technical field.The method is by constructing the three-dimensional numerical model of the intersection structure of underground main roadway and lateral laboratory, solves ventilation flow field using Reynolds average Navier-Stokes equation and shear stress transport k-ω turbulence model, the motion trajectory of different particle size granite dust particles is tracked using discrete phase model, and the relative contribution of each wall surface to particle capture is quantitatively identified by introducing the diagnostic contribution rate of split wall capture.The application can systematically evaluate the escape rate, deposition rate and main capture area of particles, and provide technical support for underground engineering ventilation optimization and dust control.

Description

Technical Field

[0001] This invention relates to the field of high-level radioactive waste geological disposal engineering and underground space dust pollution simulation and assessment technology. Specifically, it relates to a computational fluid dynamics-discrete phase model simulation and assessment method for the migration and deposition characteristics of contaminated dust in the intersection of underground tunnels and laboratories. This method is applicable to the analysis of migration patterns of contaminated granite dust and the optimization of ventilation schemes in underground laboratories and disposal repositories for high-level radioactive waste geological disposal. Background Technology

[0002] Geological disposal of high-level radioactive waste is an internationally recognized primary method for the safe disposal of such waste. Underground laboratories and storage facilities are typically constructed within deep granite rock formations. During the construction, commissioning, and operation of the storage facility, the excavation of surrounding rock, equipment disturbance, personnel activity, and ventilation inevitably induce the resuspension and migration of fine-particle rock dust. When this dust carries radioactive contaminants or acts as a contaminant carrier, its transport, retention, and deposition behavior within the underground tunnel system directly impacts pollution control effectiveness, personnel protection levels, and the development of optimized ventilation strategies.

[0003] The junction structure of underground tunnels and laboratories is a common and typical spatial configuration in underground engineering, exhibiting significant three-dimensional flow characteristics. When the ventilation airflow from the main tunnel passes through the laboratory entrance, shear layers, entrainment, and local backflow easily form in the junction area; simultaneously, low-velocity zones and large-scale circulating flow often exist within the laboratory. Compared to straight tunnels, particle transport in junction structures is no longer solely controlled by the mainstream direction, but is jointly influenced by the carrying capacity of the main tunnel, the entrainment effect in the junction area, backflow retention within the laboratory, and near-wall settling processes. Especially for micron-sized particles, particle size differences significantly affect the airflow homing, gravity settling, and inertial response of particles, thereby altering their escape, retention, and wall trapping tendencies.

[0004] Currently, computational fluid dynamics (CFD) – discrete phase models and related Eulerian-Lagrange methods have been widely applied to the study of ventilation flow fields and dust diffusion in scenarios such as underground roadways, coal mining spaces, and underground silos. In studies of tunnel construction, air curtain dust control, drilling dust removal, and industrial transport dust emissions, CFD methods have also been used to compare the effects of ventilation organization, local recirculation, and dust control configurations on particle migration. These methods can track discrete particle trajectories based on the analytical continuous phase flow field and further statistically analyze particle escape, wall trapping, and deposition outcomes. Furthermore, aerosol and ventilation duct particle deposition studies show that the transport outcomes of micron-sized particles are jointly influenced by particle size, turbulent diffusion, gravity settling, near-wall interactions, and local flow structures. While the aforementioned existing technologies provide a methodological basis for this invention, they still have significant shortcomings in the systematic evaluation of complex flows in the convergence zone and particle size effects.

[0005] However, existing research still has the following shortcomings: First, there is still a lack of research on the transport of contaminated granite dust in the intersection structure of underground laboratories and main tunnels, and there is a lack of systematic analysis on particle size effect, ventilation velocity coupling effect and diagnosis of main trapping walls.

[0006] Second, in complex converging flow fields, the particle escape rate may not simply change monotonically with ventilation velocity. Existing research has failed to fully reveal the local entrainment, backflow retention, and near-wall trapping mechanisms behind the non-monotonic change in particle escape rate, resulting in a lack of reliable theoretical basis for ventilation optimization.

[0007] Third, most existing methods lack systematic diagnosis of the capture contribution rate of different walls, making it difficult to identify the main deposition areas of particles and pollution hotspots.

[0008] Fourth, existing studies lack sufficient stability assessment of the random tracking results of discrete phase models and lack systematic testing of the adequacy of repeated tracking and discrete random walk sampling, which affects the reliability of the simulation results. Summary of the Invention

[0009] To address the aforementioned shortcomings in existing technologies, this invention provides a computational fluid dynamics-discrete phase model simulation and evaluation method for the migration and deposition characteristics of pollutant dust in the intersection of underground tunnels and laboratory environments. This method establishes a three-dimensional numerical model to systematically analyze the migration, escape, and deposition patterns of particles of different sizes under varying ventilation velocities. It also introduces wall-mounted trapping diagnostic contribution rates and discrete phase model stochastic tracking stability assessments. This aims to solve the technical problems of predicting particle fate and identifying pollution hotspots in complex converging flow fields, providing reliable technical support for ventilation optimization and dust control in underground engineering projects.

[0010] To achieve the above-mentioned objectives, the present invention provides the following technical solution: A computational fluid dynamics-discrete phase model simulation and evaluation method for the migration and deposition characteristics of pollutant dust in the intersection of underground tunnels and laboratories includes the following steps: The first step is to construct a three-dimensional geometric model of the intersection of the underground main tunnel and the lateral laboratory, set a source area base plate in a local part of the laboratory base plate, and determine the calculation area, the location of the particle release surface, and the layout scheme of each monitoring section.

[0011] The second step is to divide the computational domain into grids, conduct grid independence checks, and determine the main computational grid.

[0012] The third step is to establish the continuous phase control equations and use the steady-state incompressible Reynolds-averaged Navier-Stokes equations and the shear stress transport k-ω turbulence model to solve the ventilation flow field.

[0013] The fourth step is to establish a discrete phase model, track particle trajectories through the discrete phase model, and set particle parameters, release methods, and boundary conditions; set all walls as trapping boundaries to count particle deposition, and set the outlet as an escape boundary to count particles escaping from the outlet; and enable a discrete random walk model to consider the influence of turbulent fluctuations on particle diffusion and trajectory fluctuations.

[0014] The fifth step is to conduct a sufficiency analysis of repeated pursuit and discrete random walk sampling in the discrete phase model to evaluate the stability of the random pursuit results.

[0015] The sixth step involves calculating the escape rate and deposition rate of particles of different sizes at a preset baseline ventilation speed, and analyzing the influence of particle size on particle orientation. The escape rate refers to the percentage of particles escaping from the outlet out of the total number of tracked particles, and the deposition rate refers to the percentage of particles captured by the wall out of the total number of tracked particles.

[0016] The seventh step is to calculate the wall surface trapping diagnostic contribution rate for particles of different sizes. The wall surface trapping diagnostic contribution rate is used to characterize the relative contribution of each wall surface to particle trapping. The diagnostic calculation is performed by setting a single wall surface as the trapping boundary and the remaining wall surfaces as the escape boundary.

[0017] The eighth step involves setting multiple inlet ventilation velocities, calculating the particle escape rate and deposition rate under different combinations of wind speed and particle size, and analyzing the coupling effect between ventilation velocity and particle size.

[0018] The ninth step involves comparing and analyzing the non-monotonic change in escape rate with the results of local streamlines, particle residence time trajectories, and wall trapping diagnostics to determine the coupling mechanism between the flow field and particles.

[0019] The tenth step outputs the evaluation results of particle escape rate, deposition rate, and wall trapping diagnostic contribution rate.

[0020] In one possible embodiment, the geometric model described in the first step consists of a main tunnel and a lateral laboratory, which are connected at their intersection. The main tunnel is used to simulate the main underground ventilation channel, and the laboratory is used to characterize the space where localized contaminated dust may be released, retained, and deposited. A source area floor is set locally on the laboratory floor to characterize the initial source of contaminated granite dust. The particle release surface is set as a suspended release surface 0.10 meters above the source area floor. The source area floor is not used as a particle release surface in the discrete phase model calculation, but rather as a trapping surface for wall deposition statistics. By setting the particle release surface above the source area floor instead of releasing directly on the wall, non-physical instantaneous trapping of particles is avoided in numerical terms, improving the realism and accuracy of the simulation.

[0021] In one possible embodiment, the monitoring sections in the first step include sections at the main roadway intersection, the laboratory entrance, the middle of the laboratory, the area near the source region, and the deep part of the laboratory, used to extract parameters such as area-weighted average velocity, turbulent kinetic energy, and pressure. The setup of these monitoring sections provides a data foundation for subsequent grid independence testing and quantitative comparison of continuous phase flow field characteristics under different wind speed conditions.

[0022] In one possible embodiment, the mesh generation in the second step uses an unstructured tetrahedral mesh to discretize the computational domain, with local refinement near the junction and source regions. The unstructured tetrahedral mesh can better adapt to the discretization requirements of complex geometries, while local refinement improves the analytical accuracy of complex flows and particle release regions. Three mesh schemes—coarse, intermediate, and fine—are constructed. Inlet pressure drop, average velocity in the main tunnel junction region, laboratory inlet velocity, laboratory mid-section velocity, velocity near the source region, laboratory deep-section velocity, and turbulent kinetic energy in the junction region are selected as key monitoring indicators for mesh independence testing. Based on the fine mesh results as a reference, error analysis is performed on the coarse and intermediate mesh calculation results, and the intermediate mesh is selected as the main computational mesh. This intermediate mesh ensures computational accuracy while also considering computational cost, providing an economical and efficient computational mesh for subsequent discrete phase model calculations.

[0023] In one possible embodiment, the continuous phase control equation in the third step is solved using the Reynolds-averaged Navier-Stokes equations. The turbulence model selected is the shear stress transport k-ω turbulence model, which combines the advantages of the near-wall k-ω model and the far-field k-ε model, and is suitable for complex confluence flows with wall boundary layers, flow separation, and local backflow. The inlet boundary is set as a velocity inlet, and the outlet boundary is set as a pressure outlet, with an outlet gauge pressure of 0 Pascals. No-slip boundary conditions are applied to the wall. Five inlet ventilation velocities are set: 0.15 m / s, 0.25 m / s, 0.35 m / s, 0.45 m / s, and 0.55 m / s, with 0.35 m / s being the baseline condition. The above boundary conditions and velocity range settings provide a complete operating condition matrix for systematically studying the coupling relationship between ventilation velocity and particle orientation.

[0024] In one possible embodiment, the particles in the fourth step are assumed to be spherical inert particles, and particle breakage, agglomeration, evaporation, hygroscopic growth, and inter-particle collisions are not considered. This assumption simplifies the physical model of particle motion, allowing the calculation to focus on the particle migration trajectory in the airflow, which is beneficial for improving computational efficiency and highlighting the core research variable of particle size effect. Particle drag is modeled using a spherical particle drag model, with particle motion mainly influenced by airflow drag, gravity, and turbulent random diffusion. Feedback effects of particles relative to the continuous phase momentum field are ignored, and unidirectional coupling is adopted. Since this invention focuses on the trajectory of low-concentration polluting dust, the unidirectional coupling assumption significantly reduces computational costs while ensuring computational accuracy. The particle material is set as granite dust with a particle density of 2700 kg / m³. The studied particle sizes include 1 μm, 2 μm, 3 μm, 5 μm, 7 μm, and 10 μm to cover typical behaviors such as fine particle migration, medium-sized particle transition transport, and large-sized particle sedimentation dominance. All walls are set as trapping boundaries for statistical particle deposition; the outlet is set as an escape boundary for statistical particle escape. The discrete random walk model is enabled to account for the effects of turbulent fluctuations on particle diffusion and trajectory oscillations.

[0025] Particle escape rate and sedimentation rate Defined as: ; ; in, The total number of tracked particles, The number of particles that escaped from the outlet. This represents the number of particles trapped by the wall.

[0026] The above definition quantifies particle fate into two complementary indicators: escape rate and deposition rate, providing a unified measurement standard for quantitative comparison of particle behavior under different particle sizes and wind speeds.

[0027] In one possible embodiment, the wall-mounted trapping diagnostic contribution rate in the seventh step is defined as: ; in, For the first The number of particles captured by a single wall when one wall is set as the capture boundary and the other walls are set as the escape boundary; This refers to the total number of particles captured under the calculation condition where all the aforementioned walls are set as capture boundaries. This index characterizes the relative contribution of different walls to particle capture and is a diagnostic normalized contribution rate. By setting each target wall as a capture boundary and the remaining walls as escape boundaries, the capture contribution rate of walls such as the source area floor, laboratory floor, laboratory sidewalls, laboratory roof, laboratory endwalls, intersection rounded corners, main tunnel floor, main tunnel sidewalls, and main tunnel roof can be quantitatively evaluated. This diagnostic method overcomes the deficiency of traditional methods that cannot distinguish the contribution of each wall when all walls are captured simultaneously, providing a scientific basis for pollution source tracing, risk zoning, and prioritization of treatment.

[0028] In one possible embodiment, the discrete phase model repeated tracking in the fifth step involves three repeated tracking operations using the same continuous phase flow field, the same release surface, the same particle size, the same boundary conditions, and the same discrete random walk parameters. The discrete random walk sampling sufficiency analysis compares changes in escape rate and the contribution rate of the main wall trapping diagnostics by setting the number of attempts to 5, 10, and 20, respectively. When the number of attempts increases from 5 to 20, the escape rate of 3-micron particles remains between 33.61% and 34.34%, and the main wall trapping pattern remains stable. A setting of 10 attempts is used as the formal discrete phase model tracking setting. The above stability analysis ensures the reliability and reproducibility of the discrete phase model random tracking results.

[0029] In one possible embodiment, the eighth step generates a discrete phase model parameter matrix consisting of 5 wind velocities multiplied by 6 particle sizes, tracking a total of 9270 particle trajectories for each operating condition. All particle size and wind speed combinations use the same release surface, the same wall boundary conditions, and the same tracking settings to ensure comparability between different operating conditions. This systematic operating condition matrix design makes coupled analysis of particle size and wind speed effects possible.

[0030] In one possible embodiment, the local streamlines in the ninth step are colored by velocity, and the particle residence time trajectory is colored by residence time, to compare the differences in local flow structure and particle migration path between the confluence zone and the laboratory interior under different wind speeds. The wall-based trapping diagnostics employs a single-target wall-based trapping boundary and escape boundaries on other walls to identify changes in the main trapping area for 3-micron particles at representative inlet wind speeds.

[0031] The non-monotonic escape behavior is manifested as follows: the escape rates of 3-micron particles at 0.35 m / s, 0.45 m / s, and 0.55 m / s are 33.61%, 15.90%, and 43.02%, respectively; at 0.45 m / s, the capture contribution rates of the main roadway floor and sidewalls increase to 15.93% and 11.20%, respectively, both higher than those at adjacent wind speeds. This non-monotonic behavior is attributed to a competitive mechanism where entrainment in the confluence zone is enhanced at intermediate wind speeds, but transport in the outlet direction has not yet become dominant. The elucidation of this mechanism reveals that the particle escape rate in complex confluence flow fields does not change monotonically with ventilation velocity, providing important theoretical guidance for ventilation optimization.

[0032] In one possible embodiment, the evaluation results in the tenth step include: (a) the escape rate and deposition rate of particles of different sizes at a reference wind speed; (b) a heatmap of the contribution rate of wall-based trapping diagnosis for particles of different sizes at a reference wind speed; (c) heatmaps of particle escape rate and deposition rate under different combinations of inlet wind speed and particle size; (d) curves showing the change in escape rate of particles of different sizes with inlet wind speed; (e) local air streamline distribution map and particle residence time trajectory distribution map at a representative wind speed; and (f) a heatmap of the contribution rate of wall-based trapping diagnosis at a representative wind speed. These multi-dimensional evaluation results provide comprehensive data support for optimizing ventilation schemes and formulating dust control measures in underground engineering projects.

[0033] Compared with the prior art, the present invention has the following beneficial effects: First, this invention establishes a fluid dynamics-discrete phase model of the underground main tunnel-experimental chamber intersection structure, and for the first time systematically analyzes the migration, escape and deposition patterns of contaminated granite dust with different particle sizes (1, 2, 3, 5, 7, 10 μm) in the intersection structure, filling a research gap in this field.

[0034] Second, this invention introduces a wall-surface trapping diagnostic contribution rate index, which can identify the differences in trapping positions of particles of different sizes in various wall surface regions. By setting the target wall surface as a trap and the remaining walls as escapes, the diagnostic method can quantitatively evaluate the trapping contribution rate of walls such as the source area floor, experimental chamber floor, experimental chamber sidewalls, experimental chamber roof, experimental chamber endwalls, intersection rounded corners, main tunnel floor, main tunnel sidewalls, and main tunnel roof, providing a scientific basis for pollution source tracing, risk zoning, and prioritization of treatment.

[0035] Third, this invention reveals for the first time the phenomenon of a non-monotonic decrease in the escape rate of 3 μm particles at an inlet wind speed of 0.45 m / s. By combining local streamlines, particle residence time trajectories, and wall-side trapping diagnostic results, this invention elucidates the flow field-particle coupling mechanism of this non-monotonic behavior: at intermediate wind speeds, entrainment in the confluence zone is enhanced, but transport in the outlet direction is not yet dominant, making it easier for particles to enter the coupling circulation zone of the main roadway-experiment chamber and be trapped on the wall near the confluence zone.

[0036] Fourth, the present invention systematically conducted repeated pursuit and discrete random walk (DRW) sampling sufficiency analysis of the discrete phase model (DPM), verifying the stability and reproducibility of the particle tracking results. Taking the 3 μm and 0.35 m / s conditions as an example, the results of three repeated pursuits were completely consistent (the escape rate was 33.61% in all cases); when the number of Tries increased from 5 to 20, the escape rate remained between 33.61% and 34.34%, with a maximum difference of only 0.72 percentage points. This technical feature ensures the reliability of the simulation results of the present invention.

[0037] Fifth, this invention systematically reveals the coupled influence of ventilation velocity and particle size on particle settling by combining five inlet air velocities (0.15, 0.25, 0.35, 0.45, and 0.55 m / s) with six particle sizes. The results show that particle size controls the basic level of particle escape capability, while inlet air velocity alters the migration path of particles in the main roadway-experimental chamber intersection area. This finding provides a quantitative basis for optimizing ventilation in underground engineering projects.

[0038] Sixth, the wall-mounted trapping diagnostic results of this invention show that the bottom plate of the experimental chamber is the main trapping area for 1-7 μm particles (contribution rate 63.45%-77.09%), while the trapping contribution rate of 10 μm particles in the source area bottom plate increases to 59.55%. This finding provides direct guidance for the structural design of high-level radioactive waste geological disposal projects that are easy to clean, monitor, and isolate.

[0039] Seventh, the method of the present invention has broad application prospects. It is not only applicable to ventilation optimization, dust control and pollution risk assessment of underground laboratories and disposal facilities for geological disposal of high-level radioactive waste, but can also be extended to the field of dust pollution simulation and control in other underground engineering projects (such as mine roadways, traffic tunnels and underground storage facilities). Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a schematic diagram showing the arrangement of the calculation area, particle release surface, and monitoring section of the present invention. Figure 1 Figure a in the diagram is the overall geometric model of the main tunnel and laboratory system. Figure 1 Figure b shows a local arrangement of the particle release surface and the monitoring section.

[0041] Figure 2 This is a graph showing the results of the mesh independence test for this invention, wherein... Figure 2 Figure a in the figure shows the normalized results of the main transport indicators. Figure 2 Figure b in the figure shows the normalized results of the velocity index in the low-speed zone of the laboratory.

[0042] Figure 3 This is a velocity distribution diagram of the intersection zone and the longitudinal section of the laboratory under the reference inlet wind speed of this invention.

[0043] Figure 4 This is a comparison chart of the escape rate and deposition rate of particles of different sizes at the reference inlet wind speed of this invention.

[0044] Figure 5 This is a heatmap showing the contribution rate of the wall surface trapping and diagnosis of particles of different sizes at the reference inlet wind speed of this invention.

[0045] Figure 6 This is a thermogram showing the particle escape rate and deposition rate under different inlet wind velocities and particle sizes according to the present invention. Figure 6 Figure a in the diagram is a thermogram of particle escape rate. Figure 6 Figure b in the figure is a thermogram of particle deposition rate.

[0046] Figure 7 This is a graph showing the change in escape rate of particles of different sizes with inlet wind speed according to the present invention.

[0047] Figure 8 This is a diagram showing the airflow distribution in the convergence zone and local area of ​​the laboratory under different inlet wind velocities according to the present invention. Figure 8 Figure a shows the local air streamline distribution at an inlet ventilation velocity of 0.35 m / s. Figure 8 Figure b shows the local air streamline distribution at an inlet ventilation velocity of 0.45 m / s. Figure 8 Figure c shows the local air streamline distribution at an inlet ventilation velocity of 0.55 m / s.

[0048] Figure 9 This is a distribution diagram of the residence time trajectory of 3-micron particles under different inlet wind velocities according to the present invention. Figure 9 Figure a shows the particle residence time trajectory at an inlet ventilation velocity of 0.35 m / s. Figure 9 Figure b shows the particle residence time trajectory at an inlet ventilation velocity of 0.45 m / s. Figure 9Figure c shows the particle residence time trajectory at an inlet ventilation velocity of 0.55 m / s.

[0049] Figure 10 This is a heat map showing the contribution rate of 3-micron particle capture and diagnosis at a representative inlet wind speed of this invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. 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. Example 1

[0051] This embodiment takes the typical intersection structure of the main tunnel and laboratory in an underground laboratory for geological disposal of high-level radioactive waste as the object, and provides a detailed description of the computational fluid dynamics-discrete phase model simulation and evaluation method for the migration and deposition characteristics of pollutant dust in the intersection area of ​​underground tunnel and laboratory provided by the present invention.

[0052] Step 1: Constructing a 3D geometric model The computational domain consists of a main tunnel and lateral laboratories, which are connected at their intersection. The main tunnel simulates the main underground ventilation channel, while the laboratories characterize the spaces where localized contaminated dust may be released, retained, and deposited. To describe the initial source of contaminated granite dust, a source area floor is locally defined on the laboratory floor. Considering that releasing particles directly onto the wall surface could easily result in non-physical instantaneous trapping, the particle release surface is set as a suspended release surface located 0.10 meters above the source area floor. Therefore, the source area floor is not used as a particle release surface in the discrete phase model calculation, but rather as a trapping surface for wall deposition statistics. Figure 1 Figure a in the diagram is a schematic diagram of the overall geometric model of the main tunnel and laboratory system of this invention. Figure 1 Figure b in the diagram is a schematic diagram of the local arrangement of the particle release surface and the monitoring section.

[0053] To monitor the flow field characteristics in different regions, several monitoring sections were set up in the main channel intersection area, laboratory entrance, middle of the laboratory, near the source region, and deep within the laboratory, respectively denoted as the main channel intersection section, laboratory entrance section, middle of the laboratory section, near the source region section, and deep within the laboratory section. These sections were used to extract parameters such as area-weighted average velocity, turbulent kinetic energy, and pressure, and were also used for grid independence testing and comparison of continuous phase flow field characteristics under different wind speed conditions. The rational arrangement of the above monitoring sections ensures that this invention can comprehensively capture the airflow characteristics at key locations in the intersection area and within the laboratory, providing a sufficient data foundation for the subsequent flow field interpretation of particle migration behavior.

[0054] Step 2: Mesh generation and mesh independence check An unstructured tetrahedral mesh was used to discretize the computational domain, with local refinement near the intersection and source regions to improve the analytical capability for complex flows and particle release areas. The unstructured tetrahedral mesh has good geometric adaptability and can accurately approximate the complex three-dimensional configuration of the intersection of the main channel and the laboratory, while local refinement ensures the computational accuracy in areas with drastic flow changes.

[0055] Three mesh schemes—coarse, intermediate, and fine—were constructed. Basic information for the three mesh schemes is shown in Table 1.

[0056] Table 1. Basic Information of the Grid

[0057] The key monitoring indicators were selected as follows: inlet pressure drop, average velocity in the main roadway intersection zone, laboratory inlet velocity, laboratory mid-section velocity, velocity near the source region, laboratory deep-section velocity, and turbulent kinetic energy in the intersection zone. Based on the results of the fine-grid analysis, error analysis was performed on the calculation results of the coarse-grid and intermediate-grid analyses. Figure 2 Figure a in the figure shows the normalized results of the main transport indicators. Figure 2 Figure b in the figure shows the normalized results of the velocity index in the low-speed zone of the laboratory.

[0058] The results show that the intermediate grid exhibits good consistency with the fine grid in key flow field parameters such as the velocity in the main channel junction area and the velocity in the middle of the laboratory, indicating that the mainstream transport characteristics, such as the velocity in the main channel junction area and the velocity in the middle of the laboratory, are not sensitive to the grid. However, the inlet pressure drop, the velocity in the low-velocity region at the laboratory inlet, and the turbulent kinetic energy in the junction area show some grid sensitivity. Considering that the subsequent analysis focuses on the relative trends between different operating conditions rather than the absolute value of local turbulent kinetic energy, the intermediate grid is selected as a compromise between computational accuracy and computational cost. The overall y+ of this main computational grid is approximately 2.55, which meets the computational requirements for analyzing the flow characteristics in the near-wall and junction areas. Through the system's grid independence test, this invention ensures that the continuous phase flow field upon which the subsequent discrete phase model calculations rely is grid independent, thereby guaranteeing the reliability of the simulation results.

[0059] Step 3: Solving the continuous phase flow field The ventilation airflow is considered as a steady-state, incompressible airflow. The continuous phase is solved using the Reynolds-averaged Navier-Stokes equations. The turbulence model selected is the shear stress transport k-ω turbulence model, which combines the advantages of the near-wall k-ω model and the far-field k-ε model, and is suitable for complex confluence flows with wall boundary layers, flow separation, and local backflow. The shear stress transport k-ω turbulence model has significant advantages in capturing the development of shear layers in the confluence region and the separation of wall boundary layers, and is the preferred turbulence model for handling complex wall flows in this field.

[0060] The inlet boundary is set as a velocity inlet, and the outlet boundary is set as a pressure outlet (outlet gauge pressure is 0 Pascal). The wall surface adopts a no-slip boundary condition. Five inlet ventilation velocities are set: 0.15 m / s, 0.25 m / s, 0.35 m / s, 0.45 m / s, and 0.55 m / s, with 0.35 m / s being the baseline condition.

[0061] Figure 3 The velocity distribution at the intersection of the laboratory and the main aisle is presented at a baseline inlet wind speed of 0.35 m / s. It can be seen that the airflow in the main aisle exhibits a clear mainstream channel characteristic, while the velocity inside the laboratory is significantly lower than that in the main aisle, forming a low-velocity stagnation zone. This velocity difference indicates that the lateral laboratory is not completely ventilated synchronously with the mainstream, but rather experiences weak air exchange and localized backflow stagnation conditions, providing the flow field basis for subsequent particle retention and deposition on the floor. This flow field characteristic directly explains why the laboratory floor becomes the primary trapping area for particles of most sizes in this invention—the low-velocity stagnation zone reduces the probability of particles being carried away by the mainstream and escapes, increasing the chance of particles settling inside the chamber.

[0062] The characteristic parameters of the continuous phase flow field under different inlet wind velocities are shown in Table 2.

[0063] Table 2 Characteristic parameters of continuous phase flow field under different inlet wind velocities

[0064] As shown in Table 2, with the increase of inlet wind speed from 0.15 m / s to 0.55 m / s, the inlet-outlet pressure drop increased from 0.004171 Pascals to 0.044820 Pascals, indicating that the ventilation resistance of the roadway system significantly increased with the increase of inlet wind speed. The average velocity of the junction section was basically consistent with the inlet wind speed, indicating that a stable main roadway ventilation field satisfying the set conditions was formed under all wind speed conditions. The velocities of each monitoring section inside the laboratory also increased with the increase of inlet wind speed, indicating that enhancing inlet ventilation can improve the air exchange intensity inside the laboratory. At the same time, the average turbulent kinetic energy of the junction section increased from... The number of square meters per square second increased to The wind speed of 100 m² / s² indicates that the increased wind speed enhances shear disturbance and turbulent mixing in the convergence zone. This enhanced turbulent kinetic energy directly affects the intensity of turbulent diffusion of particles and is an important background flow field characteristic of the subsequent non-monotonic change in particle escape rate.

[0065] Step 4: Discrete Phase Model and Particle Setup The particulate phase is modeled using a discrete phase model with Lagrangian tracing. Particles are assumed to be spherical and inert, and particle breakage, agglomeration, evaporation, hygroscopic growth, and interparticle collisions are not considered. Particle drag is modeled using a spherical particle drag model, with particle motion primarily influenced by airflow drag, gravity, and turbulent random diffusion. Since this study focuses on the trajectory of low-concentration pollutant dust, the feedback effect of particles relative to the continuous phase momentum field is ignored, and unidirectional coupling is employed. These assumptions allow the calculations to focus on the passive migration behavior of particles in a known flow field, avoiding the additional computational complexity caused by interparticle interactions and multiphase coupling, and enabling the systematic analysis of particle size and wind speed effects.

[0066] The particulate material was set as granite dust with a particle density of 2700 kg / m³. The particle sizes studied included 1 μm, 2 μm, 3 μm, 5 μm, 7 μm, and 10 μm to cover typical behaviors such as fine particle migration, medium particle size transition transport, and large particle size settling.

[0067] Particles are released from the suspension release surface to characterize the equivalent resuspension process of contaminated floor dust entering the near-wall air layer after disturbance. All walls are set as trapping boundaries for statistical particle deposition; the outlet is set as an escape boundary. A discrete random walk model is enabled to account for the impact of turbulent fluctuations on particle diffusion and trajectory fluctuations.

[0068] Particle escape rate and sedimentation rate Calculate using Equations 1 and 2: in, The total number of tracked particles, The number of particles that escaped from the outlet. This represents the number of particles trapped by the wall.

[0069] The operating condition design and discrete phase model parameter settings in this embodiment are shown in Table 3.

[0070] Table 3 Operating Condition Design and Discrete Phase Model Parameter Settings

[0071] Step 5: Sufficiency Analysis of Repeated Pursuit and Discrete Random Walk Sampling in Discrete Phase Model To verify the reproducibility of the repeated tracking results of the discrete phase model, three repeated tracking operations were performed using an intermediate grid condition with 3-micrometer particles and an inlet wind speed of 0.35 m / s. Under the same continuous phase flow field, release surface, particle size, boundary conditions, and discrete random walk parameters, the three tracking results were consistently 9270 tracked items, 3116 escaped items, and 6154 captured items, corresponding to an escape rate of 33.61% and a deposition rate of 66.39%. This indicates that under the current Fluent tracking settings, the repeated calculation results are consistent and have good reproducibility. This result verifies the numerical stability of the discrete phase model tracking settings of this invention and ensures the reliability of subsequent analytical conclusions.

[0072] To evaluate the impact of the number of discrete random walk random tracking samples on particle fate statistics, the number of attempts was set to 5, 10, and 20, respectively, and the escape rate and the contribution rate of the main wall trapping diagnosis were compared under the conditions of 3 micrometers and 0.35 meters per second. The results are shown in Table 4.

[0073] Table 4. Escape rate and major wall diagnostic capture contribution rate of 3-micron particles under different discrete random walk attempt settings.

[0074] As shown in Table 4, when the number of attempts increased from 5 to 20, the escape rate of 3-micron particles remained between 33.61% and 34.33%, with a maximum difference of only 0.72 percentage points. The trapping pattern of the main walls also remained stable: the laboratory floor was consistently the most important trapping wall, contributing 75.58% to 76.44%; the source area floor was the second most important trapping wall, contributing 17.21% to 18.75%; the main tunnel floor contributed 5.18% to 5.95%; and the main tunnel sidewalls consistently contributed less than 0.20%. These results indicate that a trial count of 10 is sufficient to meet the stability requirements for particle escape rate and the contribution rate of the main wall diagnostic trapping. This sampling sufficiency analysis provides a quantitative basis for the reliability of the discrete phase model calculation results of this invention.

[0075] Step Six: Analysis of the Influence of Particle Size on Escape and Deposition at Baseline Wind Velocity To compare the fate of particles of different sizes at a baseline inlet wind velocity (0.35 m / s), the escape rate and deposition rate of particles with diameters of 1, 2, 3, 5, 7, and 10 micrometers were calculated. The results are shown in […]. Figure 4 .

[0076] Depend on Figure 4It is evident that the particle escape rate decreases significantly with increasing particle size, while the deposition rate increases. At a reference wind speed of 0.35 m / s, the escape rate of 1-micron particles is 73.27%, and the deposition rate is 26.72%; for 10-micron particles, the escape rate decreases to 0.01%, while the deposition rate increases to 99.99%. This indicates that, under the geometric structure, release conditions, and wall trapping boundary conditions of this invention, increasing particle size causes the particle tendency to gradually shift from flow-driven escape to wall trapping.

[0077] The aforementioned particle size effect mainly stems from differences in particle flow characteristics, inertial response, and gravity settling ability. Small-diameter particles exhibit strong airflow responsiveness, making them more likely to migrate with the main roadway ventilation airflow and escape from the outlet. As particle size increases, particle inertia and gravity settling strengthen, increasing the probability of collision and capture with the floor or other walls. When the particle size increases to 5 micrometers, the deposition rate approaches 90%, indicating that the particle transport mechanism gradually shifts from flow migration to deposition control. Under the geometry and ventilation conditions of this invention, 3-micrometer and 5-micrometer particles exhibit transitional characteristics between flow migration and gravity deposition, thus their escape and deposition rates are more sensitive to changes in local flow structure. This systematic quantification of the particle size effect provides a benchmark for subsequent wall-specific capture diagnosis and wind speed coupling analysis in this invention.

[0078] Step 7: Analysis of the contribution rate of wall-based trapping diagnosis at the baseline wind speed To analyze the differences in the collection positions of particles of different sizes in different wall regions, the contribution rate of wall-side collection diagnosis for each particle size at the baseline inlet wind velocity was calculated. The results are shown below. Figure 5 .

[0079] The contribution rate of wall-mounted trapping for diagnosis is calculated according to Equation 3:

[0080] in, For the first The number of particles captured by a single wall when one wall is set as the capture boundary and the other walls are set as the escape boundary; This represents the total number of particles captured under the calculation condition where all the aforementioned walls are set as capture boundaries.

[0081] The source area floor, laboratory floor, laboratory side wall, laboratory top, laboratory end wall, intersection rounded corner, main tunnel floor, main tunnel side wall, and main tunnel top represent the corresponding wall surfaces.

[0082] Depend on Figure 5It is evident that, at the baseline wind speed, particle capture of different sizes is primarily concentrated on the source region floor and the laboratory floor. For particles ranging from 1 to 7 micrometers, the laboratory floor is the most significant capture area, with its contribution rate ranging from 63.45% to 77.09%. This indicates that low-velocity flow, near-wall settling, and recirculation within the laboratory collectively promote particle capture on the laboratory floor. In contrast, the contribution rates of the laboratory sidewalls, laboratory roof, laboratory endwalls, junction rounded corners, main tunnel sidewalls, and main tunnel roof are generally lower, suggesting that these areas are not the primary capture locations at the baseline wind speed.

[0083] As particle size increases, the capture hotspot gradually shifts from the laboratory floor to the source area floor. The contribution rate of the source area floor increases from 9.25% at 1 μm to 59.55% at 10 μm, indicating that larger particles are more easily controlled by gravity settling and inertia after release, resulting in rapid capture near the source area. Conversely, the contribution rate of the main tunnel floor decreases from 10.78% at 1 μm to 0.06% at 10 μm, indicating that smaller particles are more easily carried into the main tunnel by airflow and transported and captured far from the source area. Therefore, the wall capture pattern at the baseline wind speed is jointly determined by low-speed retention in the laboratory, near-wall settling, and the inertial response controlled by particle size. These wall capture diagnostic results provide direct evidence for identifying pollution hotspots and prioritizing remediation in high-level radioactive waste geological disposal projects.

[0084] Step 8: Analysis of the Coupled Influence of Ventilation Velocity and Particle Size on Particle Escape Rate To further analyze the combined effect of inlet wind velocity and particle size, the escape rate and deposition rate were calculated under five inlet wind velocities and six particle size combinations. The results are shown in Tables 5 and 6. Figure 6 Figure a in the middle and Figure 6 Figure b in the diagram.

[0085] Table 5. Particle escape rate under different inlet velocities and particle sizes (%)

[0086] Table 6. Particle deposition rate under different inlet wind velocities and particle sizes (%)

[0087] From Tables 5 to 6 and Figure 6It is evident that particle size remains the primary factor controlling particle escape ability. Overall, smaller particles exhibit higher escape rates, while larger particles are primarily captured by the wall. 1-micron particles maintain a high escape rate under various inlet velocities, while 10-micron particles show a low escape rate across all conditions, even at 0.55 m / s, at only 1.77%. This indicates that larger particles have stronger settling and near-wall capture effects, and simply increasing the inlet velocity cannot completely counteract their deposition tendency. This finding suggests that for coarse dust particles, in-situ capture and cleanup at the source are more effective than relying on ventilation to carry them away.

[0088] The inlet wind velocity has a more significant impact on small and medium-sized particles. For 2, 3, and 5 micrometer particles, the escape rates all reach their highest values ​​at 0.55 m / s, at 53.89%, 43.02%, and 27.02%, respectively, indicating that higher wind velocities enhance the mainstream carrying capacity, causing some particles that might otherwise be captured in the laboratory or near the confluence area to continue migrating towards the outlet. In contrast, 7-micrometer and 10-micrometer particles still exhibit low escape rates even at high wind velocities, suggesting that sedimentation and wall capture remain dominant as particle size increases.

[0089] Figure 7 The escape rate of particles of different sizes as a function of inlet wind velocity is presented. Figure 7 It is evident that the escape rate does not increase monotonically with varying inlet wind speed. At 0.45 m / s, the escape rates for 1, 2, 3, and 5 micrometer particles are all lower than those at the adjacent 0.35 m / s and 0.55 m / s. For example, the escape rate for 3 micrometer particles decreases from 33.61% at 0.35 m / s to 15.90% at 0.45 m / s, before rising to 43.02% at 0.55 m / s. This phenomenon indicates that particle convergence at intermediate wind speeds is not solely controlled by the average inlet wind speed but is also influenced by the competitive relationships between entrainment in the confluence zone, laboratory backflow retention, and near-zone wall trapping. The discovery of this non-monotonic phenomenon is one of the important technical contributions of this invention, overturning the simplistic notion that "the higher the wind speed, the more particles escape," and providing more refined guidance for ventilation optimization.

[0090] Step Nine: Analysis of the Flow Field-Particle Coupling Mechanism for the Non-Monotonic Escape Behavior of 3-Micrometer Particles To explain the localized low escape rate of 3-micrometer particles at 0.45 m / s, three representative operating conditions of 0.35, 0.45, and 0.55 m / s were selected, and the local air streamlines, particle residence time trajectories, and wall-mounted trapping results were compared and analyzed.

[0091] Figure 8 Figure a in the middle Figure 8 Figure b in the middle and Figure 8 Figure c in the diagram shows the local air streamline distribution under operating conditions of 0.35 m / s, 0.45 m / s, and 0.55 m / s. (From...) Figure 8 It is evident that at 0.35 m / s, the airflow in the main channel has a certain entrainment effect on the laboratory, but the streamline intensity entering the laboratory is relatively limited, resulting in low-speed circulation within the laboratory. When the inlet wind speed increases to 0.45 m / s, the shearing effect in the junction area intensifies, with more streamlines being entrained into the laboratory, forming a more pronounced circulation path within it. This indicates that at moderate inlet wind speeds, the mainstream already possesses strong entrainment capacity, but overall scouring transport in the outlet direction has not yet become dominant. Further increasing the speed to 0.55 m / s enhances the mainstream transport capacity, and the streamlines extend more significantly downstream, suggesting enhanced transport in the outlet direction. This streamline evolution process intuitively demonstrates the non-monotonic effect of increased wind speed on the flow structure in the junction area—enhanced entrainment does not necessarily lead to enhanced transport; there is a wind speed threshold between the two.

[0092] Figure 9 Figure a in the middle Figure 9 Figure b in the middle and Figure 9 Figure c in the figure shows the residence time trajectories of 3-micrometer particles at inlet ventilation velocities of 0.35 m / s, 0.45 m / s, and 0.55 m / s, respectively. (From...) Figure 9 It is evident that at 0.45 m / s, 3-micron particles, after being entrained into the laboratory and confluence zone, do not immediately escape along the mainstream direction. Instead, they form a complex cyclical migration path within the laboratory and near the confluence zone. In contrast, at 0.35 m / s, particle entrainment is weaker, with sedimentation and retention concentrated more in the release area and near the laboratory floor. At 0.55 m / s, although local backflow still exists, the enhanced mainstream transport increases the probability of particles migrating towards the outlet. Therefore, 0.45 m / s can be understood as an intermediate wind speed state characterized by "enhanced entrainment but outlet transport not yet dominating." Comparative analysis of particle residence time trajectories verifies the direct impact of the aforementioned flow field evolution on particle behavior at the particle level.

[0093] To further verify this explanation, a diagnostic test was conducted on the wall-mounted trapping of 3-micron particles. The results are shown in Tables 7 and 8. Figure 10 .

[0094] Table 7. Overall trend statistics of 3-micron particles at representative inlet wind velocities.

[0095] Table 8. Contribution rate of 3-micron particle capture and diagnosis by wall surface at representative inlet wind velocities (%)

[0096] As shown in Tables 7 and 8, at a wind speed of 0.35 m / s, 3116 3-micron particles escaped, and 6154 were captured, resulting in an escape rate of 33.61%. At this point, the contribution rate of the laboratory floor was 75.77%, the source area floor was 18.22%, and the main tunnel floor and sidewalls contributed only 5.39% and 0.19%, respectively. This indicates that at low wind speeds, particles are mainly captured near the source area and on the laboratory floor, with a relatively low proportion entering the near-field zone of the main tunnel and being captured by the walls.

[0097] When the inlet wind speed increased to 0.45 m / s, the number of 3-micron particles escaping decreased to 1474, while the number captured increased to 7796, resulting in a decrease in the escape rate to 15.90%. At this point, the laboratory floor remained the primary capture area, but its contribution rate decreased from 75.77% to 52.46%. Simultaneously, the contribution rate of the main tunnel floor increased from 5.39% to 15.93%, and the contribution rate of the main tunnel sidewalls increased from 0.19% to 11.20%. This change indicates that the increase in escapable particles at 0.45 m / s is not merely due to more deposition on the laboratory floor, but rather to more particles entering the main tunnel and coupling with the laboratory flow field, where they are captured on the main tunnel floor and sidewalls in the near-convergence zone. This finding reveals the spatial pattern of particle capture location shifting from the laboratory interior to the near-convergence zone at intermediate wind speeds.

[0098] When the inlet wind speed further increased to 0.55 m / s, the number of 3-micron particles escaping increased to 3988, while the number captured decreased to 5282, resulting in an escape rate of 43.02%. Correspondingly, the contribution rates of the main roadway floor and main roadway sidewalls decreased to 7.08% and 5.23%, respectively, significantly lower than the 0.45 m / s condition. This indicates that under high wind speeds, the mainstream scouring and downstream transport capacity are enhanced, making it easier for particles to cross the near-confluence wall capture zone and migrate towards the outlet.

[0099] Figure 10 A heatmap showing the contribution rate of 3-micron particle capture and diagnosis based on the wall surface is presented. Figure 10 It is evident that the laboratory floor is an important collection area under all three wind speeds, but its contribution rate decreases significantly at 0.45 m / s. Meanwhile, the floor and sidewalls of the main tunnel show localized rise at 0.45 m / s, indicating enhanced collection in the near-zone floor and sidewalls of the main tunnel.

[0100] In summary, the non-monotonic escape behavior of 3-micron particles can be attributed to a competition of two types of effects: moderate wind speeds enhance entrainment in the convergence zone and promote near-wall trapping, while higher wind speeds enhance transport in the exit direction and increase the escape probability. This mechanism explanation is consistent with the results of continuous phase local streamlines, particle residence time trajectories, and partial wall trapping diagnostics. The systematic elucidation of this mechanism is a significant technical contribution of this invention, providing theoretical guidance for ventilation optimization in underground engineering that goes beyond a "single wind speed index."

[0101] Step 10: Output and Application of Evaluation Results Based on the above analysis, the following evaluation results are output: (a) At the baseline wind speed (0.35 m / s), the escape rate of 1-micron particles is 73.27%, and the escape rate of 10-micron particles is 0.01%, indicating that small-diameter particles are more likely to migrate to the outlet with the ventilation airflow, while large-diameter particles are more likely to be captured by the wall surface.

[0102] (ii) At the reference wind speed, the laboratory floor trapping contribution rate for 1 to 7 micrometer particles is 63.45% to 77.09%, and the source floor trapping contribution rate for 10 micrometer particles is 59.55%.

[0103] (iii) The escape rates of 3-micrometer particles at speeds of 0.35, 0.45 and 0.55 meters per second were 33.61%, 15.90% and 43.02%, respectively, showing a non-monotonic variation.

[0104] (iv) The sufficiency test of repeated pursuit and discrete random walk sampling of the discrete phase model shows that 10 attempts can meet the statistical stability requirements.

[0105] The above assessment results can be used to guide ventilation optimization, dust control, and pollution risk assessment for underground laboratories and storage facilities for the geological disposal of high-level radioactive waste. Specifically: The source area base plate, laboratory base plate, and confluence near-area wall are the more important collection areas under the working conditions of this invention. The structural design of these areas should prioritize easy cleaning, easy monitoring, and easy isolation.

[0106] Ventilation optimization should not only consider average wind speed, but also take into account wind speed distribution, pressure gradient, shear strength in the junction area, and local recirculation structure. For critical nodes in the treatment reservoir, a combined strategy of "main ventilation for stable transport, local directional extraction, and on-site capture in the source area" can be adopted.

[0107] For fine particles that may carry radioactive nuclides, their strong flow characteristics mean that once they enter the main ventilation flow, they are more likely to cross local control boundaries and spread to more distant areas; while larger particles tend to settle near the source area, forming local pollution hotspots. Tiered control measures should be implemented based on particle size characteristics. Example 2

[0108] This embodiment takes model credibility evaluation as the object to further illustrate the reliability of the method of the present invention.

[0109] Since this invention studies the intersection of an underground main tunnel and a laboratory structure, there is currently a lack of publicly available experimental data that fully corresponds to the geometric scale, release location, ventilation boundary, and particle parameters of this invention. Therefore, this invention does not present the calculation results as direct experimental verification results. Instead, it evaluates the model's reliability based on aspects such as continuous phase grid independence, discrete phase model repeatability, sufficiency of discrete random walk sampling, and consistency with existing research trends. This approach aligns with the fundamental principle in computational fluid dynamics research of discussing numerical verification, model validation, and applicability separately.

[0110] First, the continuous phase flow field was tested for grid independence using coarse, intermediate, and fine grids. The results show that the intermediate and fine grids maintain good consistency in key transport parameters such as the average velocity in the main roadway intersection area and the average velocity in the middle of the laboratory, indicating that the average velocity field on which the subsequent discrete phase model calculations rely has basic stability.

[0111] Secondly, the repeated tracking results of the discrete phase model have good reproducibility. Taking the intermediate grid case with 3-micron particles and an inlet wind speed of 0.35 m / s as an example, the results of the three repeated trackings are completely consistent.

[0112] Furthermore, discrete random walk sampling adequacy analysis showed that as the number of attempts increased from 5 to 20, the escape rate of 3-micron particles remained between 33.61% and 34.33%. The main wall trapping pattern also remained stable.

[0113] Finally, the results of this invention are consistent with the basic trends in existing studies on dust transport and particle deposition in underground tunnel ventilation. Previous research has shown that the fate of micron-sized particles is jointly controlled by particle size, inertial response, gravity settling, turbulent diffusion, and near-wall trapping; smaller particles generally have stronger flow migration capabilities, while larger particles are more prone to near-source settling and wall trapping. The results of this invention, showing a higher escape rate for 1-micron particles and predominantly deposition for 10-micron particles, are consistent with these trends.

[0114] The model of this invention still has clearly defined boundaries of application. Particles are assumed to be spherical and inert; particle collisions, resuspension, electrostatic interactions, hygroscopic growth, radioactive decay, pollutant adsorption or desorption, and dose conversion processes are not considered in the calculations. The wall trapping boundary represents the trapping statistics after the initial contact of particles with the wall, and is not equivalent to the long-term deposition, rebound, and resuspension processes in actual tunnels. Therefore, the results of this invention are applicable to comparing the physical migration and trapping trends of contaminated granite dust under given ventilation structures, release conditions, and wall boundaries, and should not be directly extrapolated to actual radioactive aerosol dose evaluation or long-term pollution evolution results. Example 3

[0115] This embodiment describes the application of the method of the present invention in geological disposal engineering of high-level radioactive waste.

[0116] High-level radioactive waste geological disposal projects are typically located in deep underground environments. During the construction, commissioning, and operation phases of underground laboratories and disposal facilities, excavation disturbances, equipment operation, and ventilation organization can all induce the resuspension and migration of fine particles from the surrounding rock, construction dust, and potential contaminants. This invention considers contaminated granite dust in the intersection structure of the underground main tunnel and the laboratory as a potential radioactive contaminant carrier. The related results have direct reference significance for understanding the contamination diffusion during the construction period of the disposal facility and in the auxiliary experimental space.

[0117] Figure 3 The flow field characteristics shown in Table 2 indicate that the confluence zone exhibits a clear mainstream channel, localized low-velocity zones, and enhanced turbulence. This suggests that complex confluence zones in underground engineering are not simply ventilation channels, but rather key control units that determine whether pollutants escape or accumulate locally. For treatment projects, in addition to the overall ventilation design, confluence nodes, turning sections, laboratory entrances, and localized low-velocity zones should be prioritized for treatment.

[0118] The results of this invention show that particle size has a significant impact on escape and deposition. These findings suggest that pollution risk identification in high-level radioactive waste disposal projects should not only focus on "whether there is dust," but should also be further analyzed in conjunction with particle size distribution and carrying capacity. During operations such as excavation, support, equipment replacement, and waste container handling in underground laboratories and disposal facilities, tiered control measures should be implemented based on particle size characteristics: fine particles should be prioritized for capture and escape prevention, while medium and large-sized particles should be subject to enhanced cleaning and collection of the source area floor, working surface, and near-source sediments.

[0119] This invention reveals a phenomenon that is particularly important for treatment engineering: an increase in inlet wind speed does not necessarily lead to a monotonically decreasing or monotonically increasing particle escape rate; some particle sizes will exhibit localized low escape rates at intermediate wind speeds. Figure 7 As shown in Tables 5 and 6, the escape rate of 3-micron particles at 0.45 m / s is actually lower than that at 0.35 m / s and 0.55 m / s. This indicates that in complex confluence structures, simply increasing the wind speed may simultaneously enhance entrainment, alter particle trajectories, and trigger stronger near-field trapping or localized circulation within a specific particle size range. Therefore, ventilation optimization should not only consider the average wind speed but also take into account wind speed distribution, pressure gradient, shear strength in the confluence zone, and localized recirculation structures.

[0120] From the perspective of spatial layout and engineering configuration Figure 5 and Tables 7 to 8, Figure 10The results indicate that the laboratory floor, source area floor, and the walls of the confluence area are the main capture zones. This suggests that the high-level radioactive waste repository should prioritize structural designs that facilitate cleaning, monitoring, and isolation at the laboratory floor, the bottom of the connecting alley, and the rounded corners of the confluence. For long-term maintenance and management of the repository, denser deposition monitoring points, air quality monitoring points, and local extraction ports can be deployed in these high-risk areas.

[0121] This invention also emphasizes the value of numerical diagnostic methods in engineering evaluation. The diagnostic results of wall-level capture (see...) Figure 5 Tables 7 to 8 and Figure 10 It can identify the main deposition sites of particles of different sizes, providing a methodological reference for pollution source tracking, risk zoning, and prioritization of treatment in high-level radioactive waste geological disposal projects.

[0122] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0123] It should be understood that although the present invention has been specifically disclosed through preferred embodiments and optional features, modifications, improvements, and variations can be made to the disclosed content by those skilled in the art, and such modifications, improvements, and variations are considered to be within the scope of the present invention. The materials, methods, and embodiments provided herein are representative and exemplary of preferred embodiments and are not intended to limit the scope of the present invention. The above is a detailed description of the preferred embodiments of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A computational fluid dynamics-discrete phase model simulation and evaluation method for the migration and deposition characteristics of pollutant dust in the intersection area of ​​underground tunnels and laboratories, characterized in that, Includes the following steps: A three-dimensional geometric model of the intersection of the underground main tunnel and the lateral laboratory was constructed. A source area floor was set in a local part of the laboratory floor to determine the calculation area, the location of the particle release surface, and the layout scheme of each monitoring section. The computational domain is divided into grids, grid independence is checked, and the main computational grid is determined. The continuous phase control equations are established, and the ventilation flow field is solved by the steady-state incompressible Reynolds-averaged Navier-Stokes equations and the shear stress transport k-ω turbulence model. A discrete phase model is established to track particle trajectories. Particle parameters, release methods, and boundary conditions are set. All walls are set as trapping boundaries to count particle deposition, and the outlet is set as an escape boundary to count particles escaping from the outlet. A discrete random walk model is enabled to consider the influence of turbulent fluctuations on particle diffusion and trajectory fluctuations. Adequacy analysis of repeated pursuit and discrete random walk sampling in discrete phase models was conducted to evaluate the stability of the random pursuit results; At a preset baseline ventilation velocity, the escape rate and deposition rate of particles of different sizes are calculated, and the influence of particle size on particle orientation is analyzed. The escape rate refers to the percentage of particles escaping from the outlet out of the total number of tracked particles, and the deposition rate refers to the percentage of particles captured by the wall out of the total number of tracked particles. The wall surface trapping diagnostic contribution rate of particles of different sizes is calculated. The wall surface trapping diagnostic contribution rate is used to characterize the relative contribution of each wall surface to particle trapping. The diagnostic calculation is performed by setting a single wall surface as the trapping boundary and the remaining wall surfaces as the escape boundary. Multiple inlet ventilation velocities were set up, and the particle escape rate and deposition rate under different combinations of wind speed and particle size were calculated to analyze the coupling effect between ventilation velocity and particle size. To address the non-monotonic change in escape rate, a comparative analysis was conducted using local streamlines, particle residence time trajectories, and wall-mounted trapping diagnostic results to determine the coupling mechanism between the flow field and particles. Output the particle escape rate, deposition rate, and wall trapping diagnostic contribution rate assessment results.

2. The method according to claim 1, characterized in that, The geometric model consists of a main tunnel and a lateral laboratory, which are connected in the intersection zone; the particle release surface is a suspended release surface located 0.10 meters above the source area floor; the source area floor participates in the wall deposition statistics as a trapping surface in the discrete phase model calculation.

3. The method according to claim 1, characterized in that, The computational domain is discretized using an unstructured tetrahedral grid, with local densification in the intersection zone and the area where the source zone floor is located and its adjacent areas. Three grid schemes—coarse, intermediate, and fine—are constructed. The inlet pressure drop, average velocity in the main roadway intersection zone, average velocity at the laboratory inlet, average velocity in the middle of the laboratory, average velocity near the source zone, average velocity in the deep part of the laboratory, and turbulent kinetic energy in the intersection zone are selected as monitoring indicators for grid independence testing. Using the fine mesh results as a reference, the intermediate mesh is selected as the main computational mesh.

4. The method according to claim 1, characterized in that, The continuous phase control equations are solved using the Reynolds-averaged Navier-Stokes equations; the inlet boundary is set as a velocity inlet, the outlet boundary is set as a pressure outlet, and the wall surface uses no-slip boundary conditions.

5. The method according to claim 1, characterized in that, The particles are assumed to be spherical inert particles, and their motion is mainly affected by the combined effects of airflow drag, gravity, and turbulent random diffusion. The feedback effect of the particles relative to the continuous phase momentum field is ignored, and unidirectional coupling is adopted. The particle material is set as granite dust with a particle density of 2700 kg per cubic meter. The particle size includes 1 micrometer, 2 micrometer, 3 micrometer, 5 micrometer, 7 micrometer, and 10 micrometer.

6. The method according to claim 1, characterized in that, Particle escape rate and sedimentation rate Defined as: ; ;in, The total number of tracked particles, The number of particles that escaped from the outlet. This represents the number of particles trapped by the wall.

7. The method according to claim 1, characterized in that, The contribution rate of the wall-mounted trapping diagnostics is defined as follows: ; in, For the first The number of particles captured by a single wall when one wall is set as the capture boundary and the other walls are set as the escape boundary; The total number of particles captured is calculated under the condition that all the aforementioned walls are set as capture boundaries; the walls include the source area floor plate, laboratory floor plate, laboratory side wall, laboratory top plate, laboratory end wall, intersection rounded corner, main tunnel floor plate, main tunnel side wall and main tunnel top plate.

8. The method according to claim 1, characterized in that, The discrete phase model repeated tracking uses the same continuous phase flow field, the same release surface, the same particle size, the same boundary conditions, and the same discrete random walk parameters for repeated tracking. The discrete random walk sampling sufficiency analysis compares the changes in escape rate and the contribution rate of the main wall trapping diagnosis by setting the number of attempts to 5, 10, and 20, respectively, with 10 attempts as the setting parameter for discrete phase model tracking.

9. The method according to claim 1, characterized in that, Five inlet ventilation velocities were set: 0.15 m / s, 0.25 m / s, 0.35 m / s, 0.45 m / s, and 0.55 m / s. Among them, 0.35 m / s was the preset benchmark ventilation velocity, forming a combination of discrete phase model parameters for five wind velocities and six particle sizes.

10. The method according to claim 1, characterized in that, The non-monotonic change in escape rate is manifested as follows: the escape rate at an inlet ventilation velocity of 0.45 m / s is lower than that at inlet ventilation velocities of 0.35 m / s and 0.55 m / s; at an inlet ventilation velocity of 0.45 m / s, the capture contribution rates of the main roadway floor and main roadway sidewalls are higher than those at inlet ventilation velocities of 0.35 m / s and 0.55 m / s, respectively; the non-monotonic change in escape rate is determined by a competitive mechanism where entrainment in the confluence zone is enhanced at intermediate wind speeds, but transport in the outlet direction has not yet become dominant.

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