CFD-based dust explosion calculation method

Abstract

The invention discloses a CFD-based dust explosion calculation method, and particularly relates to the technical field of dust explosion risk assessment. Firstly, initial dust parameters of a target scene are obtained, then a three-dimensional computational domain is established in combination with a geometric structure, ventilation conditions and obstacle distribution, and a dust deposition area is marked; constructing a gas-solid phase coupled dust explosion solving equation set in the CFD framework; outputting time sequence data of pressure, temperature, speed and dust concentration in the whole explosion process, and generating a risk grade distribution diagram based on the maximum explosion pressure and flame coverage time; according to the invention, the propagation and damage process of dust explosion can be accurately reproduced, the simulation precision and efficiency are improved, and the evaluation result can directly guide protection design and safety management.

Description

Technical Field

[0001] The present invention relates to the technical field of dust explosion risk assessment, and in particular to a dust explosion calculation method based on CFD. Background Art

[0002] Dust explosions occur when combustible dust at a certain concentration mixes with air and encounters an ignition source, resulting in a short burst of combustion and explosion. These explosions are common in industries such as coal mining, grain processing, metal smelting, and chemical production. They can cause severe damage to equipment and factory buildings, pose a significant threat to human life, and result in significant economic losses.

[0003] At present, dust explosion risk analysis mostly relies on experimental testing and empirical formula calculations. However, experimental testing has problems such as high cost, high risk, and limited experimental conditions. Empirical formulas often assume idealized conditions and cannot accurately reflect the impact of different factory spatial structures, dust particle size distribution, ventilation conditions, and obstacle layouts on the explosion process, resulting in deviations between the calculated results and the actual situation.

[0004] Computational fluid dynamics (CFD) methods accurately simulate the spatiotemporal distribution of flow, temperature, and pressure fields by solving governing equations. By combining dust particle dynamics, chemical reaction dynamics, and turbulence models, they can recreate the propagation and impact of dust explosions in a virtual environment. The urgent challenge in this field is to develop a highly accurate, repeatable, and scalable dust explosion calculation method within the CFD framework, tailored to the initial dust conditions and spatial distribution of different scenarios. This method, in turn, provides a reliable basis for risk assessment and protective design. Summary of the Invention

[0005] The purpose of the present invention is to provide a dust explosion calculation method based on CFD to solve the shortcomings of the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a CFD-based dust explosion calculation method, comprising: S100, obtaining initial parameters of dust in the target scene; S200. In the CFD calculation framework, a three-dimensional calculation domain is established based on the geometric structure, ventilation conditions, and obstacle distribution of the target scene. Combined with the initial parameters of the dust, the dust deposition distribution matrix is ​​generated by integrating gravity sedimentation prediction with on-site measurements, and the dust deposition areas are marked. S300, automatically selecting a turbulence model, particle dynamics model, combustion reaction model, and dust secondary suspension model that are suitable for the three-dimensional calculation domain, and constructing a dust explosion solution equation group including gas-solid phase coupling; S400: During the solution process, the impact pressure, flow field shear force, and temperature change rate of each grid cell in the dust deposition area are extracted, the particle desorption probability and the secondary suspension mass flow rate are calculated, and the secondary suspension mass flow rate is fed back into the reactant concentration field as a source term in real time to dynamically correct the combustion reaction rate and heat release distribution; S500. After the CFD calculation is completed, the time series data of the pressure field, temperature field, velocity field and dust concentration field during the entire explosion process are output. For each dust deposition area, the maximum explosion pressure and flame coverage time are extracted to construct a comprehensive risk index. The risk level is divided based on the risk index and mapped to the three-dimensional calculation domain space to generate a risk level distribution map.

[0007] Preferably, in S100, the initial parameters include dust particle size distribution, particle density, moisture content, initial concentration field, dust deposition distribution, and ignition source position and energy parameters.

[0008] Preferably, in S200; S201, generating an original 3D geometric model of the 3D computational domain based on on-site laser scanning data of the target scene, and dividing the 3D computational domain into a plurality of sub-regions with different flow characteristics in combination with information on vent locations, pipeline directions, and obstacle dimensions; S202. For each sub-region, using a gravity sedimentation prediction algorithm in a three-dimensional geometric model, combined with on-site dust deposition measurement data, a weighted calculation is performed on the dust deposition degree of each sub-region, and a dust deposition distribution matrix is ​​generated; S203 , mapping the dust deposition distribution matrix to a three-dimensional computational domain grid, and marking grid cells with dust deposition weights higher than a preset threshold as dust deposition areas.

[0009] Preferably, in S300; S301. Automatically match a combination of a turbulence model and a particle dynamics model based on the target scene's three-dimensional computational domain geometry, ventilation conditions, and dust deposition area distribution. The turbulence model is used to describe gas phase flow characteristics, and the particle dynamics model is used to describe the migration, sedimentation, and resuspension behavior of dust particles. S302. In the combustion reaction model, the mass flow rate of particles released from the dust deposition area under the action of the explosion shock wave and heat flow is coupled to the reactant concentration field in real time to dynamically correct the combustion reaction rate and heat release distribution; S303. When constructing the gas-solid phase coupled dust explosion solution equation group, the turbulent transport equation, particle motion equation, chemical reaction kinetics equation and secondary suspension source term equation are solved synchronously at a unified time step.

[0010] Preferably, in S400; S401. During the solution process, the shock wave pressure, flow field shear force, and temperature change rate of each grid cell in the dust deposition area are extracted, and the particle desorption probability is obtained by weighted summation calculation. S402, when the particle desorption probability exceeds a preset probability, the secondary suspension mass flow rate per unit time is calculated in combination with the dust deposition mass of the unit; S403. The calculated secondary suspension mass flow rate is fed back to the combustion reaction model as a source term. When updating the reactant concentration field, the newly added particle mass is distributed to adjacent grid cells through spatial interpolation method, and a higher risk weighting coefficient is assigned to areas with high dust deposition weight to dynamically correct the combustion reaction rate and heat release distribution.

[0011] Preferably, in S500; For each dust deposition area, the maximum explosion pressure value of the area during the entire explosion process is searched in the time series data of the pressure field, and the flame coverage time is determined in the temperature field and flame propagation data; Based on the maximum explosion pressure and flame coverage time, a risk level distribution model is constructed, and the model output is a comprehensive risk index; According to the size of the comprehensive risk index, the dust deposition area is divided into several risk level intervals; The risk level results of each dust deposition area are mapped to the spatial coordinates of the three-dimensional calculation domain to generate a risk level distribution map.

[0012] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. This method extracts time-series data on the pressure, temperature, velocity, and dust concentration fields throughout the explosion process, and constructs a comprehensive risk index based on the maximum explosion pressure and flame coverage duration in the dust deposition area. This method enables a multi-dimensional quantitative assessment of explosion risk. This method simultaneously reflects two primary destructive effects: mechanical impact and thermal hazards. Through normalization and weighting strategies, it adapts to the assessment needs of different scenarios, thereby enhancing the scientific and targeted nature of risk assessment.

[0013] 2. Compared with existing risk assessment methods that rely solely on a single physical quantity or local monitoring data, this method not only generates a comprehensive three-dimensional risk level distribution map but also intuitively displays the spatial distribution characteristics of high-risk areas, facilitating protection design, emergency plan development, and accident source analysis. This method improves the accuracy and visualization of risk assessments, providing reliable technical support for the safe management of dust explosions. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0015] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] Example 1, please refer to Figure 1 As shown, the CFD-based dust explosion calculation method described in this embodiment includes: S100, obtaining initial parameters of dust in the target scene; S200. In the CFD calculation framework, a three-dimensional calculation domain is established based on the geometric structure, ventilation conditions, and obstacle distribution of the target scene. Combined with the initial parameters of the dust, the dust deposition distribution matrix is ​​generated by integrating gravity sedimentation prediction with on-site measurements, and the dust deposition areas are marked. S300, automatically selecting a turbulence model, particle dynamics model, combustion reaction model, and dust secondary suspension model that are suitable for the three-dimensional calculation domain, and constructing a dust explosion solution equation group including gas-solid phase coupling; S400: During the solution process, the impact pressure, flow field shear force, and temperature change rate of each grid cell in the dust deposition area are extracted, the particle desorption probability and the secondary suspension mass flow rate are calculated, and the secondary suspension mass flow rate is fed back into the reactant concentration field as a source term in real time to dynamically correct the combustion reaction rate and heat release distribution; S500. After the CFD calculation is completed, the time series data of the pressure field, temperature field, velocity field and dust concentration field during the entire explosion process are output. For each dust deposition area, the maximum explosion pressure and flame coverage time are extracted to construct a comprehensive risk index. The risk level is divided based on the risk index and mapped to the three-dimensional calculation domain space to generate a risk level distribution map.

[0018] In the embodiment of the present invention, it is first necessary to obtain the initial parameters of the dust in the target scene to provide boundary conditions and initial conditions for the subsequent computational fluid dynamics solution. The initial parameters of the dust include the following: Dust particle size distribution refers to the distribution of dust particles within different size ranges within a target scene. This parameter can be measured by sampling on-site using a laser particle size analyzer or sieving methods, and the mass percentage of each size range is calculated. Particle size distribution directly affects the dust's suspension characteristics, combustion reaction rate, and flame propagation speed.

[0019] Dust particle density refers to the mass of a single dust particle per unit volume. It is typically determined by the inherent properties of the dust material and can be measured using a pycnometer or gas displacement method. Particle density affects the particle's trajectory in an airflow, its settling velocity, and its acceleration when subjected to shock waves.

[0020] Dust moisture content refers to the mass fraction of water contained in dust particles and can be measured using a drying method or an infrared moisture meter. Dust moisture content not only affects the minimum ignition energy of the dust but also alters the distribution of heat released during combustion, significantly influencing the evolution of the temperature and pressure fields.

[0021] The initial dust concentration field refers to the spatial distribution of dust mass concentration at each location within the computational domain. This is measured using multiple dust concentration sensors and obtained through three-dimensional interpolation. This concentration field serves as the initial condition for the gas-solid two-phase coupling solution within the computational domain and determines the distribution pattern of the combustible mixture at the initial explosion stage.

[0022] Dust deposition distribution refers to the distribution of dust mass attached to or accumulated on solid surfaces within the computational domain. It can be determined through image acquisition combined with mass weighing. Dust deposition distribution determines the potential for secondary suspension processes caused by explosion shock waves or heat fluxes, and is a key factor in explosion risk assessment.

[0023] The ignition source location refers to the spatial coordinates of the energy release point that triggers a dust explosion within the computational domain. This can be inferred through on-site layout design or accident reconstruction. The ignition source energy parameter refers to the total energy released during the ignition process and the energy release rate. This parameter can be determined by the rated parameters of the ignition device. The ignition source location and energy parameter together determine the region where the initial flame core of the explosion is generated and its initial propagation characteristics.

[0024] In an embodiment of the present invention, in order to accurately reproduce the dust distribution and explosion risk characteristics in the target scene in the computational fluid dynamics simulation, it is necessary to establish an accurate three-dimensional calculation domain based on the aforementioned acquisition of the initial dust parameters, according to the geometric structure, ventilation conditions and obstacle distribution of the target scene, and mark the dust deposition area in the three-dimensional calculation domain as the key calculation area for subsequent numerical solution.

[0025] Specifically, the target scene is first measured by on-site laser scanning, capturing high-density point cloud data encompassing walls, floors, equipment shapes, and other spatial features. Laser scanning can be performed using a 3D laser scanner or structured light scanning device, with a measurement accuracy of preferably less than five millimeters to ensure the generated geometric information accurately reflects the on-site structural details. After filtering, registration, and denoising the collected point cloud data, 3D reconstruction software is used to generate a raw 3D geometric model encompassing the scene's overall spatial outline and local structural features.

[0026] Based on the original 3D geometric model, the model is annotated with attributes, combining the 3D dimensions of vent locations, duct routing, and cross-sectional dimensions obtained from previous surveying and mapping, as well as on-site obstacles (including equipment, racks, and support structures). The purpose of attribute annotation is to establish a correspondence between different geometric features and airflow characteristics. For example, vents can be marked as air inlets or outlets, duct walls as high-velocity channels, and obstruction surfaces as interference surfaces that may cause backflow and turbulence.

[0027] Combining this attribute information, the original 3D geometric model is partitioned using an airflow characteristics analysis algorithm, dividing the computational domain into subregions with distinct flow characteristics. This partitioning can be performed using either automated streamline tracing or manually assisted methods, based on factors such as the prevailing ventilation airflow direction, velocity gradient distribution, and obstacle density. The goal of this partitioning is to differentiate between regions with different flow characteristics during subsequent dust deposition distribution calculations and mesh refinement, thereby improving the computational efficiency and accuracy of the simulation.

[0028] To determine the extent of dust deposition in each sub-area, the dust deposition process must first be predicted. This prediction method utilizes a gravity sedimentation prediction algorithm. Based on the dust particle size distribution, particle density, air viscosity, and velocity field parameters, this algorithm estimates the probability and rate of gravity deposition of particles onto solid surfaces during airflow transport. This calculation takes into account the relative velocity between the gas and solid phases, the turbulent diffusion coefficient, and local flow field changes caused by obstacles to achieve a more realistic deposition probability distribution.

[0029] In addition to predictive calculations, the predictions must be corrected using on-site dust deposition measurement data. Field measurements can obtain deposition thickness and mass data using dust sampling panels placed at different heights and locations, surface mass weighing, or high-definition imaging recognition. The field measurement data is spatially matched with the predictions, and the predictions are corrected using weighted distribution. This ensures that the predictions converge toward the measured values ​​at points with measured data, while maintaining the predicted model's calculated results at points without measured data. This results in a more accurate distribution of dust deposition levels.

[0030] The corrected results are stored in matrix form, with each matrix element corresponding to a subregion or a refined grid cell within the three-dimensional computational domain. The value of the matrix element represents the dust deposition weight of that region. The dust deposition weight can be normalized to a value between 0 and 1, where zero represents no deposition and values ​​closer to 1 indicate higher deposition. This matrix is ​​the dust deposition distribution matrix.

[0031] The dust deposition distribution matrix is ​​mapped to the computational domain grid. The mapping process matches the matrix elements to the three-dimensional computational domain grid cells one by one through the correspondence between spatial coordinates. After matching, the dust deposition weight is assigned to the corresponding grid cell.

[0032] Grid cells with dust deposition weights above a preset threshold are marked as dust deposition areas. The preset threshold can be determined based on the explosion risk level or the critical mass concentration of secondary dust suspension. For example, if the dust deposition mass in a grid cell is likely to be completely suspended by the explosion shock wave and significantly change the fuel concentration field, the dust deposition weight of the cell should be judged as high risk and marked as a dust deposition area.

[0033] The marking of dust deposition areas is not only a static label, but also triggers a local mesh refinement strategy in the subsequent computational fluid dynamics solution process, that is, automatically refining the mesh cell size in these areas to improve the computational resolution of the flow field and combustion reaction in these critical areas.

[0034] In an embodiment of the present invention, after establishing a three-dimensional calculation domain and marking the dust deposition area, it is necessary to select applicable turbulence models, particle dynamics models, combustion reaction models, and dust secondary suspension models for specific scenarios in the computational fluid dynamics framework, and construct a set of dust explosion solution equations that can reflect the gas-solid phase coupling effect.

[0035] First, the system automatically matches a turbulence model with a particle dynamics model based on the target scene's three-dimensional computational domain geometry, ventilation conditions, and dust deposition area distribution. Geometric features include the computational domain's spatial dimensions, obstacle layout, and the presence of local narrow passages; ventilation conditions include the wind speed, temperature, and flow direction distribution at the airflow inlet and outlet; and the dust deposition area distribution is derived from the high-weighted region information obtained in the previous labeling step. The system analyzes these features to determine whether the flow regime is laminar, transitional, or fully turbulent. It then automatically selects a turbulence model based on the dust particle size distribution and deposition area location. For example, a large eddy simulation model may be used for complex scenarios with strong backflow and eddies, while a Reynolds-averaged method may be used for scenarios with uniform ventilation. The particle dynamics model, meanwhile, selects an appropriate Euler-Lagrangian or multiphase Euler method based on the motion characteristics of the dust particles to accurately describe their migration, settling, and resuspension behavior under impact.

[0036] In order to fully consider the influence of dust secondary suspension on explosion propagation characteristics during the combustion reaction, it is necessary to couple the particle mass flow rate released in the dust deposition area under the action of explosion shock wave and heat flow to the reactant concentration field in real time, and dynamically correct the combustion reaction rate and heat release distribution accordingly.

[0037] Specifically, during the transient calculation process, the local peak pressure and temperature gradient are first extracted over time within each grid cell in the dust deposition area. The peak pressure is derived from the instantaneous pressure change when the shock wave propagates to that cell, and the temperature gradient is calculated from the relationship between the temperature change caused by heat flow and its spatial position. These two parameters are then weighted and summed to obtain the superposition effect value, which reflects the desorption tendency of dust particles under the combined effects of the mechanical action of the shock wave and the pyrolysis effect of the heat flow.

[0038] The desorption probability is calculated using a combination of empirical correlations and numerical regression. Historical experimental data is used to establish a weighted relationship between the effects of pressure and temperature on particle binding. This relationship is then applied to the physical properties of the cells within the dust deposition area to determine the probability of particle release. The release mass per unit time is calculated by multiplying the particle desorption probability by the deposited mass within the cell, representing the mass of dust released from the cell into the gas phase during a single calculation time step.

[0039] Once the particle mass flow rate is obtained, it is directly incorporated into the reactant concentration field update process. During this update, the released dust mass is first added to the gaseous combustible concentration based on its location, while also accounting for the time response characteristics of particle release. To simulate the physical delay of particles transitioning from a settled state to full suspension, a time response function is introduced. This function can be weighted incrementally, allowing the particle concentration to increase gradually over multiple time steps rather than reaching a maximum instantaneously. This approach avoids undesirable concentration jumps in the simulation and improves computational stability.

[0040] After the concentration field is updated, the combustion reaction rate and heat release distribution are dynamically corrected based on the new reactant concentration distribution. This reaction rate correction is based on a chemical reaction kinetic model, converting the added dust mass into a change in reactant volume fraction and recalculating the combustion rate constant and reaction heat release. In areas where dust deposition weights exceed a threshold, a localized mesh refinement and time step reduction strategy are implemented to improve simulation accuracy in high-risk areas. This refines the mesh element size within these areas and reduces the single-step time length to more accurately capture rapid combustion and high-temperature and high-pressure variations.

[0041] After model selection and coupling parameter setting, it is necessary to construct a dust explosion solution equation system that incorporates the gas-solid phase coupling effect. This system includes the turbulent transport equation, the particle motion equation, the chemical reaction kinetics equation, and the quadratic suspension source term equation, and solve them all simultaneously at a unified time step.

[0042] The purpose of synchronous solution is to ensure the temporal consistency of gas phase flow, solid phase particle migration and combustion reaction processes, and to avoid the accumulation of numerical errors caused by time step mismatch. In the specific implementation, the global time step length is first determined based on the maximum characteristic velocity and minimum grid size of the entire computational domain, and then the calculation modules of each physical process exchange data at the same time node. For example, at the beginning of each time step, the velocity and pressure distribution of the gas phase flow field are first calculated by the turbulent transport equation, and then the position and velocity distribution of the particles in the flow field are calculated by the particle motion equation. Subsequently, the secondary suspension source term equation is used to calculate the mass flow rate of the newly released dust and update the reactant concentration field. Finally, the chemical reaction kinetics equation is used to calculate the combustion reaction rate and heat release distribution. The above steps are executed cyclically until the entire simulation time ends.

[0043] In this embodiment of the present invention, during the solution process, the shock wave pressure, flow field shear force, and temperature change rate at the current moment are extracted for each grid cell within the dust deposition area. The shock wave pressure is calculated by taking the difference between the local pressure field and the background static pressure; the flow field shear force is obtained by solving the velocity gradient tensor and extracting its tangential component; and the temperature change rate is obtained by comparing the temperature difference between the current time step and the previous time step and dividing it by the time step length.

[0044] The three physical quantities above are assigned preset weight coefficients and then weighted and summed to obtain the comprehensive action value. The weight coefficients can be determined empirically based on the physical properties and adhesion strength of different dust types. For example, for dense and firmly attached dust particles, the weight of the shock wave pressure term can be increased; for easily pyrolyzed dust, the weight of the temperature change rate term can be increased. The comprehensive action value is then converted into a particle desorption probability through an empirical model or fitting function. This probability is used to describe the probability of dust particles within the grid cell being released under the current transient conditions.

[0045] When the calculated particle desorption probability exceeds the preset probability, the system determines that a valid secondary suspension event has occurred in that grid cell. At this point, the secondary suspension mass flow rate per unit time is calculated based on the dust deposition mass in that cell. This mass flow rate is calculated by multiplying the dust mass deposited in the grid cell by the particle desorption probability and dividing the result by the current time step to obtain the dust mass released into the gas phase per unit time.

[0046] The calculated secondary suspension mass flow rate is input as a source term into the combustion reaction model. During the concentration field update process, the newly added dust mass must first be appropriately distributed to adjacent grid cells to reflect its diffusion and transport characteristics in gas-solid two-phase flow. To this end, a spatial interpolation method is used to distribute the newly added particle mass from the source grid cell to adjacent cells.

[0047] During the interpolation process, an adaptive interpolation method is dynamically selected based on the velocity distribution and grid topology at the current calculation moment. In areas with high velocity gradients and drastic flow variations, a high-order interpolation method is used to capture the nonlinear transport characteristics of particles in high-speed shear or vortex flows. In areas of relatively stable flow, a low-order interpolation method is used to reduce computational complexity and minimize numerical dissipation. This adaptive interpolation strategy improves computational efficiency while maintaining accuracy.

[0048] After completing the spatial distribution of particle mass, it is necessary to further assign risk weighting coefficients to different grid cells to reflect the contribution of each area to the explosion propagation process. The calculation of the risk weighting coefficient comprehensively considers three factors: dust deposition weight, local temperature, and turbulence intensity. A higher dust deposition weight indicates a greater potential for combustible reserves in the area; a higher local temperature indicates a greater likelihood of sustained combustion; and a greater turbulence intensity indicates a more efficient mixing of reactants and oxidizers, potentially significantly increasing the combustion rate.

[0049] During the calculation process, the three factors are first normalized to a range of 0 to 1, and then weighted and summed according to the preset weight coefficients to obtain the final risk weighted coefficient. Regions with high risk coefficients will be prioritized in subsequent combustion reaction calculations and may trigger higher calculation accuracy requirements.

[0050] Applying the risk-weighted coefficient to the combustion reaction model not only updates the numerical distribution of the reactant concentration field but also simultaneously modifies the combustion reaction rate and heat release distribution for each grid cell. For regions with high risk coefficients, the model appropriately increases the local reaction rate constant to reflect the potential for accelerated combustion, while also increasing the resolution of heat release to more accurately capture the evolutionary characteristics of high-temperature and high-pressure regions.

[0051] In this embodiment of the present invention, to comprehensively assess the spatiotemporal evolution of dust explosions and the degree of risk within different regions, it is necessary to extract and analyze the various physical fields of the entire explosion process after numerical calculations are completed, and further generate a risk level distribution map based on the characteristics of the dust deposition area. This step aims to transform the time series data obtained by CFD calculations into intuitive risk visualization results that can be used for protection design.

[0052] First, after the CFD solution is complete, time series data is extracted for the pressure, temperature, velocity, and dust concentration fields throughout the explosion simulation. Time series data refers to the set of physical quantity values ​​for each grid cell within the computational domain at each computational time step. Because CFD calculations typically employ fixed or adaptive time steps, this time series data captures the dynamics of the physical field from the initial triggering of the explosion to its final, stable dissipation.

[0053] The extraction of pressure field time series data includes instantaneous pressure values ​​and the distribution of pressure increments relative to the initial static pressure, which is of great significance for analyzing the propagation path, reflection, and diffraction behavior of shock waves. The temperature field time series data records the formation, expansion, and attenuation of the high-temperature zone and is a key basis for evaluating the intensity and persistence of combustion. The velocity field time series data provides velocity distribution information for gas-solid two-phase flow, which can be used to determine the flow direction and velocity peak position driven by the explosion. The dust concentration field time series data reflects the dynamic change of dust from initial distribution to global diffusion or local aggregation during the explosion process, and is an important indicator for assessing the risk of secondary combustion.

[0054] After extracting the time-series data for the aforementioned physical fields, this example focuses on analyzing each dust deposition area. Dust deposition areas are specific regions identified through deposition distribution measurement and weighted labeling in the early stages of the simulation. These areas are particularly important for risk assessment due to their potential for secondary suspension and combustion during the explosion.

[0055] For each dust deposition area, the pressure field time series data is first searched for the maximum explosion pressure value in that area throughout the entire explosion process. This value typically occurs at the time step when the blast wave first passes through the area or at the time step when the reflected waves overlap. To avoid false peaks caused by numerical noise, this embodiment uses a sliding window averaging method to smooth the pressure time series curve, and then extracts the peak value from the smoothed curve.

[0056] Secondly, the flame coverage time is determined from the temperature field and flame propagation data. This is defined as the time interval from the first arrival of the flame front at any grid cell in the dust deposition area until the temperature of the last grid cell in the area drops below the combustion cessation threshold. The combustion cessation threshold can be set based on the dust type and the minimum self-sustaining combustion temperature. This metric reflects the duration of a region's participation in the combustion reaction and is directly related to the degree of thermal damage posed by the explosion to the area.

[0057] After obtaining the maximum explosion pressure and flame coverage time, this embodiment uses these two as primary risk assessment parameters to construct a risk level distribution model. Specifically, the maximum explosion pressure and flame coverage time are converted into comprehensive feature vectors, which are used as inputs to a machine learning model. The machine learning model uses each set of comprehensive feature vectors to predict the comprehensive risk index label for each dust deposition area as its prediction objective. The training objective is to minimize the sum of the prediction errors for the comprehensive risk index labels of all dust deposition areas. The machine learning model is trained until the sum of the prediction errors converges, terminating the model training. The comprehensive risk index for each dust deposition area is determined based on the model output. The machine learning model is a polynomial regression model.

[0058] Based on the size of the comprehensive risk index, it is divided into several risk level intervals, for example, 0 to 0.3 is low risk, 0.3 to 0.6 is medium risk, and 0.6 and above is high risk. Each dust deposition area will be assigned a risk level label.

[0059] Finally, the risk level results for each dust deposition area are mapped to the spatial coordinates of the 3D computational domain to generate a risk level distribution map. This map uses different colors or brightness levels to represent different risk levels and can be overlaid with the 3D scene geometry model, providing safety assessors with intuitive spatial risk layout information. This map not only illustrates the risk distribution of a single explosion event but also allows comparison with simulation results under different operating conditions, helping to identify key risk control areas and weak links.

[0060] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A CFD-based dust explosion calculation method, characterized by: include: S100, obtaining initial parameters of dust in the target scene; S200. In the CFD calculation framework, a three-dimensional calculation domain is established based on the geometric structure, ventilation conditions, and obstacle distribution of the target scene. Combined with the initial parameters of the dust, the dust deposition distribution matrix is ​​generated by integrating gravity sedimentation prediction with on-site measurements, and the dust deposition areas are marked. S300, automatically selecting a turbulence model, particle dynamics model, combustion reaction model, and dust secondary suspension model that are suitable for the three-dimensional calculation domain, and constructing a dust explosion solution equation group including gas-solid phase coupling; S400: During the solution process, the impact pressure, flow field shear force, and temperature change rate of each grid cell in the dust deposition area are extracted, the particle desorption probability and the secondary suspension mass flow rate are calculated, and the secondary suspension mass flow rate is fed back into the reactant concentration field as a source term in real time to dynamically correct the combustion reaction rate and heat release distribution; S500: After the CFD calculation is completed, the time series data of the pressure field, temperature field, velocity field and dust concentration field during the entire explosion process are output. For each dust deposition area, the maximum explosion pressure and flame coverage time are extracted to construct a comprehensive risk index; Risk levels are divided based on risk index and mapped to the three-dimensional computational domain space to generate a risk level distribution map.

2. The CFD-based dust explosion calculation method according to claim 1, characterized in that: In S100, the initial parameters include dust particle size distribution, particle density, moisture content, initial concentration field, dust deposition distribution, and ignition source position and energy parameters.

3. The CFD-based dust explosion calculation method according to claim 2, characterized in that: S200; S201, generating an original 3D geometric model of the 3D computational domain based on on-site laser scanning data of the target scene, and dividing the 3D computational domain into a plurality of sub-regions with different flow characteristics in combination with information on vent locations, pipeline directions, and obstacle dimensions; S202. For each sub-region, using a gravity sedimentation prediction algorithm in a three-dimensional geometric model, combined with on-site dust deposition measurement data, a weighted calculation is performed on the dust deposition degree of each sub-region, and a dust deposition distribution matrix is ​​generated; S203 , mapping the dust deposition distribution matrix to a three-dimensional computational domain grid, and marking grid cells with dust deposition weights higher than a preset threshold as dust deposition areas.

4. The CFD-based dust explosion calculation method according to claim 3, characterized in that: S300; S301. Automatically match a combination of a turbulence model and a particle dynamics model based on the target scene's three-dimensional computational domain geometry, ventilation conditions, and dust deposition area distribution. The turbulence model is used to describe gas phase flow characteristics, and the particle dynamics model is used to describe the migration, sedimentation, and resuspension behavior of dust particles. S302. In the combustion reaction model, the mass flow rate of particles released from the dust deposition area under the action of the explosion shock wave and heat flow is coupled to the reactant concentration field in real time to dynamically correct the combustion reaction rate and heat release distribution; S303. When constructing the gas-solid phase coupled dust explosion solution equation group, the turbulent transport equation, particle motion equation, chemical reaction kinetics equation and secondary suspension source term equation are solved synchronously at a unified time step.

5. The CFD-based dust explosion calculation method according to claim 4, characterized in that: S400; S401. During the solution process, the shock wave pressure, flow field shear force, and temperature change rate of each grid cell in the dust deposition area are extracted, and the particle desorption probability is obtained by weighted summation calculation. S402, when the particle desorption probability exceeds a preset probability, the secondary suspension mass flow rate per unit time is calculated in combination with the dust deposition mass of the unit; S403. The calculated secondary suspension mass flow rate is fed back to the combustion reaction model as a source term. When updating the reactant concentration field, the newly added particle mass is distributed to adjacent grid cells through spatial interpolation method, and a higher risk weighting coefficient is assigned to areas with high dust deposition weight to dynamically correct the combustion reaction rate and heat release distribution.

6. The CFD-based dust explosion calculation method according to claim 5, characterized in that: S500; For each dust deposition area, the maximum explosion pressure value of the area during the entire explosion process is searched in the time series data of the pressure field, and the flame coverage time is determined in the temperature field and flame propagation data; Based on the maximum explosion pressure and flame coverage time, a risk level distribution model is constructed, and the model output is a comprehensive risk index; According to the size of the comprehensive risk index, the dust deposition area is divided into several risk level intervals; The risk level results of each dust deposition area are mapped to the spatial coordinates of the three-dimensional calculation domain to generate a risk level distribution map.

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