A CFD-based method for calculating dust explosions

Abstract

This invention discloses a CFD-based dust explosion calculation method, specifically relating to the field of dust explosion risk assessment technology. First, initial dust parameters of the target scenario are obtained. Then, a three-dimensional computational domain is established by combining geometric structure, ventilation conditions, and obstacle distribution, and dust deposition areas are marked. A gas-solid coupled dust explosion solution equation set is constructed within the CFD framework. Time-series data of pressure, temperature, velocity, and dust concentration throughout the entire explosion process are output, and a risk level distribution map is generated based on the maximum explosion pressure and flame coverage time. This invention can accurately reproduce the propagation and destruction process of dust explosions, improving simulation accuracy and efficiency. The assessment results can directly guide protective design and safety management.

Description

Technical Field

[0001] This invention relates to the field of dust explosion risk assessment technology, specifically to a CFD-based dust explosion calculation method. Background Technology

[0002] A dust explosion refers to the combustion and explosion of combustible dust within a very short time after it mixes with air at a certain concentration and encounters an ignition source. Dust explosions are widespread in industries such as coal mining, grain processing, metal smelting, and chemical production. Dust explosions can cause severe damage to equipment and factory buildings, pose a significant threat to human life, and result in enormous economic losses.

[0003] Currently, dust explosion risk analysis relies heavily on experimental testing and empirical formula calculations. However, experimental testing suffers from high costs, high risks, and limited experimental conditions. Empirical formulas, on the other hand, often assume idealized conditions and are difficult to accurately reflect the impact of different plant spatial structures, dust particle size distributions, ventilation conditions, and obstacle layouts on the explosion process, resulting in discrepancies between the calculation results and the actual situation.

[0004] Computational fluid dynamics (CFD) methods can 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, the propagation and impact processes of dust explosions can be reproduced in a virtual environment. How to establish a high-precision, repeatable, and scalable dust explosion calculation method within the CFD framework, considering the initial dust conditions and spatial layout of different scenarios, to provide a reliable basis for risk assessment and protective design, has become a pressing technical problem in this field. Summary of the Invention

[0005] The purpose of this invention is to provide a CFD-based dust explosion calculation method to address the shortcomings of the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a CFD-based dust explosion calculation method, comprising:

[0007] S100, Obtain the initial parameters of the dust in the target scene;

[0008] S200. In the CFD calculation framework, a three-dimensional calculation domain is established based on the geometry, ventilation conditions and obstacle distribution of the target scene. Combined with the initial parameters of dust, a dust deposition distribution matrix is ​​generated by fusing gravity settling prediction with on-site measurements, and the dust deposition area is marked.

[0009] S300 automatically selects suitable turbulence models, particle dynamics models, combustion reaction models, and dust secondary suspension models for the three-dimensional computational domain, and constructs a set of dust explosion solution equations that include gas-solid phase coupling.

[0010] 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 secondary suspension mass flow rate are calculated, and the secondary suspension mass flow rate is fed back to the reactant concentration field in real time as a source term to dynamically correct the combustion reaction rate and heat release distribution.

[0011] S500: After the CFD calculation is completed, output the time series data of pressure field, temperature field, velocity field and dust concentration field during the entire explosion process. For each dust deposition area, extract the maximum explosion pressure and flame coverage time to construct a comprehensive risk index. Based on the risk index, classify the risk level and map it to the three-dimensional computational domain space to generate a risk level distribution map.

[0012] Preferably, in S100, the initial parameters include dust particle size distribution, particle density, moisture content, initial concentration field, dust deposition distribution, ignition source location, and energy parameters.

[0013] Preferably, in S200;

[0014] S201. Based on the on-site laser scanning data of the target scene, generate the original three-dimensional geometric model of the three-dimensional computing domain, and combine the information on the location of the ventilation opening, the direction of the pipeline and the size of the obstacle to divide the three-dimensional computing domain into several sub-regions with different flow characteristics.

[0015] S202. For each sub-region, the gravity settling prediction algorithm is used in the three-dimensional geometric model, combined with the on-site dust deposition measurement data, to calculate the degree of dust deposition in each sub-region by weighting, and to generate a dust deposition distribution matrix.

[0016] S203. Map the dust deposition distribution matrix to the three-dimensional computational domain grid, and mark the grid cells with dust deposition weights higher than the preset threshold as dust deposition regions.

[0017] Preferably, in S300;

[0018] S301. Based on the geometric features of the three-dimensional computational domain, ventilation conditions, and dust deposition area distribution of the target scene, automatically match the combination scheme of turbulence model and particle dynamics model, where the turbulence model is used to describe the gas phase flow characteristics, and the particle dynamics model is used to describe the migration, settling, and resuspension behavior of dust particles.

[0019] S302. In the combustion reaction model, the particle mass flow rate released by the dust deposition area under the action of 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.

[0020] S303. When constructing the gas-solid coupled dust explosion solution equation set, 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.

[0021] Preferably, in S400;

[0022] 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.

[0023] S402. When the particle desorption probability exceeds the preset probability, calculate the secondary suspension mass flow rate per unit time based on the dust deposition mass of the unit.

[0024] S403. The calculated secondary suspended mass flow rate is fed back to the combustion reaction model as a source term. When updating the reactant concentration field, the mass of newly added particles is distributed to adjacent grid cells through spatial interpolation. Higher risk weighting coefficients are assigned to regions with high dust deposition weights to dynamically correct the combustion reaction rate and heat release distribution.

[0025] Preferably, in S500;

[0026] 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.

[0027] 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.

[0028] Based on the magnitude of the comprehensive risk index, the dust deposition area is divided into several risk level intervals;

[0029] The risk level results of each dust deposition area are mapped onto the spatial coordinates of the three-dimensional computational domain to generate a risk level distribution map.

[0030] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0031] 1. This invention extracts time-series data of the pressure, temperature, velocity, and dust concentration fields throughout the entire explosion process, and constructs a comprehensive risk index by combining the maximum explosion pressure in the dust deposition area with the flame coverage time, thus achieving a multi-dimensional quantitative assessment of explosion risk. This method can simultaneously reflect the two main destructive effects of mechanical impact and thermal hazards, and adapts to the assessment needs of different scenarios through normalization and weight allocation strategies, thereby improving the scientific rigor and relevance of risk assessment.

[0032] 2. Compared with existing methods that rely solely on a single physical quantity or local monitoring data for risk assessment, this invention not only generates a three-dimensional risk level distribution map covering the entire area, but also visually presents the spatial distribution characteristics of high-risk areas, facilitating protective design, emergency plan development, and accident source tracing analysis. This method improves the accuracy and visualization level of risk assessment, providing reliable technical support for the safety management of dust explosions. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0034] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Example 1, please refer to Figure 1 As shown in this embodiment, a CFD-based dust explosion calculation method includes:

[0037] S100, Obtain the initial parameters of the dust in the target scene;

[0038] S200. In the CFD calculation framework, a three-dimensional calculation domain is established based on the geometry, ventilation conditions and obstacle distribution of the target scene. Combined with the initial parameters of dust, a dust deposition distribution matrix is ​​generated by fusing gravity settling prediction with on-site measurements, and the dust deposition area is marked.

[0039] S300 automatically selects suitable turbulence models, particle dynamics models, combustion reaction models, and dust secondary suspension models for the three-dimensional computational domain, and constructs a set of dust explosion solution equations that include gas-solid phase coupling.

[0040] 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 secondary suspension mass flow rate are calculated, and the secondary suspension mass flow rate is fed back to the reactant concentration field in real time as a source term to dynamically correct the combustion reaction rate and heat release distribution.

[0041] S500: After the CFD calculation is completed, output the time series data of pressure field, temperature field, velocity field and dust concentration field during the entire explosion process. For each dust deposition area, extract the maximum explosion pressure and flame coverage time to construct a comprehensive risk index. Based on the risk index, classify the risk level and map it to the three-dimensional computational domain space to generate a risk level distribution map.

[0042] In this embodiment of the invention, it is first necessary to obtain the initial parameters of the dust in the target scene, which are used to provide boundary conditions and initial conditions for subsequent computational fluid dynamics solutions. The initial parameters of the dust include the following:

[0043] Dust particle size distribution refers to the proportional distribution of dust particles within different size ranges within a target environment. This parameter can be measured by taking samples on-site and using a laser particle size analyzer or sieving method, and then statistically analyzing the mass percentage of each particle size range. Particle size distribution directly affects the suspension characteristics of dust, combustion reaction rate, and flame propagation speed.

[0044] Dust particle density refers to the mass of a single dust particle per unit volume. It is usually determined by the inherent properties of the dust material and can be measured using the specific gravity bottle method or the gas displacement method. Particle density affects the trajectory of particles in airflow, their settling velocity, and their acceleration process under the action of shock waves.

[0045] Dust moisture content refers to the mass fraction of water contained in dust particles, which can be obtained by drying or using an infrared moisture analyzer. Dust moisture content not only affects the minimum ignition energy of dust, but also alters the distribution of heat released during combustion, significantly influencing the evolution of temperature and pressure fields.

[0046] The initial dust concentration field refers to the spatial distribution of dust mass concentration at various locations within the computational domain. It can be obtained by measuring dust concentration at multiple points using a dust concentration sensor and performing three-dimensional interpolation. This concentration field serves as the initial condition for solving the gas-solid two-phase coupling problem in the computational domain, determining the distribution pattern of the combustible mixture in the initial stage of the explosion.

[0047] Dust deposition distribution refers to the mass distribution of dust adhering to or accumulating on solid surfaces within the computational domain, which can be determined through image acquisition combined with mass weighing. Dust deposition distribution determines the potential secondary resuscitation process under the influence of explosive shock waves or heat flow, and is an important basis for explosion risk assessment.

[0048] The ignition source location refers to the spatial coordinates of the energy release point that triggers a dust explosion within the computational domain, which can be deduced through on-site layout design or accident reconstruction. Ignition source energy parameters refer to the total energy released during ignition and the energy release rate, which can be determined by the rated parameters of the ignition device. The ignition source location and energy parameters together determine the initial flame nucleus generation area and its initial propagation characteristics.

[0049] In this embodiment of the invention, in order to accurately reproduce the dust distribution and explosion risk characteristics in the target scene in computational fluid dynamics simulation, it is necessary to establish an accurate three-dimensional computational domain based on the aforementioned initial dust parameters, the geometric structure of the target scene, ventilation conditions and obstacle distribution, and mark the dust deposition area in the three-dimensional computational domain as the key computational area for subsequent numerical solution.

[0050] Specifically, the target scene is first subjected to on-site laser scanning measurement to collect high-density point cloud data including walls, floors, equipment shapes, and other spatial features. Laser scanning can be performed using a 3D laser scanner or a structured light scanning device, with a measurement accuracy preferably less than five millimeters to ensure that the generated geometric information accurately reflects the structural details of the scene. After filtering, registration, and denoising, the collected point cloud data is used to generate an original 3D geometric model using 3D reconstruction software. This model includes the overall spatial outline of the scene and its various local structural features.

[0051] Based on the original 3D geometric model, and combined with the 3D dimension information of ventilation opening locations, ventilation duct routes, cross-sectional dimensions, and existing obstacles (including equipment, shelves, supporting structures, etc.) obtained from previous surveys, the model is annotated with attributes. The purpose of attribute annotation is to establish a correspondence between different geometric features and airflow characteristics. For example, ventilation openings are marked as airflow inlets or outlets, the inner walls of ducts are marked as high-velocity channels, and the surfaces of obstacles are marked as interference surfaces that may form backflow and turbulence.

[0052] Based on the aforementioned attribute information, the original 3D geometric model is partitioned using an airflow characteristic analysis algorithm, dividing the computational domain into several sub-regions with different flow characteristics. The partitioning can be based on factors such as the mainstream direction of the ventilation airflow, velocity gradient distribution, and obstacle density, employing either an automatic partitioning method based on streamline tracing or a manually assisted partitioning method. The purpose of partitioning is to enable differentiated processing for regions with different flow characteristics in subsequent dust deposition distribution calculations and mesh refinement, thereby improving the computational efficiency and accuracy of the simulation.

[0053] To determine the degree of dust deposition in each sub-region, it is first necessary to predict the dust deposition process. The prediction method employs a gravity settling prediction algorithm, which, based on the particle size distribution, particle density, air viscosity, and flow velocity field parameters, calculates the probability and settling rate of particles settling onto the solid surface due to gravity during airflow transport. In the calculation process, the relative velocity of the gas and solid phases, the turbulent diffusion coefficient, and local flow field changes caused by obstacles are comprehensively considered to obtain a more accurate deposition probability distribution.

[0054] In addition to predictive calculations, the predicted results need to be corrected by incorporating on-site dust deposition measurement data. On-site measurements can be performed using dust sampling plates deployed at different heights and locations, surface mass weighing, or high-definition imaging to obtain deposition thickness and mass data. The on-site measurement data is then spatially matched with the predicted results, and the predicted values ​​are corrected through weighted adjustments. This ensures that the predicted values ​​converge to the measured values ​​at locations with actual data points, while maintaining the calculated results of the prediction model at locations without actual data points, thus obtaining a more accurate distribution of dust deposition levels.

[0055] The corrected results are stored in matrix form, where each matrix element corresponds to a sub-region or a refined mesh cell within the three-dimensional computational domain. The value of each matrix element represents the dust deposition weight for that region. The range of dust deposition weights can be restricted to between 0 and 1 using a normalization method, where zero represents no deposition and a value closer to 1 indicates a higher degree of deposition. This matrix is ​​the dust deposition distribution matrix.

[0056] The dust deposition distribution matrix is ​​mapped to the computational domain grid. The mapping process matches matrix elements with three-dimensional computational domain grid cells one by one through the correspondence of spatial coordinates, and assigns dust deposition weights to the corresponding grid cells after matching.

[0057] For grid cells with a dust deposition weight higher than a preset threshold, they 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 re-suspension. For example, when the dust deposition mass of a grid cell may be completely suspended under the action of an explosion shock wave and significantly change the fuel concentration field, the dust deposition weight of that cell should be judged as high risk and marked as a dust deposition area.

[0058] The marking of dust deposition areas is not only a static label, but can also trigger a local mesh refinement strategy in the subsequent computational fluid dynamics solution process. That is, the mesh cell size is automatically refined in these areas to improve the computational resolution of the flow field and combustion reaction in these key areas.

[0059] In this embodiment of the invention, after establishing the three-dimensional computational domain and marking the dust deposition area, it is necessary to select the appropriate turbulence model, particle dynamics model, combustion reaction model and dust secondary suspension model for the specific scenario in the computational fluid dynamics framework, and construct a set of dust explosion solution equations that can reflect the gas-solid phase coupling effect.

[0060] First, based on the geometric features of the 3D computational domain, ventilation conditions, and dust deposition area distribution of the target scene, the system automatically matches a combination of turbulence and particle dynamics models. Geometric features include the spatial dimensions of the computational domain, obstacle layout, and the presence of local narrow channels; ventilation conditions include the wind speed, temperature, and flow direction distribution at the air inlet and outlet; and the dust deposition area distribution is derived from the high-weighted region information obtained in the aforementioned labeling step. By analyzing these features, the system determines whether the flow state is laminar, transitional, or fully turbulent, and automatically selects the turbulence model based on the dust particle size distribution and deposition area location. For example, the large eddy simulation model is used in complex scenarios with strong backflow and eddies, while the Reynolds average method model is used in scenarios with uniform ventilation. Simultaneously, the particle dynamics model selects the appropriate Eulerian-Lagrange method or multiphase Eulerian method based on the motion characteristics of the dust particles to accurately describe the migration, settling, and resuspension behavior of dust particles under impact.

[0061] In order to fully consider the impact of secondary dust resuspension on the explosion propagation characteristics during the combustion reaction, it is necessary to couple the particle mass flow rate released by 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.

[0062] Specifically, in the transient calculation process, the local pressure peak and temperature gradient, which vary with time, are first extracted within each grid cell of the dust deposition region. The pressure peak is obtained from the instantaneous pressure change when the shock wave propagates to the cell, and the temperature gradient is calculated from the relationship between the temperature change caused by heat transfer and spatial location. Then, these two parameters are weighted and summed to obtain the superposition effect value, which reflects the desorption tendency of dust particles under the combined action of the mechanical action of the shock wave and the pyrolysis effect of the heat flow.

[0063] The desorption probability is calculated using a combination of empirical correlation and numerical regression. Historical experimental data is used to establish a weighted relationship between pressure and temperature on particle binding force. This relationship is then applied to the physical properties of the cells within the dust deposition region to obtain the probability of particle release. The mass released per unit time is obtained by multiplying the particle desorption probability by the deposition mass within that cell, representing the mass of dust released from that cell into the gas phase within one calculation time step.

[0064] After obtaining the particle mass flow rate, it is directly incorporated into the reactant concentration field update process. During the update, the mass of released dust is first added to the gaseous combustible concentration according to its location, while considering the time response characteristics of particle release. To simulate the physical delay of particles from a sedimentary state to complete suspension, a time response function is introduced. This function can adopt an incremental weighting form, so that the particle concentration gradually increases over multiple time steps, rather than reaching its maximum instantaneous value. This treatment can avoid unreasonable concentration abrupt changes in the simulation and improve computational stability.

[0065] After the concentration field is updated, the combustion reaction rate and heat release distribution are dynamically corrected based on the new reactant concentration distribution. The reaction rate correction is based on a chemical reaction kinetic model, converting the added dust mass into changes in reactant volume fraction and recalculating the combustion rate constant and reaction heat release. In regions where the dust deposition weight exceeds a threshold, a local mesh refinement and time step shortening strategy is implemented to improve the simulation accuracy of high-risk areas. This involves refining the mesh cell size and reducing the single-step time in these regions to more accurately capture rapid combustion and high-temperature, high-pressure changes.

[0066] After completing the model selection and coupling parameter setting, it is necessary to construct a set of equations for solving dust explosions that incorporates gas-solid phase coupling effects. This set of equations includes turbulent transport equations, particle motion equations, chemical reaction kinetic equations, and quadratic suspension source term equations, and they are solved synchronously at a unified time step.

[0067] The purpose of simultaneous solving is to ensure the temporal consistency of gas-phase flow, solid-phase particle migration, and combustion reaction processes, avoiding 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 mesh size of the entire computational domain. Then, the computational modules for each physical process exchange data at the same time node. For example, at the beginning of each time step, the gas-phase flow field velocity and pressure distribution are calculated using the turbulent transport equations, followed by the particle motion equations to calculate the particle position and velocity distribution within that flow field. Subsequently, the second-order suspended source term equations are used to calculate the mass flow rate of newly released dust and update the reactant concentration field. Finally, the combustion reaction rate and heat release distribution are calculated using the chemical reaction kinetics equations. These steps are repeated until the entire simulation time ends.

[0068] In this embodiment of the invention, during the solution process, for each grid cell within the dust deposition region, the shock wave pressure, flow field shear force, and temperature change rate of that cell at the current moment are extracted. Specifically, the shock wave pressure is calculated by 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 by the time step length.

[0069] The three physical quantities are assigned preset weighting coefficients and then summed using a weighted average to obtain the comprehensive effect value. The weighting coefficients can be empirically determined based on the physical characteristics and adhesion strength of different types of dust. For example, for dense and firmly adhered 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 effect value is then converted into a particle desorption probability using an empirical model or fitting function. This probability describes the likelihood of dust particles within the grid cell being released under the current transient conditions.

[0070] 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 time, the secondary suspension mass flow rate per unit time is calculated based on the dust deposition mass of that cell. The calculation process for this mass flow rate is as follows: multiply the dust deposition mass in the grid cell by the particle desorption probability, and divide by the current time step to obtain the dust mass released into the gas phase per unit time.

[0071] The calculated secondary suspended mass flow rate was used as a source term input into the combustion reaction model. During the concentration field update process, it is first necessary to reasonably distribute the newly added dust mass to adjacent grid cells to reflect its diffusion and transport characteristics in the gas-solid two-phase flow. Therefore, a spatial interpolation method was used to allocate the newly added particle mass from the source grid cells to adjacent cells.

[0072] During interpolation, an appropriate interpolation method is dynamically selected based on the flow field velocity distribution and mesh topology at the current calculation moment. In regions with high velocity gradients and drastic flow field changes, a higher-order interpolation method is used to capture the nonlinear transport characteristics of particles in high-speed shear or vortex flows; in regions with relatively stable flow fields, a lower-order interpolation method is used to reduce computational complexity and numerical dissipation. This adaptive interpolation strategy improves computational efficiency while maintaining accuracy.

[0073] After determining the spatial distribution of particle mass, risk weighting coefficients need to be assigned to different grid cells to reflect the contribution of each region to the explosion propagation process. The calculation of risk weighting coefficients comprehensively considers three factors: dust deposition weight, local temperature, and turbulence intensity. A higher dust deposition weight means a larger potential flammable material reserve in the region; a higher local temperature means a greater likelihood of initiating sustained combustion; and a greater turbulence intensity means a higher mixing efficiency between reactants and oxidizers, which may significantly increase the combustion rate.

[0074] In the specific calculation process, the three factors are first normalized to the range of 0 to 1, and then weighted and summed according to preset weight coefficients to obtain the final risk weight coefficient. Areas with high risk coefficients will be given priority in subsequent combustion reaction calculations and may trigger higher calculation accuracy requirements.

[0075] When the risk weighting coefficient is applied to the combustion reaction model, not only is the numerical distribution of the reactant concentration field updated, but the combustion reaction rate and heat release distribution of each grid cell are also corrected simultaneously. For regions with high risk coefficients, the model appropriately increases the local reaction rate constant to reflect potential accelerated combustion effects, while also increasing the resolution of heat release to more accurately capture the evolution characteristics of high-temperature and high-pressure regions.

[0076] In this embodiment of the invention, in order to comprehensively assess the spatiotemporal evolution of the dust explosion process and its risk level in different regions, it is necessary to extract and analyze multiple physical fields of the entire explosion process after numerical calculation, 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 from CFD calculations into intuitive risk visualization results that can be used for protective design.

[0077] First, after the CFD solution is completed, time-series data of the pressure field, temperature field, velocity field, and dust concentration field are extracted throughout the explosion simulation process. Time-series data refers to the set of physical quantity values ​​for each grid cell within the computational domain at each computational time step. Since CFD calculations typically use fixed or adaptive time steps, time-series data can reflect the entire variation of the physical field from the initial triggering of the explosion to its final stable dissipation.

[0078] The extraction of time-series pressure field data includes instantaneous pressure values ​​and the distribution of pressure increments relative to the initial static pressure, which is crucial for analyzing shock wave propagation paths, reflection, and diffraction behavior. Time-series temperature field data records the formation, expansion, and decay of the high-temperature zone, providing key information for assessing combustion intensity and persistence. Time-series velocity field data provides velocity distribution information for gas-solid two-phase flow, which can be used to determine the flow direction and peak velocity location driven by the explosion. Time-series dust concentration field data reflects the dynamic changes of dust during the explosion process, from initial distribution to global diffusion or local accumulation, and is an important indicator for assessing the risk of secondary combustion.

[0079] After extracting the time-series data of the aforementioned physical fields, this embodiment focuses on the analysis of each dust deposition region. These dust deposition regions are specific areas identified in the early stages of the simulation through deposition distribution measurement and weighting. These regions may undergo secondary suspension and participate in combustion during the explosion, making their risk assessment particularly important.

[0080] For each dust deposition region, the maximum explosion pressure value for that region during the entire explosion process is first searched in the time series data of the pressure field. This value typically appears at the time step when the explosion wave first passes through the region or at the time step when the reflected wave superimposes. To avoid spurious 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.

[0081] Secondly, the flame coverage time is determined from the temperature field and flame propagation data. The flame coverage time is defined as the time interval from the moment the flame front first reaches any grid cell in the dust deposition area until the temperature of the last grid cell in that area drops below the combustion cessation threshold. The combustion cessation threshold can be set according to the dust type and the minimum self-sustaining combustion temperature. This indicator reflects the area's sustained participation time in the combustion reaction and is directly related to the degree of thermal hazard caused by the explosion to that area.

[0082] After obtaining the maximum explosion pressure and flame coverage time, this embodiment uses both as the main 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. These comprehensive feature vectors are used as input to a machine learning model. The machine learning model aims to predict the comprehensive risk index label of each dust deposition area using each set of comprehensive feature vectors. The training objective is to minimize the sum of prediction errors for the comprehensive risk index labels of all dust deposition areas. The machine learning model is trained until the sum of prediction errors converges, at which point the model training stops. The comprehensive risk index of each dust deposition area is determined based on the model output. The machine learning model is a multinomial regression model.

[0083] Based on the magnitude of the comprehensive risk index, the area is divided into several risk level ranges, for example, 0 to 0.3 is low risk, 0.3 to 0.6 is medium risk, and above 0.6 is high risk. Each dust deposition area will be assigned a risk level label.

[0084] Finally, the risk level results for each dust deposition area are mapped onto the spatial coordinates of the three-dimensional 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 three-dimensional scene geometric model, providing safety assessors with intuitive spatial risk layout information. This map not only displays the risk distribution of a single explosion event but can also be compared with simulation results under different operating conditions, helping to identify key areas and weak points for risk control.

[0085] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A CFD-based method for calculating dust explosions, characterized in that: include: S100, Obtain the initial parameters of the dust in the target scene; S200. In the CFD calculation framework, a three-dimensional calculation domain is established based on the geometry, ventilation conditions and obstacle distribution of the target scene. Combined with the initial parameters of dust, a dust deposition distribution matrix is ​​generated by fusing gravity settling prediction with on-site measurements, and the dust deposition area is marked. S300 automatically selects suitable turbulence models, particle dynamics models, combustion reaction models, and dust secondary suspension models for the three-dimensional computational domain, and constructs a set of dust explosion solution equations that include 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 secondary suspension mass flow rate are calculated, and the secondary suspension mass flow rate is fed back to the reactant concentration field in real time as a source term to dynamically correct the combustion reaction rate and heat release distribution. S500: After the CFD calculation is completed, output the time series data of pressure field, temperature field, velocity field and dust concentration field during the entire explosion process. For each dust deposition area, extract the maximum explosion pressure and flame coverage time to construct a comprehensive risk index. Risk levels are classified based on risk indices and mapped to a 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, ignition source location, and energy parameters.

3. The CFD-based dust explosion calculation method according to claim 2, characterized in that: In S200; S201. Based on the on-site laser scanning data of the target scene, generate the original three-dimensional geometric model of the three-dimensional computing domain, and combine the information on the location of the ventilation opening, the direction of the pipeline and the size of the obstacle to divide the three-dimensional computing domain into several sub-regions with different flow characteristics. S202. For each sub-region, the gravity settling prediction algorithm is used in the three-dimensional geometric model, combined with the on-site dust deposition measurement data, to calculate the degree of dust deposition in each sub-region by weighting, and to generate a dust deposition distribution matrix. S203. Map the dust deposition distribution matrix to the three-dimensional computational domain grid, and mark the grid cells with dust deposition weights higher than the preset threshold as dust deposition regions.

4. The CFD-based dust explosion calculation method according to claim 3, characterized in that: In S300; S301. Based on the geometric features of the three-dimensional computational domain, ventilation conditions, and dust deposition area distribution of the target scene, automatically match the combination scheme of turbulence model and particle dynamics model, where the turbulence model is used to describe the gas phase flow characteristics, and the particle dynamics model is used to describe the migration, settling, and resuspension behavior of dust particles. S302. In the combustion reaction model, the particle mass flow rate released by the dust deposition area under the action of 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 coupled dust explosion solution equation set, 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: 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. S402. When the particle desorption probability exceeds the preset probability, calculate the secondary suspension mass flow rate per unit time based on the dust deposition mass of the unit. S403. The calculated secondary suspended mass flow rate is fed back to the combustion reaction model as a source term. When updating the reactant concentration field, the mass of newly added particles is distributed to adjacent grid cells through spatial interpolation. Higher risk weighting coefficients are assigned to regions with high dust deposition weights 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: 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. Based on the magnitude 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 onto the spatial coordinates of the three-dimensional computational domain to generate a risk level distribution map.

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