Fire modeling method based on lattice Boltzmann method

Abstract

The invention discloses a three-dimensional fire modeling method based on a lattice Boltzmann method, and the method comprises the following steps: building a fire model which comprises a calculation module and a self-adaptive mesh refinement module, the calculation module comprises a lattice Boltzmann flow module, a buoyancy driving module, a combustion module based on gradient dynamic triggering, an energy equation module, a flue gas transportation module containing particle sedimentation and visibility calculation, and a multi-component transportation module. According to the fire modeling method based on the lattice Boltzmann method, LBM flow field data serves as core drive and is transmitted to the combustion module, the flue gas transportation module and the multi-component transportation module in real time, accurate convection and diffusion information is provided, meanwhile, the self-adaptive grid refining module dynamically adjusts the grid resolution according to the gradient of key physical quantities, and therefore the fire modeling accuracy is improved. The accuracy can be ensured, the calculation amount is reduced, and the calculation speed is increased. And the prediction precision, robustness and real fire complexity capturing capability of the model are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fire simulation modeling methods, and in particular to a fire modeling method based on a lattice Boltzmann method. Background Art

[0002] Fire modeling uses computer simulation techniques and mathematical models to simulate the onset, development, spread, and impacts of fires, such as smoke propagation, temperature distribution, toxic gas concentrations, and structural response. Its core goal is to predict and understand fire behavior, thereby providing a scientific basis and decision-making support for fire safety design, fire risk assessment, firefighting and rescue command, and fire investigation.

[0003] According to the publication number: CN113111518A, the publication date is 2021-07-13, which discloses a fire simulation processing method based on the Internet of Things, which involves a security simulation method and solves the problem that the existing forest fire spread determination method adopts a single calculation method. The present invention includes a fire scene model that performs three-dimensional spatial modeling of the forest farm and splits it into unit blocks with consistent attributes based on the vegetation type, terrain trend and real-time wind factors of the forest farm; the fire scene model collects the fire spread factor for each unit block, performs multivariate linear regression based on multiple spread factors using mathematical statistics methods, collects the real-time fire spread factor of the current unit block, fits the linear regression function of each unit block, and generates the fire scene model hyperparameters and multivariate curve clusters of the target forest farm. Its main technical effect is: using a simplified forest fire spread model, combined with the characteristics of the macroscopic spread of forest fires, it is possible to quickly analyze and judge the development of fire disasters.

[0004] The current numerical simulation technology of fire and smoke spread is mainly based on the traditional computational fluid dynamics (CFD) method, the core of which is to solve the Navier-Stokes equations by the finite volume method (FVM) or the finite element method (FEM), combined with turbulence models (such as 、 ) and simplified combustion models (such as preset heat release rate or single-step reaction) for simulation. Due to insufficient grid resolution and the complexity of turbulence-combustion interaction modeling, it is difficult to accurately capture the impact of transient vortex structure on flame propagation, especially in buoyancy-driven flow, which is prone to decoupling errors between velocity field and temperature field. The Boussinesq buoyancy approximation assumption (density only changes linearly with temperature) is usually adopted in buoyancy drive, which will cause significant deviations in the prediction of smoke rise velocity and stratification. To this end, a fire modeling method based on the lattice Boltzmann method is proposed to solve the technical problems existing in the existing technology, such as the difficulty in accurately capturing the impact of transient vortex structure on flame propagation and the easy generation of decoupling errors between velocity field and temperature field in buoyancy drive. Summary of the Invention

[0005] The purpose of the present invention is to provide a fire modeling method based on the lattice Boltzmann method, which aims to solve the technical problems existing in the prior art of accurately capturing the influence of transient vortex structures on flame propagation and the decoupling errors easily generated between the velocity field and the temperature field in buoyancy drive.

[0006] To achieve the above object, the present invention provides the following technical solution: a fire modeling method based on the lattice Boltzmann method, comprising the following steps: Constructing a fire model, the fire model including a calculation module and an adaptive grid refinement module, the calculation module including a lattice Boltzmann flow module, a buoyancy drive module, a combustion module, an energy equation module, and a smoke transport module; The lattice Boltzmann flow module outputs the density and velocity distribution of the flow field in real time, providing the combustion module and the flue gas transport module with the driving velocity of convection and diffusion and output velocity gradient data; The buoyancy drive module is specifically an ideal gas state equation and a "Kuo" force model, which calculates density changes in real time through the ideal gas state equation and couples it to the lattice Boltzmann equation through the "Kuo" force model; The combustion module solves the mixture fraction transport equation under LES subgrid viscosity correction. The mixture fraction gradient norm is used to identify the three-dimensional flame front position and calculate the heat release rate. The heat release rate is input as a source term into the subsequent energy equation module to update the temperature field. The smoke transport module relies on the three-dimensional transport of pollutants by the flow field, and is affected by the combined effects of buoyancy-driven convection and turbulent diffusion, ultimately outputting visibility parameters and smoke concentration; The adaptive grid refinement module identifies key areas during the simulation process and dynamically adjusts the grid resolution according to the physical gradient of the grid resolution in different areas of the flow field.

[0007] Preferably, the dynamic adjustment of the gradient specifically includes: A hierarchical nested grid structure is used to divide the grid structure into several grid blocks; Set the refinement threshold and coarsening threshold; Dynamically adjusting the grid structure based on multiple physical quantity gradients, wherein the multiple physical quantity gradients include velocity gradient, temperature gradient, and mixture fraction gradient; When the local gradient value of any of the above-mentioned physical quantity gradients of a certain grid block exceeds the refinement threshold, the adaptive grid refinement module refines the grid block; when the local gradient value of any of the above-mentioned physical quantity gradients of a certain grid block is lower than the coarsening threshold, the adaptive grid refinement module coarsens the grid block.

[0008] Preferably, the adaptive grid refinement module refines the grid blocks by an interpolation method, and the adaptive grid refinement module coarsens the grid blocks by an averaging method.

[0009] Preferably, the lattice Boltzmann flow module is specifically: Discrete Boltzmann equation, expressed as: ; in, For direction The distribution function of is the discrete velocity of the discrete velocity model; is the dynamic relaxation time; Contributions to buoyancy and external forces.

[0010] Preferably, the lattice Boltzmann flow module further includes a LES coupling equation, specifically expressed as: ; in, is the Smagorinsky constant, is the grid resolution, is the filtered strain rate tensor.

[0011] Preferably, in the buoyancy driven module, the ideal gas state equation is specifically: ; in, is the environmental density, is the air gas constant, is the temperature field.

[0012] Preferably, the "Guo" force model in the buoyancy driven module is specifically: ; in, is the directional weight of the discrete velocity model, is the lattice speed of sound.

[0013] Preferably, the combustion module is specifically a mixture fraction transport equation, which is specifically expressed as: ; in, is the molecular diffusion coefficient, is the turbulent Schmidt number.

[0014] Preferably, the smoke transport module specifically includes a smoke transport equation, which is expressed as: ; in, is the flue gas mass fraction, is the diffusion coefficient of smoke molecules, is the flue gas turbulence Schmidt number, is the source term, is the particle settling velocity.

[0015] Preferably, the calculation module further includes an energy equation module, and the energy equation module is specifically an energy equation, which is expressed as: ; in, is the specific heat capacity of air at constant pressure, is the thermal conductivity of air, is the heat release rate of the chemical reaction, is the radiation source term.

[0016] In the above technical solution, the present invention provides a three-dimensional fire modeling method based on the lattice Boltzmann method, which has the following beneficial effects: This invention uses LBM flow field data as its core driver, transmitting it in real time to the combustion and flue gas transport modules, providing accurate convection and diffusion information for three-dimensional simulation. Simultaneously, the adaptive mesh refinement module dynamically adjusts the mesh resolution based on the gradients of key physical quantities such as velocity, temperature, and mixture fraction. This feedback directly impacts the computational domain and accuracy of all modules in the computational model. Heat release from the combustion process (efficiently calculated using the dynamic triggering mechanism described in this invention) and the radiative heat flux calculated by the energy equation module jointly drive the energy equation to update the temperature field. Changes in the temperature field, in turn, provide real-time feedback to correct the density (via a non-Boussinesq buoyancy model) and trigger buoyancy effects, which in turn react on the flow field, completing the core "temperature-density-buoyancy-flow field" closed-loop feedback loop. Furthermore, the flue gas transport module updates flue gas concentration and visibility based on the flow field and the particle settling velocity that accounts for gravity. This comprehensive and self-consistent multi-physics field strong coupling mechanism, combined with efficient adaptive grid technology, significantly improves the model's predictive accuracy, robustness, and ability to capture the complexity of real fires, particularly in three-dimensional fire scenarios characterized by high temperature gradients, strong buoyancy drive, and complex multi-scale features. This invention is not a simple stacking of existing technologies, but rather a deep and organic integration of technologies such as LBM, multi-physics gradient-driven adaptive grids, and dynamically triggered combustion models, resulting in a synergistic effect of "1+1>2." While maintaining high accuracy, it significantly improves computational efficiency and resolves the inherent contradiction between accuracy and efficiency in traditional CFD methods. This systematic and innovative design, while not obvious to those skilled in the art, reflects the outstanding substantive features and significant advancements of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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.

[0018] Figure 1 A schematic diagram of the fire modeling structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, a fire modeling method based on the lattice Boltzmann method includes the following steps: Constructing a fire model, the fire model including a calculation module and an adaptive grid refinement module, the calculation module including a lattice Boltzmann flow module, a buoyancy drive module, a combustion module, an energy equation module, and a smoke transport module; The lattice Boltzmann flow module outputs the density and velocity distribution of the flow field in real time, providing the driving velocity of convection and diffusion for the combustion module and the flue gas transport module; The buoyancy drive module is specifically an ideal gas state equation and a "Kuo" force model, which calculates density changes in real time through the ideal gas state equation and couples it to the lattice Boltzmann equation through the "Kuo" force model; The mixture fraction combustion module is solved under LES subgrid viscosity correction, and its gradient modulus is used to identify the three-dimensional flame front position; The smoke transport module relies on the three-dimensional transport of pollutants by the flow field, and is affected by the combined effects of buoyancy-driven convection and turbulent diffusion, ultimately outputting visibility parameters and smoke concentration; The adaptive grid refinement module identifies key areas during the simulation process and dynamically adjusts the grid resolution of different areas of the flow field by gradient.

[0021] Specifically, before conducting a fire simulation, the geometric model needs to be input into the fire model and boundary conditions need to be set, including the three-dimensional ventilation outlet wind speed and the fire source location.

[0022] Set the initial flow field and the distribution function of the lattice Boltzmann method.

[0023] To effectively balance computational efficiency and local high-precision requirements for large-scale 3D fire simulations, this paper introduces an Adaptive Mesh Refinement (AMR) module based on multi-physics gradients. This AMR module dynamically adjusts the computational mesh in real time based on the degree of change (gradient) of physical quantities. High-resolution meshes are automatically used in critical areas such as the flame front and smoke plume boundary to capture details, while coarser meshes are used in non-critical areas where physical quantities vary more gently. This significantly optimizes computational resource allocation while ensuring computational accuracy, maximizing the synergy between simulation efficiency and accuracy.

[0024] As an embodiment provided by the present invention, the grid structure used is an octree structure, which organizes the three-dimensional computational domain. Each octree node represents a grid block, which can be recursively refined into eight child nodes (finer grid blocks) or coarsened into a parent node (coarser grid blocks). The adaptive grid refinement module performs refinement based on multi-physical quantity gradients. Specifically, the multi-physical quantity gradients include: velocity gradient, temperature gradient, and mixture fraction gradient; Specifically, the velocity gradient is the velocity vector The modulus or its Laplace operator , the temperature gradient is temperature The modulus or its Laplace operator The gradient of the mixture fraction is the mixture fraction The modulus of the flame is particularly useful for identifying the flame front area.

[0025] Specifically, when all the physical quantity gradient values ​​of a particular grid block fall below a preset refinement threshold, indicating a dramatic change in that grid block, the grid block is marked as refined and refined via the adaptive grid refinement module. When all the physical quantity gradient values ​​of a particular grid block fall below a preset refinement threshold, the grid block is marked as coarsened and coarsened via the adaptive grid refinement module. This fusion of multiple criteria ensures comprehensive capture of complex fire phenomena.

[0026] Specific implementation of the lattice Boltzmann method in the adaptive grid refinement module: In the framework of the multi-scale lattice Boltzmann method, information transfer between different grid levels, conservation of physical quantities and numerical stability are key challenges.

[0027] Boundary processing and information transfer between grids of different scales: When a grid block is refined or coarsened, it is necessary to ensure the accurate transfer of the lattice Boltzmann method distribution function and the conservation of physical quantities at the interface between the coarse and fine grids. Interpolation is required from coarse to fine grids: When a coarse grid cell is refined into multiple sub-grid cells, the newly generated fine grid cells need to have their lattice Boltzmann method distribution function initialized.

[0028] Specifically, the interpolation methods include: The macroscopic quantities at the fine grid cell position are calculated from the macroscopic quantities (such as density ρ, velocity u, temperature T) of the coarse grid cell and its adjacent coarse grid cells through bilinear / trilinear interpolation; Using these interpolated macroscopic quantities, according to the equilibrium distribution function formula of the lattice Boltzmann method Recalculate and initialize the Lattice Boltzmann method equilibrium distribution function for the fine mesh cells.

[0029] This interpolation method ensures the interpolation accuracy and physical conservation of macroscopic quantities.

[0030] When the grid block needs to be coarsened, the average method of physical quantity conservation is used, specifically: The macroscopic quantities of fine grid cells (such as density, momentum ( ),energy( )) Perform arithmetic averaging; Assign the average value to the corresponding coarse grid cell.

[0031] LBM distribution function of all fine grid cells in the subgrid block Aggregated to the parent coarse grid cell by weighted averaging , thus ensuring the conservation of mass, momentum and energy at the interface between coarse and fine grids.

[0032] The collision and migration steps of the lattice Boltzmann method are performed at their respective local time steps on grid blocks of different resolutions. Collision and migration are performed at a higher resolution on the fine grid, capturing finer physical details; the coarse grid maintains the original resolution. At the interface between the coarse and fine grids, distribution functions flowing out of the fine grid and into the coarse grid are transferred directly; however, distribution functions flowing out of the coarse grid and into the fine grid are calculated using the interpolation method described above.

[0033] As an embodiment provided by the present invention, in order to ensure the physical consistency and stability of the calculation, a hierarchical time stepping strategy is adopted. Specifically: On grids of different resolution levels, the time step and grid scale is proportional to, expressed as: ; in is the lattice sound velocity, i.e. the fine grid (small ) have a smaller time step.

[0034] In each coarse grid time step, its sub-fine grid will perform multiple time step calculations until it is aligned with the coarse grid time synchronization point.

[0035] For the treatment of interface stability: strictly follow the interpolation method and restriction strategy of the above-mentioned macroscopic conservation to avoid the generation of numerical pseudo-diffusion or pseudo-source terms at the interface of coarse and fine grids, and ensure the accurate conservation of mass, momentum and energy.

[0036] Specifically, the restriction strategy is that within each coarse grid time step, its sub-fine grid will perform multiple time step calculations until it is aligned with the coarse grid time synchronization point.

[0037] Application of boundary condition smoothing and virtual grid layer at the interface of coarse and fine grids: At the interface between coarse and fine grids, in order to ensure the accuracy and numerical stability of information transmission and effectively suppress non-physical oscillations that may be caused by sudden resolution changes, the present invention adopts a sophisticated interface processing strategy, which includes boundary condition smoothing technology and the application of virtual grid layers.

[0038] Construction and Function of Ghost Cells: One or more layers of ghost cells can be placed at the coarse-fine interface (CFI), either where a fine grid is adjacent to a coarse grid, or where a coarse grid is adjacent to a fine grid. These ghost cells do not participate in the standard LBM collision and migration calculations at their respective grid levels. Instead, they store distribution function values ​​constructed based on the actual flow field information of adjacent grids of different resolutions. Their purpose is to provide closure conditions for the Lattice Boltzmann equation on both sides of the interface.

[0039] Fine-grid virtual grids are used to support the flow of fluid from the fine grid to the coarse grid. Specifically, when fluid flows from a fine-grid region to a coarse-grid region, the distribution functions of the fine-grid boundary that point toward the coarse grid must be determined. These distribution functions can be obtained from the values ​​at their corresponding fine-grid virtual nodes. The distribution functions at the fine-grid virtual nodes are obtained by high-order spatiotemporal interpolation of the distribution functions of adjacent coarse-grid nodes.

[0040] The coarse-grid virtual grid supports the flow of fluid from the coarse grid to the fine grid. Specifically, when fluid flows from a coarse-grid region to a fine-grid region, the distribution function of the portion of the coarse-grid boundary that points toward the fine grid must be determined. Similarly, these distribution functions can be obtained from the values ​​at their corresponding coarse-grid virtual nodes. The distribution functions at the coarse-grid virtual nodes are reconstructed by spatially averaging or flux-conserving macroscopic quantities (such as density and velocity) from multiple adjacent fine-grid nodes. These aggregated macroscopic quantities are then aggregated using equilibrium distribution functions and non-equilibrium corrections.

[0041] Implementation of boundary condition smoothing technology: To further reduce the numerical error caused by resolution mismatch at the CFI, the present invention can further combine boundary condition smoothing technology based on the above-mentioned virtual grid layer distribution function reconstruction. This includes the following content.

[0042] Gradient smoothing, that is, smoothing the gradient of macroscopic physical quantities (such as velocity, temperature) obtained by interpolation or aggregation near the CFI, using a weighted average method or a convolution operation based on a specific smoothing kernel function to avoid sharp gradient changes.

[0043] Flux correction and conservation, that is, strictly enforcing the conservation principles of mass, momentum, and energy during the transfer of distribution functions or the reconstruction of macroscopic quantities. By locally correcting the interface flux, it is ensured that at each time step, the physical quantity flowing out of the coarse grid is exactly the same as the physical quantity flowing into the fine grid, and vice versa.

[0044] Through the above-mentioned fine adaptive grid refinement module implementation mechanism, the present invention can greatly improve the computational efficiency. By avoiding unnecessary fine calculations in areas where physical quantities change slowly, the total number of grids and the amount of calculation are significantly reduced, and the computing resource requirements can be reduced by more than 50% at the same accuracy, so that large-scale three-dimensional fire simulations can be completed within a reasonable time. It provides higher resolution in key areas such as flame fronts, smoke stratification interfaces, and strong vortices, and more accurately captures complex physical processes such as microscopic vortex structures, heat transfer, and component diffusion, thereby improving the authenticity of the simulation. It can effectively handle complex fire scenes with large-scale span characteristics, maintain numerical stability under different fire conditions, and greatly expand the application boundaries of the model.

[0045] As an embodiment provided by the present invention, the collision migration step of the lattice Boltzmann method outputs the density and velocity distribution of the flow field in real time, providing the driving velocity of convection and diffusion for the combustion and smoke models.

[0046] The lattice Boltzmann flow module specifically includes the discrete Boltzmann equation and the LES coupling equation.

[0047] Specifically, the discrete Boltzmann equation is expressed as: ; in, For direction The distribution function of is the discrete velocity of the discrete velocity model; is the dynamic relaxation time, see the LES coupling equation for details; Contributions to buoyancy and external forces.

[0048] As an embodiment provided by the present invention, the discrete velocity model is specifically D3Q27 in three-dimensional space, and the discrete velocity can be: .

[0049] The LES coupling equation is specifically expressed as: ; in, is the Smagorinsky constant, which is 0.16, is the grid resolution in m, is the filtered strain rate tensor, specifically .

[0050] In this module, macroscopic calculations are expressed as: ; in, is the density of the lattice Boltzmann equation, For speed, is the grid rate, is the distribution function.

[0051] As an embodiment provided by the present invention, the buoyancy body force is calculated by the buoyancy drive module Specifically, it is calculated by the ideal gas state equation, which is: ; in, is the environmental density, specifically, , set to is the air gas constant, is the temperature field, through the state equation renew.

[0052] The specific "Guo" force model is: ; in, is the directional weight of the discrete velocity model, and as an embodiment provided by the present invention, is the directional weight of the three-dimensional space D3Q27, such as , wait, is the lattice speed of sound.

[0053] A non-Boussinesq buoyancy model is used to calculate density changes in real time using the ideal gas equation of state, coupled to the lattice Boltzmann equation via the Guo force model. This provides more accurate results for high-temperature fire scenarios with dramatic density changes.

[0054] As another embodiment of the buoyancy driven model provided by the present invention, a segmented Boussinesq model can be used, specifically: the calculation domain is divided into low temperature zones and high temperature areas , the low temperature region uses the linear Boussinesq approximation The non-Boussinesq model is used in high-temperature areas. This balances computational efficiency and accuracy and is suitable for scenarios with distinct temperature gradients.

[0055] As an embodiment provided by the present invention, the combustion module is specifically a mixture fraction transport equation, which is expressed as: ; in, is the molecular diffusion coefficient, specifically , is the diffusion coefficient of methane to air, is the turbulent Schmidt number.

[0056] The mixed fraction transport equation is solved under the LES sub-grid viscosity correction, and its gradient modulus is Used to identify the three-dimensional flame front position.

[0057] When the gradient modulus of the mixture fraction is Exceeding the preset threshold When (ie, When the local chemical reaction is triggered in this area, the heat release rate calculation is performed. For areas that do not reach the threshold, the calculation and update of the combustion source term is skipped. It can be a constant set based on experience, or adaptively adjusted according to the dynamic characteristics of the flow field to take into account both accuracy and robustness of the calculation.

[0058] The heat release rate calculation formula is as follows: ; in, is the calorific value of fuel, is an approximation of the scalar dissipation rate of the reaction.

[0059] By activating the calculation of the combustion source term only in the three-dimensional flame front and adjacent areas, unnecessary global calculations of vast non-combustion areas are effectively avoided. While ensuring the physical properties of the three-dimensional combustion process, the computational cost of the three-dimensional simulation is effectively controlled, thereby significantly saving computing resources and improving simulation efficiency.

[0060] Another embodiment of the combustion module provided by the present invention can be a detailed chemical reaction model (EDC model). Specifically, it uses a detailed chemical reaction mechanism to couple turbulence with finite-rate reactions through the eddy dissipation concept (EDC). This model can predict the generation of intermediate products (CO, NOx), making it suitable for toxicity assessment scenarios.

[0061] As an embodiment provided by the present invention, the smoke transport module includes a smoke transport equation, specifically: ; in, is the flue gas mass fraction, is the diffusion coefficient of smoke molecules, is the flue gas turbulence Schmidt number, is the source term, is the particle settling velocity.

[0062] As another embodiment of the smoke transport module provided by the present invention, the smoke transport module can be a discrete particle model (DPM), which specifically treats smoke particles as discrete phases, tracks particle motion through the Lagrangian method, and considers turbulent diffusion, sedimentation and collision. It can accurately simulate large particles. 's dynamic behavior.

[0063] As another embodiment of the smoke transport module provided by the present invention, the smoke transport module can be a population balance model (PBM), specifically: through the particle size distribution function Describes the breakup and coalescence of polydisperse particles and couples them to the smoke concentration equation. This approach is applicable to complex particle distributions (e.g., the coexistence of 0.1-10 μm particles in smoke).

[0064] The specific steps are as follows: According to the local gradient of physical quantities such as velocity, temperature, mixture fraction, and flue gas / component concentration, the grid is refined or coarsened. The octree grid structure is updated and the information transfer between the coarse and fine grid interfaces is prepared. Preferably, the flue gas / component concentration can be directly calculated from the flue gas component concentration. If the flue gas gradient can be calculated using If you want to calculate the temperature gradient, you can use , the velocity gradient is judge; Perform a hierarchical time-stepping cycle (starting from the coarsest grid level and working down to the finest grid level); Lattice Boltzmann Method Collision and Migration, Update and ; At the current grid level, the Lattice Boltzmann method collision and migration steps are executed. At the interface between the coarse and fine grids, the distribution function flowing out of the fine grid and into the coarse grid is directly transferred; the distribution function flowing out of the coarse grid and into the fine grid is calculated by macro-quantity interpolation. Calculate the energy field (including radiation heat transfer) and update the temperature field T; Solve the energy equation including radiation source terms, radiation heat flux Calculated by integrated radiation model (such as DOM), the temperature field T is updated; Buoyancy calculation ;

[0065] Based on the updated temperature field T, the density is calculated in real time using the ideal gas state equation. , and update the buoyancy .

[0066] As an embodiment provided by the present invention, the smoke transport module is specifically a smoke transport equation, which is expressed as: ; in, is the flue gas mass fraction, is the diffusion coefficient of smoke molecules, is the flue gas turbulence Schmidt number, is the source term, , is the particle settling velocity, .

[0067] The flue gas concentration field relies on the three-dimensional transport of pollutants by the flow field, and is influenced by both convection and turbulent diffusion in the buoyancy-driven module. This invention specifically considers the settling behavior of soot particles under gravity. By incorporating particle settling velocity into the flue gas transport equation to modify the transport path, it more realistically reflects the movement and deposition of large soot particles.

[0068] Finally, the visibility parameters based on smoke concentration and density are Calculation converts complex smoke fields into intuitive risk indicators that are directly related to personnel evacuation safety.

[0069] The flue gas concentration equation is updated under the action of flow field convection and turbulent diffusion, and the particle settling velocity is introduced at the same time. Correct the transport path. Large particles of smoke and dust Under the action of gravity, it deviates from the three-dimensional streamline trajectory and settles near the ground, but the traditional passive scalar model cannot reflect this phenomenon. ,in, . Real-time output through the post-processing module is directly linked to the personnel evacuation safety assessment.

[0070] As an embodiment provided by the present invention, the energy equation module is used to update the temperature field according to the chemical reaction heat and radiation heat; wherein the chemical reaction heat release rate calculated by the combustion module is The radiation source term for the radiation heat transfer calculation They are collectively input as source terms into the energy equation module, which is specifically an energy equation.

[0071] The energy equation updates the temperature field according to the heat released by combustion and Density is corrected in real time, forming a closed-loop feedback loop of buoyancy drive, flow response, and temperature change. Heat transfer in fire smoke involves more than just convection and conduction. Radiation heat transfer plays a crucial role in high-temperature fire scenarios (especially in areas of flame and high smoke concentration), decisively affecting fire spread, structural thermal damage, and occupant heat exposure. This invention introduces a radiation source term into the energy equation and employs a high-fidelity radiation model for calculations.

[0072] The energy equation is specifically expressed as: ; in, is the specific heat capacity of air at constant pressure, is the thermal conductivity of air, is the heat release rate of the chemical reaction, calculated through the above dynamic trigger mechanism, is the radiation source term.

[0073] As an embodiment provided by the present invention, the calculation of the radiation source term specifically uses the Discrete Ordinates Method (DOM) to solve the Radiative Transfer Equation (RTE). DOM can handle complex radiation phenomena such as non-gray gas and anisotropic scattering, and effectively couples with the LBM framework. Specifically, RTE is discretized in each direction and wave number to obtain the radiation intensity I. Then, by integrating the radiation intensity, the radiation heat flux divergence within each calculation grid is calculated, that is, the radiation source term ,Right now In the present invention, the radiation intensity I obtained by DOM can be used to calculate the radiation heat flux density , and then we get the radiation source term This radiation source term is then coupled into the energy equation within the LBM framework, interacting with the convection and conduction terms to accurately update the temperature field. This coupling approach effectively accounts for the thermal radiation effects of high-temperature flames and smoke on the surrounding environment and structures, which is crucial for accurately simulating fire spread and assessing thermal hazards.

[0074] LBM flow field data (velocity and density) serves as the core driver and is transmitted in real time to the combustion module, flue gas transport module, and multi-component transport module, providing accurate convection and diffusion information. As an embodiment of the present invention, the large eddy turbulence kinetic energy module provides precise convection and diffusion information. Simultaneously, the adaptive mesh refinement module dynamically adjusts the mesh resolution based on the gradients of key physical quantities such as velocity gradients, temperature gradients, and mixture fraction gradients. This feedback directly impacts the computational domain and accuracy of all modules in the computational module. Heat release generated by the combustion process (calculated via a dynamic triggering mechanism) and the radiative heat flux calculated by the energy equation module jointly drive the energy equation to update the temperature field. Changes in the temperature field, in turn, provide real-time feedback to correct the density and trigger non-Boussinesq buoyancy effects, which in turn react on the flow field, completing the core "temperature-density-buoyancy-flow field" closed-loop feedback loop. Furthermore, the flue gas transport module updates flue gas concentration and visibility based on the flow field and particle settling velocity, while the multi-component transport module accurately predicts the concentrations of various toxic and hazardous gases based on combustion products and flow field information. This comprehensive and self-consistent multi-physics field strong coupling mechanism significantly improves the model's prediction accuracy, robustness and ability to capture the complexity of real fires, especially in fire scenarios with high temperature gradients, strong buoyancy drive and complex multi-scale characteristics.

[0075] It should be pointed out that the multi-component transport module and the large eddy turbulent kinetic energy module are conventional modules in the existing technology, and their calculation principles, formulas and data transmission principles are not described in detail.

[0076] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0078] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0080] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

[0081] The embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all steps of the method in the above embodiments. The electronic device specifically includes the following contents: Processor, memory, communications interface, and bus; The processor, memory, and communication interface communicate with each other via the bus. The processor is configured to call the computer program in the memory. When the processor executes the computer program, all steps of the method in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented: The following steps are involved: Constructing a fire model, the fire model including a calculation module and an adaptive grid refinement module, the calculation module including a lattice Boltzmann flow module, a buoyancy drive module, a combustion module, and a smoke transport module; The lattice Boltzmann flow module outputs the density and velocity distribution of the flow field in real time, providing the combustion module and the flue gas transport module with the driving velocity of convection and diffusion and output velocity gradient data; The buoyancy drive module is specifically an ideal gas state equation and a "Kuo" force model, which calculates density changes in real time through the ideal gas state equation and couples it to the lattice Boltzmann equation through the "Kuo" force model; The combustion module solves the mixture fraction transport equation under the LES subgrid viscosity correction. The mixture fraction gradient modulus is used to identify the three-dimensional flame front position and calculate the heat release rate. The temperature field is then updated based on the heat release rate. The smoke transport module relies on the three-dimensional transport of pollutants by the flow field, and is affected by the combined effects of buoyancy-driven convection and turbulent diffusion, ultimately outputting visibility parameters and smoke concentration; The adaptive grid refinement module identifies key areas during the simulation process and dynamically adjusts the grid resolution according to the physical gradient of the grid resolution in different areas of the flow field.

[0082] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the method in the above embodiments. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps of the method in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented: The following steps are involved: Constructing a fire model, the fire model including a calculation module and an adaptive grid refinement module, the calculation module including a lattice Boltzmann flow module, a buoyancy drive module, a combustion module, and a smoke transport module; The lattice Boltzmann flow module outputs the density and velocity distribution of the flow field in real time, providing the combustion module and the flue gas transport module with the driving velocity of convection and diffusion and output velocity gradient data; The buoyancy drive module is specifically an ideal gas state equation and a "Kuo" force model, which calculates density changes in real time through the ideal gas state equation and couples it to the lattice Boltzmann equation through the "Kuo" force model; The combustion module solves the mixture fraction transport equation under the LES subgrid viscosity correction. The mixture fraction gradient modulus is used to identify the three-dimensional flame front position and calculate the heat release rate. The temperature field is then updated based on the heat release rate. The smoke transport module relies on the three-dimensional transport of pollutants by the flow field, and is affected by the combined effects of buoyancy-driven convection and turbulent diffusion, ultimately outputting visibility parameters and smoke concentration; The adaptive grid refinement module identifies key areas during the simulation process and dynamically adjusts the grid resolution according to the physical gradient of the grid resolution in different areas of the flow field.

[0083] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between the various embodiments can be referenced across them. Each embodiment focuses on the differences from other embodiments. In particular, for hardware + program embodiments, since they are generally similar to method embodiments, their description is relatively simple. For relevant parts, reference can be made to the description of the method embodiments. Although the embodiments in this specification provide method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one of many possible execution sequences and does not represent the only execution sequence. When implemented in a practical device or end product, the methods shown in the embodiments or figures may be executed sequentially or in parallel (for example, in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprise," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, product, or apparatus comprising a set of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, product, or apparatus. Without further limitation, this does not preclude the presence of additional identical or equivalent elements in a process, method, product, or apparatus comprising the elements described. For ease of description, the above devices are described as functionally divided into various modules. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same or multiple software and / or hardware components, or a module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely illustrative. For example, the division of units described is merely a logical functional division. In actual implementation, other divisions may be used, such as combining or integrating multiple units or components into another system, or omitting or disabling some features. Furthermore, the coupling or direct coupling or communication connection shown or discussed between devices or units may be through interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms. The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce the instructions for implementing the process Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0084] Those skilled in the art will appreciate that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk drives, CD-ROMs, optical storage devices, etc.) containing computer-usable program code. The various embodiments in this specification are described in a progressive manner, and similar parts between the various embodiments can be referenced across them. Each embodiment focuses on the differences from other embodiments. In particular, since the system embodiments are generally similar to the method embodiments, their description is relatively simple; for relevant parts, reference can be made to the description of the method embodiments. Throughout this specification, reference to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the embodiments of this specification.

[0085] In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples, unless they contradict each other. The above is only an embodiment of the embodiment of this specification and is not intended to limit the embodiment of this specification. For those skilled in the art, the embodiment of this specification may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiment of this specification shall be included within the scope of the claims of the embodiment of this specification.

Claims

1. A fire modeling method based on the lattice Boltzmann method, characterized in that: The following steps are involved: Constructing a fire model, the fire model including a calculation module and an adaptive grid refinement module, the calculation module including a lattice Boltzmann flow module, a buoyancy drive module, a combustion module, and a smoke transport module; The lattice Boltzmann flow module outputs the density and velocity distribution of the flow field in real time, providing the combustion module and the flue gas transport module with the driving velocity of convection and diffusion and output velocity gradient data; The buoyancy drive module, specifically the ideal gas state equation and the "Kuo" force model, calculates the density change in real time through the ideal gas state equation and couples it to the lattice Boltzmann equation through the "Kuo" force model; The combustion module solves the mixture fraction transport equation under the LES subgrid viscosity correction. The mixture fraction gradient modulus is used to identify the three-dimensional flame front position and calculate the heat release rate. The temperature field is then updated based on the heat release rate. The smoke transport module relies on the three-dimensional transport of pollutants by the flow field, and is affected by the combined effects of buoyancy-driven convection and turbulent diffusion, ultimately outputting visibility parameters and smoke concentration; The adaptive grid refinement module identifies key areas during the simulation process and dynamically adjusts the grid resolution according to the physical gradient of the grid resolution in different areas of the flow field.

2. The fire modeling method based on the lattice Boltzmann method according to claim 1, characterized in that: The dynamic adjustment of the gradient specifically includes: A hierarchical nested grid structure is used to divide the grid structure into several grid blocks; Set the refinement threshold and coarsening threshold; Dynamically adjusting the grid structure based on multiple physical quantity gradients, wherein the multiple physical quantity gradients include velocity gradient, temperature gradient, and mixture fraction gradient; When the local gradient value of any of the above-mentioned physical quantity gradients of a certain grid block exceeds the refinement threshold, the adaptive grid refinement module refines the grid block; when the local gradient value of any of the above-mentioned physical quantity gradients of a certain grid block is lower than the coarsening threshold, the adaptive grid refinement module coarsens the grid block.

3. The fire modeling method based on the lattice Boltzmann method according to claim 2, characterized in that: The adaptive grid refinement module refines the grid blocks by an interpolation method, and the adaptive grid refinement module coarsens the grid blocks by an averaging method.

4. The fire modeling method based on the lattice Boltzmann method according to claim 1, characterized in that: The lattice Boltzmann flow module is specifically: Discrete Boltzmann equation, expressed as: ； in, For direction The distribution function of is the discrete velocity of the discrete velocity model; is the dynamic relaxation time; Contributions to buoyancy and external forces.

5. The fire modeling method based on the lattice Boltzmann method according to claim 1, characterized in that: The lattice Boltzmann flow module also includes the LES coupling equation, which is specifically expressed as: ； in, is the Smagorinsky constant, is the grid resolution, is the filtered strain rate tensor.

6. The fire modeling method based on the lattice Boltzmann method according to claim 1, characterized in that: In the buoyancy drive module, the ideal gas state equation is specifically: ； in, is the environmental density, is the air gas constant, is the temperature field.

7. The fire modeling method based on the lattice Boltzmann method according to claim 1, characterized in that: The "Guo" force model in the buoyancy drive module is specifically: ； in, is the directional weight of the discrete velocity model, is the lattice speed of sound.

8. The fire modeling method based on the lattice Boltzmann method according to claim 1, characterized in that: The combustion module is specifically a mixture fraction transport equation, which is specifically expressed as: ； in, is the molecular diffusion coefficient, is the turbulent Schmidt number.

9. The fire modeling method based on the lattice Boltzmann method according to claim 1, characterized in that: The smoke transport module specifically includes a smoke transport equation, which is expressed as: ； in, is the flue gas mass fraction, is the diffusion coefficient of smoke molecules, is the flue gas turbulence Schmidt number, is the source term, is the particle settling velocity.

10. The fire modeling method based on the lattice Boltzmann method according to claim 1, characterized in that: The energy equation module is specifically an energy equation, which is expressed as: ； in, is the specific heat capacity of air at constant pressure, is the thermal conductivity of air, is the heat release rate of the chemical reaction, is the radiation source term.

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