A method for evaluating virus aerosol transmission characteristics based on CFD-DEM coupled solver

Abstract

The present invention discloses a method for evaluating the propagation characteristics of viral aerosols based on a CFD-DEM coupled solver. The method uses a CFD solver and a DEM solver to respectively solve the continuous phase physical parameters and the discrete phase physical parameters of the fluid, and exchanges data through a data exchange interface, so that the DEM solver can couple the solution value of the CFD solver to numerically describe the motion and distribution state of the viral aerosol particles, thereby determining the propagation path and adsorption state of the viral aerosols. The method comprises the following steps: obtaining a flow field geometric model and importing the flow field geometric model into a CFD-DEM coupled solver; determining the computational domain boundary parameters based on the flow field geometric model; establishing a CFD solver numerical model and a DEM solver numerical model based on the computational domain boundary parameters, and initializing them; performing CFD-DEM solution calculations on the viral aerosol particles in the flow field geometric model based on the CFD solver numerical model and the DEM solver numerical model; and obtaining the propagation characteristics of the viral aerosol in the aircraft cabin environment based on the CFD-DEM solution results.

Description

Technical Field

[0001] The present invention relates to the field of fluid-particle coupling calculation, and in particular to a method for evaluating virus aerosol propagation characteristics based on a CFD-DEM coupling solver. Background Art

[0002] Aircraft cabins are semi-enclosed spaces densely populated with people and objects, creating a complex and ever-changing environment. The internal air environment is primarily controlled by engine bleed air, which is regulated by the Environmental Control System (ECS) and provides the required air for different cabin areas. While the carrier fluid, as a continuous phase, continuously improves the cabin air flow environment, it can also cause the spread of contaminants within the cabin and further adsorb onto surfaces such as seats, walls, and even clothing, causing prolonged contamination. Cabin fluids exhibit low Reynolds numbers and high turbulence, making airflow susceptible to significant changes in characteristics due to factors such as air supply boundaries, internal layout, and the flow of people. If viral aerosols, especially small particles, are present, they can be easily inhaled and sink to the lower respiratory tract, reaching the lower lungs, forming a focal point of infection.

[0003] For example, one of the primary modes of transmission of respiratory infectious diseases is viral aerosols. Viral aerosols are dispersed systems consisting of solid or liquid particles ranging in size from approximately 0.001 to 100 μm suspended in the air. Pathogens attach to viral aerosols in the atmosphere or other environments and are inhaled into the respiratory tract, causing infection. The physical properties of these systems differ from those of continuous gases. Therefore, constitutive equations related to fluids and solids, along with appropriate coupling strategies, are required to incorporate these dispersed systems. This allows researchers to study momentum, heat, and mass transfer, as well as chemical reactions, at the particle scale, with virtually no detail. Current experimental approaches to studying the transmission pathways of viral aerosols are limited by high costs and the difficulty of rapid analysis under multiple parameters and environments. Furthermore, the finite volume method, which solves the Reynolds-averaged Navier-Stokes equations (RANS) to describe gas flow under multiple parameters and environments, cannot accurately simulate the flow and adsorption of viral aerosol particles. Summary of the Invention

[0004] The following is a brief summary of one or more aspects to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceivable aspects and is neither intended to identify key or critical elements of all aspects nor to define the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that will be provided later.

[0005] The purpose of the present invention is to solve the above-mentioned problems. It provides a method for evaluating the transmission characteristics of viral aerosols based on a CFD-DEM coupling solver. The method uses a CFD (Computational Fluid Dynamics) solver and a DEM (Discrete Element Method) solver to respectively calculate the continuous phase physical parameters and discrete phase physical parameters of the fluid in the aircraft cabin environment. The physical parameters are exchanged through a data exchange interface, so that the DEM solver can couple the continuous phase physical parameters sent by the CFD solver to numerically describe the movement and distribution state of viral aerosol particles, thereby determining the transmission path and adsorption state of viral aerosols in the aircraft cabin environment.

[0006] The technical solution of the present invention is:

[0007] The present invention provides a method for evaluating the aerosol propagation characteristics of viruses based on a CFD-DEM coupled solver, comprising the following steps:

[0008] Obtain the flow field geometry model and import it into the CFD-DEM coupling solver;

[0009] Determine the boundary parameters of the computational domain according to the flow field geometric model;

[0010] CFD solver numerical model and DEM solver numerical model are established based on the computational domain boundary parameters and initialized;

[0011] Based on the CFD solver numerical model and the DEM solver numerical model, CFD-DEM solution calculation is performed on the virus aerosol particles in the flow field geometric model;

[0012] The virus aerosol transmission characteristics in the aircraft cabin environment are obtained based on the CFD-DEM solution results.

[0013] According to an embodiment of the virus aerosol transmission characteristics evaluation method based on the CFD-DEM coupling solver of the present invention, the virus aerosol transmission characteristics include the virus aerosol particle motion trajectory and the virus aerosol particle adsorption state, and the calculation domain boundary parameters include the calculation domain boundary range and the calculation domain boundary conditions; wherein,

[0014] The computational domain boundary range is used to determine the spatial area for CFD-DEM solution calculation;

[0015] The computational domain boundary conditions are used to determine the physical conditions or physical model of the surface of an object in contact with viral aerosol particles in the computational domain.

[0016] According to an embodiment of the method for evaluating viral aerosol propagation characteristics based on a CFD-DEM coupled solver of the present invention, the CFD-DEM solution calculation includes the following steps:

[0017] Based on the external real state, the CFD solver is given the continuous phase physical parameters of the initial state of the fluid in the aircraft cabin environment, and the continuous phase physical parameters of the initial state are sent to the DEM solver;

[0018] The DEM solver calculates the initial grid unit physical quantity mean based on the continuous phase physical parameters of the fluid initial state, obtains the discrete phase physical parameters, and sends the calculated discrete phase physical parameters to the CFD solver for initial value update;

[0019] The CFD solver updates the continuous phase physical parameters based on the received discrete phase physical parameters, and then performs fluid numerical calculations through the CFD solver numerical model;

[0020] Determine whether the CFD solver has completed the current CFD time step; if so, stop the fluid numerical calculation of the current time step and send the output new continuous phase physical parameters to the DEM solver; if not, continue the fluid numerical calculation;

[0021] The DEM solver performs numerical iterative calculation of viral aerosols based on the received continuous phase physical parameters through DEM solution;

[0022] Determine whether the DEM solver has completed a CFD time step; if so, stop the viral aerosol numerical iterative calculation of the current DEM time step and send the output new continuous phase physical parameters to the CFD solver; if not, continue the viral aerosol numerical iterative calculation;

[0023] Determine whether the CFD solver and DEM solver have reached the set termination conditions; if so, end the CFD-DEM solution calculation; if not, continue with the fluid numerical calculation and viral aerosol numerical iterative calculation for the next time step.

[0024] According to an embodiment of the virus aerosol propagation characteristic evaluation method based on the CFD-DEM coupling solver of the present invention, the CFD solver and the DEM solver exchange continuous phase physical parameters and discrete phase physical parameters through a data exchange interface; wherein,

[0025] The continuous phase physical parameters include velocity, pressure and CFD time step,

[0026] The discrete phase physical parameters include interphase forces, discrete phase velocity, discrete phase volume forces and updated CFD time steps.

[0027] According to an embodiment of the method for evaluating viral aerosol propagation characteristics based on a CFD-DEM coupled solver of the present invention, the CFD solver uses a RANS model for fluid numerical calculation; wherein the governing equation of the RANS model is:

[0028]

[0029] in, represents the differential surface area,

[0030] represents an arbitrary controlled volume,

[0031] W represents the original variable in the RANS model,

[0032] F represents the inviscid flux term,

[0033] G represents the viscous flux term,

[0034] H represents the body force vector.

[0035] According to an embodiment of the method for evaluating viral aerosol propagation characteristics based on the CFD-DEM coupled solver of the present invention, W, F, and G are calculated using the following formulas:

[0036]

[0037]

[0038]

[0039] in, represents the fluid density,

[0040] v represents the fluid velocity,

[0041] E represents the total energy per unit mass,

[0042] I represents the identity matrix,

[0043] H represents the total enthalpy,

[0044] T represents the viscous stress tensor,

[0045] represents the heat flux vector.

[0046] According to an embodiment of the method for evaluating the propagation characteristics of aerosol viruses based on the CFD-DEM coupling solver of the present invention, the control method of the RANS model can also be combined with the preprocessing matrix Effectively solve for compressible and incompressible fluids within the aircraft cabin environment at all speeds. The fluid solution formula is as follows:

[0047]

[0048] in, represents the preprocessing matrix,

[0049] Q represents the original variables of the preprocessed RANS model,

[0050] F represents the inviscid flux term,

[0051] G represents the viscous flux term,

[0052] H represents the body force vector.

[0053] According to an embodiment of the method for evaluating the propagation characteristics of aerosol viruses based on the CFD-DEM coupling solver of the present invention, the preprocessing matrix The calculation formula is as follows:

[0054]

[0055]

[0056] in, represents the derivative of density with respect to temperature at constant pressure,

[0057] v represents the fluid velocity,

[0058] θ represents a specific parameter,

[0059] C p is the specific heat capacity,

[0060] δ is the value under different gas conditions. If the gas condition is an ideal gas, the δ value is 1; if the gas condition is an incompressible fluid, the δ value is 0.

[0061] According to an embodiment of the method for evaluating viral aerosol propagation characteristics based on a CFD-DEM coupled solver of the present invention, the fluid solution formula can also be applied to control the fluid volume in a specially designated grid cell 0, and the calculation formula is as follows:

[0062]

[0063] in, and denote the inviscid flux and viscous flux, respectively.

[0064] represents the volume of grid cell 0,

[0065] h represents the body force of grid cell 0,

[0066] Represents the preconditioning matrix computed in grid cell 0.

[0067] According to an embodiment of the method for evaluating viral aerosol propagation characteristics based on a CFD-DEM coupled solver of the present invention, the CFD-DEM coupled solver uses an unsteady flow field solver for solution calculation and combines pre-processed pseudo-time derivatives for fluid numerical calculation. The calculation formula is as follows:

[0068]

[0069] Where t represents the physical time step uniformly applied to all grid cells in the computational domain,

[0070] represents the pseudo-time derivative of the local change used during the time progression,

[0071] Q represents the original variables of the preprocessed RANS model,

[0072] W represents the original variable,

[0073] F represents the inviscid flux term,

[0074] G represents the viscous flux term,

[0075] H represents the body force vector.

[0076] According to an embodiment of the method for evaluating viral aerosol propagation characteristics based on a CFD-DEM coupled solver of the present invention, the CFD-DEM coupled solver approaches the pseudo-time derivative to zero through the following iterative algorithm, as shown in the following formula:

[0077]

[0078]

[0079] in, represents the preprocessing matrix,

[0080] i represents the internal iteration counter,

[0081] n represents any given level of physical time.

[0082] According to an embodiment of the virus aerosol propagation characteristic evaluation method based on the CFD-DEM coupling solver of the present invention, the DEM solver receives the continuous phase physical parameters of the fluid sent by the CFD solver and performs a viral aerosol numerical iterative calculation on the viral aerosol in the aircraft cabin environment; wherein, the viral aerosol numerical iterative calculation includes the calculation of the volume fraction of the viral aerosol particle phase and the calculation of the fluid-solid interaction force term.

[0083] According to an embodiment of the method for evaluating viral aerosol propagation characteristics based on a CFD-DEM coupled solver of the present invention, the fluid-solid interaction force is calculated by the following formula:

[0084]

[0085] in, represents the fluid-solid interaction force,

[0086] represents the drag force acting on the virus aerosol particles,

[0087] Non-drag forces acting on viral aerosol particles.

[0088] According to an embodiment of the method for evaluating virus aerosol propagation characteristics based on the CFD-DEM coupling solver of the present invention, the drag Calculated using the following formula:

[0089]

[0090] in, represents the relative velocity between sol particles and local fluid,

[0091] represents the projected area of ​​virus aerosol particles along the flow direction,

[0092] represents the drag coefficient.

[0093] According to an embodiment of the method for evaluating the propagation characteristics of aerosols of viruses based on the CFD-DEM coupling solver of the present invention, the non-drag Calculated using the following formula:

[0094]

[0095] in, represents the pressure gradient force,

[0096] represents lift,

[0097] represents the virtual mass force,

[0098] Represents a custom force.

[0099] According to an embodiment of the virus aerosol transmission characteristics evaluation method based on the CFD-DEM coupling solver of the present invention, the DEM solver also includes a CFD time step correction, which accelerates and solves a single CFD time step and n DEM time steps through the critical time step of the DEM solver to obtain an updated CFD time step.

[0100] According to an embodiment of the method for evaluating viral aerosol propagation characteristics based on a CFD-DEM coupled solver of the present invention, the critical time step of the DEM solver is corrected by the following formula:

[0101]

[0102] in, represents the critical time step, Represents the radius of the virus aerosol particle; when the DEM time step in the DEM solver is higher than or equal to the critical time step, the CFD time step is corrected.

[0103] According to an embodiment of the virus aerosol propagation characteristic evaluation method based on the CFD-DEM coupling solver of the present invention, after the CFD-DEM coupling solver completes the solution calculation, the trajectory motion trajectory and adsorption state of the virus aerosol particles in the aircraft cabin environment are obtained based on the CFD-DEM solution results.

[0104] Compared to existing technologies, the present invention has the following advantages: It utilizes a CFD solver and a discrete element method (DEM) solver to respectively calculate the continuous and discrete phase physical parameters of the fluid within an aircraft cabin environment. This physical parameter exchange occurs via a data exchange interface, enabling the DEM solver to combine the various continuous phase physical parameters transmitted by the CFD solver to numerically describe the motion and distribution of viral aerosol particles within the computational domain. This enables rapid, multi-parameter, and multi-environment analysis of viral aerosol particles within the aircraft cabin environment, offering advantages such as low cost and ease of operation. Furthermore, the numerical solution method, coupled with computational fluid dynamics (CFD) and discrete element method (DEM), utilizes the present invention to numerically calculate not only the propagation pathways of viral aerosol particles under multiple parameters and environments, but also the adsorption state of the discrete phase of viral aerosol particles attached to surfaces within the aircraft cabin environment. This provides a more accurate description of the airborne propagation characteristics of viral aerosol particles within the aircraft cabin environment compared to existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] The above features and advantages of the present invention will be better understood after reading the detailed description of the embodiments of the present disclosure in conjunction with the following drawings. In the drawings, the components are not necessarily drawn to scale, and components with similar related properties or characteristics may have the same or similar reference numerals.

[0106] Figure 1 1 is a flow chart showing an embodiment of a method for evaluating virus aerosol propagation characteristics based on a CFD-DEM coupled solver according to the present invention.

[0107] Figure 2 FIG. 1 is a flow chart showing an embodiment of the CFD-DEM solution calculation method of the present invention.

[0108] Figure 3 FIG. 1 is a data flow diagram illustrating an embodiment of the CFD-DEM solution calculation method of the present invention. DETAILED DESCRIPTION

[0109] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Note that the various aspects described below with reference to the accompanying drawings and specific embodiments are merely exemplary and should not be construed as limiting the scope of protection of the present invention.

[0110] Disclosed herein is an embodiment of a method for evaluating viral aerosol propagation characteristics based on a CFD-DEM coupled solver. Figure 1 This is a flow chart showing an embodiment of the method for evaluating the viral aerosol propagation characteristics based on the CFD-DEM coupling solver of the present invention. Figure 1 ,The following is a detailed description of each step of the virus aerosol transmission characteristics evaluation method based on the CFD-DEM coupled solver.

[0111] Step S1: Obtain the flow field geometric model and import the flow field geometric model into the CFD-DEM coupling solver.

[0112] Step S2: Determine the boundary parameters of the calculation domain according to the geometric model of the flow field calculation domain.

[0113] Specifically, the flow field geometric model refers to a geometric model that includes the surface of macroscopic objects, viral aerosol particles, and the boundary of the computational domain. It is usually imported into the CFD-DEM coupling solver in the form of iges, stp, and stl format files for solution.

[0114] In this embodiment, viral aerosol refers to a particle cluster or particle clump formed by the aggregation of viral particles. The particle size is very small. Therefore, a single viral aerosol particle is usually regarded as a whole. There is no concept of range. Only the size of the computational domain space needs to consider the range issue. Through the flow field geometry model, the computational domain boundary parameters in the CFD-DEM numerical calculation can be obtained, thereby establishing the physical boundary of the computational domain and the state of collision and contact of multiple objects. Among them, the computational domain boundary range is used to determine the spatial area for CFD-DEM solution calculation, and the computational domain boundary conditions are used to determine the physical conditions or physical models of the surface of objects in the computational domain that come into contact with viral aerosol particles. For example, when a particle contacts and collides with the boundary, the mathematical model required is used.

[0115] Furthermore, in this embodiment, the flow field geometry model is the aircraft cabin geometry model, and the flow field computational domain is the interior of the aircraft cabin geometry model. The aircraft cabin interior space is the physical spatial region used for the computational domain. Only physical processes occurring within the aircraft cabin space, such as particle contact and collision, fluid motion, and interphase interactions, are computed by the CFD-DEM coupled solver. All other processes outside this region are not computed by the CFD-DEM coupled solver.

[0116] Step S3: Based on the computational domain boundary parameters, a CFD solver numerical model and a DEM solver numerical model are established and initialized.

[0117] Step S4: Based on the CFD solver numerical model and the DEM solver numerical model, CFD-DEM solution calculation is performed on the virus aerosol particles in the flow field geometric model to obtain the virus aerosol transmission characteristics.

[0118] In this embodiment, a CFD solver numerical model for solving the flow field and a DEM solver numerical model for calculating the force conditions of viral aerosol movement are established based on the computational domain boundary parameters, and the CFD solver numerical model and the DEM solver numerical model are initialized before the CFD-DEM solution is started. Figure 2 This is a flow chart showing an embodiment of the CFD-DEM solution calculation method of the present invention. Figure 2 ,The following is a detailed description of each step of the CFD-DEM solution calculation method.

[0119] Step S41: assigning the initial state continuous phase physical parameters of the fluid in the aircraft cabin environment to the CFD solver based on the external real state, and sending the initial state continuous phase physical parameters to the DEM solver.

[0120] Step S42: The DEM solver calculates the mean value of the initial grid unit physical quantity based on the continuous phase physical parameters of the initial state of the fluid, obtains the discrete phase physical parameters, and sends the calculated discrete phase physical parameters to the CFD solver for initial value update.

[0121] In this embodiment, the initial state continuous phase physical parameters include velocity, pressure, and time, and the data obtained by the CFD solver are all discrete. These initial values ​​are usually manually assigned directly to the CFD solver based on the actual external situation. The CFD solver then sends them to the DEM solver via a data exchange interface. The DEM solver calculates the mean value of the initial grid unit physical quantity and obtains the new discrete phase physical parameters. Similarly, the DEM solver sends the obtained new discrete phase physical parameters to the CFD solver via the data exchange interface to update the continuous phase physical parameters of the initial state. Figure 3 This is a data flow chart showing an embodiment of the CFD-DEM solution calculation method of the present invention. Figure 3 , further illustrating this embodiment.

[0122] like Figure 3 As shown, in this embodiment, the CDF solver and the DEM solver transmit physical parameters via a data exchange interface. The data exchange interface can be built into different solvers, meaning it can be integrated within the solver or exist independently of the solver. However, during the CFD-DEM coupled solution process, the CFD solver and the DEM solver must operate independently.

[0123] Step S43: The CFD solver performs fluid numerical calculations using a CFD solver numerical model based on the received discrete phase physical parameters.

[0124] In this embodiment, the CFD solver uses the RANS model to solve the ground effect flow field. The CFD-DEM coupled solver uses the governing equation in the form of Cartesian integrals to constrain the RANS model. The calculation formula is as follows:

[0125]

[0126] in, represents the differential surface area, represents the volume of arbitrary control, W represents the primitive variables in the Navier-Stokes equations, F represents the inviscid flux term, G represents the viscous flux term, and H represents the body force vector.

[0127] Specifically, in this embodiment, vectors W, F, and G are calculated using the following formulas:

[0128]

[0129]

[0130]

[0131] in, represents the fluid density, v represents the fluid velocity, and E represents the total energy per unit mass. I represents the identity matrix, H represents the volume force vector, T represents the viscous stress tensor, represents the heat flux vector.

[0132] In addition, in this embodiment, the control method of the RANS model can also be combined with the preprocessing matrix , by multiplying the transient terms of the governing equations, the preconditioning matrix is ​​incorporated into the control scheme described above, thereby enabling effective solution for both compressible and incompressible flows within the aircraft cabin environment at all speeds. The fluid solution formula is as follows:

[0133]

[0134] in, Represents the preprocessing matrix, Q represents the original variables of the RANS model after preprocessing, F represents the inviscid flux term, G represents the viscous flux term, and H represents the body force vector. Can be simplified to To express.

[0135] Specifically, in this embodiment, the preprocessing matrix The calculation formula is as follows:

[0136]

[0137]

[0138] in, represents the derivative of density with respect to temperature at constant pressure, v represents the fluid velocity,

[0139] θ represents a specific parameter, C p is the specific heat capacity, and δ is the value under different gas conditions. If the gas condition is ideal gas, δ is 1; if the gas condition is incompressible fluid, δ is 0.

[0140] Furthermore, in this embodiment, the fluid solution formula can also be applied to control the fluid volume in grid unit 0. When the above fluid solution formula is applied to the grid unit centered control volume of grid unit 0, the following discrete equations can be obtained:

[0141]

[0142] in, and denote the inviscid flux and viscous flux, respectively. represents the volume of grid cell 0, h represents the volume force of grid cell 0, Represents the preconditioning matrix computed in grid cell 0.

[0143] In addition, in this embodiment, the continuity, mass and momentum conservation equations are solved by a coupled fluid solver, namely a CFD-DEM coupled solver, using an implicit integration format, and the gas in the flow field is set to affect the ideal gas.

[0144] Step S44: Determine whether the CFD solver has completed the current CFD time step; if so, stop the fluid numerical calculation of the current time step and send the output new continuous phase physical parameters to the DEM solver; if not, continue the fluid numerical calculation.

[0145] In this embodiment, the discrete phase in the DEM solver can only update its subsequent motion state after receiving the continuous phase physical parameters (i.e., fluid physical property information) from the CFD solver. In bidirectionally coupled numerical calculations, the subsequent motion state of the discrete phase of the viral aerosol particles also affects the physical state of the continuous phase. Therefore, the physical state of the discrete phase of the viral aerosol particles in the CFD solver must be updated to calculate the impact of the updated continuous phase physical parameters on the fluid in the aircraft cabin environment. This impact is then fed back to the CFD solver for iterative updates; otherwise, the calculation will not converge.

[0146] In addition, in this embodiment, the CFD-DEM coupled solution method must be solved using an unsteady flow field solver. Therefore, this embodiment adopts a dual-time method and performs internal iteration in pseudo-time. The pre-processed pseudo-time derivative term is introduced into the conservation equation and the fluid numerical calculation is performed in combination with the pre-processed pseudo-time derivative. The calculation formula is as follows:

[0147]

[0148] Where t represents the physical time step uniformly applied to all grid cells in the computational domain, represents the pseudo-time derivative of the local variation used during the time march. Q represents the preprocessed raw variables of the RANS model, W represents the raw variables of the RANS model, F represents the inviscid flux term, G represents the viscous flux term, and H represents the body force vector. The calculation methods for W, F, and G are the same as those for the vectors W, F, and G above and are not repeated here.

[0149] In addition, in this embodiment, as the internal iteration of the CFD-DEM coupled solver gradually converges, the following iterative algorithm is used to bring the pseudo-time derivative toward zero:

[0150]

[0151]

[0152] in, represents the preprocessing matrix, i represents the internal iteration counter, and n represents any given physical time level. In the entire pseudo-time iteration process, and Keep constant, according to The calculations were performed by selecting the two-equation k-ω turbulence numerical model based on Menter shear stress transport (SST) to more accurately describe the air flow in the cabin.

[0153] Step S45: The DEM solver performs numerical iterative calculation of viral aerosols based on the received continuous phase physical parameters through DEM solution.

[0154] In this embodiment, after the CFD solver completes the solution calculation within the current CFD time step, the DEM solver receives the continuous phase physical parameters of the fluid sent by the CFD solver and begins to perform numerical iterative calculations of the viral aerosols in the aircraft cabin environment. This numerical iterative calculation includes calculating the volume fraction of the viral aerosol particles and the fluid-solid interaction force terms (interaction force and heat transfer rate (if any)). The calculated results are then sent to the CFD solver.

[0155] Specifically, in this embodiment, the transmission process of viral aerosol particles in the cabin environment mainly follows the momentum conservation equation, and the momentum change of the particle-fluid interaction written in the Lagrangian framework is mainly balanced by the drag and non-drag parts acting on the particles. Among them, the drag force is the force exerted by the fluid on the solid with relative velocity. This force is opposite to the direction of the solid relative to the fluid velocity and is the resistance to relative motion. Non-drag refers to external forces other than drag, including pressure gradient force, lift, virtual mass force, etc. that are not generated by the velocity difference of the fluid relative to the solid. Therefore, in this embodiment, the fluid-solid interaction force is calculated by the following formula:

[0156]

[0157] in, represents the fluid-solid interaction force, represents the drag force acting on the virus aerosol particles, Non-drag forces acting on viral aerosol particles.

[0158] Specifically, in this embodiment, the drag force Calculated using the following formula:

[0159]

[0160] in, represents the relative velocity between sol particles and local fluid, Indicates the projected area of ​​virus aerosol particles along the flow direction, drag coefficient A function that represents the small-scale fluid characteristics surrounding a single particle. Because it is impractical to resolve these characteristics spatially, the drag coefficient needs to be correlated from experimental or theoretical studies. For example, droplets, bubbles, and solid particles all have different correlations, and the particle shape, mass, and energy transfer all affect the correlation.

[0161] Furthermore, in this embodiment, non-drag Calculated using the following formula:

[0162]

[0163] in, represents the pressure gradient force, represents lift, represents the virtual mass force, Represents a custom force. Specifically, the pressure gradient force Lift refers to the force acting on a unit mass of air due to uneven distribution of fluid pressure, with its direction pointing from high pressure to low pressure. It means that when a fluid flows through the surface of an object, it will generate a surface force on it, and the component of this force perpendicular to the direction of fluid flow is the lift. It refers to the additional force generated by the accelerated motion of the discrete phase relative to the continuous phase. Other forces include gravity, Coulomb force, magnetic force, etc. Among them, Coulomb force is actually electrostatic force, which is the interaction force between two charges in an electrostatic field.

[0164] Step S45: Determine whether the DEM solver has completed a CFD time step; if so, stop the viral aerosol numerical iterative calculation of the current DEM time step and send the output new continuous phase physical parameters to the CFD solver; if not, continue the viral aerosol numerical iterative calculation.

[0165] In this embodiment, a CFD time step contains n DEM time steps, where n is a positive integer, typically between 20 and 100. During the DEM solver's iterative numerical calculation of viral aerosols, the CFD time step is corrected. A single CFD time step and n DEM time steps are accelerated and solved using the DEM solver's critical time step, thereby obtaining an updated CFD time step. The updated CFD time step is then sent to the CFD solver for updating.

[0166] Specifically, in this embodiment, in order to make the CFD-DEM coupling calculation results converge, the time step of the CFD solver must be an integer multiple of the time step of the DEM solver, and the setting of the DEM time step must meet the basic principle that the propagation distance of the disturbance wave will not exceed the center distance of the mutually contacting particles within an appropriate time step, that is, within the critical time step of the DEM solver, so as to meet the display time integration framework of the DEM solver in solving the motion equation.

[0167] Therefore, in this embodiment, the critical time step of the DEM solver following Rayleigh analysis in the CFD-DEM coupled solution process can be corrected by the following formula:

[0168]

[0169] in, represents the critical time step, Represents the radius of the virus aerosol particle. When the DEM time step in the DEM solver is greater than or equal to the critical time step, the CFD time step is corrected.

[0170] Step S46: Determine whether the CFD solver and the DEM solver have reached the set termination conditions; if so, terminate the CFD-DEM solution calculation; if not, continue with the fluid numerical calculation and viral aerosol numerical iterative calculation for the next time step.

[0171] In this embodiment, when the solution time of the CFD solver and the DEM solver both reaches the set solution time node, the solution stops.

[0172] Step S5: Obtain the virus aerosol transmission characteristics in the aircraft cabin environment based on the CFD-DEM solution results.

[0173] In this embodiment, after the CFD-DEM coupling solver completes the solution calculation, the trajectory motion trajectory and adsorption state of the virus aerosol particles are obtained based on the CFD-DEM solution results.

[0174] Specifically, the DEM solver connects all spatial coordinates of each viral aerosol particle at each DEM time step, forming polylines or Bézier curves over time. These polylines or connecting lines represent the trajectory of the viral aerosol particles. Furthermore, the DEM solver can not only describe the trajectory of viral aerosol particles within the computational domain and use this trajectory to represent the transmission pathway of viral aerosols, but also describe the physical state of viral aerosol particles after contact with surfaces within the computational domain, such as the adsorption state of viral aerosols.

[0175] In addition, in this embodiment, the trajectory of viral aerosol particles within the computational domain can be set in the DEM solver as a separately exported trajectory file. The trajectory file contains two types of data: scalar data containing search information for locating individual viral aerosol particles, including the number of viral aerosol particles and time information; and vector data for determining the velocity components of individual viral aerosol particles in the Cartesian coordinate system, respectively, in the x, y, and z coordinates.

[0176] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0177] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of the two. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. A skilled person may implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present invention.

[0178] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein may be implemented or executed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0179] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read and write information from / to the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside in a user terminal as discrete components.

[0180] In one or more exemplary embodiments, the described functions may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted via a computer-readable medium as one or more instructions or code. Computer-readable media includes both computer storage media and communication media, including any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium that is accessible by a computer. By way of example and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that is accessible by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. As used herein, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks typically reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

Claims

1. A method for evaluating the propagation characteristics of viral aerosols based on a CFD-DEM coupled solver, characterized in that: The following steps are involved: Obtain the flow field geometry model and import it into the CFD-DEM coupling solver; Determine the boundary parameters of the computational domain according to the flow field geometric model; Based on the computational domain boundary parameters, the numerical models of the CFD solver and the DEM solver are established and initialized respectively; Based on the CFD solver numerical model and the DEM solver numerical model, CFD-DEM solution calculation is performed on the virus aerosol particles in the flow field geometric model; Obtain the virus aerosol transmission characteristics in the aircraft cabin environment based on CFD-DEM solution results; Wherein, the virus aerosol transmission characteristics include the virus aerosol particle motion trajectory and the virus aerosol particle adsorption state, and the calculation domain boundary parameters include the calculation domain boundary range and the calculation domain boundary conditions; wherein, The computational domain boundary range is used to determine the spatial area for CFD-DEM solution calculation; The computational domain boundary conditions are used to determine the physical conditions or physical models of the surface of the object in contact with the viral aerosol particles in the computational domain; The CFD-DEM solution calculation includes the following steps: Based on the external real state, the CFD solver is given the continuous phase physical parameters of the initial state of the fluid in the aircraft cabin environment, and the continuous phase physical parameters of the initial state are sent to the DEM solver; The DEM solver calculates the initial grid unit physical quantity mean based on the continuous phase physical parameters of the fluid initial state, obtains the discrete phase physical parameters, and sends the calculated discrete phase physical parameters to the CFD solver for initial value update; The CFD solver updates the continuous phase physical parameters based on the received discrete phase physical parameters, and then performs fluid numerical calculations through the CFD solver numerical model; Determine whether the CFD solver has completed the current CFD time step; if so, stop the fluid numerical calculation of the current time step and send the output new continuous phase physical parameters to the DEM solver; if not, continue the fluid numerical calculation; The DEM solver performs numerical iterative calculation of viral aerosols based on the received continuous phase physical parameters through DEM solution; Determine whether the DEM solver has completed a CFD time step; if so, stop the viral aerosol numerical iterative calculation of the current DEM time step and send the output new continuous phase physical parameters to the CFD solver; if not, continue the viral aerosol numerical iterative calculation; Determine whether the CFD solver and DEM solver have reached the set termination conditions; if so, terminate the CFD-DEM solution calculation; if not, continue with the fluid numerical calculation and viral aerosol numerical iterative calculation for the next time step; The DEM solver receives the continuous phase physical parameters of the fluid sent by the CFD solver and performs a numerical iterative calculation of the viral aerosol in the aircraft cabin environment; wherein the numerical iterative calculation of the viral aerosol includes the calculation of the volume fraction of the viral aerosol particle phase and the calculation of the fluid-solid interaction force term; The fluid-solid interaction force is calculated using the following formula: F f→p =F d +F non-d Among them, F f→p represents the fluid-solid interaction force, F d represents the drag force acting on the virus aerosol particles, F non-d Non-drag forces acting on viral aerosol particles; Among them, in the DEM solver, all spatial coordinates of each viral aerosol particle obtained at each DEM time step are connected according to the development of time to form polylines or connecting lines including Bezier curves. These polylines or connecting lines are used to represent the trajectory of the viral aerosol particles. At the same time, the DEM solver not only describes the trajectory of the viral aerosol particles in the calculation domain and represents the viral aerosol transmission path through the trajectory, but also describes the physical state of the viral aerosol particles in the calculation domain after contact with the surface, which includes the adsorption state of the viral aerosol. Among them, the DEM solver sets the motion trajectory of the viral aerosol particles in the computational domain as a separately exported trajectory file. The trajectory file contains two types of data: one is scalar data containing retrieval information for locating individual viral aerosol particles, viral aerosol particle quantity information and time information; the other is vector data for determining the velocity components of individual viral aerosol particles in the Cartesian coordinate system at the x, y and z coordinates.

2. The method for evaluating virus aerosol propagation characteristics based on the CFD-DEM coupled solver according to claim 1 is characterized in that: The CFD solver and the DEM solver exchange continuous phase physical parameters and discrete phase physical parameters through a data exchange interface; wherein, The continuous phase physical parameters include velocity, pressure and CFD time step, The discrete phase physical parameters include interphase forces, discrete phase velocity, discrete phase volume forces and updated CFD time steps.

3. The method for evaluating viral aerosol propagation characteristics based on the CFD-DEM coupled solver according to claim 1 is characterized in that: The CFD solver uses the RANS model to perform fluid numerical calculations; wherein the control equation of the RANS model is: Where da represents the differential surface area, dV represents the volume of arbitrary control, W represents the original variable in the RANS model, F represents the inviscid flux term, G represents the viscous flux term, H represents the body force vector.

4. The method for evaluating viral aerosol propagation characteristics based on the CFD-DEM coupled solver according to claim 3 is characterized in that: W, F, and G are calculated using the following formulas: Where ρ represents the fluid density, v represents the fluid velocity, E represents the total energy per unit mass, I represents the identity matrix, H represents the total enthalpy, T represents the viscous stress tensor, represents the heat flux vector.

5. The method for evaluating viral aerosol propagation characteristics based on the CFD-DEM coupled solver according to claim 3 is characterized in that: The control method of the RANS model can also be combined with the preprocessing matrix Γ to effectively solve the compressible and incompressible fluids in the aircraft cabin environment at all speeds. The fluid solution formula is as follows: Where Γ represents the preprocessing matrix, Q represents the original variables of the preprocessed RANS model, F represents the inviscid flux term, G represents the viscous flux term, H represents the body force vector.

6. The method for evaluating viral aerosol propagation characteristics based on the CFD-DEM coupled solver according to claim 5 is characterized in that: The calculation formula of the preprocessing matrix Γ is as follows: Among them, ρ T represents the derivative of density with respect to temperature at constant pressure, v represents the fluid velocity, θ represents a specific parameter, C p is the specific heat capacity, δ is the value under different gas conditions. If the gas condition is an ideal gas, the δ value is 1; if the gas condition is an incompressible fluid, the δ value is 0.

7. The method for evaluating viral aerosol propagation characteristics based on a CFD-DEM coupled solver according to claim 3 is characterized in that: The fluid solution formula can also be applied to control the fluid volume in a specially specified grid cell 0. The calculation formula is as follows: Among them, f f and g f denote the inviscid flux and viscous flux respectively, V0 represents the volume of grid cell 0, h represents the body force of grid cell 0, Γ0 represents the preconditioning matrix calculated in grid cell 0.

8. The method for evaluating viral aerosol propagation characteristics based on a CFD-DEM coupled solver according to claim 3 is characterized in that: The CFD-DEM coupling solver uses an unsteady flow field solver to perform calculations and combines the pre-processed pseudo-time derivatives to perform fluid numerical calculations. The calculation formula is as follows: Where t represents the physical time step uniformly applied to all grid cells in the computational domain, τ represents the pseudo-time derivative of the local variation used during time advancement, Q represents the original variables of the preprocessed RANS model, W represents the original variable, F represents the inviscid flux term, G represents the viscous flux term, H represents the body force vector.

9. The method for evaluating viral aerosol propagation characteristics based on a CFD-DEM coupled solver according to claim 3 is characterized in that: The CFD-DEM coupled solver uses the following iterative algorithm to reduce the pseudo-time derivative to zero, as shown in the following formula: Q (0) =Q n Q (n+1) =Q (m) ΔQ≡Q (i) -Q (0) Where Γ represents the preprocessing matrix, i represents the internal iteration counter, n represents any given level of physical time.

10. The method for evaluating viral aerosol propagation characteristics based on a CFD-DEM coupled solver according to claim 1, characterized in that: The drag force F d Calculated using the following formula: Among them, v f -v p represents the relative velocity between sol particles and local fluid, A p represents the projected area of ​​virus aerosol particles along the flow direction, C d represents the drag coefficient.

11. The method for evaluating viral aerosol propagation characteristics based on a CFD-DEM coupled solver according to claim 1, characterized in that: The non-drag force F non-d Calculated using the following formula: F non-d =F p +F l +F vm +F others Among them, F p represents the pressure gradient force, F l represents lift, F vm represents the virtual mass force, F others Represents a custom force.

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