Special aerosol diffusion analysis method, device, equipment and medium

Abstract

The invention discloses a special aerosol diffusion analysis method, device, equipment and medium, and relates to the technical field of diffusion prediction.According to the method, different kinetic equations are adopted for diffusion areas of different heights to calculate the average wind speed, and the calculation speed can be increased while the calculation precision is guaranteed. The problem that a traditional large-scale special model and a traditional medium-scale special model are tedious in operation process and use is solved. By adopting the method provided by the embodiment of the invention, one model can adapt to different scales. A kernel function method is adopted to process the fluctuation problem of concentration calculation. When the method is used for calculating the concentration on the spatial coordinate point, fluctuation of a concentration field is greatly reduced, sampling calculation times can be reduced, and resources and time required by calculation are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of diffusion prediction, and in particular to a special aerosol diffusion analysis method, device, equipment and medium based on a Lagrangian particle random walk model. Background Art

[0002] With the development of society and the advancement of science and technology, users are increasingly demanding new technologies. Disasters caused by nuclear, biological, and chemical hazards have become a significant part of global disasters. Toxic and harmful gases, radioactive contamination (special aerosols), and other hazards pose a serious threat to the human living environment and human life.

[0003] In order to respond to possible emergency accidents, it is necessary to simulate and analyze the diffusion of special aerosols generated in the atmosphere after the accident, so as to analyze and predict the impact of special aerosols on the surrounding environment and organisms based on the simulation analysis results, so that decision makers can provide corresponding prevention plans based on the prediction results.

[0004] The diffusion of specialty aerosols in the atmosphere is inherently random. This characteristic of atmospheric diffusion can be simulated using a random walk of a large number of particles, known as the particle random walk model. This model is not restricted by the grid and is suitable for atmospheric diffusion calculations at various scales and complex terrains. By simply modifying the wind speed fluctuation properties, this model can be adapted to diffusion calculations at various scales.

[0005] The Lagrangian particle random walk model, based on the inherent randomness of atmospheric diffusion, uses a random walk of a large number of particles to simulate the atmospheric diffusion of specialized aerosols. The basic concept is to treat each pollutant particle as a signature particle, and the release of pollutants can be represented by the release of a large number of signature particles. The overall distribution of these particles in space and time constitutes a picture of the pollutant distribution. Each particle's trajectory can be divided into a number of discrete intervals, each of which experiences the same time interval, called a step. Within each step, the horizontal and vertical velocity components remain constant. Each velocity component consists of a mean velocity and a superimposed perturbation (or fluctuation) component. The mean motion component is provided by wind field diagnostic or forecasting models, while the fluctuation component is represented by the sum of a correlation component (the influence of the fluctuation at the previous moment on the fluctuation at the next moment) and a random component (using a random process to simulate the randomness of turbulent fluctuations). By linking diffusion calculations with the turbulent properties of the atmosphere, the assumption of uniformity and stability is not required, and the local turbulent motion conditions can be reflected. Moreover, there is no numerical dissipation (pseudo-diffusion) and numerical instability problems similar to the Euler model, and the calculation method is simple.

[0006] However, because Lagrangian particle random walk models require average wind speeds in three directions—positive east, positive north, and positive upward—diffusion simulations, traditional Lagrangian particle random walk models do not distinguish between the altitudes of the diffusion region. These three required average wind speeds are typically calculated using a single formula, which requires extensive data processing and makes the model parameter acquisition process time-consuming. Due to the numerous factors influencing average wind speed, diffusion regions at certain altitudes have little impact on simulation accuracy. Therefore, traditional large-scale and mesoscale specialized models are cumbersome to use and contribute little to improving simulation accuracy.

[0007] At the same time, the existing particle random walk model can only directly provide the spatial density distribution of simulated particles, but cannot directly provide the concentration field. The usual approach is to count the number of particles in a volume unit (defined grid unit) and then calculate the average concentration of that grid based on the "mass" of each particle. However, since the number of simulated particles is limited, the density of simulated particles becomes increasingly dilute during large-scale diffusion. Therefore, the concentration fluctuations obtained by traditional concentration calculation methods become increasingly large, and even completely distorted. Summary of the Invention

[0008] In view of the above problems, the present invention provides a special aerosol diffusion analysis method, device, equipment and medium for overcoming the above problems or at least partially solving the above problems.

[0009] The present invention provides the following solutions:

[0010] A special aerosol diffusion analysis method, comprising:

[0011] Acquiring particle initialization data, wherein the particle initialization data at least includes an initial position, an initial velocity, and a diffusion area;

[0012] Determining a target regional scale mode in which the diffusion region is located, where the target regional scale mode includes any one of a first high-altitude global mode, a second high-altitude global mode, and a low-altitude scale mode;

[0013] According to the target regional scale model, the corresponding particle dynamics equation is called to calculate the average wind speed in the east direction within the time step required by the Lagrangian particle random walk model;

[0014] The special aerosol diffusion analysis results are obtained by calculating the average wind speed in combination with the Lagrangian particle random walk model;

[0015] Calling a corresponding kernel function to simulate the spatial concentration distribution of particles according to the target area scale pattern;

[0016] The particle dynamics equation corresponding to the first high-altitude global model takes into account the influence of atmospheric drag and gravity and ignores the atmospheric turbulent wind field speed; the particle dynamics equation corresponding to the second high-altitude global model does not consider the influence of atmospheric drag, gravity and Brownian motion; the low-altitude scale model takes into account the influence of atmospheric drag, Brownian force and gravity and does not consider the effect of particles on gas flow;

[0017] The kernel function is used to calculate the spatial concentration distribution at the spatial coordinate point using a distribution function centered on the current coordinates of the particle.

[0018] Preferably, the particle dynamics equation corresponding to the first high-altitude global model is expressed by the following formula:

[0019]

[0020] Where u p represents the velocity of the particle, x p Represents the position of the particle; a D is the drag acceleration, a G Represents the acceleration due to gravity.

[0021] Preferably, the particle dynamics equation corresponding to the second high-altitude global model is expressed by the following formula:

[0022]

[0023] Where u represents the wind speed, which satisfies the following formula:

[0024]

[0025] Where, represents the average wind speed within the time step Δt, and u' represents the fluctuation of wind speed, which satisfies the following formula: u'(t)=u'(t-Δt)R(Δt)+u"

[0026] Where, R(Δt)=exp(-Δt / T L ) represents the Lagrangian coupling coefficient, u″ represents the random fluctuation that satisfies the Gaussian distribution, with a mean of 0 and a variance of

[0027] Preferably, the particle dynamics equation corresponding to the low airscale mode is expressed by the following formula:

[0028]

[0029] Where u p represents the velocity of the particle, x p Represents the position of the particle; a D is the drag acceleration, a Brepresents the Brownian acceleration, a G Represents the acceleration due to gravity.

[0030] Preferably, the kernel function corresponding to the first high-altitude global model is expressed by the following formula:

[0031]

[0032] Where: C ij (t) represents the average ground-layer air concentration of the (i, j)th grid at time t, Q k (t) represents the source intensity of the kth particle at time t, and the kernel function f(xx k ,yy k ) represents the normalized mass distribution of the kth particle, S ij Represents the area of the (i,j)th grid.

[0033] Preferably, the kernel functions corresponding to the second high-altitude global model and the low-altitude airscale model are expressed by the following formula:

[0034]

[0035] Where: C ij (t) represents the average ground-layer air concentration of the (i, j)th grid at time t, Q k (t) represents the source intensity of the kth particle at time t, and the kernel function f(xx k ,yy k ,zz k ) represents the normalized mass distribution of the kth particle, V ij Represents the area of the (i,j)th grid on the ground layer.

[0036] Preferably, the Lagrangian particle random walk model is represented by the following formula:

[0037]

[0038] Where: (x, y, z) represents the particle position, x represents the coordinate value in the positive direction facing east, y represents the coordinate value in the positive direction facing north, and z represents the coordinate value in the positive direction facing upward; represents the average wind speed at the spatial position (x(t), y(t), z(t)) at time t, (K x ,K y ,K z ) represents the turbulent diffusion coefficient, (dW x ,dW y ,dW z ) is a random number that satisfies the Gaussian distribution with a mean of 0 and a variance of dt.

[0039] A special aerosol diffusion analysis device, used to perform the above-mentioned special aerosol diffusion analysis method, comprising:

[0040] A data acquisition unit, configured to acquire particle initialization data, wherein the particle initialization data includes at least an initial position, an initial velocity, and a diffusion area;

[0041] a scale mode determination unit, configured to determine a scale mode of a target region in which the diffusion region is located, wherein the scale mode of the target region includes any one of a first high-altitude global mode, a second high-altitude global mode, and a low-altitude scale mode;

[0042] An average wind speed calculation unit is used to call the corresponding particle dynamics equation according to the target area scale model to calculate the average wind speed in the positive east direction within the time step required by the Lagrangian particle random walk model;

[0043] A diffusion result calculation unit, configured to calculate a special aerosol diffusion analysis result using the average wind speed in combination with the Lagrangian particle random walk model;

[0044] A concentration distribution calculation unit, configured to simulate the spatial concentration distribution of particles by calling a corresponding kernel function according to the target area scale pattern;

[0045] The particle dynamics equation corresponding to the first high-altitude global model takes into account the influence of atmospheric drag and gravity and ignores the atmospheric turbulent wind field speed; the particle dynamics equation corresponding to the second high-altitude global model does not consider the influence of atmospheric drag, gravity and Brownian motion; the low-altitude scale model takes into account the influence of atmospheric drag, Brownian force and gravity and does not consider the effect of particles on gas flow;

[0046] The kernel function is used to calculate the spatial concentration distribution at the spatial coordinate point using a distribution function centered on the current coordinates of the particle.

[0047] A special aerosol diffusion analysis device, comprising a processor and a memory:

[0048] The memory is used to store program code and transmit the program code to the processor;

[0049] The processor is used to execute the above-mentioned special aerosol diffusion analysis method according to the instructions in the program code.

[0050] A computer-readable storage medium is used to store program code, and the program code is used to execute the above-mentioned special aerosol diffusion analysis method.

[0051] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0052] A special aerosol diffusion analysis method, device, equipment and medium provided in the embodiment of the present application adopt different kinetic equations to calculate the average wind speed for diffusion areas at different heights, which can improve the calculation speed while ensuring the calculation accuracy. It solves the problem that the traditional large-scale special model and the mesoscale special model operation process and the use are cumbersome. The method provided by the embodiment of the present application can realize a model adaptive to different scales. At the same time, the kernel function method is adopted to deal with the concentration calculation fluctuation problem. The kernel function method means that the mass of each simulated particle is no longer regarded as a point (δ function), but a distribution function (such as Gaussian function, etc.) centered on the current coordinates of the simulated particle. Using this method to calculate the concentration at the spatial coordinate point will greatly reduce the fluctuation of the concentration field, and can also reduce the number of sampling calculations, reduce the resources required for calculation and the time required.

[0053] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] To more clearly illustrate the embodiments of the present invention 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 of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0055] Figure 1 This is a flow chart of a special aerosol diffusion analysis method provided by an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of a special aerosol diffusion analysis device provided by an embodiment of the present invention;

[0057] Figure 3 It is a schematic diagram of a special aerosol diffusion analysis device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described 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 are within the scope of protection of the present invention.

[0059] See also Figure 1 , is a special aerosol diffusion analysis method provided by an embodiment of the present invention, such as Figure 1 As shown, the method may include:

[0060] S101: Acquire particle initialization data, where the particle initialization data includes at least an initial position, an initial velocity, and a diffusion area;

[0061] S102: Determine the target area scale mode where the diffusion area is located, where the target area scale mode includes any one of a first high-altitude global mode, a second high-altitude global mode, and a low-altitude scale mode;

[0062] S103: Calling the corresponding particle dynamics equation according to the target area scale model to calculate the average wind speed in the positive east direction within the time step required by the Lagrangian particle random walk model;

[0063] S104: Calculating a special aerosol diffusion analysis result using the average wind speed in combination with the Lagrangian particle random walk model;

[0064] S105: Calling a corresponding kernel function to simulate the spatial concentration distribution of particles according to the target area scale model;

[0065] The particle dynamics equation corresponding to the first high-altitude global model takes into account the influence of atmospheric drag and gravity and ignores the atmospheric turbulent wind field speed; the particle dynamics equation corresponding to the second high-altitude global model does not consider the influence of atmospheric drag, gravity and Brownian motion; the low-altitude scale model takes into account the influence of atmospheric drag, Brownian force and gravity and does not consider the effect of particles on gas flow;

[0066] The kernel function is used to calculate the spatial concentration distribution at the spatial coordinate point using a distribution function centered on the current coordinates of the particle.

[0067] The special aerosol diffusion analysis method provided in the examples of this application is suitable for high-altitude global models (200km×200km, 100km×100km), where atmospheric turbulence can be ignored. Here, the movement of pollutants in the atmosphere is mainly reflected in the process of transport. Removing or significantly reducing the wind speed fluctuation factor during simulation can adapt calculations in this environment. For low-altitude mesoscale models (50km×50km, 5km×5km), where atmospheric turbulence is intense and the stratified structure is disrupted, the movement of pollutants is more random. Increasing the wind speed fluctuation factor during simulation can adapt calculations in this environment.

[0068] In a specific implementation, the embodiment of the present application can provide that the particle dynamics equation corresponding to the first high-altitude global model is expressed by the following formula:

[0069]

[0070] Where u p represents the velocity of the particle, xp Represents the position of the particle; a D is the drag acceleration, a G Represents the acceleration due to gravity.

[0071] The particle dynamics equation corresponding to the second high-altitude global model is expressed by the following formula:

[0072]

[0073] Where u represents the wind speed, which satisfies the following formula:

[0074]

[0075] Where, represents the average wind speed within the time step Δt, and u' represents the fluctuation of wind speed, which satisfies the following formula: u'(t)=u'(t-Δt)R(Δt)+u"

[0076] Where, R(Δt)=exp(-Δt / T L ) represents the Lagrangian coupling coefficient, u″ represents the random fluctuation that satisfies the Gaussian distribution, with a mean of 0 and a variance of

[0077] The particle dynamics equation corresponding to the low airscale mode is expressed by the following formula:

[0078]

[0079] Where u p represents the velocity of the particle, x p Represents the position of the particle; a D is the drag acceleration, a B represents the Brownian acceleration, a G Represents the acceleration due to gravity.

[0080] The special aerosol diffusion analysis method provided in the present application employs two kernel function equations designed for different scale models, employing a kernel function approach to address concentration fluctuations. The kernel function approach treats the mass of each simulated particle not as a single point (delta function), but as a distribution function (such as a Gaussian function) centered at the particle's current coordinates. Using this approach to calculate concentration at spatial coordinate points significantly reduces concentration field fluctuations.

[0081] In a specific implementation, the embodiment of the present application may provide that the kernel function corresponding to the first high-altitude global model is expressed by the following formula:

[0082]

[0083] Where: C ij(t) represents the ground average concentration of the (i, j)th grid at time t, Q k (t) represents the source intensity of the kth particle at time t, and the kernel function f(xx k ,yy k ) represents the normalized mass distribution of the kth particle. In this model, Gaussian distribution is used, S ij Represents the area of the (i,j)th grid.

[0084] The kernel functions corresponding to the second upper-altitude global model and the lower-altitude airscale model are expressed by the following formula:

[0085]

[0086] Where: Ci j (t) represents the ground average concentration of the (i, j)th grid at time t, Q k (t) represents the source intensity of the kth particle at time t, and the kernel function f(xx k ,yy k ,zz k ) represents the normalized mass distribution of the kth particle, and Gaussian distribution is used in this model, V ij Represents the area of the (i,j)th grid on the ground layer.

[0087] The special aerosol diffusion analysis method provided in the embodiment of the present application adopts a Lagrangian particle random walk model to simulate atmospheric diffusion. In specific implementation, the embodiment of the present application can provide that the Lagrangian particle random walk model is expressed by the following formula:

[0088]

[0089] Where: (x, y, z) represents the particle position, x represents the coordinate value in the positive direction facing east, y represents the coordinate value in the positive direction facing north, and z represents the coordinate value in the positive direction facing upward; is the average wind speed at the spatial position (x(t), y(t), z(t)) at time t, (K x ,K y ,K z ) is the turbulent diffusion coefficient, (dW x ,dW y ,dW z ) is a random number that satisfies the Gaussian distribution with a mean of 0 and a variance of dt.

[0090] The following is a detailed introduction to the special aerosol diffusion analysis method provided in the examples of the present application.

[0091] The method provided in the embodiment of the present application is applicable to the simulation of the evolution and diffusion of hazard levels in different regional scale scenarios (the first high-altitude global model is 200km×200km, the second high-altitude global model is 100km×100km, and the low-altitude scale models are 50km×50km and 5km×5km).

[0092] The average motion part is provided by the model's own wind field diagnostic mode or the external weather forecast mode, and the fluctuation part is represented by the sum of the correlation part (the influence of the fluctuation at the previous moment on the fluctuation at the next moment) and the random part (using random processes to simulate the randomness of turbulent pulsations).

[0093] Compared to other diffusion models, the Lagrangian particle random walk model offers a simpler computational approach. By simply modifying the wind speed fluctuation properties, the model can be adapted to diffusion calculations at various scales. For regional-scale models (200 km × 200 km, 100 km × 100 km), atmospheric turbulence is negligible. Here, the movement of pollutants in the atmosphere primarily occurs through transport. Removing or significantly reducing the wind speed fluctuation factor during simulation can adapt to these environments. For local-scale models (50 km × 50 km, 5 km × 5 km), atmospheric turbulence is intense, disrupting the layer structure and causing more random movement of pollutants. Therefore, increasing the wind speed fluctuation factor during simulation is necessary to accommodate these environments.

[0094] In practical applications, the coordinate system can be determined first.

[0095] a. Spatial coordinates

[0096] Physical coordinate system (x, y, z), the positive direction of x is east, the positive direction of y is north, and the positive direction of z is upward;

[0097] Logical coordinates (i, j, k);

[0098] Physical transformation coordinate system:

[0099] x'=x

[0100] y'=y

[0101]

[0102] b. Time coordinate

[0103] The calculation time in seconds includes the start time, end time, and calculation period, and available meteorological data must be guaranteed; the source release time includes the start time, end time, and a series of release rate times; the output time interval includes the sampling period (used to calculate the average concentration).

[0104] (2) Computational Grid

[0105] LPM does not require a grid to calculate particle movement in space. The purpose of establishing a grid is to define the calculation area; provide meteorological data space nodes; and calculate the concentration dose field.

[0106] The deterministic Lagrangian particle random walk model is expressed as follows:

[0107]

[0108] in, is the average wind speed at the spatial position (x(t), y(t), z(t)) at time t, (K x ,K y ,K z ) is the turbulent diffusion coefficient, where the spatial variation of the horizontal turbulent diffusion coefficient is neglected, (dW x ,dW y ,dW z ) is a random number that satisfies a Gaussian distribution with mean 0 and variance dt.

[0109] b. Parameterized turbulence.

[0110] Vertical K z , in the planetary boundary layer, i.e. zz g <h

[0111]

[0112] in,

[0113]

[0114] u * is the friction velocity, L is the Monin length. Above the boundary layer, it is a constant and can be used with the value z = h.

[0115] ② Horizontal K y

[0116] Turbulent Diffusion (Draxler)

[0117]

[0118] Where D = 0.9, we can deduce:

[0119]

[0120] Among them, σ v is the standard deviation of the lateral wind fluctuation, T i is the Lagrangian time scale.

[0121] c. Standard deviation of transverse wind fluctuation σ v

[0122] If there is no σ v When the unstable atmospheric boundary layer (1 / L<0,zz g <h).

[0123]

[0124] Where A=4.25, B=0.34,

[0125] For stable and neutral planetary boundary layers (1 / L≥0,zz g <h)

[0126]

[0127] Experimental proof Not with u * and z decrease infinitely, but there is a limit, then there is

[0128] When z>h,

[0129] d. Lagrangian time scale T i

[0130] If there is no T i For a stable and neutral planetary boundary layer (1 / L≥0,zz g <h) and z>h, T i =1000s, otherwise

[0131]

[0132] e. Particle initialization

[0133] Depending on the release scenario of the accident, the particle initialization is different. For point source release, the particle initialization coordinates are the same. For area source release (combustion) and volume source release (explosion), the particle initialization coordinates need to have a distribution. This system uses uniform random distribution to handle area source and volume source problems. The specific operations are as follows:

[0134] Divide the surface source or volume source into several regions; uniformly and randomly distribute a number of particles in each region, with 200 particles taken per region by default (the number depends on computer performance); assign mass to each particle according to the region.

[0135] f. Sedimentation effect.

[0136] The particle transport and diffusion process requires consideration of particle sedimentation. This module considers three types of sedimentation: gravity sedimentation, dry sedimentation on the underlying surface, and wet sedimentation. This sedimentation effect can be used to guide particle motion in the positive upward direction.

[0137] ① Gravity sedimentation: In the entire vertical space, aerosol particles must consider gravity sedimentation, and the gravity sedimentation velocity is solved using the Stokes equation

[0138]

[0139] Where ρ is the particle density, r is the particle radius, and μ is the air viscosity. Since the Stokes equation describes the interaction between a ball and a free fluid, which is not the case in reality, we take v in the system. g 2 / 3 of the velocity is the gravity sedimentation velocity.

[0140] ② Dry deposition on the underlying surface: The deposition of particles due to the adsorption of particles on the underlying surface is called dry deposition on the underlying surface (abbreviated as dry deposition). Dry deposition is very complex and is related to the physical properties of the particles and the underlying surface. However, its effect height is very small and is generally considered within the underlying surface height. This method uses a simplified model and introduces the dry deposition velocity V d , it is assumed that the deposition flux ω d Proportional to the ground concentration C0, that is:

[0141] ω d =V d C0

[0142] ③ Wet deposition: Wet deposition is caused by precipitation, and its effect height is the height of the precipitation layer. In order to simplify the calculation, the flushing coefficient Λ is introduced. It is considered that the mass of particles reduced per unit time dt is

[0143] dQ=ΛQdt

[0144] Then its deposition flux ω w Expressed as

[0145]

[0146] When simulating particle diffusion at different regional scales, the corresponding particle dynamics equation can be determined according to the type of regional scale where the particles are located. Specifically:

[0147] 1. For simulations on a 200km×200km regional scale

[0148] Considering the low overall particle concentration, ignoring the effect of particles on gas flow, particle motion is mainly affected by atmospheric drag and gravity. At this time, ignoring the atmospheric turbulence wind field velocity u can be used Where u is the average wind speed within the time step Δt.

[0149] The particle dynamics equation is as follows:

[0150]

[0151] Among them, u p 、x p is the velocity and position of the particle; a D is the drag acceleration, a G is the acceleration due to gravity.

[0152] In order to solve the problem of large errors in concentration statistics caused by insufficient sampling number of Lagrangian particle model, the kernel function calculation method is used to calculate the concentration on the ground. The concentration distribution is:

[0153]

[0154] Among them, C ij (t) represents the ground average concentration of the (i, j)th grid at time t, Q k (t) represents the source intensity of the kth particle at time t, and the kernel function f(xx k ,yy k ) represents the normalized mass distribution of the kth particle. In this model, Gaussian distribution is used, S ij Represents the area of the (i,j)th grid.

[0155] 2. Simulation of a 100km×100km regional scale

[0156] At this scale, the particle size is small, the evaluation area is large, and the time step is long. The effects of atmospheric drag, gravity, and Brownian motion are not considered. The particle dynamics equation is as follows:

[0157]

[0158] Where u is the wind speed; it satisfies the following formula:

[0159]

[0160] in, is the average wind speed within the time step Δt, u′ is the fluctuation of wind speed (also known as turbulent energy velocity), which satisfies:

[0161] u′(t)=u′(t-Δt)R(Δt)+u″

[0162] Among them, the Lagrangian coupling coefficient R(Δt)=exp(-Δt / T L ), random fluctuation u″ satisfies Gaussian distribution, with mean 0 and variance

[0163] In this scale mode, the kernel function calculation method is used to calculate the concentration on the ground, and the kernel function calculation method is used to calculate the concentration in the air at the ground level. The concentration distribution is:

[0164]

[0165] Among them, C ij (t) represents the average ground-layer air concentration of the (i, j)th grid at time t, Q k (t) represents the source intensity of the kth particle at time t, and the kernel function f(xx k ,yy k ,zz k ) represents the normalized mass distribution of the kth particle, and Gaussian distribution is used in this model, V ij Represents the area of the (i,j)th grid on the ground layer.

[0166] 3. For simulations at the regional scale of 50km×50km or 5km×5km.

[0167] At this scale, the particle concentration is low, and the effect of particles on gas flow is not considered. Particle motion is mainly affected by atmospheric drag, Brownian force, and gravity. Therefore, the particle dynamics equation is as follows:

[0168]

[0169] Among them, u p 、x p is the velocity and position of the particle; a D is the drag acceleration, a B is the Brownian acceleration, a G is the acceleration due to gravity.

[0170] Where u is the wind speed; it satisfies the following formula:

[0171]

[0172] in, is the average wind speed within the time step Δt, u′ is the fluctuation of wind speed (also known as turbulent energy velocity), which satisfies:

[0173] u′(t)=u′(t-Δt)R(Δt)+u″

[0174] Among them, the Lagrangian coupling coefficient R(Δt)=exp(-Δt / T L ), random fluctuation u″ satisfies Gaussian distribution, with mean 0 and variance

[0175] In this scale mode, the kernel function calculation method is used to calculate the concentration on the ground, and the kernel function calculation method is used to calculate the concentration in the air at the ground level. The concentration distribution is:

[0176]

[0177] Among them, C ij (t) represents the average ground-layer air concentration of the (i, j)th grid at time t, Q k (t) represents the source intensity of the kth particle at time t, and the kernel function f(xx k ,yy k ,zz k ) represents the normalized mass distribution of the kth particle, and Gaussian distribution is used in this model, V ij Represents the (i,j)th grid volume of the ground layer.

[0178] This application provides two kernel function equations for different scale models, employing a kernel function approach to address concentration fluctuations. The kernel function approach treats the mass of each simulated particle not as a single point (delta function), but as a distribution function (such as a Gaussian function) centered on the particle's current coordinates. Using this approach to calculate concentration at spatial coordinate points significantly reduces concentration field fluctuations.

[0179] The calculation is complete when it reaches a certain level of accuracy. Without this (kernel function) method, sampling and calculation would have required 20,000 calculations. Now, convergence is achieved with 2,000 calculations. Previously, one point was sampled, but now each sample represents n samples, representing the sampling of a cluster of particles. This achieves acceleration. Sampling can be done in this way at every scale. Think of the atmosphere as many air particles, and count where each particle will be at each moment in the future. Each particle count count counts as one sample. Each sample performs the aforementioned diffusion calculation.

[0180] In short, the special aerosol diffusion analysis method provided by the present application adopts different kinetic equations to calculate the average wind speed for diffusion areas at different heights, which can improve the calculation speed while ensuring the calculation accuracy. It solves the problem that the traditional large-scale special model and the mesoscale special model operation process and the use are cumbersome. The method provided by the embodiment of the present application can realize a model adaptive to different scales. At the same time, the kernel function method is adopted to deal with the concentration calculation fluctuation problem. The kernel function method means that the mass of each simulated particle is no longer regarded as a point (δ function), but a distribution function (such as Gaussian function, etc.) centered on the current coordinates of the simulated particle. Using this method to calculate the concentration at the spatial coordinate point will greatly reduce the fluctuation of the concentration field, and can also reduce the number of sampling calculations, reduce the required resources and time for calculation.

[0181] See also Figure 2 , the embodiment of the present application can also provide a special aerosol diffusion analysis device, such as Figure 2 As shown, the device for performing the above-mentioned special aerosol diffusion analysis method may include:

[0182] The data acquisition unit 201 is used to acquire particle initialization data, wherein the particle initialization data at least includes an initial position, an initial velocity, and a diffusion area;

[0183] A scale mode determination unit 202 is configured to determine a scale mode of a target region in which the diffusion region is located, wherein the scale mode of the target region includes any one of a first high-altitude global mode, a second high-altitude global mode, and a low-altitude scale mode;

[0184] The average wind speed calculation unit 203 is used to call the corresponding particle dynamics equation according to the target area scale model to calculate the average wind speed in the positive east direction within the time step required by the Lagrangian particle random walk model;

[0185] The diffusion result calculation unit 204 is used to calculate and obtain the special aerosol diffusion analysis result by using the average wind speed in combination with the Lagrangian particle random walk model;

[0186] A concentration distribution calculation unit 205 is configured to call a corresponding kernel function according to the target area scale model to simulate the spatial concentration distribution of particles;

[0187] The particle dynamics equation corresponding to the first high-altitude global model takes into account the influence of atmospheric drag and gravity and ignores the atmospheric turbulent wind field speed; the particle dynamics equation corresponding to the second high-altitude global model does not consider the influence of atmospheric drag, gravity and Brownian motion; the low-altitude scale model takes into account the influence of atmospheric drag, Brownian force and gravity and does not consider the effect of particles on gas flow;

[0188] The kernel function is used to calculate the spatial concentration distribution at the spatial coordinate point using a distribution function centered on the current coordinates of the particle.

[0189] The embodiment of the present application may also provide a special aerosol diffusion analysis device, the device comprising a processor and a memory:

[0190] The memory is used to store program code and transmit the program code to the processor;

[0191] The processor is used to execute the steps of the above-mentioned special aerosol diffusion analysis method according to the instructions in the program code.

[0192] like Figure 3 As shown, a special aerosol diffusion analysis device provided in an embodiment of the present application may include: a processor 10, a memory 11, a communication interface 12, and a communication bus 13. The processor 10, the memory 11, and the communication interface 12 all communicate with each other via the communication bus 13.

[0193] In the embodiment of the present application, the processor 10 can be a central processing unit (CPU), a graphics processing unit (GPU), an application-specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic devices, etc.

[0194] The processor 10 may call a program stored in the memory 11 . Specifically, the processor 10 may execute operations in an embodiment of the special aerosol diffusion analysis method.

[0195] The memory 11 is used to store one or more programs. The program may include program code, and the program code includes computer operating instructions. In the embodiment of the present application, the memory 11 stores at least a program for implementing the following functions:

[0196] Acquiring particle initialization data, wherein the particle initialization data at least includes an initial position, an initial velocity, and a diffusion area;

[0197] Determining a target regional scale mode in which the diffusion region is located, where the target regional scale mode includes any one of a first high-altitude global mode, a second high-altitude global mode, and a low-altitude scale mode;

[0198] According to the target regional scale model, the corresponding particle dynamics equation is called to calculate the average wind speed in the east direction within the time step required by the Lagrangian particle random walk model;

[0199] The special aerosol diffusion analysis results are obtained by calculating the average wind speed in combination with the Lagrangian particle random walk model;

[0200] Calling a corresponding kernel function to simulate the spatial concentration distribution of particles according to the target area scale pattern;

[0201] The particle dynamics equation corresponding to the first high-altitude global model takes into account the influence of atmospheric drag and gravity and ignores the atmospheric turbulent wind field speed; the particle dynamics equation corresponding to the second high-altitude global model does not consider the influence of atmospheric drag, gravity and Brownian motion; the low-altitude scale model takes into account the influence of atmospheric drag, Brownian force and gravity and does not consider the effect of particles on gas flow;

[0202] The kernel function is used to calculate the spatial concentration distribution at the spatial coordinate point using a distribution function centered on the current coordinates of the particle.

[0203] In one possible implementation, the memory 11 may include a program storage area and a data storage area, wherein the program storage area can store an operating system and application programs required for at least one function (such as a file creation function, a data reading and writing function), etc.; the data storage area can store data created during use, such as initialization data, etc.

[0204] In addition, the memory 11 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0205] The communication interface 12 may be an interface of a communication model, used for connecting to other devices or systems.

[0206] Of course, it needs to be explained that Figure 3 The structure shown does not constitute a limitation on the special aerosol diffusion analysis device in the embodiment of the present application. In actual application, the special aerosol diffusion analysis device may include Figure 3 More or fewer components than shown, or combinations of certain components.

[0207] The embodiment of the present application may also provide a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the steps of the above-mentioned special aerosol diffusion analysis method.

[0208] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0209] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present application.

[0210] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the models can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without making any creative efforts.

[0211] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

[0212] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0213] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present application.

[0214] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0215] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A special aerosol diffusion analysis method, characterized in that: include: Acquiring particle initialization data, wherein the particle initialization data at least includes an initial position, an initial velocity, and a diffusion area; Determining a target regional scale mode in which the diffusion region is located, where the target regional scale mode includes any one of a first high-altitude global mode, a second high-altitude global mode, and a low-altitude scale mode; According to the target regional scale model, the corresponding particle dynamics equation is called to calculate the average wind speed in the east direction within the time step required by the Lagrangian particle random walk model; The special aerosol diffusion analysis results are obtained by calculating the average wind speed in combination with the Lagrangian particle random walk model; Calling a corresponding kernel function to simulate the spatial concentration distribution of particles according to the target area scale pattern; The particle dynamics equation corresponding to the first high-altitude global model takes into account the influence of atmospheric drag and gravity and ignores the atmospheric turbulent wind field speed; the particle dynamics equation corresponding to the second high-altitude global model does not consider the influence of atmospheric drag, gravity and Brownian motion; the low-altitude scale model takes into account the influence of atmospheric drag, Brownian force and gravity and does not consider the effect of particles on gas flow; The kernel function is used to calculate the spatial concentration distribution at the spatial coordinate point using a distribution function centered on the current coordinates of the particle.

2. The special aerosol diffusion analysis method according to claim 1, characterized in that: The particle dynamics equation corresponding to the first high-altitude global model is expressed by the following formula: Where u p represents the velocity of the particle, x p Represents the position of the particle; a D is the drag acceleration, a G Represents the acceleration due to gravity.

3. The special aerosol diffusion analysis method according to claim 1, characterized in that: The particle dynamics equation corresponding to the second high-altitude global model is expressed by the following formula: Where u represents the wind speed, which satisfies the following formula: Where, represents the average wind speed within the time step Δt, and u' represents the fluctuation of wind speed, which satisfies the following formula: u'(t)=u'(t-Δt)R(Δt)+u" Where, R(Δt)=exp(-Δt / T L ) represents the Lagrangian coupling coefficient, u″ represents the random fluctuation that satisfies the Gaussian distribution, with a mean of 0 and a variance of 4. The special aerosol diffusion analysis method according to claim 1, characterized in that: The particle dynamics equation corresponding to the low airscale mode is expressed by the following formula: Where u p represents the velocity of the particle, x p Represents the position of the particle; a D is the drag acceleration, a B represents the Brownian acceleration, a G Represents the acceleration due to gravity.

5. The special aerosol diffusion analysis method according to claim 1, characterized in that: The kernel function corresponding to the first upper-altitude global model is expressed by the following formula: Where: C ij (t) represents the average ground-layer air concentration of the (i, j)th grid at time t, Q k (t) represents the source intensity of the kth particle at time t, and the kernel function f(xx k ,yy k ) represents the normalized mass distribution of the kth particle, S ij Represents the area of the (i,j)th grid.

6. The special aerosol diffusion analysis method according to claim 1, characterized in that: The kernel functions corresponding to the second upper-altitude global model and the lower-altitude airscale model are expressed by the following formula: Where: C ij (t) represents the average ground-layer air concentration of the (i, j)th grid at time t, Q k (t) represents the source intensity of the kth particle at time t, and the kernel function f(xx k ,yy k ,zz k ) represents the normalized mass distribution of the kth particle, V ij Represents the area of the (i,j)th grid on the ground layer.

7. The special aerosol diffusion analysis method according to claim 1, characterized in that: The Lagrangian particle random walk model is expressed by the following formula: Where: (x, y, z) represents the particle position, x represents the coordinate value in the positive direction facing east, y represents the coordinate value in the positive direction facing north, and z represents the coordinate value in the positive direction facing upward; represents the average wind speed at the spatial position (x(t), y(t), z(t)) at time t, (K x ,K y ,K z ) represents the turbulent diffusion coefficient, (dW x ,dW y ,dW z ) is a random number that satisfies the Gaussian distribution with a mean of 0 and a variance of dt.

8. A special aerosol diffusion analysis device, characterized in that: A device for performing the special aerosol diffusion analysis method according to any one of claims 1 to 7, comprising: A data acquisition unit, configured to acquire particle initialization data, wherein the particle initialization data includes at least an initial position, an initial velocity, and a diffusion area; a scale mode determination unit, configured to determine a scale mode of a target region in which the diffusion region is located, wherein the scale mode of the target region includes any one of a first high-altitude global mode, a second high-altitude global mode, and a low-altitude scale mode; An average wind speed calculation unit is used to call the corresponding particle dynamics equation according to the target area scale model to calculate the average wind speed in the positive east direction within the time step required by the Lagrangian particle random walk model; A diffusion result calculation unit, configured to calculate a special aerosol diffusion analysis result using the average wind speed in combination with the Lagrangian particle random walk model; A concentration distribution calculation unit, configured to simulate the spatial concentration distribution of particles by calling a corresponding kernel function according to the target area scale pattern; The particle dynamics equation corresponding to the first high-altitude global model takes into account the influence of atmospheric drag and gravity and ignores the atmospheric turbulent wind field speed; the particle dynamics equation corresponding to the second high-altitude global model does not consider the influence of atmospheric drag, gravity and Brownian motion; the low-altitude scale model takes into account the influence of atmospheric drag, Brownian force and gravity and does not consider the effect of particles on gas flow; The kernel function is used to calculate the spatial concentration distribution at the spatial coordinate point using a distribution function centered on the current coordinates of the particle.

9. A special aerosol diffusion analysis device, characterized in that: The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the special aerosol diffusion analysis method according to any one of claims 1 to 7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program code, and the program code is used to execute the special aerosol diffusion analysis method according to any one of claims 1 to 7.

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