CN116911214A

CN116911214A - Method and device for simulating diffusion of block-scale aerosol

Info

Publication number: CN116911214A
Application number: CN202311107644.6A
Authority: CN
Inventors: 陈焕盛; 王哲; 杨文夷; 葛宝珠; 文质彬; 肖林鸿; 王文丁; 王自发
Original assignee: Institute of Atmospheric Physics of CAS
Current assignee: Institute of Atmospheric Physics of CAS
Priority date: 2023-08-31
Filing date: 2023-08-31
Publication date: 2023-10-20
Anticipated expiration: 2043-08-31
Also published as: CN116911214B

Abstract

The application provides a block scale aerosol diffusion simulation method and a block scale aerosol diffusion simulation device, and relates to the technical field of environmental protection, wherein the method comprises the following steps: generating a regional three-dimensional grid of the target block; simulating a plurality of three-dimensional flow fields respectively corresponding to the target block under a plurality of wind directions and wind power levels based on the regional three-dimensional grid; selecting an initial flow field from the plurality of three-dimensional flow fields according to wind directions and wind power levels of the simulated aerosol diffusion event under the corresponding diffusion period; and acquiring the aerosol concentration distribution of the aerosol to be simulated in the corresponding diffusion period according to the initial flow field and the preset aerosol diffusion parameters of the simulated aerosol diffusion event. By the method, the concentration distribution under the real-time flow field is obtained, and the problems of inaccurate block simulation results or overlong simulation time are effectively solved.

Description

Method and device for simulating diffusion of block-scale aerosol

Technical Field

The application relates to the technical field of environmental protection, in particular to a block-scale aerosol diffusion simulation method and device.

Background

The neighborhood (including industrial park and close-connected building area) is taken as one of areas with more concentrated aerosol emission, so that the rule of aerosol diffusion and transportation in the neighborhood is researched, and the method has important significance in the aspects of reasonably distributing environmental resources in the areas, designing an avoidance scheme for harmful aerosol leakage events and the like.

In the related technology, the diffusion simulation of the block aerosol is usually carried out based on a city canopy model under the city scale, the non-uniformity complexity of the city building layout is not considered, and the method is more suitable for researching the area scale, and can achieve higher simulation efficiency, but the simulation effect of the block aerosol is difficult to meet; the block scale mode based on computational fluid dynamics (Computational Fluid Dynamics, CFD) at present mainly aims at a steady-state flow field with a fixed initial value, so that a reasonable initial value needs to be calculated, the time consumed in solving the flow field is too long due to the fact that the initial value is too large, the timeliness of simulation is affected, and the problem that the accuracy of a simulation result is low due to the fact that the initial value is too small; in addition, the flow field also changes with the passage of time, and a steady flow field with a fixed initial value is adopted, so that the simulation result is difficult to accurately reflect the diffusion characteristic of the aerosol.

Disclosure of Invention

In order to solve the problems, namely the problems of accuracy and timeliness of aerosol diffusion simulation at the block scale, the application provides a block scale aerosol diffusion simulation method and device.

In order to achieve the above object, the present application provides the following technical solutions:

According to a first aspect of the present application, there is provided a block-scale aerosol diffusion simulation method comprising:

generating a regional three-dimensional grid of the target block;

simulating a plurality of three-dimensional flow fields respectively corresponding to the target block under a plurality of wind directions and wind power levels based on the regional three-dimensional grid;

selecting an initial flow field from the plurality of three-dimensional flow fields according to wind directions and wind power levels of the simulated aerosol diffusion event under the corresponding diffusion period;

and acquiring the aerosol concentration distribution of the aerosol to be simulated in the corresponding diffusion period according to the initial flow field and the preset aerosol diffusion parameters of the simulated aerosol diffusion event.

In one embodiment, the generating the regional three-dimensional grid of the target neighborhood includes:

generating a three-dimensional model based on space data open shp file data of a target block, wherein the three-dimensional model carries building information of the target block;

and inputting the three-dimensional model into a preset background grid, generating a building body-attaching grid, and determining the building body-attaching grid as the regional three-dimensional grid of the target block.

In one embodiment, the modeling the three-dimensional flow fields of the target block under a plurality of wind directions and wind levels based on the regional three-dimensional grid includes:

Determining each flow field boundary of the three-dimensional flow field based on each side boundary of the regional three-dimensional grid; wherein the types of flow field boundaries include an inflow boundary and an outflow boundary;

for each wind direction and wind power level, determining the type of each flow field boundary and boundary conditions thereof under the wind direction and wind power level respectively;

and simulating a plurality of three-dimensional flow fields of the target block under a plurality of wind directions and wind levels based on the types of the flow field boundaries and boundary conditions thereof under the wind directions and the wind levels.

In one embodiment, the determining, for each wind direction and wind level, the type of each flow field boundary and the boundary condition thereof under the wind direction and wind level includes:

for each wind direction and wind power level, respectively determining a reference wind vector under a reference altitude under the wind direction and the wind power level;

and determining the types of all flow field boundaries under the wind direction and the wind level based on the reference wind vector, and respectively determining the boundary conditions of the inflow boundary and the outflow boundary according to the types of all flow field boundaries.

In one embodiment, the method further comprises:

determining wind vectors of the inflow boundary at different boundary heights thereof based on the reference wind vectors and the profile functions;

The determining the boundary conditions of the inflow boundary and the outflow boundary according to the types of the flow field boundaries respectively comprises the following steps:

setting a fixed value boundary condition corresponding to the wind vector and a zero gradient boundary condition related to pressure respectively aiming at an inflow boundary;

and setting entrainment boundary conditions corresponding to the wind vectors and total pressure boundary conditions related to pressure respectively for the outflow boundary.

In one embodiment, the modeling the three-dimensional flow fields of the target block under the wind direction and wind level based on the type of each flow field boundary under the wind direction and wind level and the boundary conditions thereof includes:

and solving a first N-S equation based on the type of each flow field boundary and boundary conditions thereof under the wind direction and wind force level to obtain initial wind speed and initial pressure of each grid position in the regional grid under the wind direction and wind force level so as to simulate a plurality of three-dimensional flow fields of the target block under a plurality of wind directions and wind force levels.

In one embodiment, the preset aerosol diffusion parameters include: the range, source intensity and diffusion coefficient of the region to be simulated for aerosol leakage;

the obtaining, according to the initial flow field and the preset aerosol diffusion parameter of the simulated aerosol diffusion event, the aerosol concentration distribution of the aerosol to be simulated in the corresponding diffusion period includes:

Based on the initial wind speed and the initial pressure of each grid position in the initial flow field, the area range, the source intensity and the diffusion coefficient of the aerosol leakage to be simulated, solving a second N-S equation and a component mass conservation equation simultaneously, and obtaining the aerosol concentration distribution of the aerosol to be simulated, which changes with time in the corresponding diffusion period.

In one embodiment, after acquiring the aerosol concentration distribution of the aerosol to be simulated at the corresponding diffusion period, the method further comprises:

and judging the risk area of the target neighborhood based on the aerosol concentration distribution of the aerosol to be simulated in the corresponding diffusion period.

In one embodiment, the risk area determination of the target neighborhood based on the aerosol concentration distribution of the aerosol to be simulated in the corresponding diffusion period includes:

drawing a concentration distribution diagram of the aerosol to be simulated based on the concentration distribution of the aerosol to be simulated in the corresponding diffusion period;

and judging whether each position area of the target block is a risk area according to a preset concentration critical value and the concentration distribution diagram.

According to a second aspect of the present application, there is provided a neighborhood-scale aerosol diffusion simulation apparatus comprising:

A grid generation module configured to generate a regional three-dimensional grid of the target neighborhood;

the flow field simulation module is arranged to simulate a plurality of three-dimensional flow fields of the target neighborhood under a plurality of wind directions and wind levels based on the regional three-dimensional grid;

the flow field selection module is arranged to select an initial flow field from the plurality of three-dimensional flow fields according to wind direction and wind power level of the simulated aerosol diffusion event under the corresponding diffusion period;

the concentration acquisition module is used for acquiring the aerosol concentration distribution of the simulated aerosol diffusion event under the corresponding diffusion period according to the initial flow field and the preset aerosol diffusion parameter of the simulated aerosol diffusion event.

According to a third aspect of the present application, there is provided a server comprising: a memory and a processor;

the memory stores computer-executable instructions;

and the processor executes the computer-executed instructions stored in the memory, so that the server executes the block-scale aerosol diffusion simulation method.

According to a fourth aspect of the present application there is provided a computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the block-scale aerosol diffusion simulation method.

According to a fifth aspect of the present application, there is provided a computer program product comprising: the computer program product comprises computer program code which, when run on a computer, causes the computer to perform the block-scale aerosol diffusion simulation method.

It can be appreciated that, according to the block-scale aerosol diffusion simulation method and device provided by the embodiment of the application, by generating the regional three-dimensional grid of the target block, simulating a plurality of three-dimensional flow fields respectively corresponding to the target block under a plurality of wind directions and wind power levels based on the regional three-dimensional grid, then selecting an initial flow field from the plurality of three-dimensional flow fields according to the wind directions and wind power levels of the simulated aerosol diffusion event under the corresponding diffusion period, and then acquiring the aerosol concentration distribution of the aerosol to be simulated under the corresponding diffusion period according to the initial flow field and the preset aerosol diffusion parameters of the simulated aerosol diffusion event. In the process, a three-dimensional grid suitable for the neighborhood is generated, a plurality of groups of three-dimensional flow fields with different initial values under different wind directions and wind levels are simulated based on the three-dimensional grid, the corresponding three-dimensional flow fields are selected as initial flow fields according to corresponding diffusion time periods for carrying out diffusion solving on simulated aerosol events of the neighborhood, the concentration distribution condition under the real-time flow fields can be obtained, the problems that the initial value setting and the true value of the initial flow fields are difficult to converge and easy to scatter due to the fact that the initial value setting and the true value of the initial flow fields are different are effectively avoided, the simulation calculation time required by emergency response is greatly saved, and the concentration diffusion change can be reflected more accurately.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.

Fig. 1 is a schematic flow chart of a block-scale aerosol diffusion simulation method according to an embodiment of the present application;

FIG. 2a is an exemplary diagram of wind partitioning in an embodiment of the present application;

FIG. 2b is an exemplary diagram of wind power classification in an embodiment of the application;

FIG. 3 is a schematic flow chart of another block-scale aerosol diffusion simulation method according to an embodiment of the present application;

FIG. 4a is an exemplary graph of surface roughness differentiation in an embodiment of the present application;

FIG. 4b is a graph illustrating a vertical distribution of parameters of wind speed in an embodiment of the present application;

FIG. 4c is an exemplary diagram of an inflow boundary and an outflow boundary in an embodiment of the application;

FIG. 4d is a diagram illustrating a boundary condition setting according to an embodiment of the present application;

FIG. 5 is an exemplary diagram of a simulation process of a three-dimensional flow field in an embodiment of the present application;

FIG. 6 is an exemplary diagram of a diffusion solving process given an initial flow field in an embodiment of the present application;

FIG. 7a is a schematic flow chart of another block-scale aerosol diffusion simulation method according to an embodiment of the present application;

FIG. 7b is a second flow chart of a block-scale aerosol diffusion simulation method according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of a block-scale aerosol diffusion simulation device according to an embodiment of the present application;

fig. 9 is a schematic structural diagram of a server according to an embodiment of the present application.

Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the specific embodiments.

Detailed Description

In the related art, the numerical simulation technology is widely applied to the study of an atmospheric boundary layer and the study of atmospheric aerosol delivery and diffusion as an effective means, and a better effect is obtained. The currently more commonly accepted development process of the air quality model is divided into the third generation, the 60 th century, the demand of the public for air quality prediction is continuously increased due to the rapid deterioration of air quality, related scholars give diffusion parameter curves under different stability conditions on the basis of analyzing field monitoring data, a first generation air quality model system is developed, the recommended mode of the environmental protection agency of each country basically adopts the system, the first generation air quality model comprises a Box model (Box models) based on the law of conservation of mass, a Gaussian model (Gaussian models) based on the theory of turbulence diffusion statistics, and a Lagrange model (Lagrangian models) based on the theory of conservation of mass and the theory of turbulence diffusion statistics. Wherein the box model comprises AURORA, CPB, etc., the gaussian model comprises AERMOD, CALPUFF, etc., and the lagrangian locus model comprises SPRAY, HYSPLIT, etc. The second generation air quality model for solving the problem of single pollutant is generated from the 80 s to the 90 s of the 20 th century based on a three-dimensional Euler mathematical model, a nonlinear chemical reaction mechanism and a complex meteorological model, and specifically comprises UAM, ROM, RADM and the like. In the 90 s of the 20 th century, based on the "one atmosphere" theory, the whole atmosphere was taken as a research object, all the physical and chemical processes of the atmosphere were simulated on various spatial scales, and a third generation air quality model suitable for treating complex problems such as multiple pollutant composite pollution appears, which is mainly represented by a regional environmental quality model (Community Multi-scale Air Quality, CMAQ), a comprehensive air quality model (Comprehensive Air Quality Model with extensions, CAMx), a nested air quality prediction modeling system (Nested Air Quality Prediction Modeling System, NAQPMS) and the like.

The model commonly used for simulating the distribution characteristics of pollutants on the urban scale is mainly a first generation model based on statistical theory. The first generation model based on statistical theory is used for simulating the block scale aerosol diffusion, and the solved equation is simplified, so that calculation is fast and convenient, but the method has a plurality of limitations in use, for example, the basic assumption that the Gaussian plume model is satisfied includes that a discharge source keeps steady state, the wind speed cannot be too small or no wind exists, the wind direction change is continuous, the radius of a research area does not exceed the local scale, namely 50 km, the meteorological data input of the required hour level is needed, and the like, so that the rationality of the simulation result of the Gaussian plume model can be ensured. Although the Gaussian smoke mass model (such as CALPUFF) has a few improvement on the limitation of the Gaussian smoke mass model as a whole, the applicable simulation range is mostly in city scale (tens to hundreds of km), and the capability of describing flow field distribution in a complex building group in block scale is extremely limited. Particle tracking models (such as HYSPLIT) are commonly used to study forward and backward trajectory tracking of aerosols and potential source regions, and the applicable simulation range is mostly in city scale, but not applicable to simulating the region scale aerosol concentration. A semi-empirical model based on gaussian diffusion theory (a semi-empirical model refers to a model in which parameters of a mathematical model are experimentally checked and corrected) represented by OSPM, and a large number of parameters are included based on empirical assumptions, and selective adjustment of parameters is required for different research areas. Although it has some descriptive capacity for aerosol diffusion within a neighborhood, the difference between a real building group and a simplified neighborhood is large, and the descriptive capacity for flow fields within a complex building group is limited.

Therefore, the air quality model generally takes the urban canopy below the building height as a whole to build the canopy model for parametric description, and the non-uniform complexity of urban building layout is not considered, so that the model is more suitable for researching the area above and cannot accurately simulate the aerosol in the block scale.

The diffusion of aerosols between regions is significantly affected by the local flow characteristics of the atmosphere and the turbulent flow characteristics of the atmosphere. The presence of dense building clusters within a city block affects the local boundary layer structure, changing the dynamic environment within the area, resulting in changes in the characteristics of the atmospheric flow field and turbulence within the area. The neighborhood (including industrial park and close-coupled building area) is also generally a region with more concentrated aerosol emission, and the change of local flow field characteristics leads to the change of the rules of aerosol diffusion, conversion, accumulation and the like in the region.

The block scale model based on computational fluid dynamics (Computational Fluid Dynamics, CFD) can meet the requirement just by researching the material diffusion characteristics of the complex underlying surface area where the block scale building is dense, and requiring a more accurate model to consider the morphological characteristics of the building such as height, density and the like. By means of CFD model, inputting the data of the sublevel of the neighborhood, and taking the peripheral observation station or the meteorological field simulated by mesoscale meteorological mode (The Weather Research and Forecasting Model, WRF) as the boundary field driving model, the small-scale pollutant diffusion characteristics under different wind directions, different emission sources and different building forms can be discussed.

In general, the core building groups of a neighborhood vary in floor space from hundreds of square meters to thousands of square meters, and the computational grids are refined to the point where the building can be resolved, with a large grid count. In general, to save computing resources, a three-dimensional building model may be selected to be simplified (e.g., an irregular building is simplified into a cube with a proper proportion), and although the grid quality can be kept high under the condition of thicker grid resolution, the accuracy of the simulation value is affected to a certain extent. Without simplifying the building model, the time consumed in solving the flow field is long (the solving convergence speed of the flow field is related to the setting of the initial value, and the given reasonable initial value greatly shortens the convergence time of the flow field), which affects the timeliness of emergency response.

In view of this, an embodiment of the present application provides a method and an apparatus for simulating block-scale aerosol diffusion, by generating a regional three-dimensional grid of a target block, and simulating a plurality of three-dimensional flow fields corresponding to the target block under a plurality of wind directions and wind power levels, respectively, and then selecting an initial flow field from the plurality of three-dimensional flow fields according to the wind directions and wind power levels of a simulated aerosol diffusion event under a corresponding diffusion period, and then obtaining an aerosol concentration distribution of an aerosol to be simulated under the corresponding diffusion period according to the initial flow field and a preset aerosol diffusion parameter of the simulated aerosol diffusion event. In the process, a three-dimensional grid suitable for the neighborhood is generated, and for simulated aerosol events, a corresponding three-dimensional flow field is selected as an initial flow field according to a corresponding diffusion period by simulating a plurality of groups of three-dimensional flow fields under different wind directions and wind power levels, so that the problems of difficult convergence and easy dispersion due to larger difference between the initial value setting and the actual value of the initial flow field in transient solution are effectively avoided, the simulation calculation time required by emergency response is greatly saved, and the concentration diffusion change can be reflected more accurately.

In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the accompanying drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals refer to the same or similar components or components having the same or similar functions throughout. The described embodiments are some, but not all, embodiments of the application. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

In a possible application scenario, taking a prototype of a certain block in a certain city as an example, aerosol diffusion simulation is performed on the block, where the application scenario may include a terminal device and a server, where the terminal device and the server are connected to each other through a wired or wireless network. Optionally, the terminal device may be configured to provide various parameters required for simulation, such as spatial data opening (shaping) file data or other data of a target block required for generating a three-dimensional grid of the region, various data of aerosol to be simulated, and the like, to the server, and the server is configured to perform aerosol diffusion simulation based on the block scale based on the data provided by the terminal device, and acquire concentration distribution in each period. Optionally, in the process of performing aerosol diffusion simulation, the server takes over primary computing work, and the terminal device takes over secondary computing work; or the server bears secondary computing work, and the terminal equipment bears primary computing work; alternatively, the server or the terminal device, respectively, can solely take on the computing effort.

The terminal device may include, but is not limited to, a computer, a smart phone, a tablet computer, an electronic book reader, a dynamic image expert compression standard audio layer 3 (Moving Picture experts group audio layer III, MP3 for short) player, a dynamic image expert compression standard audio layer 4 (Moving Picture experts group audio layerIV, MP4 for short) player, a portable computer, a car computer, a wearable device, a desktop computer, a set-top box, a smart television, and the like.

The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), basic cloud computing services such as big data and artificial intelligent platforms, and the like.

Alternatively, the number of the terminal devices or the servers may be more or less, which is not limited in the embodiment of the present application. In some embodiments, the terminal device and the server may be used as nodes in a blockchain system to synchronize the simulation result to other nodes of the blockchain system, so as to realize data synchronization of the simulation result, and facilitate further processing of the data.

It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region, and provide corresponding operation entries for the user to select authorization or rejection.

The application scenario of the present application is briefly described above, and a server is taken as an example to describe in detail the block-scale aerosol diffusion simulation method provided by the embodiment of the present application.

Referring to fig. 1, fig. 1 is a block-scale aerosol diffusion simulation method according to an embodiment of the present application, which includes steps S101-S104.

And step S101, generating a regional three-dimensional grid of the target neighborhood.

It will be appreciated that the target neighborhood is the neighborhood to be subjected to aerosol diffusion simulation, also known as the simulated neighborhood, and may be predetermined by those skilled in the art.

In this embodiment, the three-dimensional grid of the area is generated for the target block, so that simulation within the block range can be realized, the city scale is reduced to the block scale, and the simulation accuracy is improved.

In one embodiment, step S101 generates a regional three-dimensional grid of the target neighborhood, which may include the steps of:

generating a three-dimensional model based on the space data open shp file data of the target block, wherein the three-dimensional model carries building information of the target block;

and inputting the three-dimensional model into a preset background grid, generating a building body-attaching grid, and determining the building body-attaching grid as a regional three-dimensional grid of the target block.

It can be understood that the shp file format is a file format based on vector data and is used for storing geographic elements and attribute information, and the embodiment generates a three-dimensional model by utilizing the shp file data, so that the quick establishment of the three-dimensional model can be realized. The building information comprises building height information of a target block, and can be obtained by performing height conversion on floor information in the shp file.

In one implementation, the shp file of the simulated block may be imported into a spatial data conversion processing tool to perform longitude and latitude projection conversion, and the floor information in the shp file may be converted into building height, so as to generate a three-dimensional model identifiable by a subsequent simulated three-dimensional flow field (e.g., CFD model). After the preset background grid is generated, three-dimensional model data is input, grids are thinned, grids inside the building are removed, and finally building body-attached grids which can be used for subsequent simulation, namely, regional three-dimensional grids of the target block, are generated.

It should be noted that, a person skilled in the art may combine the actual application and the prior art to generate the preset background grid, and optionally, when generating the background grid, the 4 side boundaries may be named as: north boundary (side), south boundary (side), west boundary (side), east boundary (side), and facilitate distinguishing inflow boundary and outflow boundary of flow field when flow field simulation is performed subsequently.

Step S102, based on the regional three-dimensional grid, simulating a plurality of three-dimensional flow fields respectively corresponding to the target block under a plurality of wind directions and wind power levels.

Alternatively, the embodiment may employ a CFD model, and simulate a three-dimensional flow field under multiple wind directions and wind levels in combination with a regional three-dimensional grid. The wind directions and wind power grades can be obtained in any combination mode, for example, the wind directions can be selected from a method for dividing the total of 16 wind directions by N, NNE and NE … …, other wind direction dividing methods can be selected as required to divide a plurality of wind directions, a Ty wind meter is adopted, 12 grades are adopted, and each wind direction and each wind power grade are combined in any mode according to a plurality of wind power grades divided by wind speeds at a reference height, for example, a 10m height, so that a three-dimensional flow field under each combination condition is obtained. The division schematic diagram of the 16 wind directions is shown in fig. 2a, and the cattail wind meter is shown in fig. 2 b.

Step S103, selecting an initial flow field from a plurality of three-dimensional flow fields according to wind directions and wind power levels of the simulated aerosol diffusion event in the corresponding diffusion period.

In the related art, taking a CFD model as an example, the simulation of an initial flow field is usually a three-dimensional flow field simulating a steady state, that is, the initial flow field is a three-dimensional flow field determined by taking the initial flow field as an initial value, on one hand, the setting of the initial value may possibly affect the simulation accuracy or consume longer time and calculation resources, on the other hand, when the initial value corresponds to the three-dimensional flow field and has larger difference between the wind direction and the wind level corresponding to the initial value and the actual wind direction and the wind level, the problems of difficult convergence, easy dispersion and the like are generated, which cannot reflect the aerosol diffusion characteristics under time variation, and in order to balance the contradiction between the accuracy and the calculation resources, and simultaneously realize the simulation of the aerosol diffusion characteristics under the real-time flow field, the embodiment models a plurality of flow fields in advance as the initial flow fields for the follow-up diffusion solution according to different wind directions and wind levels in the actual simulation, and selects the closest initial flow field according to the wind direction and the wind level under the corresponding time period.

It will be appreciated that the material diffusion solver built into the CFD model can only solve for material diffusion in a steady-state wind field (steady-state wind field is a time-independent quantity, which can be approximately understood as wind-invariant, and material concentration varies with time; for example, the same source is strong, the concentration field after 10 seconds of release of material from 0 seconds is a, and the concentration distribution of material concentration fields B at 30 seconds from 20 seconds, a and B are nearly identical), and material diffusion in a time-variant wind field (i.e., transient wind field) cannot be accurately simulated (it can be understood that wind varies with time, material concentration varies with time; for the above example, the concentration distributions of a and B are not approximately considered to be identical because the wind fields for the two time periods of "0 seconds to 10 seconds" and "20 seconds to 30 seconds" are different). Aerosol diffusion is one form of material diffusion. In other words, a CFD model is adopted to simulate a steady-state flow field, and a built-in steady-state substance diffusion solver is utilized to simulate the delivery and diffusion of substances, so that the substance diffusion characteristics under a transient flow field which continuously changes along with time cannot be reflected, namely, the aerosol diffusion characteristics under the transient flow field which continuously changes cannot be accurately reflected.

Step S104, according to the initial flow field and the preset aerosol diffusion parameters of the simulated aerosol diffusion event, acquiring the aerosol concentration distribution of the aerosol to be simulated in the corresponding diffusion period.

Optionally, the aerosol diffusion parameters may include a range of areas where the aerosol leaks, a source strength (kg/s) of the leakage, a diffusion coefficient, and the like, so as to support that the same aerosol simultaneously sets a plurality of leakage areas, different source strengths, and the same diffusion coefficient; supporting the simulation of multiple aerosols in the same flow field, different diffusion coefficients can be set (assuming that the diffusion coefficients are related only to the aerosol species).

In this embodiment, the aerosol to be simulated is the aerosol corresponding to the simulated aerosol diffusion event.

In this embodiment, the corresponding period, i.e. the simulation period, can be adaptively set by those skilled in the art in combination with practical applications, for example, when no significant change of wind direction and wind level occurs in the wind farm in the simulation period, the settable simulation duration is the total simulation duration. When the wind direction and the wind power level obviously change in the simulation time period, the segmentation simulation is needed, namely, the simulation time period comprises a plurality of time periods, and an initial flow field under each time period is acquired, wherein turning time points are included between the time periods; when solving, the turning time point can be set to output the aerosol concentration field at the moment, the corresponding changed flow field is used as the initial flow field for solving the next section, the final aerosol concentration field in the last section of simulation time is used as the initial concentration field for solving the next section, and the solution is carried out again until the final simulation time or the transition time of the next flow field is reached, so that the accurate simulation of aerosol diffusion in each period is realized.

Therefore, according to the block scale aerosol diffusion simulation method provided by the embodiment, the steady-state flow fields under different wind directions and wind levels are solved in advance, the corresponding steady-state flow fields are used as the initial flow fields for diffusion solving after the leakage event occurs, the problems that the initial value setting and the actual value of the initial flow fields are large in difference and difficult to converge and easy to scatter in transient solving are effectively avoided, and the simulation calculation time required by emergency response is greatly saved.

Referring to fig. 3, fig. 3 is a schematic flow chart of another block-scale aerosol diffusion simulation method according to an embodiment of the present application, on the basis of the above embodiment, the present embodiment further describes a flow field simulation scheme under a plurality of wind directions and wind levels, so as to realize automatic and efficient simulation of a flow field, and further improve aerosol diffusion simulation efficiency. Specifically, in addition to the above steps S101 to S104, step S102 is further divided into the following steps S301 to S303 by simulating a plurality of three-dimensional flow fields of the target block under a plurality of wind directions and wind levels based on the regional three-dimensional grid.

Step S301, determining each flow field boundary of the three-dimensional flow field based on each side boundary of the regional three-dimensional grid; the types of flow field boundaries include inflow boundaries and outflow boundaries, among others.

Alternatively, in the process of generating the regional three-dimensional grid, the 4 side boundaries of the background grid may be named as a north boundary (side), a south boundary (side), a west boundary (side), and an east boundary (side), respectively, and the 4 side boundaries may be determined as flow field boundaries of the three-dimensional flow field, which may be an inflow boundary or an outflow boundary according to different wind directions.

Step S302, for each wind direction and wind power level, determining the type of each flow field boundary and the boundary conditions thereof under the wind direction and the wind power level respectively.

In this embodiment, three-dimensional flow field simulation is performed under each wind power and wind power level, and flow field initial values under a plurality of wind power and wind power levels need to be determined.

In one embodiment, in step S302, for each wind direction and wind power level, the type of each flow field boundary and its boundary condition under the wind direction and wind power level are determined respectively, which may specifically be the following steps:

Determining a reference wind vector at a reference altitude under the wind direction and the wind level respectively for each wind direction and wind level;

Alternatively, the reference wind vector may be 10m (i.e., z _ref Reference wind vector U when=10m) ₁₀ (i.e. U) _ref ) In some embodiments, the reference altitude may also be adapted by those skilled in the art in connection with the actual application to determine the reference wind vector. The reference wind vector comprises the direction and the magnitude of wind, the type of a flow field boundary is firstly determined, and then the boundary conditions of an inflow boundary and an outflow boundary are respectively determined according to the type of the flow field boundary.

Further, the present embodiment determines wind vectors of the inflow boundary at different boundary heights using the reference wind vector and the profile function to determine boundary conditions of the respective boundaries, and the method may further include the steps of:

according to the type of each flow field boundary, boundary conditions of an inflow boundary and an outflow boundary are respectively determined, and the method comprises the following steps:

Setting a fixed value boundary condition corresponding to the wind vector and a zero gradient boundary condition related to pressure respectively aiming at the inflow boundary;

for the outflow boundary, a entrainment boundary condition corresponding to the wind vector and a total pressure boundary condition concerning the pressure are set, respectively.

In this embodiment, the reference wind vector U can be used _ref And substituting the grid center height z into an exponential type wind profile function to obtain wind vectors U at different heights required by the boundary places. The formula is as follows:

wherein U is the wind vector (i.e. wind speed), U _ref For wind vectors at reference altitude, z is altitude (e.g., grid center height in CFD simulation), z _ref For reference altitude, α is the wind profile index. The value of α can be selected according to the surface roughness of the block, the surface roughness corresponding to the block of the general city dense building is iv, and the wind profile index α=0.27 can be set. For ease of understanding, an outline example of the ground surface roughness distinction and an example of the wind speed vertical direction distribution parameter are shown in connection with fig. 4a and 4b, respectively.

Exemplary, for example, the wind direction is positive west (W), the wind power level is level 4 (taking the mean value of wind speed corresponding to the Bose wind meter of 6.7 m/s), the wind vector U is referenced ₁₀ For (6.7,0,0), i.e. the weft wind component u=6.7 m/s at the reference height (10 m), the warp wind component v=0 m/s, and the vertical wind component w=0 m/s. The wind speed at the rest of the altitude can be obtained by substituting the altitude z into the formula. It will be appreciated that the wind components in the x, y, z directions are u, v, w, respectively, since the wind component in the z direction is typically much smaller than the horizontal component, it can be assumed that w=0 on the boundary, considering only the horizontal wind component u &v. In another example, as shown in connection with fig. 4c, fig. 4c is a schematic diagram of inflow and outflow boundaries when the wind direction is Northwest Wind (NW).

As shown in fig. 4d, the wind direction and the wind level can be achieved through automatic programming, wind vectors (u, v are components in x, y directions respectively) on a reference height are obtained, 4 side boundaries are initially set as outflow boundaries, reference wind vectors are input, and the inflow boundaries are automatically judged according to the wind direction, for example, the wind direction is positive west (W, i.e. the reference wind vectors (u v W, corresponding to wind components in x\y\z directions respectively) have u >0, v=0), the western boundary (side W) is the inflow boundary, and other side boundaries are outflow boundaries; the wind direction is south-east (SE, i.e. u <0, v > 0), then south boundary (side) and east boundary (side) are inflow boundaries, and the other side boundaries are outflow boundaries. After updating the inflow and outflow boundary decisions, the reference wind vectors and boundary types are input into a wind profile function, and wind vectors U of all grids on the flow boundary are obtained.

The wind speed U inflow boundary may be set to a specified fixed value boundary condition (fixed values are set for 3 wind components at different grid heights based on the above formula programming), the outflow boundary is set to entrainment boundary condition (zero gradient boundary condition is applied to all components, fixed value boundary condition is applied to the tangential components of the boundary when the flow is inflow); the pressure p is set to the inflow boundary condition of zero gradient and the outflow boundary is set to the total pressure boundary condition (a fixed value boundary condition, calculated using the specified total pressure p0 and the local velocity U).

In the embodiment, different wind profile functions are classified and given according to the surface roughness corresponding to the research area, so that the calculated inflow wind speed is more reasonable, inflow and outflow boundaries are automatically judged, and the flow field boundary is more reasonable.

Step S303, based on the type of each flow field boundary and the boundary conditions thereof under the wind direction and the wind force level, a plurality of three-dimensional flow fields of the target block under a plurality of wind directions and the wind force level are simulated.

After the determination of the boundary type and the boundary condition is completed, solving a plurality of groups of flow fields under different wind directions and wind levels, specifically solving a steady-state flow field and monitoring residual errors until the flow field calculation is converged, and then using the calculated flow fields as an initial flow field for solving (according to the difference of diffusion periods) transient diffusion, traversing the combination of different wind directions (N wind directions, N=1, 2, …, i) and different wind levels (M wind levels, M=1, 2, …, j), and outputting three-dimensional flow fields under various (N×M) combinations. The simulation of the three-dimensional flow field is shown in fig. 5. In the process, a steady state solver (a first N-S equation) is utilized in advance to simulate a flow field (a corresponding flow field is selected as an initial flow field for diffusion solving) to be converged, and it can be understood that a smaller time step is often required for transient solving to avoid solving divergence, the demand on computing resources is higher, and the steady state solver can solve to be converged under less computing resources.

Specifically, step S303 simulates a plurality of three-dimensional flow fields of the target block under a plurality of wind directions and wind levels based on the types of the flow field boundaries and boundary conditions thereof under the wind directions and wind levels, including:

based on the type of each flow field boundary and boundary conditions thereof under the wind direction and wind force level, a first N-S equation is solved, and initial wind speeds and initial pressures of all grid positions in the regional grid under the wind direction and wind force level are obtained so as to simulate a plurality of three-dimensional flow fields of a target block under a plurality of wind directions and wind force levels.

It should be noted that, in this embodiment, the first N-S equation and the second N-S equation are only used to distinguish similar objects, and have no other special meaning, and the first N-S equation and the second N-S equation may be the same equation set. The first N-S equation may be:

where ρ is the density, U is the velocity, p is the pressure, τ is the shear stress, and t is the time.

In this embodiment, the initial flow field is modeled based on given boundary conditions (including U-wind speed; p-air pressure, p= (p-p 0)/, p0 is the reference air pressure being a constant, representing the air density, being a constant, these being known quantities and using turbulence related parameters such as turbulence model employed in the simulation, k etc. to calculate the shear stress τ), the principle being that the values of the whole area (all grids) are calculated by the simulation based on the grid values at the boundary.

It will be appreciated that for the N-S equation, since the air under investigation can be approximated as an incompressible fluid (density almost unchanged), the general equation is divided by the density on both sides, given by the (p-p 0)/, for the boundary pressure.

Alternatively, to improve flow field simulation efficiency, the fluid flow problem under investigation may be simplified during flow field simulation, with the fluid considered incompressible, despite its temperature change. When the initial flow field is solved in advance, a pressure velocity coupling algorithm (Semi-Implicit Method for Pressure Linked Equations, SIMPLE) can be used, a proper matrix solver, a discrete method, a differential format and other solving control information are set, and a proper turbulence model and a proper turbulence closing scheme are selected.

After selecting a proper pre-simulated steady-state flow field as an initial flow field input for aerosol transient diffusion simulation according to the wind direction and the wind power level of the simulated aerosol diffusion period, simulating the simulated aerosol diffusion. Optionally, the preset aerosol diffusion parameters in the present embodiment include: the range of the region where the aerosol leaks, the source strength and the diffusion coefficient are to be simulated. The parameters such as the area range of aerosol leakage, the leakage source intensity (kg/s), the diffusion coefficient and the like can be set by a person skilled in the art according to the leakage situation of target simulation.

In step S104, according to the initial flow field and the preset aerosol diffusion parameters for simulating the aerosol diffusion event, the aerosol concentration distribution of the aerosol to be simulated in the corresponding diffusion period is obtained, which specifically includes the following steps:

based on the initial wind speed and the initial pressure of each grid position in the initial flow field, and the region range, the source intensity and the diffusion coefficient of the aerosol leakage to be simulated, the second N-S equation and the component mass conservation equation are solved simultaneously, and the aerosol concentration distribution of the aerosol to be simulated, which changes with time in the corresponding diffusion period, is obtained.

In the embodiment, the initial flow field selected by the corresponding time period (namely the diffusion period) is combined with the leakage condition, the flow field and the concentration field which are diffused for a period of time are simulated, the concentration distribution of the aerosol is obtained, the flow field condition under different time states is considered, and the precise simulation of the aerosol diffusion under the transient condition is realized.

Further, compared with a material diffusion solver in the related art, a transient diffusion solver (UTFoam) is provided for different flow fields, and the transient diffusion solver calculates a concentration field in a transient wind field by solving an N-S equation and a component mass conservation equation successively. Specifically, as shown in fig. 6, in the diffusion solving process under the initial flow field selected in a certain time period, in one implementation, a simulation time, a proper matrix solver, a discrete method and other solving control information and other solving control parameters are set, a self-compiled transient diffusion simulation solver (UTFoam) is operated, and flow field wind speed and aerosol concentration field information at a target simulation moment are output.

The second N-S equation adopts the following formula (the second N-S equation and the first N-S equation in this embodiment adopt the same N-S equation), and the PIMPLE method can be used to solve the second N-S equation.

Where ρ is the air density, U is the velocity, p is the pressure, τ is the shear stress, and t is the time.

Wherein, the conservation equation of the component mass adopts the following formula,

wherein T represents the transmitted scalar quantity, i.e. the concentration to be solved, U is the wind speed, D _T Is the diffusion coefficient, t is time, and S is the source term. Where the source term is a known quantity obtained from the range of regions and the source intensity.

The N-S equation and the component mass conservation equation illustrated in the present embodiment are general and most general expressions, and other equations may be used in other examples.

For ease of understanding, this embodiment is described in connection with flow field simulation and selection of an initial flow field to solve for diffusion concentration. The first step: flow field simulation, namely, calculating values (namely, initial flow fields) of all grids by giving boundary values and boundary conditions, and only solving an N-S equation in the step; and the second step is to simulate a flow field and a concentration field which are diffused for a period of time according to the initial flow field and the leakage situation, and the N-S equation and the component mass conservation equation are solved in the second step.

In this embodiment, instead of directly and simultaneously solving the N-S equation and the component mass conservation equation, the three-dimensional flow field is simulated by solving the N-S equation, and the concentration distribution is obtained by solving the N-S equation and the component mass conservation equation after the initial flow field is selected. The reason for this is that the present embodiment considers that if the direct simultaneous solution would likely result in an unrealistic diffusion under the streaming field, and leads to inaccuracy of the calculation result. For example, the calculation area is 100 grids, the leakage source is set on the 50 th grid, the 1 st grid is given an inflow speed of 1 second, one grid is run, the 100 th grid is an outflow boundary, and the initial values of the internal grid speeds are all set to 0. If solved at the same time, the 1 st second wind speed actually reaches the 2 nd grid, and the speeds on other grids are also inaccurate, and the speed is estimated. The speed at this time can be considered accurate only when the wind speed actually reaches the last outflow grid after 100 s. The wind speed provided by the inaccurate flow field is used to calculate the concentration before the accurate flow field is obtained, and it is apparent that the calculated concentration is problematic. Of course, the actual calculation can not only depend on the blowing-out time of the flow field to define the initial flow field simulation time, but also can set an error tolerance value, and when the error is smaller than the critical value, the flow field is considered to be more accurate, and the flow field can be used for solving the diffusion. At this time, the initial flow field is selected, and the concentration distribution obtained by solving the N-S equation and the component mass conservation equation approaches to the diffusion characteristic under the real flow field. In this embodiment, the transient diffusion solver is customized by combining the N-S equation and the component mass conservation equation, and simultaneously outputs a concentration field under a flow field that varies with time, and the simulation result is closer to the diffusion characteristics under a real flow field than a substance diffusion solver built in a CFD model in the related art. According to different preset aerosol diffusion parameters, the method can solve the convection diffusion of aerosols with different types, multiple source areas and different sources under the flow field which continuously changes along with time.

Referring to fig. 7a and fig. 7b, fig. 7a and fig. 7b are schematic flow diagrams of another block-scale aerosol diffusion simulation method according to an embodiment of the present application, after obtaining concentration distribution of aerosol diffusion in the embodiment, risk area determination is performed on a target block according to the corresponding concentration distribution, and specifically, as shown in fig. 7a, after obtaining the concentration distribution of aerosol to be simulated in the corresponding diffusion period in step S104, the embodiment may further include step S701.

And step 701, judging a risk area of the target neighborhood based on the aerosol concentration distribution of the aerosol to be simulated in the corresponding diffusion period.

In this embodiment, compared with the risk area determination in the related art, since the concentration distribution is simulated according to the time variation, the risk area determination also has real-time performance, so that corresponding emergency risk avoidance measures can be adopted according to different diffusion periods, and the emergency response effect is better.

Further, the risk area determination on the target neighborhood based on the aerosol concentration distribution of the aerosol to be simulated in the corresponding diffusion period may include the following steps:

And judging whether each position area of the target block is a risk area according to the preset concentration critical value and the concentration distribution diagram.

Specifically, a concentration field distribution diagram can be drawn by using post-processing software, whether the grid points are risk areas or not is judged according to a preset risk concentration critical value and the concentration distribution diagram, and further risk avoidance suggestions after leakage events occur are provided according to the distribution situation of the risk areas.

In this embodiment, by generating a three-dimensional grid of a research area required by flow field simulation, simulating three-dimensional steady-state flow fields of the research area under different wind directions and different wind power levels in the three-dimensional grid of the area, selecting a corresponding steady-state flow field as an initial flow field under the wind directions and wind power levels of a simulated aerosol event under a corresponding time period, further performing diffusion simulation (setting an aerosol leakage area, a strong source and a diffusion coefficient corresponding to the aerosol) by using a transient diffusion solver, obtaining real-time flow field and aerosol diffusion concentration data, judging a risk area through post-processing drawing forms, and effectively providing a reference for emergency risk avoidance command decision after aerosol leakage occurs.

The embodiment of the application correspondingly provides a block-scale aerosol diffusion simulation device, as shown in fig. 8, comprising a grid generating module 81, a flow field simulation module 82, a flow field selecting module 83 and a concentration obtaining module 84, wherein,

A grid generation module 81 arranged to generate a regional three-dimensional grid of the target neighborhood;

a flow field simulation module 82 configured to simulate a plurality of three-dimensional flow fields of the target block under a plurality of wind directions and wind levels based on the regional three-dimensional grid;

a flow field selection module 83 configured to select an initial flow field from a plurality of three-dimensional flow fields according to wind direction and wind power level of the simulated aerosol diffusion event at the corresponding diffusion period;

the concentration acquisition module 84 is configured to acquire an aerosol concentration distribution of the simulated aerosol diffusion event at the corresponding diffusion period according to the initial flow field and a preset aerosol diffusion parameter of the simulated aerosol diffusion event.

In one embodiment, the mesh generation module 81 includes:

the first generation unit is used for generating a three-dimensional model based on the space data open shp file data of the target block, wherein the three-dimensional model carries building information of the target block;

and a second generation unit configured to input the three-dimensional model into a preset background grid, generate a building body-in-place grid, and determine the building body-in-place grid as a regional three-dimensional grid of the target block.

In one embodiment, the flow field simulation module 83 includes:

A first determining unit configured to determine respective flow field boundaries of the three-dimensional flow field based on respective side boundaries of the regional three-dimensional grid; wherein the types of flow field boundaries include inflow boundaries and outflow boundaries;

a second determining unit configured to determine, for each wind direction and wind power level, a type of each flow field boundary under the wind direction and wind power level, respectively, and boundary conditions thereof;

and the simulation unit is used for simulating a plurality of three-dimensional flow fields of the target neighborhood in a plurality of wind directions and wind power levels based on the types of the flow field boundaries and boundary conditions of the flow field boundaries in the wind directions and the wind power levels.

In one embodiment, the second determining unit is specifically configured to determine, for each wind direction and wind level, a reference wind vector at a reference altitude for the wind direction and the wind level, respectively; and determining the types of all flow field boundaries under the wind direction and the wind level based on the reference wind vector, and respectively determining the boundary conditions of the inflow boundary and the outflow boundary according to the types of all flow field boundaries.

In one embodiment, the apparatus further comprises:

a wind vector determination module arranged to determine wind vectors of the inflow boundary at different boundary heights thereof based on the reference wind vector and the profile function;

According to the types of the boundaries of each flow field, the boundary conditions of the inflow boundary and the outflow boundary are respectively determined, namely, a fixed value boundary condition corresponding to a wind vector and a zero gradient boundary condition related to pressure are respectively set for the inflow boundary; for the outflow boundary, a entrainment boundary condition corresponding to the wind vector and a total pressure boundary condition concerning the pressure are set, respectively.

In one embodiment, the simulation unit is specifically configured to solve the first N-S equation based on the type of each flow field boundary and its boundary conditions under the wind direction and the wind direction level, and obtain the initial wind speed and the initial pressure of each grid position in the regional grid under the wind direction and the wind direction level, so as to simulate a plurality of three-dimensional flow fields of the target block under a plurality of wind directions and wind direction levels.

the concentration obtaining module 84 is specifically configured to solve the second N-S equation and the component mass conservation equation simultaneously based on the initial wind speed and the initial pressure of each grid position in the initial flow field, and the area range, the source intensity and the diffusion coefficient of the aerosol leakage to be simulated, so as to obtain the aerosol concentration distribution of the aerosol to be simulated, which changes with time under the corresponding diffusion period.

In one embodiment, the apparatus further comprises:

and the region judging module is used for judging the risk region of the target neighborhood based on the aerosol concentration distribution of the aerosol to be simulated under the corresponding diffusion period.

In one embodiment, the area determining module is specifically configured to draw a concentration distribution map of the aerosol to be simulated based on the concentration distribution of the aerosol to be simulated in the corresponding diffusion period; and judging whether each position area of the target block is a risk area according to the preset concentration critical value and the concentration distribution diagram.

The embodiment of the application correspondingly provides a server, as shown in fig. 9, which may include: a transceiver 91, a processor 92, a memory 93.

The processor 92 executes computer-executable instructions stored in the memory 93, causing the processor 92 to execute the arrangements of the above-described embodiments. The processor 92 may be a general purpose processor including a central processing unit CPU, a network processor (network processor, NP), etc.; but may also be a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

The memory 93 is coupled to the processor 92 via a system bus and communicates with each other, and the memory 93 is adapted to store computer program instructions.

The transceiver 91 may be used to obtain a task to be run and configuration information of the task to be run.

The system bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industrystandard architecture, EISA) bus, among others. The system bus may be classified into an address bus, a data bus, a control bus, and the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus. The transceiver is used to enable communication between the database access device and other computers (e.g., clients, read-write libraries, and read-only libraries). The memory may include random access memory (random access memory, RAM) and may also include non-volatile memory (non-volatile memory).

According to a fourth aspect of the present application there is provided a computer readable storage medium having stored therein computer executable instructions for a real block scale aerosol diffusion simulation method when executed by a processor.

According to a fifth aspect of the present application, there is provided a computer program product comprising: the computer program product comprises computer program code which, when run on a computer, causes the computer to perform a block-scale aerosol diffusion simulation method.

It should be noted that, the computer readable storage medium provided by the present application can correspondingly implement all the method steps implemented by the server in the method embodiment, and can achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those of the method embodiment in the embodiment are omitted.

The embodiment of the application correspondingly provides a computer program product, and the computer program product comprises computer program code which, when run on a computer, enables the computer to execute the block-scale aerosol diffusion simulation method.

The embodiment of the application correspondingly provides a chip which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor is used for calling and running the computer program from the memory to execute the block-scale aerosol diffusion simulation method.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, e.g., the division of modules is merely a logical function division, and there may be additional divisions of actual implementation, e.g., multiple modules may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.

The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to implement the solution of this embodiment.

In addition, each functional module in the embodiments of the present application may be integrated in one processing unit, or each module may exist alone physically, or two or more modules may be integrated in one unit. The units formed by the modules can be realized in a form of hardware or a form of hardware and software functional units.

The integrated modules, which are implemented in the form of software functional modules, may be stored in a computer readable storage medium. The software functional modules described above are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or processor to perform some of the steps of the methods of the various embodiments of the application.

It should be understood that the above processor may be a central processing unit (Central Processing Unit, abbreviated as CPU), but may also be other general purpose processors, digital signal processors (Digital SignalProcessor, abbreviated as DSP), application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.

The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile memory NVM, such as at least one magnetic disk memory, and may also be a U-disk, a removable hard disk, a read-only memory, a magnetic disk or optical disk, etc.

The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral ComponentInterconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or to one type of bus.

The storage medium may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). Of course, the processor and the storage medium may reside as discrete components in an electronic control unit or master control device.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.

Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A block-scale aerosol diffusion simulation method, comprising:

generating a regional three-dimensional grid of the target block;

2. The method of claim 1, wherein the generating the regional three-dimensional grid of the target neighborhood comprises:

3. The method of claim 1, wherein simulating a plurality of three-dimensional flow fields of the target block at a plurality of wind directions and wind levels based on the regional three-dimensional grid comprises:

4. A method according to claim 3, wherein said determining the type of respective flow field boundary and its boundary conditions for each wind direction and wind level, respectively, comprises:

5. The method according to claim 4, wherein the method further comprises:

6. The method according to claim 3 or 4, wherein simulating a plurality of three-dimensional flow fields of the target block under a plurality of wind direction and wind level based on the type of each flow field boundary under the wind direction and wind level and boundary conditions thereof comprises:

7. The method of claim 6, wherein the preset aerosol diffusion parameters comprise: the range, source intensity and diffusion coefficient of the region to be simulated for aerosol leakage;

8. The method according to claim 1, further comprising, after acquiring the aerosol concentration profile of the aerosol to be simulated at the corresponding diffusion period:

9. The method of claim 8, wherein the risk area determination of the target neighborhood based on the aerosol concentration distribution of the aerosol to be simulated for the corresponding diffusion period comprises:

10. A neighborhood-scale aerosol diffusion simulation device, comprising:

11. A server, comprising: a memory and a processor;

the memory stores computer-executable instructions;

the processor executing computer-executable instructions stored in the memory, causing the server to perform the block-scale aerosol diffusion simulation method of any one of claims 1 to 9.

12. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are for implementing the block scale aerosol diffusion simulation method according to any of claims 1 to 9.

13. A computer program product, comprising: the computer program product comprising computer program code which, when run on a computer, causes the computer to perform the block-scale aerosol diffusion simulation method according to any of claims 1 to 9.