JP2012008089A - Device, method, and program for gas flow field data generation - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a device, method, and program for gas flow field data generation capable of generating detailed gas flow field data despite the specifications of a computer.SOLUTION: Gas flow field data is used for estimating diffusion states of diffusing substances at a plurality of estimation points in a specific region A3, and the use of the gas flow field data in which estimation points are finely set allows further detailed estimation of the diffusion states of the diffusing substances. A diffusion state estimating device generates the gas flow field data of each of the divided areas 50 formed by dividing the specific region A3 into a plurality of areas, combines the gas flow field data of each of the plurality of generated divided areas 50, sets the specific region A3 of wider range, and generates the gas flow field data corresponding to the specific region A3.

Description

  The present invention relates to an airflow field data generation device, an airflow field data generation method, and an airflow field data generation program.

  Conventionally, there is a diffusion prediction method for predicting a diffusion state indicating a diffusion range and a diffusion concentration of a diffusion substance such as a gas or dust released by an accident of nuclear power generation, a chemical factory, and other facilities, or an incident.

  As this diffusion prediction method, Patent Literature 1 previously obtains an airflow field database including a plurality of airflow field data having different wind directions and atmospheric stability at a target point where a diffusion source that discharges diffused substances into the atmosphere exists. In addition, the wind direction and atmospheric stability are obtained based on the weather data of the target date and time, and the airflow field data having the wind direction and atmospheric stability similar to the wind direction and atmospheric stability of the weather data of the target date and time are obtained from the airflow field database. A technique for obtaining diffusion field data of a diffusing material by substituting the read out airflow field data into the diffusion equation for calculating the diffusion state of the diffusing material is described.

Japanese Patent No. 4209354

  In the technique described in Patent Document 1, a calculation region (a target region for predicting the diffusion state of a diffusing material) is divided into a grid shape, and variable differential equations are time-integrated for variables such as wind speed and temperature at each lattice point. Airflow field data is generated by computational fluid dynamics (CFD). In order to generate detailed airflow field data, it is necessary to set grid points with a short distance interval for a wider calculation area. Then, by using the detailed airflow field data, the diffusion state of the diffusing substance can be predicted in more detail.

  However, in order to generate detailed airflow field data, the computer specifications (RAM (Random Access Memory) capacity (so-called memory capacity), etc.) of the airflow field data must be high. That is, the calculation area when generating the airflow field data depends on the specifications of the computer that generates the airflow field data, and as a result, the prediction accuracy of the diffusion state of the diffusing material depends on the specifications of the computer. there were.

  The present invention has been made in view of such circumstances, and an airflow field data generation device, an airflow field data generation method, and an airflow field data generation that can generate detailed airflow field data regardless of the specifications of the computer. The purpose is to provide a program.

  In order to solve the above problems, the airflow field data generation apparatus of the present invention employs the following means.

  That is, the airflow field data generation device of the present invention is an airflow field data generation device that generates airflow field data used for predicting the diffusion state of the diffusing substance at a plurality of evaluation points in the attention region, Generating means for generating the airflow field data for each of the divided areas divided into a plurality of areas, and combining means for combining the airflow field data for each of the plurality of divided areas generated by the generating means.

  According to the present invention, airflow field data is generated for each divided region obtained by dividing the region of interest into a plurality of regions by the generating unit, and airflow field data for each of the plurality of divided regions generated by the generating unit is generated by the combining unit. Combined.

  Airflow field data is data used to predict the diffusion status of diffusing substances at multiple evaluation points in the region of interest. By using airflow field data with detailed evaluation points, the diffusion state of diffusing substances is determined. Can be predicted in more detail. However, if the region of interest for generating airflow field data with finely set evaluation points is set wider, a computer having a larger memory area is required to generate airflow field data.

  Therefore, the present invention does not generate the airflow field data corresponding to the attention area by a single process, but generates airflow field data for each divided area obtained by dividing the attention area, and then combines them. Even if it is not the computer which has, the airflow field data by which the evaluation point corresponding to the attention area of a wider area | region was set finely can be produced | generated.

  From the above, the present invention can generate detailed airflow field data regardless of the computer specifications.

  In the present invention, the region of interest may be set in an enlarged region where the region of interest is wider than the region of interest and the evaluation point is set coarser than the region of interest.

  According to the present invention, since the region of interest is set in the enlarged region, detailed airflow field data corresponding to the region where the diffusion state of the diffusing material needs to be predicted in more detail can be obtained regardless of the computer specifications. Can be generated.

  In the present invention, a plurality of the attention areas may be set in the enlargement area.

  According to the present invention, since a plurality of regions of interest are set in the enlarged region, it is possible to predict the diffusion state of the diffusing material with respect to the regions of interest away from each other in detail.

  In addition, the present invention may further include a correcting unit that corrects the airflow field data of the adjacent divided regions so as to have continuity.

  According to the present invention, the correction unit corrects the airflow field data of adjacent divided regions so as to have continuity, so that the accuracy of the combined airflow field data can be further increased.

  Further, in the present invention, the generating unit generates a plurality of airflow field data such that some regions of the adjacent divided regions overlap each other, and the correcting unit includes a plurality of airflow field data generated by the generating unit. The partial areas of the airflow field data that overlap each other may be averaged.

  According to the present invention, a plurality of airflow field data is generated by the generating unit so that some areas of adjacent divided areas overlap each other. Then, the correction unit corrects the airflow field data of adjacent divided regions to have continuity by averaging a part of the overlapping areas of the plurality of airflow field data generated by the generation unit. .

  Accordingly, the present invention can easily increase the accuracy of the combined airflow field data.

  On the other hand, in order to solve the above problems, the airflow field data generation method of the present invention employs the following means.

  That is, the airflow field data generation method of the present invention is an airflow field data generation method for generating airflow field data used for predicting the diffusion state of the diffusing material at a plurality of evaluation points in the attention area, A first step of generating the airflow field data for each divided region divided into a plurality of regions, and a second step of combining the airflow field data for each of the plurality of divided regions generated by the first step. Including.

  According to the present invention, airflow field data is generated for each divided region obtained by dividing the region of interest into a plurality of regions, and the generated airflow field data for each divided region is combined. Detailed airflow field data can be generated regardless of specifications.

  On the other hand, in order to solve the above problems, the airflow field data generation program of the present invention employs the following means.

  That is, the airflow field data generation program of the present invention is an airflow field data generation program for generating airflow field data used for predicting the diffusion state of the diffusing material at a plurality of evaluation points in the region of interest, the computer comprising: Generating means for generating the airflow field data for each divided area obtained by dividing the region of interest into a plurality of areas; and combining means for combining the airflow field data for each of the plurality of divided areas generated by the generating means; To function.

  According to the present invention, airflow field data is generated for each divided region obtained by dividing the region of interest into a plurality of regions, and the generated airflow field data for each divided region is combined. Detailed airflow field data can be generated regardless of specifications.

  According to the present invention, there is an excellent effect that detailed airflow field data can be generated regardless of computer specifications.

It is a figure which shows schematic structure of the spreading | diffusion condition prediction apparatus which concerns on 1st Embodiment of this invention. It is a figure which shows an example of the data memorize | stored in the auxiliary storage device which concerns on 1st Embodiment of this invention. It is a schematic diagram which shows the example of a setting of the evaluation point in the attention area which concerns on 1st Embodiment of this invention. It is a schematic diagram required for description of the airflow field data which concern on 1st Embodiment of this invention. It is a schematic diagram which shows the divided | segmented specific area | region which concerns on 1st Embodiment of this invention. It is a flowchart which shows the flow of a process of the airflow field data generation program which concerns on 1st Embodiment of this invention. It is a schematic diagram of the diffusion state of the diffusing material when a plurality of specific regions according to the first embodiment of the present invention are set. It is a flowchart which shows the flow of a process of the airflow field data generation program which concerns on 2nd Embodiment of this invention. It is a schematic diagram which shows the divided | segmented specific area | region which concerns on 2nd Embodiment of this invention. It is a schematic diagram which shows the divided | segmented specific area | region which concerns on other embodiment of this invention.

  Hereinafter, an airflow field data generation device, an airflow field data generation method, and an airflow field data generation method according to an embodiment of the present invention will be described with reference to the drawings.

[First Embodiment]
FIG. 1 is a block diagram showing a schematic configuration of a diffusion state prediction apparatus 10 (airflow field data generation apparatus) according to the first embodiment of the present invention. The diffusion state prediction apparatus 10 according to the first embodiment is a gas state of a region of interest that is a predetermined region including a point of interest such as a nuclear power plant, a chemical factory, or a central point of a major city. This is a system for predicting the diffusion state of a diffusing substance such as gas or dust released from the discharge point using the predicted gas state.

  The diffusion status prediction device 10 is a so-called computer system, and a CPU (Central Processing Unit) 12 that controls the operation of the diffusion status prediction device 10 as a whole, and a main memory such as a RAM that is used as a work area when the CPU 12 executes various programs. Device 14, Auxiliary storage device (storage means) 16 such as ROM (Read Only Memory) and HDD (Hard Disk Drive) in which various programs and various parameters are stored in advance, Input device 20 such as keyboard and mouse, Display and printer And the like, and a communication device 22 that communicates with an external device.

  The auxiliary storage device 16 stores each atmospheric condition and airflow field data of a region of interest under each atmospheric condition in association with each other. The atmospheric condition is determined by, for example, a combination of the wind direction and the atmospheric stability. In the first embodiment, as shown in FIG. 2, the wind direction is 16 azimuths, and the atmospheric stability is changed from 6 levels A to F. Is set.

  The airflow field data is three-dimensional airflow field data in an attention area including a point of interest and an enlarged area that is wider than the attention area. Calculated by CPU 12 using fluid analysis (CFD). Here, as shown in FIG. 3, for example, the attention area is set in a rectangular shape with one side having a predetermined length (in FIG. 3, the case of 10 km is shown). The evaluation points of the airflow field are set in a lattice shape at a predetermined distance interval (in FIG. 3, the case of 10 m is shown). In the enlarged area, the evaluation point is set to be coarser than the attention area.

  The diffusion state prediction device 10 obtains various weather elements such as wind direction, wind speed, turbulent energy, humidity, and temperature at each of these evaluation points. Specifically, it is set within the region of interest under all combinations determined by the above 16 azimuth wind directions and six levels of atmospheric stability, ie, 96 (= 16 × 6) weather conditions. The airflow field at each evaluation point is calculated using a fluid dynamic model (CFD model), and the airflow field data as the calculation result and the weather condition obtained from the airflow field data are stored in the auxiliary storage device 16 in association with each other. .

  The communication device 22 has a function capable of connecting to a weather database 24 installed on the network. The weather database 24 stores past weather data and future predicted weather data. Examples of weather data include GPV (Grid Point Value) data and AMEDAS.

  Then, the CPU 12 extracts the airflow field data stored in the auxiliary storage device 16 according to the atmospheric conditions of the region of interest, and performs the diffusion calculation using the extracted airflow field data, thereby releasing the airflow from the discharge point. The diffusion state of the diffusing material is predicted at every predetermined time interval (for example, every 10 minutes). The diffusion state prediction device 10 outputs the prediction result of the diffusion state of the diffusing material using the extracted airflow field data to the output device 20 (display on a display and recording on a recording medium (paper medium, etc.) by a printer) )

  Here, a method for extracting airflow field data will be described.

  First, a release point from which a diffusing substance is released is set, and a predetermined time interval (for example, in an evaluation period from a prediction start time to a prediction end time input as an initial condition necessary for calculation such as calculation start time by an operator (for example, In 10 minutes increments), meteorological elements at a plurality of evaluation points set in the attention area and the enlarged area are obtained using a weather model.

  Specifically, the diffusion status prediction device 10 is connected to the weather database 24 via the communication device 22 and downloads weather data in the evaluation period, for example, GPV data that is national-scale weather observation data. Subsequently, initial conditions and boundary conditions are determined based on the GPV data, and high-resolution weather elements are sequentially obtained from an enlarged region to a region of interest using a nesting method.

  In this process, first, a time interpolation interpolation calculation and a spatial interpolation calculation are performed using GPV data, thereby obtaining boundary conditions for the enlarged region and obtaining initial conditions for each predetermined time interval. Here, for details of the calculation method of the boundary condition and the initial condition, for example, a method that is currently well known can be adopted, and for example, disclosed in Japanese Patent Application Laid-Open No. 2002-202383 as a prior art. Can be adopted.

  When the initial conditions and boundary conditions in the enlarged region corresponding to the GPV data are determined in this way, the RAMS code, which is a partial differential equation for analyzing atmospheric phenomena using these conditions, is shown. The basic equation of the wind velocity field analysis is subjected to a differential decomposition operation, and this variable is output as a differential decomposition (that is, a weather element at each evaluation point for each predetermined time interval).

  Then, when the diffusion state prediction device 10 determines the meteorological element of each evaluation point in the enlarged area, it determines the atmospheric condition of the attention area from the meteorological element in the enlarged area, and auxiliary storage of the airflow field data corresponding to the atmospheric condition Extract from device 16. For example, the diffusion state prediction device 10 selects a weather element at one evaluation point corresponding to the attention area, for example, the wind speed, the wind direction, and the solar radiation amount, and obtains the atmospheric stability from the selected wind speed and solar radiation amount. Then, airflow field data corresponding to the atmospheric condition determined by the combination of the wind direction and the atmospheric stability is extracted from the auxiliary storage device 16.

  Next, generation of airflow field data that has been conventionally performed will be described.

  First, as shown in FIG. 4, an attention area that is a target for generating airflow field data is set as a specific area A3. A plurality of enlarged regions A2 (A2> A3, hereinafter referred to as “middle regions”) and enlarged regions A1 (A1> A2), which include the specific region A3 and whose area is gradually expanded. , Hereinafter referred to as “large area”). For example, the large area A1 is set to 40 km square, the middle area A2 is set to 10 km square, and the specific area A3 is set to 3 km square. In the specific area A3 in this example, when the required damage evaluation range is 2 km square, the range that can be set as the release point of the diffused substance is 1 km square.

  Evaluation points for evaluating airflow elements are set in the specific area A3, the middle area A2, and the large area A1, respectively. For example, in the large area A1, the evaluation points are set in a grid pattern with a distance of 1 km (40 × 40 mesh) in the east-west direction and the north-south direction. Similarly, in the middle region A2, evaluation points are set in a lattice pattern at a distance interval (40 × 40 mesh) of 0.25 km in the east-west direction and the north-south direction, and in the specific region A3, in the east-west direction and the north-south direction. Evaluation points are set in a grid pattern at a distance of 10 m (300 × 300 mesh).

  When airflow elements are predicted in the large area A1, the middle area A2, and the specific area A3, first, the airflow elements at the respective evaluation points set in the large area A1, which is the widest area, are met with weather observation data. Calculate based on Here, for example, a case where weather GPV data is used as the weather observation data will be specifically described.

  First, data obtained by spatial interpolation of GPV data is used as an initial condition, and data obtained by spatial / temporal interpolation of GPV data is used as a boundary condition. And the airflow element of each evaluation point in large area | region A1 is calculated by solving the partial differential equation regarding weather using these data.

  Subsequently, the meteorological element at the evaluation point in the middle region A2 is calculated by the following processing procedure. First, as an initial condition, among the evaluation points in the middle region A2, those at the same position as the evaluation point set in the large region A1 have already been obtained in the calculation of the large region A1, and the data is used as it is. At other evaluation points, data obtained by interpolating the diverted data is used. Next, as the boundary condition, among the evaluation points on the boundary of the middle region A2, those that are at the same position as the evaluation point set in the large region A1 use the data of the large region A1, and on the other boundaries At the evaluation point, data obtained by interpolating the diverted data is used. Then, using these initial conditions and boundary conditions, partial differential equations relating to weather are solved to calculate the air flow elements at each evaluation point.

  Similarly, the initial condition and boundary condition at the evaluation point in the specific area A3 are obtained by the same procedure as the above-described middle area A2, and by using the obtained initial condition and boundary condition, the partial differential equation relating to weather is solved, The airflow element at each evaluation point is calculated.

  As described above, the specific area A3 is an area wider than the specific area A3 and is set in the large area A1 and the middle area A2 in which the evaluation points are set to be coarser than the specific area A3. For this reason, it becomes possible to shorten processing time compared with the case where it calculates by setting a detailed evaluation point in all the area | regions of large area | region A1. Such a processing method for gradually narrowing down the calculation area is called a multigrid method.

  Here, the diffusion state prediction device 10 can predict the diffusion state of the diffusing material in more detail by using the airflow field data in which the evaluation points are set finely. However, if the region of interest for which airflow field data in which evaluation points are set finely is set wider, a computer (diffusion state prediction device 10) having a larger memory area is required to generate airflow field data. Become.

  Therefore, the diffusion state prediction apparatus 10 according to the first embodiment divides the specific area A3 into a plurality of areas as shown in the example of FIG. 5, and generates airflow field data for each of the divided areas 50A to 50B. (Hereinafter referred to as “airflow field data generation processing”). In the following description, when distinguishing each divided region, any one of A to B is added to the end of the code, and when not distinguishing each divided region, AB is omitted.

  FIG. 6 is a flowchart showing the flow of the airflow field data generation program executed by the CPU 12 when the airflow field data generation process is performed. The airflow field data generation program is stored in advance in a predetermined area of the auxiliary storage device 16. It is remembered.

  First, in step 100, the specific area A3 (6 km square in the example of FIG. 5) is divided. For example, the number of divisions (the number of divisions in the vertical direction and the number of divisions in the horizontal direction) of the specific area A3 is input by the operator via the input device 20, whereby the range of each division area 50 is determined. In the example of FIG. 5, 2 is input as the number of divisions in the vertical direction, and 2 is input as the number of divisions in the horizontal direction, whereby the specific area A <b> 3 is divided into four.

  In addition, each divided region 50 includes a part of a releasable region 52 (a region surrounded by a one-dot chain line in the specific region A3 in FIGS. 4 and 5) that can be set as a discharge point for releasing the diffusion substance.

  In the next step 102, airflow field data is generated for each divided region 50 by the multigrid method described above.

  In the next step 104, the airflow field data for each of the plurality of divided regions 50 generated in step 102 is combined into one file, that is, airflow field data corresponding to the specific region A3.

  In the next step 106, the airflow field data combined in step 104 is stored as the airflow field data corresponding to the specific area A3 in the auxiliary storage device 16 in association with the weather conditions obtained for the airflow field data. Exit the program.

  FIG. 7 shows the diffusion state of the diffusing material when a plurality of specific regions A3 are set. Each of the specific areas A3-2 and A3-3 shown in FIG. 7 is, for example, an area where the population is dense or an attention area where important facilities are provided.

  As shown in FIG. 7A, the diffusion state prediction apparatus 10 generates airflow field data corresponding to a plurality of specific areas A3-1, A3-2, and A3-3, and diffuses only in the specific area A3-1. A substance release point may be provided, and the diffusion state of the diffused substance released from the release point may be predicted by performing diffusion calculation using the airflow field data.

  Further, as illustrated in FIG. 7B, the diffusion state prediction device 10 generates airflow field data corresponding to the plurality of specific areas A3-1, A3-2, and A3-3, and the specific areas A3-1, A3-1. The diffusion state of the diffusing material released from each of the discharge points may be predicted by providing a discharge point of the diffusing material in each of A3-2 and A3-3, and performing diffusion calculation using the airflow field data.

  As described above, the diffusion state prediction apparatus 10 according to the first embodiment generates airflow field data for each divided region 50 obtained by dividing the region of interest into a plurality of regions, and for each of the generated plurality of divided regions 50. Combine airflow field data. Thus, instead of generating the airflow field data corresponding to the attention area in a single process, the airflow field data is generated for each divided area obtained by dividing the attention area, and then combined. The diffusion state prediction apparatus 10 can generate detailed airflow field data regardless of the computer specifications (specifically, the capacity of the RAM included in the main storage device 14).

  Specifically, for example, in a computer in which the attention area can be set only up to 3 km square, the diffusion state prediction apparatus 10 according to the first embodiment can set a further area as the attention area (example in FIG. 5). Then, 6km square).

  Further, when the evaluation range of the diffusion state is 2 km, for example, in a computer that can set the attention area only to 3 km square, the range of the dischargeable area 52 is the center 1 km square of the attention area. However, in the diffusion state prediction apparatus 10 according to the first embodiment, since the attention area can be set up to 6 km square, the dischargeable area 52 can be set at the center 4 km square of the attention area. As described above, the diffusion state prediction apparatus 10 according to the first embodiment can set the dischargeable region 52 widely.

  Further, in the diffusion state prediction apparatus 10 according to the first embodiment, the attention area is set in an enlarged area where the evaluation area is set to be coarser than the attention area and the attention area is wider than the attention area. Therefore, it is possible to generate detailed airflow field data corresponding to an area where it is highly necessary to predict the diffusion state of the diffusing material in detail regardless of the computer specifications.

  Furthermore, the diffusion state prediction apparatus 10 according to the first embodiment predicts the diffusion state of the diffusing substance to the attention region at a distance at the same time in more detail since a plurality of attention regions are set in the enlarged region. be able to.

[Second Embodiment]
Hereinafter, a second embodiment of the present invention will be described.

  In addition, since the structure of the spreading | diffusion condition prediction apparatus 10 which concerns on this 2nd Embodiment is the same as that of the structure of the spreading | diffusion condition prediction apparatus 10 which concerns on 1st Embodiment shown by FIG. 1, description is abbreviate | omitted.

  Next, airflow field data generation processing according to the second embodiment will be described. Note that the airflow field data generation processing according to the second embodiment corrects the airflow field data in the adjacent divided areas 50 of the specific area A3 so as to have continuity.

  FIG. 8 is a flowchart showing the flow of the airflow field data generation program executed by the CPU 12 when the airflow field data generation process according to the second embodiment is performed. The airflow field data generation program is an auxiliary memory. Pre-stored in a predetermined area of the device 16. In FIG. 8, the same processes as those in FIG. 5 are denoted by the same reference numerals as those in FIG.

  First, in step 100 ′, the number of divisions of the specific area A3 (the number of divisions in the vertical direction and the number of divisions in the horizontal direction) is input by the operator via the input device 20, so that the range of each division area 50 is determined. Is done. In the airflow field data generation process according to the second embodiment, as shown in the example of FIG. 9, each divided region 50 is set so that some regions of adjacent divided regions 50 overlap each other. In the example of FIG. 9, the area surrounded by diagonal lines is an overlapping area among the adjacent divided areas 50, and the overlapping area is set to a predetermined ratio (for example, 10%) of the length of each side, for example. Has been.

  In the next step 102 ′, airflow field data is generated for each of the plurality of divided regions 50 that are divided in step 100 ′ and in which some regions of the adjacent divided regions 50 overlap each other.

  In the next step 103, the plurality of airflow field data generated in step 102 'is corrected so that the airflow field data of the adjacent divided regions 50 has continuity. More specifically, by correcting the airflow field data of a part of the overlapping areas, the airflow field data of the adjacent divided areas 50 is corrected so as to have continuity.

  As described above, the diffusion state prediction apparatus 10 according to the second embodiment generates a plurality of airflow field data such that some regions of the adjacent divided regions 50 overlap each other, and the generated plurality of airflow fields. By averaging the partial areas where the data overlap each other, the airflow field data of the adjacent divided areas 50 is corrected so as to have continuity.

  Thereby, the diffusion state prediction apparatus 10 according to the second embodiment can easily increase the accuracy of the combined airflow field data.

  As mentioned above, although this invention was demonstrated using said each embodiment, the technical scope of this invention is not limited to the range as described in the said embodiment. Various changes or improvements can be added to the above-described embodiments without departing from the gist of the invention, and embodiments to which the changes or improvements are added are also included in the technical scope of the present invention.

  For example, in each of the above embodiments, the case where the specific area A3 is divided into four and the airflow field data is generated for each divided area 50 has been described. However, the present invention is not limited to this, and FIG. As shown in A), the specific area A3 may be divided into four to generate the airflow field data for each divided area 50, or the specific area A3 may be polygonal as shown in FIG. It is good also as a form which divides | segments into the division area 50 and produces | generates airflow field data for every division area 50. FIG.

  In each of the above embodiments, the case where the attention area included in the enlarged area is divided into a plurality of divided areas and the airflow field data is generated for each of the divided areas has been described. However, the present invention is limited to this. Instead, the enlarged region may be divided into a plurality of divided regions, and airflow field data may be generated for each division.

  In each of the above embodiments, a case where a plurality of airflow field data are combined into one file has been described. However, the present invention is not limited to this, and the airflow field data is combined. Instead, it may be stored in the auxiliary storage device 16 for each divided region 50. In this embodiment, since the airflow field data for each divided area 50 has coordinates corresponding to the coordinates of the divided area 50, when performing diffusion calculation using the airflow field data, the division is performed based on the coordinates. Airflow field data for each region 50 is combined as airflow field data corresponding to the specific region A3.

  In each of the above embodiments, the airflow field data generation program is stored in the auxiliary storage device 16, but the present invention is not limited to this, and the airflow field data generation program is stored in a magnetic disk, a CD ( Compact disc (DVD), DVD (Digital Versatile Disc) and other optical discs, Memo IC (Integrated Circuit) cards, memory cards and other portable storage media, and airflow field data stored in the portable storage media The program may be read and executed by the diffusion status prediction apparatus 10.

  Furthermore, although each said embodiment demonstrated the case where the diffusion condition prediction apparatus 10 produced | generated airflow field data, this invention is not limited to this, The diffusion condition prediction apparatus 10 uses airflow field data. It is good also as a form which the other computer system used as an airflow field data generation apparatus produces | generates airflow field data, and the produced | generated airflow field data are memorize | stored in the auxiliary storage device 16 of the spreading | diffusion condition prediction apparatus 10 without producing | generating. .

10 Diffusion situation prediction device 12 CPU
14 Main storage device 16 Auxiliary storage device 18 Input device 20 Output device 22 Communication device 24 Weather database

Claims (7)

An airflow field data generation device that generates airflow field data used to predict the diffusion state of a diffused substance at a plurality of evaluation points in a region of interest,
Generating means for generating the airflow field data for each divided region obtained by dividing the region of interest into a plurality of regions;
Combining means for combining the airflow field data for each of the plurality of divided regions generated by the generating means;
An airflow field data generation device comprising:   2. The airflow field data generation device according to claim 1, wherein the attention area is an area wider than the attention area, and is set in an enlarged area where the evaluation point is set to be coarser than the attention area.   The airflow field data generation device according to claim 2, wherein a plurality of the attention areas are set in the expansion area. Correction means for correcting the airflow field data of the adjacent divided areas so as to have continuity,
The airflow field data generation device according to any one of claims 1 to 3, further comprising: The generation means generates a plurality of the airflow field data so that some regions of the adjacent divided regions overlap each other,
The airflow field data generation device according to claim 4, wherein the correction unit averages the partial areas of the plurality of airflow field data generated by the generation unit that overlap each other. An airflow field data generation method for generating airflow field data used for predicting the diffusion state of a diffusing substance at a plurality of evaluation points in a region of interest,
A first step of generating the airflow field data for each divided region obtained by dividing the region of interest into a plurality of regions;
A second step of combining the airflow field data for each of the plurality of divided regions generated by the first step;
A method for generating airflow field data including: An airflow field data generation program for generating airflow field data used for predicting the diffusion state of a diffusing material at a plurality of evaluation points in a region of interest,
Computer
Generating means for generating the airflow field data for each divided region obtained by dividing the region of interest into a plurality of regions;
Combining means for combining the airflow field data for each of the plurality of divided regions generated by the generating means;
Airflow field data generation program for functioning.

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