Sensible heat flux analysis method using computational fluid dynamics model and sensible heat flux analysis system using same

Abstract

The present invention: relates to a sensible heat flux analysis method using a computational fluid dynamics model that may simulate and analyze a high-resolution sensible heat flux by considering building and land cover characteristics of an urban area, and a sensible heat flux analysis system using same; may simulate and analyze a high-resolution sensible heat flux by considering building and land cover characteristics of an urban area; may calculate a high-resolution sensible heat flux by using the computational fluid dynamics model and local data assimilation and prediction system data, urban canopy model data comprising vegetation, a digital topographic map, and a land cover map; and may provide a sensible heat flux analysis result which may be used as a basis for the determination of urban design and environmental policies in a target area.

Description

Method for analyzing sensible heat flux using a computational fluid dynamics model and a sensible heat flux analysis system using the same

[0001] The present invention relates to a method for analyzing sensible heat flux using a computational fluid dynamics model and a system for analyzing sensible heat flux using the same. More specifically, the invention relates to a method for analyzing sensible heat flux using a computational fluid dynamics model and a system for analyzing sensible heat flux using the same, which can simulate and analyze high-resolution sensible heat flux by considering the building and land cover characteristics of urban areas.

[0002] Cities are composed of diverse land covers, including artificial structures and natural vegetation. Land covers such as concrete and asphalt, which constitute a large portion of cities, exhibit higher temperatures and sensible heat flux than natural vegetation. Sensible heat refers to the energy that raises a substance's temperature when applied thermal energy accumulates within it, while sensible heat flux refers to the energy transferred between a surface and the atmosphere via conduction without a change in state. High sensible heat flux destabilizes the atmosphere and affects precipitation. Furthermore, as sensible heat flux increases, the thermal comfort of urban pedestrians may decrease.

[0003] Accordingly, analyzing the thermal environment of urban areas is becoming increasingly important to assess the impact of urban atmospheric conditions on humans. Many previous studies have analyzed the thermal environment of urban areas using sensible heat flux. Most of these studies analyzed the thermal environment using field measurements and remote sensing data. However, because urban areas consist of diverse land covers and contain numerous buildings, there are temporal and spatial limitations to analyzing sensible heat flux in urban areas using only field measurements and remote sensing data. Furthermore, numerical weather forecasting models lack the resolution to analyze sensible heat flux in urban areas.

[0004] It is difficult to find prior art or research that simulates and analyzes high-resolution sensible heat flux considering the building and land cover characteristics of urban areas.

[0005] [Prior Art Literature]

[0006] [Patent Literature]

[0007] Korean Published Patent 10-2023-0140831 (2023.10.10)

[0008] The present invention provides a sensible heat flux analysis method using a computational fluid dynamics model capable of simulating and analyzing high-resolution sensible heat flux by considering building and land cover characteristics in urban areas, and a sensible heat flux analysis system using the same.

[0009] The present invention provides a method for analyzing sensible heat flux using a computational fluid dynamics model capable of calculating high-resolution sensible heat flux using a computational fluid dynamics model, local forecasting system data, urban canopy model data including vegetation, digital topographic maps, and land cover maps, and a sensible heat flux analysis system using the same.

[0010] The present invention provides a method for analyzing sensible heat flux using a computational fluid dynamics model and a system for analyzing sensible heat flux using the same, wherein the analysis results can be utilized as a basis for urban design and environmental policy decisions in a target area.

[0011] A sensible heat flux analysis system using a computational fluid dynamics model according to the present invention comprises: a first information receiving unit for receiving air temperature data and wind data for an area to be analyzed; a second information receiving unit for receiving land surface temperature data for the area to be analyzed; a geographic information receiving unit for receiving topographic information and building information for the area to be analyzed from a Geographic Information System (GIS); an environmental geographic information receiving unit for receiving land cover information for the area to be analyzed from an Environmental Geographic Information Service (EGIS); a modeling unit for three-dimensionally modeling the area to be analyzed based on topographic information, building information, and land cover information for the area to be analyzed; and setting initial and boundary conditions for the modeled area to be analyzed based on the received air temperature data and wind data, and setting land surface temperature conditions for the modeled area to be analyzed based on the received land surface temperature data, thereby analyzing the air temperature, wind, and sensible heat of the area to be analyzed through a computational fluid dynamics (CFD) model. It includes a computational fluid dynamics analysis unit that analyzes sensible heat flux.

[0012] In one embodiment, the first information receiving unit can receive temperature data and wind data for the analysis target area from the LDAPS (Local Data Assimilation and Prediction System) local forecasting system.

[0013] In one embodiment, the second information receiving unit can receive surface temperature data for the analysis target area from the VUCM (Vegetation Urban Canopy Model) system.

[0014] In one embodiment, a verification unit may be further included to compare and analyze the temperature, wind, and sensible heat flux calculated by the computational fluid dynamics analysis unit with the actual temperature, wind, and sensible heat flux values ​​measured in the analysis target area.

[0015] In one embodiment, the system may further include a visualization unit that visualizes the modeled analysis target area in three dimensions in virtual space and visualizes the spatial distribution of temperature, wind, or sensible heat flux calculated by the computational fluid dynamics analysis unit in the visualized analysis target area.

[0016] In one embodiment, the modeling unit can model the area to be analyzed as a grid of a predetermined size (e.g., 10m(x)×10m(y)×2m(z)) based on terrain information and building information received from the geographic information system.

[0017] In one embodiment, the system further includes an interpolation unit that interpolates temperature data and wind data received from the first information receiving unit, wherein the interpolation unit generates interpolated temperature data and wind data by averaging the values ​​of the four grid points closest to the target area of ​​the computational fluid dynamics model among the grid points from which the temperature data and wind data are calculated, and the computational fluid dynamics analysis unit may set the temperature data and wind data generated by the interpolation unit as initial and boundary conditions for the modeled target area of ​​analysis.

[0018] In one embodiment, the system further includes a reclassification unit that reclassifies land cover information received from the environmental geographic information system into five categories (building, road, bare land, tree, and grassland) to reconstruct a land cover map of the area to be analyzed, and a land cover-specific surface temperature generation unit that generates a land cover-specific surface temperature of the area to be analyzed based on the reconstructed land cover map and surface temperature data received from the second information receiving unit, and the computational fluid dynamics analysis unit can set the land cover-specific surface temperature generated by the land cover-specific surface temperature generation unit as a surface temperature condition for the modeled area to be analyzed.

[0019] In one embodiment, the computational fluid dynamics analysis unit can calculate the sensible heat flux using the following mathematical formula 12.

[0020] [Mathematical Formula 12]

[0021]

[0022] Here, Q h ε is the sensible heat flux, ρ is the dry air density, C p ε is the specific heat at constant pressure, w′ is the mean vertical wind component (w), and T′ are perturbations with respect to temperature (T).

[0023] A method for analyzing sensible heat flux using a computational fluid dynamics model according to the present invention comprises the steps of: a first information receiving unit receiving air temperature data and wind data for an area to be analyzed; a second information receiving unit receiving land surface temperature data for the area to be analyzed; a geographic information receiving unit receiving topographic information and building information for the area to be analyzed from a Geographic Information System (GIS); an environmental geographic information receiving unit receiving land cover information for the area to be analyzed from an Environmental Geographic Information Service (EGIS); a modeling unit modeling the area to be analyzed in three dimensions based on the topographic information, building information, and land cover information for the area to be analyzed; and a computational fluid dynamics analysis unit setting initial and boundary conditions for the modeled area to be analyzed based on the received air temperature data and wind data, and setting land surface temperature conditions for the modeled area to be analyzed based on the received land surface temperature data, thereby using a computational fluid dynamics (CFD) model to analyze the It includes a step of analyzing the temperature, wind, and sensible heat flux of the area to be analyzed.

[0024] In one embodiment, the method for analyzing sensible heat flux using a computational fluid dynamics model may further include the step of the computational fluid dynamics analysis unit analyzing the spatial distribution of sensible heat flux based on the calculated sensible heat flux, and analyzing by land cover of the analysis target area.

[0025] In one embodiment, the method for analyzing sensible heat flux using a computational fluid dynamics model may further include the step of a verification unit verifying the temperature and wind calculated by the computational fluid dynamics analysis unit by comparing and analyzing them with the actual temperature and wind values ​​measured in the analysis target area, the step of the computational fluid dynamics analysis unit calculating the sensible heat flux, and the step of the verification unit verifying the sensible heat flux calculated by the computational fluid dynamics analysis unit by comparing and analyzing it with the actual sensible heat flux values ​​measured in the analysis target area.

[0026] In one embodiment, the step of three-dimensional modeling of the area to be analyzed may model the area to be analyzed as a grid of a predetermined size (e.g., 10m(x)×10m(y)×2m(z)) based on the terrain information and building information.

[0027] In one embodiment, the step of setting initial and boundary conditions based on the temperature data and wind data includes an interpolation step in which an interpolation unit interpolates the temperature data and wind data received from the first information receiving unit, and a step in which a computational fluid dynamics analysis unit sets the temperature data and wind data generated by the interpolation unit as initial and boundary conditions for the modeled analysis target area, wherein the interpolation step may generate interpolated temperature data and wind data by averaging the values ​​of the four grid points closest to the target area of ​​the computational fluid dynamics model among the grid points from which the temperature data and wind data are calculated.

[0028] In one embodiment, the step of setting surface temperature conditions for the modeled target area for analysis based on the received surface temperature data may include: a reclassification unit reclassifying land cover information received from the environmental geographic information system into five categories (building, road, bare land, tree, and grassland) to reconstruct a land cover map of the target area for analysis; a land cover-specific surface temperature generation unit generating a land cover-specific surface temperature of the target area for analysis based on the reconstructed land cover map and the surface temperature data received from the second information receiving unit; and a computational fluid dynamics analysis unit setting the land cover-specific surface temperature generated by the land cover-specific surface temperature generation unit as the surface temperature condition for the modeled target area for analysis.

[0029] As described above, the sensible heat flux analysis method using a computational fluid dynamics model according to the present invention and the sensible heat flux analysis system using the same can simulate and analyze high-resolution sensible heat flux by considering the building and land cover characteristics of urban areas.

[0030] The sensible heat flux analysis method using a computational fluid dynamics model and the sensible heat flux analysis system using the same according to the present invention can calculate high-resolution sensible heat flux using a computational fluid dynamics model, local forecasting system data, urban canopy model data including vegetation, digital topographic maps, and land cover maps.

[0031] The sensible heat flux analysis method using a computational fluid dynamics model according to the present invention and the sensible heat flux analysis system using the same can provide sensible heat flux analysis results that can be utilized as a basis for urban design and environmental policy decisions in a target area.

[0032] FIG. 1 is a diagram illustrating the connection relationships of a system for analyzing sensible heat flux according to an embodiment of the present invention.

[0033] FIG. 2 is a diagram showing the configuration of a sensible heat flux analysis system using the computational fluid dynamics model of FIG. 1.

[0034] FIG. 3 is a satellite image showing the analysis target area according to the embodiment and the location of the flux tower that measured the sensible heat flux used for verification.

[0035] FIG. 4 is a diagram briefly explaining the initial input / boundary input information of the sensible heat flux analysis system of FIG. 1.

[0036] Figure 5 is a drawing showing an example of 3D building / topographic information and land cover information of the area to be analyzed.

[0037] Figure 6 is a diagram showing the values ​​measured at the flux tower during the analysis period and the temperature, wind speed, and wind direction calculated by the sensible heat flux analysis system of Figure 1 in chronological order.

[0038] Figure 7 is a drawing showing the actual measured values ​​from the flux tower during the analysis period and the sensible heat flux values ​​calculated by the sensible heat flux analysis system of Figure 1.

[0039] Figure 8 is a table showing the results of calculating statistical figures to verify the sensible heat flux calculated by the sensible heat flux analysis system using sensible heat flux measured at flux towers located within the analysis area during the entire analysis period.

[0040] Figure 9 is a diagram showing the hourly average sensible heat flux and the standard deviation of each hour in chronological order during the analysis period.

[0041] Figure 10 is a diagram showing the hourly averaged normalized surface temperature during the analysis period.

[0042] Figure 11 is a diagram showing the spatial distribution of averaged sensible heat flux by land cover for the analysis period.

[0043] Figure 12 is a diagram showing the spatial distribution of the standard deviation of the averaged sensible heat flux for the analysis period by land cover.

[0044] Figure 13 is a diagram showing the spatial distribution of sensible heat flux averaged hourly at 4:00, 9:00, 12:00, 14:00, 17:00, and 22:00 during the analysis period.

[0045] Figure 14 is a diagram showing the range of magnitudes of hourly averaged temperature gradient, turbulent heat diffusion coefficient, and sensible heat flux by land cover during the analysis period of the analysis area.

[0046] FIG. 15 is a flowchart illustrating a method for analyzing sensible heat flux using a computational fluid dynamics model according to an embodiment of the present invention.

[0047] The following describes specific details for implementing the sensible heat flux analysis method using a computational fluid dynamics model according to the present invention and the sensible heat flux analysis system using the same.

[0048]

[0049] FIG. 1 is a diagram illustrating the connection relationships of a system for analyzing sensible heat flux according to one embodiment of the present invention.

[0050] Referring to FIG. 1, a system for sensible heat flux analysis includes a sensible heat flux analysis system (110) using a computational fluid dynamics model, and the sensible heat flux analysis system (110) using a computational fluid dynamics model can be connected via a network to a geographic information system (120), an environmental geographic information system (130), a local forecasting system (140), a Vegetation Urban Canopy Model (VUCM) (150), etc.

[0051] The geographic information system (120) may correspond to a geographic information system (GIS). The geographic information system (120) includes a server that collects, stores, manipulates, and analyzes geographic information. The geographic information includes topography information and building information.

[0052] For example, a geographic information system (120) includes a geographic information database, and the geographic information database includes graphic data in the form of a map or drawing representing the location and form of various geographic phenomena such as administrative boundaries, roads, and building shapes, and descriptive or attribute data in the form of numbers and characters describing spatial characteristics such as population, building area, and place names. For example, a geographic information system (120) provided by the National Geographic Information Institute of Korea provides topography contours in a vector format with a resolution of 1m and building information in a raster format.

[0053] The sensible heat flux analysis system (110) can connect to a geographic information system (120) to receive geographic information for a specific area (e.g., an area to be analyzed).

[0054] The environmental geographic information system (130) may include an Environmental Geographic Information Service (EGS). In one embodiment, the environmental geographic information system (130) may correspond to the Environmental Geographic Information Service (EGS) of the Ministry of Environment, which provides various environmental spatial information. The Environmental Geographic Information Service (EGS) may provide land cover maps, environmental thematic maps, maps of land use regulated areas and districts, and individual spatial information systems. For example, the Environmental Geographic Information Service (EGS) provided by the Ministry of Environment of Korea provides a sub-divided land cover map with a resolution of 1m classified into 41 land cover items.

[0055] The sensible heat flux analysis system (110) can connect to the environmental geographic information system (130) to receive land cover information for a specific area (e.g., an area to be analyzed).

[0056] The local forecasting system (140) simulates the air temperature and wind for a pre-set area to produce air temperature data and wind data. In one embodiment, the local forecasting system (140) may correspond to a Local Data Assimilation and Prediction System (LDAS) system.

[0057] The urban vegetation canopy model system (VUCM) (150) simulates the land surface temperature for a pre-set area and calculates land surface temperature data.

[0058] The sensible heat flux analysis system (110) can set a target area for analysis according to the user's control. The sensible heat flux analysis system (110) receives temperature data and wind data for the target area for analysis from the local forecasting system (140), and receives surface temperature data for the target area for analysis from the urban vegetation canopy model system (150).

[0059] The sensible heat flux analysis system (110) models the area to be analyzed in three dimensions based on topographic information, building information, and land cover information received from the geographic information system (120) and the environmental geographic information system (130), and analyzes the temperature, wind, and sensible heat flux of the area to be analyzed through a Computational Fluid Dynamics (CFD) model based on temperature data and wind data received from the local forecasting system (140) and surface temperature data received from the urban vegetation canopy model system (150). The sensible heat flux analysis system (110) can display the analyzed results on a screen under the control of a user.

[0060] Below, the process of the sensible heat flux analysis system (110) analyzing the temperature, wind, and sensible heat flux of the area to be analyzed will be explained in detail.

[0061]

[0062] Figure 2 is a diagram showing the configuration of a sensible heat flux analysis system using the computational fluid dynamics model of Figure 1.

[0063] Referring to FIG. 2, the sensible heat flux analysis system (110) includes a first information receiving unit (202), a second information receiving unit (204), an environmental geographic information receiving unit (206), a geographic information receiving unit (208), an interpolation unit (210), a surface temperature generation unit by land cover (212), a reclassification unit (214), a modeling unit (216), a computational fluid dynamics analysis unit (218), a verification unit (220), a memory (222), and a visualization unit (224).

[0064] The network unit (not shown) is connected to an external network via wired or wireless connection and can access a geographic information system (120), an environmental geographic information system (130), a local forecasting system (140), and an urban vegetation canopy model system (150) through the external network. The memory (222) can store data used for the operation of the sensible heat flux analysis system (110), data used for sensible heat flux analysis, and analysis result data. In one embodiment, the memory (222) can store actual measured temperature, wind, and sensible heat flux values ​​in the analysis target area.

[0065] The geographic information receiving unit (208) receives geographic information (e.g., topography information, building information) for at least one region from the geographic information system (120). In one embodiment, the geographic information receiving unit (208) receives at least one region to be analyzed (e.g., one region of a city) from a user and may request geographic information for the region to be analyzed from the geographic information system (120).

[0066] The environmental geographic information receiving unit (206) requests land cover information for at least one target area for analysis from the environmental geographic information system (130) and receives land cover information for each area. The geographic information receiving unit (208) and the environmental geographic information receiving unit (206) can each receive information for the same target area for analysis.

[0067] In one embodiment, the area to be analyzed may correspond to a measurement area where a weather observation device or a sensible heat flux measuring device (flux tower) is installed.

[0068] The first information receiving unit (202) receives air temperature data and wind data for the area to be analyzed. In one embodiment, the first information receiving unit (202) may receive air temperature data and wind data for the area to be analyzed from a local forecasting system (140). For example, the air temperature data may include hourly air temperature values, and the wind data may include hourly east-west wind components (u-wind component) and north-south wind components (v-wind component). In one embodiment, the local forecasting system (140) may correspond to an LDAPS local forecasting system.

[0069] The second information receiving unit (204) receives land surface temperature data for the area to be analyzed. In one embodiment, the second information receiving unit (204) may receive land surface temperature data for the area to be analyzed from the urban vegetation canopy model system (150). In one embodiment, the urban vegetation canopy model system (150) may correspond to a VUCM system.

[0070] The modeling unit (216) models the area to be analyzed in three dimensions based on topographic information, building information, and land cover information for the area to be analyzed received through the geographic information receiving unit (208) and the environmental geographic information receiving unit (206). In one embodiment, the modeling unit (216) can model the area to be analyzed in a grid of a pre-set size (e.g., 10m(x)×10m(y)×2m(z)) based on topographic information and building information received from the geographic information system (120).

[0071] The reclassification unit (214) reclassifies land cover information received from the environmental geographic information system through the environmental geographic information receiving unit (206) into five categories (building, road, bare land, tree, and grassland) and reconstructs the land cover map of the area to be analyzed. In one embodiment, the number of reclassification categories (e.g., 5) may vary depending on the embodiment.

[0072] The surface temperature generation unit (212) by land cover generates the surface temperature by land cover of the analysis target area based on the reconstructed land cover map and the surface temperature data received from the second information receiving unit (204). For example, the surface temperature generation unit (212) by land cover can generate the surface temperature by land cover for the land cover map of the analysis target area reconstructed into five items. This is because the urban vegetation canopy model system (150) calculates the surface temperature by land cover for the entire target area, and calculates the surface temperature in a form different from the actual land cover of the analysis target area.

[0073] The interpolation unit (210) interpolates the temperature data and wind data received from the first information receiving unit (202). In one embodiment, the interpolation unit (210) may generate interpolated temperature data and wind data by averaging the values ​​of the four grid points closest to the target area of ​​the computational fluid dynamics model among the grid points from which temperature data and wind data are calculated in the local forecasting system (140).

[0074] The computational fluid dynamics analysis unit (218) sets initial and boundary conditions for the modeled analysis target area based on the received temperature data and wind data, and sets surface temperature conditions for the modeled analysis target area based on the received surface temperature data, and analyzes the temperature, wind, and sensible heat flux of the analysis target area through a computational fluid dynamics (CFD) model.

[0075] In one embodiment, the computational fluid dynamics analysis unit (218) may set the temperature data and wind data generated by the interpolation unit (210) as initial and boundary conditions for the area to be analyzed, and set the surface temperature generated by the surface temperature generation unit (212) by land cover as a surface temperature condition for the area to be analyzed.

[0076] The verification unit (220) analyzes the temperature, wind, and sensible heat flux calculated by the computational fluid dynamics analysis unit (218) by comparing them with the actual temperature, wind, and sensible heat flux values ​​measured in the analysis target area. For example, the verification unit (220) can verify the accuracy of the analysis by the computational fluid dynamics analysis unit (218) by analyzing the error between the values ​​calculated by the computational fluid dynamics analysis unit (218) and the values ​​actually measured.

[0077] The visualization unit (224) visualizes the analysis target area modeled by the modeling unit (216) in three dimensions in virtual space, and visualizes the spatial distribution of temperature, wind, or sensible heat flux calculated by the computational fluid dynamics analysis unit (218) in the visualized analysis target area. The display unit (not shown) displays the results visualized by the visualization unit (224) on a screen.

[0078] Below, the process of the sensible heat flux analysis system (110) analyzing the temperature, wind, and sensible heat flux of the analysis target area will be explained in detail, using a specific analysis target area as an example.

[0079]

[0080] Figure 3 is a satellite image showing the location of the flux tower that measured the sensible heat flux used for verification and the analysis target area according to the embodiment.

[0081] Referring to Fig. 3, this figure shows an example where 3D building and terrain data of the analysis target area are configured according to the COST Action 732 (guidelines for numerical simulation of CFD models). For example, the size of the analysis target area is set by establishing boundary areas of 500 m in the x and y directions (east, west, south, and north), respectively, and is defined as 2000 m, 2000 m, and 630 m in the x, y, and z directions, respectively. The grid size is set to 10 m, 10 m, and 2 m in the x, y, and z directions, respectively. The land cover information is set to the same drawing size and grid size as the 3D building and terrain data. The point indicated in the center is the sensible heat flux (Q h Indicates the location of the flux tower where ) was measured.

[0082]

[0083] Figure 4 is a diagram briefly explaining the initial input / boundary input information of the sensible heat flux analysis system of Figure 1.

[0084] For convenience of explanation, the following description assumes that the local forecasting system (140) uses the LDAPS system and the urban vegetation canopy model system (150) uses the VUCM system.

[0085] LDAPS produces air temperature and wind data for the area under analysis. In one embodiment, LDAPS may use one of the numerical weather prediction systems operated by the Korea Meteorological Administration (KMA). LDAPS produces air temperature and wind data every hour (at 1-hour intervals) during the analysis period, and the sensible heat flux analysis system (110) receives the data. LDAPS operated by the Korea Meteorological Administration (KMA) is based on the integrated model of the UK Met Office and uses the Arakawa C-grid for the horizontal-vertical grid system and Charney-Phillips grid staggering for the vertical grid system. LDAPS has a horizontal resolution of 1.5 km and consists of 744 grids in the east-west direction and 928 grids in the north-south direction, with a maximum vertical range of 39 km and 70 layers. LDAPS uses a semi-implicit and semi-Lagrangian time integration method. LDAPS performs a 36-hour forecast for weather forecasting at 00:00, 06:00, 12:00, and 18:00 UTC, and a 3-hour forecast to provide background fields for the 36-hour forecast at 03:00, 09:00, 15:00, and 21:00 UTC. LDAPS's 3-hour forecast data (horizontal wind components and air temperatures) is used as boundary and initial conditions for CFD model simulation in the sensible heat flux analysis system (110).

[0086] VUCM calculates land surface temperature data for the area to be analyzed. In one embodiment, VUCM calculates land surface temperature data by land cover at every hour (1-hour interval) during the analysis period, and the sensible heat flux analysis system (110) receives the data.

[0087] VUCM simulates surface temperatures by land cover. VUCM simulates surface temperatures by parameterizing the physical processes within urban canopy layers, such as long-wave and short-wave radiation transfer, heat transfer between artificial surfaces (roofs, roads, and soil), and hydrological processes by urban vegetation. For example, VUCM can analyze air temperature, humidity, wind speed, and surface temperatures (roofs, walls, and floors) in street canyons using input variables that reflect meteorological forcing and urban morphology.

[0088]

[0089] The modeling unit (216) of the sensible heat flux analysis system (110) models the area to be analyzed in three dimensions using a grid system of a pre-set size based on topographic information, building information, and land cover information for the area to be analyzed. In one embodiment, the modeling unit (216) may receive topographic information and building information for the area to be analyzed from a geographic information system (GIS). In one embodiment, the modeling unit (216) may model the area to be analyzed using a grid of a pre-set size (e.g., 10m(x)×10m(y)×2m(z)) based on the received topographic information and building information.

[0090] In one embodiment, the interpolation unit (210) can generate interpolated temperature data and wind data by interpolating temperature data and wind data received from LDAPS. For example, the interpolation unit (210) can generate interpolated temperature data and wind data by averaging the values ​​(temperature and wind) of the four grid points closest to a specific analysis target point to be analyzed in the CFD model among the grid points from which temperature data and wind data are calculated in LDAPS. This is because the grid system of the analysis target area modeled in LDAPS does not match the grid system of the analysis target area to be analyzed in the CFD model.

[0091] In one embodiment, the reclassification unit (214) can reclassify land cover based on pre-set criteria based on land cover information for the analysis target area received from the Environmental Geographic Information System (EGIS). For example, the reclassification unit (214) can reclassify the subdivided land cover map received from the Environmental Geographic Information System (EGIS) into five categories (building, road, bare land, tree, and grassland) to regenerate the land cover map of the analysis target area. The land cover-specific surface temperature generation unit (212) generates the land cover-specific surface temperature of the analysis target area based on the reconstructed land cover map and the surface temperature data received from the second information receiving unit (204).

[0092]

[0093] Figure 5 is a diagram showing an example of three-dimensional building / terrain information and land cover information of the area to be analyzed.

[0094] Figure 5(a) is a drawing showing an example of 3D building information and terrain information, and Figure 5(b) is a drawing showing an example of land cover information. Referring to Figure 5, the x and y direction dimensions of the actual analysis target area are 1000 m and 1000 m, but the 3D building information and terrain information of the analysis target area were set to 2000 m, 2000 m, and 630 m in the x, y, and z directions, respectively, by setting boundary areas of 500 m in the x and y directions (east, west, south, and north directions) in accordance with COST Action 732 standards. The grid size was set to 10 m, 10 m, and 2 m in the x, y, and z directions, respectively, and the land cover information was set to the same drawing size and grid size as the 3D building / terrain information.

[0095] The land cover map in Fig. 5(b) is an example of modeling the area to be analyzed by reclassifying the land cover into five categories (building, road, bare land, tree, and grassland).

[0096]

[0097] The computational fluid dynamics analysis unit (218) sets the temperature data and wind data generated by the interpolation unit (210) as initial and boundary conditions for the modeled analysis target area, and sets the surface temperature generated by the surface temperature generation unit (212) by land cover as the surface temperature condition for the modeled analysis target area, and analyzes the temperature, wind, and sensible heat flux of the analysis target area through a computational fluid dynamics model (CFD).

[0098]

[0099] Computational Fluid Dynamics Analysis Unit (218) can analyze the temperature, wind, and sensible heat flux of the area to be analyzed using a CFD model based on the RANS model.

[0100] CFD models based on the RANS model solve the unsteady (time-dependent) Reynolds averaged Navier-Stokes (RANS) equations in a staggered grid system using the Finite Volume Method and the SIMPLE (Semi-Implicit Method for Pressure-Linked Equation) algorithm.

[0101] A CFD model based on a RANS model assumes a three-dimensional, non-hydrostatic, and Boussinesq air flow system and analyzes the wind field of the analysis area based on a k-ε turbulence closure scheme based on the Renormalization Group (RNG) theory. For example, the computational fluid dynamics analysis unit (218) can analyze the wind streamline, dimensionless average vorticity, vertical streamline, velocity field, vortex and recirculation area, stagnation-point height, and maximum downdraft in the analysis area through the CFD model based on a RANS model. In one embodiment, the computational fluid dynamics analysis unit (218) may be set to analyze the wind field for 3600 seconds at a time step of 0.5s.

[0102] The RANS model-based CFD model implements the wall boundary conditions proposed by Versteeg and Malalasekera to explain the effects of turbulent boundary layers near solid walls, such as buildings. The momentum and mass conservation equations and the thermodynamic energy equation of the RANS model-based CFD model can be expressed as Equations 1, 2, and 3 below, respectively.

[0103] [Mathematical Formula 1]

[0104]

[0105] [Mathematical Formula 2]

[0106]

[0107] [Mathematical Formula 3]

[0108]

[0109] Here, , , are respectively the i-th Cartesian coordinates (i th Cartesian coordinate)(i=1,2,3), i-th mean velocity component, fluctuation in the i-th mean velocity component th Represents the mean velocity component, t, , , represents time, deviation of pressure from the reference value, air density, and Kronecker Delta values, respectively.

[0110] ν and κ represent the kinematic viscosity and thermal diffusivity of air, respectively, and g, T, T′T0, T*, S h represents gravitational acceleration, average temperature, fluctuation from T, reference temperature, temperature deviation from the reference value, and source / sink term of heat, respectively.

[0111] The Reynolds stresses of Equation 1 can be parameterized as shown in Equations 4 and 5 below.

[0112] [Mathematical Formula 4]

[0113]

[0114] [Mathematical Formula 5]

[0115]

[0116] Here, ν t , κ t , k represent the turbulent diffusion coefficients of momentum, heat, and turbulent kinetic energy (TKE), respectively.

[0117] ν t It can be calculated as shown in Equation 6 below based on turbulent kinetic energy (TKE) and loss rate (ε).

[0118] [Mathematical Formula 6]

[0119]

[0120] Here, Cμ is an empirical constant.

[0121] In the RNG k-ε turbulence closure scheme, the prediction equations for TKE and ε can be expressed as Equations 7 and 8 below, respectively.

[0122] [Mathematical Formula 7]

[0123]

[0124] [Mathematical Formula 8]

[0125]

[0126] Here, R represents the strain rate term given as in Equations 9 and 10 below.

[0127] [Mathematical Formula 9]

[0128]

[0129] [Mathematical Formula 10]

[0130]

[0131] Here, C μ , C ε1 , C ε2 , σ k , σ ε , η0 and β0 are established empirical constants. For example, the empirical constants may be predetermined values ​​as shown in Equation 11 below.

[0132] [Mathematical Formula 11]

[0133]

[0134] The governing equation set of the CFD model can be numerically integrated for up to 3600 seconds at time intervals of 1 second.

[0135] The computational fluid dynamics analysis unit (218) uses the following mathematical formula 12 to analyze the sensible heat flux (Q h ) can be produced.

[0136] [Mathematical Formula 12]

[0137]

[0138] Here, ρ and C p are dry air density (=1.2 kgm³) respectively -3 ) and specific heat at constant pressure (=10¹² Jkg⁻¹) -1 K -1It represents ), and w′ and T′ represent the perturbations for the mean vertical wind component (w) and temperature (T), respectively. In the CFD model, the turbulent heat flux ( ′) is turbulent heat diffusivity (K h It is parameterized according to ) and the vertical temperature gradient (∂T / ∂z) ). Turbulent thermal diffusivity (K h ) is the turbulent momentum diffusivity (K m ) and turbulent Prandtl number (Pr t It can be defined as a ratio of , 0.9)( ).

[0139]

[0140] Below, the analysis results of the sensible heat flux analysis system of the present invention will be explained based on actual experimental results.

[0141] Figure 6 is a diagram showing the temperature, wind speed, and wind direction measured at the flux tower during the analysis period and calculated by the sensible heat flux analysis system of Figure 1 in chronological order.

[0142] Figure 6 shows 1) the actual temperature ((a)), wind speed ((b)), and wind direction ((c)) measured at the Flux Tower during the analysis period from September 15, 2014 to September 21, 2014, 2) the temperature, wind speed, and wind direction calculated by LDAPS, and 3) the temperature, wind speed, and wind direction simulated by the CFD model using the temperature, wind speed, and wind direction from LDAPS as initial and boundary conditions. Referring to Figure 6, it can be seen that compared to the actual measured values, the CFD model showed better simulation performance for wind speed than LDAPS, while the CFD model and LDAPS showed similar simulation performance for temperature and wind direction.

[0143]

[0144] Figure 7 is a diagram showing the actual measured values ​​from the flux tower during the analysis period and the sensible heat flux values ​​calculated by the sensible heat flux analysis system of Figure 2.

[0145] Referring to Figure 7, the sensible heat flux was calculated according to Equation 12 using the results calculated by the CFD model for the entire analysis period from September 1, 2014, to September 30, 2014. The sensible heat flux values ​​calculated by the CFD model were verified through statistical analysis by comparing them with the sensible heat flux values ​​measured at the actual flux tower. The verified statistical figures were found to be suitable for analyzing the spatial distribution of sensible heat flux. Referring to the scatter plot in Figure 7, it can be seen that the actual measured values ​​and the values ​​calculated by the CFD model are mostly clustered on the slope indicating the same value.

[0146]

[0147] Sensible heat flux value (Q) simulated through the CFD model h The sensible heat flux values ​​measured in the actual flux tower and the sensible heat flux values ​​were verified using statistical indices such as the root mean square error (RMSE), mean absolute error (MAE), index of agreement (IOA), and correlation coefficient (R) defined as follows.

[0148] The verification unit (212) is the sensible heat flux value (Q) calculated by the computational fluid dynamics analysis unit (218). hThe root mean square error (RMSE), mean absolute error (MAE), agreement index (IOA), or correlation coefficient (R) can be analyzed by comparing the actual measured sensible heat flux values ​​in the analysis target area with the ) and the sensible heat flux values ​​in the corresponding analysis target area. The verification unit (212) can verify the accuracy of the simulation results of the computational fluid dynamics analysis unit (218) by analyzing this. The root mean square error (RMSE), mean absolute error (MAE), agreement index (IOA), and correlation coefficient (R) can each be calculated through the following mathematical formulas 13 to 16.

[0149] [Mathematical Formula 13]

[0150]

[0151] [Mathematical Formula 14]

[0152]

[0153] [Mathematical Formula 15]

[0154]

[0155] [Mathematical Formula 16]

[0156]

[0157] Here, S is the sensible heat flux value (Q) calculated through the CFD model in the computational fluid dynamics analysis unit (218). h ), M is the actual measured sensible heat flux value (Q h Representing ), represents the average of the S values, and represents the average of the M values. The range of MAE and IOA is 0 to 1, and if MAE is 0 and IOA is 1, it means that the values ​​calculated by the model and the actual measured values ​​match perfectly.

[0158]

[0159] FIG. 8 is a table showing the results of calculating statistical figures to verify the sensible heat flux calculated by the sensible heat flux analysis system according to the present invention using sensible heat flux measured at a flux tower located within the analysis area during the entire analysis period (from September 1, 2014 to September 30, 2014).

[0160] FIG. 8 shows the results of calculating RMSE, MAE, IOA, and R based on the actual measured values ​​of FIG. 7 and the sensible heat flux values ​​calculated through a CFD model in the computational fluid dynamics analysis unit (218). Referring to FIG. 8, the RMSE, MAE, IOA, and R for the analysis period are 42.68 W m⁻¹, respectively. -2 , 26.95 W m -2 It can be confirmed that , 0.84 and 0.73.

[0161]

[0162] Figure 9 is a diagram showing the hourly average sensible heat flux and the standard deviation of each hour in chronological order during the analysis period, and Figure 10 is a diagram showing the hourly averaged normalized surface temperature during the analysis period.

[0163] FIG. 9 is a diagram illustrating the hourly average of the actual measured values ​​from the flux tower and the sensible heat flux values ​​calculated by the CFD model for the analysis period. Referring to FIG. 9, it can be seen that the sensible heat flux calculated by the CFD model in the computational fluid dynamics analysis unit (218) shows a daily variation similar to the actual measured sensible heat flux. In the case of the sensible heat flux calculated by the CFD model, it can be seen that the time when the daily maximum sensible heat flux occurs (13:00) is simulated to be the same as the actual measured value. However, in the case of the sensible heat flux calculated by the CFD model, it can be seen that the simulation was slightly under-simulated compared to the actual measured value between 10:00 and 14:00, and over-simulated between 17:00 and 20:00.

[0164] Figure 10 is a diagram comparing the normalized surface temperature measured at the Seoul Synoptic Observatory (ASOS), the analysis target area, with the surface temperature at the flux tower point analyzed by VUCM. Referring to Figure 10, it can be seen that the surface temperature calculated by VUCM is under-simulated from 07:00 to 14:00 and over-simulated from 16:00 to 20:00 compared to the actual measured surface temperature. In the case of the sensible heat flux analysis system using a CFD model according to the present invention, it can be confirmed that the sensible heat flux simulation results are sensitive to the surface temperature. That is, it can be seen that the sensible heat flux is under-simulated during the time when the surface temperature is under-simulated, and the sensible heat flux is over-simulated during the time when the surface temperature is over-simulated.

[0165]

[0166] Figure 11 is a diagram showing the spatial distribution of averaged sensible heat flux for the analysis period by land cover. Figure 11 shows the spatial distribution of numerically simulated sensible heat flux for the entire area, (b) the building area, (c) the road area, (d) the bare land area, (e) the grassland area, and (f) the tree area.

[0167] Referring to Fig. 11, it can be seen that the sensible heat flux calculated by the sensible heat flux analysis system using a CFD model exhibits distinct characteristics depending on the land cover. It can be observed that the calculated sensible heat flux is high in areas with buildings, roads, and bare land, while it is low in areas with trees and grasslands. In particular, it can be seen that downward sensible heat flux appears in areas with trees and grasslands.

[0168] Figure 12 is a diagram showing the spatial distribution of the standard deviation of sensible heat flux averaged over the analysis period, categorized by land cover. Figure 12 shows the spatial distribution of the standard deviation of sensible heat flux numerically simulated for the entire area, (b) the building area, (c) the road area, (d) the bare land area, (e) the grassland area, and (f) the tree area.

[0169] Referring to Figure 12, for the sensible heat flux calculated by the sensible heat flux analysis system using a CFD model, it can be seen that the spatial distribution of the standard deviation is high in areas with high sensible heat flux. When examined by land cover, it can be seen that the standard deviation is highest in building areas and lowest in grass areas.

[0170]

[0171] Figure 13 is a diagram showing the spatial distribution of hourly averaged sensible heat flux at 4:00, 9:00, 12:00, 14:00, 17:00, and 22:00 during the analysis period. Figure 13 shows the spatial distribution of hourly averaged sensible heat flux according to Local Solar Time (LST), where (a) shows the spatial distribution of average sensible heat flux at 04 LST, (b) at 09 LST, (c) at 12 LST, (d) at 14 LST, (e) at 17 LST, and (f) at 22 LST.

[0172] Referring to Figure 13, the hourly average sensible heat flux calculated by the sensible heat flux analysis system using a CFD model was found to be highest at 14:00 in the building, road, and bare land areas. The lowest sensible heat flux was calculated at 5:00 in the building area and at 4:00 in the road and bare land areas, while a downward trend in sensible heat flux was mainly observed in the afternoon in the tree and grassland areas. The upward sensible heat flux was highest at 9:00 in the tree area and at 12:00 in the grassland area, and the downward sensible heat flux was highest at 17:00 in the tree and grassland areas. The lowest sensible heat flux was found at 22:00 in the tree area and at 23:00 in the grassland area.

[0173]

[0174] Figure 14 is a diagram showing the range of magnitudes of temperature gradient, turbulent heat diffusion coefficient, and sensible heat flux by land cover, averaged over time during the analysis period of the analysis area.

[0175] Figure 14 shows a box plot of temperature gradients, (b) turbulent heat diffusivity, and (c) time-averaged sensible heat flux by land cover type.

[0176] Referring to Figure 14, for roads, buildings, and bare land, the average temperature gradient and turbulent heat diffusivity were higher during the day than at night, except immediately after sunrise. For wooded and grassland areas, the turbulent heat diffusivity was high during the day, but the temperature gradient was very low during some time periods of the day, which coincides with the times when sensible heat flux was low.

[0177] For building, bare land, and grassland areas, the turbulent heat diffusivity was relatively low at the time of highest sensible heat flux, but the contribution of the temperature gradient is analyzed to be significant. Road and tree areas are confirmed to have high turbulent heat diffusivity and temperature gradients.

[0178] During the night (19:00–6:00), spatial variation in sensible heat flux was found to be small, and differences between land covers were also minimal. However, during the day (7:00–18:00), significant spatial variation in sensible heat flux was observed even within the same land cover. This is because the difference between the distribution of the temperature gradient and the distribution of the turbulent heat diffusion coefficient is greater during the day than at night.

[0179] Sensible heat flux in building areas was highest during the day but was found to be lower at night than in road, bare land, and tree areas. The daily variation trend in road areas was found to be similar to that of building areas. However, most road areas located near buildings exhibited low sensible heat flux due to small temperature gradients caused by low turbulent heat diffusion coefficients and the influence of building shadows.

[0180] Sensible heat flux in bare areas was highest during nighttime hours, excluding 19:00, and was second only to built areas during the day. This is attributed to the fact that most bare areas within the analyzed region are located at a considerable distance from buildings. Similar to road areas, sensible heat flux was low in bare areas located near buildings. Sensible heat flux in open bare areas without surrounding buildings was similar to that of built areas.

[0181] The grassland area showed the smallest hourly variation. The tree area showed a maximum upward sensible heat flux at 9:00, a downward sensible heat flux from 12:00, and reached a maximum downward flux at 17:00.

[0182] Downward sensible heat flux was observed between 13:00 and 23:00 in most areas. It can be confirmed that even with the same land cover, sensible heat flux shows a significant difference depending on the surrounding environment.

[0183]

[0184] FIG. 15 is a flowchart illustrating a method for analyzing sensible heat flux using a computational fluid dynamics model according to one embodiment of the present invention.

[0185] Referring to FIG. 15, the first information receiving unit (202) receives air temperature data and wind data for the area to be analyzed (step S1510). In one embodiment, the first information receiving unit (202) can receive air temperature data and wind data from LDAPS (Local Data Assimilation and Prediction System) local forecasting system.

[0186] The second information receiving unit (204) receives land surface temperature data for the area to be analyzed (step S1520). In one embodiment, the second information receiving unit (204) can receive land surface temperature data from a VUCM (Vegetation Urban Canopy Model) system.

[0187] The geographic information receiving unit (208) receives topographic information and building information for the area to be analyzed from a geographic information system (GIS) (step S1530).

[0188] The environmental geographic information receiving unit (206) receives land cover information for the area to be analyzed from the Environmental Geographic Information Service (EGIS) (step S1540).

[0189] The modeling unit (216) models the area to be analyzed in three dimensions based on topographic information, building information, and land cover information for the area to be analyzed (step S1550).

[0190] In one embodiment, the modeling unit (216) can model the area to be analyzed as a grid of a predetermined size (e.g., 10m(x)×10m(y)×2m(z)) based on terrain information and building information.

[0191] The reclassification unit (214) can model the area to be analyzed by reclassifying land cover information received from the Environmental Geographic Information System (EGIS) into five categories (buildings, roads, bare land, trees and grassland areas). The land cover-specific surface temperature generation unit (212) generates the land cover-specific surface temperature of the area to be analyzed based on the reconstructed land cover map and the surface temperature data received from the second information receiving unit (204).

[0192] The interpolation unit (210) interpolates the temperature data and wind data received from the first information receiving unit (202) to generate interpolated temperature data and wind data. For example, the interpolation unit (210) can average the temperature and wind values ​​of the four grid points closest to the specific target area to be analyzed in the computational fluid dynamics model among the grid points where temperature and wind are analyzed in LDAPS, and interpolate the result to the temperature and wind values ​​of the specific target area to be analyzed in the computational fluid dynamics model.

[0193] The computational fluid dynamics analysis unit (218) sets the initial and boundary conditions of the area to be analyzed using the temperature and wind data generated by the interpolation unit (210), and sets the surface temperature data calculated by the surface temperature generation unit (212) by land cover as the surface temperature condition, and analyzes the temperature, wind, and sensible heat flux of the area to be analyzed through a computational fluid dynamics model (CFD) (step S1550).

[0194] In one embodiment, the computational fluid dynamics analysis unit (218) analyzes the spatial distribution of sensible heat flux based on the calculated sensible heat flux, and may analyze it by land cover of the area to be analyzed. For example, the computational fluid dynamics analysis unit (218) may analyze and display the spatial distribution of the averaged sensible heat flux by land cover as shown in FIG. 11, and may display the spatial distribution of the standard deviation of the averaged sensible heat flux by land cover as shown in FIG. 12. In addition, the computational fluid dynamics analysis unit (218) may analyze and display the spatial distribution of the hourly averaged sensible heat flux as shown in FIG. 13, and may analyze and display the hourly averaged temperature gradient, turbulent heat diffusion coefficient, and magnitude range of the sensible heat flux by land cover of the area to be analyzed as shown in FIG. 14.

[0195] In one embodiment, the verification unit (220) can verify the temperature and wind calculated by the computational fluid dynamics analysis unit (218) by comparing and analyzing them with the actual temperature and wind values ​​measured in the analysis target area. In another embodiment, the verification unit (220) verifies the temperature and wind calculated by LDAPS by comparing and analyzing them with the actual temperature and wind values ​​measured in the analysis target area, and if the temperature and wind calculated by LDAPS are within a preset error range as a result of the verification, the computational fluid dynamics analysis unit (218) can calculate the sensible heat flux.

[0196] The verification unit (220) can verify the accuracy of the computational fluid dynamics analysis unit (218) by comparing and analyzing the sensible heat flux calculated by the computational fluid dynamics analysis unit (218) with the sensible heat flux value actually measured in the analysis target area. For example, the verification unit (220) can verify the sensible heat flux value (Q) calculated by the computational fluid dynamics analysis unit (218). h Accuracy can be verified by analyzing the root mean square error (RMSE), mean absolute error (MAE), index of agreement (IOA), or correlation coefficient (R) by comparing the actual measured sensible heat flux values ​​in the analysis target area with the ) and the actual measured sensible heat flux values.

[0197] Although the present invention has been described above with reference to embodiments, the technical concept of the present invention is not limited to the above embodiments, and various methods for analyzing sensible heat flux using computational fluid dynamics models and systems for analyzing sensible heat flux using the same can be implemented within the scope of the technical concept of the present invention.

[0198] [Explanation of the symbol]

[0199] 110: Sensible heat flux analysis system 202: First information receiving unit

[0200] 204: Second Information Receiving Unit 206: Environmental Geographic Information Receiving Unit

[0201] 208 : Geographic Information Receiving Unit 210 : Interpolation Unit

[0202] 212: Surface temperature generation section by land cover 214: Reclassification section

[0203] 216 : Modeling Department 218 : Computational Fluid Dynamics Analysis Department

[0204] 220 : Verification Unit 222 : Memory

[0205] 224 : Visualization Section

Claims

1. A first information receiving unit that receives air temperature data and wind data for an analysis target area; A second information receiving unit that receives land surface temperature data for the above-mentioned analysis target area; A geographic information receiving unit that receives topographic information and building information regarding the analysis target area from a Geographic Information System (GIS); An environmental geographic information receiving unit that receives land cover information for the analysis target area from an Environmental Geographic Information Service (EGIS); A modeling unit that performs a three-dimensional modeling of the analysis target area based on topographic information, building information, and land cover information regarding the analysis target area; and A sensible heat flux analysis system using a computational fluid dynamics model comprising a computational fluid dynamics analysis unit that analyzes the temperature, wind, and sensible heat flux of the analysis target area through a computational fluid dynamics (CFD) model by setting initial and boundary conditions for the modeled analysis target area based on the received temperature data and wind data, and setting surface temperature conditions for the modeled analysis target area based on the received surface temperature data.

2. In paragraph 1, the first information receiving unit Sensible heat flux analysis system using a computational fluid dynamics model that receives temperature data and wind data for the analysis target area from LDAPS (Local Data Assimilation and Prediction System).

3. In paragraph 1, the second information receiving unit Sensible heat flux analysis system using a computational fluid dynamics model that receives surface temperature data for the analysis target area from a VUCM (Vegetation Urban Canopy Model) system.

4. In Paragraph 1, A sensible heat flux analysis system using a computational fluid dynamics model, further comprising a verification unit that compares and analyzes the temperature, wind, and sensible heat flux calculated by the above computational fluid dynamics analysis unit with the actual temperature, wind, and sensible heat flux values ​​measured in the above analysis target area.

5. In Paragraph 1, A sensible heat flux analysis system using a computational fluid dynamics model, further comprising a visualization unit that visualizes the modeled analysis target area in three dimensions in a virtual space and visualizes the spatial distribution of temperature, wind, or sensible heat flux calculated by the computational fluid dynamics analysis unit by adding it to the visualized analysis target area.

6. In paragraph 1, the above modeling part Sensible heat flux analysis system using a computational fluid dynamics model that models the target area of ​​analysis as a grid of a preset size (e.g., 10m(x)×10m(y)×2m(z)) based on terrain information and building information received from the above geographic information system.

7. In Paragraph 1, It further includes an interpolation unit that interpolates temperature data and wind data received from the first information receiving unit, The above interpolation unit generates interpolated temperature and wind data by averaging the values ​​of the four grid points closest to the target area of ​​the computational fluid dynamics model among the grid points where the temperature and wind data were calculated. The above computational fluid dynamics analysis unit is a sensible heat flux analysis system using a computational fluid dynamics model that sets temperature data and wind data generated by the above interpolation unit as initial and boundary conditions for the above-modeled analysis target area.

8. In Paragraph 1, A reclassification unit that reclassifies land cover information received from the above-mentioned environmental geographic information system into five categories (building, road, bare land, tree, and grassland) and reconstructs a land cover map of the above-mentioned analysis target area; and It further includes a surface temperature generation unit for each land cover that generates a surface temperature for each land cover of the analysis target area based on the reconstructed land cover map and surface temperature data received from the second information receiving unit. The above computational fluid dynamics analysis unit is a sensible heat flux analysis system using a computational fluid dynamics model that sets the surface temperature for each land cover generated by the above land cover-specific surface temperature generation unit as the surface temperature condition for the above-modeled analysis target area.

9. In paragraph 1, the computational fluid dynamics analysis unit Sensible heat flux analysis system using a computational fluid dynamics model that calculates sensible heat flux using the following mathematical formula 12. [Mathematical Formula 12] Here, Q h ε is the sensible heat flux, ρ is the dry air density, C p ε is the specific heat at constant pressure, w′ is the mean vertical wind component (w), and T′ are perturbations with respect to temperature (T).

10. A step in which the first information receiving unit receives air temperature data and wind data for the analysis target area; A step in which a second information receiving unit receives land surface temperature data for the analysis target area; A step in which a geographic information receiving unit receives topographic information and building information regarding the analysis target area from a Geographic Information System (GIS); A step in which an environmental geographic information receiving unit receives land cover information for the analysis target area from an Environmental Geographic Information Service (EGIS); A step in which a modeling unit performs a three-dimensional modeling of the analysis target area based on topographic information, building information, and land cover information regarding the analysis target area; and A method for analyzing sensible heat flux using a computational fluid dynamics model, comprising the step of a computational fluid dynamics analysis unit setting initial and boundary conditions for the modeled analysis target area based on the received temperature data and wind data, and setting surface temperature conditions for the modeled analysis target area based on the received surface temperature data, and analyzing the temperature, wind, and sensible heat flux of the analysis target area through a computational fluid dynamics (CFD) model.

11. In Paragraph 10, A method for analyzing sensible heat flux using a computational fluid dynamics model, wherein the above-mentioned computational fluid dynamics analysis unit analyzes the spatial distribution of sensible heat flux based on the calculated sensible heat flux, and further includes the step of analyzing by land cover of the above-mentioned analysis target area.

12. In Paragraph 10, A step in which the verification unit verifies the temperature and wind calculated by the computational fluid dynamics analysis unit by comparing and analyzing them with the actual temperature and wind values ​​measured in the analysis target area; The step of the above computational fluid dynamics analysis unit calculating sensible heat flux; and A method for analyzing sensible heat flux using a computational fluid dynamics model, comprising the step of the verification unit verifying the sensible heat flux calculated by the computational fluid dynamics analysis unit by comparing it with the sensible heat flux value actually measured in the analysis target area.

13. In Clause 10, the step of three-dimensionally modeling the analysis target area above A method for analyzing sensible heat flux using a computational fluid dynamics model that models the area to be analyzed as a grid of a predetermined size (e.g., 10m(x)×10m(y)×2m(z)) based on the above terrain information and building information.

14. In Clause 10, the step of setting initial and boundary conditions based on the above temperature data and wind data A step of interpolating temperature data and wind data received from the first information receiving unit by an interpolation unit; and The above computational fluid dynamics analysis unit includes the step of setting temperature data and wind data generated by the above interpolation unit as initial and boundary conditions for the above-modeled analysis target area, The above interpolation step is a method for analyzing sensible heat flux using a computational fluid dynamics model, wherein the interpolation step generates interpolated temperature data and wind data by averaging the values ​​of the four grid points closest to the target area of ​​the computational fluid dynamics model among the grid points where the temperature data and wind data are calculated.

15. In paragraph 10, the step of setting surface temperature conditions for the modeled analysis target area based on the received surface temperature data A step in which a reclassification unit reclassifies land cover information received from the above-mentioned environmental geographic information system into five categories (building, road, bare land, tree, and grassland) and reconstructs a land cover map of the above-mentioned analysis target area; A step in which a surface temperature generation unit by land cover generates a surface temperature by land cover of the analysis target area based on the reconstructed land cover map and surface temperature data received from the second information receiving unit; and A method for analyzing sensible heat flux using a computational fluid dynamics model, comprising the step of the above computational fluid dynamics analysis unit setting the surface temperature for each land cover generated by the above land cover-specific surface temperature generation unit as the surface temperature condition for the above-modeled analysis target area.

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