JP2009075885A - Method and system for simulating heat transfer - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To easily and accurately simulate the transfer of heat due to wind. <P>SOLUTION: Digital image data acquired by imaging a partial region on the earth from a communication satellite are input, and the surface temperature distribution data of the partial region are input, and the three-dimensional data of a building included in the partial region are created by using the digital image data, and wind amount and wind direction at the position of the building are designated, and the transfer of heat due to the wind amount and wind direction designated in the designation step in the periphery of the three-dimensional data is simulated by using the input surface temperature distribution data as boundary conditions. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a technique for simulating heat transfer.

  In recent years, the heat island phenomenon caused by global warming has become increasingly problematic. This can be seen at a glance by looking at the transition of the distribution of high-temperature areas near Tokyo (Fig. 7, provided by the Ministry of the Environment). The range in which the total temperature exceeds 30 degrees and close to 400 hours is overwhelmingly larger in 1999 (right side) than in 1981 (left side). For example, compared with 1930, natural coverage (rivers, trees and vegetation) in Tokyo decreased by 43.6%, pavement area increased 11 times, and building area increased 3.2 times. As a result, the average temperature has increased by about 2 ° C.

  Various proposals have been made in order to live a little comfortably under such circumstances, but in particular, it is more energy efficient to let the cool air that has accumulated around parks, rivers, etc., flow into the residential area by wind. It is considered effective. In order to obtain maximum comfort with minimum energy, that is, to live cool in the summer without relying on a cooler, the creation of a city where naturally generated cold air flows in the residential area by riding the wind, especially the realization of an environmentally symbiotic housing complex Is emphasized.

JP 2007-104972 A

  However, in the conventional measures, there has been no clear analysis on the actual size and shape of the building, the arrangement, the amount and arrangement of trees, the air volume and the wind direction, and the heat transfer in and around the building.

  An object of this invention is to analyze the heat transfer in consideration of the influence of a wind and the influence of surrounding environment.

In order to achieve the above object, the method according to the present invention comprises:
A first input step of inputting digital image data obtained by imaging a part of the earth from a communication satellite;
A second input step for inputting surface temperature distribution data of the partial area;
Using the digital image data, a generating step for generating three-dimensional data of buildings included in the partial area;
A designation step for designating an air volume and a wind direction at the position of the building;
A display step of simulating and displaying the movement of heat by the air volume and the wind direction specified in the specifying step around the three-dimensional data, using the surface temperature distribution data input in the second input step as a boundary condition;
It is characterized by including.

In order to achieve the above object, a system according to the present invention provides:
First input means for inputting digital image data obtained by imaging a part of the earth from a communication satellite;
Second input means for inputting surface temperature distribution data of the partial area;
Generating means for generating three-dimensional data of a building included in the partial area using the digital image data;
A designation means for designating an air volume and a wind direction at the position of the building;
Display means for simulating the movement of heat by the air volume and the wind direction designated by the designation means around the three-dimensional data, using the surface temperature distribution data inputted by the second input means as a boundary condition, and displaying in three dimensions ,
It is characterized by including.

  According to the present invention, heat transfer due to wind can be simulated easily and accurately.

  Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the drawings. However, the relative arrangement of components, the display screen, and the like described in this embodiment are not intended to limit the scope of the present invention only to those unless otherwise specified.

<System configuration>
A system for realizing the method according to the present invention will be described. FIG. 1 is a diagram showing a schematic configuration of a heat transfer simulation system. The heat transfer simulation system 100 is a system configured by installing an OS (basic software) and several application programs on a general-purpose computer.

  As shown in FIG. 1, the system 100 includes a CPU (central processing unit) 101, a ROM (read only memory) 102, a RAM (random access memory) 103, an HD (hard disk) 104, and an I / O (input / output interface). An input device 106 such as a mouse and a keyboard and a display 107 are connected to a computer main body having 105.

  The CPU 101 is an arithmetic / control processor that controls the entire system 110. The ROM 102 is a non-volatile memory that stores programs executed by the CPU 101, fixed values, and the like. A RAM 103 is a volatile memory for temporarily storing data and programs, and an HD (hard disk) 104 is a storage medium that stores an OS executed by the system 100 and various application programs. An input / output interface (I / O) 105 is an interface for inputting / outputting image data between the computer main body and its peripheral devices, and the CPU 101 receives an input device 106 and a display 107 via the I / O. Exchange data with.

  In the present system 100, the RAM 103 performs a heat transfer simulation process in addition to a program execution area 103a for temporarily storing a simulation program executed by the CPU 101, a sanitary image data storage area 103b, a tie point data storage area 103c, a GCP (ground control). Point) A data storage area 103d, a digital elevation data storage area 103e, a surface temperature data storage area 103f, a wind direction / air volume data storage area 103g, and the like are provided.

  Here, the tie point data is data indicating corresponding points of a plurality of satellite image data. GCP data is data of a target (ground reference point) whose absolute coordinates (latitude, longitude, altitude) are known and whose image position can be specified also in image data. Location data is included. The digital elevation data is digital elevation data for each mesh that is necessary when creating a flood hazard map.

  In the system 100, the HD 104 stores various data (satellite image data and the like) developed in the RAM 103 in addition to the three-dimensional data generation module 104a and the heat transfer simulation module 104b.

<Flow of heat transfer simulation>
Next, the flow of the heat transfer simulation realized by this system will be described with reference to FIG.

  First, in step S201, DEM (Digital Elevation Model) and DSM (digital Structure Model) are calculated from the satellite image (stereo pair image), and 3D polygons such as buildings and plants are generated.

  Next, in step S202, a land use classification map is generated using NDVI (Normalized Difference Vegetation Index). NDVI is the most widely used vegetation index in the world and can be derived using the following formula.

NDVI = (NIR-RED) / (NIR + RED)
Here, NIR is the near infrared region, and RED is the red reflectance in the visible region. Plants have low red reflectance and high near-infrared reflectance. When the amount of plants is large or healthy, the difference in reflectance between the near infrared region and red increases. NDVI (an integer value from 0 to 255) is a normalization index using this. That is, it is possible to accurately determine whether the structure is an artificial structure or a vegetation distribution based on the determination of NDVI.

  Specifically, the CPU 101 obtains the value of NDVI and compares it with a predetermined threshold according to the season and place, for example, divided into 4 to 6 classifications, and the land use classification map (the so-called supervised land use interpretation classification). Data).

  In step S203, a surface temperature distribution is generated from the satellite image data. At this time, a detailed mesh (for example, 3 m × 3 m) is obtained in order to correspond to the boundary condition of the heat transfer simulation calculation. At this time, although it is determined to be an artificial structure in the land use classification, the part classified as a cool spot, such as a tree, becomes cold due to some temporary cause, as a normal building that is not a cool spot Reclassify. For an area that includes a building that has been erroneously determined as a cool spot (the temperature distribution is lower than that for a special reason), correct it by giving an average temperature distribution around the area, and then simulate the entire area. At the same time.

  In other words, the condition of the cool spot needs to satisfy two conditions that the surface temperature is a distribution sufficiently lower than the surroundings and that the land use classification of the NDVI determination is a tree.

  Next, in step S204, a wind direction and a wind speed are calculated from weather data. In step S205, the surface temperature of step S203 on the surface of the 3D polygon generated in step S201 is used as a boundary condition to simulate how the surface heat is moved by the wind obtained in step S204, and the result is imaged. indicate. FIG. 3A shows an example of heat distribution display around a building (housing complex) as viewed obliquely from above, and the black dots shown like sand are tracers representing the flow of wind. This figure shows the movement of the surface temperature distribution when a south wind of 3.2 m blows from the right to the left of the screen. Actually, it is possible to grasp the movement of heat more clearly by displaying it as a moving image instead of a still image. FIG. 3B shows an example of display display showing the heat distribution around the building as viewed from the side. If FIG.3 (b) is looked at, the cold air (blue part) in a river or a green field will see a mode that it flows on the building according to the direction and speed of a wind, and descends to the neighborhood of a housing complex.

  The unsteady incompressible Navier-Stokes equation is used as the fluid equation of motion. This is combined with the law of conservation of mass and Poisson's equation of pressure to make the governing equation. For discretization of these equations, a multi-directional difference method is adopted. Third-order upwind differences are used for nonlinear terms, and second-order accuracy center differences are used for spatial differential terms other than nonlinear terms.

Crank-Nicolson quadratic precision implicit method is used for time integration of Navier-Stokes equations. These equations are solved using the SOR method and Multi-grid method at each time step. The pressure is obtained by solving the Poisson equation, and high speed and high accuracy are realized by adopting the Multi-grid method.
The governing equation is a continuity equation and a three-dimensional incompressible Navier-Stokes equation.

The symbols are explained below (unit system must be unified).
u velocity vector
T temperature [K]
T ₀ Reference temperature [K]
t hours
p (upper bar) Pressure, but the following relationship.
p ₀ a reference density corresponding to a reference pressure ρ ₀ p ₀
Pressure fluctuation from p 'p ₀

ν Kinematic viscosity β Fluid expansion coefficient ΔT Temperature difference [K]
K external force vector
g Gravity acceleration α Temperature propagation rate (thermal diffusivity)
Q Calorific value ρ density per unit volume
Cp specific heat
k Thermal conductivity

  Nagare has been developed mainly to solve the Navier-Stokes equation correctly and physically, and only the physical parameters are set at the time of calculation. An orthogonal equidistant grid is adopted as the calculation grid. By using orthogonal equidistant grids, the flow field can be set very easily, and the distribution of grid points is uniform throughout the calculation area, so there is little influence of local numerical diffusion due to the grid. .

  For visualization, fluid visualization software Clef3D is used. Visualize physical quantities (velocity, pressure, density, temperature, vorticity in the X, Y, and Z directions) calculated at each lattice point analyzed by Nagare. Visualization of analysis results is a very effective means for observing phenomena.

<Digital elevation data extraction process>
Next, the digital elevation data extraction process in step S201 will be described with reference to the flowchart of FIG. First, in step S301, satellite image data is input. The satellite image data input in step S301 is, for example, image data remotely sensed by the QuickBird satellite, and has been subjected to radiation correction and sensor correction. Here, the radiation correction is a correction for relative radiation reaction between sensor elements, compensation for non-reactive detection sensor elements, and absolute radiation measurement. The sensor correction is correction in consideration of the sensor internal structure, optical distortion, scanning distortion, and the like.

  The state of remote sensing by satellite is shown in FIG. An imaging satellite such as a QuickBird satellite remotely senses the ground surface 402 with a line sensor while moving on the satellite orbit 401 in the direction of the arrow at a speed of about 8 km / s. The line sensor mounted on the satellite projects the electromagnetic wave received from the ground surface 402 onto the image plane 403 and stores it as digital data. The digital data subjected to radiation correction and sensor correction is input in step S301. In the case of the QuickBird satellite, the spatial resolution of the remotely sensed image data is about 61 cm (directly below) to 72 cm (25 degrees off nadir angle), and it is possible to capture an image of the ground surface about 16.5 km square at a time. it can.

  In step S301, at least two types of image data thus remotely sensed are prepared. They are image data obtained by remotely sensing the same area from sensors of satellites at different positions or from different sensors provided on one satellite. The relationship is shown in FIG. Reference numerals 501 and 502 denote satellite orbits, and reference numeral 503 denotes the ground surface. Reference numeral 504 denotes an imaging target area. When using the QuickBird satellite, it is possible to image the same area from different orbits with one satellite, but the same area may be captured from different satellites. The QuickBird satellite is a satellite that orbits from north to south, and its orbit is shifted from east to west. In addition, commercial satellites such as QuickBird satellites have a function of changing the direction of the sensor while the satellite moves from one north to the other and capturing images of the same region from two directions almost simultaneously to obtain a stereo pair image. . When this function is used, reference numerals 501 and 502 in FIG. 6 indicate satellite positions at different times on the same orbit.

  In step S301, not only image data but also image support data (ISD) is input. The image support data includes at least information on the position and time of the satellite that captured the image. Image support data includes, for example, attitude data (first data point time, points, point interval and attitude information), satellite orbital calendar data (first data point time, points, point interval and satellite orbit information), geometric Correction data (parameters for photogrammetry of a virtual camera model that models satellite sensors and optical systems: focal length, central axis coordinates, etc.), image metadata (product level, coordinate values of four corners of the image (latitude, longitude) ), Main attributes such as image products including map projection information, image acquisition time), RPC (RAPID POSITIONING CAPABILITY EXTENSION FORMAT) data (coordinate values of four corners of space and coordinate values of four corners of image) Mathematical data).

  Since the satellite image data input in step S301 has been subjected to sensor correction, distortion caused by the mechanism of the satellite / sensor is corrected, but distortion due to movement of the sensor and rotation of the earth is included. Therefore, in step S302, such distortion is corrected. This correction is referred to as geometric correction in a narrow sense. In step S302, the conversion coefficient from the pixel coordinates to the satellite coordinates is further determined geometrically using geometric characteristic parameters such as the focal length and viewing angle of the satellite sensor.

  Next, GCP is set in step S303, and aerial triangulation is performed in step S304. For the GCP, a triangular point, a reference point, or coordinates of points obtained by surveying, altitude, or the like is used. In Japan, since a topographic map of 1/5000 is easily available, latitude and longitude such as an intersection and approximate altitude can be read and used as GCP.

  Aerial triangulation refers to the relationship between the coordinates on the planar image captured by the satellite line array sensor and the ground coordinate system. The sensor center, the GCP coordinates on the image, and the GCP position on the ground are in a straight line. A survey that is analyzed using collinear conditions.

  If the coordinates (x, y) of the image of the object are known in the satellite image data input in step S301, two primary relational expressions indicating the relationship between the longitude X, the latitude Y, and the altitude Z can be obtained.

  Next, in step S305, tie point setting and stereo matching are performed. A tie point is a corresponding point representing the same ground object (intersection, characteristic building, etc.) in a plurality of satellite image data. Stereo matching is a process of comparing the coordinates of tie points in two satellite image data.

  The tie point may be set by the operator by comparing two images, but is basically obtained automatically by an image processing program. It is possible to extract small window areas (for example, about 7 × 7 pixels) from the two pieces of image data, search for areas having the highest similarity, and define them as tie points. By setting tie points in this way, two image data can be associated with each other.

  When the coordinates of the same tie point in the two image data are derived by stereo matching, the altitude can be calculated from the displacement of the position. Due to the two satellite images, the position of the corresponding ground object is displaced by the difference from the reference altitude (parallax) between the acquired stereo images for the same region. By measuring this displacement, the difference from the reference elevation, that is, the elevation can be obtained.

  In step S305, when the latitude, longitude, and altitude are obtained for all the searched tie points, the process proceeds to step S306.

  In step S306, digital elevation data of all pixels on the image data is obtained from a plurality of tie points and GCP coordinates using a successive approximation method such as a least square method. At this time, since the mean square error is also obtained, the accuracy evaluation of the obtained elevation value can be performed simultaneously.

  Next, in step S307, orthogeometric transformation is performed on the satellite image data using the accurate digital elevation data extracted in step S306. The ortho-geometric transformation is a process for converting the central projection image into a parallel projection image. The data subjected to the ortho-geometric transformation becomes the three-dimensional data of the urban area for the heat transfer simulation.

  Here, remote sensing from the QuickBird satellite has been described, but the present invention is not limited to this. That is, orbit data and the like can be used, and any satellite capable of extracting digital elevation data from the image data can be used.

  As described above, if digital elevation data is extracted from satellite image data, three-dimensional data (not only buildings but also trees) of a desired area can be obtained easily and accurately. By using, heat distribution and movement can be accurately simulated.

  As a result, based on the heat transfer simulation results, as a hot spot countermeasure, parking lots are arranged in the building to reduce the outdoor asphalt surface, utilize existing trees, and implement rooftop greening. Can do.

It is a figure showing a schematic structure of a simulation system concerning an embodiment of the present invention. It is a flowchart which shows the simulation process which concerns on embodiment of this invention. It is a figure which shows the result of the simulation process which concerns on embodiment of this invention. It is a flowchart which shows the digital elevation data extraction process which concerns on embodiment of this invention. It is a figure explaining the remote sensing by a satellite. It is a figure explaining imaging of a plurality of images from a sensor in a different position. It is a figure for demonstrating the background of this invention.

Claims (3)

A first input step of inputting digital image data obtained by imaging a part of the earth from a communication satellite;
A second input step for inputting surface temperature distribution data of the partial area;
Using the digital image data, a generating step for generating three-dimensional data of buildings included in the partial area;
A designation step for designating an air volume and a wind direction at the position of the building;
A display step of simulating and displaying the movement of heat by the air volume and the wind direction specified in the specifying step around the three-dimensional data, using the surface temperature distribution data input in the second input step as a boundary condition;
A heat transfer simulation method comprising:   The heat transfer simulation method according to claim 1, wherein the display step displays the virtually calculated heat transfer in a three-dimensional manner. First input means for inputting digital image data obtained by imaging a part of the earth from a communication satellite;
Second input means for inputting surface temperature distribution data of the partial area;
Generating means for generating three-dimensional data of a building included in the partial area using the digital image data;
A designation means for designating an air volume and a wind direction at the position of the building;
Display means for simulating the movement of heat by the air volume and the wind direction designated by the designation means around the three-dimensional data, using the surface temperature distribution data inputted by the second input means as a boundary condition, and displaying in three dimensions ,
A heat transfer simulation system comprising: