WO2020209432A1

WO2020209432A1 - Rainfall simulator calibration system, and rainfall simulator calibration method

Info

Publication number: WO2020209432A1
Application number: PCT/KR2019/004749
Authority: WO
Inventors: 김학수; 고택조; 예성제; 곽용석
Original assignee: 대한민국(행정안전부 국립재난안전연구원장)
Priority date: 2019-04-12
Filing date: 2019-04-19
Publication date: 2020-10-15

Abstract

The present invention relates to a rainfall simulator calibration system, and a rainfall simulator calibration method. A rainfall simulator calibration system according to an embodiment of the present invention may comprise: a rainfall simulator installed at a designated height in an experiment room to provide rainfall downwards; a validation/calibration automated apparatus that collects provided rainfall and includes a measurement device for, when the rainfall is collected, measuring the rainfall intensity and the rainfall distribution of the rainfall; and a control device for calibrating a state of rainfall provided by the rainfall simulator, on the basis of a result of analysis of data related to rainfall intensity and rainfall distribution provided by the validation/calibration automated apparatus.

Description

Rainfall simulator correction system and rainfall simulator correction method

The present invention relates to a rainfall simulator correction system and a rainfall simulator correction method, and more specifically, a rainfall simulator to establish a method of calibrating a large-scale laboratory rainfall simulator through an automatic rainfall collection system in order to evaluate the reliability and accuracy of the rainfall simulator. It relates to a correction system and a rainfall simulator correction method.

Rainfall runoff is an important hydrogeomorphological process that affects several types of environmental factors including soil, topography, vegetation and natural resources within reservoirs. Runoff studies often rely on properties of natural rainfall such as variability in intensity, spatiotemporal distribution, particle size distribution, particle velocity and kinetic energy. Rainfall simulation is a widely used method for hydrological malformation studies, including runoff, flooding, soil properties in watersheds, and other research areas to reproduce the characteristics and processes of natural rainfall.

Rainfall simulator (or rainfall simulator) (RS) is a technology widely used as a research tool to calculate runoff, infiltration and erosion data in field and laboratory-based studies in hydrology and topography courses. 2018.01.25. Announcement) Real-time disaster occurrence time prediction system and method, Korean Patent Publication No. 10-1891237 (2018.09.28. Announcement) Rain Simulator Inspection and Correction Automation Facility, Korean Patent Publication No. 10-1820946 (2018.03. .09. Announcement) A precipitation simulation method using particle-based SPH and a rainfall simulation platform configured to execute it, as known in Korean Patent Publication No. 10-0960850 (2010.06.07. Announcement), as known in the excellent measurement information transmission system. A simulator is being developed. The main purpose of the rainfall simulator is to provide accurate and quick generation of various rainfall systems by controlling rainfall intensity and duration, as well as to provide reproducible rainfall data collection. Data collected from rainfall simulation experiments provide the basic information necessary to understand the dynamic behavior of runoff generation, infiltration and soil erosion. This information focuses on how surface properties such as slopes, soil properties, vegetation cover, and topography within the reservoir affect the processes mentioned above.

There is no universal rainfall simulator applicable to all study conditions. Many types of rainfall simulators have been developed to study various components of hydrological and topographic processes through artificial reproduction of rainfall. Rainfall simulators can be classified into two main groups depending on how the droplet formation mechanisms generate rain. The first group consists of unheated drop forming simulators in which raindrops are formed at the end of a needle-like conduit (i.e., a drop former) connected to a set of pipes, or directly from a hole in the bottom of the tank. The drop former starts dropping a drop with a speed of 0. The second group consists of pressurized nozzle simulators, where raindrops are produced continuously at significant speeds by single or multiple nozzles.

The pressureless fall formation simulator has limitations in reproducing natural rainfall in terms of the size and energy characteristics of the fall. Simulators of this type generally provide evenly distributed raindrops in terms of size. However, unless various sizes or dimensions of the drop former are used, they do not produce a drop distribution. Another drawback of the pressureless drop formation simulator is its limited application to soil erosion studies, especially in field experiments. These simulators are generally impractical for field use because raindrops need a sufficient height from the impact surface to reach the terminal velocity corresponding to the rainfall kinetic energy.

In contrast, the pressurized nozzle simulator produces a wider particle size distribution. Different nozzle types are available for rainfall simulation, and the drop size distribution is determined by the nozzle's shape characteristics and discharge. This type of simulator offers a variety of rainfall intensities. Rainfall intensity can vary depending on nozzle orifice, pump pressure, nozzle spacing and nozzle movement. This simulator provides moderate velocities and kinetic energy values for raindrops with low drop heights, but the droplet velocity is generally exaggerated because the nozzle's raindrops have an initial velocity greater than zero due to pump pressure. Continuous spraying from the nozzle can result in higher rainfall intensity than natural rainfall. Rotating disks, rotating booms, rotating rods or solenoid control simulators can be used as solutions to reduce exaggerated rainfall intensity. A rotating or rotating rod is the simplest way to closely simulate natural rainfall in terms of rainfall intensity. In addition, a box around the nozzle is introduced to control the sprayed raindrops, reducing the surface exposure time to artificial rainfall spray, thereby reducing the rainfall intensity compared to natural rainfall.

There are also indoor and outdoor rainfall simulators depending on mobility. Small, portable in-situ rainfall simulators can be useful because of their easy and rapid transport and implementation, but their small size typically limits their application to accurately assessing net runoff responses, including heterogeneity of surface properties at plot scale. On the other hand, the laboratory rainfall simulator was used to overcome the shortcomings of the small and portable field rainfall simulator. The laboratory rainfall simulator prevents the destructive effects of wind speed, temperature and humidity on the experiment.

Recent research has focused more on the assessment of the impact of changes in surface properties due to fire, agriculture, urbanization, or other disturbances on the hydrological cycle. This study includes extensive experiments evaluating the properties of the land surface on a hill-to-catch scale to better understand the interaction between surface properties and runoff generation. This requires a laboratory rainfall simulator that is large enough to produce high rainfall intensities and is suitable for capturing the heterogeneity of surface properties on the experimental surface. These measurements in large laboratory rainfall simulators can be used to increase the accuracy of effluents and hydrological models. Further research using large laboratory rainfall simulators is required to evaluate the effect of changes in surface properties on the hydrological cycle, but simple, small rainfall simulators that produce rain in small areas (less than 5 m2 and usually less than 1 m2) are widely available. Is being used. This is because the rainfall simulator covering a relatively large area (about 100 m2 or more) is expensive to install and is not easy to operate.

For accurate runoff experiments, rainfall simulators must adequately simulate natural rainfall and control rainfall intensity and duration. In addition to desirable properties to accurately simulate rainfall, rainfall simulators require other requirements such as efficiency, simplicity and economics. However, it is difficult to meet all the requirements for a rainfall simulator because there is a trade-off between desirable characteristics such as size, cost, mobility, area coverage, ease of use and operation. Desirable characteristics of rainfall simulators depend primarily on the research objectives and rainfall characteristics required for specific research conditions.

For large-scale indoor rainfall simulators, the main requirements for runoff experiments are reliability and accuracy. Reliability is related to the repeatability of storm events, and accuracy is related to the spatial uniformity of rainfall over the entire test plan. When the desired intensity and duration of a storm is controlled by a computer-driven operating system, reproducible rainfall patterns can be more reliably provided. This eliminates the problem associated with human error in changing storm patterns. Stability can also be improved by implementing appropriate instrumentation that accurately monitors storm events in the test plot. Accuracy is assessed by the uniformity of the rainfall distribution in the test plot. The accuracy can be increased, or at least set high, when selecting the appropriate spray nozzle type and placing the nozzles in series at appropriate intervals to avoid overlapping raindrops. In addition, the physical movement of the nozzle across the experimental plot can have a notable effect on the uniformity of the rainfall distribution.

In general, rainfall depth and spatial distribution are manually measured with collection vessels evenly distributed over the entire experimental design in order to provide quantitative information on the reproducibility and homogeneity of rainfall. During the experiment, the mass of water collected from each container is converted to the hourly rainfall depth and then the average simulated rainfall intensity is calculated. The spatial homogeneity of a specific rainfall event is mainly evaluated using the Christiansen Uniformity Coefficient (CuC). This type of experiment is repeated with the same intensity for rainfall and rainfall.

While these traditional measurements are widely used for small rainfall simulations, manual procedures are inefficient in terms of the time and human resources required to cover a large area, resulting in a relatively large experimental area (100 m2).Above). The rainfall intensity and spatial distribution are controlled by a combination of parameters of the rainfall simulator system such as the nozzle's orifice diameter, the pressure at the nozzle, and the nozzle motion. (For example, the rotational speed of the bar and the delay time at the end of each vibration) The larger the rainfall simulator, the more system variables in the rainfall simulator, and the rational uniformity of the rainfall distribution because the number of combinations of the rainfall simulator system variables increases. It becomes more difficult to hit the target intensity during a specified period of sex. Also, conventional measurements based on manual procedures have their own errors due to human error. These measurement errors change randomly under the same conditions as the system variables of the rainfall simulator. If errors in rainfall intensities generated by rainfall simulators are not taken into account before they are used in the analysis, researchers may draw erroneous conclusions regarding research objectives such as penetration, pavement effects in urbanized areas, and storage capacity of reservoirs.

In addition, various rainfall simulators have been calibrated in the experimental area, often not taking into account problems such as the correlation of system variables and rainfall intensity and uniform distribution of rainfall simulators. In particular, in the case of a large-scale rainfall simulator, attention is paid to grasping the relationship between the system variables of the rainfall simulator and the rainfall intensity and uniformity distribution. There are increasing problems associated with the use of large rainfall simulators for fixed and known strength, and fluid topography studies. In order to obtain rainfall intensity and rainfall distribution in a large-scale test area, the need for additional equipment has been raised to overcome the limitation due to manual measurement.

An object of the present invention is to provide a rainfall simulator correction system and a rainfall simulator correction method to establish a method of calibrating a large-scale laboratory rainfall simulator through an automatic rainfall collection system in order to evaluate the reliability and accuracy of the rainfall simulator. .

In addition, an embodiment of the present invention implements an automatic rainfall collection system (ARCS) to identify a functional relationship between system variables of a rainfall simulator and rainfall intensity and uniform distribution (i.e., motion model), and the driving model is hydrological and topographic Rainfall simulator and rainfall simulator calibration method that can be used as a guide for intuitively selecting an appropriate range of system variables for rainfall simulators to generate specific rainfall intensities and uniformity for simulated rainfall in laboratory-based studies of the rainfall simulator There is another purpose to provide.

The rainfall simulator correction system according to an embodiment of the present invention is a rainfall simulator installed at a designated height of a laboratory to provide rainfall downward, collects the provided rainfall, and when collecting the rainfall, the rainfall of the rainfall An automated calibration device including a measurement device for measuring intensity and rainfall distribution, and the rainfall provided by the rainfall simulator based on the analysis result of the rainfall intensity and data related to the rainfall distribution provided by the automated calibration device It includes a control device to correct the state of.

The calibration automation device comprises: a container forming a space for collecting the rainfall, a first measuring device in which a plurality of is installed by forming a matrix for measuring the rainfall distribution in the space of the container, and the rainfall It may include a second measuring device provided at an outlet for discharging the rainfall collected from the container for measuring the intensity.

The first measuring device may include a conductive rainfall meter, and the second measuring device may include an ultrasonic flow meter.

The conductive rainfall meter may be installed while being fixed to a designated height from the bottom surface of the space.

The control device may correct the condition of the rainfall based on a comparison result of the total rainfall intensity (Itotal) measured by the plurality of first measuring devices and the average rainfall intensity (Iaverrage) averaged the total rainfall intensity. have.

The control device may derive the analysis result by applying a Christiansen uniform distribution coefficient that calculates the rainfall distribution.

The control device configures the rainfall simulator to provide system variables including nozzle pressure (NP), rotation speed (OV), and delay time (TD) of the nozzle providing the rainfall, and a function between the rainfall intensity and the rainfall distribution. The rainfall state can be corrected by the analysis result derived based on the relationship.

The rainfall simulator includes a control motor for controlling the nozzle of the rainfall, an injection angle and a waiting time, and a pressure boosting pump for distributing water pressure to the entire area of the rainfall simulator, and the control device includes the control motor and the pressure booster The condition of the rainfall can be corrected by controlling the pump.

In addition, the rainfall simulator correction method according to an embodiment of the present invention is a rainfall simulator correction method of a rainfall simulator correction system including a rainfall simulator, an automatic calibration device and a control device, in a rainfall simulator installed at a designated height of a laboratory, Providing rainfall downward, the calibration automation device collecting the provided rainfall and measuring the rainfall intensity and rainfall distribution of the rainfall by a measuring device when collecting the rainfall, and the control device And correcting the condition of the rainfall provided by the rainfall simulator based on the analysis result of the rainfall intensity and data related to the rainfall distribution provided by the automatic calibration device.

In the step of correcting, the rainfall state may be corrected based on a comparison result of the total rainfall intensity measured by the plurality of first measuring devices and the average rainfall intensity obtained by averaging the total rainfall intensity.

The correcting may include deriving the analysis result by applying a Christiansen uniform distribution coefficient for calculating the rainfall distribution.

The correcting step includes a system variable including a nozzle pressure (NP), a rotational speed (OV), and a delay time (TD) of a nozzle providing the rainfall by configuring the rainfall simulator, and between the rainfall intensity and the rainfall distribution. The rainfall state may be corrected by the analysis result derived based on the functional relationship of.

The rainfall simulator includes a control motor for controlling the nozzle of the rainfall, an injection angle, and a waiting time, and a pressure boosting pump for distributing water pressure to the entire area of the rainfall simulator, and the step of correcting includes the control motor and the It is possible to correct the condition of the rainfall by controlling the booster pump.

According to an embodiment of the present invention, as the estimation accuracy of rainfall intensity is higher than that of the traditional manual method, the result of the traditional manual method is applied to the study of fluid topography such as changes in surface properties such as fire, agriculture, urbanization, etc. You may be able to consider the uncertainty problem more carefully than when observing the average rainfall intensity using.

In addition, by carefully considering the uncertainty problem according to an embodiment of the present invention, it is possible to increase the accuracy of the urban flood demonstration experiment, and as a result, it will be possible to construct an accurate disaster prevention system for urban flood.

1 is a view for explaining a rain simulator inspection/correction automation facility of a real-time disaster occurrence time prediction system and method in Republic of Korea Patent Publication No. 10-1821599 (announced on January 25, 2018) to which the present applicant has registered a patent;

2 is a view showing a conductive small rainfall meter installed in the rainfall simulator inspection and correction automation facility of FIG. 1;

3A is a schematic diagram illustrating a water circulation process of a rainfall simulator in a large-scale laboratory as a rainfall simulator correction system according to an embodiment of the present invention;

3B is a photograph showing the interior of the laboratory in which the simulator of FIG. 3A is implemented;

4A and 4B are views showing a nozzle configuration of the rainfall simulator of FIG. 3A;

5A and 5B are views showing a spray box for reducing rainfall intensity of the rainfall simulator of FIG. 3A;

6A and 6B are diagrams showing the design and components of an automatic rainfall collection system according to an embodiment of the present invention;

7 is a comparison graph of the total rainfall intensity measured by a flow meter and the average rainfall intensity measured using rainfall,

8 is a graph showing the amount of variation in rainfall intensity with respect to nozzle pressure (NP), rotation speed (OV), and delay time (TD);

9 is a graph showing the variation of the uniformity coefficient (CuC) value in response to nozzle pressure (NP), rotation speed (OV) and delay time (TD);

10 is a diagram showing the spatial distribution of rainfall intensity of 181 mm/h on a 10×10 m plot surface,

11 is a graph showing the distribution of the distribution of system variables of the rainfall simulator and the uniformity distribution of rainfall in NP 1.5kg/㎠;

12 is a graph showing changes in rainfall intensity (I) and uniformity coefficient (CuC) in response to system variables of a rainfall simulator, and

13 is a flowchart of a rainfall simulator correction method according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings, and the same reference numerals are designated for components that perform the same function in the drawings 1 to 6. On the other hand, in the illustration and detailed description of the drawings, a detailed description and illustration of a specific technical configuration and operation of elements not directly related to the technical features of the present invention are omitted, and only the technical configuration related to the present invention is briefly shown or Explained.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the drawings.

3A is a rainfall simulator correction system according to an embodiment of the present invention, a schematic diagram showing a water circulation process in a large-scale laboratory rainfall simulator, and FIG. 3B is a photograph showing an interior view of a laboratory in which the simulator of FIG. 3A is implemented, FIGS. 4A and 4B. 4B is a view showing a nozzle configuration of the rainfall simulator of FIG. 3A, FIGS. 5A and 5B are views showing a spray box for reducing the rainfall intensity of the rainfall simulator of FIG. 3A, and FIGS. 6A and 6B are diagrams showing an embodiment of the present invention. It is a diagram showing the design and components of the automatic rainfall collection system according to the

3A to 6B, the rainfall simulator correction system 90 according to the embodiment of the present invention includes a rainfall simulator 100, an automatic calibration device (or automatic rainfall collection system) 110, and a control device ( Or, it includes part or all of the control device (170).

Here, "including some or all" means that some components such as the control device 170 are omitted to configure the rainfall simulator correction system 90, or some components such as the control device 170 are used in the rainfall simulator ( It will be described as including all in order to help a sufficient understanding of the invention as meaning the thing that can be configured by being integrated with other components such as 100).

In the rainfall simulator correction system 90 according to an embodiment of the present invention, the applicant of the present invention developed and registered an automated rain simulator inspection and correction facility as shown in FIG. 1, Korean Patent Application Publication No. 10-1891237 (2018.09. Announcement) Rainfall simulator A large-scale laboratory rainfall simulator (RS) of the National Disaster Management Research Institute (NDMI), which is a facility of automated inspection and correction facilities, can be included, and a vibration-type nozzle system is installed in an experimental area of up to 900㎡. It is used to reproduce spatially uniform rainfall.

The rainfall simulator 100 is a compression nozzle type simulator equipped with computer-operated oscillating booms, and the rainfall simulator 100 of the National Disaster Research Institute according to an embodiment of the present invention may be composed of several parts.

Groundwater storage tank (or underground storage tank, reservoir) 120 (ex: 900㎥), submersible pump 130 (ex: 0.25㎥/s ⁴ pump), rooftop water storage tank (or high water tank) 140 (example : 63㎥), (submersible pump to nozzle) water supply system, booster pump (or rainfall pump) 150 (eg 1.7㎥/min ³ pump), control the vibration pipe 210 with nozzle 210a The rainfall simulator control motor 160, the nozzle 210a, the spray box (or rain gutter) 200, and a computer control operating system include some or all of the control device 170, where "including some or all "To do is the same as the previous meaning.

The submersible pump 130 supplies water from the underground storage tank 120 under the laboratory to the rooftop storage tank 140. Water from the rooftop storage tank 140 is supplied to each nozzle 210a of the rainfall simulator 100 through a water delivery system. The booster pump 150 controls the inflow of water from the rooftop storage tank 140 to the water supply system, keeps the pressure constant, and keeps each nozzle 210a having a similar (or the same) discharge rate. Raindrops discharged to the nozzle 210a through the above-ground drainage system are collected again in the underground storage tank 120 for reuse.

The size of the rainfall simulator 100 is, for example, 30m (length) x 30m (width). The simulator and experiment area are divided into nine different sub-sections (e.g. 10×10m) that can operate independently, allowing efficient experiments ranging from small models to full-scale testing. The simulator nozzle 210a is at a height of 12 m above the ground to ensure the distal velocity of the raindrop. Four sets of nozzles 210a are installed and evenly spaced at 2.5m intervals along the pipeline of the lower section. Each set consists of two nozzles 210a. The nozzle type is KJ 80150 (eg orifice diameter 7.5mm, flat fan spray type). The nozzle configuration is shown in Figs. 4A and 4B.

The spray box 200 under each pair of vibrating nozzles 210a is used to cut the spray to reduce the rainfall intensity. Details of the structure of the spray box 200 are shown in FIGS. 5A and 5B. The spray box 200 has a drain hole 200a at each corner or edge region, and the drain holes 200a on both sides may be connected to drain

pipes

201 and 202 having different diameters, respectively. In the rainfall simulator 100, 144 spray boxes 200 are installed in the whole (see FIG. 4A). The number of spray boxes 200 installed in the rainfall simulator 100 is not necessarily limited to the above number, and may be appropriately adjusted according to the scale of the rainfall simulator 100. In general, the vibration nozzle system without the spray box 200 can adjust the simulated rainfall characteristics by changing the type of nozzle used, that is, the water pressure at the nozzle 210a and the sweep vibration frequency of the nozzle 210a. On the other hand, in the case of the vibrating nozzle system with the spray box 200 as in the embodiment of the present invention, the variable controlling the nozzle movement that affects the rainfall intensity and uniformity is the vibrating nozzle 210a across the spray box 200 It is related to the speed of the spray box 200 as well as the delay time outside the central rectangular opening of the spray box 200. When the nozzle 210a located at the corner of the spray box 200 is used, the water spray moves through the gutter that moves water to the underground storage tank 120 without passing through the central square hole of the spray box 200.

The control motor 160 of the rainfall simulator 100 can control the nozzle 210a of the rainfall, the spray angle, and the waiting time, and in the case of the booster pump 150, the water pressure is uniformly distributed over the entire size of the rainfall simulator. It is a pump that helps you do it. The booster pump 150 and the control motor 160 are controlled by the control device 170 of the control room, for example.

The nozzle pressure (NP) in the rainfall simulator (NDMI RS) 100 of the National Disaster Research Institute according to an embodiment of the present invention is 1.3 ~ 7.0kg/㎠In the range, it increases in units of 0.1kg/㎠ and the flow rate is 0.96~3.91㎥/min. The rotational speed (OV) of the nozzle 210a varies from 6.25 to 31.25 rpm at 1.25 rpm, and the nozzle delay time (TD) of the spray box varies from 0 to 10 seconds in 0.1 second increments. System variables such as NP, OV and TD can be changed automatically using a computer controlled operating system, such as control unit 170.

The automatic calibration device 110 according to an embodiment of the present invention may be referred to as an automatic rainfall (or rainfall) collection system (ARCS), and automatically performs the repeatability and uniformity of the rainfall situation of the large laboratory rainfall simulator 100. It is designed and manufactured to measure with. The size of the automatic rainfall collection system is 10m (length) × 10m (width). The automatic rainfall collection system can be operated based on 10 mobile units (e.g., each unit is 5m (length) × 2m (width) in size) for easy assembly and transport inside the laboratory. 6A and 6B show the design and components of the automatic rainfall collection system, that is, the automatic calibration device 110.

The main components of the automatic rainfall collection system are a small tipping bucket rain gauge (hereinafter, a conductive small rainfall meter or rainfall meter) 400, a partially filled pipe ultrasonic flow meter 411, a wireless data transmission device, and a real-time data processing system. Here, the real-time data processing system can be considered to mean a system for transmitting measurement data from a container to the control device 170 through a wireless data transmission device such as a zigbee communication module. For example, it can be divided into a data transmitting device provided in a container and a data receiving device provided in a control device 170 such as an operating PC. In this case, the data transmission device may include a CPU, a memory, a high-speed multi-counter (eg, a data processor, a data logger), a power supply device, and the like. The high-speed multi-counter simultaneously measures the pulse signals of the rain gauge at 10 points, and the rainfall data is converted into a digital signal in the data processing device and transmitted to the operating PC, that is, the control device 170 through Zigbee communication in the 2.4 GHz frequency range. In addition, it is configured to supply power stably by preventing leakage and leakage by using a waterproof connector. A small tipping bucket rain gauge (e.g., 10 cm diameter with a 20 cm high metal circular container, 1 pulse per 0.25 mm conversion), i.e. rainfall meter 400, is used to efficiently cover the 1 × 1 m surface below the rainfall simulator 100. It is developed and used (Fig. 6a b). A total of 100 rainfall meters, that is, rainfall meters 400, are uniformly arranged in the entire automatic rainfall collection system plot in a grid pattern (d, e in FIG. 6A) to measure the spatial rainfall distribution. The number of rainfall meters 400 installed in the rainfall simulator 100 is not necessarily limited to the above number, and may be appropriately adjusted according to the size of the rainfall simulator 100. The upper part of each rainfall meter 400 is located about 60 cm above the surface of the automatic rainfall collecting system so that sprayed raindrops do not splash onto the upper part of the collector (FIG. 6A). According to an embodiment of the present invention, the rainfall meter 400 may be included in the first measuring device, and the flow meter 411 may be included in the second measuring device. Therefore, in the embodiment of the present invention, a rainfall meter (e.g., a conductive small rainfall meter) 400 and a flow meter (e.g., an ultrasonic flow meter) having a structure as shown in FIG. 2 are used for measuring the rainfall distribution and rainfall intensity ) It is preferable to use 411, but it will not be particularly limited thereto.

The total rainfall intensity is determined by a partially filled pipe ultrasonic flow meter 411 installed at the end of the pipe 410 (Fig. 6A, c). The rainfall delivered by the rainfall simulator 100 is collected in an automatic rainfall collection system (d in FIG. 6A), and is collected in the automatic rainfall collection system at the same time as the rain falls on an instrument. Rainfall falls to the bottom of the container of the automatic rainfall collection system through a hole under the sloping bucket (Fig. 6a, b). The collected rainfall is transferred to a PVC pipe 410 having a diameter of 250 mm under the automatic rainfall collection system (FIG. 6B). The flow meter 411 measures the water level of the pipe 410 every minute, and the total rainfall collected by the automatic rainfall collection system is calculated as an equivalent intensity value of rainfall by accumulating the rainfall measured every minute. Data of 100 lane meters, that is, the rainfall meter 400 and the flow meter 411 are transmitted through a wireless data transmission device. Data is stored, processed, and displayed in a real-time data processing system (Fig. 6A).

The developed automatic rainfall collection system is implemented to correct the rainfall simulator 100 for rainfall intensity and spatial uniformity. The uniformity of rainfall intensity and spatial rainfall distribution was determined by 75 combinations of three system variables (i.e., 1.3, 1.4 and 1.4, and 1.5 kg/cm 2 NP values) in the rainfall simulator 100 of 10 m (length) × 10 m (width). The plot sizes are evaluated with OV values of 6.25, 12.50, 18.75, 25.00 and 31.25 rpm, and TD values of 0.0, 0.5, 1.0, 1.5 and 2.0 s. When the NP exceeds 1.5 kg/cm 2, the maximum value of NP is maintained at 1.5 kg/cm 2 so that excessive water does not overflow from the spray box 200. During the non-rainfall period, the TD increases from 0.5 seconds to 2.0 seconds, taking into account the physical characteristics of natural rainfall.

The rainfall intensity is measured in two ways. In the first method, each rainfall intensity over the entire plot (10m x 10m) is measured by 100 small tipping bucket rain gauges, i.e. rainfall meters 400, evenly distributed in a square grid (1m apart from each other). Average rainfall intensity (Iaverage) is determined by averaging the rainfall intensity measurements from the rainfall instrument panel. The volume method is adopted in the second method. In the automatic rainfall collection system, the total rainfall collected in a container having a capacity of 100 m 2 is measured with a partially filled pipe ultrasonic flow meter 411. The amount of water collected by the automatic rainfall collection system is converted into an equivalent intensity value (total rainfall intensity, Itotal) of rainfall. The accuracy of the average rainfall intensity (measurement error of the average tipping bucket rain meter: 5%) based on the rainfall instrument panel (rainfall amount) is the total rainfall intensity measured using the flow meter 411 (total, measurement error of the flow meter: 2%). Compare and evaluate.

The uniformity of simulated rainfall is evaluated using the Christiansen Uniformity Coefficient (CuC) (Christiansen, 1942), the most widely used formula of spatial uniformity in a specific rainfall event. The CuC equation is shown in [Equation 1] below.

Where xi is the rainfall at location i, x is the average rainfall, and n is the total number of observations. The Christiansen's uniform distribution coefficient is the deviation from the mean normalized to the average rainfall intensity at each observation point to characterize the variability of spatial rainfall. The closer the value is to 100%, the more uniform the spatial distribution of rainfall. The spatial pattern of rainfall can be considered to be reasonably constant in the experimental plot when the Christiansen uniform distribution coefficient is 80% or more. However, there are trade-offs between uniformity and other goals such as size and cost. In general, the smaller rainfall simulator 100 has a higher CuC value. For large experimental plots, a CuC value of 70% is considered reasonably acceptable.

In order to evaluate the reliability and accuracy of the rainfall simulator 100, the variability (or consistency) of rainfall intensity and spatial uniformity was investigated. The motion model of the rainfall simulator 100 is made under the condition of system variables showing a high level of accuracy related to spatial uniformity in order to reproduce the desired rainfall intensity with a reasonably high uniform distribution. The correlation between system variables and rainfall intensity and uniform distribution was investigated to identify possible interdependencies. The correlation between the variables was tested by calculating the linear correlation coefficient. In addition, the correlation coefficient in the log-transformed space was examined whether the nonlinear relationship was terminated. A correlation analysis in consideration of the significance level was performed to confirm the dependence on the variables (that is, system variables of the rainfall simulator 100 and simulated rainfall intensity and uniform distribution). The functional relationship between the system variables of the rainfall simulator 100 and the simulated intensity and uniform distribution of rainfall was established based on multiple regression analysis methods for linear and log scales. The coefficient of determination was used to measure the performance of the working model. The driving model with the highest coefficient of determination was selected through model comparison of each scale.

Subsequently, results of the above system configuration and method will be described.

9 is a comparison graph of the average rainfall intensity measured using the total rainfall intensity measured by a flow meter and the rainfall. FIG. 10 is a variation in rainfall intensity with respect to nozzle pressure (NP), rotational speed (OV), and delay time (TD). 11 is a graph showing the variation of the uniformity coefficient (CuC) value in response to the nozzle pressure (NP), rotational speed (OV) and delay time (TD), and FIG. 12 is a 10×10 m plot on the surface. A diagram showing the spatial distribution of the rainfall intensity of 181mm/h, FIG. 13 is a graph showing the distribution of the system variables of the rainfall simulator and the uniformity distribution of the rainfall at NP 1.5kg/㎠, and FIG. 32 is the system variable of the rainfall simulator. It is a graph showing the change in rainfall intensity (I) and uniformity coefficient (CuC) in response to.

The rainfall simulator 100 has been calibrated for the uniformity of rainfall intensity and rainfall distribution in various combinations of system variables (eg, vibrational motion including pressure and velocity and delay time). Before evaluating the reliability and accuracy of the rainfall simulator 100 based on the calibration result, the suitability of the average rainfall intensity (Iaverage) of the instrument for all system variables is compared with that measured with the flow meter 411 and evaluated by graphical comparison. did. As can be seen in Fig. 7, the interaction points between Iaverage and Itotal were evenly distributed on both sides of the 1:1 line in the relatively high rainfall range of 130 to 200 mm/h, but the Iaverage was distributed in the rainfall range at 60 to 130 mm/h. It was a little overrated. However, the average rainfall intensity generally agrees well with the total rainfall intensity measured by the flow meter 411. The line of agreement between the values of Iaverage and Itotal is very close to a 1:1 correspondence. Performance statistics also show that Iaverage has a high degree of accuracy in estimating rainfall intensity (R2 between Iaverage and Itotal values is 0.97).

Sousa Junior estimated average rainfall intensity using two different methods. One was a traditional manual measurement, collected and measured in 63 collection cans arranged in 7×9 mesh at 0.25m intervals over a 3 m2 area. The other was a volumetric measurement method that measures the total rainfall collected in a container (3㎡). These authors compared rainfall intensities in two methods for rainfall in the 170-250mm/h range. The mean rainfall intensity differed significantly between 170 and 200 mm/h (the deviation between the values of the two methods was approximately 30 to 35 mm/h), and in this study, the difference between Iaverage and Itotal was approximately 2 to 2 in the same rainfall range. It was 7 mm/h.

The variability of average rainfall intensity (hereinafter, rainfall intensity) for three system variables (ie, based on 75 combinations of NP, OV and TD) is compared in Table 1 and FIG. 8. Table 1 below provides a statistical summary of the correction results for the rainfall intensity for each system variable, and visually compares the fluctuation of the rainfall intensity for each system variable using the box and whisker diagram of FIG. 8. The rainfall simulator 100 provided rainfall intensity between 44.1 and 181.7 mm/h for three system variables (FIG. 8 ). As can be seen in Fig. 8, a similar range of rainfall intensity was produced across all pressures. (NP values are 1.3, 1.4 and 1.5 kg/cm ² ). Average rainfall intensity slightly increased with increasing NP. (Average 104.5, 108.4 and 112.1 mm/h in response to NP values of 1.3, 1.4 and 1.5 kg/cm ² , respectively; Table 1), and no substantial difference was found with an increase in NP values. In contrast, increases in OV and TD values significantly decreased rainfall intensity (ranging from 82.6 to 140.5 mm/h on average and from 72.8 to 162.6 mm/h in response to OV and TD, respectively, Table 1). The lower the OV and TD, the higher the rainfall intensity of the rainfall simulator 100.

A statistical summary of the correction results for spatial rainfall uniformity is shown in Table 2 below, and FIG. 11 shows the range of CuC values calculated according to changes in three system variables. Uniformity coefficient for all cases, 70.1% (1.3kg / NP for a ㎠, refers to OV and the rainfall intensity of TD, 44.1mm / h of 2.0s of 31.25rpm) and 78.4% (1.5kg / cm ² of NP , OV was 6.25rpm, TD was 0.0 sec, and rainfall intensity was between 181.7mm/h).

The uniformity coefficient was mainly influenced by changes in NP and OV. The CuC value of the simulated rainfall was improved as the NP increased, and the RS showed a higher uniformity coefficient value at the NP value of 1.5kg/cm ² . (CuC value is 74.2 to 77.4% in Fig. 9, and rainfall intensity in Fig. 8 is in the range of 49.1 to 181.7 mm/h). It was found that the spatial distribution of the simulated rainfall is improved with a decrease in OV. The increase in OV is inversely proportional to both the rainfall intensity and the uniformity coefficient (Figs. 8 and 9), and the uniformity coefficient is not significantly affected by the change in TD. A sample plot for the spatial rainfall distribution (rainfall intensity 381.7 mm/h and CuC 78.4%) is presented in FIG. 10. The overall spatial pattern of the distribution for different rainfall intensities was largely similar in terms of high and low rainfall regions despite variations in rainfall intensity.

The correction results for rainfall intensity and spatial rainfall uniformity for each system variable showed that OV had a strong correlation with both rainfall intensity and uniformity coefficient. Higher uniformity was observed in response to an NP of 1.5 kg/cm 2, and included the full range of rainfall intensities obtained from all combinations of system variables (NP, OV and TD) of the rainfall simulator 100. In the embodiment of the present invention, the driving model of the rainfall simulator 100 was derived under the pressure condition (NP 1.5kg/㎠) with the highest spatial rainfall uniformity, and the system variables, rainfall intensity and uniform distribution of the rainfall simulator 100 We included a range of rainfall intensities for all combinations of the three system variables according to the correlation between them.

[Table 3] below shows the correlation between OV and rainfall intensity and uniform distribution for each TD on linear and log scales. As can be seen in FIG. 11, when the correlation between rainfall and uniformity is classified into five TDs, the closer the OV is to 0, the higher the simulated rainfall intensity. Under the condition of increasing rainfall intensity, the uniformity of rainfall distribution was slightly improved. However, as the TD decreased, the rainfall intensity increased, whereas the change in TD did not significantly affect the change in the CuC value. In all TD values, OV showed a strong negative correlation with both rainfall intensity and spatial uniformity of rainfall distribution (Table 3). In particular, the correlation was significant at the linear and logarithmic scale 1% to 5% level.

The linear and nonlinear motion models used multiple regression to establish an appropriate functional relationship between the system variables of the rainfall simulator 100 and the simulated intensity and the uniform distribution of rainfall of statistical significance. The performance of the computational model for linear and logarithmic scales was measured based on the coefficient of determination. The linear model shows high model performance at rainfall intensity (R2 of 0.93 and 0.83 on the linear and log scale, respectively) and spatial uniformity coefficient (R2 of 0.84 and 0.82 on the linear and log scale, respectively). The operating model with the corresponding coefficient of determination is presented in Table 4 below. The operating model showed good overall performance in both rainfall intensity and uniformity coefficient (R2 value of 0.8 or higher).

In the developed driving model, the simulated rainfall intensity and the uniformity of the distribution affected by the system variables (OV and TD) are visually compared in FIG. 12 based on the specific values of the rainfall intensity and coefficient. (Rain intensity is 50, 90, 130 and 170 mm/h and CuC is 75, 76, 77 and 78). Rainfall intensity increased as OV and TD decreased. The change in TD did not have a significant effect on the CuC value, but the uniformity coefficient improved with a decrease in OV. (I.e., 75, 76 and 77% CuC values were obtained over the entire TD range of 0 to 2 seconds; FIG. 12).

In summary, in the embodiment of the present invention, an automatic rainfall collection system was developed to overcome the disadvantages of manual measurement in order to obtain rainfall intensity and rainfall distribution in a large-scale experimental area. The developed automatic rainfall collection system was implemented to calibrate the large laboratory rainfall simulator 100 for rainfall intensity and spatial rainfall uniformity at various combinations of system variables such as pressure (NP) and vibrational motion, including speed and delay time. (OV and TD, respectively). Finally, the motion model of the rainfall simulator 100 was derived from a functional relationship between the system variables of the rainfall simulator 100 and the rainfall intensity and uniform distribution.

Before evaluating the reliability and accuracy of the rainfall simulator 100, it was compared with the volume measurement method using the flow meter 411 to evaluate the appropriateness of the average rainfall intensity automatically collected by the rain meter 400 of the small conduction type rainfall meter. A comparative evaluation of the estimation method for rainfall generally indicates that the rain of small conducted rainfall provides accurate and consistent predictions for the high and low range of rainfall of 40-200 mm/h with the average rainfall intensity automatically collected by the meter. In particular, the automatic method in the embodiment of the present invention has higher estimation accuracy for rainfall intensity than the traditional manual method when compared with the results reported by Sousa Junior and the like. Therefore, when the results of traditional manual methods are applied to fluid topography studies such as changes in surface properties such as fire, agriculture, urbanization, etc., the uncertainty problem must be carefully considered than when observing average rainfall intensity using traditional methods.

The calibration results showed that the simulated rainfall intensity and the uniformity of the rainfall distribution are affected by the system variables of the rainfall simulator 100. (That is, the pressurized oscillation nozzle simulator having the spray box 200) The rainfall intensity was inversely proportional to the increase of OV and TD, but there was no significant difference in the rainfall intensity as NP increased. Rainfall was evenly distributed within the maximum and minimum rainfall values at each NP value (1.3, 1.4, 1.5kg/cm ² ). This indicates that the rainfall intensity changes more sensitively to changes in system variables related to the vibrational motion of the nozzle 210a compared to the pump pressure. In general, the rainfall intensity of a pressurized nozzle simulator is controlled by the nozzle type, nozzle orifice diameter, and pump pressure at the nozzle. Nozzle pressure primarily affects rainfall intensity under conditions such as nozzle type and nozzle orifice diameter.

The uniformity of rainfall distribution improved with decreasing OV and increasing NP. The improvement in the coefficient of uniformity of the response to a decrease in OV is associated with an increase in the exposure time of the oscillating nozzle to the experimental plot surface. This indicates that as the OV (i.e., increasing the exposure time of the nozzle) is slower, the rainfall increases and the uniformity coefficient increases. This is because the increase in the simulated rainfall contributes to the rainfall distribution in the experimental plot affected by nozzle vibration. Increasing NP also increased the uniformity of the rainfall distribution.

As the pump pressure in the nozzle 210a increases, it seems that raindrops are sprayed more uniformly. In addition, the CuC value range was high at the pump pressure of 1.5kg/cm ² (CuC in FIG. 9 ranged from 74.2 to 78.4%), and the change in the CuC value was small (SD in Table 2 was 1.3), and the CuC value The range of was relatively low (CuC was 70.1 to 76.7% and 71.6 to 77.7%, respectively, at the NP values of 1.3 and 1.4 kg/cm ² , FIG. 9), and the variability of the CuC value was 1.3 and 1.4 kg/cm 2 1.9 and 1.8 at each NP value; Table 2) shows under the pressure condition of 1.5kg/cm2. Considering these conditions, it would be appropriate to set the pressure condition of the rainfall simulator 100 higher than 1.5 kg/cm 2 in order to ensure a high and consistent performance of the spatial distribution of the simulated rainfall.

The operating model of the rainfall simulator 100 (that is, the functional relationship between the system variable of RS and the simulated intensity and the uniform distribution of rainfall) was set to NP of 1.5 kg/cm 2, indicating the highest level of spatial rainfall uniformity. The entire range of rainfall intensities was included simultaneously for all combinations of system variables. The operating model was calculated based on multiple regression analysis, including correlation analysis on linear and logarithmic scales, taking into account significance levels. The driving model showed high accuracy for both rainfall intensity and uniformity coefficient (R2 value was 0.8 or higher, see Table 4). The rainfall intensity and uniformity coefficient increased with decreasing OV and TD. In particular, the change in OV is notable for the simulated rainfall intensity and uniform distribution of rainfall, and the TD change has little effect on the CuC value.

The simulated rainfall intensity and rainfall uniformity coefficient for each value were plotted for OV and TD, and the changes in rainfall intensity and uniform distribution affected by system variables were visually inspected. Information on graphical plots can be used as a guide for the appropriate range of system variables that produce specific rainfall intensity and uniformity for simulated rainfall. In addition, information on intuitive selection criteria of the range of system variables for calculating specific rainfall and rainfall uniformity for specific applications is provided.

However, in order to generalize the driving model of the rainfall simulator 100 for various rainfall conditions, additional experiments using an automatic rainfall collection system for various types of nozzles 210a and pump pressures are required. In particular, in order to increase the pump pressure of the nozzle (1.3 ~ 1.5kg/cm ² ) used in the embodiment of the present invention, the spray box 200 is used to discharge excess water when the nozzle pressure exceeds 1.5 kg/cm 2. There is a need to improve the drainage capacity. In order to further improve the spatial diversity of rainfall intensity, it is necessary to measure the pressure drop in the water supply system (from the booster pump to the nozzle) and the pressure change in each nozzle 210a. In addition, further research or development of the comparison of manual and automatic methods based on various conditions such as container size, grid size and rainfall range will be needed to quantify the size of rainfall due to the existing manual method.

13 is a flowchart illustrating a rainfall simulator correction method according to an embodiment of the present invention.

Referring to FIG. 13 together with FIG. 3 for convenience of explanation, the rainfall simulator 100 according to an embodiment of the present invention is a simulator installed at a designated height (from the floor surface) of the laboratory, and provides rainfall downward (S1100). .

In addition, the calibration automation device 110 is an automatic rainfall collection system that collects the provided rainfall and measures the rainfall intensity and distribution of rainfall by a measuring device (e.g., a rainfall meter, a flow meter, etc.) when collecting rainfall. (S1110).

Subsequently, the control device 170, such as a control computer in the control room, is based on the analysis result of the rainfall intensity provided by the calibration automation device 110 and data related to the rainfall distribution, and the rainfall state provided by the rainfall simulator 100 It corrects (S1120).

Here, the analysis result of the data includes system variables including the nozzle pressure (NP), rotation speed (OV), and delay time (TD) of the nozzle 210a providing rainfall by configuring the rainfall simulator 100. In addition, a value derived based on a functional relationship between rainfall intensity and rainfall distribution may be further included. In addition, pump pressure, injection speed, nozzle type, pipe feed water, etc. may be further considered as control variables.

Accordingly, the control device 170 constitutes the rainfall simulator 100 to control the nozzle of the rainfall, the spray angle and the waiting time, and the control motor 160 to distribute the water pressure to the entire area of the rainfall simulator 100. By controlling the pump 150, the condition of rainfall may be corrected.

In addition to the above, other detailed correction methods of the rainfall simulator 100 have been sufficiently described above, and thus the contents will be substituted.

On the other hand, even if all the constituent elements constituting an embodiment of the present invention are described as being combined into one or operating in combination, the present invention is not necessarily limited to this embodiment. That is, within the scope of the object of the present invention, all the constituent elements may be selectively combined and operated in one or more. In addition, although all the components may be implemented as one independent hardware, a program module that performs some or all functions combined in one or more hardware by selectively combining some or all of the components. It may be implemented as a computer program having Codes and code segments constituting the computer program may be easily inferred by those skilled in the art of the present invention. Such a computer program is stored in a non-transitory computer readable media that can be read by a computer and is read and executed by a computer, thereby implementing an embodiment of the present invention.

Here, the non-transitory readable recording medium is not a medium that stores data for a short moment, such as a register, cache, memory, etc., but a medium that stores data semi-permanently and can be read by a device. . Specifically, the above-described programs may be provided by being stored in a non-transitory readable recording medium such as a CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM, or the like.

In the above, preferred embodiments of the present invention have been illustrated and described, but the present invention is not limited to the specific embodiments described above, and is generally used in the technical field to which the present invention pertains without departing from the gist of the present invention claimed in the claims. Various modifications are possible by those skilled in the art of course, and these modifications should not be individually understood from the technical idea or perspective of the present invention.

The present invention is a rainfall simulator installed at a designated height of a laboratory to provide a downward rainfall (rainfall);

A calibration automation device comprising a measuring device for collecting the provided rainfall and measuring the rainfall intensity and distribution of the rainfall when collecting the rainfall; And

A control device for correcting the condition of the rainfall provided by the rainfall simulator based on the analysis result of the data related to the rainfall intensity and rainfall distribution provided by the automatic calibration device;

The included rainfall simulator correction system is in a form for implementation of the invention.

In addition, the present invention is a rainfall simulator correction method of a rainfall simulator correction system including a rainfall simulator, an automatic calibration device and a control device,

In a rainfall simulator installed at a designated height of the laboratory, providing rainfall downward;

Measuring, by the calibration automation device, the rainfall intensity and distribution of the rainfall by a measuring device when collecting the provided rainfall and collecting the rainfall; And

Compensating, by the control device, the condition of the rainfall provided by the rainfall simulator based on the analysis result of the rainfall intensity and data related to the rainfall distribution provided by the automatic calibration device. To another form for the implementation of the invention.

According to the present invention, as the estimation accuracy of rainfall intensity is higher than that of the traditional manual method, the results of the traditional manual method are averaged using the traditional method when applied to the study of fluid topography such as changes in surface properties such as fire, agriculture, urbanization, etc. It is possible to carefully consider the uncertainty problem than when observing the rainfall intensity, and also, by carefully considering the uncertainty problem according to an embodiment of the present invention, it is possible to increase the accuracy of the urban flood demonstration experiment. It is possible to establish an accurate disaster prevention system, so it is expected to be used in the industry.

Claims

A rainfall simulator installed at a designated height in the laboratory to provide rainfall downward;

A calibration automation device comprising a measuring device for collecting the provided rainfall and measuring the rainfall intensity and distribution of the rainfall when collecting the rainfall; And

A control device for correcting the condition of the rainfall provided by the rainfall simulator based on the analysis result of the data related to the rainfall intensity and rainfall distribution provided by the automatic calibration device;

Rainfall simulator correction system including.
The method of claim 1,

The calibration automation device,

A container forming a space for collecting the rainfall;

A first measuring device configured to form a matrix for measuring the rainfall distribution in the space of the container and to have a plurality of installed ones; And

A second measuring device provided at an outlet for discharging the rainfall collected from the container for measuring the rainfall intensity;

Rainfall simulator correction system including.
The method of claim 2,

The first measuring device includes a conductive rainfall meter, and the second measuring device includes an ultrasonic flow meter.
The method of claim 3,

The conductive rainfall meter is a rainfall simulator correction system that is fixed and installed at a designated height from the floor surface of the space.
The method of claim 2,

The control device is a rainfall for correcting the condition of the rainfall based on a comparison result of the total rainfall intensity (Itotal) measured by the plurality of first measuring devices and the average rainfall intensity (Iaverrage) averaged the total rainfall intensity. Simulator calibration system.
The method of claim 2,

The control device is a rainfall simulator correction system that derives the analysis result by applying a Christiansen Uniformity Coefficient for calculating the rainfall distribution.
The method of claim 1,

The control device configures the rainfall simulator to provide system variables including nozzle pressure (NP), rotation speed (OV), and delay time (TD) of the nozzle providing the rainfall, and a function between the rainfall intensity and the rainfall distribution. A rainfall simulator correction system for correcting the condition of the rainfall based on the analysis result derived based on the relationship.
The method of claim 7,

The rainfall simulator,

A control motor for controlling the rainfall nozzle, spray angle and waiting time; And

Includes; a pressure boosting pump for distributing water pressure to the entire area of the rainfall simulator,

The control device is a rainfall simulator correction system for correcting the state of the rainfall by controlling the control motor and the boosting pump.
A rainfall simulator correction method of a rainfall simulator correction system including a rainfall simulator, an automatic calibration device and a control device,

In a rainfall simulator installed at a designated height of the laboratory, providing rainfall downward;

Measuring, by the calibration automation device, the rainfall intensity and distribution of the rainfall by a measuring device when collecting the provided rainfall and collecting the rainfall; And

Compensating, by the control device, the condition of the rainfall provided by the rainfall simulator based on the analysis result of the data related to the rainfall intensity and the rainfall distribution provided by the automatic calibration device.

Rainfall simulator calibration method to include.