KR20240100609A - Apparatus and method for predicting aircraft icing based on the atmospheric numerical weather prediction model - Google Patents

Abstract

An aircraft icing prediction device based on a numerical meteorological model according to an embodiment of the present invention includes a data transmission and reception module; A memory in which an aircraft icing prediction program is stored; and a processor that executes a program stored in a memory, wherein the aircraft icing prediction program calculates the icing potential by inputting the collected weather variable information into the icing prediction model, and determines whether the icing potential is above the threshold. It provides an icing expected area according to icing potential and an icing risk area above a threshold, but the icing prediction model is a fuzzy logic model built using a membership function defined for each meteorological variable.

Description

Aircraft icing prediction device and method based on meteorological numerical model {APPARATUS AND METHOD FOR PREDICTING AIRCRAFT ICING BASED ON THE ATMOSPHERIC NUMERICAL WEATHER PREDICTION MODEL}

The present invention relates to an aircraft icing prediction device and method based on a meteorological numerical model.

Aircraft icing is a phenomenon in which supercooled water droplets that remain unfrozen in clouds even at sub-zero temperatures collide with the surface of the aircraft, forming ice. Icing reduces an aircraft's lift and increases drag, friction, and weight, reducing fuel efficiency and stability. If icing occurs on an observation device attached to an aircraft, such as a pitot tube, it may cause instrument error and failure. If icing occurs in the engine of an aircraft without an anti-icing device, the aircraft may lose power and crash. Therefore, when operating an aircraft, it is necessary to identify icing risk areas in advance and prepare for them by modifying the flight path.

There is a way to predict icing by using a numerical meteorological model, and the meteorological variables related to icing calculated from the numerical model can be used. There is a direct prediction method that uses two variables: the amount of supercooled water, which is directly related to the presence of supercooled water droplets, and the central volume diameter, which represents the size distribution of the droplets. However, prediction performance varies greatly depending on the configuration of the numerical model (horizontal grid resolution, etc.), and is not widely used in actual predictions due to a lack of supercomputer computing resources. Therefore, foreign meteorological offices consider the limitations of direct prediction methods and adopt indirect prediction methods for efficient system operation. The indirect prediction method they use is to use meteorological variables such as temperature, relative humidity, and cloud sap volume, which indirectly indicate the presence of supercooled water droplets.

The Korea Aviation Meteorological Administration uses an icing prediction system that uses temperature and relative humidity, an indirect prediction method developed by the U.S. National Institute of Meteorology in the 1990s. However, predicting icing with only two meteorological variables has limitations in terms of precision and specificity. The icing prediction results of existing systems cannot clearly distinguish between mild icing that does not pose a significant threat to the safety of the aviation industry and strong icing that must be avoided. Accordingly, developed countries (USA, UK, etc.) have stopped using the existing system since the 2000s.

On the other hand, end users such as weather forecasters and airlines first need predictions of relatively strong icing areas of medium or higher intensity. Therefore, it is necessary to increase the accuracy and precision of icing prediction by using additional meteorological variables.

The occurrence of icing is also related to aerodynamics, and the range of environments in which it can occur from a meteorological point of view is very wide. In other words, it is difficult to accurately predict icing using only two meteorological variables: temperature and relative humidity. Therefore, developed countries have been developing fuzzy logic-based algorithms used in machine learning since the 2000s and using them for practical forecasting.

However, the fuzzy logic algorithm used overseas has been developed to fit the numerical model of the operating agency. In addition, numerical models are designed to ensure forecast accuracy according to the characteristics of weather phenomenon and the purpose of weather forecasting in the country in which they operate, so each model has its own unique forecast characteristics. Therefore, in order to ensure forecast accuracy and operational efficiency of Korea's airspace forecast, an aircraft icing prediction method optimized for the numerical model used by the Korea Meteorological Administration is required.

Republic of Korea registered patent 10-10-2043227 (Title of invention: “System and method for predicting and controlling ice formation”)

The present invention is intended to solve the above-mentioned problems, and its purpose is to provide an aircraft icing prediction device and method based on a meteorological numerical model that presents prediction results of the icing risk area using meteorological variables simulated in the numerical model.

However, the technical challenge that this embodiment aims to achieve is not limited to the technical challenges described above, and other technical challenges may exist.

As a technical means for solving the above-described technical problem, an aircraft icing prediction device based on a numerical meteorological model according to an embodiment of the present invention includes a data transmission and reception module; A memory in which an aircraft icing prediction program is stored; and a processor that executes a program stored in a memory, wherein the aircraft icing prediction program calculates the icing potential by inputting the collected weather variable information into the icing prediction model, and compares the icing potential with the threshold value. It provides information on the icing expected area and the icing risk area, but the icing risk area represents the area where the icing potential is above the threshold, and the icing prediction model is a fuzzy logic model built using a membership function defined for each meteorological variable. .

An aircraft icing prediction method performed by an aircraft icing prediction device based on a meteorological numerical model according to another embodiment of the present invention includes the steps of (a) inputting collected meteorological variable information into an icing prediction model to calculate icing potential; (b) comparing the icing potential to a threshold; and (c) providing information on the icing expected area and icing risk area according to the compared results, where the icing prediction model is a fuzzy logic model built using a membership function defined for each meteorological variable.

According to one of the above-described means of solving the problems of the present invention, unlike the conventional technology that predicts icing with only two meteorological variables, temperature and relative humidity, by providing a fuzzy logic-based numerical model that uses additional meteorological variables related to icing. , it is possible to predict the icing area more accurately and precisely than the prior art.

In addition, by building an icing prediction model based on long-term observation data optimized for the Korea Meteorological Administration's operational numerical model, it is effective in securing aircraft icing prediction accuracy and operational efficiency in domestic airspace.

In addition, since the present invention provides an icing risk area, it is possible to determine whether to perform de-icing and anti-icing work before departure of the aircraft and to induce route correction to avoid hazardous weather in advance.

Figure 1 is a configuration diagram of an aircraft icing prediction device based on a numerical meteorological model according to an embodiment of the present invention.
Figure 2 is a block diagram showing detailed modules of an aircraft icing prediction device based on a numerical meteorological model according to an embodiment of the present invention.
Figures 3 and 4 are diagrams for explaining the construction process of an icing prediction model according to an embodiment of the present invention.
Figures 5A to 5C are diagrams showing examples of icing prediction areas provided by a result output unit according to an embodiment of the present invention.
Figure 6 is a flow chart illustrating a method for predicting aircraft icing based on a numerical meteorological model according to another embodiment of the present invention.

Below, with reference to the attached drawings, embodiments of the present invention will be described in detail so that those skilled in the art can easily implement the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. In order to clearly explain the present invention in the drawings, parts that are not related to the description are omitted, and similar parts are given similar reference numerals throughout the specification.

Throughout the specification, when a part is said to be “connected” to another part, this includes not only cases where it is “directly connected,” but also cases where it is “electrically connected” with another element in between. . Additionally, when a part "includes" a certain component, this means that it may further include other components rather than excluding other components, unless specifically stated to the contrary.

In this specification, 'part' includes a unit realized by hardware, a unit realized by software, and a unit realized using both. Additionally, one unit may be realized using two or more pieces of hardware, and two or more units may be realized using one piece of hardware. Meanwhile, '~ part' is not limited to software or hardware, and '~ part' may be configured to reside in an addressable storage medium or may be configured to reproduce one or more processors. Therefore, as an example, '~ part' refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, and procedures. , subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and 'parts' may be combined into a smaller number of components and 'parts' or may be further separated into additional components and 'parts'. Additionally, components and 'parts' may be implemented to refresh one or more CPUs within the device.

Network refers to a connection structure that allows information exchange between nodes such as terminals and servers, including Local Area Network (LAN), Wide Area Network (WAN), and World Wide Area Network (WWW). Wide Web), wired and wireless data communication networks, telephone networks, and wired and wireless television communication networks. Examples of wireless data communication networks include 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi, Bluetooth communication, infrared communication, and ultrasound. This includes, but is not limited to, communication, Visible Light Communication (VLC), LiFi, etc.

Figure 1 is a configuration diagram of an aircraft icing prediction device based on a numerical meteorological model according to an embodiment of the present invention.

Referring to FIG. 1 , the aircraft icing prediction device 100 may include a data transmission/reception module 110, a memory 120, a processor 130, and a database 140.

The data transmission/reception module 110 may receive weather variable information from the weather numerical model 10 and transmit it to the processor 130. Here, the data transmission/reception module 110 may be a device that includes hardware and software required to transmit/receive signals such as control signals or data signals through wired or wireless connections with other network devices.

At this time, the weather numerical model (10) refers to the Korea Meteorological Administration's Global Unified Model (Unified Model-Global Data Assimilation and Prediction System; hereinafter referred to as UM), and the weather variable information includes temperature, relative humidity, and vertical wind predicted in the Global Unified Model (UM). Velocity, cloud water (liquid + solid phase) content variables.

The memory 120 may have an aircraft icing prediction program recorded therein. The aircraft icing prediction program calculates the icing potential by inputting the collected weather variable information into the icing prediction model, and provides information on the icing expected area and icing risk area according to the results of comparing the icing potential with the threshold. Here, the memory 120 may include magnetic storage media or flash storage media in addition to volatile storage devices that require power to maintain stored information, but the scope of the present invention is limited thereto. It doesn't work.

The memory 120 may store a separate program such as an operating system for processing and controlling the processor 130, and may perform a function for temporarily storing input or output data.

The processor 130 executes an aircraft icing prediction program (hereinafter referred to as program) stored in the memory 120 and provides a function to control the hardware of the aircraft icing prediction device 100 according to the execution of the program. That is, the processor 130 performs hardware control functions such as file system, memory allocation, network, basic library, timer, device control (display, media, input device, 3D, etc.), and other utilities required as the program is executed. You can.

The processor 130 inputs the collected weather variable information into the icing prediction model to calculate the icing potential, and provides information on the icing expected area and the icing risk area according to the result of comparing the icing potential with the threshold. In addition, each specific step of the icing prediction process according to the execution of the program will be described later with reference to FIGS. 2 to 5C.

The processor 130 may include all types of devices capable of processing data. For example, it may refer to a data processing device built into hardware that has a physically structured circuit to perform a function expressed by code or instructions included in a program. Examples of data processing devices built into hardware include a microprocessor, central processing unit (CPU), processor core, multiprocessor, and application-specific (ASIC). It may encompass processing devices such as integrated circuits and FPGAs (field programmable gate arrays), but the scope of the present invention is not limited thereto.

The database 140 stores or provides data necessary for the aircraft icing prediction device 100 under the control of the processor 130. As an example, the database 140 may store the icing potential calculated from an icing prediction model. This database 140 may be included as a separate component from the memory 120 or may be built in a partial area of the memory 120.

Figure 2 is a block diagram showing detailed modules of an aircraft icing prediction device based on a numerical meteorological model according to an embodiment of the present invention.

Referring to FIG. 2, the processor 130 may include detailed modules that perform various functions depending on the execution of the aircraft icing prediction program. Here, the detailed module may include a data collection unit 210, an icing prediction model 220, a post-processing unit 230, and a result output unit 240.

The program can calculate the icing potential by inputting weather variable information collected through the data collection unit 210 into the icing prediction model 220. Here, the icing prediction model 220 may be a fuzzy logic model built using a membership function defined for each meteorological variable. A detailed description of the construction process of the icing prediction model 220 will be described later with reference to FIGS. 3 and 4.

Subsequently, the program compares the icing potential with a threshold value through the post-processing unit 230, and can provide information about the icing expected area and the icing risk area through the result output unit 240 according to the comparison result. At this time, the icing expected area may include all areas where icing is expected to occur regardless of the risk intensity of icing. Icing hazard areas may include areas where the icing potential is above a threshold.

In other words, the present invention provides information on the icing expected area and the icing risk area, so it can help determine whether to perform de-icing and anti-icing work before departure of the aircraft and determine route changes to avoid dangerous weather in advance.

The data collection unit 210 may receive weather variable information predicted in a three-dimensional coordinate system from the global unified model (UM) according to a preset period.

For example, the data collection unit 210 can collect forecast data of the global unified model (UM) generated up to 288 hours at 6-hour intervals four times a day, at 03, 09, 15, and 21 KST. At this time, weather variable information can utilize temperature, relative humidity, vertical wind speed, and cloud water content among UM's forecast data.

Meanwhile, the existing icing prediction method used by the Aviation Meteorological Administration is a categorical algorithm that collects only temperature and relative humidity as meteorological variables, but there is a problem with precision as discontinuous results appear depending on the range. Additionally, icing generally occurs when the temperature is between minus 40 degrees and 0 degrees and the relative humidity is close to 100%, but there is a limit to predicting the intensity of icing occurrence using only two meteorological variables. To solve this limitation, the present invention can predict the intensity of icing occurrence by additionally using vertical wind speed and cloud water content related to icing occurrence.

For example, vertical wind speed is a meteorological variable related to the creation and dissipation of clouds. When rising air currents exist, clouds are created, and the adiabatic expansion of the air parcel lowers the internal temperature and reduces the saturated water vapor pressure, leading to condensation of water vapor. Conversely, the descending airflow increases the internal temperature due to the adiabatic compression of the air parcel, suppressing water vapor condensation and leading to the dissipation of the cloud. In other words, strong rising air currents can create more water droplets and cause strong icing. Therefore, the vertical wind speed can be used as a variable in the icing prediction model 220 to predict a dangerous icing area where strong icing occurs.

In addition, the amount of cloud sap directly indicates the amount of supercooled water when the temperature is below freezing, so the greater the amount of sap, the stronger the intensity of icing. For example, the mixing ratio for each phase of cloud water droplets calculated in a numerical model may be different even in the same environment depending on the microphysical parameterization method. In other words, even if supercooled water droplets that cause icing exist in the actual environment, depending on the parameterization method, the numerical model can simulate the presence of particles such as snow that are unrelated to icing, instead of having no or low liquid phase (water droplet) mixing ratio. In this case, a problem may arise where the risk of icing is underestimated. Therefore, by using the cloud water content, which includes the cloud mixing ratio of both liquid and solid phases, as a variable in the icing prediction model 220, it is possible to prevent incorrect classification of phases and accurately predict the hazardous icing area.

The icing prediction model 220 is a fuzzy logic model built using membership functions defined for each meteorological variable, and is a membership function for temperature, a membership function for relative humidity, and a membership function for vertical wind speed according to preset fuzzy logic. And the icing potential can be output by performing the calculation of the membership function for the cloud water content.

That is, the icing prediction model 220 can calculate the icing potential when temperature, relative humidity, vertical wind speed, and cloud water content are input as weather variable information.

Figures 3 and 4 are diagrams for explaining the construction process of an icing prediction model according to an embodiment of the present invention.

Because the icing prediction model 220 has its own unique prediction characteristics, the importance of the prediction accuracy of each variable for the icing environment may be different. At this time, importance can be viewed as the weight assigned to each variable in the fuzzy logic algorithm. In order to select a weight combination suitable for the Korea Meteorological Administration's global integrated model, the icing prediction model 220 statistically verified 230 weight combinations against observation data and set the combination that showed the highest prediction accuracy.

For example, icing observation data for building the icing prediction model 220 is collected indirectly by a pilot subjectively determining the severity of icing and reporting it to the ground control center. This indirect collection method is called Pilot Report (PIREP), and when icing occurs during the operation of an (irregular) flight, the pilot reports to ground control after escaping the emergency situation (after responding to the icing). Report location, time, and severity of icing. Since there are thousands of flights per day around the world, the number of data is much larger than direct observation data. Here, direct observation data refers to data that directly observes the meteorological environment by intentionally flying over an area where icing occurs for purposes such as research.

Therefore, the present invention utilized PIREP to secure as much data as possible when building the icing prediction model 220. Here, since PIREP includes information on the location of occurrence (longitude, latitude, altitude), time, intensity of icing occurrence, etc., prediction results of the icing prediction model 220 for icing-related variables can be obtained for the location and time. there is.

As described above, in the present invention, in order to improve icing predictability, the amount of ice crystals in the cloud, which is solid as well as liquid, can be used as a meteorological variable. However, PIREP includes a type of engine icing (many ice crystals are sucked into the engine and engine failure) that is different from the type of icing intended to be predicted in the present invention. Therefore, excluding engine icing types, PIREPs reported below 25000 ft were used. The PIREP data used in the present invention was collected over a period of approximately 3 years from October 2015 to July 2018. In the data, the icing intensity is divided into null, trace-light, light-moderate, moderate, moderate-severe, and severe. The number of reports for each intensity is as shown in Figure 3.

The membership function defined for each meteorological variable represents the potential for icing for each variable's value. Therefore, the frequency of specific values of the predicted (observed) variable at icing occurrence points can be considered to correspond to the potential for icing. Based on this fact, the icing prediction model (20) calculated the predicted values for each algorithm variable simulated by UM at the point where icing of medium or higher intensity was reported using the probability density function and cumulative distribution function. Each membership function is defined by Equation 1 to Equation 4 to follow a probability density function or cumulative distribution function.

The membership function (M _T ) for temperature is as shown in Equation 1.

<Formula 1>

At this time, T is the temperature and weather variable information predicted from UM, and T ₁ = -30℃, T ₂ = -20℃, T ₃ = -10℃, T ₄ = -5℃, T ₅ = 1℃.

The membership function (M _RH ) for relative humidity is as shown in Equation 2.

<Formula 2>

At this time, RH is relative humidity and weather variable information predicted from UM, and RH ₁ = 30%, RH ₂ = 60%, RH ₃ = 80%, RH ₄ = 90%, and RH ₅ = 100%.

The membership function (M _w ) for vertical wind speed is given in Equation 3.

<Formula 3>

At this time, w is the vertical wind speed and weather variable information predicted from UM, w ₁ = -0.1 m/s, w ₂ = 0 m/s, w ₃ = 0.05 m/s, w ₄ = 0.1 m/s. .

The membership function (M _CWC ) for cloud water content is given in Equation 4.

<Formula 4>

At this time, CWC is the cloud water content and weather variable information predicted from UM, and CWC ₁ = 0.05g/kg, CWC ₂ = 0.1g/kg, CWC ₃ = 0.2g/kg, and CWC ₄ = 0.4g/kg.

As an example, the icing prediction model 220 may express the membership functions of Equations 1 to 4 defined for each meteorological variable as Equation 5 according to a preset fuzzy logic. That is, the icing prediction model 220 can calculate the icing potential of a single forecast field through Equation 5.

<Formula 5>

Here, M _T represents the membership function for temperature, M _RH represents the membership function for relative humidity, M _w represents the membership function for vertical wind speed, and M _CWC represents the membership function for cloud water content. The coefficients for each membership function may be set to, for example, a=0.5, b=0.15, and c=0.35, but the present invention is not limited thereto. The icing severity is calculated as a number between 0 and 1, and the larger the number, the greater the possibility of icing occurring and the stronger the expected occurrence intensity.

As another example, the icing prediction model 220 may include Equation 6 based on a time-lagged ensemble that uses multiple prediction results simultaneously.

Time lag ensemble is a method of utilizing forecast data produced at multiple times for one forecast effective time. For example, if icing forecast information is produced at 00 UTC on October 15, 2022, the 6-hour forecast produced at 18 UTC on the 14th, the 12-hour forecast produced at 12 UTC on the 14th, and the 6-hour forecast produced at 06 UTC on the 14th. Multiple forecast data can be used simultaneously, such as 18-hour forecast data produced at 00 UTC on the 14th.

As an example, Figure 4 is an example of using a 6-hour interval, 12-36 hour time lag ensemble. The current weather numerical model produces forecast data every day at 00, 06, 12, and 18 Coordinated Universal Time (UTC) for 3 to 84 hours at 3-hour intervals and 84 to 288 hours (00 and 12 UTC only) at 6-hour intervals. do. In other words, due to the nature of numerical weather models, the longer the prediction time, the lower the prediction accuracy. Therefore, the icing prediction model 220 designed Equation 6 based on time lag ensemble, using only forecast data up to 48 hours at 6-hour intervals.

<Formula 6>

Accordingly, Equation 6 can predict a value (icing potential) that is closer to the actual value than a single forecast field (Equation 5) by using multiple prediction results simultaneously.

Here, M _T is a membership function for temperature, M _RH is a membership function for relative humidity, M _w is a membership function for vertical wind speed, and M _CWC is a membership function for cloud water content. In addition, the subscript 6t refers to the prediction time in 6-hour intervals (6, 12, ..., 48), N refers to the number of prediction data used (8), and W refers to the weight for each prediction time.

Considering that prediction accuracy decreases as the prediction time becomes longer, the weight (W) for each prediction time can be defined as Equation 7.

At this time, the prediction time, number of prediction data, and weights can be set differently depending on the data of the numerical weather model used. To determine the weight, you can use future observation data, etc., and use one of the predictive quantitative indicators, such as Area Under Receiver Opearting Characteristics Curve (AUC), Root Mean Square Error (RMSE), and bias.

<Formula 7>

The post-processing unit 230 vertically converts the icing potential on a three-dimensional coordinate system to the flight altitude on grid coordinates, and can calculate the icing potential for each unit of flight altitude (FL) using an interpolation method. Subsequently, the post-processing unit 230 may filter out areas where the icing potential is below the threshold on the grid coordinates and calculate the highest and lowest altitudes of the areas where the icing potential is above the threshold.

For example, the post-processing unit 230 can interpolate the icing potential into 30 layers in units of FL010 from FL010 to FL300 of the flight altitude. Afterwards, in order to visualize areas where strong icing is expected to occur, areas with an icing potential of less than 0.2 are filtered on the dimensionally transformed grid coordinates, and the lowest and highest elevations of areas with an icing potential of 0.2 or more on the grid coordinates can be calculated. In other words, the area with 0.2 or more can be provided as the icing risk area.

Figures 5A to 5C are diagrams showing examples of icing prediction areas provided by a result output unit according to an embodiment of the present invention.

The result output unit 240 provides an icing expected area by visualizing the maximum icing potential for the entire flight altitude, and provides each icing risk area by visualizing the icing potential calculated by the highest and lowest altitudes. .

For example, the expected icing area provides the maximum icing potential in the vertical direction as shown in the horizontal plane (shown in Figure 5a), and the icing risk area provides the result showing the highest elevation where strong icing is expected (shown in Figure 5b). and the lowest elevation can be provided as a result (shown in Figure 5c). That is, the present invention can quickly and simply confirm the expected area of strong icing in three dimensions through the results shown in Figures 5a to 5c.

By way of example, the icing potential shown in FIG. 5A may be calculated as a value between 0 and 1. An icing potential between 0 and 0.07 can be interpreted as low-intensity icing (a level that may be problematic when flying in that environment for more than an hour). An icing potential between 0.07 and 0.2 can be interpreted as mild-to-medium intensity icing occurring. An icing potential of 0.2 or higher can be interpreted as icing of medium or higher intensity (a level that requires preparation, such as operation of an icing prevention system).

Hereinafter, descriptions of the same components among those shown in FIGS. 1 to 5C will be omitted.

Figure 6 is a flow chart illustrating a method for predicting aircraft icing based on a numerical meteorological model according to another embodiment of the present invention.

Referring to FIG. 6, the aircraft icing prediction method performed by the aircraft icing prediction device based on a meteorological numerical model includes inputting the collected meteorological variable information into the icing prediction model to calculate the icing potential (S110), and setting the icing potential to a threshold value. It includes a step of comparing with (S120) and a step of providing information on the icing expected area and the icing risk area according to the comparison result (S130).

At this time, the icing prediction model is a fuzzy logic model built using membership functions defined for each meteorological variable.

The icing prediction model is meteorological variable information, and when temperature, relative humidity, vertical wind speed, and cloud water content are input, a membership function for temperature, a membership function for relative humidity, and a membership function for vertical wind speed follow a preset fuzzy logic. The icing potential can be output by performing calculations on the membership function for the function and cloud water content.

Step S110 may include receiving weather variable information predicted in a three-dimensional coordinate system from the global integrated model according to a preset period.

Step S120 is a step of vertically converting the icing potential on the 3D coordinate system to the flight altitude on grid coordinates, and calculating the icing potential for each unit of flight altitude using an interpolation method, where the icing potential on the grid coordinates is less than the threshold. It may include filtering the area, and calculating the highest and lowest altitudes of the area that are above the threshold.

Step S130 may include providing an icing expected area by visualizing all of the maximum icing potential for the entire flight altitude, and providing each icing risk area by visualizing the icing potential calculated by the highest and lowest altitudes. .

One embodiment of the present invention may also be implemented in the form of a recording medium containing instructions executable by a computer, such as program modules executed by a computer. Computer-readable media can be any available media that can be accessed by a computer and includes both volatile and non-volatile media, removable and non-removable media. Additionally, computer-readable media may include computer storage media. Computer storage media includes both volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data.

Although the devices and methods of the present invention have been described with respect to specific embodiments, some or all of their components or operations may be implemented using a computer system having a general-purpose hardware architecture.

The description of the present invention described above is for illustrative purposes, and those skilled in the art will understand that the present invention can be easily modified into other specific forms without changing the technical idea or essential features of the present invention. will be. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive. For example, each component described as unitary may be implemented in a distributed manner, and similarly, components described as distributed may also be implemented in a combined form.

The scope of the present invention is indicated by the claims described below rather than the detailed description above, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be construed as being included in the scope of the present invention. do.

10: Numerical meteorological model
100: Aircraft icing prediction device
110: Data transmission/reception module
120: memory
130: processor
140: database

Claims (10)

In the aircraft icing prediction device based on a meteorological numerical model,
Data transmission/reception module;
A memory in which an aircraft icing prediction program is stored; and
It includes a processor that executes a program stored in the memory,
The aircraft icing prediction program calculates the icing potential by inputting the collected weather variable information into the icing prediction model, and provides information on the icing expected area and icing risk area according to the result of comparing the icing potential with the threshold. Provide,
The icing risk area represents an area where the icing potential is above a threshold,
The icing prediction model is a fuzzy logic model built using a membership function defined for each meteorological variable.
According to paragraph 1,
The aircraft icing prediction program is
An aircraft icing prediction device that receives the weather variable information predicted in a three-dimensional coordinate system from a global integrated model according to a preset period.
According to paragraph 1,
The aircraft icing prediction program is
The icing potential on a three-dimensional coordinate system is vertically converted to a flight altitude on grid coordinates, the icing potential is calculated for each unit of the flight altitude using an interpolation method, and the icing potential on the grid coordinates is set to a threshold value. An aircraft icing prediction device that filters the area below the threshold and calculates the highest and lowest altitudes of the area above the threshold.
According to paragraph 3,
The aircraft icing prediction program is
An aircraft icing prediction device that provides an icing expected area by visualizing the maximum icing potential for the entire flight altitude, and provides each icing risk area by visualizing the icing potential calculated for the highest and lowest altitudes.
According to paragraph 1,
The icing prediction model is
When temperature, relative humidity, vertical wind speed, and cloud water content are input as the weather variable information,
An aircraft that outputs the icing potential by performing calculations of a membership function for temperature, a membership function for relative humidity, a membership function for vertical wind speed, and a membership function for cloud water content according to a preset fuzzy logic. Icing prediction device.
In the aircraft icing prediction method performed by an aircraft icing prediction device based on a meteorological numerical model,
(a) inputting the collected weather variable information into an icing prediction model to calculate the icing potential;
(b) comparing the icing potential to a threshold; and
(c) including providing information on the icing expected area and icing risk area according to the comparison results,
The icing risk area represents an area where the icing potential is above a threshold,
The icing prediction model is a fuzzy logic model built using a membership function defined for each meteorological variable.
According to clause 6,
The step (a) is
A method for predicting aircraft icing, comprising receiving the weather variable information predicted in a three-dimensional coordinate system from a global integrated model according to a preset period.
According to clause 6,
Step (b) above is
Vertically converting the icing potential in a three-dimensional coordinate system to a flight altitude in grid coordinates, and calculating the icing potential for each unit of the flight altitude using an interpolation method,
filtering out areas where the icing potential is below a threshold on the grid coordinates, and
A method for predicting aircraft icing, comprising the step of calculating the highest altitude and lowest altitude of the area above the threshold.
According to clause 8,
The step (c) is
An aircraft comprising the steps of providing an icing expected area by visualizing all of the maximum icing potential for the entire flight altitude, and providing each icing risk area by visualizing the icing potential calculated for the highest and lowest altitudes. How to predict icing.
According to clause 6,
The icing prediction model is
When temperature, relative humidity, vertical wind speed, and cloud water content are input as the weather variable information,
An aircraft that outputs the icing potential by performing calculations of a membership function for temperature, a membership function for relative humidity, a membership function for vertical wind speed, and a membership function for cloud water content according to a preset fuzzy logic. How to predict icing.

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