Method for analyzing risk of encountering wake of small single-engine training aircraft operating in mixed traffic

Abstract

The application relates to the technical field of aircraft wake risk analysis, and discloses a mixed operation airport small single-engine training aircraft wake encounter risk analysis method. Flight operation programs and training flight subject information are collected, key flight features are extracted, average motion trajectories of typical flight tasks of public transport aircraft and small training aircraft are obtained through cluster analysis, then trajectory and intersection point analysis is carried out, wake numerical simulation analysis of the trajectory intersection point is carried out based on CFD, a flight wake vortex wake dissipation database is constructed, finally, the improved strip band method is used to calculate the change of the additional lift caused by the wake vortex field of each part, and then the rolling moment coefficient of the small training aircraft when encountering the wake is calculated, and the wake encounter risk is evaluated. The application can accurately evaluate the risk of a general single-engine trainer aircraft encountering the wake of a front civil aviation transport aircraft during take-off, approach and other stages in a mixed use airport, and thus improve the operation efficiency of the mixed operation airport.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft wake turbulence risk analysis, and in particular to a method for analyzing the wake turbulence encounter risk of a small single-engine training aircraft at a mixed-operation airport. Background Art

[0002] As a byproduct of aircraft lift, aircraft wake vortices are characterized by being intense, stable, and large in spatial scale. During takeoff, cruising, and approach phases, due to their long residence time, when a trailing aircraft enters the wake region of the preceding aircraft, the rolling torque induced by their strong vortex structure can cause the fuselage of the trailing aircraft to vibrate, sink, and change its flight state, leading to flight accidents. This threat is particularly pronounced for small, general-purpose, single-engine trainer aircraft. Exploring the dissipation characteristics of aircraft wake vortices in the atmospheric environment, clarifying the operating modes of mixed-use airports, and rationally planning the wake vortex separation between general-purpose, single-engine trainer aircraft and civil transport aircraft are effective ways to ensure civil aviation operational safety, ensure normal flight training, and increase the airspace capacity of mixed-use airports.

[0003] To mitigate potential safety hazards posed by wake turbulence from preceding aircraft, the International Civil Aviation Organization (ICAO) and the U.S. Federal Aviation Administration (FAA) have established comprehensive wake turbulence separation standards based on meteorological conditions, air traffic control measures, and runway availability. Based on ICAO standards, my country's Air Traffic Management Committee and the Civil Aviation Administration of my country have also promulgated their own wake turbulence separation standards and established relevant operational specifications to ensure the operational safety of arriving and departing aircraft.

[0004] The above standards achieved relatively significant results in the initial stage of implementation, and to a large extent avoided the occurrence of aviation safety accidents caused by aircraft wake turbulence. However, the dissipation and transmission patterns of wake turbulence vary greatly with different meteorological conditions. The existing separation standards are too conservative under most meteorological conditions. If strictly enforced, they will severely limit the capacity of airport runways, greatly affecting the operating efficiency of airports and restricting the development of the aviation transportation industry. In addition, the current wake turbulence separation standards are mainly applied to the approach phase of large civil aviation transport aircraft. There is still a lack of research on wake turbulence safety separation standards between civil aviation transport aircraft and general single-engine trainer aircraft at mixed-use airports.

[0005] At present, domestic and foreign scholars mainly use computational fluid dynamics methods to study the wake dissipation mechanism and use the strip method to study the risk of following the aircraft wake.

[0006] 1) Numerical simulation method of aircraft wake vortex:

[0007] The numerical simulation methods commonly used to solve turbulence are also applicable to solving aircraft wake vortices. The basic idea is to describe the evolution of turbulence by solving the three-dimensional Navier-Stokes equations (NS equations) while satisfying the conservation laws of the fluid. However, due to different computational conditions, different purposes for studying turbulence, and different levels of sophistication in solving turbulence, the numerical simulation methods used also vary. Generally speaking, the numerical simulation methods for aircraft wake vortices can be divided into two categories: direct numerical simulation and indirect numerical simulation. Reynolds-averaged numerical simulation is the most widely used method in indirect numerical simulation calculations.

[0008] In actual research, if the only target of study is the time-averaged quantities of turbulent motion characteristics (such as the mean velocity field and mean force), the NS equations can be ensemble-averaged to quickly simulate turbulent motion by solving the time-averaged NS equations. This method of averaging the unsteady NS equations over time to solve the time-averaged quantities required in engineering is known as Reynolds-averaged simulation.

[0009] 2) Aircraft wake vortex encounter risk assessment method:

[0010] The strip method is a calculation method used to analyze the rolling moment experienced by an aircraft encountering the wake turbulence of a preceding aircraft. This method divides the aircraft's wing into multiple strips and calculates the aerodynamic forces within each strip, ultimately deriving the aerodynamic forces and moments for the entire wing. This method is widely used in aircraft wake turbulence encounter risk analysis because it accurately simulates the aerodynamic response of an aircraft in complex flow fields.

[0011] The basic idea of ​​the strip method is to simplify the aircraft wing into a series of parallel strips, each with identical aerodynamic characteristics. The effects of the fuselage are ignored during the calculation, and only the aerodynamic effects of the wing, horizontal stabilizer, and vertical stabilizer are considered. For each strip, the change in lift caused by the trailing vortex of the leading aircraft is calculated, thereby deriving the induced moment on that strip.

[0012] In summary, existing technologies are mainly developed for the wake turbulence separation of public transport aircraft during the approach phase. Generally, numerical simulation or lidar observation is used to obtain the aircraft wake turbulence dissipation conditions at a single meteorological condition and a single flight altitude glide path. With the help of the strip method, the wing of the trailing aircraft is selected as the main wake action surface for analysis, and the rolling moment and rolling moment coefficient of the wake of the leading aircraft on the trailing aircraft are obtained, thereby determining the risk of the trailing aircraft encountering the wake turbulence.

[0013] However, at mixed-operation airports, due to the limited performance and anti-disturbance capabilities of small single-engine training aircraft, wake turbulence encounters pose a serious threat to flight safety. Existing wake turbulence risk analysis methods lack comprehensive consideration of the airport operating environment, aircraft performance differences, and wake turbulence characteristics. This makes it difficult to accurately and comprehensively assess the wake turbulence risk faced by small single-engine training aircraft when operating alongside large aircraft, and thus fail to provide effective risk warnings and decision-making support for pilots and air traffic controllers. This is specifically reflected in the following three aspects:

[0014] 1) Lack of aircraft trajectory analysis at mixed-operation airports:

[0015] Due to their weight and engine thrust, different types of aircraft have different takeoff and landing trajectories. This is especially true for small training aircraft, whose flight paths differ significantly from traditional takeoff and landing routes due to their different training subjects. Existing wake separation analysis methods fail to fully consider the trajectories of public transport aircraft and training aircraft, and tend to overlook the overlap between their wake turbulences, resulting in excessive wake separation and impacting airport operational efficiency.

[0016] 2) The numerical simulation environment for aircraft wake vortex is limited:

[0017] Public transport aircraft typically approach at a 3° glide path angle, with their altitude above the ground and approach speed constantly changing. Furthermore, due to complex meteorological conditions, wind direction and speed in the glide path area can also vary significantly. However, existing wake vortex analysis methods are mostly conducted under standardized meteorological conditions, such as stable wind speed and pressure, with a specific approach altitude selected for evolution analysis. This single simulation environment cannot accurately reflect the complex and variable real-world meteorological conditions, such as air turbulence and wind direction changes. Furthermore, it is difficult to characterize where and when the wake vortex impact is strongest in the glide path area, which directly affects the accuracy of wake vortex risk assessment.

[0018] 3) Conventional strip method is not applicable:

[0019] The currently used strip method is mainly designed for large aircraft. Since the fuselage of public transport aircraft is approximately cylindrical or conical, the rolling moment generated by the wake effect is relatively small and is often ignored. However, when applied to small single-engine training aircraft, this method slightly reduces its accuracy and applicability because it ignores the differences in the physical properties and operating characteristics of the rectangular cross-section fuselage of small training aircraft. Summary of the Invention

[0020] To address the above issues, the present invention aims to provide a method for analyzing the risk of small single-engine training aircraft encountering wake turbulence at mixed-use airports. This method can accurately assess the risk of a general-purpose single-engine trainer aircraft encountering the wake turbulence of a preceding civil transport aircraft during takeoff and approach at mixed-use airports. This method provides a theoretical basis for reducing the wake turbulence separation between civil transport aircraft and general-purpose single-engine trainer aircraft at mixed-use airports, thereby improving the operational efficiency of mixed-use airports. The technical solution is as follows:

[0021] The wake turbulence encounter risk analysis method for small single-engine training aircraft at mixed-operation airports includes the following steps:

[0022] Step 1: Collect flight operating procedures and training flight subject information, extract key flight characteristics; use ADS-B data receiving equipment to obtain real-time flight data of public transport aircraft and small training aircraft, and store ADS-B data;

[0023] Step 2: Perform data cleaning, data interpolation, coordinate conversion, and unit conversion on the collected ADS-B data;

[0024] Step 3: Based on the processed ADS-B data, perform cluster analysis to obtain the average trajectory of typical flight missions for public transport aircraft and small training aircraft.

[0025] Step 4: Operational track and intersection analysis: Compare the average motion tracks of public transport aircraft and small training aircraft, analyze their relative positions and motion relationships in the air, and calculate the position and time of the track intersection, as well as the track angle and time interval at the intersection;

[0026] Step 5: Based on the analysis results of step 4, numerical simulation analysis of the wake vortex at the track intersection is performed using computational fluid dynamics technology to construct a database of aircraft wake dissipation under different meteorological conditions, different altitudes, and different speeds.

[0027] Step 6: Combined with the aircraft wake dissipation database, using the improved strip method, the aircraft is divided into three parts: the wing, fuselage, and tail. The additional lift changes caused by the wake vortex field in each part are calculated separately. Based on this, the rolling moment coefficient of the small training aircraft when encountering the wake is calculated to assess the risk of wake encounter.

[0028] The beneficial effects of the present invention are:

[0029] 1) This invention comprehensively considers the differences in flight missions and flight performance between different aircraft types at mixed-operation airports, and introduces a trajectory analysis technology that comprehensively considers the operation of different aircraft types (including training aircraft and commercial transport aircraft), ensuring a more comprehensive and detailed wake turbulence risk assessment.

[0030] 2) The present invention constructs a database of wake evolution and dissipation under different meteorological conditions, different altitudes, and multiple speeds for multiple aircraft models to improve the applicability and accuracy of risk assessment.

[0031] 3) This paper designs a wake turbulence risk assessment method suitable for small training aircraft. By optimizing the strip method, it overcomes the shortcomings of traditional methods that approximate the fuselage as a cylinder or cone and ignore the treatment. It further refines the calculation process of the rolling moment coefficient of small training aircraft, and improves the adaptability and accuracy of the calculation of changes in the aerodynamic performance of small aircraft due to wake turbulence. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The present invention is a flowchart for the specific implementation of the method for analyzing the wake turbulence encounter risk of a small single-engine training aircraft at a mixed-operation airport.

[0033] Figure 2 Flowchart of aircraft trajectory analysis for mixed-operation airports.

[0034] Figure 3 The flowchart of the numerical simulation analysis and database construction of aircraft wake in the present invention.

[0035] Figure 4 This is a flow chart of wake risk area analysis based on the improved strip method of the present invention.

[0036] Figure 5 Schematic diagram of the strip model. DETAILED DESCRIPTION

[0037] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, it mainly includes three parts: analysis of the operation trajectories of each aircraft at mixed-operation airports, numerical simulation analysis of aircraft wakes and construction of a wake database, and analysis of wake risk areas based on the improved strip method. Through the analysis of the operation trajectories of each aircraft at mixed-operation airports, the types of aircraft operations, flight procedures, and related operating parameters (such as speed, altitude, etc.) are mastered. Combined with the aerodynamic configuration and motion state of the aircraft, with the help of fluid simulation software, numerical simulation of aircraft wake evolution and dissipation is carried out, the wake dissipation characteristics under different meteorological environments are extracted, and a wake evolution database is constructed. With reference to the types and configurations of following small training aircraft, an improved strip method is developed to carry out analysis of the aerodynamic response characteristics of following aircraft wake encounters, and risk areas for small single-engine training aircraft wake encounters are delineated to provide safety guarantees for training flights at mixed-operation airports. The specific steps are as follows:

[0038] 1. Analysis of the flight paths of various aircraft at mixed-operation airports.

[0039] The process of analyzing the trajectory of each aircraft in a mixed-operation airport of the present invention is as follows: Figure 2 The specific process is as follows:

[0040] Step 1: Data collection;

[0041] (1) Collect information on flight operation procedures and various training flight subjects;

[0042] Flight operation procedures: Combined with the flight procedure chart published in the "Domestic Navigation Information Compilation", extract information including runway selection for takeoff and landing, climb and descent paths, cruising altitude, etc.

[0043] Training flight subject information: Check the flight training institution's syllabus, training plan, flight records and other information to obtain training flight subject information for small training aircraft, including training area, flight altitude range, flight speed range, flight path, etc.

[0044] (2) Obtain flight data;

[0045] ADS-B (Automatic Dependent Surveillance-Broadcast) Data Reception: Due to the relatively high cost and poor real-time performance of ground-to-air data link transmission, small training aircraft, due to their short mission times, have limited suitability for data link communication modules and are not always equipped with them. However, ADS-B modules offer relatively low transmission costs and faster update rates, making them suitable for small aircraft surveillance. Therefore, ADS-B receivers can be used to receive real-time flight data from public transport aircraft and small training aircraft. ADS-B signals contain key information such as the aircraft's identification number, position (latitude and longitude), altitude, speed, and heading.

[0046] Data storage: The received ADS-B data is stored in a database for subsequent processing and analysis. This can be done using a relational database (such as MySQL) or a non-relational database (such as MongoDB).

[0047] Step 2: Data processing;

[0048] (1) Data cleaning;

[0049] Outlier removal: Data cleaning is a crucial step in data preprocessing, aiming to remove noise, errors, and inconsistencies, thereby improving data quality and reliability. ADS-B data can experience jumps due to the surrounding terrain and electromagnetic environment. A boxplot analysis is used to examine ADS-B data for outliers, such as unreasonable location coordinates or abnormal speeds. The data distribution is described using quartiles (Q1, Q2, and Q3) and the interquartile range (IQR). Data points with a value less than Q1 minus 1.5 × IQR or greater than Q3 plus 1.5 × IQR are generally considered outliers and are removed.

[0050] Data interpolation: For missing data points, linear interpolation is used to supplement them to ensure data continuity. The formula is as follows:

[0051] ;

[0052] Where, is a missing value, and is a known value, and is a known point in time, t is a time variable.

[0053] (2) Data conversion;

[0054] Coordinate conversion: The Mercator projection is used to convert the geographic coordinates (longitude and latitude) in the ADS-B data into coordinates in the Cartesian coordinate system for trajectory analysis. The formula is as follows:

[0055] ;

[0056] Where, and are Cartesian coordinates, is the radius of the Earth, is the longitude, It is latitude.

[0057] Unit conversion: Convert the units of speed, altitude and other data to a unified level, such as converting speed from knots to meters per second and converting altitude from feet to meters.

[0058] Step 3: Obtain the average motion trajectory of typical aircraft for typical flight missions through cluster analysis: Based on the processed ADS-B data, the average motion trajectory of typical flight missions of public transport aircraft and small training aircraft is obtained through cluster analysis.

[0059] Step 3.1: Feature extraction;

[0060] Extract flight features: Extract key features from the processed flight data, such as position coordinates, speed, altitude, heading, etc., for cluster analysis.

[0061] Feature standardization: The extracted features are standardized using the Z-score standardization method to eliminate the dimensional differences between different features.

[0062] Step 3.2: Cluster analysis;

[0063] Select clustering algorithm: Select K-Means clustering algorithm for cluster analysis of motion data.

[0064] Determine the number of clusters: The number of clusters is determined using the elbow rule. The elbow rule calculates the sum of squared clustering errors for different numbers of clusters and selects the number of clusters that produces the fastest decrease in the sum of squared errors.

[0065] Step 3.3: Get the average motion trajectory;

[0066] Calculate the average trajectory: The flight trajectories generated by each cluster are averaged to obtain the average trajectory for a typical aircraft and typical flight missions. Using multiple linear regression, we automatically fit flight trajectories within a fixed time interval to form a short-term flight trajectory formula for trajectory analysis. The time interval is calculated based on the time it takes for five ADS-B data releases to avoid problems such as overly complex formula fitting and lengthy calculation times.

[0067] Step 4: Analysis of the operational trajectories of public transport aircraft and small training aircraft: Analyze the operational trajectories and intersection points, compare the average motion trajectories of public transport aircraft and small training aircraft, analyze their relative positions and motion relationships in the air, calculate the position and time of the track intersection, as well as the track angle and time interval at the intersection.

[0068] Step 4.1: Run trajectory comparison;

[0069] Comparing operational trajectories: Compare the average trajectories of public transport aircraft and small training aircraft to analyze their relative positions and motion relationships in the air. This comparison can be done by plotting trajectories and calculating the distance between trajectories.

[0070] Analyze flight altitude and speed: Compare the flight altitude and speed changes of two types of aircraft during different flight phases to analyze the differences in their flight performance. This analysis can be performed by plotting altitude-time curves or speed-time curves.

[0071] Step 4.2: Flight area analysis;

[0072] Determine the flight area: Based on the average motion trajectories of the two types of aircraft, calculate the boundaries of the trajectories and determine their main flight areas.

[0073] Analyze flight density: Calculate the flight density of two types of aircraft in different flight areas and analyze the distribution of flight activities. Flight density can be calculated by counting flight time and flight count in different areas.

[0074] Step 4.3: Determine the intersection point;

[0075] Intersection point calculation based on trajectory model: Utilize the average motion trajectory model of two types of aircraft, combined with the runway usage plan of mixed-operation airports, to calculate the position and time of air intersection points.

[0076] Intersection verification based on actual flight data: Utilize actual flight data to verify the location and time of intersections to ensure their accuracy.

[0077] Step 4.4: Crossing angle and time interval analysis;

[0078] Intersection angle calculation: Calculate the angle between the flight paths of two types of aircraft at the intersection point, and analyze the size and distribution of the intersection angle.

[0079] Time interval calculation: Calculate the time interval between two types of aircraft at the intersection point and analyze the size and distribution of the time interval. The size of the time interval is also an important factor in the risk of wake turbulence encounter.

[0080] The calculated air trajectory intersection information is summarized and used for auxiliary configuration of wake numerical simulation environment, etc.

[0081] 2. Numerical simulation analysis of aircraft wake and database construction.

[0082] The present invention carries out the numerical simulation analysis and database construction process of aircraft wake as follows Figure 3 shown.

[0083] Step 5: Numerical simulation and analysis of the wake vortex at the intersection of the track based on CFD (Computational Fluid Dynamics): Based on the analysis results of Step 4, a numerical simulation and analysis of the wake vortex at the intersection of the track is performed using computational fluid dynamics technology to construct a database of aircraft wake vortex dissipation under different meteorological conditions, different altitudes, and multiple speeds.

[0084] Step 5.1: Analyze and construct the aircraft wake vortex evolution environment;

[0085] The intersection points of public transport aircraft and small training flight trajectories or the areas covered by wake vortices include approach and landing areas, takeoff and go-around areas, etc., which are mostly near the ground. In order to better simulate the evolution and dissipation process of the wake vortex and save computing resources, the near-ground computational flow field can be constructed based on the intersection time situation, and the grid size is set to 0.021b0, where b0 is the initial vortex core spacing (approximately 0.25π times the wingspan). The flow field is set to more than 10 times the wingspan length along the wingspan direction and perpendicular to the wingspan direction to reduce the influence of boundary effects on the numerical simulation results.

[0086] Step 5.2: Select the initial tail vortex tangential velocity model;

[0087] After the aircraft tail vortex is formed, it evolves and merges to reach a stable state at a distance of about 10 times the wingspan. The Hallock-Burnham tangential velocity model, as a classic aircraft tail vortex tangential velocity model, can effectively describe the velocity field distribution in the tail vortex region at this state. The model formula is expressed as follows:

[0088] ;

[0089] Where, is the radius from the vortex center r The tangential velocity at is the initial vortex ring volume, is the radius of the initial vortex core. This model is selected as the initial tail vortex model for research, and the UDF file is written based on the analytical expression of the tail vortex tangential velocity model.

[0090] Turbulence model selection;

[0091] Considering the computational resources and the calculation cost of the example, the Reynolds time-averaged numerical simulation method can be used, and the The model closes the NS equations and realizes the numerical simulation of the evolution and dissipation behavior of the tail vortex of the intersection aircraft under the influence of different environments.

[0092] Step 5.3: Boundary condition setting;

[0093] The process of wake vortex dissipation varies significantly under the influence of different heights above the ground, crosswind speeds, turbulence intensity, and temperature. Crosswind speeds are particularly influential, resulting in the most severe consequences. However, due to the large number of variables, numerical simulations would be prohibitively expensive in terms of computational time and resource consumption, and would be difficult to achieve full coverage of the entire scenario. Therefore, combined with actual measurements from airport weather stations, data such as wind speed profiles, turbulence intensity, and temperature gradients can be mapped to computational domain boundary conditions and subjected to isometric decomposition. This allows wake vortex calculations to be performed under typical environmental parameters, reducing the computational effort required for actual numerical simulations.

[0094] Step 5.4: Solver configuration;

[0095] A pressure-based solver with faster convergence speed was selected, and the Simple algorithm was chosen for the solution. The node-based Green-Gauss method was used for the gradient term, and the pressure, momentum, turbulent kinetic energy, and turbulent diffusion terms were all second-order upwind.

[0096] Step 5.5: Extract key output parameters of wake flow;

[0097] The wake dissipation data obtained from numerical simulation are sorted out, including the temporal and spatial variation patterns of parameters such as wake area velocity, turbulence intensity, circulation attenuation, and vortex core motion trajectory under different flight conditions.

[0098] Step 5.6: Construct an LSTM-based wake vortex dissipation prediction model, including an input layer, an LSTM layer, and an output layer; the input layer parameters include the speed, weight, altitude, wind speed, wind direction, temperature, humidity, and turbulence intensity of the leading aircraft; collect meteorological data in real time, fuse the real-time meteorological data with the wake data to form a complete data set, and input the data set into the LSTM-based wake vortex dissipation prediction model to perform wake dissipation prediction under typical conditions.

[0099] Construction and update of aircraft wake vortex dissipation database.

[0100] (1) Construction of a multi-dimensional parameterized aircraft wake vortex dissipation database:

[0101] Design the database table structure, including the wake data table, meteorological data table, and wake dissipation parameter table. The wake data table stores wake parameters such as velocity, turbulence intensity, and pressure; the meteorological data table stores parameters such as wind speed, wind direction, temperature, and humidity; and the wake dissipation parameter table stores parameters such as the wake dissipation rate and safety interval threshold. Index design: Create indexes for database tables to improve the efficiency of data query and update. For example, you can create an index on the time field to quickly query wake data within a certain time period. Organize the calculation results and perform multi-dimensional hierarchical storage. The format can be referred to in Table 1.

[0102] Table 1 Data storage style

[0103] .

[0104] Construction of a tail vortex dissipation prediction model based on LSTM (Long short term memory);

[0105] Build an LSTM network, consisting of an input layer, an LSTM layer, and an output layer. Multiple LSTM layers can be stacked to increase model complexity and predictive power. The input layer vector dimension must be at least 9, and parameters include the leading aircraft's speed, weight, altitude, wind speed, wind direction, temperature, humidity, and turbulence intensity.

[0106] (2) Real-time meteorological data input and fusion:

[0107] Meteorological data collection: Meteorological data, including wind speed, wind direction, temperature, humidity, etc., are collected in real time through meteorological sensors (such as anemometers, temperature sensors, etc.).

[0108] Data cleaning: Collected meteorological data is cleaned to remove outliers and noise. For example, wind speed data may be affected by sensor failure or environmental interference, requiring statistical methods to detect and remove outliers.

[0109] Data interpolation: Linear interpolation of missing meteorological data.

[0110] Data fusion: Fusion of real-time meteorological data with wake data to form a complete data set. Data fusion can be achieved by time synchronization or spatial matching.

[0111] Data input: The fused data is input into the LSTM model as the input feature of the model to carry out wake dissipation prediction under other typical environments.

[0112] (3) Database dynamic update mechanism:

[0113] Data Update: Batch updates can be performed by setting update trigger conditions and using MySQL's dynamic SQL update functionality to batch update the database's wake dissipation parameters. Incremental updates are performed based on real-time data changes, updating only the changed data records. The database dynamic update mechanism is shown in Table 2.

[0114] Table 2 Database dynamic update mechanism

[0115] .

[0116] 3. Analysis of wake risk areas based on the improved strip method.

[0117] Step 6: Combined with the aircraft wake vortex dissipation database, the aircraft is divided into three parts: wing, fuselage, and tail. By treating the vortex surface arranged on the fuselage surface as a plate, a vortex plate numerical method is introduced, and the calculation logic of the strip method is improved. The additional lift change caused by the wake vortex field of each part is calculated separately. Based on this, the rolling moment coefficient of the small training aircraft when encountering the wake vortex is calculated to assess the wake encounter risk.

[0118] The present invention is based on the improved strip method wake risk zone analysis process as follows Figure 4 The specific steps are as follows:

[0119] Selection of wake turbulence encounter safety indicators:

[0120] Due to the uneven distribution of induced velocity in the wake of the preceding aircraft, the magnitude and direction of the induced forces on both sides of the aircraft following the aircraft are inconsistent when encountering the wake. This in turn triggers a rolling moment in the aircraft following the aircraft, causing the aircraft to roll clockwise or counterclockwise. Simultaneously, as the roll occurs, the anhedral angle on the aircraft's wings, the wing sweep angle, and the vertical tail generate a roll damping moment, which helps automatically restore lateral balance. Furthermore, the pilot's manipulation of the ailerons also generates a stabilizing moment in the opposite direction of the roll, slowing the aircraft's roll tendency and thus restoring the aircraft to a state of lateral balance.

[0121] However, when the induced rolling moment caused by a wake turbulence encounter exceeds the aircraft's inherent controllable performance limit, the aircraft will lose lateral stability, potentially leading to uncontrollable roll and, in turn, a serious aviation accident. The RMC (Rolling Moment Coefficient) is an important indicator for measuring the severity of a wake turbulence encounter. Its formula is:

[0122] ;

[0123] Where, It is the rolling moment generated by the following aircraft encountering the wake turbulence. is the atmospheric density, 、 and They are the true airspeed, wing area and wingspan of the trailing aircraft respectively. During takeoff and approach, the limit value of the roll moment coefficient of light aircraft is approximately 0.05~0.07. When the overall roll moment coefficient of the aircraft exceeds this limit value, serious consequences may occur. This includes the possibility of losing control of the aircraft and causing uncontrolled rolling motion, causing the aircraft to continue to rotate and difficult to restore to normal attitude. Excessive roll moment may also cause the aircraft's roll angle to exceed the design limit, adversely affecting the aircraft structure and control system, resulting in structural damage or control system failure. In addition, strong rolling motion may also cause dangerous lateral motion, such as sideslip or tumbling, posing a threat to the safety of passengers and crew members. In summary, for safety reasons, 0.05 can be selected as the critical value of the roll moment coefficient.

[0124] Aerodynamic response analysis of small training aircraft:

[0125] When the following aircraft enters the induced velocity field formed by the front tail vortex flow field, the lift of the aircraft will change. Due to the different geometric parameters of each part of the aircraft, the corresponding lift calculation method is also different. In order to accurately calculate the aerodynamic force of the subsequent aircraft, the aircraft is divided into three parts: wing, fuselage and tail, combined with the fuselage structure. By treating the vortex surface arranged on the fuselage surface as a plate, the vortex plate numerical method is introduced to improve the strip method calculation logic. The strip model is as follows: Figure 5 shown.

[0126] For a small single-engine training aircraft with a rectangular fuselage cross-section, the specific calculation process is as follows:

[0127] Step 6.1: For a small single-engine training aircraft with a rectangular or similar fuselage cross-section, such as a Cessna 172, the additional lift change on the wing or tail caused by the wake vortex field can be obtained using the following formula:

[0128] ;

[0129] Where, is the atmospheric density, is the inflow velocity (i.e. the true airspeed of the aircraft), B is the wingspan of the aircraft, is the chord length of the wing at that coordinate, is the change in lift coefficient.

[0130] The change in lift coefficient is calculated by the following formula:

[0131] ;

[0132] Where, is the induced velocity of the trailing vortex field of the leading aircraft on the wing section of the trailing aircraft, is the slope of the wing lift line, which can be obtained from the following formula:

[0133] ;

[0134] ;

[0135] Where, is the aircraft aspect ratio; is the sweep angle of the wing's half-chord line; is the sweep angle of the leading edge; is the airfoil efficiency, usually taken as 0.95; Mɑ is the aircraft Mach number; It is the aircraft tip-to-root ratio.

[0136] Step 6.2: The chord length of a wing or tail section can be approximated as:

[0137] ;

[0138] Where, is the wing root chord length, S is the wing area, d is the distance between this point and the plane of symmetry of the aircraft.

[0139] Therefore, the torque can be obtained by The rolling moment of the wing is obtained L R It can be expressed as:

[0140] ;

[0141] Similarly, the rolling moment of the tail It can be expressed as:

[0142] ;

[0143] Where, is the tail wingspan, is the slope of the tail lift line.

[0144] Step 6.3: Based on the characteristics of the Cessna 172 fuselage and the vortex plate numerical method, the fuselage rolling moment is calculated as follows:

[0145] ;

[0146] Where, is the additional force generated by the fuselage, s ( x ) is the longitudinal length of the fuselage section, b is the span length of the fuselage.

[0147] Therefore, the rolling moment of the fuselage for:

[0148] ;

[0149] Step 6.4: Finally, using the rolling moment formula, we can find the rolling moment coefficient RMC :

[0150] ;

[0151] Step 6.5: Calculation of the danger zone for small training aircraft wake turbulence encounter:

[0152] (1) Calculation of wake turbulence hazard area;

[0153] Calculation of wake turbulence hazard areas for different aircraft types, altitude levels, and takeoff weights: Calculates the wake turbulence hazard area for different aircraft types, altitude levels, and takeoff weights. The extent of the wake turbulence hazard area can be determined through numerical simulation or empirical formulas. For example, the size and shape of the wake turbulence hazard area can be calculated for different aircraft types and combinations.

[0154] Analysis of the impact range of dangerous areas: Analyze the impact range of the wake dangerous area, including the impact range in the horizontal and vertical directions.

[0155] (2) Determination of safety interval;

[0156] Combine the overlapping range of the danger zone and the following aircraft's channel: Based on the intersection of the danger zone boundary and the following aircraft's channel, obtain the overlapping range of the wake turbulence danger zone and the following aircraft's channel, and determine the minimum wake turbulence safety interval.

[0157] Consider different aircraft model combinations: For different aircraft model combinations, such as super heavy aircraft, heavy aircraft, general heavy aircraft, medium aircraft and light aircraft, calculate their wake turbulence hazard areas and safety intervals respectively.

[0158] (3) Small training aircraft wake encounter risk database;

[0159] The calculated data of wake turbulence hazard areas are integrated with the aircraft wake vortex wake dissipation database to form a small training aircraft wake turbulence encounter risk database. The risk prediction, real-time data introduction, and dynamic database update steps are repeated to achieve precise control of risk areas.

Claims

1. A method for analyzing the risk of wake turbulence encounters for small single-engine training aircraft at mixed-operation airports, characterized by: The following steps are involved: Step 1: Collect flight operating procedures and training flight subject information, extract key flight characteristics; use ADS-B data receiving equipment to obtain real-time flight data of public transport aircraft and small training aircraft, and store ADS-B data; Step 2: Perform data cleaning, data interpolation, coordinate conversion, and unit conversion on the collected ADS-B data; Step 3: Based on the processed ADS-B data, perform cluster analysis to obtain the average trajectory of typical flight missions for public transport aircraft and small training aircraft. Step 4: Operational track and intersection analysis: Compare the average motion tracks of public transport aircraft and small training aircraft, analyze their relative positions and motion relationships in the air, and calculate the position and time of the track intersection, as well as the track angle and time interval at the intersection; Step 5: Based on the analysis results of step 4, numerical simulation analysis of the wake vortex at the track intersection is performed using computational fluid dynamics technology to construct a database of aircraft wake dissipation under different meteorological conditions, different altitudes, and different speeds. Step 6: Based on the aircraft wake dissipation database, the aircraft is divided into three parts: the wing, fuselage, and tail. By treating the vortex surface arranged on the fuselage surface as a plate, a vortex plate numerical method is introduced, and the calculation logic of the strip method is improved. The additional lift change caused by the wake vortex field of each part is calculated separately. Based on this, the rolling moment coefficient of the small training aircraft when encountering the wake vortex is calculated to assess the wake encounter risk. In step 6, for a small single-engine training aircraft with a rectangular fuselage cross-section, the specific calculation process is as follows: Step 6.1: Additional lift changes on the wing or tail caused by the vortex field Obtained by the following formula: ； Where, is the atmospheric density, is the true airspeed of the rear aircraft, is the wing span; is the chord length of the wing at the corresponding coordinate, is the change in lift coefficient; are Cartesian coordinates; Changes in lift coefficient Calculated by the following formula: ； Where, is the induced velocity of the trailing vortex field of the leading aircraft on the wing section of the trailing aircraft; is the slope of the wing lift line, which is obtained from the following formula: ； ； Where, is the aircraft aspect ratio; is the sweep angle of the wing's half-chord line; is the sweep angle of the leading edge; is the airfoil efficiency; Mɑ is the aircraft Mach number; is the aircraft tip-to-root ratio; Step 6.2: The chord length of a wing or tail section is approximately: ； Where, is the wing root chord length, d is the distance from any point on the wing to the plane of symmetry of the aircraft; S is the wing area; The rolling moment of the wing is L R Expressed as: ； Similarly, the rolling moment of the tail Expressed as: ； Where, is the tail wingspan, is the slope of the tail lift line; Step 6.3: Combined with the vortex plate numerical method, the approximate calculation method for the fuselage rolling moment is as follows: Calculate the additional force generated by the body: ； Where, is the additional force generated by the fuselage, s ( x ) is the longitudinal length of the fuselage section, b is the span length of the fuselage; then the rolling moment of the fuselage for: ； Step 6.4: Calculate the rolling moment coefficient according to the rolling moment formula RMC : ； Step 6.5: Use the rolling moment coefficient as a wake turbulence encounter safety indicator to calculate the wake turbulence danger zone for different aircraft types, different altitude levels, and different takeoff weights. Analyze the impact range of the wake turbulence danger zone. Based on the intersection of the danger zone boundary and the following aircraft's flight path, determine the overlap range between the wake turbulence danger zone and the following aircraft's flight path and determine the minimum wake turbulence safety interval.

2. The method for analyzing the risk of wake turbulence encounter of a small single-engine training aircraft at a mixed-operation airport according to claim 1 is characterized in that: Step 2 is as follows: Data cleaning: Use the boxplot method to check whether there are outliers in the ADS-B data. Use quartiles and interquartile ranges to describe the data distribution, identify data points that are outliers, and remove them. Data interpolation: For missing data points, linear interpolation is used to supplement them to ensure data continuity; the formula is as follows: ； Where, is a missing value, and is a known value, and is a known point in time; t is a time variable; Coordinate transformation: The geographic coordinates in the ADS-B data are transformed into coordinates in the Cartesian coordinate system using the Mercator projection for trajectory analysis. The formula is as follows: ； Where, and are Cartesian coordinates, The direction is along the horizontal direction of the wing. The direction is perpendicular to the wing direction; is the radius of the Earth; is the longitude, is latitude; Unit conversion: Convert the units of speed data and altitude data uniformly.

3. The method for analyzing the risk of wake turbulence encounter of a small single-engine training aircraft at a mixed-operation airport according to claim 1, characterized in that: Step 3 is as follows: Step 3.1: Feature extraction: Extract key features from the processed flight data, including position coordinates, speed, altitude, and heading, for cluster analysis. Use the Z-score normalization method to standardize the extracted features to eliminate dimensional differences between different features. Step 3.2: Cluster analysis: K-Means clustering algorithm was used to perform cluster analysis on motion data, and the number of clusters was determined by the elbow rule; Step 3.3: Get the average motion trajectory: The flight trajectories obtained from each cluster are averaged to obtain the average motion trajectory of a typical aircraft and a typical flight mission.

4. The method for analyzing the risk of wake turbulence encounter of a small single-engine training aircraft at a mixed-operation airport according to claim 1, characterized in that: Step 4 is as follows: Step 4.1: Run trajectory comparison: By plotting trajectory diagrams and calculating the distances between the trajectories, the average motion trajectories of two types of aircraft, public transport aircraft and small training aircraft, were compared to analyze the relative positions and motion relationships of the two types of aircraft in the air. By plotting altitude-time curves and speed-time curves, the changes in flight altitude and speed of the two types of aircraft during different flight phases were compared to analyze the differences in their flight performance. Step 4.2: Flight area analysis: Based on the average motion trajectories of the two types of aircraft, the trajectory boundaries are calculated to determine the flight areas of the two types respectively; the flight density of the two types of aircraft in different flight areas is calculated by counting the flight time and number of flights in different areas to analyze the distribution of flight activities; Step 4.3: Intersection point determination: The average motion trajectory model of the two types of aircraft is used in combination with the runway utilization plan of the mixed-operation airport to calculate the position and time of the air intersection; Step 4.4: Intersection angle and time interval analysis: Calculate the track angle between the two types of aircraft at the intersection point, and analyze the size and distribution pattern of the intersection angle; calculate the time interval between the two types of aircraft at the intersection point, and analyze the size and distribution pattern of the time interval.

5. The method for analyzing the risk of wake turbulence encounter of a small single-engine training aircraft at a mixed-operation airport according to claim 4 is characterized in that: Step 5 is as follows: Step 5.1: Construct the near-surface computational flow domain based on the intersection time. Set the grid size to meet the grid independence requirement. Set the flow domain to be longer than W times the wingspan length in the spanwise and perpendicular directions to simulate the evolution and dissipation of the wake vortex. Step 5.2: Select the Hallock-Burnham tangential velocity model as the initial tail vortex tangential velocity model; use the Reynolds time-averaged numerical simulation method and select The model is used as a turbulence model to close the NS equations and realize the numerical simulation of the evolution and dissipation behavior of the aircraft tail vortex at the intersection under different environmental influences; Step 5.3: Combined with the measured data from the airport meteorological station, the acquired wind speed profile, turbulence intensity, and temperature gradient data are mapped to the computational domain boundary conditions and subjected to isometric decomposition to achieve wake vortex calculation under typical environmental parameters. Step 5.4: When configuring the solver, select the pressure-based solver and use the Simple algorithm for the solution. Then, organize the wake dissipation data obtained from the numerical simulation, including the temporal and spatial variations of wake velocity, turbulence intensity, circulation attenuation, and vortex core trajectory parameters under different flight conditions. Step 5.5: Construct a multi-dimensional parameterized aircraft wake dissipation database and design the database table structure, including the wake data table, meteorological data table, and wake dissipation parameter table; Step 5.6: Construct a LSTM-based wake vortex dissipation prediction model, which includes an input layer, an LSTM layer, and an output layer. The input layer parameters include the leading aircraft's speed, weight, altitude, wind speed, wind direction, temperature, humidity, and turbulence intensity. Meteorological data is collected in real time, and the real-time meteorological data is fused with wake data to form a complete data set, which is input into the LSTM-based wake vortex dissipation prediction model to perform wake dissipation prediction under typical environments.

6. The method for analyzing the risk of wake turbulence encounter of a small single-engine training aircraft at a mixed-operation airport according to claim 1, characterized in that: Also includes: The calculated data of wake turbulence hazard areas are integrated with the aircraft wake vortex wake dissipation database to form a small-scale training aircraft wake turbulence encounter risk database. The risk prediction, real-time data introduction and dynamic database update steps are repeated to achieve precise control of risk areas.

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