Aircraft landing performance analysis method and system based on runway perception-airborne QAR data fusion

Abstract

The present invention relates to a method and system for analyzing aircraft landing performance based on runway perception-onboard QAR data fusion. The method comprises collecting data during aircraft landing; analyzing key factors affecting the aircraft's landing distance and identifying variables that significantly affect the aircraft's landing distance; establishing a simulation model for the entire aircraft landing process and obtaining simulation data; combining the simulation data with field data to jointly establish a mapping model from runway perception-onboard QAR data to aircraft landing distance; and calculating a predicted landing distance value based on the runway perception-onboard QAR data before the aircraft landed using the mapping model. The predicted landing distance value is then applied to a probability model to obtain a probability distribution of the aircraft's landing distance, thereby analyzing the aircraft's landing performance. The present invention significantly improves the accuracy, adaptability, and real-time performance of the aircraft landing distance prediction model, providing scientific support for the safety of aircraft landing operations at airports and ensuring the safe operation of airport runways.

Description

Technical Field

[0001] The present invention relates to the field of airport engineering, and in particular to an aircraft landing performance analysis method and system based on runway perception-airborne QAR data fusion, which are used to improve aircraft landing operation safety. Background Art

[0002] The landing phase is a critical stage in flight and a frequent source of accidents. On rainy days, runways are prone to localized or uneven accumulation of water. This water film degrades the runway's skid resistance, impairing the aircraft's braking and maneuverability, leading to frequent accidents. Therefore, real-time, accurate analysis of aircraft landing performance based on landing distance is crucial to ensuring landing roll safety and improving runway operational efficiency.

[0003] In the prior art, the following methods are usually used to predict the landing distance of an aircraft and analyze its landing performance: (1) Prediction method based on mathematical model: This method establishes a mathematical model of the required landing distance and its influencing factors under different runway surface conditions, and makes corrections to obtain a prediction model for the landing distance of an aircraft on a wet runway or a contaminated runway. Although this method can make a preliminary estimate of the required landing distance of an aircraft, it ignores the physical process of the aircraft landing process, resulting in low prediction accuracy when facing extreme weather and complex meteorological conditions. (2) Prediction method based on empirical model: This method is based on the ESDU model and uses aircraft speed and empirical coefficients to predict the landing performance of an aircraft. However, the empirical coefficients in this empirical model are based on assumptions and experimental data, and have poor adaptability to different application scenarios. In addition, the model is difficult to update in a timely manner, resulting in low accuracy.

[0004] Therefore, existing technologies for aircraft landing distance prediction suffer from multiple issues, including oversimplified models, low adaptability, low accuracy, and low real-time performance. To address these issues, this paper proposes an aircraft landing performance analysis method and system that integrates runway perception and airborne QAR data. By combining simulation models with machine learning methods, this method significantly improves the accuracy, adaptability, and real-time performance of the aircraft landing distance prediction model, providing scientific support for safe airport aircraft landing operations. Summary of the Invention

[0005] The present invention aims to overcome the shortcomings of the above-mentioned existing technologies and provide an aircraft landing performance analysis method and system based on runway perception and airborne QAR data fusion. By combining simulation models and machine learning methods, the accuracy, adaptability and real-time performance of the aircraft landing distance prediction model are significantly improved, providing scientific support for the safety of airport aircraft landing operations and ensuring the safe operation of airport runways.

[0006] The technical solution adopted to achieve the purpose of the present invention is an aircraft landing performance analysis method based on runway perception-airborne QAR data fusion, which includes:

[0007] S1, collects runway perception data, airborne QAR data and aviation meteorological data when the aircraft lands;

[0008] S2. Analyze the key factors that affect aircraft landing distance and conduct sensitivity analysis to identify the variables that have a significant impact on aircraft landing distance;

[0009] S3. Based on runway perception data, airborne QAR data, and aviation meteorological data, the variables that significantly affect the aircraft landing distance are used as key simulation factors to establish a simulation model for the entire aircraft landing behavior process;

[0010] S4. Setting the operating conditions of the aircraft during landing in the full-process simulation model of the aircraft landing behavior to obtain simulation data, combining the simulation data with the field data to jointly establish a mapping model from runway perception-airborne QAR data to aircraft landing distance, wherein the operating conditions of the aircraft during landing include the variables that significantly affect the aircraft landing distance;

[0011] S5. Based on the runway perception and onboard QAR data before the aircraft lands, a mapping model is used to calculate a predicted landing distance value for the aircraft. The predicted landing distance value is applied to a probability model to obtain a probability distribution of the aircraft landing distance, thereby analyzing the aircraft landing performance.

[0012] In addition, an aircraft landing performance analysis system based on runway perception and airborne QAR data fusion is also provided. The system includes:

[0013] Data acquisition module, used to collect runway perception data, airborne QAR data and aviation meteorological data when the aircraft lands;

[0014] The aircraft landing variable analysis module is used to conduct sensitivity analysis on key factors affecting aircraft landing distance and identify variables that have a significant impact on aircraft landing distance;

[0015] a modeling module for establishing a full-process simulation model of aircraft landing behavior based on the data collected by the data acquisition module and taking the variables that significantly affect the aircraft landing distance as key simulation elements;

[0016] a mapping model establishment module for combining simulation data obtained from the full-process aircraft landing behavior simulation model with field data to jointly establish a mapping model from runway perception-airborne QAR data to aircraft landing distance;

[0017] The aircraft landing performance analysis module is used to use the mapping model to predict the aircraft landing distance based on real-time monitoring of runway perception-airborne QAR data, and to obtain the probability distribution of the aircraft landing distance through the probability model. Finally, the aircraft landing performance is determined based on the aircraft landing distance probability function.

[0018] The present invention has the following advantages:

[0019] 1. The present invention effectively improves the accuracy of aircraft landing distance prediction under complex and extreme weather conditions by combining field data with simulation data.

[0020] 2. The present invention deeply combines data with physical models, improving the accuracy and adaptability of aircraft landing distance prediction.

[0021] 3. The present invention can achieve real-time and accurate prediction of aircraft landing distance through runway sensing data, airborne QAR data and aviation meteorological data. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The figure is a flow chart of the aircraft landing performance analysis method and system based on runway perception and airborne QAR data fusion according to the present invention.

[0023] Figure 2 Flowchart for establishing a simulation model of aircraft landing behavior throughout the entire process. DETAILED DESCRIPTION

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] like Figure 1 As shown, the present invention is based on the aircraft landing performance analysis method of runway perception-airborne QAR data fusion, the method comprising:

[0026] S1. Collect runway perception data, airborne QAR data, and aviation meteorological data during aircraft landing, including:

[0027] S1.1. Connect the aircraft's internal QAR equipment to the data extraction tool and use professional software to read, decode, and convert it to obtain various operational and status data of the aircraft during flight and landing. Extract key variables from the airborne QAR data, including speed, braking status, aircraft attitude, aircraft weight, etc. Airborne QAR data is usually stored in binary or proprietary formats. Use specific airborne QAR data decoding software to convert it into a readable format (such as CSV or Excel) to facilitate subsequent analysis and selection of appropriate flight samples. Select multiple aircraft landing data at different water film thicknesses to ensure that the data includes various environmental conditions, such as sunny days, rainy days, and wet runways.

[0028] S1.2. Obtain landing meteorological data from a weather station, airport meteorological department, or third-party meteorological service provider, including wind direction and speed, temperature, humidity, and rainfall. Organize the meteorological data into a standard format, with corresponding wind speed, temperature, humidity, etc. for each landing sampling time point. Ensure that the sampling time points are synchronized with the onboard QAR data.

[0029] S1.3. Obtain runway surface condition data, including average water film thickness, runway friction coefficient, pavement temperature, sampling time, etc., through water film thickness sensors installed at the airport or through on-site testing.

[0030] S1.4. Match the runway surface condition data, meteorological data, and airborne QAR data according to sampling time points to generate time series data. Clean the collected data, using interpolation to fill missing data and correct outliers. Then, standardize all data. Then, use principal component analysis to reduce the dimensionality of the time series data, identify the principal components within the data, and map the high-dimensional data into a low-dimensional space.

[0031] S2. Analyze the key factors that affect aircraft landing distance and conduct sensitivity analysis to identify the variables that significantly affect aircraft landing distance, including:

[0032] S2.1. Use statistical tools to calculate the Pearson correlation coefficient between different variables and aircraft landing distance. This is represented by r, which ranges from -1 to 1. Values ​​closer to 1 or -1 indicate a stronger linear correlation. Then calculate the significance level of the variable, represented by p. Smaller p-values ​​indicate a more significant correlation between the variable and aircraft landing distance. A p-value less than 0.05 is considered significant, and variables with a significant impact on aircraft landing distance are preliminarily screened. The formula for calculating the Pearson correlation coefficient and significance level is:

[0033]

[0034] Among them, x i represents the value of the variable data point, Represents the value of the variable data point; y i represents the value of the aircraft landing distance data point, represents the mean of the landing distance data points; r represents the correlation coefficient; and n represents the sample size. The corresponding p-value is determined based on the t-statistic.

[0035] S2.2. Build a multiple regression model, calculate the regression coefficient of each variable, and then use the standardization method to obtain the sensitivity coefficient to screen out the variables with the greatest impact on the aircraft landing distance. The formula is:

[0036]

[0037] in, represents the standardized regression coefficient, Represents the independent variable x j The standard deviation, σ y Represents the standard deviation of the dependent variable, aircraft landing distance.

[0038] Finally, several variables with a significant impact on the aircraft landing distance were selected, including wind speed, water film thickness, aircraft landing weight and target speed during the final approach phase.

[0039] S3, such as Figure 2 As shown in the figure, based on runway perception data, airborne QAR data and aviation meteorological data, a full-process simulation model of aircraft landing behavior is established, including:

[0040] S3.1. Build the aircraft fuselage model, tire and landing gear geometry model, pavement model, and aerodynamic system model for the simulation model. Simultaneously, build the control subsystem to form the complete aircraft assembly and perform braking and rolling simulation of the aircraft during landing. The modeling is implemented as follows:

[0041] When building a multibody dynamics model, it's necessary to assume that all model components are rigid bodies. The different aircraft component models are then created in CATIA, with communication interfaces established between them to form the aircraft assembly. The aircraft assembly primarily consists of six main components: the fuselage subsystem, nose landing gear subsystem, nose landing gear tire subsystem, main landing gear subsystem, main landing gear tire subsystem, and brake subsystem.

[0042] The landing gear structure in the landing gear geometric model is simplified into mass elements, beam elements, and connector elements for multibody dynamics simulation. The elastic and damping parameters of the connector elements are set to simulate the landing gear hydraulic system.

[0043] The pavement model uses solid elements. The pavement structure is composed of 32 cubes in a 4×8 configuration. Each cube measures 5m x 4.5m to cover the main wheel contact area during aircraft takeoff and landing. The joint width between cubes is 0.008m. The foundation layer of the pavement is modeled using the equivalent stiffness method, with ground springs established at node locations to simulate the stiffness of the foundation soil.

[0044] The aerodynamic system model uses the water flow state equation to model the water film distribution. The Mie-GRUNEISEN state equation is:

[0045]

[0046] Where P and P H are the water pressure and Hugoniot shock pressure, e and e respectively H are the specific internal energy and energy reference value, Γρ and Γ0 are the material parameters of water after impact and the initial values ​​of the material parameters, ρ and ρ0 are the density of water after impact and the initial density, respectively. s and U p is the shock wave velocity and particle wave velocity, c0 is the propagation speed of the sound wave in the fluid, s is U s and U p The slope between .

[0047] The modeling method uses fluid-structure interaction, and the water film modeling is based on the Abaqus CEL method. The water film uses an Eulerian mesh, and the runway and other components use a Lagrangian mesh. The water film thickness information cannot be directly used in virtual prototype simulation. Therefore, it is necessary to convert the water film thickness information into a pavement-surface combination coefficient that can be used by the simulation software.

[0048] This embodiment integrates the simulation tools XFlow and Abaqus into a single process through "Xflow-Abaqus co-simulation" to achieve coupled simulation of aerodynamics (fluid mechanics) and structural mechanics. During the co-simulation process, XFlow transmits fluid calculation results (such as surface pressure and fluid load) to Abaqus, which serves as boundary conditions or loading input for the structural model. After completing the structural calculation, Abaqus returns structural deformation or displacement information to XFlow, which is used to update the geometry and boundary conditions of the fluid domain.

[0049] The aircraft control system in the control subsystem controls the lateral and longitudinal behavior of the aircraft through Simulink in MATLAB, establishes a two-dimensional fuzzy controller based on error and error change rate, and establishes an aircraft lateral control system and ABS control system.

[0050] S3.2. In the simulation, set the aircraft's speed, attitude, and weight at runway contact to simulate the effect of water film thickness on tire friction and landing distance. Adjust air resistance based on speed and angle of attack, and consider the effects of wind speed and direction. Finally, select the variables (wind speed, water film thickness, landing weight, and target speed during final approach) that have the greatest impact on landing distance as key simulation factors.

[0051] S3.3. Repeatedly run simulations to analyze landing distance and braking deceleration under key factors, adjust water film thickness, tire friction, braking system, and aerodynamic parameters, ensure that simulation results are consistent with actual airborne QAR data, and gradually optimize the model.

[0052] S4. Simulation data is obtained based on the full-process simulation model of aircraft landing behavior. The simulation data is combined with field data to jointly establish a runway perception-airborne QAR data to aircraft landing distance mapping model, including the following steps:

[0053] S4.1. Obtain multi-condition simulation data using the full-process aircraft landing behavior simulation model established in S3. The specific operations are as follows:

[0054] Different operating conditions are set through the simulation model of aircraft landing behavior to generate a wide range of simulation data samples. These operating conditions include the variables with significant influence determined in S2, such as wind speed, water film thickness, aircraft landing weight, and target speed during the final approach phase, including changes in single factors and multiple factors. Through multiple simulation runs, landing distance data under different conditions is obtained. This simulation data not only covers common flight conditions, but also considers aircraft landing performance under extreme conditions to ensure data diversity and breadth. For example, by setting water film thickness and wind speed, the simulation model simulates the impact of these factors on aircraft tire friction, braking deceleration, and landing distance, thereby ensuring that the simulation environment is consistent with the actual situation.

[0055] S4.2. Combine the simulation data generated in step S4.1 with real data to train the mapping model. The specific operations are as follows:

[0056] Combine the simulated data generated in step S4.1 with the actual QAR field data to form a complete dataset. The significantly influential variables selected in step S2 are used as input features to train the XGBoost mapping model, enabling it to accurately learn the relationship between key variables and landing distance. During model training, focus on these significant variables and optimize the model's hyperparameters to optimize the mapping model's prediction accuracy.

[0057] In order to eliminate the dimensional differences of different variables, this embodiment normalizes all data using the Z-score normalization formula:

[0058]

[0059] Where x is the original data, μ is the mean of the variable, and σ is the standard deviation of the variable.

[0060] The standardized dataset facilitates unified machine learning training. The standardized dataset is then divided into a training set, a validation set, and a test set, typically with 70% used for model training, 15% for validation, and 15% for testing. After data processing, the XGBoost algorithm is used to build a mapping model to learn the relationship between airborne QAR data (such as speed, wind speed, braking force, etc.) and aircraft landing distance. The XGBoost model is based on a gradient boosting tree and uses the mean squared error (MSE) as the loss function. The formula is:

[0061]

[0062] in, is the landing distance predicted by the model, y i is the actual landing distance. By minimizing the loss function, the model prediction ability is gradually optimized.

[0063] S4.3. Optimize the mapping model parameters in S4.2. The specific operations are as follows:

[0064] By adjusting the hyperparameters of XGBoost, including learning rate, maximum tree depth, subsampling ratio, etc., the model performance was optimized through k-fold cross validation, and the mean square error (MSE) and coefficient of determination (R 2 ) value, iteratively optimized based on feedback, and finally used the test set to verify the prediction accuracy of the model under different working conditions to ensure that the model can accurately predict the landing distance.

[0065] S5. Before landing, runway perception data, onboard QAR data, and aviation meteorological data are acquired. The mapping model obtained in S4 is used to calculate a predicted landing distance. Due to data bias, the landing distance obtained from the mapping model is applied to a probability model to obtain the probability distribution of the landing distance. Assuming the landing distance follows a normal distribution, the predicted value follows the probability density function of the normal distribution. Using the probability distribution and confidence interval, the landing risk probability can be determined in real time, thereby evaluating the aircraft's landing performance.

Claims

1. An aircraft landing performance analysis method based on runway perception and airborne QAR data fusion, characterized in that: include: S1, collects runway perception data, airborne QAR data and aviation meteorological data when the aircraft lands; S2. Analyze the key factors that affect aircraft landing distance and conduct sensitivity analysis to identify the variables that have a significant impact on aircraft landing distance; S3. Based on runway perception data, airborne QAR data, and aviation meteorological data, the variables that significantly affect the aircraft landing distance are used as key simulation elements to establish a full-process simulation model of aircraft landing behavior; specifically, the following steps are included: S3.

1. Establish the aircraft fuselage model, tire and landing gear geometry models, pavement model, and aerodynamic system model for the simulation model. Simultaneously, establish the control subsystem to form the complete aircraft assembly and perform braking and rolling simulation during landing. S3.

2. In the simulation, set the aircraft's speed, attitude, and weight when it touches the runway. Simulate the effect of water film thickness on tire friction and applied braking force. Adjust air resistance based on speed and angle of attack. Consider the effects of wind speed and direction. Finally, select the variables that have the greatest impact on the aircraft's landing distance (S2) as key simulation factors. S3.

3. Repeatedly run simulations to analyze key factors such as landing distance and braking deceleration, adjust water film thickness, tire friction, braking system, and aerodynamic parameters, ensure that simulation results are consistent with actual airborne QAR data, and gradually optimize the model. S4. Setting the operating conditions of the aircraft during landing in the full-process simulation model of the aircraft landing behavior to obtain simulation data, combining the simulation data with the field data to jointly establish a mapping model from runway perception-airborne QAR data to aircraft landing distance, wherein the operating conditions of the aircraft during landing include the variables that significantly affect the aircraft landing distance; S5. Based on the runway perception and onboard QAR data before the aircraft lands, a mapping model is used to calculate a predicted landing distance value for the aircraft. The predicted landing distance value is applied to a probability model to obtain a probability distribution of the aircraft landing distance, thereby analyzing the aircraft landing performance.

2. The aircraft landing performance analysis method based on runway perception and airborne QAR data fusion according to claim 1 is characterized in that: Step S1 includes: S1.

1. Connect the onboard QAR device to the data extraction tool to obtain various aircraft operation and status data during flight and landing through reading, decoding, and conversion, and finally save it in a readable format; S1.

2. Obtain aviation meteorological data at the time of landing from a weather station, airport meteorological department, or third-party meteorological service provider, including wind direction and speed, temperature, humidity, and rainfall; S1.

3. Obtain runway surface condition data, including water film thickness, runway friction coefficient, runway surface temperature, and sampling time, through water film thickness sensors installed at the airport or through on-site testing; S1.

4. Match the airborne QAR data, aviation meteorological data, and runway surface condition data according to the sampling time points. Clean the collected data, use interpolation to fill in missing data and correct outliers, and then standardize all data.

3. The aircraft landing performance analysis method based on runway perception and airborne QAR data fusion according to claim 2, characterized in that: Step S2 includes: S2.

1. Use statistical tools to calculate the correlation coefficients between different variables and aircraft landing distance, and preliminarily screen out variables that significantly affect aircraft landing distance. S2.

2. Build a multivariate regression model, calculate the sensitivity coefficients of all significant influencing variables, and evaluate the impact of each variable on aircraft landing distance; S2.

3. Finally, the variables that have a high degree of influence on the aircraft landing distance are screened out, including wind speed, water film thickness, aircraft landing weight and target speed during the final approach phase.

4. The aircraft landing performance analysis method based on runway perception and airborne QAR data fusion according to claim 3 is characterized in that: The step S3.1 includes: Build models of different aircraft components in CATIA, and establish communication devices between different components to form an aircraft assembly. The aircraft assembly includes the fuselage subsystem, nose landing gear subsystem, nose landing gear tire subsystem, main landing gear subsystem, main landing gear tire subsystem, and brake subsystem; The structure of the landing gear geometric model is subjected to multi-body dynamics simulation using mass units, beam units, and connector units, wherein elasticity and damping parameters are set for the connector units to simulate the landing gear hydraulic system; The pavement model is modeled using solid elements. The pavement structure is composed of 32 cube structures in a 4×8 pattern. Each block is 5m×4.5m in size to cover the main wheel action area during aircraft takeoff and landing. The width of the joints between blocks is 0.008m. The foundation layer of the pavement is modeled using the equivalent stiffness method. Ground springs are established at the node positions to simulate the stiffness of the foundation soil. The aerodynamic system model includes water film distribution modeling through the water flow state equation. The Mie-GRUNEISEN state equation is: Where P and P H are the water pressure and Hugoniot shock pressure, e and e respectively H are the specific internal energy and energy reference value, Γ ρ and Γ0 are the material parameters of water after impact and the initial values ​​of the material parameters, ρ and ρ0 are the density of water after impact and the initial density, respectively. s and U p is the shock wave velocity and particle wave velocity, c0 is the propagation speed of the sound wave in the fluid, s is U s and U p The slope between The fluid-structure coupling method is used for modeling, and the water film is modeled based on the Abaqus CEL method. The water film adopts the Euler grid, and the runway adopts the Lagrangian grid. The control subsystem controls the lateral and longitudinal behaviors of the aircraft through Simulink in MATLAB, establishes a two-dimensional fuzzy controller based on error and error change rate, and establishes an aircraft lateral control system and ABS control system.

5. The aircraft landing performance analysis method based on runway perception and airborne QAR data fusion according to claim 4 is characterized in that: Step S4 includes: S4.

1. Based on the obtained full-process aircraft landing behavior simulation model, set different landing conditions for the aircraft, including wind speed, water film thickness, aircraft landing weight, and target speed during the final approach phase. Test the effects of water film thickness, wind speed, and aircraft braking method on landing distance using single and multiple factor combinations. Run multiple simulations to generate simulation data samples covering a wide range of conditions. S4.

2. Use both simulation data and field data as input, standardize and partition the dataset, build a mapping model using the XGBoost algorithm, train the model to learn the relationship between airborne QAR data and landing distance, and adjust hyperparameters to optimize model performance using a validation set. S4.

3. Optimize model performance by adjusting hyperparameters and using cross-validation to evaluate the model's MSE and R 2 The model is iteratively optimized based on the feedback, and finally the test set is used to verify the prediction accuracy of the model under different working conditions to ensure that the model can accurately predict the landing distance.

6. An aircraft landing performance analysis system based on runway perception and airborne QAR data fusion, characterized in that: include: Data acquisition module, used to collect runway perception data, airborne QAR data and aviation meteorological data when the aircraft lands; The aircraft landing variable analysis module is used to conduct sensitivity analysis on key factors affecting aircraft landing distance and identify variables that have a significant impact on aircraft landing distance; A modeling module is used to establish a simulation model of the entire landing process of an aircraft based on the data collected by the data acquisition module and using the variables that significantly affect the aircraft landing distance as key simulation elements; specifically, it includes: The whole aircraft modeling submodule is used to establish the aircraft fuselage model, tire and landing gear geometry model, pavement model and aerodynamic system model of the simulation model, and at the same time establish the control subsystem to form the whole aircraft assembly and perform the braking and rolling simulation of the aircraft during landing; The aircraft data setting and selection submodule is used to set the aircraft's speed, attitude, and weight when it touches the runway in the simulation. It also simulates the effect of water film thickness on tire friction and applied braking force, adjusts air resistance based on speed and angle of attack, and considers the effects of wind speed and direction. The aircraft landing variable analysis module ultimately selects several variables with the highest degree of influence on the aircraft's landing distance as key simulation factors. The simulation run submodule is used to repeatedly run simulations to analyze key factors such as landing distance and braking deceleration, adjust water film thickness, tire friction, braking system, and aerodynamic parameters, ensure that the simulation results are consistent with the actual airborne QAR data, and gradually optimize the model; a mapping model establishment module for combining simulation data obtained from the full-process aircraft landing behavior simulation model with field data to jointly establish a mapping model from runway perception-airborne QAR data to aircraft landing distance; The aircraft landing performance analysis module is used to use the mapping model to predict the aircraft landing distance based on real-time monitoring of runway perception-airborne QAR data, and to obtain the probability distribution of the aircraft landing distance through the probability model. Finally, the aircraft landing performance is determined based on the aircraft landing distance probability function.

7. The aircraft landing performance analysis system based on runway perception and airborne QAR data fusion according to claim 6, characterized in that: The full-process simulation model of aircraft landing behavior sets the operating conditions of the aircraft during landing to obtain simulation data. The operating conditions of the aircraft during landing include variables that significantly affect the landing distance of the aircraft, including wind speed, water film thickness, aircraft landing weight, and target speed during the final approach phase.

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